diff --git a/search-index.json b/search-index.json
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We introduce MathChat, a conversational framework leveraging Large Language Models (LLMs), specifically GPT-4, to solve advanced mathematical problems. MathChat improves LLM's performance on challenging math problem-solving, outperforming basic prompting and other strategies by about 6%. The improvement was especially notable in the Algebra category, with a 15% increase in accuracy. Despite the advancement, GPT-4 still struggles to solve very challenging math problems, even with effective prompting strategies. Further improvements are needed, such as the development of more specific assistant models or the integration of new tools and prompts. Recent Large Language Models (LLMs) like GTP-3.5 and GPT-4 have demonstrated astonishing abilities over previous models on various tasks, such as text generation, question answering, and code generation. Moreover, these models can communicate with humans through conversations and remember previous contexts, making it easier for humans to interact with them. These models play an increasingly important role in our daily lives assisting people with different tasks, such as writing emails, summarizing documents, and writing code. In this blog post, we probe into the problem-solving capabilities of LLMs. Specifically, we are interested in their capabilities to solve advanced math problems, which could be representative of a broader class of problems that require precise reasoning and also have deterministic solutions. We introduce MathChat, a conversational framework designed for solving challenging math problems with LLMs. This framework takes advantage of the chat-optimized feature of state-of-the-art LLMs, where a user proxy agent and an LLM assistant work together to tackle math problems. We also test previous prompting techniques for comparison.","s":"MathChat - An Conversational Framework to Solve Math Problems","u":"/FLAML/blog/2023/06/28/MathChat","h":"","p":1},{"i":4,"t":"MathChat simulates a conversation between the LLM assistant and a user proxy agent. As the name indicates, the user proxy agent acts as a proxy for the user, which is responsible for communicating with the LLM assistant and continuing the conversation in a desired manner. The proxy agent first presents a math problem to the LLM assistant, framed by an initial prompt that guides the assistant in how it should work collaboratively to solve the problem. We incorporate existing prompting methods such as CoT (chain-of-thought) and tool-using are incorporated in the initial prompt. Key components of the prompt include: Tool-using Prompt: This guides the LLM assistant to use Python code in the correct format to address the problem, allowing the user proxy agent to parse the code and provide the appropriate results. Problem-Solving Strategy Selection Prompt: The assistant is instructed to choose one of three potential problem-solving strategies, including: Writing a Python program to solve the problem directly. Solving the problem directly without using Python, which exercises LLMs' inherent reasoning capabilities. Solving the problem step by step with Python. This should be used when the first two strategies aren't suitable. Final Answer Encapsulation Prompt: This part instructs the assistant to put the final answer in \\boxed{}. The prompt design in MathChat is flexible and can easily be refined to include other tools, such as Wolfram Alpha. Let's take a look at an example between the User Proxy Agent and the LLM Assistant (GPT-4). The conversation focuses on how to solve inequality using Python. (The conversation is modified for readability.) Participant Dialogue User Proxy Agent (Initial Prompt: Let's solve a math problem with Python...) Problem: Find all x that satisfy the inequality (2x+10)(x+3) < (3x+9)(x+8). Express your answer in interval notation. LLM assistant The key idea to solve this problem is to find the critical points, which are the points where the inequality is equal. Then, test the intervals between the critical points to find the interval for which the inequality holds. LLM assistant (continued) Here's Python code to find the critical points: from sympy import symbols, Eq, solve; x = symbols(\"x\"); lhs = (2*x + 10)*(x + 3); rhs = (3*x + 9)*(x + 8); equation = Eq(lhs, rhs); critical_points = solve(equation, x); critical_points User Proxy Agent The critical points are: [-14, -3] LLM assistant We found the critical points to be x = -14 and x = -3. Now we'll test the intervals between these critical points as well as points before and after the critical points. The intervals are: x < -14, -14 < x < -3, x > -3. LLM assistant (continued) Here's Python code to test the inequality for each interval: from sympy import symbols; x = symbols(\"x\"); inequality = (2*x + 10)*(x + 3) < (3*x + 9)*(x + 8); test_points = [-15, -5, 0]; intervals = []; for point in test_points: if inequality.subs(x, point): intervals.append(point); intervals User Proxy Agent The intervals where the inequality holds true are for test points: [-15, 0] LLM assistant We have tested the intervals and found that the inequality holds for: 1. x < -14 (test point: -15) 2. x > -3 (test point: 0). Therefore, the solution to the inequality (2x+10)(x+3) < (3x+9)(x+8) is x ∈ (-∞, -14) ∪ (-3, ∞)","s":"The MathChat Framework","u":"/FLAML/blog/2023/06/28/MathChat","h":"#the-mathchat-framework","p":1},{"i":6,"t":"We evaluate the improvement brought by MathChat. For the experiment, we focus on the level-5 problems from the MATH dataset, which are composed of high school competition problems. These problems include the application of theorems and complex equation derivation and are challenging even for undergraduate students. We evaluate 6 of 7 categories from the dataset (excluding Geometry): Prealgebra, Algebra, Number Theory, Counting and Probability, Intermediate Algebra, and Precalculus. We evaluate GPT-4 and use the default configuration of the OpenAI API. To access the final performance, we manually compare the final answer with the correct answer. For the vanilla prompt, Program Synthesis, and MathChat, we have GPT-4 enclose the final answer in \\boxed{}, and we take the return of the function in PoT as the final answer. We also evaluate the following methods for comparison: Vanilla prompting: Evaluates GPT-4's direct problem-solving capability. The prompt used is: \" Solve the problem carefully. Put the final answer in \\boxed{}\". Program of Thoughts (PoT): Uses a zero-shot PoT prompt that requests the model to create a Solver function to solve the problem and return the final answer. Program Synthesis (PS) prompting: Like PoT, it prompts the model to write a program to solve the problem. The prompt used is: \"Write a program that answers the following question: {Problem}\".","s":"Experiment Setup","u":"/FLAML/blog/2023/06/28/MathChat","h":"#experiment-setup","p":1},{"i":8,"t":"The accuracy on all the problems with difficulty level-5 from different categories of the MATH dataset with different methods is shown below: We found that compared to basic prompting, which demonstrates the innate capabilities of GPT-4, utilizing Python within the context of PoT or PS strategy improved the overall accuracy by about 10%. This increase was mostly seen in categories involving more numerical manipulations, such as Counting & Probability and Number Theory, and in more complex categories like Intermediate Algebra and Precalculus. For categories like Algebra and Prealgebra, PoT and PS showed little improvement, and in some instances, even led to a decrease in accuracy. However, MathChat was able to enhance total accuracy by around 6% compared to PoT and PS, showing competitive performance across all categories. Remarkably, MathChat improved accuracy in the Algebra category by about 15% over other methods. Note that categories like Intermediate Algebra and Precalculus remained challenging for all methods, with only about 20% of problems solved accurately. The code for experiments can be found at this repository. We now provide an implementation of MathChat using the interactive agents in FLAML. See this notebook for example usage.","s":"Experiment Results","u":"/FLAML/blog/2023/06/28/MathChat","h":"#experiment-results","p":1},{"i":10,"t":"Despite MathChat's improvements over previous methods, the results show that complex math problem is still challenging for recent powerful LLMs, like GPT-4, even with help from external tools. Further work can be done to enhance this framework or math problem-solving in general: Although enabling the model to use tools like Python can reduce calculation errors, LLMs are still prone to logic errors. Methods like self-consistency (Sample several solutions and take a major vote on the final answer), or self-verification (use another LLM instance to check whether an answer is correct) might improve the performance. Sometimes, whether the LLM can solve the problem depends on the plan it uses. Some plans require less computation and logical reasoning, leaving less room for mistakes. MathChat has the potential to be adapted into a copilot system, which could assist users with math problems. This system could allow users to be more involved in the problem-solving process, potentially enhancing learning.","s":"Future Directions","u":"/FLAML/blog/2023/06/28/MathChat","h":"#future-directions","p":1},{"i":12,"t":"Research paper of MathChat Documentation about flaml.autogen Are you working on applications that involve math problem-solving? Would you appreciate additional research or support on the application of LLM-based agents for math problem-solving? Please join our Discord server for discussion.","s":"For Further Reading","u":"/FLAML/blog/2023/06/28/MathChat","h":"#for-further-reading","p":1},{"i":15,"t":"TL;DR: A case study using the HumanEval benchmark shows that an adaptive way of using multiple GPT models can achieve both much higher accuracy (from 68% to 90%) and lower inference cost (by 18%) than using GPT-4 for coding. GPT-4 is a big upgrade of foundation model capability, e.g., in code and math, accompanied by a much higher (more than 10x) price per token to use over GPT-3.5-Turbo. On a code completion benchmark, HumanEval, developed by OpenAI, GPT-4 can successfully solve 68% tasks while GPT-3.5-Turbo does 46%. It is possible to increase the success rate of GPT-4 further by generating multiple responses or making multiple calls. However, that will further increase the cost, which is already nearly 20 times of using GPT-3.5-Turbo and with more restricted API call rate limit. Can we achieve more with less? In this blog post, we will explore a creative, adaptive way of using GPT models which leads to a big leap forward.","s":"Achieve More, Pay Less - Use GPT-4 Smartly","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"","p":14},{"i":17,"t":"GPT-3.5-Turbo can alrady solve 40%-50% tasks. For these tasks if we never use GPT-4, we can save nearly 40-50% cost. If we use the saved cost to generate more responses with GPT-4 for the remaining unsolved tasks, it is possible to solve some more of them while keeping the amortized cost down. The obstacle of leveraging these observations is that we do not know a priori which tasks can be solved by the cheaper model, which tasks can be solved by the expensive model, and which tasks can be solved by paying even more to the expensive model. To overcome that obstacle, one may want to predict which task requires what model to solve and how many responses are required for each task. Let's look at one example code completion task: def vowels_count(s): \"\"\"Write a function vowels_count which takes a string representing a word as input and returns the number of vowels in the string. Vowels in this case are 'a', 'e', 'i', 'o', 'u'. Here, 'y' is also a vowel, but only when it is at the end of the given word. Example: >>> vowels_count(\"abcde\") 2 >>> vowels_count(\"ACEDY\") 3 \"\"\" Copy Can we predict whether GPT-3.5-Turbo can solve this task or do we need to use GPT-4? My first guess is that GPT-3.5-Turbo can get it right because the instruction is fairly straightforward. Yet, it turns out that GPT-3.5-Turbo does not consistently get it right, if we only give it one chance. It's not obvious (but an interesting research question!) how to predict the performance without actually trying. What else can we do? We notice that: It's \"easier\" to verify a given solution than finding a correct solution from scratch. Some simple example test cases are provided in the docstr. If we already have a response generated by a model, we can use those test cases to filter wrong implementations, and either use a more powerful model or generate more responses, until the result passes the example test cases. Moreover, this step can be automated by asking GPT-3.5-Turbo to generate assertion statements from the examples given in the docstr (a simpler task where we can place our bet) and executing the code.","s":"Observations","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#observations","p":14},{"i":19,"t":"Combining these observations, we can design a solution with two intuitive ideas: Make use of auto-generated feedback, i.e., code execution results, to filter responses. Try inference configurations one by one, until one response can pass the filter. This solution works adaptively without knowing or predicting which task fits which configuration. It simply tries multiple configurations one by one, starting from the cheapest configuration. Note that one configuration can generate multiple responses (by setting the inference parameter n larger than 1). And different configurations can use the same model and different inference parameters such as n and temperature. Only one response is returned and evaluated per task. An implementation of this solution is provided in flaml.autogen. It uses the following sequence of configurations: GPT-3.5-Turbo, n=1, temperature=0 GPT-3.5-Turbo, n=7, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"] GPT-4, n=1, temperature=0 GPT-4, n=2, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"] GPT-4, n=1, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]","s":"Solution","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#solution","p":14},{"i":21,"t":"The first figure in this blog post shows the success rate and average inference cost of the adaptive solution compared with default GPT-4. The inference cost includes the cost for generating the assertions in our solution. The generated assertions are not always correct, and programs that pass/fail the generated assertions are not always right/wrong. Despite of that, the adaptive solution can increase the success rate (referred to as pass@1 in the literature) from 68% to 90%, while reducing the cost by 18%. Here are a few examples of function definitions which are solved by different configurations in the portfolio. Solved by GPT-3.5-Turbo, n=1, temperature=0 def compare(game,guess): \"\"\"I think we all remember that feeling when the result of some long-awaited event is finally known. The feelings and thoughts you have at that moment are definitely worth noting down and comparing. Your task is to determine if a person correctly guessed the results of a number of matches. You are given two arrays of scores and guesses of equal length, where each index shows a match. Return an array of the same length denoting how far off each guess was. If they have guessed correctly, the value is 0, and if not, the value is the absolute difference between the guess and the score. example: compare([1,2,3,4,5,1],[1,2,3,4,2,-2]) -> [0,0,0,0,3,3] compare([0,5,0,0,0,4],[4,1,1,0,0,-2]) -> [4,4,1,0,0,6] \"\"\" Copy Solved by GPT-3.5-Turbo, n=7, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]: the vowels_count function presented earlier. Solved by GPT-4, n=1, temperature=0: def string_xor(a: str, b: str) -> str: \"\"\" Input are two strings a and b consisting only of 1s and 0s. Perform binary XOR on these inputs and return result also as a string. >>> string_xor('010', '110') '100' \"\"\" Copy Solved by GPT-4, n=2, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]: def is_palindrome(string: str) -> bool: \"\"\" Test if given string is a palindrome \"\"\" return string == string[::-1]def make_palindrome(string: str) -> str: \"\"\" Find the shortest palindrome that begins with a supplied string. Algorithm idea is simple: - Find the longest postfix of supplied string that is a palindrome. - Append to the end of the string reverse of a string prefix that comes before the palindromic suffix. >>> make_palindrome('') '' >>> make_palindrome('cat') 'catac' >>> make_palindrome('cata') 'catac' \"\"\" Copy Solved by GPT-4, n=1, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]: def sort_array(arr): \"\"\" In this Kata, you have to sort an array of non-negative integers according to number of ones in their binary representation in ascending order. For similar number of ones, sort based on decimal value. It must be implemented like this: >>> sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5] >>> sort_array([-2, -3, -4, -5, -6]) == [-6, -5, -4, -3, -2] >>> sort_array([1, 0, 2, 3, 4]) [0, 1, 2, 3, 4] \"\"\" Copy The last problem is an example with wrong example test cases in the original definition. It misleads the adaptive solution because a correct implementation is regarded as wrong and more trials are made. The last configuration in the sequence returns the right implementation, even though it does not pass the auto-generated assertions. This example demonstrates that: Our adaptive solution has a certain degree of fault tolerance. The success rate and inference cost for the adaptive solution can be further improved if correct example test cases are used. It is worth noting that the reduced inference cost is the amortized cost over all the tasks. For each individual task, the cost can be either larger or smaller than directly using GPT-4. This is the nature of the adaptive solution: The cost is in general larger for difficult tasks than that for easy tasks. An example notebook to run this experiment can be found at: https://github.com/microsoft/FLAML/blob/v1.2.1/notebook/research/autogen_code.ipynb","s":"Experiment Results","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#experiment-results","p":14},{"i":23,"t":"Our solution is quite simple to implement using a generic interface offered in flaml.autogen, yet the result is quite encouraging. While the specific way of generating assertions is application-specific, the main ideas are general in LLM operations: Generate multiple responses to select - especially useful when selecting a good response is relatively easier than generating a good response at one shot. Consider multiple configurations to generate responses - especially useful when: Model and other inference parameter choice affect the utility-cost tradeoff; or Different configurations have complementary effect. A previous blog post provides evidence that these ideas are relevant in solving math problems too. flaml.autogen uses a technique EcoOptiGen to support inference parameter tuning and model selection. There are many directions of extensions in research and development: Generalize the way to provide feedback. Automate the process of optimizing the configurations. Build adaptive agents for different applications. Do you find this approach applicable to your use case? Do you have any other challenge to share about LLM applications? Do you like to see more support or research of LLM optimization or automation? Please join our Discord server for discussion.","s":"Discussion","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#discussion","p":14},{"i":25,"t":"Documentation about flaml.autogen and Research paper. Blog post about a related study for math.","s":"For Further Reading","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#for-further-reading","p":14},{"i":27,"t":"TL;DR: We demonstrate how to use flaml.autogen for local LLM application. As an example, we will initiate an endpoint using FastChat and perform inference on ChatGLMv2-6b.","s":"Use flaml.autogen for Local LLMs","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"","p":26},{"i":30,"t":"FastChat provides OpenAI-compatible APIs for its supported models, so you can use FastChat as a local drop-in replacement for OpenAI APIs. However, its code needs minor modification in order to function properly. git clone https://github.com/lm-sys/FastChat.gitcd FastChat Copy","s":"Clone FastChat","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#clone-fastchat","p":26},{"i":32,"t":"ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters. ChatGLM2-6B is its second-generation version. Before downloading from HuggingFace Hub, you need to have Git LFS installed. git clone https://huggingface.co/THUDM/chatglm2-6b Copy","s":"Download checkpoint","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#download-checkpoint","p":26},{"i":34,"t":"First, launch the controller python -m fastchat.serve.controller Copy Then, launch the model worker(s) python -m fastchat.serve.model_worker --model-path chatglm2-6b Copy Finally, launch the RESTful API server python -m fastchat.serve.openai_api_server --host localhost --port 8000 Copy Normally this will work. However, if you encounter error like this, commenting out all the lines containing finish_reason in fastchat/protocol/api_protocal.py and fastchat/protocol/openai_api_protocol.py will fix the problem. The modified code looks like: class CompletionResponseChoice(BaseModel): index: int text: str logprobs: Optional[int] = None # finish_reason: Optional[Literal[\"stop\", \"length\"]]class CompletionResponseStreamChoice(BaseModel): index: int text: str logprobs: Optional[float] = None # finish_reason: Optional[Literal[\"stop\", \"length\"]] = None Copy","s":"Initiate server","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#initiate-server","p":26},{"i":36,"t":"Now the models can be directly accessed through openai-python library as well as flaml.oai.Completion and flaml.oai.ChatCompletion. from flaml import oai# create a text completion requestresponse = oai.Completion.create( config_list=[ { \"model\": \"chatglm2-6b\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", # just a placeholder } ], prompt=\"Hi\",)print(response)# create a chat completion requestresponse = oai.ChatCompletion.create( config_list=[ { \"model\": \"chatglm2-6b\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", } ], messages=[{\"role\": \"user\", \"content\": \"Hi\"}])print(response) Copy If you would like to switch to different models, download their checkpoints and specify model path when launching model worker(s).","s":"Interact with model using oai.Completion","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#interact-with-model-using-oaicompletion","p":26},{"i":38,"t":"If you would like to interact with multiple LLMs on your local machine, replace the model_worker step above with a multi model variant: python -m fastchat.serve.multi_model_worker \\ --model-path lmsys/vicuna-7b-v1.3 \\ --model-names vicuna-7b-v1.3 \\ --model-path chatglm2-6b \\ --model-names chatglm2-6b Copy The inference code would be: from flaml import oai# create a chat completion requestresponse = oai.ChatCompletion.create( config_list=[ { \"model\": \"chatglm2-6b\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", }, { \"model\": \"vicuna-7b-v1.3\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", } ], messages=[{\"role\": \"user\", \"content\": \"Hi\"}])print(response) Copy","s":"interacting with multiple local LLMs","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#interacting-with-multiple-local-llms","p":26},{"i":40,"t":"Documentation about flaml.autogen Documentation about FastChat.","s":"For Further Reading","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#for-further-reading","p":26},{"i":42,"t":"TL;DR: Celebrating FLAML's milestone: 1 million downloads Introducing Large Language Model (LLM) support in the upcoming FLAML v2 This week, FLAML has reached a significant milestone: 1 million downloads. Originating as an intern research project within Microsoft Research, FLAML has grown into an open-source library used widely across the industry and supported by an active community. As we celebrate this milestone, we want to recognize the passionate contributors and users who have played an essential role in molding FLAML into the flourishing project it is today. Our heartfelt gratitude goes out to each of you for your unwavering support, constructive feedback, and innovative contributions that have driven FLAML to new heights. A big shoutout to our industrial collaborators from Azure Core, Azure Machine Learning, Azure Synapse Analytics, Microsoft 365, ML.NET, Vowpal Wabbit, Anyscale, Databricks, and Wise; and academic collaborators from MIT, Penn State University, Stevens Institute of Technology, Tel Aviv University, Texas A & M University, University of Manchester, University of Washington, and The Chinese University of Hong Kong etc. We'd also like to take the opportunity to reflect on FLAML's past achievements and its future roadmap, with a particular focus on large language models (LLM) and LLMOps.","s":"Surpassing 1 Million Downloads - A Retrospective and a Look into the Future","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"","p":41},{"i":45,"t":"FLAML offers an off-the-shelf AutoML solution that enables users to quickly discover high-quality models or configurations for common ML/AI tasks. By automatically selecting models and hyperparameters for training or inference, FLAML saves users time and effort. FLAML has significantly reduced development time for developers and data scientists alike, while also providing a convenient way to integrate new algorithms into the pipeline, enabling easy extensions and large-scale parallel tuning. These features make FLAML a valuable tool in R&D efforts for many enterprise users. FLAML is capable of handling a variety of common ML tasks, such as classification, regression, time series forecasting, NLP tasks, and generative tasks, providing a comprehensive solution for various applications.","s":"Bring AutoML to One's Fingertips","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#bring-automl-to-ones-fingertips","p":41},{"i":47,"t":"What sets FLAML apart from other AutoML libraries is its exceptional efficiency, thanks to the economical and efficient hyperparameter optimization and model selection methods developed in our research. FLAML is also capable of handling large search spaces with heterogeneous evaluation costs, complex constraints, guidance, and early stopping. The zero-shot AutoML option further reduces the cost of AutoML, making FLAML an even more attractive solution for a wide range of applications with low resources.","s":"Speed and Efficiency: The FLAML Advantage","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#speed-and-efficiency-the-flaml-advantage","p":41},{"i":49,"t":"FLAML is designed for easy extensibility and customization, allowing users to add custom learners, metrics, search space, etc. For example, the support of hierarchical search spaces allows one to first choose an ML learner and then sampling from the hyperparameter space specific to that learner. The level of customization ranges from minimal (providing only training data and task type as input) to full (tuning a user-defined function). This flexibility and support for easy customization have led to FLAML's adoption in various domains, including security, finance, marketing, engineering, supply chain, insurance, and healthcare, delivering highly accurate results.","s":"Easy Customization and Extensibility","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#easy-customization-and-extensibility","p":41},{"i":51,"t":"As large language models continue to reshape the AI ecosystem, FLAML is poised to adapt and grow alongside these advancements. Recognizing the importance of large language models, we have recently incorporated an autogen package into FLAML, and are committed to focusing our collective efforts on addressing the unique challenges that arise in LLMOps (Large Language Model Operations). In its current iteration, FLAML offers support for model selection and inference parameter tuning for large language models. We are actively working on the development of new features, such as low-level inference API with caching, templating, filtering, and higher-level components like LLM-based coding and interactive agents, to enable more effective and economical usage of LLM. We are eagerly preparing for the launch of FLAML v2, where we will place special emphasis on incorporating and enhancing features specifically tailored for large language models (LLMs), further expanding FLAML's capabilities. We invite contributions from anyone interested in this topic and look forward to collaborating with the community as we shape the future of FLAML and LLMOps together.","s":"Embracing Large Language Models in FLAML v2","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#embracing-large-language-models-in-flaml-v2","p":41},{"i":53,"t":"Documentation about flaml.autogen Code Example: Tune chatGPT for Math Problem Solving with FLAML Do you have any experience to share about LLM applications? Do you like to see more support or research of LLMOps? Please join our Discord server for discussion.","s":"For Further Reading","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#for-further-reading","p":41},{"i":55,"t":"On this page","s":"Contributing","u":"/FLAML/docs/Contribute","h":"","p":54},{"i":57,"t":"When you submit an issue to GitHub, please do your best to follow these guidelines! This will make it a lot easier to provide you with good feedback: The ideal bug report contains a short reproducible code snippet. This way anyone can try to reproduce the bug easily (see this for more details). If your snippet is longer than around 50 lines, please link to a gist or a GitHub repo. If an exception is raised, please provide the full traceback. Please include your operating system type and version number, as well as your Python, flaml, scikit-learn versions. The version of flaml can be found by running the following code snippet: import flamlprint(flaml.__version__) Copy Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks for more details.","s":"How to make a good bug report","u":"/FLAML/docs/Contribute","h":"#how-to-make-a-good-bug-report","p":54},{"i":59,"t":"There is currently no formal reviewer solicitation process. Current reviewers identify reviewers from active contributors. If you are willing to become a reviewer, you are welcome to let us know on discord.","s":"Becoming a Reviewer","u":"/FLAML/docs/Contribute","h":"#becoming-a-reviewer","p":54},{"i":62,"t":"git clone https://github.com/microsoft/FLAML.gitpip install -e FLAML[notebook,autogen] Copy In case the pip install command fails, try escaping the brackets such as pip install -e FLAML\\[notebook,autogen\\].","s":"Setup","u":"/FLAML/docs/Contribute","h":"#setup","p":54},{"i":64,"t":"We provide a simple Dockerfile. docker build https://github.com/microsoft/FLAML.git#main -t flaml-devdocker run -it flaml-dev Copy","s":"Docker","u":"/FLAML/docs/Contribute","h":"#docker","p":54},{"i":66,"t":"If you use vscode, you can open the FLAML folder in a Container. We have provided the configuration in devcontainer.","s":"Develop in Remote Container","u":"/FLAML/docs/Contribute","h":"#develop-in-remote-container","p":54},{"i":68,"t":"Run pre-commit install to install pre-commit into your git hooks. Before you commit, run pre-commit run to check if you meet the pre-commit requirements. If you use Windows (without WSL) and can't commit after installing pre-commit, you can run pre-commit uninstall to uninstall the hook. In WSL or Linux this is supposed to work.","s":"Pre-commit","u":"/FLAML/docs/Contribute","h":"#pre-commit","p":54},{"i":70,"t":"Any code you commit should not decrease coverage. To run all unit tests, install the [test] option under FLAML/: pip install -e.\"[test]\"coverage run -m pytest test Copy Then you can see the coverage report by coverage report -m or coverage html.","s":"Coverage","u":"/FLAML/docs/Contribute","h":"#coverage","p":54},{"i":72,"t":"To build and test documentation locally, install Node.js. For example, nvm install --lts Copy Then: npm install --global yarn # skip if you use the dev container we providedpip install pydoc-markdown==4.5.0 # skip if you use the dev container we providedcd websiteyarn install --frozen-lockfile --ignore-enginespydoc-markdownyarn start Copy The last command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. Note: some tips in this guide are based off the contributor guide from ray, scikit-learn, or hummingbird.","s":"Documentation","u":"/FLAML/docs/Contribute","h":"#documentation","p":54},{"i":74,"t":"AutoGen - Tune GPT Models flaml.autogen offers a cost-effective hyperparameter optimization technique EcoOptiGen for tuning Large Language Models. The research study finds that tuning hyperparameters can significantly improve the utility of them. Please find documentation about this feature here. Links to notebook examples: Optimize for Code Generation | Open in colab Optimize for Math | Open in colab","s":"AutoGen - Tune GPT Models","u":"/FLAML/docs/Examples/AutoGen-OpenAI","h":"","p":73},{"i":76,"t":"AutoGen - Automated Multi Agent Chat Please refer to https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat.","s":"AutoGen - Automated Multi Agent Chat","u":"/FLAML/docs/Examples/AutoGen-AgentChat","h":"","p":75},{"i":78,"t":"TL;DR: Just by tuning the inference parameters like model, number of responses, temperature etc. without changing any model weights or prompt, the baseline accuracy of untuned gpt-4 can be improved by 20% in high school math competition problems. For easy problems, the tuned gpt-3.5-turbo model vastly outperformed untuned gpt-4 in accuracy (e.g., 90% vs. 70%) and cost efficiency. For hard problems, the tuned gpt-4 is much more accurate (e.g., 35% vs. 20%) and less expensive than untuned gpt-4. FLAML can help with model selection, parameter tuning, and cost-saving in LLM applications. Large language models (LLMs) are powerful tools that can generate natural language texts for various applications, such as chatbots, summarization, translation, and more. GPT-4 is currently the state of the art LLM in the world. Is model selection irrelevant? What about inference parameters? In this blog post, we will explore how model and inference parameter matter in LLM applications, using a case study for MATH, a benchmark for evaluating LLMs on advanced mathematical problem solving. MATH consists of 12K math competition problems from AMC-10, AMC-12 and AIME. Each problem is accompanied by a step-by-step solution. We will use the new subpackage flaml.autogen to automatically find the best model and inference parameter for LLMs on a given task and dataset given an inference budget, using a novel low-cost search & pruning strategy. FLAML currently supports all the LLMs from OpenAI, such as GPT-3.5 and GPT-4. We will use FLAML to perform model selection and inference parameter tuning. Then we compare the performance and inference cost on solving algebra problems with the untuned gpt-4. We will also analyze how different difficulty levels affect the results.","s":"Does Model and Inference Parameter Matter in LLM Applications? - A Case Study for MATH","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"","p":77},{"i":80,"t":"We use FLAML to select between the following models with a target inference budget $0.02 per instance: gpt-3.5-turbo, a relatively cheap model that powers the popular ChatGPT app gpt-4, the state of the art LLM that costs more than 10 times of gpt-3.5-turbo We adapt the models using 20 examples in the train set, using the problem statement as the input and generating the solution as the output. We use the following inference parameters: temperature: The parameter that controls the randomness of the output text. A higher temperature means more diversity but less coherence. We search for the optimal temperature in the range of [0, 1]. top_p: The parameter that controls the probability mass of the output tokens. Only tokens with a cumulative probability less than or equal to top-p are considered. A lower top-p means more diversity but less coherence. We search for the optimal top-p in the range of [0, 1]. max_tokens: The maximum number of tokens that can be generated for each output. We search for the optimal max length in the range of [50, 1000]. n: The number of responses to generate. We search for the optimal n in the range of [1, 100]. prompt: We use the template: \"{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed{{}}.\" where {problem} will be replaced by the math problem instance. In this experiment, when n > 1, we find the answer with highest votes among all the responses and then select it as the final answer to compare with the ground truth. For example, if n = 5 and 3 of the responses contain a final answer 301 while 2 of the responses contain a final answer 159, we choose 301 as the final answer. This can help with resolving potential errors due to randomness. We use the average accuracy and average inference cost as the metric to evaluate the performance over a dataset. The inference cost of a particular instance is measured by the price per 1K tokens and the number of tokens consumed.","s":"Experiment Setup","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#experiment-setup","p":77},{"i":82,"t":"The first figure in this blog post shows the average accuracy and average inference cost of each configuration on the level 2 Algebra test set. Surprisingly, the tuned gpt-3.5-turbo model is selected as a better model and it vastly outperforms untuned gpt-4 in accuracy (92% vs. 70%) with equal or 2.5 times higher inference budget. The same observation can be obtained on the level 3 Algebra test set. However, the selected model changes on level 4 Algebra. This time gpt-4 is selected as the best model. The tuned gpt-4 achieves much higher accuracy (56% vs. 44%) and lower cost than the untuned gpt-4. On level 5 the result is similar. We can see that FLAML has found different optimal model and inference parameters for each subset of a particular level, which shows that these parameters matter in cost-sensitive LLM applications and need to be carefully tuned or adapted. An example notebook to run these experiments can be found at: https://github.com/microsoft/FLAML/blob/v1.2.1/notebook/autogen_chatgpt.ipynb","s":"Experiment Results","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#experiment-results","p":77},{"i":84,"t":"While gpt-3.5-turbo demonstrates competitive accuracy with voted answers in relatively easy algebra problems under the same inference budget, gpt-4 is a better choice for the most difficult problems. In general, through parameter tuning and model selection, we can identify the opportunity to save the expensive model for more challenging tasks, and improve the overall effectiveness of a budget-constrained system. There are many other alternative ways of solving math problems, which we have not covered in this blog post. When there are choices beyond the inference parameters, they can be generally tuned via flaml.tune. The need for model selection, parameter tuning and cost saving is not specific to the math problems. The Auto-GPT project is an example where high cost can easily prevent a generic complex task to be accomplished as it needs many LLM inference calls.","s":"Analysis and Discussion","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#analysis-and-discussion","p":77},{"i":86,"t":"Research paper about the tuning technique Documentation about flaml.autogen Do you have any experience to share about LLM applications? Do you like to see more support or research of LLM optimization or automation? Please join our Discord server for discussion.","s":"For Further Reading","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#for-further-reading","p":77},{"i":88,"t":"On this page","s":"AutoML - Classification","u":"/FLAML/docs/Examples/AutoML-Classification","h":"","p":87},{"i":90,"t":"Install the [automl] option. pip install \"flaml[automl]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Classification","h":"#prerequisites","p":87},{"i":92,"t":"from flaml import AutoMLfrom sklearn.datasets import load_iris# Initialize an AutoML instanceautoml = AutoML()# Specify automl goal and constraintautoml_settings = { \"time_budget\": 1, # in seconds \"metric\": \"accuracy\", \"task\": \"classification\", \"log_file_name\": \"iris.log\",}X_train, y_train = load_iris(return_X_y=True)# Train with labeled input dataautoml.fit(X_train=X_train, y_train=y_train, **automl_settings)# Predictprint(automl.predict_proba(X_train))# Print the best modelprint(automl.model.estimator) Copy Sample of output​ [flaml.automl: 11-12 18:21:44] {1485} INFO - Data split method: stratified[flaml.automl: 11-12 18:21:44] {1489} INFO - Evaluation method: cv[flaml.automl: 11-12 18:21:44] {1540} INFO - Minimizing error metric: 1-accuracy[flaml.automl: 11-12 18:21:44] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1'][flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-12 18:21:44] {1944} INFO - Estimated sufficient time budget=1285s. Estimated necessary time budget=23s.[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.2s, estimator lgbm's best error=0.0733, best estimator lgbm's best error=0.0733[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.3s, estimator lgbm's best error=0.0733, best estimator lgbm's best error=0.0733[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.4s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 4, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 5, current learner xgboost[flaml.automl: 11-12 18:21:45] {2029} INFO - at 0.9s, estimator xgboost's best error=0.0600, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-12 18:21:45] {2029} INFO - at 1.0s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 7, current learner extra_tree[flaml.automl: 11-12 18:21:45] {2029} INFO - at 1.1s, estimator extra_tree's best error=0.0667, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:45] {2242} INFO - retrain lgbm for 0.0s[flaml.automl: 11-12 18:21:45] {2247} INFO - retrained model: LGBMClassifier(learning_rate=0.2677050123105203, max_bin=127, min_child_samples=12, n_estimators=4, num_leaves=4, reg_alpha=0.001348364934537134, reg_lambda=1.4442580148221913, verbose=-1)[flaml.automl: 11-12 18:21:45] {1608} INFO - fit succeeded[flaml.automl: 11-12 18:21:45] {1610} INFO - Time taken to find the best model: 0.3756711483001709 Copy","s":"A basic classification example","u":"/FLAML/docs/Examples/AutoML-Classification","h":"#a-basic-classification-example","p":87},{"i":94,"t":"Link to notebook | Open in colab","s":"A more advanced example including custom learner and metric","u":"/FLAML/docs/Examples/AutoML-Classification","h":"#a-more-advanced-example-including-custom-learner-and-metric","p":87},{"i":96,"t":"On this page","s":"AutoML for XGBoost","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"","p":95},{"i":98,"t":"Install the [automl] option. pip install \"flaml[automl] matplotlib openml\" Copy","s":"Prerequisites for this example","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"#prerequisites-for-this-example","p":95},{"i":100,"t":"from flaml import AutoMLfrom flaml.automl.data import load_openml_dataset# Download [houses dataset](https://www.openml.org/d/537) from OpenML. The task is to predict median price of the house in the region based on demographic composition and a state of housing market in the region.X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")automl = AutoML()settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"r2\", # primary metrics for regression can be chosen from: ['mae','mse','r2'] \"estimator_list\": [ \"xgboost\" ], # list of ML learners; we tune XGBoost in this example \"task\": \"regression\", # task type \"log_file_name\": \"houses_experiment.log\", # flaml log file \"seed\": 7654321, # random seed}automl.fit(X_train=X_train, y_train=y_train, **settings) Copy Sample output​ [flaml.automl: 09-29 23:06:46] {1446} INFO - Data split method: uniform[flaml.automl: 09-29 23:06:46] {1450} INFO - Evaluation method: cv[flaml.automl: 09-29 23:06:46] {1496} INFO - Minimizing error metric: 1-r2[flaml.automl: 09-29 23:06:46] {1533} INFO - List of ML learners in AutoML Run: ['xgboost'][flaml.automl: 09-29 23:06:46] {1763} INFO - iteration 0, current learner xgboost[flaml.automl: 09-29 23:06:47] {1880} INFO - Estimated sufficient time budget=2621s. Estimated necessary time budget=3s.[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.3s, estimator xgboost's best error=2.1267, best estimator xgboost's best error=2.1267[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 1, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.5s, estimator xgboost's best error=2.1267, best estimator xgboost's best error=2.1267[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 2, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.6s, estimator xgboost's best error=0.8485, best estimator xgboost's best error=0.8485[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 3, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.8s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 4, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 1.0s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 5, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 1.2s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 6, current learner xgboost[flaml.automl: 09-29 23:06:48] {1952} INFO - at 1.5s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992[flaml.automl: 09-29 23:06:48] {1763} INFO - iteration 7, current learner xgboost[flaml.automl: 09-29 23:06:48] {1952} INFO - at 1.9s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992[flaml.automl: 09-29 23:06:48] {1763} INFO - iteration 8, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.2s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 9, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.5s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 10, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.8s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 11, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 3.0s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 12, current learner xgboost[flaml.automl: 09-29 23:06:50] {1952} INFO - at 3.3s, estimator xgboost's best error=0.2113, best estimator xgboost's best error=0.2113[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 13, current learner xgboost[flaml.automl: 09-29 23:06:50] {1952} INFO - at 3.5s, estimator xgboost's best error=0.2113, best estimator xgboost's best error=0.2113[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 14, current learner xgboost[flaml.automl: 09-29 23:06:50] {1952} INFO - at 4.0s, estimator xgboost's best error=0.2090, best estimator xgboost's best error=0.2090[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 15, current learner xgboost[flaml.automl: 09-29 23:06:51] {1952} INFO - at 4.5s, estimator xgboost's best error=0.2090, best estimator xgboost's best error=0.2090[flaml.automl: 09-29 23:06:51] {1763} INFO - iteration 16, current learner xgboost[flaml.automl: 09-29 23:06:51] {1952} INFO - at 5.2s, estimator xgboost's best error=0.1919, best estimator xgboost's best error=0.1919[flaml.automl: 09-29 23:06:51] {1763} INFO - iteration 17, current learner xgboost[flaml.automl: 09-29 23:06:52] {1952} INFO - at 5.5s, estimator xgboost's best error=0.1919, best estimator xgboost's best error=0.1919[flaml.automl: 09-29 23:06:52] {1763} INFO - iteration 18, current learner xgboost[flaml.automl: 09-29 23:06:54] {1952} INFO - at 8.0s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:06:54] {1763} INFO - iteration 19, current learner xgboost[flaml.automl: 09-29 23:06:55] {1952} INFO - at 9.0s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:06:55] {1763} INFO - iteration 20, current learner xgboost[flaml.automl: 09-29 23:07:08] {1952} INFO - at 21.8s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:07:08] {1763} INFO - iteration 21, current learner xgboost[flaml.automl: 09-29 23:07:11] {1952} INFO - at 24.4s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:07:11] {1763} INFO - iteration 22, current learner xgboost[flaml.automl: 09-29 23:07:16] {1952} INFO - at 30.0s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:16] {1763} INFO - iteration 23, current learner xgboost[flaml.automl: 09-29 23:07:20] {1952} INFO - at 33.5s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:20] {1763} INFO - iteration 24, current learner xgboost[flaml.automl: 09-29 23:07:29] {1952} INFO - at 42.3s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:29] {1763} INFO - iteration 25, current learner xgboost[flaml.automl: 09-29 23:07:30] {1952} INFO - at 43.2s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:30] {1763} INFO - iteration 26, current learner xgboost[flaml.automl: 09-29 23:07:50] {1952} INFO - at 63.4s, estimator xgboost's best error=0.1663, best estimator xgboost's best error=0.1663[flaml.automl: 09-29 23:07:50] {2059} INFO - selected model: <xgboost.core.Booster object at 0x7f6399005910>[flaml.automl: 09-29 23:07:55] {2122} INFO - retrain xgboost for 5.4s[flaml.automl: 09-29 23:07:55] {2128} INFO - retrained model: <xgboost.core.Booster object at 0x7f6398fc0eb0>[flaml.automl: 09-29 23:07:55] {1557} INFO - fit succeeded[flaml.automl: 09-29 23:07:55] {1558} INFO - Time taken to find the best model: 63.427649974823[flaml.automl: 09-29 23:07:55] {1569} WARNING - Time taken to find the best model is 106% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy Retrieve best config​ print(\"Best hyperparmeter config:\", automl.best_config)print(\"Best r2 on validation data: {0:.4g}\".format(1 - automl.best_loss))print(\"Training duration of best run: {0:.4g} s\".format(automl.best_config_train_time))print(automl.model.estimator)# Best hyperparmeter config: {'n_estimators': 473, 'max_leaves': 35, 'max_depth': 0, 'min_child_weight': 0.001, 'learning_rate': 0.26865031351923346, 'subsample': 0.9718245679598786, 'colsample_bylevel': 0.7421362469066445, 'colsample_bytree': 1.0, 'reg_alpha': 0.06824336834995245, 'reg_lambda': 250.9654222583276}# Best r2 on validation data: 0.8384# Training duration of best run: 2.194 s# XGBRegressor(base_score=0.5, booster='gbtree',# colsample_bylevel=0.7421362469066445, colsample_bynode=1,# colsample_bytree=1.0, gamma=0, gpu_id=-1, grow_policy='lossguide',# importance_type='gain', interaction_constraints='',# learning_rate=0.26865031351923346, max_delta_step=0, max_depth=0,# max_leaves=35, min_child_weight=0.001, missing=nan,# monotone_constraints='()', n_estimators=473, n_jobs=-1,# num_parallel_tree=1, random_state=0, reg_alpha=0.06824336834995245,# reg_lambda=250.9654222583276, scale_pos_weight=1,# subsample=0.9718245679598786, tree_method='hist',# use_label_encoder=False, validate_parameters=1, verbosity=0) Copy Plot feature importance​ import matplotlib.pyplot as pltplt.barh(automl.feature_names_in_, automl.feature_importances_) Copy Compute predictions of testing dataset​ y_pred = automl.predict(X_test)print(\"Predicted labels\", y_pred)# Predicted labels [139062.95 237622. 140522.03 ... 182125.5 252156.36 264884.5 ] Copy Compute different metric values on testing dataset​ from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))print(\"mse\", \"=\", sklearn_metric_loss_score(\"mse\", y_pred, y_test))print(\"mae\", \"=\", sklearn_metric_loss_score(\"mae\", y_pred, y_test))# r2 = 0.8456494234135888# mse = 2040284106.2781258# mae = 30212.830996680445 Copy Compare with untuned XGBoost​ from xgboost import XGBRegressorxgb = XGBRegressor()xgb.fit(X_train, y_train)y_pred = xgb.predict(X_test)from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"default xgboost r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))# default xgboost r2 = 0.8265451174596482 Copy Plot learning curve​ How does the model accuracy improve as we search for different hyperparameter configurations? from flaml.automl.data import get_output_from_logimport numpy as nptime_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = get_output_from_log(filename=settings['log_file_name'], time_budget=60)plt.title('Learning Curve')plt.xlabel('Wall Clock Time (s)')plt.ylabel('Validation r2')plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')plt.show() Copy","s":"Use built-in XGBoostSklearnEstimator","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"#use-built-in-xgboostsklearnestimator","p":95},{"i":102,"t":"You can easily enable a custom objective function by adding a customized XGBoost learner (inherit XGBoostEstimator or XGBoostSklearnEstimator) in FLAML. In the following example, we show how to add such a customized XGBoost learner with a custom objective function. import numpy as np# define your customized objective functiondef logregobj(preds, dtrain): labels = dtrain.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight grad = preds - labels hess = preds * (1.0 - preds) return grad, hessfrom flaml.automl.model import XGBoostEstimatorclass MyXGB1(XGBoostEstimator): \"\"\"XGBoostEstimator with the logregobj function as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=logregobj, **config)class MyXGB2(XGBoostEstimator): \"\"\"XGBoostEstimator with 'reg:squarederror' as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=\"reg:gamma\", **config) Copy Add the customized learners and tune them​ automl = AutoML()automl.add_learner(learner_name=\"my_xgb1\", learner_class=MyXGB1)automl.add_learner(learner_name=\"my_xgb2\", learner_class=MyXGB2)settings[\"estimator_list\"] = [\"my_xgb1\", \"my_xgb2\"] # change the estimator listautoml.fit(X_train=X_train, y_train=y_train, **settings) Copy Link to notebook | Open in colab","s":"Use a customized XGBoost learner","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"#use-a-customized-xgboost-learner","p":95},{"i":104,"t":"On this page","s":"AutoML for LightGBM","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"","p":103},{"i":106,"t":"Install the [automl] option. pip install \"flaml[automl] matplotlib openml\" Copy","s":"Prerequisites for this example","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"#prerequisites-for-this-example","p":103},{"i":108,"t":"from flaml import AutoMLfrom flaml.automl.data import load_openml_dataset# Download [houses dataset](https://www.openml.org/d/537) from OpenML. The task is to predict median price of the house in the region based on demographic composition and a state of housing market in the region.X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")automl = AutoML()settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"r2\", # primary metrics for regression can be chosen from: ['mae','mse','r2'] \"estimator_list\": [\"lgbm\"], # list of ML learners; we tune lightgbm in this example \"task\": \"regression\", # task type \"log_file_name\": \"houses_experiment.log\", # flaml log file \"seed\": 7654321, # random seed}automl.fit(X_train=X_train, y_train=y_train, **settings) Copy Sample output​ [flaml.automl: 11-15 19:46:44] {1485} INFO - Data split method: uniform[flaml.automl: 11-15 19:46:44] {1489} INFO - Evaluation method: cv[flaml.automl: 11-15 19:46:44] {1540} INFO - Minimizing error metric: 1-r2[flaml.automl: 11-15 19:46:44] {1577} INFO - List of ML learners in AutoML Run: ['lgbm'][flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-15 19:46:44] {1944} INFO - Estimated sufficient time budget=3232s. Estimated necessary time budget=3s.[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.5s, estimator lgbm's best error=0.7383, best estimator lgbm's best error=0.7383[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.4774, best estimator lgbm's best error=0.4774[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.7s, estimator lgbm's best error=0.4774, best estimator lgbm's best error=0.4774[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.9s, estimator lgbm's best error=0.2985, best estimator lgbm's best error=0.2985[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 4, current learner lgbm[flaml.automl: 11-15 19:46:45] {2029} INFO - at 1.3s, estimator lgbm's best error=0.2337, best estimator lgbm's best error=0.2337[flaml.automl: 11-15 19:46:45] {1826} INFO - iteration 5, current learner lgbm[flaml.automl: 11-15 19:46:45] {2029} INFO - at 1.4s, estimator lgbm's best error=0.2337, best estimator lgbm's best error=0.2337[flaml.automl: 11-15 19:46:45] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-15 19:46:46] {2029} INFO - at 2.5s, estimator lgbm's best error=0.2219, best estimator lgbm's best error=0.2219[flaml.automl: 11-15 19:46:46] {1826} INFO - iteration 7, current learner lgbm[flaml.automl: 11-15 19:46:46] {2029} INFO - at 2.9s, estimator lgbm's best error=0.2219, best estimator lgbm's best error=0.2219[flaml.automl: 11-15 19:46:46] {1826} INFO - iteration 8, current learner lgbm[flaml.automl: 11-15 19:46:48] {2029} INFO - at 4.5s, estimator lgbm's best error=0.1764, best estimator lgbm's best error=0.1764[flaml.automl: 11-15 19:46:48] {1826} INFO - iteration 9, current learner lgbm[flaml.automl: 11-15 19:46:54] {2029} INFO - at 10.5s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:46:54] {1826} INFO - iteration 10, current learner lgbm[flaml.automl: 11-15 19:46:56] {2029} INFO - at 12.4s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:46:56] {1826} INFO - iteration 11, current learner lgbm[flaml.automl: 11-15 19:47:13] {2029} INFO - at 29.0s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:47:13] {1826} INFO - iteration 12, current learner lgbm[flaml.automl: 11-15 19:47:15] {2029} INFO - at 31.1s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:47:15] {1826} INFO - iteration 13, current learner lgbm[flaml.automl: 11-15 19:47:29] {2029} INFO - at 45.8s, estimator lgbm's best error=0.1564, best estimator lgbm's best error=0.1564[flaml.automl: 11-15 19:47:33] {2242} INFO - retrain lgbm for 3.2s[flaml.automl: 11-15 19:47:33] {2247} INFO - retrained model: LGBMRegressor(colsample_bytree=0.8025848209352517, learning_rate=0.09100963138990374, max_bin=255, min_child_samples=42, n_estimators=363, num_leaves=216, reg_alpha=0.001113000336715291, reg_lambda=76.50614276906414, verbose=-1)[flaml.automl: 11-15 19:47:33] {1608} INFO - fit succeeded[flaml.automl: 11-15 19:47:33] {1610} INFO - Time taken to find the best model: 45.75616669654846[flaml.automl: 11-15 19:47:33] {1624} WARNING - Time taken to find the best model is 76% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy Retrieve best config​ print(\"Best hyperparmeter config:\", automl.best_config)print(\"Best r2 on validation data: {0:.4g}\".format(1 - automl.best_loss))print(\"Training duration of best run: {0:.4g} s\".format(automl.best_config_train_time))print(automl.model.estimator)# Best hyperparmeter config: {'n_estimators': 363, 'num_leaves': 216, 'min_child_samples': 42, 'learning_rate': 0.09100963138990374, 'log_max_bin': 8, 'colsample_bytree': 0.8025848209352517, 'reg_alpha': 0.001113000336715291, 'reg_lambda': 76.50614276906414}# Best r2 on validation data: 0.8436# Training duration of best run: 3.229 s# LGBMRegressor(colsample_bytree=0.8025848209352517,# learning_rate=0.09100963138990374, max_bin=255,# min_child_samples=42, n_estimators=363, num_leaves=216,# reg_alpha=0.001113000336715291, reg_lambda=76.50614276906414,# verbose=-1) Copy Plot feature importance​ import matplotlib.pyplot as pltplt.barh(automl.feature_names_in_, automl.feature_importances_) Copy Compute predictions of testing dataset​ y_pred = automl.predict(X_test)print(\"Predicted labels\", y_pred)# Predicted labels [143391.65036562 245535.13731811 153171.44071629 ... 184354.52735963# 235510.49470445 282617.22858956] Copy Compute different metric values on testing dataset​ from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))print(\"mse\", \"=\", sklearn_metric_loss_score(\"mse\", y_pred, y_test))print(\"mae\", \"=\", sklearn_metric_loss_score(\"mae\", y_pred, y_test))# r2 = 0.8505434326526395# mse = 1975592613.138005# mae = 29471.536046068788 Copy Compare with untuned LightGBM​ from lightgbm import LGBMRegressorlgbm = LGBMRegressor()lgbm.fit(X_train, y_train)y_pred = lgbm.predict(X_test)from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"default lgbm r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))# default lgbm r2 = 0.8296179648694404 Copy Plot learning curve​ How does the model accuracy improve as we search for different hyperparameter configurations? from flaml.automl.data import get_output_from_logimport numpy as nptime_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = get_output_from_log(filename=settings['log_file_name'], time_budget=60)plt.title('Learning Curve')plt.xlabel('Wall Clock Time (s)')plt.ylabel('Validation r2')plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')plt.show() Copy","s":"Use built-in LGBMEstimator","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"#use-built-in-lgbmestimator","p":103},{"i":110,"t":"The native API of LightGBM allows one to specify a custom objective function in the model constructor. You can easily enable it by adding a customized LightGBM learner in FLAML. In the following example, we show how to add such a customized LightGBM learner with a custom objective function. Create a customized LightGBM learner with a custom objective function​ import numpy as np# define your customized objective functiondef my_loss_obj(y_true, y_pred): c = 0.5 residual = y_pred - y_true grad = c * residual / (np.abs(residual) + c) hess = c ** 2 / (np.abs(residual) + c) ** 2 # rmse grad and hess grad_rmse = residual hess_rmse = 1.0 # mae grad and hess grad_mae = np.array(residual) grad_mae[grad_mae > 0] = 1. grad_mae[grad_mae <= 0] = -1. hess_mae = 1.0 coef = [0.4, 0.3, 0.3] return coef[0] * grad + coef[1] * grad_rmse + coef[2] * grad_mae, coef[0] * hess + coef[1] * hess_rmse + coef[2] * hess_maefrom flaml.automl.model import LGBMEstimatorclass MyLGBM(LGBMEstimator): \"\"\"LGBMEstimator with my_loss_obj as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=my_loss_obj, **config) Copy Add the customized learner and tune it​ automl = AutoML()automl.add_learner(learner_name=\"my_lgbm\", learner_class=MyLGBM)settings[\"estimator_list\"] = [\"my_lgbm\"] # change the estimator listautoml.fit(X_train=X_train, y_train=y_train, **settings) Copy Link to notebook | Open in colab","s":"Use a customized LightGBM learner","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"#use-a-customized-lightgbm-learner","p":103},{"i":112,"t":"On this page","s":"AutoML - NLP","u":"/FLAML/docs/Examples/AutoML-NLP","h":"","p":111},{"i":114,"t":"This example requires GPU. Install the [automl,hf] option: pip install \"flaml[automl,hf]\" Copy","s":"Requirements","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#requirements","p":111},{"i":116,"t":"from flaml import AutoMLfrom datasets import load_datasettrain_dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\").to_pandas()dev_dataset = load_dataset(\"glue\", \"mrpc\", split=\"validation\").to_pandas()test_dataset = load_dataset(\"glue\", \"mrpc\", split=\"test\").to_pandas()custom_sent_keys = [\"sentence1\", \"sentence2\"]label_key = \"label\"X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]X_test = test_dataset[custom_sent_keys]automl = AutoML()automl_settings = { \"time_budget\": 100, \"task\": \"seq-classification\", \"fit_kwargs_by_estimator\": { \"transformer\": { \"output_dir\": \"data/output/\" # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base } }, # setting the huggingface arguments: output directory \"gpu_per_trial\": 1, # set to 0 if no GPU is available}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)automl.predict(X_test) Copy Notice that after you run automl.fit, the intermediate checkpoints are saved under the specified output_dir data/output. You can use the following code to clean these outputs if they consume a large storage space: if os.path.exists(\"data/output/\"): shutil.rmtree(\"data/output/\") Copy Sample output​ [flaml.automl: 12-06 08:21:39] {1943} INFO - task = seq-classification[flaml.automl: 12-06 08:21:39] {1945} INFO - Data split method: stratified[flaml.automl: 12-06 08:21:39] {1949} INFO - Evaluation method: holdout[flaml.automl: 12-06 08:21:39] {2019} INFO - Minimizing error metric: 1-accuracy[flaml.automl: 12-06 08:21:39] {2071} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 12-06 08:21:39] {2311} INFO - iteration 0, current learner transformer{'data/output/train_2021-12-06_08-21-53/train_8947b1b2_1_n=1e-06,s=9223372036854775807,e=1e-05,s=-1,s=0.45765,e=32,d=42,o=0.0,y=0.0_2021-12-06_08-21-53/checkpoint-53': 53}[flaml.automl: 12-06 08:22:56] {2424} INFO - Estimated sufficient time budget=766860s. Estimated necessary time budget=767s.[flaml.automl: 12-06 08:22:56] {2499} INFO - at 76.7s, estimator transformer's best error=0.1740, best estimator transformer's best error=0.1740[flaml.automl: 12-06 08:22:56] {2606} INFO - selected model: <flaml.nlp.huggingface.trainer.TrainerForAuto object at 0x7f49ea8414f0>[flaml.automl: 12-06 08:22:56] {2100} INFO - fit succeeded[flaml.automl: 12-06 08:22:56] {2101} INFO - Time taken to find the best model: 76.69802761077881[flaml.automl: 12-06 08:22:56] {2112} WARNING - Time taken to find the best model is 77% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy","s":"A simple sequence classification example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-sequence-classification-example","p":111},{"i":118,"t":"from flaml import AutoMLfrom datasets import load_datasettrain_dataset = load_dataset(\"glue\", \"stsb\", split=\"train\").to_pandas()dev_dataset = load_dataset(\"glue\", \"stsb\", split=\"train\").to_pandas()custom_sent_keys = [\"sentence1\", \"sentence2\"]label_key = \"label\"X_train = train_dataset[custom_sent_keys]y_train = train_dataset[label_key]X_val = dev_dataset[custom_sent_keys]y_val = dev_dataset[label_key]automl = AutoML()automl_settings = { \"gpu_per_trial\": 0, \"time_budget\": 20, \"task\": \"seq-regression\", \"metric\": \"rmse\",}automl_settings[\"fit_kwargs_by_estimator\"] = { # setting the huggingface arguments \"transformer\": { \"model_path\": \"google/electra-small-discriminator\", # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base \"output_dir\": \"data/output/\", # setting the output directory \"fp16\": False, } # setting whether to use FP16}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings) Copy Sample output​ [flaml.automl: 12-20 11:47:28] {1965} INFO - task = seq-regression[flaml.automl: 12-20 11:47:28] {1967} INFO - Data split method: uniform[flaml.automl: 12-20 11:47:28] {1971} INFO - Evaluation method: holdout[flaml.automl: 12-20 11:47:28] {2063} INFO - Minimizing error metric: rmse[flaml.automl: 12-20 11:47:28] {2115} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 12-20 11:47:28] {2355} INFO - iteration 0, current learner transformer Copy","s":"A simple sequence regression example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-sequence-regression-example","p":111},{"i":120,"t":"from flaml import AutoMLfrom datasets import load_datasettrain_dataset = load_dataset(\"xsum\", split=\"train\").to_pandas()dev_dataset = load_dataset(\"xsum\", split=\"validation\").to_pandas()custom_sent_keys = [\"document\"]label_key = \"summary\"X_train = train_dataset[custom_sent_keys]y_train = train_dataset[label_key]X_val = dev_dataset[custom_sent_keys]y_val = dev_dataset[label_key]automl = AutoML()automl_settings = { \"gpu_per_trial\": 1, \"time_budget\": 20, \"task\": \"summarization\", \"metric\": \"rouge1\",}automl_settings[\"fit_kwargs_by_estimator\"] = { # setting the huggingface arguments \"transformer\": { \"model_path\": \"t5-small\", # if model_path is not set, the default model is t5-small: https://huggingface.co/t5-small \"output_dir\": \"data/output/\", # setting the output directory \"fp16\": False, } # setting whether to use FP16}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings) Copy Sample Output​ [flaml.automl: 12-20 11:44:03] {1965} INFO - task = summarization[flaml.automl: 12-20 11:44:03] {1967} INFO - Data split method: uniform[flaml.automl: 12-20 11:44:03] {1971} INFO - Evaluation method: holdout[flaml.automl: 12-20 11:44:03] {2063} INFO - Minimizing error metric: -rouge[flaml.automl: 12-20 11:44:03] {2115} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 12-20 11:44:03] {2355} INFO - iteration 0, current learner transformerloading configuration file https://huggingface.co/t5-small/resolve/main/config.json from cache at /home/xliu127/.cache/huggingface/transformers/fe501e8fd6425b8ec93df37767fcce78ce626e34cc5edc859c662350cf712e41.406701565c0afd9899544c1cb8b93185a76f00b31e5ce7f6e18bbaef02241985Model config T5Config { \"_name_or_path\": \"t5-small\", \"architectures\": [ \"T5WithLMHeadModel\" ], \"d_ff\": 2048, \"d_kv\": 64, \"d_model\": 512, \"decoder_start_token_id\": 0, \"dropout_rate\": 0.1, \"eos_token_id\": 1, \"feed_forward_proj\": \"relu\", \"initializer_factor\": 1.0, \"is_encoder_decoder\": true, \"layer_norm_epsilon\": 1e-06, \"model_type\": \"t5\", \"n_positions\": 512, \"num_decoder_layers\": 6, \"num_heads\": 8, \"num_layers\": 6, \"output_past\": true, \"pad_token_id\": 0, \"relative_attention_num_buckets\": 32, \"task_specific_params\": { \"summarization\": { \"early_stopping\": true, \"length_penalty\": 2.0, \"max_length\": 200, \"min_length\": 30, \"no_repeat_ngram_size\": 3, \"num_beams\": 4, \"prefix\": \"summarize: \" }, \"translation_en_to_de\": { \"early_stopping\": true, \"max_length\": 300, \"num_beams\": 4, \"prefix\": \"translate English to German: \" }, \"translation_en_to_fr\": { \"early_stopping\": true, \"max_length\": 300, \"num_beams\": 4, \"prefix\": \"translate English to French: \" }, \"translation_en_to_ro\": { \"early_stopping\": true, \"max_length\": 300, \"num_beams\": 4, \"prefix\": \"translate English to Romanian: \" } }, \"transformers_version\": \"4.14.1\", \"use_cache\": true, \"vocab_size\": 32128} Copy","s":"A simple summarization example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-summarization-example","p":111},{"i":122,"t":"There are two ways to define the label for a token classification task. The first is to define the token labels: from flaml import AutoMLimport pandas as pdtrain_dataset = { \"id\": [\"0\", \"1\"], \"ner_tags\": [ [\"B-ORG\", \"O\", \"B-MISC\", \"O\", \"O\", \"O\", \"B-MISC\", \"O\", \"O\"], [\"B-PER\", \"I-PER\"], ], \"tokens\": [ [ \"EU\", \"rejects\", \"German\", \"call\", \"to\", \"boycott\", \"British\", \"lamb\", \".\", ], [\"Peter\", \"Blackburn\"], ],}dev_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [\"O\"], ], \"tokens\": [[\"1996-08-22\"]],}test_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [\"O\"], ], \"tokens\": [[\".\"]],}custom_sent_keys = [\"tokens\"]label_key = \"ner_tags\"train_dataset = pd.DataFrame(train_dataset)dev_dataset = pd.DataFrame(dev_dataset)test_dataset = pd.DataFrame(test_dataset)X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]X_test = test_dataset[custom_sent_keys]automl = AutoML()automl_settings = { \"time_budget\": 10, \"task\": \"token-classification\", \"fit_kwargs_by_estimator\": { \"transformer\": { \"output_dir\": \"data/output/\" # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base } }, # setting the huggingface arguments: output directory \"gpu_per_trial\": 1, # set to 0 if no GPU is available \"metric\": \"seqeval:overall_f1\",}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)automl.predict(X_test) Copy The second is to define the id labels + a token label list: from flaml import AutoMLimport pandas as pdtrain_dataset = { \"id\": [\"0\", \"1\"], \"ner_tags\": [ [3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], ], \"tokens\": [ [ \"EU\", \"rejects\", \"German\", \"call\", \"to\", \"boycott\", \"British\", \"lamb\", \".\", ], [\"Peter\", \"Blackburn\"], ],}dev_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [0], ], \"tokens\": [[\"1996-08-22\"]],}test_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [0], ], \"tokens\": [[\".\"]],}custom_sent_keys = [\"tokens\"]label_key = \"ner_tags\"train_dataset = pd.DataFrame(train_dataset)dev_dataset = pd.DataFrame(dev_dataset)test_dataset = pd.DataFrame(test_dataset)X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]X_test = test_dataset[custom_sent_keys]automl = AutoML()automl_settings = { \"time_budget\": 10, \"task\": \"token-classification\", \"fit_kwargs_by_estimator\": { \"transformer\": { \"output_dir\": \"data/output/\", # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base \"label_list\": [ \"O\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\", \"B-MISC\", \"I-MISC\", ], } }, # setting the huggingface arguments: output directory \"gpu_per_trial\": 1, # set to 0 if no GPU is available \"metric\": \"seqeval:overall_f1\",}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)automl.predict(X_test) Copy Sample Output​ [flaml.automl: 06-30 03:10:02] {2423} INFO - task = token-classification[flaml.automl: 06-30 03:10:02] {2425} INFO - Data split method: stratified[flaml.automl: 06-30 03:10:02] {2428} INFO - Evaluation method: holdout[flaml.automl: 06-30 03:10:02] {2497} INFO - Minimizing error metric: seqeval:overall_f1[flaml.automl: 06-30 03:10:02] {2637} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 06-30 03:10:02] {2929} INFO - iteration 0, current learner transformer Copy For tasks that are not currently supported, use flaml.tune for customized tuning.","s":"A simple token classification example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-token-classification-example","p":111},{"i":124,"t":"To run more examples, especially examples using Ray Tune, please go to: Link to notebook | Open in colab","s":"Link to Jupyter notebook","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#link-to-jupyter-notebook","p":111},{"i":126,"t":"On this page","s":"AutoML - Rank","u":"/FLAML/docs/Examples/AutoML-Rank","h":"","p":125},{"i":128,"t":"Install the [automl] option. pip install \"flaml[automl]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Rank","h":"#prerequisites","p":125},{"i":130,"t":"from sklearn.datasets import fetch_openmlfrom flaml import AutoMLX_train, y_train = fetch_openml(name=\"credit-g\", return_X_y=True, as_frame=False)y_train = y_train.cat.codes# not a real learning to rank dataasetgroups = [200] * 4 + [100] * 2 # group countsautoml = AutoML()automl.fit( X_train, y_train, groups=groups, task=\"rank\", time_budget=10, # in seconds) Copy Sample output​ [flaml.automl: 11-15 07:14:30] {1485} INFO - Data split method: group[flaml.automl: 11-15 07:14:30] {1489} INFO - Evaluation method: holdout[flaml.automl: 11-15 07:14:30] {1540} INFO - Minimizing error metric: 1-ndcg[flaml.automl: 11-15 07:14:30] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'xgboost'][flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-15 07:14:30] {1944} INFO - Estimated sufficient time budget=679s. Estimated necessary time budget=1s.[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.1s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.1s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 4, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 5, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.3s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 7, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.3s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 8, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 9, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 10, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 11, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 12, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 13, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 14, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.5s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 15, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.5s, estimator xgboost's best error=0.0233, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 16, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.5s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 17, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.5s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 18, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 19, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 20, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 21, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.7s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 22, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.7s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 23, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.8s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 24, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.8s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 25, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.8s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 26, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.9s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 27, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.9s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 28, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 1.0s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 29, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 1.0s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {2242} INFO - retrain lgbm for 0.0s[flaml.automl: 11-15 07:14:31] {2247} INFO - retrained model: LGBMRanker(colsample_bytree=0.9852774042640857, learning_rate=0.034918421933217675, max_bin=1023, min_child_samples=22, n_estimators=6, num_leaves=23, reg_alpha=0.0009765625, reg_lambda=21.505295697527654, verbose=-1)[flaml.automl: 11-15 07:14:31] {1608} INFO - fit succeeded[flaml.automl: 11-15 07:14:31] {1610} INFO - Time taken to find the best model: 0.8846545219421387[flaml.automl: 11-15 07:14:31] {1624} WARNING - Time taken to find the best model is 88% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy","s":"A simple learning-to-rank example","u":"/FLAML/docs/Examples/AutoML-Rank","h":"#a-simple-learning-to-rank-example","p":125},{"i":132,"t":"On this page","s":"Default - Flamlized Estimator","u":"/FLAML/docs/Examples/Default-Flamlized","h":"","p":131},{"i":135,"t":"This example requires the [autozero] option. pip install flaml[autozero] lightgbm openml Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#prerequisites","p":131},{"i":137,"t":"from flaml.automl.data import load_openml_datasetfrom flaml.default import LGBMRegressorfrom flaml.automl.ml import sklearn_metric_loss_scoreX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")lgbm = LGBMRegressor()lgbm.fit(X_train, y_train)y_pred = lgbm.predict(X_test)print(\"flamlized lgbm r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))print(lgbm) Copy Sample output​ load dataset from ./openml_ds537.pklDataset name: housesX_train.shape: (15480, 8), y_train.shape: (15480,);X_test.shape: (5160, 8), y_test.shape: (5160,)flamlized lgbm r2 = 0.8537444671194614LGBMRegressor(colsample_bytree=0.7019911744574896, learning_rate=0.022635758411078528, max_bin=511, min_child_samples=2, n_estimators=4797, num_leaves=122, reg_alpha=0.004252223402511765, reg_lambda=0.11288241427227624, verbose=-1) Copy","s":"Zero-shot AutoML","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#zero-shot-automl","p":131},{"i":139,"t":"from flaml.automl.data import load_openml_datasetfrom flaml.default import LGBMRegressorfrom flaml.ml import sklearn_metric_loss_scoreX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")lgbm = LGBMRegressor()hyperparams, estimator_name, X_transformed, y_transformed = lgbm.suggest_hyperparams(X_train, y_train)print(hyperparams) Copy Sample output​ load dataset from ./openml_ds537.pklDataset name: housesX_train.shape: (15480, 8), y_train.shape: (15480,);X_test.shape: (5160, 8), y_test.shape: (5160,){'n_estimators': 4797, 'num_leaves': 122, 'min_child_samples': 2, 'learning_rate': 0.022635758411078528, 'colsample_bytree': 0.7019911744574896, 'reg_alpha': 0.004252223402511765, 'reg_lambda': 0.11288241427227624, 'max_bin': 511, 'verbose': -1} Copy Link to notebook | Open in colab","s":"Suggest hyperparameters without training","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#suggest-hyperparameters-without-training","p":131},{"i":142,"t":"This example requires xgboost, sklearn, openml==0.10.2.","s":"Prerequisites","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#prerequisites-1","p":131},{"i":144,"t":"from flaml.automl.data import load_openml_datasetfrom flaml.default import XGBClassifierfrom flaml.automl.ml import sklearn_metric_loss_scoreX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir=\"./\")xgb = XGBClassifier()xgb.fit(X_train, y_train)y_pred = xgb.predict(X_test)print( \"flamlized xgb accuracy\", \"=\", 1 - sklearn_metric_loss_score(\"accuracy\", y_pred, y_test),)print(xgb) Copy Sample output​ load dataset from ./openml_ds1169.pklDataset name: airlinesX_train.shape: (404537, 7), y_train.shape: (404537,);X_test.shape: (134846, 7), y_test.shape: (134846,)flamlized xgb accuracy = 0.6729009388487608XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.4601573737792679, colsample_bynode=1, colsample_bytree=1.0, gamma=0, gpu_id=-1, grow_policy='lossguide', importance_type='gain', interaction_constraints='', learning_rate=0.04039771837785377, max_delta_step=0, max_depth=0, max_leaves=159, min_child_weight=0.3396294979905001, missing=nan, monotone_constraints='()', n_estimators=540, n_jobs=4, num_parallel_tree=1, random_state=0, reg_alpha=0.0012362430984376035, reg_lambda=3.093428791531145, scale_pos_weight=1, subsample=1.0, tree_method='hist', use_label_encoder=False, validate_parameters=1, verbosity=0) Copy","s":"Zero-shot AutoML","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#zero-shot-automl-1","p":131},{"i":146,"t":"On this page","s":"AutoML - Regression","u":"/FLAML/docs/Examples/AutoML-Regression","h":"","p":145},{"i":148,"t":"Install the [automl] option. pip install \"flaml[automl]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Regression","h":"#prerequisites","p":145},{"i":150,"t":"from flaml import AutoMLfrom sklearn.datasets import fetch_california_housing# Initialize an AutoML instanceautoml = AutoML()# Specify automl goal and constraintautoml_settings = { \"time_budget\": 1, # in seconds \"metric\": \"r2\", \"task\": \"regression\", \"log_file_name\": \"california.log\",}X_train, y_train = fetch_california_housing(return_X_y=True)# Train with labeled input dataautoml.fit(X_train=X_train, y_train=y_train, **automl_settings)# Predictprint(automl.predict(X_train))# Print the best modelprint(automl.model.estimator) Copy Sample output​ [flaml.automl: 11-15 07:08:19] {1485} INFO - Data split method: uniform[flaml.automl: 11-15 07:08:19] {1489} INFO - Evaluation method: holdout[flaml.automl: 11-15 07:08:19] {1540} INFO - Minimizing error metric: 1-r2[flaml.automl: 11-15 07:08:19] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree'][flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-15 07:08:19] {1944} INFO - Estimated sufficient time budget=846s. Estimated necessary time budget=2s.[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.2s, estimator lgbm's best error=0.7393, best estimator lgbm's best error=0.7393[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.3s, estimator lgbm's best error=0.7393, best estimator lgbm's best error=0.7393[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.3s, estimator lgbm's best error=0.5446, best estimator lgbm's best error=0.5446[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.4s, estimator lgbm's best error=0.2807, best estimator lgbm's best error=0.2807[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 4, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.5s, estimator lgbm's best error=0.2712, best estimator lgbm's best error=0.2712[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 5, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.5s, estimator lgbm's best error=0.2712, best estimator lgbm's best error=0.2712[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.6s, estimator lgbm's best error=0.2712, best estimator lgbm's best error=0.2712[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 7, current learner lgbm[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.7s, estimator lgbm's best error=0.2197, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 8, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.8s, estimator xgboost's best error=1.4958, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 9, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.8s, estimator xgboost's best error=1.4958, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 10, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.9s, estimator xgboost's best error=0.7052, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 11, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.9s, estimator xgboost's best error=0.3619, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 12, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.9s, estimator xgboost's best error=0.3619, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 13, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 1.0s, estimator xgboost's best error=0.3619, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 14, current learner extra_tree[flaml.automl: 11-15 07:08:20] {2029} INFO - at 1.1s, estimator extra_tree's best error=0.7197, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {2242} INFO - retrain lgbm for 0.0s[flaml.automl: 11-15 07:08:20] {2247} INFO - retrained model: LGBMRegressor(colsample_bytree=0.7610534336273627, learning_rate=0.41929025492645006, max_bin=255, min_child_samples=4, n_estimators=45, num_leaves=4, reg_alpha=0.0009765625, reg_lambda=0.009280655005879943, verbose=-1)[flaml.automl: 11-15 07:08:20] {1608} INFO - fit succeeded[flaml.automl: 11-15 07:08:20] {1610} INFO - Time taken to find the best model: 0.7289648056030273[flaml.automl: 11-15 07:08:20] {1624} WARNING - Time taken to find the best model is 73% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy","s":"A basic regression example","u":"/FLAML/docs/Examples/AutoML-Regression","h":"#a-basic-regression-example","p":145},{"i":152,"t":"We can combine sklearn.MultiOutputRegressor and flaml.AutoML to do AutoML for multi-output regression. from flaml import AutoMLfrom sklearn.datasets import make_regressionfrom sklearn.model_selection import train_test_splitfrom sklearn.multioutput import MultiOutputRegressor# create regression dataX, y = make_regression(n_targets=3)# split into train and test dataX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.30, random_state=42)# train the modelmodel = MultiOutputRegressor(AutoML(task=\"regression\", time_budget=60))model.fit(X_train, y_train)# predictprint(model.predict(X_test)) Copy It will perform AutoML for each target, each taking 60 seconds.","s":"Multi-output regression","u":"/FLAML/docs/Examples/AutoML-Regression","h":"#multi-output-regression","p":145},{"i":154,"t":"On this page","s":"AutoML - Time Series Forecast","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"","p":153},{"i":156,"t":"Install the [automl,ts_forecast] option. pip install \"flaml[automl,ts_forecast]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#prerequisites","p":153},{"i":158,"t":"import numpy as npfrom flaml import AutoMLX_train = np.arange(\"2014-01\", \"2022-01\", dtype=\"datetime64[M]\")y_train = np.random.random(size=84)automl = AutoML()automl.fit( X_train=X_train[:84], # a single column of timestamp y_train=y_train, # value for each timestamp period=12, # time horizon to forecast, e.g., 12 months task=\"ts_forecast\", time_budget=15, # time budget in seconds log_file_name=\"ts_forecast.log\", eval_method=\"holdout\",)print(automl.predict(X_train[84:])) Copy Sample output​ [flaml.automl: 01-21 08:01:20] {2018} INFO - task = ts_forecast[flaml.automl: 01-21 08:01:20] {2020} INFO - Data split method: time[flaml.automl: 01-21 08:01:20] {2024} INFO - Evaluation method: holdout[flaml.automl: 01-21 08:01:20] {2124} INFO - Minimizing error metric: mape[flaml.automl: 01-21 08:01:21] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'][flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 0, current learner lgbm[flaml.automl: 01-21 08:01:21] {2547} INFO - Estimated sufficient time budget=1429s. Estimated necessary time budget=1s.[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 1, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 2, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 3, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 4, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - 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iteration 100, current learner xgb_limitdepth[flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.3s, estimator xgb_limitdepth's best error=0.9683, best estimator sarimax's best error=0.5600 Copy","s":"Simple NumPy Example","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#simple-numpy-example","p":153},{"i":160,"t":"import statsmodels.api as smdata = sm.datasets.co2.load_pandas().data# data is given in weeks, but the task is to predict monthly, so use monthly averages insteaddata = data[\"co2\"].resample(\"MS\").mean()data = data.bfill().ffill() # makes sure there are no missing valuesdata = data.to_frame().reset_index()num_samples = data.shape[0]time_horizon = 12split_idx = num_samples - time_horizontrain_df = data[ :split_idx] # train_df is a dataframe with two columns: timestamp and labelX_test = data[split_idx:][ \"index\"].to_frame() # X_test is a dataframe with dates for predictiony_test = data[split_idx:][ \"co2\"] # y_test is a series of the values corresponding to the dates for predictionfrom flaml import AutoMLautoml = AutoML()settings = { \"time_budget\": 10, # total running time in seconds \"metric\": \"mape\", # primary metric for validation: 'mape' is generally used for forecast tasks \"task\": \"ts_forecast\", # task type \"log_file_name\": \"CO2_forecast.log\", # flaml log file \"eval_method\": \"holdout\", # validation method can be chosen from ['auto', 'holdout', 'cv'] \"seed\": 7654321, # random seed}automl.fit( dataframe=train_df, # training data label=\"co2\", # label column period=time_horizon, # key word argument 'period' must be included for forecast task) **settings) Copy Sample output​ [flaml.automl: 01-21 07:54:04] {2018} INFO - task = ts_forecast[flaml.automl: 01-21 07:54:04] {2020} INFO - Data split method: time[flaml.automl: 01-21 07:54:04] {2024} INFO - Evaluation method: holdout[flaml.automl: 01-21 07:54:04] {2124} INFO - Minimizing error metric: mapeImporting plotly failed. Interactive plots will not work.[flaml.automl: 01-21 07:54:04] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'][flaml.automl: 01-21 07:54:04] {2434} INFO - iteration 0, current learner lgbm[flaml.automl: 01-21 07:54:05] {2547} INFO - Estimated sufficient time budget=2145s. 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fit succeeded[flaml.automl: 01-21 07:54:14] {2211} INFO - Time taken to find the best model: 9.339771270751953[flaml.automl: 01-21 07:54:14] {2222} WARNING - Time taken to find the best model is 93% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy Compute and plot predictions​ The example plotting code requires matplotlib. flaml_y_pred = automl.predict(X_test)import matplotlib.pyplot as pltplt.plot(X_test, y_test, label=\"Actual level\")plt.plot(X_test, flaml_y_pred, label=\"FLAML forecast\")plt.xlabel(\"Date\")plt.ylabel(\"CO2 Levels\")plt.legend() Copy","s":"Univariate time series","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#univariate-time-series","p":153},{"i":162,"t":"import pandas as pd# pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)multi_df = pd.read_csv( \"https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/nyc_energy_consumption.csv\")# preprocessing datamulti_df[\"timeStamp\"] = pd.to_datetime(multi_df[\"timeStamp\"])multi_df = multi_df.set_index(\"timeStamp\")multi_df = multi_df.resample(\"D\").mean()multi_df[\"temp\"] = multi_df[\"temp\"].fillna(method=\"ffill\")multi_df[\"precip\"] = multi_df[\"precip\"].fillna(method=\"ffill\")multi_df = multi_df[:-2] # last two rows are NaN for 'demand' column so remove themmulti_df = multi_df.reset_index()# Using temperature values create categorical values# where 1 denotes daily tempurature is above monthly average and 0 is below.def get_monthly_avg(data): data[\"month\"] = data[\"timeStamp\"].dt.month data = data[[\"month\", \"temp\"]].groupby(\"month\") data = data.agg({\"temp\": \"mean\"}) return datamonthly_avg = get_monthly_avg(multi_df).to_dict().get(\"temp\")def above_monthly_avg(date, temp): month = date.month if temp > monthly_avg.get(month): return 1 else: return 0multi_df[\"temp_above_monthly_avg\"] = multi_df.apply( lambda x: above_monthly_avg(x[\"timeStamp\"], x[\"temp\"]), axis=1)del multi_df[\"month\"] # remove temperature column to reduce redundancy# split data into train and testnum_samples = multi_df.shape[0]multi_time_horizon = 180split_idx = num_samples - multi_time_horizonmulti_train_df = multi_df[:split_idx]multi_test_df = multi_df[split_idx:]multi_X_test = multi_test_df[ [\"timeStamp\", \"precip\", \"temp\", \"temp_above_monthly_avg\"]] # test dataframe must contain values for the regressors / multivariate variablesmulti_y_test = multi_test_df[\"demand\"]# initialize AutoML instanceautoml = AutoML()# configure AutoML settingssettings = { \"time_budget\": 10, # total running time in seconds \"metric\": \"mape\", # primary metric \"task\": \"ts_forecast\", # task type \"log_file_name\": \"energy_forecast_categorical.log\", # flaml log file \"eval_method\": \"holdout\", \"log_type\": \"all\", \"label\": \"demand\",}# train the modelautoml.fit(dataframe=df, **settings, period=time_horizon)# predictionsprint(automl.predict(multi_X_test)) Copy Sample Output​ [flaml.automl: 08-13 01:03:11] {2540} INFO - task = ts_forecast[flaml.automl: 08-13 01:03:11] {2542} INFO - Data split method: time[flaml.automl: 08-13 01:03:11] {2545} INFO - Evaluation method: holdout[flaml.automl: 08-13 01:03:11] {2664} INFO - Minimizing error metric: mape[flaml.automl: 08-13 01:03:12] {2806} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'][flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 0, current learner lgbm[flaml.automl: 08-13 01:03:12] {3241} INFO - Estimated sufficient time budget=7681s. Estimated necessary time budget=8s.[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.8s, estimator lgbm's best error=0.0854, best estimator lgbm's best error=0.0854[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 1, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0854, best estimator lgbm's best error=0.0854[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 2, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0525, best estimator lgbm's best error=0.0525[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 3, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0525, best estimator lgbm's best error=0.0525[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 4, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 5, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 6, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 7, current learner lgbm[flaml.automl: 08-13 01:03:13] {3288} INFO - at 1.1s, estimator lgbm's best error=0.0393, best estimator lgbm's best error=0.0393[flaml.automl: 08-13 01:03:13] {3108} INFO - iteration 8, current learner lgbm[flaml.automl: 08-13 01:03:13] {3288} INFO - at 1.1s, estimator lgbm's best error=0.0393, best estimator lgbm's best error=0.0393[flaml.automl: 08-13 01:03:13] {3108} INFO - iteration 9, current learner lgbm... silent=True, subsample=1.0, subsample_for_bin=200000, subsample_freq=0, verbose=-1)[flaml.automl: 08-13 01:03:22] {2837} INFO - fit succeeded[flaml.automl: 08-13 01:03:22] {2838} INFO - Time taken to find the best model: 3.4941744804382324 Copy","s":"Multivariate Time Series (Forecasting with Exogenous Variables)","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#multivariate-time-series-forecasting-with-exogenous-variables","p":153},{"i":164,"t":"from hcrystalball.utils import get_sales_dataimport numpy as npfrom flaml import AutoMLtime_horizon = 30df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)df = df[[\"Sales\", \"Open\", \"Promo\", \"Promo2\"]]# feature engineering - create a discrete value column# 1 denotes above mean and 0 denotes below meandf[\"above_mean_sales\"] = np.where(df[\"Sales\"] > df[\"Sales\"].mean(), 1, 0)df.reset_index(inplace=True)# train-test splitdiscrete_train_df = df[:-time_horizon]discrete_test_df = df[-time_horizon:]discrete_X_train, discrete_X_test = ( discrete_train_df[[\"Date\", \"Open\", \"Promo\", \"Promo2\"]], discrete_test_df[[\"Date\", \"Open\", \"Promo\", \"Promo2\"]],)discrete_y_train, discrete_y_test = ( discrete_train_df[\"above_mean_sales\"], discrete_test_df[\"above_mean_sales\"],)# initialize AutoML instanceautoml = AutoML()# configure the settingssettings = { \"time_budget\": 15, # total running time in seconds \"metric\": \"accuracy\", # primary metric \"task\": \"ts_forecast_classification\", # task type \"log_file_name\": \"sales_classification_forecast.log\", # flaml log file \"eval_method\": \"holdout\",}# train the modelautoml.fit( X_train=discrete_X_train, y_train=discrete_y_train, **settings, period=time_horizon)# make predictionsdiscrete_y_pred = automl.predict(discrete_X_test)print(\"Predicted label\", discrete_y_pred)print(\"True label\", discrete_y_test) Copy Sample Output​ [flaml.automl: 02-28 21:53:03] {2060} INFO - task = ts_forecast_classification[flaml.automl: 02-28 21:53:03] {2062} INFO - Data split method: time[flaml.automl: 02-28 21:53:03] {2066} INFO - Evaluation method: holdout[flaml.automl: 02-28 21:53:03] {2147} INFO - Minimizing error metric: 1-accuracy[flaml.automl: 02-28 21:53:03] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth'][flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 0, current learner lgbm[flaml.automl: 02-28 21:53:03] {2573} INFO - Estimated sufficient time budget=269s. 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iteration 25, current learner xgb_limitdepth[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 26, current learner xgb_limitdepth[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 27, current learner xgboost[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgboost's best error=0.0333, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 28, current learner extra_tree[flaml.automl: 02-28 21:53:04] {2620} INFO - at 1.0s, estimator extra_tree's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 29, current learner xgb_limitdepth[flaml.automl: 02-28 21:53:04] {2620} INFO - at 1.0s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2850} INFO - retrain xgboost for 0.0s[flaml.automl: 02-28 21:53:04] {2857} INFO - retrained model: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.9826753651836615, colsample_bynode=1, colsample_bytree=0.9725493834064914, gamma=0, gpu_id=-1, grow_policy='lossguide', importance_type='gain', interaction_constraints='', learning_rate=0.1665803484560213, max_delta_step=0, max_depth=0, max_leaves=4, min_child_weight=0.5649012460525115, missing=nan, monotone_constraints='()', n_estimators=4, n_jobs=-1, num_parallel_tree=1, objective='binary:logistic', random_state=0, reg_alpha=0.009638363373006869, reg_lambda=0.143703802530408, scale_pos_weight=1, subsample=0.9643606787051899, tree_method='hist', use_label_encoder=False, validate_parameters=1, verbosity=0)[flaml.automl: 02-28 21:53:04] {2234} INFO - fit succeeded[flaml.automl: 02-28 21:53:04] {2235} INFO - Time taken to find the best model: 0.8547139167785645 Copy","s":"Forecasting Discrete Variables","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#forecasting-discrete-variables","p":153},{"i":166,"t":"Panel time series datasets involves multiple individual time series. For example, see Stallion demand dataset from PyTorch Forecasting, orginally from Kaggle. def get_stalliion_data(): from pytorch_forecasting.data.examples import get_stallion_data data = get_stallion_data() # add time index - For datasets with no missing values, FLAML will automate this process data[\"time_idx\"] = data[\"date\"].dt.year * 12 + data[\"date\"].dt.month data[\"time_idx\"] -= data[\"time_idx\"].min() # add additional features data[\"month\"] = data.date.dt.month.astype(str).astype( \"category\" ) # categories have be strings data[\"log_volume\"] = np.log(data.volume + 1e-8) data[\"avg_volume_by_sku\"] = data.groupby( [\"time_idx\", \"sku\"], observed=True ).volume.transform(\"mean\") data[\"avg_volume_by_agency\"] = data.groupby( [\"time_idx\", \"agency\"], observed=True ).volume.transform(\"mean\") # we want to encode special days as one variable and thus need to first reverse one-hot encoding special_days = [ \"easter_day\", \"good_friday\", \"new_year\", \"christmas\", \"labor_day\", \"independence_day\", \"revolution_day_memorial\", \"regional_games\", \"beer_capital\", \"music_fest\", ] data[special_days] = ( data[special_days] .apply(lambda x: x.map({0: \"-\", 1: x.name})) .astype(\"category\") ) return data, special_daysdata, special_days = get_stalliion_data()time_horizon = 6 # predict six monthstraining_cutoff = data[\"time_idx\"].max() - time_horizondata[\"time_idx\"] = data[\"time_idx\"].astype(\"int\")ts_col = data.pop(\"date\")data.insert(0, \"date\", ts_col)# FLAML assumes input is not sorted, but we sort here for comparison purposes with y_testdata = data.sort_values([\"agency\", \"sku\", \"date\"])X_train = data[lambda x: x.time_idx <= training_cutoff]X_test = data[lambda x: x.time_idx > training_cutoff]y_train = X_train.pop(\"volume\")y_test = X_test.pop(\"volume\")automl = AutoML()# Configure settings for FLAML modelsettings = { \"time_budget\": budget, # total running time in seconds \"metric\": \"mape\", # primary metric \"task\": \"ts_forecast_panel\", # task type \"log_file_name\": \"test/stallion_forecast.log\", # flaml log file \"eval_method\": \"holdout\",}# Specify kwargs for TimeSeriesDataSet used by TemporalFusionTransformerEstimatorfit_kwargs_by_estimator = { \"tft\": { \"max_encoder_length\": 24, \"static_categoricals\": [\"agency\", \"sku\"], \"static_reals\": [\"avg_population_2017\", \"avg_yearly_household_income_2017\"], \"time_varying_known_categoricals\": [\"special_days\", \"month\"], \"variable_groups\": { \"special_days\": special_days }, # group of categorical variables can be treated as one variable \"time_varying_known_reals\": [ \"time_idx\", \"price_regular\", \"discount_in_percent\", ], \"time_varying_unknown_categoricals\": [], \"time_varying_unknown_reals\": [ \"y\", # always need a 'y' column for the target column \"log_volume\", \"industry_volume\", \"soda_volume\", \"avg_max_temp\", \"avg_volume_by_agency\", \"avg_volume_by_sku\", ], \"batch_size\": 256, \"max_epochs\": 1, \"gpu_per_trial\": -1, }}# Train the modelautoml.fit( X_train=X_train, y_train=y_train, **settings, period=time_horizon, group_ids=[\"agency\", \"sku\"], fit_kwargs_by_estimator=fit_kwargs_by_estimator,)# Compute predictions of testing datasety_pred = automl.predict(X_test)print(y_test)print(y_pred)# best modelprint(automl.model.estimator) Copy Sample Output​ [flaml.automl: 07-28 21:26:03] {2478} INFO - task = ts_forecast_panel[flaml.automl: 07-28 21:26:03] {2480} INFO - Data split method: time[flaml.automl: 07-28 21:26:03] {2483} INFO - Evaluation method: holdout[flaml.automl: 07-28 21:26:03] {2552} INFO - Minimizing error metric: mape[flaml.automl: 07-28 21:26:03] {2694} INFO - List of ML learners in AutoML Run: ['tft'][flaml.automl: 07-28 21:26:03] {2986} INFO - iteration 0, current learner tftGPU available: False, used: FalseTPU available: False, using: 0 TPU coresIPU available: False, using: 0 IPUs | Name | Type | Params----------------------------------------------------------------------------------------0 | loss | QuantileLoss | 01 | logging_metrics | ModuleList | 02 | input_embeddings | MultiEmbedding | 1.3 K3 | prescalers | ModuleDict | 2564 | static_variable_selection | VariableSelectionNetwork | 3.4 K5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K10 | static_context_enrichment | GatedResidualNetwork | 1.1 K11 | lstm_encoder | LSTM | 4.4 K12 | lstm_decoder | LSTM | 4.4 K13 | post_lstm_gate_encoder | GatedLinearUnit | 54414 | post_lstm_add_norm_encoder | AddNorm | 3215 | static_enrichment | GatedResidualNetwork | 1.4 K16 | multihead_attn | InterpretableMultiHeadAttention | 67617 | post_attn_gate_norm | GateAddNorm | 57618 | pos_wise_ff | GatedResidualNetwork | 1.1 K19 | pre_output_gate_norm | GateAddNorm | 57620 | output_layer | Linear | 119----------------------------------------------------------------------------------------33.6 K Trainable params0 Non-trainable params33.6 K Total params0.135 Total estimated model params size (MB)Epoch 19: 100%|██████████| 129/129 [00:56<00:00, 2.27it/s, loss=45.9, v_num=2, train_loss_step=43.00, val_loss=65.20, train_loss_epoch=46.50][flaml.automl: 07-28 21:46:46] {3114} INFO - Estimated sufficient time budget=12424212s. Estimated necessary time budget=12424s.[flaml.automl: 07-28 21:46:46] {3161} INFO - at 1242.6s,\\testimator tft's best error=1324290483134574.7500,\\tbest estimator tft's best error=1324290483134574.7500GPU available: False, used: FalseTPU available: False, using: 0 TPU coresIPU available: False, using: 0 IPUs | Name | Type | Params----------------------------------------------------------------------------------------0 | loss | QuantileLoss | 01 | logging_metrics | ModuleList | 02 | input_embeddings | MultiEmbedding | 1.3 K3 | prescalers | ModuleDict | 2564 | static_variable_selection | VariableSelectionNetwork | 3.4 K5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K10 | static_context_enrichment | GatedResidualNetwork | 1.1 K11 | lstm_encoder | LSTM | 4.4 K12 | lstm_decoder | LSTM | 4.4 K13 | post_lstm_gate_encoder | GatedLinearUnit | 54414 | post_lstm_add_norm_encoder | AddNorm | 3215 | static_enrichment | GatedResidualNetwork | 1.4 K16 | multihead_attn | InterpretableMultiHeadAttention | 67617 | post_attn_gate_norm | GateAddNorm | 57618 | pos_wise_ff | GatedResidualNetwork | 1.1 K19 | pre_output_gate_norm | GateAddNorm | 57620 | output_layer | Linear | 119----------------------------------------------------------------------------------------33.6 K Trainable params0 Non-trainable params33.6 K Total params0.135 Total estimated model params size (MB)Epoch 19: 100%|██████████| 145/145 [01:03<00:00, 2.28it/s, loss=45.2, v_num=3, train_loss_step=46.30, val_loss=67.60, train_loss_epoch=48.10][flaml.automl: 07-28 22:08:05] {3425} INFO - retrain tft for 1279.6s[flaml.automl: 07-28 22:08:05] {3432} INFO - retrained model: TemporalFusionTransformer( (loss): QuantileLoss() (logging_metrics): ModuleList( (0): SMAPE() (1): MAE() (2): RMSE() (3): MAPE() ) (input_embeddings): MultiEmbedding( (embeddings): ModuleDict( (agency): Embedding(58, 16) (sku): Embedding(25, 10) (special_days): TimeDistributedEmbeddingBag(11, 6, mode=sum) (month): Embedding(12, 6) ) ) (prescalers): ModuleDict( (avg_population_2017): Linear(in_features=1, out_features=8, bias=True) (avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True) (encoder_length): Linear(in_features=1, out_features=8, bias=True) (y_center): Linear(in_features=1, out_features=8, bias=True) (y_scale): Linear(in_features=1, out_features=8, bias=True) (time_idx): Linear(in_features=1, out_features=8, bias=True) (price_regular): Linear(in_features=1, out_features=8, bias=True) (discount_in_percent): Linear(in_features=1, out_features=8, bias=True) (relative_time_idx): Linear(in_features=1, out_features=8, bias=True) (y): Linear(in_features=1, out_features=8, bias=True) (log_volume): Linear(in_features=1, out_features=8, bias=True) (industry_volume): Linear(in_features=1, out_features=8, bias=True) (soda_volume): Linear(in_features=1, out_features=8, bias=True) (avg_max_temp): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True) ) (static_variable_selection): VariableSelectionNetwork( (flattened_grn): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=66, out_features=7, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=7, out_features=7, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=7, out_features=14, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True) ) ) ) (single_variable_grns): ModuleDict( (agency): ResampleNorm( (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (sku): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (avg_population_2017): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_yearly_household_income_2017): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (encoder_length): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (y_center): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (y_scale): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) ) (prescalers): ModuleDict( (avg_population_2017): Linear(in_features=1, out_features=8, bias=True) (avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True) (encoder_length): Linear(in_features=1, out_features=8, bias=True) (y_center): Linear(in_features=1, out_features=8, bias=True) (y_scale): Linear(in_features=1, out_features=8, bias=True) ) (softmax): Softmax(dim=-1) ) (encoder_variable_selection): VariableSelectionNetwork( (flattened_grn): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=100, out_features=13, bias=True) (elu): ELU(alpha=1.0) (context): Linear(in_features=16, out_features=13, bias=False) (fc2): Linear(in_features=13, out_features=13, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=13, out_features=26, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True) ) ) ) (single_variable_grns): ModuleDict( (special_days): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (month): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (price_regular): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (discount_in_percent): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (relative_time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (y): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (log_volume): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (industry_volume): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (soda_volume): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_max_temp): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_volume_by_agency): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_volume_by_sku): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) ) (prescalers): ModuleDict( (time_idx): Linear(in_features=1, out_features=8, bias=True) (price_regular): Linear(in_features=1, out_features=8, bias=True) (discount_in_percent): Linear(in_features=1, out_features=8, bias=True) (relative_time_idx): Linear(in_features=1, out_features=8, bias=True) (y): Linear(in_features=1, out_features=8, bias=True) (log_volume): Linear(in_features=1, out_features=8, bias=True) (industry_volume): Linear(in_features=1, out_features=8, bias=True) (soda_volume): Linear(in_features=1, out_features=8, bias=True) (avg_max_temp): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True) ) (softmax): Softmax(dim=-1) ) (decoder_variable_selection): VariableSelectionNetwork( (flattened_grn): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=44, out_features=6, bias=True) (elu): ELU(alpha=1.0) (context): Linear(in_features=16, out_features=6, bias=False) (fc2): Linear(in_features=6, out_features=6, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=6, out_features=12, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True) ) ) ) (single_variable_grns): ModuleDict( (special_days): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (month): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (price_regular): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (discount_in_percent): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (relative_time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) ) (prescalers): ModuleDict( (time_idx): Linear(in_features=1, out_features=8, bias=True) (price_regular): Linear(in_features=1, out_features=8, bias=True) (discount_in_percent): Linear(in_features=1, out_features=8, bias=True) (relative_time_idx): Linear(in_features=1, out_features=8, bias=True) ) (softmax): Softmax(dim=-1) ) (static_context_variable_selection): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (static_context_initial_hidden_lstm): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (static_context_initial_cell_lstm): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (static_context_enrichment): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (lstm_encoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1) (lstm_decoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1) (post_lstm_gate_encoder): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (post_lstm_gate_decoder): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (post_lstm_add_norm_encoder): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (post_lstm_add_norm_decoder): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (static_enrichment): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (context): Linear(in_features=16, out_features=16, bias=False) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (multihead_attn): InterpretableMultiHeadAttention( (dropout): Dropout(p=0.1, inplace=False) (v_layer): Linear(in_features=16, out_features=4, bias=True) (q_layers): ModuleList( (0): Linear(in_features=16, out_features=4, bias=True) (1): Linear(in_features=16, out_features=4, bias=True) (2): Linear(in_features=16, out_features=4, bias=True) (3): Linear(in_features=16, out_features=4, bias=True) ) (k_layers): ModuleList( (0): Linear(in_features=16, out_features=4, bias=True) (1): Linear(in_features=16, out_features=4, bias=True) (2): Linear(in_features=16, out_features=4, bias=True) (3): Linear(in_features=16, out_features=4, bias=True) ) (attention): ScaledDotProductAttention( (softmax): Softmax(dim=2) ) (w_h): Linear(in_features=4, out_features=16, bias=False) ) (post_attn_gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) (pos_wise_ff): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (pre_output_gate_norm): GateAddNorm( (glu): GatedLinearUnit( (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) (output_layer): Linear(in_features=16, out_features=7, bias=True))[flaml.automl: 07-28 22:08:05] {2725} INFO - fit succeeded[flaml.automl: 07-28 22:08:05] {2726} INFO - Time taken to find the best model: 1242.6435902118683[flaml.automl: 07-28 22:08:05] {2737} WARNING - Time taken to find the best model is 414% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\\n\" ] } ], Copy Link to notebook | Open in colab","s":"Forecasting with Panel Datasets","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#forecasting-with-panel-datasets","p":153},{"i":168,"t":"On this page","s":"Integrate - Scikit-learn Pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"","p":167},{"i":170,"t":"Install the [automl] option. pip install \"flaml[automl] openml\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#prerequisites","p":167},{"i":172,"t":"from flaml.automl.data import load_openml_dataset# Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.X_train, X_test, y_train, y_test = load_openml_dataset( dataset_id=1169, data_dir=\"./\", random_state=1234, dataset_format=\"array\") Copy","s":"Load data","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#load-data","p":167},{"i":174,"t":"from sklearn import set_configfrom sklearn.pipeline import Pipelinefrom sklearn.impute import SimpleImputerfrom sklearn.preprocessing import StandardScalerfrom flaml import AutoMLset_config(display=\"diagram\")imputer = SimpleImputer()standardizer = StandardScaler()automl = AutoML()automl_pipeline = Pipeline( [(\"imputuer\", imputer), (\"standardizer\", standardizer), (\"automl\", automl)])automl_pipeline Copy","s":"Create a pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#create-a-pipeline","p":167},{"i":176,"t":"automl_settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"accuracy\", # primary metrics can be chosen from: ['accuracy', 'roc_auc', 'roc_auc_weighted', 'roc_auc_ovr', 'roc_auc_ovo', 'f1', 'log_loss', 'mae', 'mse', 'r2'] Check the documentation for more details (https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#optimization-metric) \"task\": \"classification\", # task type \"estimator_list\": [\"xgboost\", \"catboost\", \"lgbm\"], \"log_file_name\": \"airlines_experiment.log\", # flaml log file}pipeline_settings = {f\"automl__{key}\": value for key, value in automl_settings.items()}automl_pipeline.fit(X_train, y_train, **pipeline_settings) Copy","s":"Run AutoML in the pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#run-automl-in-the-pipeline","p":167},{"i":178,"t":"automl = automl_pipeline.steps[2][1]# Get the best config and best learnerprint(\"Best ML leaner:\", automl.best_estimator)print(\"Best hyperparmeter config:\", automl.best_config)print(\"Best accuracy on validation data: {0:.4g}\".format(1 - automl.best_loss))print(\"Training duration of best run: {0:.4g} s\".format(automl.best_config_train_time)) Copy Link to notebook | Open in colab","s":"Get the automl object from the pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#get-the-automl-object-from-the-pipeline","p":167},{"i":180,"t":"On this page","s":"Integrate - AzureML","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"","p":179},{"i":182,"t":"Install the [automl,azureml] option. pip install \"flaml[automl,azureml]\" Copy Setup a AzureML workspace: from azureml.core import Workspacews = Workspace.create( name=\"myworkspace\", subscription_id=\"<azure-subscription-id>\", resource_group=\"myresourcegroup\",) Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#prerequisites","p":179},{"i":184,"t":"import mlflowfrom azureml.core import Workspacews = Workspace.from_config()mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri()) Copy","s":"Enable mlflow in AzureML workspace","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#enable-mlflow-in-azureml-workspace","p":179},{"i":186,"t":"from flaml.automl.data import load_openml_datasetfrom flaml import AutoML# Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir=\"./\")automl = AutoML()settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"accuracy\", # metric to optimize \"task\": \"classification\", # task type \"log_file_name\": \"airlines_experiment.log\", # flaml log file}experiment = mlflow.set_experiment(\"flaml\") # the experiment name in AzureML workspacewith mlflow.start_run() as run: # create a mlflow run automl.fit(X_train=X_train, y_train=y_train, **settings) mlflow.sklearn.log_model(automl, \"automl\") Copy The metrics in the run will be automatically logged in an experiment named \"flaml\" in your AzureML workspace. They can be retrieved by mlflow.search_runs: mlflow.search_runs( experiment_ids=[experiment.experiment_id], filter_string=\"params.learner = 'xgboost'\",) Copy The logged model can be loaded and used to make predictions: automl = mlflow.sklearn.load_model(f\"{run.info.artifact_uri}/automl\")print(automl.predict(X_test)) Copy Link to notebook | Open in colab","s":"Start an AutoML run","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#start-an-automl-run","p":179},{"i":188,"t":"When you have a compute cluster in AzureML, you can distribute flaml.AutoML or flaml.tune with ray. Build a ray environment in AzureML​ Create a docker file such as .Docker/Dockerfile-cpu. Make sure RUN pip install flaml[blendsearch,ray] is included in the docker file. Then build a AzureML environment in the workspace ws. ray_environment_name = \"aml-ray-cpu\"ray_environment_dockerfile_path = \"./Docker/Dockerfile-cpu\"# Build CPU image for Rayray_cpu_env = Environment.from_dockerfile( name=ray_environment_name, dockerfile=ray_environment_dockerfile_path)ray_cpu_env.register(workspace=ws)ray_cpu_build_details = ray_cpu_env.build(workspace=ws)import timewhile ray_cpu_build_details.status not in [\"Succeeded\", \"Failed\"]: print( f\"Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}\" ) time.sleep(10) Copy You only need to do this step once for one workspace. Create a compute cluster with multiple nodes​ from azureml.core.compute import AmlCompute, ComputeTargetcompute_target_name = \"cpucluster\"node_count = 2# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6compute_target_size = \"STANDARD_D2_V2\"if compute_target_name in ws.compute_targets: compute_target = ws.compute_targets[compute_target_name] if compute_target and type(compute_target) is AmlCompute: if compute_target.provisioning_state == \"Succeeded\": print(\"Found compute target; using it:\", compute_target_name) else: raise Exception( \"Found compute target but it is in state\", compute_target.provisioning_state, )else: print(\"creating a new compute target...\") provisioning_config = AmlCompute.provisioning_configuration( vm_size=compute_target_size, min_nodes=0, max_nodes=node_count ) # Create the cluster compute_target = ComputeTarget.create(ws, compute_target_name, provisioning_config) # Can poll for a minimum number of nodes and for a specific timeout. # If no min node count is provided it will use the scale settings for the cluster compute_target.wait_for_completion( show_output=True, min_node_count=None, timeout_in_minutes=20 ) # For a more detailed view of current AmlCompute status, use get_status() print(compute_target.get_status().serialize()) Copy If the computer target \"cpucluster\" already exists, it will not be recreated. Run distributed AutoML job​ Assuming you have an automl script like ray/distribute_automl.py. It uses n_concurrent_trials=k to inform AutoML.fit() to perform k concurrent trials in parallel. Submit an AzureML job as the following: from azureml.core import Workspace, Experiment, ScriptRunConfig, Environmentfrom azureml.core.runconfig import RunConfiguration, DockerConfigurationcommand = [\"python distribute_automl.py\"]ray_environment_name = \"aml-ray-cpu\"env = Environment.get(workspace=ws, name=ray_environment_name)aml_run_config = RunConfiguration(communicator=\"OpenMpi\")aml_run_config.target = compute_targetaml_run_config.docker = DockerConfiguration(use_docker=True)aml_run_config.environment = envaml_run_config.node_count = 2config = ScriptRunConfig( source_directory=\"ray/\", command=command, run_config=aml_run_config,)exp = Experiment(ws, \"distribute-automl\")run = exp.submit(config)print(run.get_portal_url()) # link to ml.azure.comrun.wait_for_completion(show_output=True) Copy Run distributed tune job​ Prepare a script like ray/distribute_tune.py. Replace the command in the above eample with: command = [\"python distribute_tune.py\"] Copy Everything else is the same.","s":"Use ray to distribute across a cluster","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#use-ray-to-distribute-across-a-cluster","p":179},{"i":190,"t":"On this page","s":"Integrate - Spark","u":"/FLAML/docs/Examples/Integrate - Spark","h":"","p":189},{"i":192,"t":"FLAML integrates estimators based on Spark ML models. These models are trained in parallel using Spark, so we called them Spark estimators. To use these models, you first need to organize your data in the required format.","s":"Spark ML Estimators","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#spark-ml-estimators","p":189},{"i":194,"t":"For Spark estimators, AutoML only consumes Spark data. FLAML provides a convenient function to_pandas_on_spark in the flaml.automl.spark.utils module to convert your data into a pandas-on-spark (pyspark.pandas) dataframe/series, which Spark estimators require. This utility function takes data in the form of a pandas.Dataframe or pyspark.sql.Dataframe and converts it into a pandas-on-spark dataframe. It also takes pandas.Series or pyspark.sql.Dataframe and converts it into a pandas-on-spark series. If you pass in a pyspark.pandas.Dataframe, it will not make any changes. This function also accepts optional arguments index_col and default_index_type. index_col is the column name to use as the index, default is None. default_index_type is the default index type, default is \"distributed-sequence\". More info about default index type could be found on Spark official documentation Here is an example code snippet for Spark Data: import pandas as pdfrom flaml.automl.spark.utils import to_pandas_on_spark# Creating a dictionarydata = { \"Square_Feet\": [800, 1200, 1800, 1500, 850], \"Age_Years\": [20, 15, 10, 7, 25], \"Price\": [100000, 200000, 300000, 240000, 120000],}# Creating a pandas DataFramedataframe = pd.DataFrame(data)label = \"Price\"# Convert to pandas-on-spark dataframepsdf = to_pandas_on_spark(dataframe) Copy To use Spark ML models you need to format your data appropriately. Specifically, use VectorAssembler to merge all feature columns into a single vector column. Here is an example of how to use it: from pyspark.ml.feature import VectorAssemblercolumns = psdf.columnsfeature_cols = [col for col in columns if col != label]featurizer = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")psdf = featurizer.transform(psdf.to_spark(index_col=\"index\"))[\"index\", \"features\"] Copy Later in conducting the experiment, use your pandas-on-spark data like non-spark data and pass them using X_train, y_train or dataframe, label.","s":"Data","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#data","p":189},{"i":196,"t":"Model List​ lgbm_spark: The class for fine-tuning Spark version LightGBM models, using SynapseML API. Usage​ First, prepare your data in the required format as described in the previous section. By including the models you intend to try in the estimators_list argument to flaml.automl, FLAML will start trying configurations for these models. If your input is Spark data, FLAML will also use estimators with the _spark postfix by default, even if you haven't specified them. Here is an example code snippet using SparkML models in AutoML: import flaml# prepare your data in pandas-on-spark format as we previously mentionedautoml = flaml.AutoML()settings = { \"time_budget\": 30, \"metric\": \"r2\", \"estimator_list\": [\"lgbm_spark\"], # this setting is optional \"task\": \"regression\",}automl.fit( dataframe=psdf, label=label, **settings,) Copy Link to notebook | Open in colab","s":"Estimators","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#estimators","p":189},{"i":198,"t":"You can activate Spark as the parallel backend during parallel tuning in both AutoML and Hyperparameter Tuning, by setting the use_spark to true. FLAML will dispatch your job to the distributed Spark backend using joblib-spark. Please note that you should not set use_spark to true when applying AutoML and Tuning for Spark Data. This is because only SparkML models will be used for Spark Data in AutoML and Tuning. As SparkML models run in parallel, there is no need to distribute them with use_spark again. All the Spark-related arguments are stated below. These arguments are available in both Hyperparameter Tuning and AutoML: use_spark: boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable FLAML_MAX_CONCURRENT to override the detected num_executors. The final number of concurrent trials will be the minimum of n_concurrent_trials and num_executors. n_concurrent_trials: int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, FLAML performes parallel tuning. force_cancel: boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. Spark jobs include parallel tuning jobs and Spark-based model training jobs. An example code snippet for using parallel Spark jobs: import flamlautoml_experiment = flaml.AutoML()automl_settings = { \"time_budget\": 30, \"metric\": \"r2\", \"task\": \"regression\", \"n_concurrent_trials\": 2, \"use_spark\": True, \"force_cancel\": True, # Activating the force_cancel option can immediately halt Spark jobs once they exceed the allocated time_budget.}automl.fit( dataframe=dataframe, label=label, **automl_settings,) Copy Link to notebook | Open in colab","s":"Parallel Spark Jobs","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#parallel-spark-jobs","p":189},{"i":200,"t":"On this page","s":"Tune - AzureML pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"","p":199},{"i":203,"t":"We recommend using conda or venv to create a virtual env to install the dependencies. # set up new conda environmentconda create -n pipeline_tune python=3.8 pip=20.2 -yconda activate pipeline_tune# install azureml packages for runnig AzureML pipelinespip install azureml-core==1.39.0pip install azure-ml-component[notebooks]==0.9.10.post1pip install azureml-dataset-runtime==1.39.0# install hydra-core for passing AzureML pipeline parameterspip install hydra-core==1.1.1# install flamlpip install flaml[blendsearch,ray]==1.0.9 Copy","s":"Requirements","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#requirements","p":199},{"i":205,"t":"Before we are ready for tuning, we must first have an Azure ML pipeline. In this example, we use the following toy pipeline for illustration. The pipeline consists of two steps: (1) data preparation and (2) model training. . The code example discussed in the page is included in test/pipeline_tuning_example/. We will use the relative path in the rest of the page.","s":"Azure ML training pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#azure-ml-training-pipeline","p":199},{"i":207,"t":"The example data exsits in data/data.csv. It will be uploaded to AzureML workspace to be consumed by the training pipeline using the following code. Dataset.File.upload_directory( src_dir=to_absolute_path(LOCAL_DIR / \"data\"), target=(datastore, \"classification_data\"), overwrite=True,)dataset = Dataset.File.from_files(path=(datastore, \"classification_data\")) Copy","s":"Data","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#data","p":199},{"i":209,"t":"The pipeline configuration is defined in configs/train_config.yaml. hydra: searchpath: - file://.aml_config: workspace_name: your_workspace_name resource_group: your_resource_group subscription_id: your_subscription_id cpu_target: cpuclustertrain_config: exp_name: sklearn_breast_cancer_classification test_train_ratio: 0.4 learning_rate: 0.05 n_estimators: 50 Copy","s":"Configurations for the pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#configurations-for-the-pipeline","p":199},{"i":211,"t":"The pipeline was defined in submit_train_pipeline.py. To submit the pipeline, please specify your AzureML resources in the configs/train_config.yaml and run cd test/pipeline_tuning_examplepython submit_train_pipeline.py Copy To get the pipeline ready for HPO, in the training step, we need to log the metrics of interest to AzureML using run.log(f\"{data_name}_{eval_name}\", result) Copy","s":"Define and submit the pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#define-and-submit-the-pipeline","p":199},{"i":213,"t":"We are now ready to set up the HPO job for the AzureML pipeline, including: config the HPO job, set up the interaction between the HPO job and the training job. These two steps are done in tuner/tuner_func.py.","s":"Hyperparameter Optimization","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#hyperparameter-optimization","p":199},{"i":215,"t":"tuner_func.tune_pipeline sets up the search space, metric to optimize, mode, etc. def tune_pipeline(concurrent_run=1): start_time = time.time() # config the HPO job search_space = { \"train_config.n_estimators\": flaml.tune.randint(50, 200), \"train_config.learning_rate\": flaml.tune.uniform(0.01, 0.5), } hp_metric = \"eval_binary_error\" mode = \"max\" num_samples = 2 if concurrent_run > 1: import ray # For parallel tuning ray.init(num_cpus=concurrent_run) use_ray = True else: use_ray = False # launch the HPO job analysis = flaml.tune.run( run_with_config, config=search_space, metric=hp_metric, mode=mode, num_samples=num_samples, # number of trials use_ray=use_ray, ) # get the best config best_trial = analysis.get_best_trial(hp_metric, mode, \"all\") metric = best_trial.metric_analysis[hp_metric][mode] print(f\"n_trials={len(analysis.trials)}\") print(f\"time={time.time()-start_time}\") print(f\"Best {hp_metric}: {metric:.4f}\") print(f\"Best coonfiguration: {best_trial.config}\") Copy","s":"Set up the tune job","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#set-up-the-tune-job","p":199},{"i":217,"t":"The interaction between FLAML and AzureML pipeline jobs is in tuner_func.run_with_config. def run_with_config(config: dict): \"\"\"Run the pipeline with a given config dict\"\"\" # pass the hyperparameters to AzureML jobs by overwriting the config file. overrides = [f\"{key}={value}\" for key, value in config.items()] print(overrides) run = submit_train_pipeline.build_and_submit_aml_pipeline(overrides) print(run.get_portal_url()) # retrieving the metrics to optimize before the job completes. stop = False while not stop: # get status status = run._core_run.get_status() print(f\"status: {status}\") # get metrics metrics = run._core_run.get_metrics(recursive=True) if metrics: run_metrics = list(metrics.values()) new_metric = run_metrics[0][\"eval_binary_error\"] if type(new_metric) == list: new_metric = new_metric[-1] print(f\"eval_binary_error: {new_metric}\") tune.report(eval_binary_error=new_metric) time.sleep(5) if status == \"FAILED\" or status == \"Completed\": stop = True print(\"The run is terminated.\") print(status) return Copy Overall, to tune the hyperparameters of the AzureML pipeline, run: # the training job will run remotely as an AzureML job in both choices# run the tuning job locallypython submit_tune.py --local# run the tuning job remotelypython submit_tune.py --remote --subscription_id <your subscription_id> --resource_group <your resource_group> --workspace <your workspace> Copy The local option runs the tuner/tuner_func.py in your local machine. The remote option wraps up the tuner/tuner_func.py as an AzureML component and starts another AzureML job to tune the AzureML pipeline.","s":"Interact with AzureML pipeline jobs","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#interact-with-azureml-pipeline-jobs","p":199},{"i":219,"t":"On this page","s":"Tune - HuggingFace","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"","p":218},{"i":221,"t":"This example requires GPU. Install dependencies: pip install torch transformers datasets \"flaml[blendsearch,ray]\" Copy","s":"Requirements","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#requirements","p":218},{"i":223,"t":"Tokenizer​ from transformers import AutoTokenizerMODEL_NAME = \"distilbert-base-uncased\"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)COLUMN_NAME = \"sentence\"def tokenize(examples): return tokenizer(examples[COLUMN_NAME], truncation=True) Copy Define training method​ import flamlimport datasetsfrom transformers import AutoModelForSequenceClassificationTASK = \"cola\"NUM_LABELS = 2def train_distilbert(config: dict): # Load CoLA dataset and apply tokenizer cola_raw = datasets.load_dataset(\"glue\", TASK) cola_encoded = cola_raw.map(tokenize, batched=True) train_dataset, eval_dataset = cola_encoded[\"train\"], cola_encoded[\"validation\"] model = AutoModelForSequenceClassification.from_pretrained( MODEL_NAME, num_labels=NUM_LABELS ) metric = datasets.load_metric(\"glue\", TASK) def compute_metrics(eval_pred): predictions, labels = eval_pred predictions = np.argmax(predictions, axis=1) return metric.compute(predictions=predictions, references=labels) training_args = TrainingArguments( output_dir=\".\", do_eval=False, disable_tqdm=True, logging_steps=20000, save_total_limit=0, **config, ) trainer = Trainer( model, training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=tokenizer, compute_metrics=compute_metrics, ) # train model trainer.train() # evaluate model eval_output = trainer.evaluate() # report the metric to optimize & the metric to log flaml.tune.report( loss=eval_output[\"eval_loss\"], matthews_correlation=eval_output[\"eval_matthews_correlation\"], ) Copy","s":"Prepare for tuning","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#prepare-for-tuning","p":218},{"i":225,"t":"We are now ready to define our search. This includes: The search_space for our hyperparameters The metric and the mode ('max' or 'min') for optimization The constraints (n_cpus, n_gpus, num_samples, and time_budget_s) max_num_epoch = 64search_space = { # You can mix constants with search space objects. \"num_train_epochs\": flaml.tune.loguniform(1, max_num_epoch), \"learning_rate\": flaml.tune.loguniform(1e-6, 1e-4), \"adam_epsilon\": flaml.tune.loguniform(1e-9, 1e-7), \"adam_beta1\": flaml.tune.uniform(0.8, 0.99), \"adam_beta2\": flaml.tune.loguniform(98e-2, 9999e-4),}# optimization objectiveHP_METRIC, MODE = \"matthews_correlation\", \"max\"# resourcesnum_cpus = 4num_gpus = 4 # change according to your GPU resources# constraintsnum_samples = -1 # number of trials, -1 means unlimitedtime_budget_s = 3600 # time budget in seconds Copy","s":"Define the search","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#define-the-search","p":218},{"i":227,"t":"We are now ready to launch the tuning using flaml.tune.run: import rayray.init(num_cpus=num_cpus, num_gpus=num_gpus)print(\"Tuning started...\")analysis = flaml.tune.run( train_distilbert, search_alg=flaml.CFO( space=search_space, metric=HP_METRIC, mode=MODE, low_cost_partial_config={\"num_train_epochs\": 1}, ), resources_per_trial={\"gpu\": num_gpus, \"cpu\": num_cpus}, local_dir=\"logs/\", num_samples=num_samples, time_budget_s=time_budget_s, use_ray=True,) Copy This will run tuning for one hour. At the end we will see a summary. == Status ==Memory usage on this node: 32.0/251.6 GiBUsing FIFO scheduling algorithm.Resources requested: 0/4 CPUs, 0/4 GPUs, 0.0/150.39 GiB heap, 0.0/47.22 GiB objects (0/1.0 accelerator_type:V100)Result logdir: /home/chiw/FLAML/notebook/logs/train_distilbert_2021-05-07_02-35-58Number of trials: 22/infinite (22 TERMINATED)Trial name status loc adam_beta1 adam_beta2 adam_epsilon learning_rate num_train_epochs iter total time (s) loss matthews_correlationtrain_distilbert_a0c303d0 TERMINATED 0.939079 0.991865 7.96945e-08 5.61152e-06 1 1 55.6909 0.587986 0train_distilbert_a0c303d1 TERMINATED 0.811036 0.997214 2.05111e-09 2.05134e-06 1.44427 1 71.7663 0.603018 0train_distilbert_c39b2ef0 TERMINATED 0.909395 0.993715 1e-07 5.26543e-06 1 1 53.7619 0.586518 0train_distilbert_f00776e2 TERMINATED 0.968763 0.990019 4.38943e-08 5.98035e-06 1.02723 1 56.8382 0.581313 0train_distilbert_11ab3900 TERMINATED 0.962198 0.991838 7.09296e-08 5.06608e-06 1 1 54.0231 0.585576 0train_distilbert_353025b6 TERMINATED 0.91596 0.991892 8.95426e-08 6.21568e-06 2.15443 1 98.3233 0.531632 0.388893train_distilbert_5728a1de TERMINATED 0.926933 0.993146 1e-07 1.00902e-05 1 1 55.3726 0.538505 0.280558train_distilbert_9394c2e2 TERMINATED 0.928106 0.990614 4.49975e-08 3.45674e-06 2.72935 1 121.388 0.539177 0.327295train_distilbert_b6543fec TERMINATED 0.876896 0.992098 1e-07 7.01176e-06 1.59538 1 76.0244 0.527516 0.379177train_distilbert_0071f998 TERMINATED 0.955024 0.991687 7.39776e-08 5.50998e-06 2.90939 1 126.871 0.516225 0.417157train_distilbert_2f830be6 TERMINATED 0.886931 0.989628 7.6127e-08 4.37646e-06 1.53338 1 73.8934 0.551629 0.0655887train_distilbert_7ce03f12 TERMINATED 0.984053 0.993956 8.70144e-08 7.82557e-06 4.08775 1 174.027 0.523732 0.453549train_distilbert_aaab0508 TERMINATED 0.940707 0.993946 1e-07 8.91979e-06 3.40243 1 146.249 0.511288 0.45085train_distilbert_14262454 TERMINATED 0.99 0.991696 4.60093e-08 4.83405e-06 3.4954 1 152.008 0.53506 0.400851train_distilbert_6d211fe6 TERMINATED 0.959277 0.994556 5.40791e-08 1.17333e-05 6.64995 1 271.444 0.609851 0.526802train_distilbert_c980bae4 TERMINATED 0.99 0.993355 1e-07 5.21929e-06 2.51275 1 111.799 0.542276 0.324968train_distilbert_6d0d29d6 TERMINATED 0.965773 0.995182 9.9752e-08 1.15549e-05 13.694 1 527.944 0.923802 0.549474train_distilbert_b16ea82a TERMINATED 0.952781 0.993931 2.93182e-08 1.19145e-05 3.2293 1 139.844 0.533466 0.451307train_distilbert_eddf7cc0 TERMINATED 0.99 0.997109 8.13498e-08 1.28515e-05 15.5807 1 614.789 0.983285 0.56993train_distilbert_43008974 TERMINATED 0.929089 0.993258 1e-07 1.03892e-05 12.0357 1 474.387 0.857461 0.520022train_distilbert_b3408a4e TERMINATED 0.99 0.993809 4.67441e-08 1.10418e-05 11.9165 1 474.126 0.828205 0.526164train_distilbert_cfbfb220 TERMINATED 0.979454 0.9999 1e-07 1.49578e-05 20.3715 Copy","s":"Launch the tuning","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#launch-the-tuning","p":218},{"i":229,"t":"best_trial = analysis.get_best_trial(HP_METRIC, MODE, \"all\")metric = best_trial.metric_analysis[HP_METRIC][MODE]print(f\"n_trials={len(analysis.trials)}\")print(f\"time={time.time()-start_time}\")print(f\"Best model eval {HP_METRIC}: {metric:.4f}\")print(f\"Best model parameters: {best_trial.config}\")# n_trials=22# time=3999.769361972809# Best model eval matthews_correlation: 0.5699# Best model parameters: {'num_train_epochs': 15.580684188655825, 'learning_rate': 1.2851507818900338e-05, 'adam_epsilon': 8.134982521948352e-08, 'adam_beta1': 0.99, 'adam_beta2': 0.9971094424784387} Copy Link to notebook | Open in colab","s":"Retrieve the results","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#retrieve-the-results","p":218},{"i":231,"t":"On this page","s":"Tune - PyTorch","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"","p":230},{"i":234,"t":"pip install torchvision \"flaml[blendsearch,ray]\" Copy Before we are ready for tuning, we first need to define the neural network that we would like to tune.","s":"Requirements","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#requirements","p":230},{"i":236,"t":"import torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torch.utils.data import random_splitimport torchvisionimport torchvision.transforms as transformsclass Net(nn.Module): def __init__(self, l1=120, l2=84): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, l1) self.fc2 = nn.Linear(l1, l2) self.fc3 = nn.Linear(l2, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x Copy","s":"Network Specification","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#network-specification","p":230},{"i":238,"t":"def load_data(data_dir=\"data\"): transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] ) trainset = torchvision.datasets.CIFAR10( root=data_dir, train=True, download=True, transform=transform ) testset = torchvision.datasets.CIFAR10( root=data_dir, train=False, download=True, transform=transform ) return trainset, testset Copy","s":"Data","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#data","p":230},{"i":240,"t":"from ray import tunedef train_cifar(config, checkpoint_dir=None, data_dir=None): if \"l1\" not in config: logger.warning(config) net = Net(2 ** config[\"l1\"], 2 ** config[\"l2\"]) device = \"cpu\" if torch.cuda.is_available(): device = \"cuda:0\" if torch.cuda.device_count() > 1: net = nn.DataParallel(net) net.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=config[\"lr\"], momentum=0.9) # The `checkpoint_dir` parameter gets passed by Ray Tune when a checkpoint # should be restored. if checkpoint_dir: checkpoint = os.path.join(checkpoint_dir, \"checkpoint\") model_state, optimizer_state = torch.load(checkpoint) net.load_state_dict(model_state) optimizer.load_state_dict(optimizer_state) trainset, testset = load_data(data_dir) test_abs = int(len(trainset) * 0.8) train_subset, val_subset = random_split( trainset, [test_abs, len(trainset) - test_abs] ) trainloader = torch.utils.data.DataLoader( train_subset, batch_size=int(2 ** config[\"batch_size\"]), shuffle=True, num_workers=4, ) valloader = torch.utils.data.DataLoader( val_subset, batch_size=int(2 ** config[\"batch_size\"]), shuffle=True, num_workers=4, ) for epoch in range( int(round(config[\"num_epochs\"])) ): # loop over the dataset multiple times running_loss = 0.0 epoch_steps = 0 for i, data in enumerate(trainloader, 0): # get the inputs; data is a list of [inputs, labels] inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics running_loss += loss.item() epoch_steps += 1 if i % 2000 == 1999: # print every 2000 mini-batches print( \"[%d, %5d] loss: %.3f\" % (epoch + 1, i + 1, running_loss / epoch_steps) ) running_loss = 0.0 # Validation loss val_loss = 0.0 val_steps = 0 total = 0 correct = 0 for i, data in enumerate(valloader, 0): with torch.no_grad(): inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) outputs = net(inputs) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() loss = criterion(outputs, labels) val_loss += loss.cpu().numpy() val_steps += 1 # Here we save a checkpoint. It is automatically registered with # Ray Tune and will potentially be passed as the `checkpoint_dir` # parameter in future iterations. with tune.checkpoint_dir(step=epoch) as checkpoint_dir: path = os.path.join(checkpoint_dir, \"checkpoint\") torch.save((net.state_dict(), optimizer.state_dict()), path) tune.report(loss=(val_loss / val_steps), accuracy=correct / total) print(\"Finished Training\") Copy","s":"Training","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#training","p":230},{"i":242,"t":"def _test_accuracy(net, device=\"cpu\"): trainset, testset = load_data() testloader = torch.utils.data.DataLoader( testset, batch_size=4, shuffle=False, num_workers=2 ) correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels = data images, labels = images.to(device), labels.to(device) outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return correct / total Copy","s":"Test Accuracy","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#test-accuracy","p":230},{"i":244,"t":"import numpy as npimport flamlimport osdata_dir = os.path.abspath(\"data\")load_data(data_dir) # Download data for all trials before starting the run Copy","s":"Hyperparameter Optimization","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#hyperparameter-optimization","p":230},{"i":246,"t":"max_num_epoch = 100config = { \"l1\": tune.randint(2, 9), # log transformed with base 2 \"l2\": tune.randint(2, 9), # log transformed with base 2 \"lr\": tune.loguniform(1e-4, 1e-1), \"num_epochs\": tune.loguniform(1, max_num_epoch), \"batch_size\": tune.randint(1, 5), # log transformed with base 2} Copy","s":"Search space","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#search-space","p":230},{"i":248,"t":"time_budget_s = 600 # time budget in secondsgpus_per_trial = ( 0.5 # number of gpus for each trial; 0.5 means two training jobs can share one gpu)num_samples = 500 # maximal number of trialsnp.random.seed(7654321) Copy","s":"Budget and resource constraints","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#budget-and-resource-constraints","p":230},{"i":250,"t":"import timestart_time = time.time()result = flaml.tune.run( tune.with_parameters(train_cifar, data_dir=data_dir), config=config, metric=\"loss\", mode=\"min\", low_cost_partial_config={\"num_epochs\": 1}, max_resource=max_num_epoch, min_resource=1, scheduler=\"asha\", # Use asha scheduler to perform early stopping based on intermediate results reported resources_per_trial={\"cpu\": 1, \"gpu\": gpus_per_trial}, local_dir=\"logs/\", num_samples=num_samples, time_budget_s=time_budget_s, use_ray=True,) Copy","s":"Launch the tuning","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#launch-the-tuning","p":230},{"i":252,"t":"print(f\"#trials={len(result.trials)}\")print(f\"time={time.time()-start_time}\")best_trial = result.get_best_trial(\"loss\", \"min\", \"all\")print(\"Best trial config: {}\".format(best_trial.config))print( \"Best trial final validation loss: {}\".format( best_trial.metric_analysis[\"loss\"][\"min\"] ))print( \"Best trial final validation accuracy: {}\".format( best_trial.metric_analysis[\"accuracy\"][\"max\"] ))best_trained_model = Net(2 ** best_trial.config[\"l1\"], 2 ** best_trial.config[\"l2\"])device = \"cpu\"if torch.cuda.is_available(): device = \"cuda:0\" if gpus_per_trial > 1: best_trained_model = nn.DataParallel(best_trained_model)best_trained_model.to(device)checkpoint_value = ( getattr(best_trial.checkpoint, \"dir_or_data\", None) or best_trial.checkpoint.value)checkpoint_path = os.path.join(checkpoint_value, \"checkpoint\")model_state, optimizer_state = torch.load(checkpoint_path)best_trained_model.load_state_dict(model_state)test_acc = _test_accuracy(best_trained_model, device)print(\"Best trial test set accuracy: {}\".format(test_acc)) Copy","s":"Check the result","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#check-the-result","p":230},{"i":254,"t":"#trials=44time=1193.913584947586Best trial config: {'l1': 8, 'l2': 8, 'lr': 0.0008818671030627281, 'num_epochs': 55.9513429004283, 'batch_size': 3}Best trial final validation loss: 1.0694482081472874Best trial final validation accuracy: 0.6389Files already downloaded and verifiedFiles already downloaded and verifiedBest trial test set accuracy: 0.6294 Copy Link to notebook | Open in colab","s":"Sample of output","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#sample-of-output","p":230},{"i":256,"t":"On this page","s":"Installation","u":"/FLAML/docs/Installation","h":"","p":255},{"i":258,"t":"FLAML requires Python version >= 3.7. It can be installed from pip: pip install flaml Copy or conda: conda install flaml -c conda-forge Copy","s":"Python","u":"/FLAML/docs/Installation","h":"#python","p":255},{"i":260,"t":"Autogen​ pip install \"flaml[autogen]\" Copy Task-oriented AutoML​ pip install \"flaml[automl]\" Copy Extra learners/models​ openai models pip install \"flaml[openai]\" Copy catboost pip install \"flaml[catboost]\" Copy vowpal wabbit pip install \"flaml[vw]\" Copy time series forecaster: prophet, statsmodels pip install \"flaml[forecast]\" Copy huggingface transformers pip install \"flaml[hf]\" Copy Notebook​ To run the notebook examples, install flaml with the [notebook] option: pip install \"flaml[notebook]\" Copy Distributed tuning​ ray pip install \"flaml[ray]\" Copy spark Spark support is added in v1.1.0 pip install \"flaml[spark]>=1.1.0\" Copy For cloud platforms such as Azure Synapse, Spark clusters are provided. But you may also need to install Spark manually when setting up your own environment. For latest Ubuntu system, you can install Spark 3.3.0 standalone version with below script. For more details of installing Spark, please refer to Spark Doc. sudo apt-get update && sudo apt-get install -y --allow-downgrades --allow-change-held-packages --no-install-recommends \\ ca-certificates-java ca-certificates openjdk-17-jdk-headless \\ && sudo apt-get clean && sudo rm -rf /var/lib/apt/lists/*wget --progress=dot:giga \"https://www.apache.org/dyn/closer.lua/spark/spark-3.3.0/spark-3.3.0-bin-hadoop2.tgz?action=download\" \\ -O - | tar -xzC /tmp; archive=$(basename \"spark-3.3.0/spark-3.3.0-bin-hadoop2.tgz\") \\ bash -c \"sudo mv -v /tmp/\\${archive/%.tgz/} /spark\"export SPARK_HOME=/sparkexport PYTHONPATH=/spark/python/lib/py4j-0.10.9.5-src.zip:/spark/pythonexport PATH=$PATH:$SPARK_HOME/bin Copy nni pip install \"flaml[nni]\" Copy blendsearch pip install \"flaml[blendsearch]\" Copy synapse To install flaml in Azure Synapse and similar cloud platform pip install flaml[synapse] Copy Test and Benchmark​ test pip install flaml[test] Copy benchmark pip install flaml[benchmark] Copy","s":"Optional Dependencies","u":"/FLAML/docs/Installation","h":"#optional-dependencies","p":255},{"i":262,"t":"FLAML has a .NET implementation in ML.NET, an open-source, cross-platform machine learning framework for .NET. You can use FLAML in .NET in the following ways: Low-code Model Builder - A Visual Studio extension for training ML models using FLAML. For more information on how to install, see the install Model Builder guide. ML.NET CLI - A dotnet CLI tool for training machine learning models using FLAML on Windows, MacOS, and Linux. For more information on how to install the ML.NET CLI, see the install the ML.NET CLI guide. Code-first Microsoft.ML.AutoML - NuGet package that provides direct access to the FLAML AutoML APIs that power low-code solutions like Model Builder and the ML.NET CLI. For more information on installing NuGet packages, see the install and use a NuGet package in Visual Studio or dotnet CLI guides. To get started with the ML.NET API and AutoML, see the csharp-notebooks.","s":".NET","u":"/FLAML/docs/Installation","h":"#net","p":255},{"i":264,"t":"On this page","s":"Frequently Asked Questions","u":"/FLAML/docs/FAQ","h":"","p":263},{"i":269,"t":"Definition and purpose: The low_cost_partial_config is a dictionary of subset of the hyperparameter coordinates whose value corresponds to a configuration with known low-cost (i.e., low computation cost for training the corresponding model). The concept of low/high-cost is meaningful in the case where a subset of the hyperparameters to tune directly affects the computation cost for training the model. For example, n_estimators and max_leaves are known to affect the training cost of tree-based learners. We call this subset of hyperparameters, cost-related hyperparameters. In such scenarios, if you are aware of low-cost configurations for the cost-related hyperparameters, you are recommended to set them as the low_cost_partial_config. Using the tree-based method example again, since we know that small n_estimators and max_leaves generally correspond to simpler models and thus lower cost, we set {'n_estimators': 4, 'max_leaves': 4} as the low_cost_partial_config by default (note that 4 is the lower bound of search space for these two hyperparameters), e.g., in LGBM. Configuring low_cost_partial_config helps the search algorithms make more cost-efficient choices. In AutoML, the low_cost_init_value in search_space() function for each estimator serves the same role. Usage in practice: It is recommended to configure it if there are cost-related hyperparameters in your tuning task and you happen to know the low-cost values for them, but it is not required (It is fine to leave it the default value, i.e., None). How does it work: low_cost_partial_config if configured, will be used as an initial point of the search. It also affects the search trajectory. For more details about how does it play a role in the search algorithms, please refer to the papers about the search algorithms used: Section 2 of Frugal Optimization for Cost-related Hyperparameters (CFO) and Section 3 of Economical Hyperparameter Optimization with Blended Search Strategy (BlendSearch).","s":"About low_cost_partial_config in tune.","u":"/FLAML/docs/FAQ","h":"#about-low_cost_partial_config-in-tune","p":263},{"i":271,"t":"Currently FLAML does several things for imbalanced data. When a class contains fewer than 20 examples, we repeatedly add these examples to the training data until the count is at least 20. We use stratified sampling when doing holdout and kf. We make sure no class is empty in both training and holdout data. We allow users to pass sample_weight to AutoML.fit(). User can customize the weight of each class by setting the custom_hp or fit_kwargs_by_estimator arguments. For example, the following code sets the weight for pos vs. neg as 2:1 for the RandomForest estimator: from flaml import AutoMLfrom sklearn.datasets import load_irisX_train, y_train = load_iris(return_X_y=True)automl = AutoML()automl_settings = { \"time_budget\": 2, \"task\": \"classification\", \"log_file_name\": \"test/iris.log\", \"estimator_list\": [\"rf\", \"xgboost\"],}automl_settings[\"custom_hp\"] = { \"xgboost\": { \"scale_pos_weight\": { \"domain\": 0.5, \"init_value\": 0.5, } }, \"rf\": {\"class_weight\": {\"domain\": \"balanced\", \"init_value\": \"balanced\"}},}print(automl.model) Copy","s":"How does FLAML handle imbalanced data (unequal distribution of target classes in classification task)?","u":"/FLAML/docs/FAQ","h":"#how-does-flaml-handle-imbalanced-data-unequal-distribution-of-target-classes-in-classification-task","p":263},{"i":273,"t":"You can use automl.model.estimator.feature_importances_ to get the feature_importances_ for the best model found by automl. See an example. Packages such as azureml-interpret and sklearn.inspection.permutation_importance can be used on automl.model.estimator to explain the selected model. Model explanation is frequently asked and adding a native support may be a good feature. Suggestions/contributions are welcome. Optimization history can be checked from the log. You can also retrieve the log and plot the learning curve.","s":"How to interpret model performance? Is it possible for me to visualize feature importance, SHAP values, optimization history?","u":"/FLAML/docs/FAQ","h":"#how-to-interpret-model-performance-is-it-possible-for-me-to-visualize-feature-importance-shap-values-optimization-history","p":263},{"i":275,"t":"Set free_mem_ratio a float between 0 and 1. For example, 0.2 means try to keep free memory above 20% of total memory. Training may be early stopped for memory consumption reason when this is set. Set model_history False. If your data are already preprocessed, set skip_transform False. If you can preprocess the data before the fit starts, this setting can save memory needed for preprocessing in fit. If the OOM error only happens for some particular trials: set use_ray True. This will increase the overhead per trial but can keep the AutoML process running when a single trial fails due to OOM error. provide a more accurate size function for the memory bytes consumption of each config for the estimator causing this error. modify the search space for the estimators causing this error. or remove this estimator from the estimator_list. If the OOM error happens when ensembling, consider disabling ensemble, or use a cheaper ensemble option. (Example).","s":"How to resolve out-of-memory error in AutoML.fit()","u":"/FLAML/docs/FAQ","h":"#how-to-resolve-out-of-memory-error-in-automlfit","p":263},{"i":277,"t":"On this page","s":"Getting Started","u":"/FLAML/docs/Getting-Started","h":"","p":276},{"i":279,"t":"FLAML enables building next-gen GPT-X applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation and optimization of a complex GPT-X workflow. It maximizes the performance of GPT-X models and augments their weakness. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It is easy to customize or extend. It supports fast and economical automatic tuning, capable of handling large search space with heterogeneous evaluation cost and complex constraints/guidance/early stopping. FLAML is powered by a series of research studies from Microsoft Research and collaborators such as Penn State University, Stevens Institute of Technology, University of Washington, and University of Waterloo.","s":"Main Features","u":"/FLAML/docs/Getting-Started","h":"#main-features","p":276},{"i":281,"t":"Install FLAML from pip: pip install flaml. Find more options in Installation. There are several ways of using flaml: (New) AutoGen​ Autogen enables the next-gen GPT-X applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools and human. By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example, from flaml import autogenassistant = autogen.AssistantAgent(\"assistant\")user_proxy = autogen.UserProxyAgent(\"user_proxy\")user_proxy.initiate_chat( assistant, message=\"Show me the YTD gain of 10 largest technology companies as of today.\",)# This initiates an automated chat between the two agents to solve the task Copy Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of openai.Completion or openai.ChatCompletion with powerful functionalites like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets. # perform tuningconfig, analysis = autogen.Completion.tune( data=tune_data, metric=\"success\", mode=\"max\", eval_func=eval_func, inference_budget=0.05, optimization_budget=3, num_samples=-1,)# perform inference for a test instanceresponse = autogen.Completion.create(context=test_instance, **config) Copy Task-oriented AutoML​ With three lines of code, you can start using this economical and fast AutoML engine as a scikit-learn style estimator. from flaml import AutoMLautoml = AutoML()automl.fit(X_train, y_train, task=\"classification\", time_budget=60) Copy It automatically tunes the hyperparameters and selects the best model from default learners such as LightGBM, XGBoost, random forest etc. for the specified time budget 60 seconds. Customizing the optimization metrics, learners and search spaces etc. is very easy. For example, automl.add_learner(\"mylgbm\", MyLGBMEstimator)automl.fit( X_train, y_train, task=\"classification\", metric=custom_metric, estimator_list=[\"mylgbm\"], time_budget=60,) Copy Tune user-defined function​ You can run generic hyperparameter tuning for a custom function (machine learning or beyond). For example, from flaml import tunefrom flaml.automl.model import LGBMEstimatordef train_lgbm(config: dict) -> dict: # convert config dict to lgbm params params = LGBMEstimator(**config).params # train the model train_set = lightgbm.Dataset(csv_file_name) model = lightgbm.train(params, train_set) # evaluate the model pred = model.predict(X_test) mse = mean_squared_error(y_test, pred) # return eval results as a dictionary return {\"mse\": mse}# load a built-in search space from flamlflaml_lgbm_search_space = LGBMEstimator.search_space(X_train.shape)# specify the search space as a dict from hp name to domain; you can define your own search space same wayconfig_search_space = { hp: space[\"domain\"] for hp, space in flaml_lgbm_search_space.items()}# give guidance about hp values corresponding to low training cost, i.e., {\"n_estimators\": 4, \"num_leaves\": 4}low_cost_partial_config = { hp: space[\"low_cost_init_value\"] for hp, space in flaml_lgbm_search_space.items() if \"low_cost_init_value\" in space}# run the tuning, minimizing mse, with total time budget 3 secondsanalysis = tune.run( train_lgbm, metric=\"mse\", mode=\"min\", config=config_search_space, low_cost_partial_config=low_cost_partial_config, time_budget_s=3, num_samples=-1,) Copy Please see this script for the complete version of the above example. Zero-shot AutoML​ FLAML offers a unique, seamless and effortless way to leverage AutoML for the commonly used classifiers and regressors such as LightGBM and XGBoost. For example, if you are using lightgbm.LGBMClassifier as your current learner, all you need to do is to replace from lightgbm import LGBMClassifier by: from flaml.default import LGBMClassifier Copy Then, you can use it just like you use the original LGMBClassifier. Your other code can remain unchanged. When you call the fit() function from flaml.default.LGBMClassifier, it will automatically instantiate a good data-dependent hyperparameter configuration for your dataset, which is expected to work better than the default configuration.","s":"Quickstart","u":"/FLAML/docs/Getting-Started","h":"#quickstart","p":276},{"i":283,"t":"Understand the use cases for AutoGen, Task-oriented AutoML, Tune user-defined function and Zero-shot AutoML. Find code examples under \"Examples\": from AutoGen - AgentChat to Tune - PyTorch. Learn about research around FLAML and check blogposts. Chat on Discord. If you like our project, please give it a star on GitHub. If you are interested in contributing, please read Contributor's Guide.","s":"Where to Go Next?","u":"/FLAML/docs/Getting-Started","h":"#where-to-go-next","p":276},{"i":285,"t":"On this page","s":"autogen.agentchat.agent","u":"/FLAML/docs/reference/autogen/agentchat/agent","h":"","p":284},{"i":287,"t":"class Agent() Copy (In preview) An abstract class for AI agent. An agent can communicate with other agents and perform actions. Different agents can differ in what actions they perform in the receive method. __init__​ def __init__(name: str) Copy Arguments: name str - name of the agent. name​ @propertydef name() Copy Get the name of the agent. send​ def send(message: Union[Dict, str], recipient: \"Agent\", request_reply: Optional[bool] = None) Copy (Aabstract method) Send a message to another agent. a_send​ async def a_send(message: Union[Dict, str], recipient: \"Agent\", request_reply: Optional[bool] = None) Copy (Aabstract async method) Send a message to another agent. receive​ def receive(message: Union[Dict, str], sender: \"Agent\", request_reply: Optional[bool] = None) Copy (Abstract method) Receive a message from another agent. a_receive​ async def a_receive(message: Union[Dict, str], sender: \"Agent\", request_reply: Optional[bool] = None) Copy (Abstract async method) Receive a message from another agent. reset​ def reset() Copy (Abstract method) Reset the agent. generate_reply​ def generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[\"Agent\"] = None, **kwargs, ,) -> Union[str, Dict, None] Copy (Abstract method) Generate a reply based on the received messages. Arguments: messages list[dict] - a list of messages received. sender - sender of an Agent instance. Returns: str or dict or None: the generated reply. If None, no reply is generated. a_generate_reply​ async def a_generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[\"Agent\"] = None, **kwargs, ,) -> Union[str, Dict, None] Copy (Abstract async method) Generate a reply based on the received messages. Arguments: messages list[dict] - a list of messages received. sender - sender of an Agent instance. Returns: str or dict or None: the generated reply. If None, no reply is generated.","s":"Agent Objects","u":"/FLAML/docs/reference/autogen/agentchat/agent","h":"#agent-objects","p":284},{"i":289,"t":"On this page","s":"Tune - Lexicographic Objectives","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"","p":288},{"i":291,"t":"pip install \"flaml>=1.1.0\" thop torchvision torch Copy Tuning multiple objectives with Lexicographic preference is a new feature added in version 1.1.0 and is subject to change in future versions.","s":"Requirements","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#requirements","p":288},{"i":294,"t":"import torchimport thopimport torch.nn as nnfrom flaml import tuneimport torch.nn.functional as Fimport torchvisionimport numpy as npimport osDEVICE = torch.device(\"cpu\")BATCHSIZE = 128N_TRAIN_EXAMPLES = BATCHSIZE * 30N_VALID_EXAMPLES = BATCHSIZE * 10data_dir = os.path.abspath(\"data\")train_dataset = torchvision.datasets.FashionMNIST( data_dir, train=True, download=True, transform=torchvision.transforms.ToTensor(),)train_loader = torch.utils.data.DataLoader( torch.utils.data.Subset(train_dataset, list(range(N_TRAIN_EXAMPLES))), batch_size=BATCHSIZE, shuffle=True,)val_dataset = torchvision.datasets.FashionMNIST( data_dir, train=False, transform=torchvision.transforms.ToTensor())val_loader = torch.utils.data.DataLoader( torch.utils.data.Subset(val_dataset, list(range(N_VALID_EXAMPLES))), batch_size=BATCHSIZE, shuffle=True, Copy","s":"Data","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#data","p":288},{"i":296,"t":"def define_model(configuration): n_layers = configuration[\"n_layers\"] layers = [] in_features = 28 * 28 for i in range(n_layers): out_features = configuration[\"n_units_l{}\".format(i)] layers.append(nn.Linear(in_features, out_features)) layers.append(nn.ReLU()) p = configuration[\"dropout_{}\".format(i)] layers.append(nn.Dropout(p)) in_features = out_features layers.append(nn.Linear(in_features, 10)) layers.append(nn.LogSoftmax(dim=1)) return nn.Sequential(*layers) Copy","s":"Specific the model","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#specific-the-model","p":288},{"i":298,"t":"def train_model(model, optimizer, train_loader): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE) optimizer.zero_grad() F.nll_loss(model(data), target).backward() optimizer.step() Copy","s":"Train","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#train","p":288},{"i":300,"t":"def eval_model(model, valid_loader): model.eval() correct = 0 with torch.no_grad(): for batch_idx, (data, target) in enumerate(valid_loader): data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE) pred = model(data).argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() accuracy = correct / N_VALID_EXAMPLES flops, params = thop.profile( model, inputs=(torch.randn(1, 28 * 28).to(DEVICE),), verbose=False ) return np.log2(flops), 1 - accuracy, params Copy","s":"Metrics","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#metrics","p":288},{"i":302,"t":"def evaluate_function(configuration): model = define_model(configuration).to(DEVICE) optimizer = torch.optim.Adam(model.parameters(), configuration[\"lr\"]) n_epoch = configuration[\"n_epoch\"] for epoch in range(n_epoch): train_model(model, optimizer, train_loader) flops, error_rate, params = eval_model(model, val_loader) return {\"error_rate\": error_rate, \"flops\": flops, \"params\": params} Copy","s":"Evaluation function","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#evaluation-function","p":288},{"i":304,"t":"search_space = { \"n_layers\": tune.randint(lower=1, upper=3), \"n_units_l0\": tune.randint(lower=4, upper=128), \"n_units_l1\": tune.randint(lower=4, upper=128), \"n_units_l2\": tune.randint(lower=4, upper=128), \"dropout_0\": tune.uniform(lower=0.2, upper=0.5), \"dropout_1\": tune.uniform(lower=0.2, upper=0.5), \"dropout_2\": tune.uniform(lower=0.2, upper=0.5), \"lr\": tune.loguniform(lower=1e-5, upper=1e-1), \"n_epoch\": tune.randint(lower=1, upper=20),} Copy","s":"Search space","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#search-space","p":288},{"i":306,"t":"# Low cost initial pointlow_cost_partial_config = { \"n_layers\": 1, \"n_units_l0\": 4, \"n_units_l1\": 4, \"n_units_l2\": 4, \"n_epoch\": 1,}# Specific lexicographic preferencelexico_objectives = {}lexico_objectives[\"metrics\"] = [\"error_rate\", \"flops\"]lexico_objectives[\"tolerances\"] = {\"error_rate\": 0.02, \"flops\": 0.0}lexico_objectives[\"targets\"] = {\"error_rate\": 0.0, \"flops\": 0.0}lexico_objectives[\"modes\"] = [\"min\", \"min\"]# launch the tuning processanalysis = tune.run( evaluate_function, num_samples=-1, time_budget_s=100, config=search_space, # search space of NN use_ray=False, lexico_objectives=lexico_objectives, low_cost_partial_config=low_cost_partial_config, # low cost initial point) Copy We also support providing percentage tolerance as shown below. lexico_objectives[\"tolerances\"] = {\"error_rate\": \"5%\", \"flops\": \"0%\"} Copy Link to notebook | Open in colab","s":"Launch the tuning process","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#launch-the-tuning-process","p":288},{"i":308,"t":"On this page","s":"autogen.agentchat.assistant_agent","u":"/FLAML/docs/reference/autogen/agentchat/assistant_agent","h":"","p":307},{"i":310,"t":"class AssistantAgent(ConversableAgent) Copy (In preview) Assistant agent, designed to solve a task with LLM. AssistantAgent is a subclass of ConversableAgent configured with a default system message. The default system message is designed to solve a task with LLM, including suggesting python code blocks and debugging. human_input_mode is default to \"NEVER\" and code_execution_config is default to False. This agent doesn't execute code by default, and expects the user to execute the code. __init__​ def __init__(name: str, system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE, llm_config: Optional[Union[Dict, bool]] = None, is_termination_msg: Optional[Callable[[Dict], bool]] = None, max_consecutive_auto_reply: Optional[int] = None, human_input_mode: Optional[str] = \"NEVER\", code_execution_config: Optional[Union[Dict, bool]] = False, **kwargs, ,) Copy Arguments: name str - agent name. system_message str - system message for the ChatCompletion inference. Please override this attribute if you want to reprogram the agent. llm_config dict - llm inference configuration. Please refer to autogen.Completion.create for available options. is_termination_msg function - a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. The dict can contain the following keys: \"content\", \"role\", \"name\", \"function_call\". max_consecutive_auto_reply int - the maximum number of consecutive auto replies. default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). The limit only plays a role when human_input_mode is not \"ALWAYS\". **kwargs dict - Please refer to other kwargs in ConversableAgent.","s":"AssistantAgent Objects","u":"/FLAML/docs/reference/autogen/agentchat/assistant_agent","h":"#assistantagent-objects","p":307},{"i":312,"t":"On this page","s":"autogen.agentchat.conversable_agent","u":"/FLAML/docs/reference/autogen/agentchat/conversable_agent","h":"","p":311},{"i":314,"t":"class ConversableAgent(Agent) Copy (In preview) A class for generic conversable agents which can be configured as assistant or user proxy. After receiving each message, the agent will send a reply to the sender unless the msg is a termination msg. For example, AssistantAgent and UserProxyAgent are subclasses of this class, configured with different default settings. To modify auto reply, override generate_reply method. To disable/enable human response in every turn, set human_input_mode to \"NEVER\" or \"ALWAYS\". To modify the way to get human input, override get_human_input method. To modify the way to execute code blocks, single code block, or function call, override execute_code_blocks, run_code, and execute_function methods respectively. To customize the initial message when a conversation starts, override generate_init_message method. __init__​ def __init__(name: str, system_message: Optional[str] = \"You are a helpful AI Assistant.\", is_termination_msg: Optional[Callable[[Dict], bool]] = None, max_consecutive_auto_reply: Optional[int] = None, human_input_mode: Optional[str] = \"TERMINATE\", function_map: Optional[Dict[str, Callable]] = None, code_execution_config: Optional[Union[Dict, bool]] = None, llm_config: Optional[Union[Dict, bool]] = None, default_auto_reply: Optional[Union[str, Dict, None]] = \"\") Copy Arguments: name str - name of the agent. system_message str - system message for the ChatCompletion inference. is_termination_msg function - a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. The dict can contain the following keys: \"content\", \"role\", \"name\", \"function_call\". max_consecutive_auto_reply int - the maximum number of consecutive auto replies. default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). When set to 0, no auto reply will be generated. human_input_mode str - whether to ask for human inputs every time a message is received. Possible values are \"ALWAYS\", \"TERMINATE\", \"NEVER\". (1) When \"ALWAYS\", the agent prompts for human input every time a message is received. Under this mode, the conversation stops when the human input is \"exit\", or when is_termination_msg is True and there is no human input. (2) When \"TERMINATE\", the agent only prompts for human input only when a termination message is received or the number of auto reply reaches the max_consecutive_auto_reply. (3) When \"NEVER\", the agent will never prompt for human input. Under this mode, the conversation stops when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. function_map dict[str, callable] - Mapping function names (passed to openai) to callable functions. code_execution_config dict or False - config for the code execution. To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: work_dir (Optional, str): The working directory for the code execution. If None, a default working directory will be used. The default working directory is the \"extensions\" directory under \"path_to_flaml/autogen\". use_docker (Optional, list, str or bool): The docker image to use for code execution. If a list or a str of image name(s) is provided, the code will be executed in a docker container with the first image successfully pulled. If None, False or empty, the code will be executed in the current environment. Default is True, which will be converted into a list. If the code is executed in the current environment, the code must be trusted. timeout (Optional, int): The maximum execution time in seconds. last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. llm_config dict or False - llm inference configuration. Please refer to autogen.Completion.create for available options. To disable llm-based auto reply, set to False. default_auto_reply str or dict or None - default auto reply when no code execution or llm-based reply is generated. register_reply​ def register_reply(trigger: Union[Type[Agent], str, Agent, Callable[[Agent], bool], List], reply_func: Callable, position: Optional[int] = 0, config: Optional[Any] = None, reset_config: Optional[Callable] = None) Copy Register a reply function. The reply function will be called when the trigger matches the sender. The function registered later will be checked earlier by default. To change the order, set the position to a positive integer. Arguments: trigger Agent class, str, Agent instance, callable, or list - the trigger. If a class is provided, the reply function will be called when the sender is an instance of the class. If a string is provided, the reply function will be called when the sender's name matches the string. If an agent instance is provided, the reply function will be called when the sender is the agent instance. If a callable is provided, the reply function will be called when the callable returns True. If a list is provided, the reply function will be called when any of the triggers in the list is activated. If None is provided, the reply function will be called only when the sender is None. Note - Be sure to register None as a trigger if you would like to trigger an auto-reply function with non-empty messages and sender=None. reply_func Callable - the reply function. The function takes a recipient agent, a list of messages, a sender agent and a config as input and returns a reply message. def reply_func( recipient: ConversableAgent, messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None,) -> Union[str, Dict, None]: Copy position int - the position of the reply function in the reply function list. The function registered later will be checked earlier by default. To change the order, set the position to a positive integer. config Any - the config to be passed to the reply function. When an agent is reset, the config will be reset to the original value. reset_config Callable - the function to reset the config. The function returns None. Signature: def reset_config(config: Any) system_message​ @propertydef system_message() Copy Return the system message. update_system_message​ def update_system_message(system_message: str) Copy Update the system message. Arguments: system_message str - system message for the ChatCompletion inference. update_max_consecutive_auto_reply​ def update_max_consecutive_auto_reply(value: int, sender: Optional[Agent] = None) Copy Update the maximum number of consecutive auto replies. Arguments: value int - the maximum number of consecutive auto replies. sender Agent - when the sender is provided, only update the max_consecutive_auto_reply for that sender. max_consecutive_auto_reply​ def max_consecutive_auto_reply(sender: Optional[Agent] = None) -> int Copy The maximum number of consecutive auto replies. chat_messages​ @propertydef chat_messages() -> Dict[str, List[Dict]] Copy A dictionary of conversations from name to list of ChatCompletion messages. last_message​ def last_message(agent: Optional[Agent] = None) -> Dict Copy The last message exchanged with the agent. Arguments: agent Agent - The agent in the conversation. If None and more than one agent's conversations are found, an error will be raised. If None and only one conversation is found, the last message of the only conversation will be returned. Returns: The last message exchanged with the agent. use_docker​ @propertydef use_docker() -> Union[bool, str, None] Copy Bool value of whether to use docker to execute the code, or str value of the docker image name to use, or None when code execution is disabled. send​ def send(message: Union[Dict, str], recipient: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) -> bool Copy Send a message to another agent. Arguments: message dict or str - message to be sent. The message could contain the following fields (either content or function_call must be provided): content (str): the content of the message. function_call (str): the name of the function to be called. name (str): the name of the function to be called. role (str): the role of the message, any role that is not \"function\" will be modified to \"assistant\". context (dict): the context of the message, which will be passed to autogen.Completion.create. For example, one agent can send a message A as: { \"content\": lambda context: context[\"use_tool_msg\"], \"context\": { \"use_tool_msg\": \"Use tool X if they are relevant.\" }} Copy Next time, one agent can send a message B with a different \"use_tool_msg\". Then the content of message A will be refreshed to the new \"use_tool_msg\". So effectively, this provides a way for an agent to send a \"link\" and modify the content of the \"link\" later. recipient Agent - the recipient of the message. request_reply bool or None - whether to request a reply from the recipient. silent bool or None - (Experimental) whether to print the message sent. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. a_send​ async def a_send(message: Union[Dict, str], recipient: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) -> bool Copy (async) Send a message to another agent. Arguments: message dict or str - message to be sent. The message could contain the following fields (either content or function_call must be provided): content (str): the content of the message. function_call (str): the name of the function to be called. name (str): the name of the function to be called. role (str): the role of the message, any role that is not \"function\" will be modified to \"assistant\". context (dict): the context of the message, which will be passed to autogen.Completion.create. For example, one agent can send a message A as: { \"content\": lambda context: context[\"use_tool_msg\"], \"context\": { \"use_tool_msg\": \"Use tool X if they are relevant.\" }} Copy Next time, one agent can send a message B with a different \"use_tool_msg\". Then the content of message A will be refreshed to the new \"use_tool_msg\". So effectively, this provides a way for an agent to send a \"link\" and modify the content of the \"link\" later. recipient Agent - the recipient of the message. request_reply bool or None - whether to request a reply from the recipient. silent bool or None - (Experimental) whether to print the message sent. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. receive​ def receive(message: Union[Dict, str], sender: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) Copy Receive a message from another agent. Once a message is received, this function sends a reply to the sender or stop. The reply can be generated automatically or entered manually by a human. Arguments: message dict or str - message from the sender. If the type is dict, it may contain the following reserved fields (either content or function_call need to be provided). \"content\": content of the message, can be None. \"function_call\": a dictionary containing the function name and arguments. \"role\": role of the message, can be \"assistant\", \"user\", \"function\". This field is only needed to distinguish between \"function\" or \"assistant\"/\"user\". \"name\": In most cases, this field is not needed. When the role is \"function\", this field is needed to indicate the function name. \"context\" (dict): the context of the message, which will be passed to autogen.Completion.create. sender - sender of an Agent instance. request_reply bool or None - whether a reply is requested from the sender. If None, the value is determined by self.reply_at_receive[sender]. silent bool or None - (Experimental) whether to print the message received. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. a_receive​ async def a_receive(message: Union[Dict, str], sender: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) Copy (async) Receive a message from another agent. Once a message is received, this function sends a reply to the sender or stop. The reply can be generated automatically or entered manually by a human. Arguments: message dict or str - message from the sender. If the type is dict, it may contain the following reserved fields (either content or function_call need to be provided). \"content\": content of the message, can be None. \"function_call\": a dictionary containing the function name and arguments. \"role\": role of the message, can be \"assistant\", \"user\", \"function\". This field is only needed to distinguish between \"function\" or \"assistant\"/\"user\". \"name\": In most cases, this field is not needed. When the role is \"function\", this field is needed to indicate the function name. \"context\" (dict): the context of the message, which will be passed to autogen.Completion.create. sender - sender of an Agent instance. request_reply bool or None - whether a reply is requested from the sender. If None, the value is determined by self.reply_at_receive[sender]. silent bool or None - (Experimental) whether to print the message received. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. initiate_chat​ def initiate_chat(recipient: \"ConversableAgent\", clear_history: Optional[bool] = True, silent: Optional[bool] = False, **context, ,) Copy Initiate a chat with the recipient agent. Reset the consecutive auto reply counter. If clear_history is True, the chat history with the recipient agent will be cleared. generate_init_message is called to generate the initial message for the agent. Arguments: recipient - the recipient agent. clear_history bool - whether to clear the chat history with the agent. silent bool or None - (Experimental) whether to print the messages for this conversation. **context - any context information. \"message\" needs to be provided if the generate_init_message method is not overridden. a_initiate_chat​ async def a_initiate_chat(recipient: \"ConversableAgent\", clear_history: Optional[bool] = True, silent: Optional[bool] = False, **context, ,) Copy (async) Initiate a chat with the recipient agent. Reset the consecutive auto reply counter. If clear_history is True, the chat history with the recipient agent will be cleared. generate_init_message is called to generate the initial message for the agent. Arguments: recipient - the recipient agent. clear_history bool - whether to clear the chat history with the agent. silent bool or None - (Experimental) whether to print the messages for this conversation. **context - any context information. \"message\" needs to be provided if the generate_init_message method is not overridden. reset​ def reset() Copy Reset the agent. stop_reply_at_receive​ def stop_reply_at_receive(sender: Optional[Agent] = None) Copy Reset the reply_at_receive of the sender. reset_consecutive_auto_reply_counter​ def reset_consecutive_auto_reply_counter(sender: Optional[Agent] = None) Copy Reset the consecutive_auto_reply_counter of the sender. clear_history​ def clear_history(agent: Optional[Agent] = None) Copy Clear the chat history of the agent. Arguments: agent - the agent with whom the chat history to clear. If None, clear the chat history with all agents. generate_oai_reply​ def generate_oai_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) -> Tuple[bool, Union[str, Dict, None]] Copy Generate a reply using autogen.oai. generate_code_execution_reply​ def generate_code_execution_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) Copy Generate a reply using code execution. generate_function_call_reply​ def generate_function_call_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) Copy Generate a reply using function call. check_termination_and_human_reply​ def check_termination_and_human_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) -> Tuple[bool, Union[str, Dict, None]] Copy Check if the conversation should be terminated, and if human reply is provided. generate_reply​ def generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, exclude: Optional[List[Callable]] = None) -> Union[str, Dict, None] Copy Reply based on the conversation history and the sender. Either messages or sender must be provided. Register a reply_func with None as one trigger for it to be activated when messages is non-empty and sender is None. Use registered auto reply functions to generate replies. By default, the following functions are checked in order: check_termination_and_human_reply generate_function_call_reply generate_code_execution_reply generate_oai_reply Every function returns a tuple (final, reply). When a function returns final=False, the next function will be checked. So by default, termination and human reply will be checked first. If not terminating and human reply is skipped, execute function or code and return the result. AI replies are generated only when no code execution is performed. Arguments: messages - a list of messages in the conversation history. default_reply str or dict - default reply. sender - sender of an Agent instance. exclude - a list of functions to exclude. Returns: str or dict or None: reply. None if no reply is generated. a_generate_reply​ async def a_generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, exclude: Optional[List[Callable]] = None) -> Union[str, Dict, None] Copy (async) Reply based on the conversation history and the sender. Either messages or sender must be provided. Register a reply_func with None as one trigger for it to be activated when messages is non-empty and sender is None. Use registered auto reply functions to generate replies. By default, the following functions are checked in order: check_termination_and_human_reply generate_function_call_reply generate_code_execution_reply generate_oai_reply Every function returns a tuple (final, reply). When a function returns final=False, the next function will be checked. So by default, termination and human reply will be checked first. If not terminating and human reply is skipped, execute function or code and return the result. AI replies are generated only when no code execution is performed. Arguments: messages - a list of messages in the conversation history. default_reply str or dict - default reply. sender - sender of an Agent instance. exclude - a list of functions to exclude. Returns: str or dict or None: reply. None if no reply is generated. get_human_input​ def get_human_input(prompt: str) -> str Copy Get human input. Override this method to customize the way to get human input. Arguments: prompt str - prompt for the human input. Returns: str - human input. run_code​ def run_code(code, **kwargs) Copy Run the code and return the result. Override this function to modify the way to run the code. Arguments: code str - the code to be executed. **kwargs - other keyword arguments. Returns: A tuple of (exitcode, logs, image). exitcode int - the exit code of the code execution. logs str - the logs of the code execution. image str or None - the docker image used for the code execution. execute_code_blocks​ def execute_code_blocks(code_blocks) Copy Execute the code blocks and return the result. execute_function​ def execute_function(func_call) Copy Execute a function call and return the result. Override this function to modify the way to execute a function call. Arguments: func_call - a dictionary extracted from openai message at key \"function_call\" with keys \"name\" and \"arguments\". Returns: A tuple of (is_exec_success, result_dict). is_exec_success boolean - whether the execution is successful. result_dict - a dictionary with keys \"name\", \"role\", and \"content\". Value of \"role\" is \"function\". generate_init_message​ def generate_init_message(**context) -> Union[str, Dict] Copy Generate the initial message for the agent. Override this function to customize the initial message based on user's request. If not overriden, \"message\" needs to be provided in the context. register_function​ def register_function(function_map: Dict[str, Callable]) Copy Register functions to the agent. Arguments: function_map - a dictionary mapping function names to functions.","s":"ConversableAgent Objects","u":"/FLAML/docs/reference/autogen/agentchat/conversable_agent","h":"#conversableagent-objects","p":311},{"i":316,"t":"On this page","s":"autogen.agentchat.groupchat","u":"/FLAML/docs/reference/autogen/agentchat/groupchat","h":"","p":315},{"i":318,"t":"@dataclassclass GroupChat() Copy A group chat class that contains a list of agents and the maximum number of rounds. agent_names​ @propertydef agent_names() -> List[str] Copy Return the names of the agents in the group chat. reset​ def reset() Copy Reset the group chat. agent_by_name​ def agent_by_name(name: str) -> Agent Copy Find the next speaker based on the message. next_agent​ def next_agent(agent: Agent) -> Agent Copy Return the next agent in the list. select_speaker_msg​ def select_speaker_msg() Copy Return the message for selecting the next speaker. select_speaker​ def select_speaker(last_speaker: Agent, selector: ConversableAgent) Copy Select the next speaker.","s":"GroupChat Objects","u":"/FLAML/docs/reference/autogen/agentchat/groupchat","h":"#groupchat-objects","p":315},{"i":320,"t":"class GroupChatManager(ConversableAgent) Copy (In preview) A chat manager agent that can manage a group chat of multiple agents. run_chat​ def run_chat(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[GroupChat] = None) -> Union[str, Dict, None] Copy Run a group chat.","s":"GroupChatManager Objects","u":"/FLAML/docs/reference/autogen/agentchat/groupchat","h":"#groupchatmanager-objects","p":315},{"i":322,"t":"On this page","s":"autogen.agentchat.user_proxy_agent","u":"/FLAML/docs/reference/autogen/agentchat/user_proxy_agent","h":"","p":321},{"i":324,"t":"class UserProxyAgent(ConversableAgent) Copy (In preview) A proxy agent for the user, that can execute code and provide feedback to the other agents. UserProxyAgent is a subclass of ConversableAgent configured with human_input_mode to ALWAYS and llm_config to False. By default, the agent will prompt for human input every time a message is received. Code execution is enabled by default. LLM-based auto reply is disabled by default. To modify auto reply, register a method with (register_reply)[conversable_agent#register_reply]. To modify the way to get human input, override get_human_input method. To modify the way to execute code blocks, single code block, or function call, override execute_code_blocks, run_code, and execute_function methods respectively. To customize the initial message when a conversation starts, override generate_init_message method. __init__​ def __init__(name: str, is_termination_msg: Optional[Callable[[Dict], bool]] = None, max_consecutive_auto_reply: Optional[int] = None, human_input_mode: Optional[str] = \"ALWAYS\", function_map: Optional[Dict[str, Callable]] = None, code_execution_config: Optional[Union[Dict, bool]] = None, default_auto_reply: Optional[Union[str, Dict, None]] = \"\", llm_config: Optional[Union[Dict, bool]] = False, system_message: Optional[str] = \"\") Copy Arguments: name str - name of the agent. is_termination_msg function - a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. The dict can contain the following keys: \"content\", \"role\", \"name\", \"function_call\". max_consecutive_auto_reply int - the maximum number of consecutive auto replies. default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). The limit only plays a role when human_input_mode is not \"ALWAYS\". human_input_mode str - whether to ask for human inputs every time a message is received. Possible values are \"ALWAYS\", \"TERMINATE\", \"NEVER\". (1) When \"ALWAYS\", the agent prompts for human input every time a message is received. Under this mode, the conversation stops when the human input is \"exit\", or when is_termination_msg is True and there is no human input. (2) When \"TERMINATE\", the agent only prompts for human input only when a termination message is received or the number of auto reply reaches the max_consecutive_auto_reply. (3) When \"NEVER\", the agent will never prompt for human input. Under this mode, the conversation stops when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. function_map dict[str, callable] - Mapping function names (passed to openai) to callable functions. code_execution_config dict or False - config for the code execution. To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: work_dir (Optional, str): The working directory for the code execution. If None, a default working directory will be used. The default working directory is the \"extensions\" directory under \"path_to_flaml/autogen\". use_docker (Optional, list, str or bool): The docker image to use for code execution. If a list or a str of image name(s) is provided, the code will be executed in a docker container with the first image successfully pulled. If None, False or empty, the code will be executed in the current environment. Default is True, which will be converted into a list. If the code is executed in the current environment, the code must be trusted. timeout (Optional, int): The maximum execution time in seconds. last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. default_auto_reply str or dict or None - the default auto reply message when no code execution or llm based reply is generated. llm_config dict or False - llm inference configuration. Please refer to autogen.Completion.create for available options. Default to false, which disables llm-based auto reply. system_message str - system message for ChatCompletion inference. Only used when llm_config is not False. Use it to reprogram the agent.","s":"UserProxyAgent Objects","u":"/FLAML/docs/reference/autogen/agentchat/user_proxy_agent","h":"#userproxyagent-objects","p":321},{"i":326,"t":"On this page","s":"autogen.code_utils","u":"/FLAML/docs/reference/autogen/code_utils","h":"","p":325},{"i":328,"t":"class PassAssertionFilter() Copy pass_assertions​ def pass_assertions(context, response, **_) Copy Check if the response passes the assertions. implement​ def implement(definition: str, configs: Optional[List[Dict]] = None, assertions: Optional[Union[str, Callable[[str], Tuple[str, float]]]] = generate_assertions) -> Tuple[str, float] Copy Implement a function from a definition. Arguments: definition str - The function definition, including the signature and docstr. configs list - The list of configurations for completion. assertions Optional, str or Callable - The assertion code which serves as a filter of the responses, or an assertion generator. Returns: str - The implementation. float - The cost of the implementation. int - The index of the configuration which generates the implementation.","s":"PassAssertionFilter Objects","u":"/FLAML/docs/reference/autogen/code_utils","h":"#passassertionfilter-objects","p":325},{"i":330,"t":"On this page","s":"autogen.oai.completion","u":"/FLAML/docs/reference/autogen/oai/completion","h":"","p":329},{"i":332,"t":"class Completion(openai_Completion) Copy A class for OpenAI completion API. It also supports: ChatCompletion, Azure OpenAI API. set_cache​ @classmethoddef set_cache(cls, seed: Optional[int] = 41, cache_path_root: Optional[str] = \".cache\") Copy Set cache path. Arguments: seed int, Optional - The integer identifier for the pseudo seed. Results corresponding to different seeds will be cached in different places. cache_path str, Optional - The root path for the cache. The complete cache path will be {cache_path}/{seed}. clear_cache​ @classmethoddef clear_cache(cls, seed: Optional[int] = None, cache_path_root: Optional[str] = \".cache\") Copy Clear cache. Arguments: seed int, Optional - The integer identifier for the pseudo seed. If omitted, all caches under cache_path_root will be cleared. cache_path str, Optional - The root path for the cache. The complete cache path will be {cache_path}/{seed}. tune​ @classmethoddef tune(cls, data: List[Dict], metric: str, mode: str, eval_func: Callable, log_file_name: Optional[str] = None, inference_budget: Optional[float] = None, optimization_budget: Optional[float] = None, num_samples: Optional[int] = 1, logging_level: Optional[int] = logging.WARNING, **config, ,) Copy Tune the parameters for the OpenAI API call. TODO: support parallel tuning with ray or spark. TODO: support agg_method as in test Arguments: data list - The list of data points. metric str - The metric to optimize. mode str - The optimization mode, \"min\" or \"max. eval_func Callable - The evaluation function for responses. The function should take a list of responses and a data point as input, and return a dict of metrics. For example, def eval_func(responses, **data): solution = data[\"solution\"] success_list = [] n = len(responses) for i in range(n): response = responses[i] succeed = is_equiv_chain_of_thought(response, solution) success_list.append(succeed) return { \"expected_success\": 1 - pow(1 - sum(success_list) / n, n), \"success\": any(s for s in success_list), } Copy log_file_name str, optional - The log file. inference_budget float, optional - The inference budget, dollar per instance. optimization_budget float, optional - The optimization budget, dollar in total. num_samples int, optional - The number of samples to evaluate. -1 means no hard restriction in the number of trials and the actual number is decided by optimization_budget. Defaults to 1. logging_level optional - logging level. Defaults to logging.WARNING. **config dict - The search space to update over the default search. For prompt, please provide a string/Callable or a list of strings/Callables. If prompt is provided for chat models, it will be converted to messages under role \"user\". Do not provide both prompt and messages for chat models, but provide either of them. A string template will be used to generate a prompt for each data instance using prompt.format(**data). A callable template will be used to generate a prompt for each data instance using prompt(data). For stop, please provide a string, a list of strings, or a list of lists of strings. For messages (chat models only), please provide a list of messages (for a single chat prefix) or a list of lists of messages (for multiple choices of chat prefix to choose from). Each message should be a dict with keys \"role\" and \"content\". The value of \"content\" can be a string/Callable template. Returns: dict - The optimized hyperparameter setting. tune.ExperimentAnalysis - The tuning results. create​ @classmethoddef create(cls, context: Optional[Dict] = None, use_cache: Optional[bool] = True, config_list: Optional[List[Dict]] = None, filter_func: Optional[Callable[[Dict, Dict, Dict], bool]] = None, raise_on_ratelimit_or_timeout: Optional[bool] = True, allow_format_str_template: Optional[bool] = False, **config, ,) Copy Make a completion for a given context. Arguments: context Dict, Optional - The context to instantiate the prompt. It needs to contain keys that are used by the prompt template or the filter function. E.g., prompt=\"Complete the following sentence: {prefix}, context={\"prefix\": \"Today I feel\"}. The actual prompt will be: \"Complete the following sentence: Today I feel\". More examples can be found at templating. use_cache bool, Optional - Whether to use cached responses. config_list List, Optional - List of configurations for the completion to try. The first one that does not raise an error will be used. Only the differences from the default config need to be provided. E.g., response = oai.Completion.create( config_list=[ { \"model\": \"gpt-4\", \"api_key\": os.environ.get(\"AZURE_OPENAI_API_KEY\"), \"api_type\": \"azure\", \"api_base\": os.environ.get(\"AZURE_OPENAI_API_BASE\"), \"api_version\": \"2023-03-15-preview\", }, { \"model\": \"gpt-3.5-turbo\", \"api_key\": os.environ.get(\"OPENAI_API_KEY\"), \"api_type\": \"open_ai\", \"api_base\": \"https://api.openai.com/v1\", }, { \"model\": \"llama-7B\", \"api_base\": \"http://127.0.0.1:8080\", \"api_type\": \"open_ai\", } ], prompt=\"Hi\",) Copy filter_func Callable, Optional - A function that takes in the context, the config and the response and returns a boolean to indicate whether the response is valid. E.g., def yes_or_no_filter(context, config, response): return context.get(\"yes_or_no_choice\", False) is False or any( text in [\"Yes.\", \"No.\"] for text in oai.Completion.extract_text(response) ) Copy raise_on_ratelimit_or_timeout bool, Optional - Whether to raise RateLimitError or Timeout when all configs fail. When set to False, -1 will be returned when all configs fail. allow_format_str_template bool, Optional - Whether to allow format string template in the config. **config - Configuration for the openai API call. This is used as parameters for calling openai API. Besides the parameters for the openai API call, it can also contain a seed (int) for the cache. This is useful when implementing \"controlled randomness\" for the completion. Also, the \"prompt\" or \"messages\" parameter can contain a template (str or Callable) which will be instantiated with the context. Returns: Responses from OpenAI API, with additional fields. cost: the total cost. When config_list is provided, the response will contain a few more fields: config_id: the index of the config in the config_list that is used to generate the response. pass_filter: whether the response passes the filter function. None if no filter is provided. test​ @classmethoddef test(cls, data, eval_func=None, use_cache=True, agg_method=\"avg\", return_responses_and_per_instance_result=False, logging_level=logging.WARNING, **config, ,) Copy Evaluate the responses created with the config for the OpenAI API call. Arguments: data list - The list of test data points. eval_func Callable - The evaluation function for responses per data instance. The function should take a list of responses and a data point as input, and return a dict of metrics. You need to either provide a valid callable eval_func; or do not provide one (set None) but call the test function after calling the tune function in which a eval_func is provided. In the latter case we will use the eval_func provided via tune function. Defaults to None. def eval_func(responses, **data): solution = data[\"solution\"] success_list = [] n = len(responses) for i in range(n): response = responses[i] succeed = is_equiv_chain_of_thought(response, solution) success_list.append(succeed) return { \"expected_success\": 1 - pow(1 - sum(success_list) / n, n), \"success\": any(s for s in success_list), } Copy use_cache bool, Optional - Whether to use cached responses. Defaults to True. agg_method str, Callable or a dict of Callable - Result aggregation method (across multiple instances) for each of the metrics. Defaults to 'avg'. An example agg_method in str: agg_method = 'median' Copy An example agg_method in a Callable: agg_method = np.median Copy An example agg_method in a dict of Callable: agg_method={'median_success': np.median, 'avg_success': np.mean} Copy return_responses_and_per_instance_result bool - Whether to also return responses and per instance results in addition to the aggregated results. logging_level optional - logging level. Defaults to logging.WARNING. **config dict - parametes passed to the openai api call create(). Returns: None when no valid eval_func is provided in either test or tune; Otherwise, a dict of aggregated results, responses and per instance results if return_responses_and_per_instance_result is True; Otherwise, a dict of aggregated results (responses and per instance results are not returned). cost​ @classmethoddef cost(cls, response: dict) Copy Compute the cost of an API call. Arguments: response dict - The response from OpenAI API. Returns: The cost in USD. 0 if the model is not supported. extract_text​ @classmethoddef extract_text(cls, response: dict) -> List[str] Copy Extract the text from a completion or chat response. Arguments: response dict - The response from OpenAI API. Returns: A list of text in the responses. extract_text_or_function_call​ @classmethoddef extract_text_or_function_call(cls, response: dict) -> List[str] Copy Extract the text or function calls from a completion or chat response. Arguments: response dict - The response from OpenAI API. Returns: A list of text or function calls in the responses. logged_history​ @classmethod@propertydef logged_history(cls) -> Dict Copy Return the book keeping dictionary. start_logging​ @classmethoddef start_logging(cls, history_dict: Optional[Dict] = None, compact: Optional[bool] = True, reset_counter: Optional[bool] = True) Copy Start book keeping. Arguments: history_dict Dict - A dictionary for book keeping. If no provided, a new one will be created. compact bool - Whether to keep the history dictionary compact. Compact history contains one key per conversation, and the value is a dictionary like: { \"create_at\": [0, 1], \"cost\": [0.1, 0.2],} Copy where \"created_at\" is the index of API calls indicating the order of all the calls, and \"cost\" is the cost of each call. This example shows that the conversation is based on two API calls. The compact format is useful for condensing the history of a conversation. If compact is False, the history dictionary will contain all the API calls: the key is the index of the API call, and the value is a dictionary like: { \"request\": request_dict, \"response\": response_dict,} Copy where request_dict is the request sent to OpenAI API, and response_dict is the response. For a conversation containing two API calls, the non-compact history dictionary will be like: { 0: { \"request\": request_dict_0, \"response\": response_dict_0, }, 1: { \"request\": request_dict_1, \"response\": response_dict_1, }, Copy The first request's messages plus the response is equal to the second request's messages. For a conversation with many turns, the non-compact history dictionary has a quadratic size while the compact history dict has a linear size. reset_counter bool - whether to reset the counter of the number of API calls. stop_logging​ @classmethoddef stop_logging(cls) Copy End book keeping.","s":"Completion Objects","u":"/FLAML/docs/reference/autogen/oai/completion","h":"#completion-objects","p":329},{"i":334,"t":"class ChatCompletion(Completion) Copy A class for OpenAI API ChatCompletion.","s":"ChatCompletion Objects","u":"/FLAML/docs/reference/autogen/oai/completion","h":"#chatcompletion-objects","p":329},{"i":336,"t":"On this page","s":"autogen.oai.openai_utils","u":"/FLAML/docs/reference/autogen/oai/openai_utils","h":"","p":335},{"i":338,"t":"On this page","s":"autogen.retrieve_utils","u":"/FLAML/docs/reference/autogen/retrieve_utils","h":"","p":337},{"i":340,"t":"On this page","s":"autogen.math_utils","u":"/FLAML/docs/reference/autogen/math_utils","h":"","p":339},{"i":342,"t":"On this page","s":"automl.contrib.histgb","u":"/FLAML/docs/reference/automl/contrib/histgb","h":"","p":341},{"i":344,"t":"class HistGradientBoostingEstimator(SKLearnEstimator) Copy The class for tuning Histogram Gradient Boosting.","s":"HistGradientBoostingEstimator Objects","u":"/FLAML/docs/reference/automl/contrib/histgb","h":"#histgradientboostingestimator-objects","p":341},{"i":346,"t":"On this page","s":"automl.data","u":"/FLAML/docs/reference/automl/data","h":"","p":345},{"i":348,"t":"class DataTransformer() Copy Transform input training data. fit_transform​ def fit_transform(X: Union[DataFrame, np.ndarray], y, task: Union[str, \"Task\"]) Copy Fit transformer and process the input training data according to the task type. Arguments: X - A numpy array or a pandas dataframe of training data. y - A numpy array or a pandas series of labels. task - An instance of type Task, or a str such as 'classification', 'regression'. Returns: X - Processed numpy array or pandas dataframe of training data. y - Processed numpy array or pandas series of labels. transform​ def transform(X: Union[DataFrame, np.array]) Copy Process data using fit transformer. Arguments: X - A numpy array or a pandas dataframe of training data. Returns: X - Processed numpy array or pandas dataframe of training data.","s":"DataTransformer Objects","u":"/FLAML/docs/reference/automl/data","h":"#datatransformer-objects","p":345},{"i":350,"t":"On this page","s":"automl.automl","u":"/FLAML/docs/reference/automl/automl","h":"","p":349},{"i":352,"t":"class AutoML(BaseEstimator) Copy The AutoML class. Example: automl = AutoML()automl_settings = { \"time_budget\": 60, \"metric\": 'accuracy', \"task\": 'classification', \"log_file_name\": 'mylog.log',}automl.fit(X_train = X_train, y_train = y_train, **automl_settings) Copy __init__​ def __init__(**settings) Copy Constructor. Many settings in fit() can be passed to the constructor too. If an argument in fit() is provided, it will override the setting passed to the constructor. If an argument in fit() is not provided but provided in the constructor, the value passed to the constructor will be used. Arguments: metric - A string of the metric name or a function, e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted', 'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If passing a customized metric function, the function needs to have the following input arguments: def custom_metric( X_test, y_test, estimator, labels, X_train, y_train, weight_test=None, weight_train=None, config=None, groups_test=None, groups_train=None,): return metric_to_minimize, metrics_to_log Copy which returns a float number as the minimization objective, and a dictionary as the metrics to log. E.g., def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args,): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { \"val_loss\": val_loss, \"train_loss\": train_loss, \"pred_time\": pred_time, } Copy task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression', 'summarization', or an instance of the Task class. n_jobs - An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. log_file_name - A string of the log file name | default=\"\". To disable logging, set it to be an empty string \"\". estimator_list - A list of strings for estimator names, or 'auto'. e.g., ['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']. time_budget - A float number of the time budget in seconds. Use -1 if no time limit. max_iter - An integer of the maximal number of iterations. sample - A boolean of whether to sample the training data during search. ensemble - boolean or dict | default=False. Whether to perform ensemble after search. Can be a dict with keys 'passthrough' and 'final_estimator' to specify the passthrough and final_estimator in the stacker. The dict can also contain 'n_jobs' as the key to specify the number of jobs for the stacker. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio - A float of the valiation data percentage for holdout. n_splits - An integer of the number of folds for cross - validation. log_type - A string of the log type, one of ['better', 'all']. 'better' only logs configs with better loss than previos iters 'all' logs all the tried configs. model_history - A boolean of whether to keep the best model per estimator. Make sure memory is large enough if setting to True. log_training_metric - A boolean of whether to log the training metric for each model. mem_thres - A float of the memory size constraint in bytes. pred_time_limit - A float of the prediction latency constraint in seconds. It refers to the average prediction time per row in validation data. train_time_limit - A float of the training time constraint in seconds. verbose - int, default=3 | Controls the verbosity, higher means more messages. retrain_full - bool or str, default=True | whether to retrain the selected model on the full training data when using holdout. True - retrain only after search finishes; False - no retraining; 'budget' - do best effort to retrain without violating the time budget. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. hpo_method - str, default=\"auto\" | The hyperparameter optimization method. By default, CFO is used for sequential search and BlendSearch is used for parallel search. No need to set when using flaml's default search space or using a simple customized search space. When set to 'bs', BlendSearch is used. BlendSearch can be tried when the search space is complex, for example, containing multiple disjoint, discontinuous subspaces. When set to 'random', random search is used. starting_points - A dictionary or a str to specify the starting hyperparameter config for the estimators | default=\"static\". If str: if \"data\", use data-dependent defaults; if \"data:path\" use data-dependent defaults which are stored at path; if \"static\", use data-independent defaults. If dict, keys are the name of the estimators, and values are the starting hyperparameter configurations for the corresponding estimators. The value can be a single hyperparameter configuration dict or a list of hyperparameter configuration dicts. In the following code example, we get starting_points from the automl object and use them in the new_automl object. e.g., from flaml import AutoMLautoml = AutoML()X_train, y_train = load_iris(return_X_y=True)automl.fit(X_train, y_train)starting_points = automl.best_config_per_estimatornew_automl = AutoML()new_automl.fit(X_train, y_train, starting_points=starting_points) Copy seed - int or None, default=None | The random seed for hpo. n_concurrent_trials - [In preview] int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, flaml performes parallel tuning and installation of ray or spark is required: pip install flaml[ray] or pip install flaml[spark]. Please check here for more details about installing Spark. keep_search_state - boolean, default=False | Whether to keep data needed for model search after fit(). By default the state is deleted for space saving. preserve_checkpoint - boolean, default=True | Whether to preserve the saved checkpoint on disk when deleting automl. By default the checkpoint is preserved. early_stop - boolean, default=False | Whether to stop early if the search is considered to converge. force_cancel - boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. append_log - boolean, default=False | Whether to directly append the log records to the input log file if it exists. auto_augment - boolean, default=True | Whether to automatically augment rare classes. min_sample_size - int, default=MIN_SAMPLE_TRAIN | the minimal sample size when sample=True. use_ray - boolean or dict. If boolean: default=False | Whether to use ray to run the training in separate processes. This can be used to prevent OOM for large datasets, but will incur more overhead in time. If dict: the dict contains the keywords arguments to be passed to ray.tune.run. use_spark - boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable FLAML_MAX_CONCURRENT to override the detected num_executors. The final number of concurrent trials will be the minimum of n_concurrent_trials and num_executors. free_mem_ratio - float between 0 and 1, default=0. The free memory ratio to keep during training. metric_constraints - list, default=[] | The list of metric constraints. Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from \">=\" and \"<=\", and the third element is the constraint value. E.g., ('val_loss', '<=', 0.1). Note that all the metric names in metric_constraints need to be reported via the metrics_to_log dictionary returned by a customized metric function. The customized metric function shall be provided via the metric key word argument of the fit() function or the automl constructor. Find an example in the 4th constraint type in this doc. If pred_time_limit is provided as one of keyword arguments to fit() function or the automl constructor, flaml will automatically (and under the hood) add it as an additional element in the metric_constraints. Essentially 'pred_time_limit' specifies a constraint about the prediction latency constraint in seconds. custom_hp - dict, default=None | The custom search space specified by user. It is a nested dict with keys being the estimator names, and values being dicts per estimator search space. In the per estimator search space dict, the keys are the hyperparameter names, and values are dicts of info (\"domain\", \"init_value\", and \"low_cost_init_value\") about the search space associated with the hyperparameter (i.e., per hyperparameter search space dict). When custom_hp is provided, the built-in search space which is also a nested dict of per estimator search space dict, will be updated with custom_hp. Note that during this nested dict update, the per hyperparameter search space dicts will be replaced (instead of updated) by the ones provided in custom_hp. Note that the value for \"domain\" can either be a constant or a sample.Domain object. e.g., custom_hp = { \"transformer_ms\": { \"model_path\": { \"domain\": \"albert-base-v2\", }, \"learning_rate\": { \"domain\": tune.choice([1e-4, 1e-5]), } } } Copy skip_transform - boolean, default=False | Whether to pre-process data prior to modeling. fit_kwargs_by_estimator - dict, default=None | The user specified keywords arguments, grouped by estimator name. e.g., fit_kwargs_by_estimator = { \"transformer\": { \"output_dir\": \"test/data/output/\", \"fp16\": False, }} Copy mlflow_logging - boolean, default=True | Whether to log the training results to mlflow. This requires mlflow to be installed and to have an active mlflow run. FLAML will create nested runs. config_history​ @propertydef config_history() -> dict Copy A dictionary of iter->(estimator, config, time), storing the best estimator, config, and the time when the best model is updated each time. model​ @propertydef model() Copy An object with predict() and predict_proba() method (for classification), storing the best trained model. best_model_for_estimator​ def best_model_for_estimator(estimator_name: str) Copy Return the best model found for a particular estimator. Arguments: estimator_name - a str of the estimator's name. Returns: An object storing the best model for estimator_name. If model_history was set to False during fit(), then the returned model is untrained unless estimator_name is the best estimator. If model_history was set to True, then the returned model is trained. best_estimator​ @propertydef best_estimator() Copy A string indicating the best estimator found. best_iteration​ @propertydef best_iteration() Copy An integer of the iteration number where the best config is found. best_config​ @propertydef best_config() Copy A dictionary of the best configuration. best_config_per_estimator​ @propertydef best_config_per_estimator() Copy A dictionary of all estimators' best configuration. best_loss_per_estimator​ @propertydef best_loss_per_estimator() Copy A dictionary of all estimators' best loss. best_loss​ @propertydef best_loss() Copy A float of the best loss found. best_result​ @propertydef best_result() Copy Result dictionary for model trained with the best config. metrics_for_best_config​ @propertydef metrics_for_best_config() Copy Returns a float of the best loss, and a dictionary of the auxiliary metrics to log associated with the best config. These two objects correspond to the returned objects by the customized metric function for the config with the best loss. best_config_train_time​ @propertydef best_config_train_time() Copy A float of the seconds taken by training the best config. feature_transformer​ @propertydef feature_transformer() Copy Returns feature transformer which is used to preprocess data before applying training or inference. label_transformer​ @propertydef label_transformer() Copy Returns label transformer which is used to preprocess labels before scoring, and inverse transform labels after inference. classes_​ @propertydef classes_() Copy A numpy array of shape (n_classes,) for class labels. time_to_find_best_model​ @propertydef time_to_find_best_model() -> float Copy Time taken to find best model in seconds. predict​ def predict(X: Union[np.array, DataFrame, List[str], List[List[str]], psDataFrame], **pred_kwargs, ,) Copy Predict label from features. Arguments: X - A numpy array or pandas dataframe or pyspark.pandas dataframe of featurized instances, shape n * m, or for time series forcast tasks: a pandas dataframe with the first column containing timestamp values (datetime type) or an integer n for the predict steps (only valid when the estimator is arima or sarimax). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). **pred_kwargs - Other key word arguments to pass to predict() function of the searched learners, such as per_device_eval_batch_size. multivariate_X_test = DataFrame({ 'timeStamp': pd.date_range(start='1/1/2022', end='1/07/2022'), 'categorical_col': ['yes', 'yes', 'no', 'no', 'yes', 'no', 'yes'], 'continuous_col': [105, 107, 120, 118, 110, 112, 115]})model.predict(multivariate_X_test) Copy Returns: A array-like of shape n * 1: each element is a predicted label for an instance. predict_proba​ def predict_proba(X, **pred_kwargs) Copy Predict the probability of each class from features, only works for classification problems. Arguments: X - A numpy array of featurized instances, shape n * m. **pred_kwargs - Other key word arguments to pass to predict_proba() function of the searched learners, such as per_device_eval_batch_size. Returns: A numpy array of shape n * c. c is the # classes. Each element at (i, j) is the probability for instance i to be in class j. add_learner​ def add_learner(learner_name, learner_class) Copy Add a customized learner. Arguments: learner_name - A string of the learner's name. learner_class - A subclass of flaml.automl.model.BaseEstimator. get_estimator_from_log​ def get_estimator_from_log(log_file_name: str, record_id: int, task: Union[str, Task]) Copy Get the estimator from log file. Arguments: log_file_name - A string of the log file name. record_id - An integer of the record ID in the file, 0 corresponds to the first trial. task - A string of the task type, 'binary', 'multiclass', 'regression', 'ts_forecast', 'rank', or an instance of the Task class. Returns: An estimator object for the given configuration. retrain_from_log​ def retrain_from_log(log_file_name, X_train=None, y_train=None, dataframe=None, label=None, time_budget=np.inf, task: Optional[Union[str, Task]] = None, eval_method=None, split_ratio=None, n_splits=None, split_type=None, groups=None, n_jobs=-1, train_best=True, train_full=False, record_id=-1, auto_augment=None, custom_hp=None, skip_transform=None, preserve_checkpoint=True, fit_kwargs_by_estimator=None, **fit_kwargs, ,) Copy Retrain from log file. This function is intended to retrain the logged configurations. NOTE: In some rare case, the last config is early stopped to meet time_budget and it's the best config. But the logged config's ITER_HP (e.g., n_estimators) is not reduced. Arguments: log_file_name - A string of the log file name. X_train - A numpy array or dataframe of training data in shape n*m. For time series forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). y_train - A numpy array or series of labels in shape n*1. dataframe - A dataframe of training data including label column. For time series forecast tasks, dataframe must be specified and should have at least two columns: timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). label - A str of the label column name, e.g., 'label'; Note - If X_train and y_train are provided, dataframe and label are ignored; If not, dataframe and label must be provided. time_budget - A float number of the time budget in seconds. task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression', 'summarization', or an instance of Task class. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio - A float of the validation data percentage for holdout. n_splits - An integer of the number of folds for cross-validation. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. groups - None or array-like | Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. n_jobs - An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. train_best - A boolean of whether to train the best config in the time budget; if false, train the last config in the budget. train_full - A boolean of whether to train on the full data. If true, eval_method and sample_size in the log file will be ignored. record_id - the ID of the training log record from which the model will be retrained. By default record_id = -1 which means this will be ignored. record_id = 0 corresponds to the first trial, and when record_id >= 0, time_budget will be ignored. auto_augment - boolean, default=True | Whether to automatically augment rare classes. custom_hp - dict, default=None | The custom search space specified by user Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the domain of the custom search space can either be a value or a sample.Domain object. custom_hp = { \"transformer_ms\": { \"model_path\": { \"domain\": \"albert-base-v2\", }, \"learning_rate\": { \"domain\": tune.choice([1e-4, 1e-5]), } }} Copy fit_kwargs_by_estimator - dict, default=None | The user specified keywords arguments, grouped by estimator name. e.g., fit_kwargs_by_estimator = { \"transformer\": { \"output_dir\": \"test/data/output/\", \"fp16\": False, }} Copy **fit_kwargs - Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. Below are a few examples of estimator-specific parameters: period - int | forecast horizon for all time series forecast tasks. gpu_per_trial - float, default = 0 | A float of the number of gpus per trial, only used by TransformersEstimator, XGBoostSklearnEstimator, and TemporalFusionTransformerEstimator. group_ids - list of strings of column names identifying a time series, only used by TemporalFusionTransformerEstimator, required for 'ts_forecast_panel' task. group_ids is a parameter for TimeSeriesDataSet object from PyTorchForecasting. For other parameters to describe your dataset, refer to TimeSeriesDataSet PyTorchForecasting. To specify your variables, use static_categoricals, static_reals, time_varying_known_categoricals, time_varying_known_reals, time_varying_unknown_categoricals, time_varying_unknown_reals, variable_groups. To provide more information on your data, use max_encoder_length, min_encoder_length, lags. log_dir - str, default = \"lightning_logs\" | Folder into which to log results for tensorboard, only used by TemporalFusionTransformerEstimator. max_epochs - int, default = 20 | Maximum number of epochs to run training, only used by TemporalFusionTransformerEstimator. batch_size - int, default = 64 | Batch size for training model, only used by TemporalFusionTransformerEstimator. search_space​ @propertydef search_space() -> dict Copy Search space. Must be called after fit(...) (use max_iter=0 and retrain_final=False to prevent actual fitting). Returns: A dict of the search space. low_cost_partial_config​ @propertydef low_cost_partial_config() -> dict Copy Low cost partial config. Returns: A dict. (a) if there is only one estimator in estimator_list, each key is a hyperparameter name. (b) otherwise, it is a nested dict with 'ml' as the key, and a list of the low_cost_partial_configs as the value, corresponding to each learner's low_cost_partial_config; the estimator index as an integer corresponding to the cheapest learner is appended to the list at the end. cat_hp_cost​ @propertydef cat_hp_cost() -> dict Copy Categorical hyperparameter cost Returns: A dict. (a) if there is only one estimator in estimator_list, each key is a hyperparameter name. (b) otherwise, it is a nested dict with 'ml' as the key, and a list of the cat_hp_cost's as the value, corresponding to each learner's cat_hp_cost; the cost relative to lgbm for each learner (as a list itself) is appended to the list at the end. points_to_evaluate​ @propertydef points_to_evaluate() -> dict Copy Initial points to evaluate. Returns: A list of dicts. Each dict is the initial point for each learner. resource_attr​ @propertydef resource_attr() -> Optional[str] Copy Attribute of the resource dimension. Returns: A string for the sample size attribute (the resource attribute in AutoML) or None. min_resource​ @propertydef min_resource() -> Optional[float] Copy Attribute for pruning. Returns: A float for the minimal sample size or None. max_resource​ @propertydef max_resource() -> Optional[float] Copy Attribute for pruning. Returns: A float for the maximal sample size or None. trainable​ @propertydef trainable() -> Callable[[dict], Optional[float]] Copy Training function. Returns: A function that evaluates each config and returns the loss. metric_constraints​ @propertydef metric_constraints() -> list Copy Metric constraints. Returns: A list of the metric constraints. fit​ def fit(X_train=None, y_train=None, dataframe=None, label=None, metric=None, task: Optional[Union[str, Task]] = None, n_jobs=None, log_file_name=None, estimator_list=None, time_budget=None, max_iter=None, sample=None, ensemble=None, eval_method=None, log_type=None, model_history=None, split_ratio=None, n_splits=None, log_training_metric=None, mem_thres=None, pred_time_limit=None, train_time_limit=None, X_val=None, y_val=None, sample_weight_val=None, groups_val=None, groups=None, verbose=None, retrain_full=None, split_type=None, learner_selector=None, hpo_method=None, starting_points=None, seed=None, n_concurrent_trials=None, keep_search_state=None, preserve_checkpoint=True, early_stop=None, force_cancel=None, append_log=None, auto_augment=None, min_sample_size=None, use_ray=None, use_spark=None, free_mem_ratio=0, metric_constraints=None, custom_hp=None, time_col=None, cv_score_agg_func=None, skip_transform=None, mlflow_logging=None, fit_kwargs_by_estimator=None, **fit_kwargs, ,) Copy Find a model for a given task. Arguments: X_train - A numpy array or a pandas dataframe of training data in shape (n, m). For time series forecsat tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). When using ray, X_train can be a ray.ObjectRef. y_train - A numpy array or a pandas series of labels in shape (n, ). dataframe - A dataframe of training data including label column. For time series forecast tasks, dataframe must be specified and must have at least two columns, timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). When using ray, dataframe can be a ray.ObjectRef. label - A str of the label column name for, e.g., 'label'; Note - If X_train and y_train are provided, dataframe and label are ignored; If not, dataframe and label must be provided. metric - A string of the metric name or a function, e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted', 'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If passing a customized metric function, the function needs to have the following input arguments: def custom_metric( X_test, y_test, estimator, labels, X_train, y_train, weight_test=None, weight_train=None, config=None, groups_test=None, groups_train=None,): return metric_to_minimize, metrics_to_log Copy which returns a float number as the minimization objective, and a dictionary as the metrics to log. E.g., def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args,): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { \"val_loss\": val_loss, \"train_loss\": train_loss, \"pred_time\": pred_time, } Copy task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast_regression', 'ts_forecast_classification', 'rank', 'seq-classification', 'seq-regression', 'summarization', or an instance of Task class n_jobs - An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. log_file_name - A string of the log file name | default=\"\". To disable logging, set it to be an empty string \"\". estimator_list - A list of strings for estimator names, or 'auto'. e.g., ['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']. time_budget - A float number of the time budget in seconds. Use -1 if no time limit. max_iter - An integer of the maximal number of iterations. NOTE - when both time_budget and max_iter are unspecified, only one model will be trained per estimator. sample - A boolean of whether to sample the training data during search. ensemble - boolean or dict | default=False. Whether to perform ensemble after search. Can be a dict with keys 'passthrough' and 'final_estimator' to specify the passthrough and final_estimator in the stacker. The dict can also contain 'n_jobs' as the key to specify the number of jobs for the stacker. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio - A float of the valiation data percentage for holdout. n_splits - An integer of the number of folds for cross - validation. log_type - A string of the log type, one of ['better', 'all']. 'better' only logs configs with better loss than previos iters 'all' logs all the tried configs. model_history - A boolean of whether to keep the trained best model per estimator. Make sure memory is large enough if setting to True. Default value is False: best_model_for_estimator would return a untrained model for non-best learner. log_training_metric - A boolean of whether to log the training metric for each model. mem_thres - A float of the memory size constraint in bytes. pred_time_limit - A float of the prediction latency constraint in seconds. It refers to the average prediction time per row in validation data. train_time_limit - None or a float of the training time constraint in seconds. X_val - None or a numpy array or a pandas dataframe of validation data. y_val - None or a numpy array or a pandas series of validation labels. sample_weight_val - None or a numpy array of the sample weight of validation data of the same shape as y_val. groups_val - None or array-like | group labels (with matching length to y_val) or group counts (with sum equal to length of y_val) for validation data. Need to be consistent with groups. groups - None or array-like | Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. verbose - int, default=3 | Controls the verbosity, higher means more messages. retrain_full - bool or str, default=True | whether to retrain the selected model on the full training data when using holdout. True - retrain only after search finishes; False - no retraining; 'budget' - do best effort to retrain without violating the time budget. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. hpo_method - str, default=\"auto\" | The hyperparameter optimization method. By default, CFO is used for sequential search and BlendSearch is used for parallel search. No need to set when using flaml's default search space or using a simple customized search space. When set to 'bs', BlendSearch is used. BlendSearch can be tried when the search space is complex, for example, containing multiple disjoint, discontinuous subspaces. When set to 'random', random search is used. starting_points - A dictionary or a str to specify the starting hyperparameter config for the estimators | default=\"data\". If str: if \"data\", use data-dependent defaults; if \"data:path\" use data-dependent defaults which are stored at path; if \"static\", use data-independent defaults. If dict, keys are the name of the estimators, and values are the starting hyperparameter configurations for the corresponding estimators. The value can be a single hyperparameter configuration dict or a list of hyperparameter configuration dicts. In the following code example, we get starting_points from the automl object and use them in the new_automl object. e.g., from flaml import AutoMLautoml = AutoML()X_train, y_train = load_iris(return_X_y=True)automl.fit(X_train, y_train)starting_points = automl.best_config_per_estimatornew_automl = AutoML()new_automl.fit(X_train, y_train, starting_points=starting_points) Copy seed - int or None, default=None | The random seed for hpo. n_concurrent_trials - [In preview] int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, flaml performes parallel tuning and installation of ray or spark is required: pip install flaml[ray] or pip install flaml[spark]. Please check here for more details about installing Spark. keep_search_state - boolean, default=False | Whether to keep data needed for model search after fit(). By default the state is deleted for space saving. preserve_checkpoint - boolean, default=True | Whether to preserve the saved checkpoint on disk when deleting automl. By default the checkpoint is preserved. early_stop - boolean, default=False | Whether to stop early if the search is considered to converge. force_cancel - boolean, default=False | Whether to forcely cancel the PySpark job if overtime. append_log - boolean, default=False | Whether to directly append the log records to the input log file if it exists. auto_augment - boolean, default=True | Whether to automatically augment rare classes. min_sample_size - int, default=MIN_SAMPLE_TRAIN | the minimal sample size when sample=True. use_ray - boolean or dict. If boolean: default=False | Whether to use ray to run the training in separate processes. This can be used to prevent OOM for large datasets, but will incur more overhead in time. If dict: the dict contains the keywords arguments to be passed to ray.tune.run. use_spark - boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. free_mem_ratio - float between 0 and 1, default=0. The free memory ratio to keep during training. metric_constraints - list, default=[] | The list of metric constraints. Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from \">=\" and \"<=\", and the third element is the constraint value. E.g., ('precision', '>=', 0.9). Note that all the metric names in metric_constraints need to be reported via the metrics_to_log dictionary returned by a customized metric function. The customized metric function shall be provided via the metric key word argument of the fit() function or the automl constructor. Find examples in this test. If pred_time_limit is provided as one of keyword arguments to fit() function or the automl constructor, flaml will automatically (and under the hood) add it as an additional element in the metric_constraints. Essentially 'pred_time_limit' specifies a constraint about the prediction latency constraint in seconds. custom_hp - dict, default=None | The custom search space specified by user Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the domain of the custom search space can either be a value of a sample.Domain object. custom_hp = { \"transformer_ms\": { \"model_path\": { \"domain\": \"albert-base-v2\", }, \"learning_rate\": { \"domain\": tune.choice([1e-4, 1e-5]), } }} Copy time_col - for a time series task, name of the column containing the timestamps. If not provided, defaults to the first column of X_train/X_val cv_score_agg_func - customized cross-validation scores aggregate function. Default to average metrics across folds. If specificed, this function needs to have the following input arguments: val_loss_folds: list of floats, the loss scores of each fold; log_metrics_folds: list of dicts/floats, the metrics of each fold to log. This function should return the final aggregate result of all folds. A float number of the minimization objective, and a dictionary as the metrics to log or None. E.g., def cv_score_agg_func(val_loss_folds, log_metrics_folds): metric_to_minimize = sum(val_loss_folds)/len(val_loss_folds) metrics_to_log = None for single_fold in log_metrics_folds: if metrics_to_log is None: metrics_to_log = single_fold elif isinstance(metrics_to_log, dict): metrics_to_log = {k: metrics_to_log[k] + v for k, v in single_fold.items()} else: metrics_to_log += single_fold if metrics_to_log: n = len(val_loss_folds) metrics_to_log = ( {k: v / n for k, v in metrics_to_log.items()} if isinstance(metrics_to_log, dict) else metrics_to_log / n ) return metric_to_minimize, metrics_to_log Copy skip_transform - boolean, default=False | Whether to pre-process data prior to modeling. mlflow_logging - boolean, default=None | Whether to log the training results to mlflow. Default value is None, which means the logging decision is made based on AutoML.init's mlflow_logging argument. This requires mlflow to be installed and to have an active mlflow run. FLAML will create nested runs. fit_kwargs_by_estimator - dict, default=None | The user specified keywords arguments, grouped by estimator name. For TransformersEstimator, available fit_kwargs can be found from TrainingArgumentsForAuto. e.g., fit_kwargs_by_estimator = { \"transformer\": { \"output_dir\": \"test/data/output/\", \"fp16\": False, }, \"tft\": { \"max_encoder_length\": 1, \"min_encoder_length\": 1, \"static_categoricals\": [], \"static_reals\": [], \"time_varying_known_categoricals\": [], \"time_varying_known_reals\": [], \"time_varying_unknown_categoricals\": [], \"time_varying_unknown_reals\": [], \"variable_groups\": {}, \"lags\": {}, }} Copy **fit_kwargs - Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. Below are a few examples of estimator-specific parameters: period - int | forecast horizon for all time series forecast tasks. gpu_per_trial - float, default = 0 | A float of the number of gpus per trial, only used by TransformersEstimator, XGBoostSklearnEstimator, and TemporalFusionTransformerEstimator. group_ids - list of strings of column names identifying a time series, only used by TemporalFusionTransformerEstimator, required for 'ts_forecast_panel' task. group_ids is a parameter for TimeSeriesDataSet object from PyTorchForecasting. For other parameters to describe your dataset, refer to TimeSeriesDataSet PyTorchForecasting. To specify your variables, use static_categoricals, static_reals, time_varying_known_categoricals, time_varying_known_reals, time_varying_unknown_categoricals, time_varying_unknown_reals, variable_groups. To provide more information on your data, use max_encoder_length, min_encoder_length, lags. log_dir - str, default = \"lightning_logs\" | Folder into which to log results for tensorboard, only used by TemporalFusionTransformerEstimator. max_epochs - int, default = 20 | Maximum number of epochs to run training, only used by TemporalFusionTransformerEstimator. batch_size - int, default = 64 | Batch size for training model, only used by TemporalFusionTransformerEstimator.","s":"AutoML Objects","u":"/FLAML/docs/reference/automl/automl","h":"#automl-objects","p":349},{"i":354,"t":"On this page","s":"automl.ml","u":"/FLAML/docs/reference/automl/ml","h":"","p":353},{"i":356,"t":"On this page","s":"automl.model","u":"/FLAML/docs/reference/automl/model","h":"","p":355},{"i":358,"t":"class BaseEstimator() Copy The abstract class for all learners. Typical examples: XGBoostEstimator: for regression. XGBoostSklearnEstimator: for classification. LGBMEstimator, RandomForestEstimator, LRL1Classifier, LRL2Classifier: for both regression and classification. __init__​ def __init__(task=\"binary\", **config) Copy Constructor. Arguments: task - A string of the task type, one of 'binary', 'multiclass', 'regression', 'rank', 'seq-classification', 'seq-regression', 'token-classification', 'multichoice-classification', 'summarization', 'ts_forecast', 'ts_forecast_classification'. config - A dictionary containing the hyperparameter names, 'n_jobs' as keys. n_jobs is the number of parallel threads. model​ @propertydef model() Copy Trained model after fit() is called, or None before fit() is called. estimator​ @propertydef estimator() Copy Trained model after fit() is called, or None before fit() is called. feature_names_in_​ @propertydef feature_names_in_() Copy if self.model has attribute feature_names_in, return it. otherwise, if self.model has attribute feature_name, return it. otherwise, if self._model has attribute feature_names, return it. otherwise, if self._model has method get_booster, return the feature names. otherwise, return None. feature_importances_​ @propertydef feature_importances_() Copy if self.model has attribute feature_importances, return it. otherwise, if self.model has attribute coef, return it. otherwise, return None. fit​ def fit(X_train, y_train, budget=None, free_mem_ratio=0, **kwargs) Copy Train the model from given training data. Arguments: X_train - A numpy array or a dataframe of training data in shape n*m. y_train - A numpy array or a series of labels in shape n*1. budget - A float of the time budget in seconds. free_mem_ratio - A float between 0 and 1 for the free memory ratio to keep during training. Returns: train_time - A float of the training time in seconds. predict​ def predict(X, **kwargs) Copy Predict label from features. Arguments: X - A numpy array or a dataframe of featurized instances, shape n*m. Returns: A numpy array of shape n*1. Each element is the label for a instance. predict_proba​ def predict_proba(X, **kwargs) Copy Predict the probability of each class from features. Only works for classification problems Arguments: X - A numpy array of featurized instances, shape n*m. Returns: A numpy array of shape n*c. c is the # classes. Each element at (i,j) is the probability for instance i to be in class j. score​ def score(X_val: DataFrame, y_val: Series, **kwargs) Copy Report the evaluation score of a trained estimator. Arguments: X_val - A pandas dataframe of the validation input data. y_val - A pandas series of the validation label. kwargs - keyword argument of the evaluation function, for example: metric: A string of the metric name or a function e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If metric is given, the score will report the user specified metric. If metric is not given, the metric is set to accuracy for classification and r2 for regression. You can also pass a customized metric function, for examples on how to pass a customized metric function, please check test/nlp/test_autohf_custom_metric.py and test/automl/test_multiclass.py. Returns: The evaluation score on the validation dataset. search_space​ @classmethoddef search_space(cls, data_size, task, **params) Copy [required method] search space. Arguments: data_size - A tuple of two integers, number of rows and columns. task - A str of the task type, e.g., \"binary\", \"multiclass\", \"regression\". Returns: A dictionary of the search space. Each key is the name of a hyperparameter, and value is a dict with its domain (required) and low_cost_init_value, init_value, cat_hp_cost (if applicable). e.g., {'domain': tune.randint(lower=1, upper=10), 'init_value': 1}. size​ @classmethoddef size(cls, config: dict) -> float Copy [optional method] memory size of the estimator in bytes. Arguments: config - A dict of the hyperparameter config. Returns: A float of the memory size required by the estimator to train the given config. cost_relative2lgbm​ @classmethoddef cost_relative2lgbm(cls) -> float Copy [optional method] relative cost compared to lightgbm. init​ @classmethoddef init(cls) Copy [optional method] initialize the class. config2params​ def config2params(config: dict) -> dict Copy [optional method] config dict to params dict Arguments: config - A dict of the hyperparameter config. Returns: A dict that will be passed to self.estimator_class's constructor.","s":"BaseEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#baseestimator-objects","p":355},{"i":360,"t":"class SparkEstimator(BaseEstimator) Copy The base class for fine-tuning spark models, using pyspark.ml and SynapseML API. fit​ def fit(X_train: psDataFrame, y_train: psSeries = None, budget=None, free_mem_ratio=0, index_col: str = \"tmp_index_col\", **kwargs, ,) Copy Train the model from given training data. Arguments: X_train - A pyspark.pandas DataFrame of training data in shape n*m. y_train - A pyspark.pandas Series in shape n*1. None if X_train is a pyspark.pandas Dataframe contains y_train. budget - A float of the time budget in seconds. free_mem_ratio - A float between 0 and 1 for the free memory ratio to keep during training. Returns: train_time - A float of the training time in seconds. predict​ def predict(X, index_col=\"tmp_index_col\", return_all=False, **kwargs) Copy Predict label from features. Arguments: X - A pyspark or pyspark.pandas dataframe of featurized instances, shape n*m. index_col - A str of the index column name. Default to \"tmp_index_col\". return_all - A bool of whether to return all the prediction results. Default to False. Returns: A pyspark.pandas series of shape n*1 if return_all is False. Otherwise, a pyspark.pandas dataframe. predict_proba​ def predict_proba(X, index_col=\"tmp_index_col\", return_all=False, **kwargs) Copy Predict the probability of each class from features. Only works for classification problems Arguments: X - A pyspark or pyspark.pandas dataframe of featurized instances, shape n*m. index_col - A str of the index column name. Default to \"tmp_index_col\". return_all - A bool of whether to return all the prediction results. Default to False. Returns: A pyspark.pandas dataframe of shape n*c. c is the # classes. Each element at (i,j) is the probability for instance i to be in class j.","s":"SparkEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#sparkestimator-objects","p":355},{"i":362,"t":"class SparkLGBMEstimator(SparkEstimator) Copy The class for fine-tuning spark version lightgbm models, using SynapseML API.","s":"SparkLGBMEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#sparklgbmestimator-objects","p":355},{"i":364,"t":"class TransformersEstimator(BaseEstimator) Copy The class for fine-tuning language models, using huggingface transformers API.","s":"TransformersEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#transformersestimator-objects","p":355},{"i":366,"t":"class SKLearnEstimator(BaseEstimator) Copy The base class for tuning scikit-learn estimators. Subclasses can modify the function signature of __init__ to ignore the values in config that are not relevant to the constructor of their underlying estimator. For example, some regressors in scikit-learn don't accept the n_jobs parameter contained in config. For these, one can add n_jobs=None, before **config to make sure config doesn't contain an n_jobs key.","s":"SKLearnEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#sklearnestimator-objects","p":355},{"i":368,"t":"class LGBMEstimator(BaseEstimator) Copy The class for tuning LGBM, using sklearn API.","s":"LGBMEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#lgbmestimator-objects","p":355},{"i":370,"t":"class XGBoostEstimator(SKLearnEstimator) Copy The class for tuning XGBoost regressor, not using sklearn API.","s":"XGBoostEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#xgboostestimator-objects","p":355},{"i":372,"t":"class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator) Copy The class for tuning XGBoost with unlimited depth, using sklearn API.","s":"XGBoostSklearnEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#xgboostsklearnestimator-objects","p":355},{"i":374,"t":"class XGBoostLimitDepthEstimator(XGBoostSklearnEstimator) Copy The class for tuning XGBoost with limited depth, using sklearn API.","s":"XGBoostLimitDepthEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#xgboostlimitdepthestimator-objects","p":355},{"i":376,"t":"class RandomForestEstimator(SKLearnEstimator, LGBMEstimator) Copy The class for tuning Random Forest.","s":"RandomForestEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#randomforestestimator-objects","p":355},{"i":378,"t":"class ExtraTreesEstimator(RandomForestEstimator) Copy The class for tuning Extra Trees.","s":"ExtraTreesEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#extratreesestimator-objects","p":355},{"i":380,"t":"class LRL1Classifier(SKLearnEstimator) Copy The class for tuning Logistic Regression with L1 regularization.","s":"LRL1Classifier Objects","u":"/FLAML/docs/reference/automl/model","h":"#lrl1classifier-objects","p":355},{"i":382,"t":"class LRL2Classifier(SKLearnEstimator) Copy The class for tuning Logistic Regression with L2 regularization.","s":"LRL2Classifier Objects","u":"/FLAML/docs/reference/automl/model","h":"#lrl2classifier-objects","p":355},{"i":384,"t":"class CatBoostEstimator(BaseEstimator) Copy The class for tuning CatBoost.","s":"CatBoostEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#catboostestimator-objects","p":355},{"i":386,"t":"On this page","s":"automl.nlp.huggingface.trainer","u":"/FLAML/docs/reference/automl/nlp/huggingface/trainer","h":"","p":385},{"i":388,"t":"class TrainerForAuto(Seq2SeqTrainer) Copy evaluate​ def evaluate(eval_dataset=None, ignore_keys=None, metric_key_prefix=\"eval\") Copy Overriding transformers.Trainer.evaluate by saving metrics and checkpoint path.","s":"TrainerForAuto Objects","u":"/FLAML/docs/reference/automl/nlp/huggingface/trainer","h":"#trainerforauto-objects","p":385},{"i":390,"t":"On this page","s":"automl.nlp.huggingface.training_args","u":"/FLAML/docs/reference/automl/nlp/huggingface/training_args","h":"","p":389},{"i":392,"t":"@dataclassclass TrainingArgumentsForAuto(TrainingArguments) Copy FLAML custom TrainingArguments. Arguments: task str - the task name for NLP tasks, e.g., seq-classification, token-classification output_dir str - data root directory for outputing the log, etc. model_path str, optional, defaults to \"facebook/muppet-roberta-base\" - A string, the path of the language model file, either a path from huggingface model card huggingface.co/models, or a local path for the model. fp16 bool, optional, defaults to \"False\" - A bool, whether to use FP16. max_seq_length int, optional, defaults to 128 - An integer, the max length of the sequence. For token classification task, this argument will be ineffective. pad_to_max_length (bool, optional, defaults to \"False\"): whether to pad all samples to model maximum sentence length. If False, will pad the samples dynamically when batching to the maximum length in the batch. per_device_eval_batch_size int, optional, defaults to 1 - An integer, the per gpu evaluation batch size. label_list List[str], optional, defaults to None - A list of string, the string list of the label names. When the task is sequence labeling/token classification, there are two formats of the labels: (1) The token labels, i.e., [B-PER, I-PER, B-LOC]; (2) Id labels. For (2), need to pass the label_list (e.g., [B-PER, I-PER, B-LOC]) to convert the Id to token labels when computing the metric with metric_loss_score. See the example in a simple token classification example.","s":"TrainingArgumentsForAuto Objects","u":"/FLAML/docs/reference/automl/nlp/huggingface/training_args","h":"#trainingargumentsforauto-objects","p":389},{"i":394,"t":"On this page","s":"automl.nlp.huggingface.utils","u":"/FLAML/docs/reference/automl/nlp/huggingface/utils","h":"","p":393},{"i":396,"t":"On this page","s":"automl.spark.metrics","u":"/FLAML/docs/reference/automl/spark/metrics","h":"","p":395},{"i":398,"t":"On this page","s":"automl.state","u":"/FLAML/docs/reference/automl/state","h":"","p":397},{"i":400,"t":"class AutoMLState() Copy sanitize​ @classmethoddef sanitize(cls, config: dict) -> dict Copy Make a config ready for passing to estimator.","s":"AutoMLState Objects","u":"/FLAML/docs/reference/automl/state","h":"#automlstate-objects","p":397},{"i":402,"t":"On this page","s":"automl.spark.utils","u":"/FLAML/docs/reference/automl/spark/utils","h":"","p":401},{"i":404,"t":"On this page","s":"automl.nlp.utils","u":"/FLAML/docs/reference/automl/nlp/utils","h":"","p":403},{"i":406,"t":"On this page","s":"automl.task.time_series_task","u":"/FLAML/docs/reference/automl/task/time_series_task","h":"","p":405},{"i":408,"t":"On this page","s":"automl.task.task","u":"/FLAML/docs/reference/automl/task/task","h":"","p":407},{"i":410,"t":"class Task(ABC) Copy Abstract base class for a machine learning task. Class definitions should implement abstract methods and provide a non-empty dictionary of estimator classes. A Task can be suitable to be used for multiple machine-learning tasks (e.g. classification or regression) or be implemented specifically for a single one depending on the generality of data validation and model evaluation methods implemented. The implementation of a Task may optionally use the training data and labels to determine data and task specific details, such as in determining if a problem is single-label or multi-label. FLAML evaluates at runtime how to behave exactly, relying on the task instance to provide implementations of operations which vary between tasks. __init__​ def __init__(task_name: str, X_train: Optional[Union[np.ndarray, DataFrame, psDataFrame]] = None, y_train: Optional[Union[np.ndarray, DataFrame, Series, psSeries]] = None) Copy Constructor. Arguments: task_name - String name for this type of task. Used when the Task can be generic and implement a number of types of sub-task. X_train - Optional. Some Task types may use the data shape or features to determine details of their usage, such as in binary vs multilabel classification. y_train - Optional. Some Task types may use the data shape or features to determine details of their usage, such as in binary vs multilabel classification. __str__​ def __str__() -> str Copy Name of this task type. evaluate_model_CV​ @abstractmethoddef evaluate_model_CV(config: dict, estimator: \"flaml.automl.ml.BaseEstimator\", X_train_all: Union[np.ndarray, DataFrame, psDataFrame], y_train_all: Union[np.ndarray, DataFrame, Series, psSeries], budget: int, kf, eval_metric: str, best_val_loss: float, log_training_metric: bool = False, fit_kwargs: Optional[dict] = {}) -> Tuple[float, float, float, float] Copy Evaluate the model using cross-validation. Arguments: config - configuration used in the evaluation of the metric. estimator - Estimator class of the model. X_train_all - Complete training feature data. y_train_all - Complete training target data. budget - Training time budget. kf - Cross-validation index generator. eval_metric - Metric name to be used for evaluation. best_val_loss - Best current validation-set loss. log_training_metric - Bool defaults False. Enables logging of the training metric. fit_kwargs - Additional kwargs passed to the estimator's fit method. Returns: validation loss, metric value, train time, prediction time validate_data​ @abstractmethoddef validate_data(automl: \"flaml.automl.automl.AutoML\", state: \"flaml.automl.state.AutoMLState\", X_train_all: Union[np.ndarray, DataFrame, psDataFrame, None], y_train_all: Union[np.ndarray, DataFrame, Series, psSeries, None], dataframe: Union[DataFrame, None], label: str, X_val: Optional[Union[np.ndarray, DataFrame, psDataFrame]] = None, y_val: Optional[Union[np.ndarray, DataFrame, Series, psSeries]] = None, groups_val: Optional[List[str]] = None, groups: Optional[List[str]] = None) Copy Validate that the data is suitable for this task type. Arguments: automl - The AutoML instance from which this task has been constructed. state - The AutoMLState instance for this run. X_train_all - The complete data set or None if dataframe is supplied. y_train_all - The complete target set or None if dataframe is supplied. dataframe - A dataframe constaining the complete data set with targets. label - The name of the target column in dataframe. X_val - Optional. A data set for validation. y_val - Optional. A target vector corresponding to X_val for validation. groups_val - Group labels (with matching length to y_val) or group counts (with sum equal to length of y_val) for validation data. Need to be consistent with groups. groups - Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. Raises: AssertionError - The data provided is invalid for this task type and configuration. prepare_data​ @abstractmethoddef prepare_data(state: \"flaml.automl.state.AutoMLState\", X_train_all: Union[np.ndarray, DataFrame, psDataFrame], y_train_all: Union[np.ndarray, DataFrame, Series, psSeries, None], auto_augment: bool, eval_method: str, split_type: str, split_ratio: float, n_splits: int, data_is_df: bool, sample_weight_full: Optional[List[float]] = None) Copy Prepare the data for fitting or inference. Arguments: automl - The AutoML instance from which this task has been constructed. state - The AutoMLState instance for this run. X_train_all - The complete data set or None if dataframe is supplied. Must contain the target if y_train_all is None y_train_all - The complete target set or None if supplied in X_train_all. auto_augment - If true, task-specific data augmentations will be applied. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. split_ratio - A float of the valiation data percentage for holdout. n_splits - An integer of the number of folds for cross - validation. data_is_df - True if the data was provided as a DataFrame else False. sample_weight_full - A 1d arraylike of the sample weight. Raises: AssertionError - The configuration provided is invalid for this task type and data. decide_split_type​ @abstractmethoddef decide_split_type(split_type: str, y_train_all: Union[np.ndarray, DataFrame, Series, psSeries, None], fit_kwargs: dict, groups: Optional[List[str]] = None) -> str Copy Choose an appropriate data split type for this data and task. If split_type is 'auto' then this is determined based on the task type and data. If a specific split_type is requested then the choice is validated to be appropriate. Arguments: split_type - Either 'auto' or a task appropriate split type. y_train_all - The complete set of targets. fit_kwargs - Additional kwargs passed to the estimator's fit method. groups - Optional. Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. Returns: The determined appropriate split type. Raises: AssertionError - The requested split_type is invalid for this task, configuration and data. preprocess​ @abstractmethoddef preprocess(X: Union[np.ndarray, DataFrame, psDataFrame], transformer: Optional[\"flaml.automl.data.DataTransformer\"] = None) -> Union[np.ndarray, DataFrame] Copy Preprocess the data ready for fitting or inference with this task type. Arguments: X - The data set to process. transformer - A DataTransformer instance to be used in processing. Returns: The preprocessed data set having the same type as the input. default_estimator_list​ @abstractmethoddef default_estimator_list(estimator_list: Union[List[str], str] = \"auto\", is_spark_dataframe: bool = False) -> List[str] Copy Return the list of default estimators registered for this task type. If 'auto' is provided then the default list is returned, else the provided list will be validated given this task type. Arguments: estimator_list - Either 'auto' or a list of estimator names to be validated. is_spark_dataframe - True if the data is a spark dataframe. Returns: A list of valid estimator names for this task type. default_metric​ @abstractmethoddef default_metric(metric: str) -> str Copy Return the default metric for this task type. If 'auto' is provided then the default metric for this task will be returned. Otherwise, the provided metric name is validated for this task type. Arguments: metric - The name of a metric to be used in evaluation of models during fitting or validation. Returns: The default metric, or the provided metric if it is valid for this task type. __eq__​ def __eq__(other: str) -> bool Copy For backward compatibility with all the string comparisons to task estimator_class_from_str​ def estimator_class_from_str(estimator_name: str) -> \"flaml.automl.ml.BaseEstimator\" Copy Determine the estimator class corresponding to the provided name. Arguments: estimator_name - Name of the desired estimator. Returns: The estimator class corresponding to the provided name. Raises: ValueError - The provided estimator_name has not been registered for this task type.","s":"Task Objects","u":"/FLAML/docs/reference/automl/task/task","h":"#task-objects","p":407},{"i":412,"t":"On this page","s":"automl.time_series.sklearn","u":"/FLAML/docs/reference/automl/time_series/sklearn","h":"","p":411},{"i":414,"t":"X : pandas.DataFrame Input features. y : array_like, (1d) Target vector. horizon : int length of X for predict method","s":"Parameters","u":"/FLAML/docs/reference/automl/time_series/sklearn","h":"#parameters","p":411},{"i":416,"t":"pandas.DataFrame shifted dataframe with lags columns","s":"Returns","u":"/FLAML/docs/reference/automl/time_series/sklearn","h":"#returns","p":411},{"i":418,"t":"On this page","s":"automl.time_series.ts_data","u":"/FLAML/docs/reference/automl/time_series/ts_data","h":"","p":417},{"i":420,"t":"@dataclassclass TimeSeriesDataset() Copy to_univariate​ def to_univariate() -> Dict[str, \"TimeSeriesDataset\"] Copy Convert a multivariate TrainingData to a dict of univariate ones @param df: @return: fourier_series​ def fourier_series(feature: pd.Series, name: str) Copy Assume feature goes from 0 to 1 cyclically, transform that into Fourier @param feature: input feature @return: sin(2pifeature), cos(2pifeature)","s":"TimeSeriesDataset Objects","u":"/FLAML/docs/reference/automl/time_series/ts_data","h":"#timeseriesdataset-objects","p":417},{"i":422,"t":"class DataTransformerTS() Copy Transform input time series training data. fit​ def fit(X: Union[DataFrame, np.array], y) Copy Fit transformer. Arguments: X - A numpy array or a pandas dataframe of training data. y - A numpy array or a pandas series of labels. Returns: X - Processed numpy array or pandas dataframe of training data. y - Processed numpy array or pandas series of labels.","s":"DataTransformerTS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_data","h":"#datatransformerts-objects","p":417},{"i":424,"t":"On this page","s":"automl.time_series.ts_model","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"","p":423},{"i":426,"t":"class Prophet(TimeSeriesEstimator) Copy The class for tuning Prophet.","s":"Prophet Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#prophet-objects","p":423},{"i":428,"t":"class ARIMA(StatsModelsEstimator) Copy The class for tuning ARIMA.","s":"ARIMA Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#arima-objects","p":423},{"i":430,"t":"class SARIMAX(StatsModelsEstimator) Copy The class for tuning SARIMA.","s":"SARIMAX Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#sarimax-objects","p":423},{"i":432,"t":"class HoltWinters(StatsModelsEstimator) Copy The class for tuning Holt Winters model, aka 'Triple Exponential Smoothing'.","s":"HoltWinters Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#holtwinters-objects","p":423},{"i":434,"t":"class TS_SKLearn(TimeSeriesEstimator) Copy The class for tuning SKLearn Regressors for time-series forecasting","s":"TS_SKLearn Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#ts_sklearn-objects","p":423},{"i":436,"t":"class LGBM_TS(TS_SKLearn) Copy The class for tuning LGBM Regressor for time-series forecasting","s":"LGBM_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#lgbm_ts-objects","p":423},{"i":438,"t":"class XGBoost_TS(TS_SKLearn) Copy The class for tuning XGBoost Regressor for time-series forecasting","s":"XGBoost_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#xgboost_ts-objects","p":423},{"i":440,"t":"class RF_TS(TS_SKLearn) Copy The class for tuning Random Forest Regressor for time-series forecasting","s":"RF_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#rf_ts-objects","p":423},{"i":442,"t":"class ExtraTrees_TS(TS_SKLearn) Copy The class for tuning Extra Trees Regressor for time-series forecasting","s":"ExtraTrees_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#extratrees_ts-objects","p":423},{"i":444,"t":"class XGBoostLimitDepth_TS(TS_SKLearn) Copy The class for tuning XGBoost Regressor with unlimited depth for time-series forecasting","s":"XGBoostLimitDepth_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#xgboostlimitdepth_ts-objects","p":423},{"i":446,"t":"On this page","s":"automl.time_series.tft","u":"/FLAML/docs/reference/automl/time_series/tft","h":"","p":445},{"i":448,"t":"class TemporalFusionTransformerEstimator(TimeSeriesEstimator) Copy The class for tuning Temporal Fusion Transformer","s":"TemporalFusionTransformerEstimator Objects","u":"/FLAML/docs/reference/automl/time_series/tft","h":"#temporalfusiontransformerestimator-objects","p":445},{"i":450,"t":"On this page","s":"default.greedy","u":"/FLAML/docs/reference/default/greedy","h":"","p":449},{"i":452,"t":"On this page","s":"default.estimator","u":"/FLAML/docs/reference/default/estimator","h":"","p":451},{"i":454,"t":"On this page","s":"onlineml.autovw","u":"/FLAML/docs/reference/onlineml/autovw","h":"","p":453},{"i":456,"t":"class AutoVW() Copy Class for the AutoVW algorithm. __init__​ def __init__(max_live_model_num: int, search_space: dict, init_config: Optional[dict] = {}, min_resource_lease: Optional[Union[str, float]] = \"auto\", automl_runner_args: Optional[dict] = {}, scheduler_args: Optional[dict] = {}, model_select_policy: Optional[str] = \"threshold_loss_ucb\", metric: Optional[str] = \"mae_clipped\", random_seed: Optional[int] = None, model_selection_mode: Optional[str] = \"min\", cb_coef: Optional[float] = None) Copy Constructor. Arguments: max_live_model_num - An int to specify the maximum number of 'live' models, which, in other words, is the maximum number of models allowed to update in each learning iteraction. search_space - A dictionary of the search space. This search space includes both hyperparameters we want to tune and fixed hyperparameters. In the latter case, the value is a fixed value. init_config - A dictionary of a partial or full initial config, e.g. {'interactions': set(), 'learning_rate': 0.5} min_resource_lease - string or float | The minimum resource lease assigned to a particular model/trial. If set as 'auto', it will be calculated automatically. automl_runner_args - A dictionary of configuration for the OnlineTrialRunner. If set {}, default values will be used, which is equivalent to using the following configs. Example: automl_runner_args = { \"champion_test_policy\": 'loss_ucb', # the statistic test for a better champion \"remove_worse\": False, # whether to do worse than test} Copy scheduler_args - A dictionary of configuration for the scheduler. If set {}, default values will be used, which is equivalent to using the following config. Example: scheduler_args = { \"keep_challenger_metric\": 'ucb', # what metric to use when deciding the top performing challengers \"keep_challenger_ratio\": 0.5, # denotes the ratio of top performing challengers to keep live \"keep_champion\": True, # specifcies whether to keep the champion always running} Copy model_select_policy - A string in ['threshold_loss_ucb', 'threshold_loss_lcb', 'threshold_loss_avg', 'loss_ucb', 'loss_lcb', 'loss_avg'] to specify how to select one model to do prediction from the live model pool. Default value is 'threshold_loss_ucb'. metric - A string in ['mae_clipped', 'mae', 'mse', 'absolute_clipped', 'absolute', 'squared'] to specify the name of the loss function used for calculating the progressive validation loss in ChaCha. random_seed - An integer of the random seed used in the searcher (more specifically this the random seed for ConfigOracle). model_selection_mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. cb_coef - A float coefficient (optional) used in the sample complexity bound. predict​ def predict(data_sample) Copy Predict on the input data sample. Arguments: data_sample - one data example in vw format. learn​ def learn(data_sample) Copy Perform one online learning step with the given data sample. Arguments: data_sample - one data example in vw format. It will be used to update the vw model. get_ns_feature_dim_from_vw_example​ @staticmethoddef get_ns_feature_dim_from_vw_example(vw_example) -> dict Copy Get a dictionary of feature dimensionality for each namespace singleton.","s":"AutoVW Objects","u":"/FLAML/docs/reference/onlineml/autovw","h":"#autovw-objects","p":453},{"i":458,"t":"On this page","s":"default.suggest","u":"/FLAML/docs/reference/default/suggest","h":"","p":457},{"i":460,"t":"On this page","s":"onlineml.trial","u":"/FLAML/docs/reference/onlineml/trial","h":"","p":459},{"i":462,"t":"class OnlineResult() Copy Class for managing the result statistics of a trial. __init__​ def __init__(result_type_name: str, cb_coef: Optional[float] = None, init_loss: Optional[float] = 0.0, init_cb: Optional[float] = 100.0, mode: Optional[str] = \"min\", sliding_window_size: Optional[int] = 100) Copy Constructor. Arguments: result_type_name - A String to specify the name of the result type. cb_coef - a string to specify the coefficient on the confidence bound. init_loss - a float to specify the inital loss. init_cb - a float to specify the intial confidence bound. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. sliding_window_size - An int to specify the size of the sliding window (for experimental purpose). update_result​ def update_result(new_loss, new_resource_used, data_dimension, bound_of_range=1.0, new_observation_count=1.0) Copy Update result statistics.","s":"OnlineResult Objects","u":"/FLAML/docs/reference/onlineml/trial","h":"#onlineresult-objects","p":459},{"i":464,"t":"class BaseOnlineTrial(Trial) Copy Class for the online trial. __init__​ def __init__(config: dict, min_resource_lease: float, is_champion: Optional[bool] = False, is_checked_under_current_champion: Optional[bool] = True, custom_trial_name: Optional[str] = \"mae\", trial_id: Optional[str] = None) Copy Constructor. Arguments: config - The configuration dictionary. min_resource_lease - A float specifying the minimum resource lease. is_champion - A bool variable indicating whether the trial is champion. is_checked_under_current_champion - A bool indicating whether the trial has been used under the current champion. custom_trial_name - A string of a custom trial name. trial_id - A string for the trial id. set_resource_lease​ def set_resource_lease(resource: float) Copy Sets the resource lease accordingly. set_status​ def set_status(status) Copy Sets the status of the trial and record the start time.","s":"BaseOnlineTrial Objects","u":"/FLAML/docs/reference/onlineml/trial","h":"#baseonlinetrial-objects","p":459},{"i":466,"t":"class VowpalWabbitTrial(BaseOnlineTrial) Copy The class for Vowpal Wabbit online trials. __init__​ def __init__(config: dict, min_resource_lease: float, metric: str = \"mae\", is_champion: Optional[bool] = False, is_checked_under_current_champion: Optional[bool] = True, custom_trial_name: Optional[str] = \"vw_mae_clipped\", trial_id: Optional[str] = None, cb_coef: Optional[float] = None) Copy Constructor. Arguments: config dict - the config of the trial (note that the config is a set because the hyperparameters are). min_resource_lease float - the minimum resource lease. metric str - the loss metric. is_champion bool - indicates whether the trial is the current champion or not. is_checked_under_current_champion bool - indicates whether this trials has been paused under the current champion. trial_id str - id of the trial (if None, it will be generated in the constructor). train_eval_model_online​ def train_eval_model_online(data_sample, y_pred) Copy Train and evaluate model online. predict​ def predict(x) Copy Predict using the model.","s":"VowpalWabbitTrial Objects","u":"/FLAML/docs/reference/onlineml/trial","h":"#vowpalwabbittrial-objects","p":459},{"i":468,"t":"On this page","s":"default.portfolio","u":"/FLAML/docs/reference/default/portfolio","h":"","p":467},{"i":470,"t":"On this page","s":"onlineml.trial_runner","u":"/FLAML/docs/reference/onlineml/trial_runner","h":"","p":469},{"i":472,"t":"class OnlineTrialRunner() Copy Class for the OnlineTrialRunner. __init__​ def __init__(max_live_model_num: int, searcher=None, scheduler=None, champion_test_policy=\"loss_ucb\", **kwargs) Copy Constructor. Arguments: max_live_model_num - The maximum number of 'live'/running models allowed. searcher - A class for generating Trial objects progressively. The ConfigOracle is implemented in the searcher. scheduler - A class for managing the 'live' trials and allocating the resources for the trials. champion_test_policy - A string to specify what test policy to test for champion. Currently can choose from ['loss_ucb', 'loss_avg', 'loss_lcb', None]. champion_trial​ @propertydef champion_trial() -> Trial Copy The champion trial. running_trials​ @propertydef running_trials() Copy The running/'live' trials. step​ def step(data_sample=None, prediction_trial_tuple=None) Copy Schedule one trial to run each time it is called. Arguments: data_sample - One data example. prediction_trial_tuple - A list of information containing (prediction_made, prediction_trial). get_top_running_trials​ def get_top_running_trials(top_ratio=None, top_metric=\"ucb\") -> list Copy Get a list of trial ids, whose performance is among the top running trials. get_trials​ def get_trials() -> list Copy Return the list of trials managed by this TrialRunner. add_trial​ def add_trial(new_trial) Copy Add a new trial to this TrialRunner. Trials may be added at any time. Arguments: new_trial Trial - Trial to queue. stop_trial​ def stop_trial(trial) Copy Stop a trial: set the status of a trial to be Trial.TERMINATED and perform other subsequent operations. pause_trial​ def pause_trial(trial) Copy Pause a trial: set the status of a trial to be Trial.PAUSED and perform other subsequent operations. run_trial​ def run_trial(trial) Copy Run a trial: set the status of a trial to be Trial.RUNNING and perform other subsequent operations.","s":"OnlineTrialRunner Objects","u":"/FLAML/docs/reference/onlineml/trial_runner","h":"#onlinetrialrunner-objects","p":469},{"i":474,"t":"On this page","s":"tune.sample","u":"/FLAML/docs/reference/tune/sample","h":"","p":473},{"i":476,"t":"class Domain() Copy Base class to specify a type and valid range to sample parameters from. This base class is implemented by parameter spaces, like float ranges (Float), integer ranges (Integer), or categorical variables (Categorical). The Domain object contains information about valid values (e.g. minimum and maximum values), and exposes methods that allow specification of specific samplers (e.g. uniform() or loguniform()). cast​ def cast(value) Copy Cast value to domain type is_valid​ def is_valid(value: Any) Copy Returns True if value is a valid value in this domain.","s":"Domain Objects","u":"/FLAML/docs/reference/tune/sample","h":"#domain-objects","p":473},{"i":478,"t":"class Grid(Sampler) Copy Dummy sampler used for grid search uniform​ def uniform(lower: float, upper: float) Copy Sample a float value uniformly between lower and upper. Sampling from tune.uniform(1, 10) is equivalent to sampling from np.random.uniform(1, 10)) quniform​ def quniform(lower: float, upper: float, q: float) Copy Sample a quantized float value uniformly between lower and upper. Sampling from tune.uniform(1, 10) is equivalent to sampling from np.random.uniform(1, 10)) The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. loguniform​ def loguniform(lower: float, upper: float, base: float = 10) Copy Sugar for sampling in different orders of magnitude. Arguments: lower float - Lower boundary of the output interval (e.g. 1e-4) upper float - Upper boundary of the output interval (e.g. 1e-2) base int - Base of the log. Defaults to 10. qloguniform​ def qloguniform(lower: float, upper: float, q: float, base: float = 10) Copy Sugar for sampling in different orders of magnitude. The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. Arguments: lower float - Lower boundary of the output interval (e.g. 1e-4) upper float - Upper boundary of the output interval (e.g. 1e-2) q float - Quantization number. The result will be rounded to an integer increment of this value. base int - Base of the log. Defaults to 10. choice​ def choice(categories: Sequence) Copy Sample a categorical value. Sampling from tune.choice([1, 2]) is equivalent to sampling from np.random.choice([1, 2]) randint​ def randint(lower: int, upper: int) Copy Sample an integer value uniformly between lower and upper. lower is inclusive, upper is exclusive. Sampling from tune.randint(10) is equivalent to sampling from np.random.randint(10) lograndint​ def lograndint(lower: int, upper: int, base: float = 10) Copy Sample an integer value log-uniformly between lower and upper, with base being the base of logarithm. lower is inclusive, upper is exclusive. qrandint​ def qrandint(lower: int, upper: int, q: int = 1) Copy Sample an integer value uniformly between lower and upper. lower is inclusive, upper is also inclusive (!). The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. qlograndint​ def qlograndint(lower: int, upper: int, q: int, base: float = 10) Copy Sample an integer value log-uniformly between lower and upper, with base being the base of logarithm. lower is inclusive, upper is also inclusive (!). The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. randn​ def randn(mean: float = 0.0, sd: float = 1.0) Copy Sample a float value normally with mean and sd. Arguments: mean float - Mean of the normal distribution. Defaults to 0. sd float - SD of the normal distribution. Defaults to 1. qrandn​ def qrandn(mean: float, sd: float, q: float) Copy Sample a float value normally with mean and sd. The value will be quantized, i.e. rounded to an integer increment of q. Arguments: mean - Mean of the normal distribution. sd - SD of the normal distribution. q - Quantization number. The result will be rounded to an integer increment of this value.","s":"Grid Objects","u":"/FLAML/docs/reference/tune/sample","h":"#grid-objects","p":473},{"i":480,"t":"On this page","s":"tune.scheduler.trial_scheduler","u":"/FLAML/docs/reference/tune/scheduler/trial_scheduler","h":"","p":479},{"i":482,"t":"class TrialScheduler() Copy Interface for implementing a Trial Scheduler class.","s":"TrialScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/trial_scheduler","h":"#trialscheduler-objects","p":479},{"i":484,"t":"On this page","s":"tune.scheduler.online_scheduler","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"","p":483},{"i":486,"t":"class OnlineScheduler(TrialScheduler) Copy Class for the most basic OnlineScheduler. on_trial_result​ def on_trial_result(trial_runner, trial: Trial, result: Dict) Copy Report result and return a decision on the trial's status. choose_trial_to_run​ def choose_trial_to_run(trial_runner) -> Trial Copy Decide which trial to run next.","s":"OnlineScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"#onlinescheduler-objects","p":483},{"i":488,"t":"class OnlineSuccessiveDoublingScheduler(OnlineScheduler) Copy class for the OnlineSuccessiveDoublingScheduler algorithm. __init__​ def __init__(increase_factor: float = 2.0) Copy Constructor. Arguments: increase_factor - A float of multiplicative factor used to increase resource lease. Default is 2.0. on_trial_result​ def on_trial_result(trial_runner, trial: Trial, result: Dict) Copy Report result and return a decision on the trial's status.","s":"OnlineSuccessiveDoublingScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"#onlinesuccessivedoublingscheduler-objects","p":483},{"i":490,"t":"class ChaChaScheduler(OnlineSuccessiveDoublingScheduler) Copy class for the ChaChaScheduler algorithm. __init__​ def __init__(increase_factor: float = 2.0, **kwargs) Copy Constructor. Arguments: increase_factor - A float of multiplicative factor used to increase resource lease. Default is 2.0. on_trial_result​ def on_trial_result(trial_runner, trial: Trial, result: Dict) Copy Report result and return a decision on the trial's status.","s":"ChaChaScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"#chachascheduler-objects","p":483},{"i":492,"t":"On this page","s":"tune.analysis","u":"/FLAML/docs/reference/tune/analysis","h":"","p":491},{"i":494,"t":"class ExperimentAnalysis() Copy Analyze results from a Tune experiment. best_trial​ @propertydef best_trial() -> Trial Copy Get the best trial of the experiment The best trial is determined by comparing the last trial results using the metric and mode parameters passed to tune.run(). If you didn't pass these parameters, use get_best_trial(metric, mode, scope) instead. best_config​ @propertydef best_config() -> Dict Copy Get the config of the best trial of the experiment The best trial is determined by comparing the last trial results using the metric and mode parameters passed to tune.run(). If you didn't pass these parameters, use get_best_config(metric, mode, scope) instead. results​ @propertydef results() -> Dict[str, Dict] Copy Get the last result of all the trials of the experiment get_best_trial​ def get_best_trial(metric: Optional[str] = None, mode: Optional[str] = None, scope: str = \"last\", filter_nan_and_inf: bool = True) -> Optional[Trial] Copy Retrieve the best trial object. Compares all trials' scores on metric. If metric is not specified, self.default_metric will be used. If mode is not specified, self.default_mode will be used. These values are usually initialized by passing the metric and mode parameters to tune.run(). Arguments: metric str - Key for trial info to order on. Defaults to self.default_metric. mode str - One of [min, max]. Defaults to self.default_mode. scope str - One of [all, last, avg, last-5-avg, last-10-avg]. If scope=last, only look at each trial's final step for metric, and compare across trials based on mode=[min,max]. If scope=avg, consider the simple average over all steps for metric and compare across trials based on mode=[min,max]. If scope=last-5-avg or scope=last-10-avg, consider the simple average over the last 5 or 10 steps for metric and compare across trials based on mode=[min,max]. If scope=all, find each trial's min/max score for metric based on mode, and compare trials based on mode=[min,max]. filter_nan_and_inf bool - If True (default), NaN or infinite values are disregarded and these trials are never selected as the best trial. get_best_config​ def get_best_config(metric: Optional[str] = None, mode: Optional[str] = None, scope: str = \"last\") -> Optional[Dict] Copy Retrieve the best config corresponding to the trial. Compares all trials' scores on metric. If metric is not specified, self.default_metric will be used. If mode is not specified, self.default_mode will be used. These values are usually initialized by passing the metric and mode parameters to tune.run(). Arguments: metric str - Key for trial info to order on. Defaults to self.default_metric. mode str - One of [min, max]. Defaults to self.default_mode. scope str - One of [all, last, avg, last-5-avg, last-10-avg]. If scope=last, only look at each trial's final step for metric, and compare across trials based on mode=[min,max]. If scope=avg, consider the simple average over all steps for metric and compare across trials based on mode=[min,max]. If scope=last-5-avg or scope=last-10-avg, consider the simple average over the last 5 or 10 steps for metric and compare across trials based on mode=[min,max]. If scope=all, find each trial's min/max score for metric based on mode, and compare trials based on mode=[min,max]. best_result​ @propertydef best_result() -> Dict Copy Get the last result of the best trial of the experiment The best trial is determined by comparing the last trial results using the metric and mode parameters passed to tune.run(). If you didn't pass these parameters, use get_best_trial(metric, mode, scope).last_result instead.","s":"ExperimentAnalysis Objects","u":"/FLAML/docs/reference/tune/analysis","h":"#experimentanalysis-objects","p":491},{"i":496,"t":"On this page","s":"tune.searcher.blendsearch","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"","p":495},{"i":498,"t":"class BlendSearch(Searcher) Copy class for BlendSearch algorithm. __init__​ def __init__(metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, time_budget_s: Union[int, float] = None, num_samples: Optional[int] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, global_search_alg: Optional[Searcher] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, seed: Optional[int] = 20, cost_attr: Optional[str] = \"auto\", cost_budget: Optional[float] = None, experimental: Optional[bool] = False, lexico_objectives: Optional[dict] = None, use_incumbent_result_in_evaluation=False, allow_empty_config=False) Copy Constructor. Arguments: metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. space - A dictionary to specify the search space. low_cost_partial_config - A dictionary from a subset of controlled dimensions to the initial low-cost values. E.g., {'n_estimators': 4, 'max_leaves': 4}. cat_hp_cost - A dictionary from a subset of categorical dimensions to the relative cost of each choice. E.g., {'tree_method': [1, 1, 2]}. I.e., the relative cost of the three choices of 'tree_method' is 1, 1 and 2 respectively. points_to_evaluate - Initial parameter suggestions to be run first. evaluated_rewards list - If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same or shorter length than points_to_evaluate. When provided, mode must be specified. time_budget_s - int or float | Time budget in seconds. num_samples - int | The number of configs to try. -1 means no limit on the number of configs to try. resource_attr - A string to specify the resource dimension and the best performance is assumed to be at the max_resource. min_resource - A float of the minimal resource to use for the resource_attr. max_resource - A float of the maximal resource to use for the resource_attr. reduction_factor - A float of the reduction factor used for incremental pruning. global_search_alg - A Searcher instance as the global search instance. If omitted, Optuna is used. The following algos have known issues when used as global_search_alg: HyperOptSearch raises exception sometimes TuneBOHB has its own scheduler config_constraints - A list of config constraints to be satisfied. E.g., config_constraints = [(mem_size, '<=', 1024**3)]. mem_size is a function which produces a float number for the bytes needed for a config. It is used to skip configs which do not fit in memory. metric_constraints - A list of metric constraints to be satisfied. E.g., ['precision', '>=', 0.9]. The sign can be \">=\" or \"<=\". seed - An integer of the random seed. cost_attr - None or str to specify the attribute to evaluate the cost of different trials. Default is \"auto\", which means that we will automatically choose the cost attribute to use (depending on the nature of the resource budget). When cost_attr is set to None, cost differences between different trials will be omitted in our search algorithm. When cost_attr is set to a str different from \"auto\" and \"time_total_s\", this cost_attr must be available in the result dict of the trial. cost_budget - A float of the cost budget. Only valid when cost_attr is a str different from \"auto\" and \"time_total_s\". lexico_objectives - dict, default=None | It specifics information needed to perform multi-objective optimization with lexicographic preferences. This is only supported in CFO currently. When lexico_objectives is not None, the arguments metric, mode will be invalid. This dictionary shall contain the following fields of key-value pairs: \"metrics\": a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. \"modes\" (optional): a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives. \"targets\" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. \"tolerances\" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. E.g., lexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": 0.01, \"pred_time\": 0.0}, \"targets\" - {\"error_rate\": 0.0}, } We also support percentage tolerance.E.g.,```pythonlexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": \"5%\", \"pred_time\": \"0%\"}, \"targets\" - {\"error_rate\": 0.0}, } Copy experimental - A bool of whether to use experimental features. save​ def save(checkpoint_path: str) Copy save states to a checkpoint path. restore​ def restore(checkpoint_path: str) Copy restore states from checkpoint. on_trial_complete​ def on_trial_complete(trial_id: str, result: Optional[Dict] = None, error: bool = False) Copy search thread updater and cleaner. on_trial_result​ def on_trial_result(trial_id: str, result: Dict) Copy receive intermediate result. suggest​ def suggest(trial_id: str) -> Optional[Dict] Copy choose thread, suggest a valid config. results​ @propertydef results() -> List[Dict] Copy A list of dicts of results for each evaluated configuration. Each dict has \"config\" and metric names as keys. The returned dict includes the initial results provided via evaluated_reward.","s":"BlendSearch Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#blendsearch-objects","p":495},{"i":500,"t":"class BlendSearchTuner(BlendSearch, NNITuner) Copy Tuner class for NNI. receive_trial_result​ def receive_trial_result(parameter_id, parameters, value, **kwargs) Copy Receive trial's final result. Arguments: parameter_id - int. parameters - object created by generate_parameters(). value - final metrics of the trial, including default metric. generate_parameters​ def generate_parameters(parameter_id, **kwargs) -> Dict Copy Returns a set of trial (hyper-)parameters, as a serializable object. Arguments: parameter_id - int. update_search_space​ def update_search_space(search_space) Copy Required by NNI. Tuners are advised to support updating search space at run-time. If a tuner can only set search space once before generating first hyper-parameters, it should explicitly document this behaviour. Arguments: search_space - JSON object created by experiment owner.","s":"BlendSearchTuner Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#blendsearchtuner-objects","p":495},{"i":502,"t":"class CFO(BlendSearchTuner) Copy class for CFO algorithm.","s":"CFO Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#cfo-objects","p":495},{"i":504,"t":"class RandomSearch(CFO) Copy Class for random search.","s":"RandomSearch Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#randomsearch-objects","p":495},{"i":506,"t":"On this page","s":"tune.searcher.cfo_cat","u":"/FLAML/docs/reference/tune/searcher/cfo_cat","h":"","p":505},{"i":508,"t":"class FLOW2Cat(FLOW2) Copy Local search algorithm optimized for categorical variables.","s":"FLOW2Cat Objects","u":"/FLAML/docs/reference/tune/searcher/cfo_cat","h":"#flow2cat-objects","p":505},{"i":510,"t":"class CFOCat(CFO) Copy CFO optimized for categorical variables.","s":"CFOCat Objects","u":"/FLAML/docs/reference/tune/searcher/cfo_cat","h":"#cfocat-objects","p":505},{"i":512,"t":"On this page","s":"tune.searcher.online_searcher","u":"/FLAML/docs/reference/tune/searcher/online_searcher","h":"","p":511},{"i":514,"t":"class BaseSearcher() Copy Abstract class for an online searcher.","s":"BaseSearcher Objects","u":"/FLAML/docs/reference/tune/searcher/online_searcher","h":"#basesearcher-objects","p":511},{"i":516,"t":"class ChampionFrontierSearcher(BaseSearcher) Copy The ChampionFrontierSearcher class. NOTE about the correspondence about this code and the research paper: ChaCha for Online AutoML. This class serves the role of ConfigOralce as described in the paper. __init__​ def __init__(init_config: Dict, space: Optional[Dict] = None, metric: Optional[str] = None, mode: Optional[str] = None, random_seed: Optional[int] = 2345, online_trial_args: Optional[Dict] = {}, nonpoly_searcher_name: Optional[str] = \"CFO\") Copy Constructor. Arguments: init_config - A dictionary of initial configuration. space - A dictionary to specify the search space. metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. random_seed - An integer of the random seed. online_trial_args - A dictionary to specify the online trial arguments for experimental purpose. nonpoly_searcher_name - A string to specify the search algorithm for nonpoly hyperparameters. set_search_properties​ def set_search_properties(metric: Optional[str] = None, mode: Optional[str] = None, config: Optional[Dict] = {}, setting: Optional[Dict] = {}, init_call: Optional[bool] = False) Copy Construct search space with the given config, and setup the search. next_trial​ def next_trial() Copy Return a trial from the _challenger_list.","s":"ChampionFrontierSearcher Objects","u":"/FLAML/docs/reference/tune/searcher/online_searcher","h":"#championfrontiersearcher-objects","p":511},{"i":518,"t":"On this page","s":"tune.searcher.flow2","u":"/FLAML/docs/reference/tune/searcher/flow2","h":"","p":517},{"i":520,"t":"class FLOW2(Searcher) Copy Local search algorithm FLOW2, with adaptive step size. __init__​ def __init__(init_config: dict, metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, resource_multiple_factor: Optional[float] = None, cost_attr: Optional[str] = \"time_total_s\", seed: Optional[int] = 20, lexico_objectives=None) Copy Constructor. Arguments: init_config - a dictionary of a partial or full initial config, e.g., from a subset of controlled dimensions to the initial low-cost values. E.g., {'epochs': 1}. metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. space - A dictionary to specify the search space. resource_attr - A string to specify the resource dimension and the best performance is assumed to be at the max_resource. min_resource - A float of the minimal resource to use for the resource_attr. max_resource - A float of the maximal resource to use for the resource_attr. resource_multiple_factor - A float of the multiplicative factor used for increasing resource. cost_attr - A string of the attribute used for cost. seed - An integer of the random seed. lexico_objectives - dict, default=None | It specifics information needed to perform multi-objective optimization with lexicographic preferences. When lexico_objectives is not None, the arguments metric, mode will be invalid. This dictionary shall contain the following fields of key-value pairs: \"metrics\": a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. \"modes\" (optional): a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives \"targets\" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. \"tolerances\" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. E.g., lexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": 0.01, \"pred_time\": 0.0}, \"targets\" - {\"error_rate\": 0.0}, } We also support percentage tolerance.E.g.,```pythonlexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": \"5%\", \"pred_time\": \"0%\"}, \"targets\" - {\"error_rate\": 0.0}, } Copy complete_config​ def complete_config(partial_config: Dict, lower: Optional[Dict] = None, upper: Optional[Dict] = None) -> Tuple[Dict, Dict] Copy Generate a complete config from the partial config input. Add minimal resource to config if available. normalize​ def normalize(config, recursive=False) -> Dict Copy normalize each dimension in config to [0,1]. denormalize​ def denormalize(config) Copy denormalize each dimension in config from [0,1]. on_trial_complete​ def on_trial_complete(trial_id: str, result: Optional[Dict] = None, error: bool = False) Copy Compare with incumbent. If better, move, reset num_complete and num_proposed. If not better and num_complete >= 2*dim, num_allowed += 2. on_trial_result​ def on_trial_result(trial_id: str, result: Dict) Copy Early update of incumbent. suggest​ def suggest(trial_id: str) -> Optional[Dict] Copy Suggest a new config, one of the following cases: same incumbent, increase resource. same resource, move from the incumbent to a random direction. same resource, move from the incumbent to the opposite direction. can_suggest​ @propertydef can_suggest() -> bool Copy Can't suggest if 2*dim configs have been proposed for the incumbent while fewer are completed. config_signature​ def config_signature(config, space: Dict = None) -> tuple Copy Return the signature tuple of a config. converged​ @propertydef converged() -> bool Copy Whether the local search has converged. reach​ def reach(other: Searcher) -> bool Copy whether the incumbent can reach the incumbent of other.","s":"FLOW2 Objects","u":"/FLAML/docs/reference/tune/searcher/flow2","h":"#flow2-objects","p":517},{"i":522,"t":"On this page","s":"tune.searcher.search_thread","u":"/FLAML/docs/reference/tune/searcher/search_thread","h":"","p":521},{"i":524,"t":"class SearchThread() Copy Class of global or local search thread. __init__​ def __init__(mode: str = \"min\", search_alg: Optional[Searcher] = None, cost_attr: Optional[str] = TIME_TOTAL_S, eps: Optional[float] = 1.0) Copy When search_alg is omitted, use local search FLOW2. suggest​ def suggest(trial_id: str) -> Optional[Dict] Copy Use the suggest() of the underlying search algorithm. on_trial_complete​ def on_trial_complete(trial_id: str, result: Optional[Dict] = None, error: bool = False) Copy Update the statistics of the thread. reach​ def reach(thread) -> bool Copy Whether the incumbent can reach the incumbent of thread. can_suggest​ @propertydef can_suggest() -> bool Copy Whether the thread can suggest new configs.","s":"SearchThread Objects","u":"/FLAML/docs/reference/tune/searcher/search_thread","h":"#searchthread-objects","p":521},{"i":526,"t":"On this page","s":"tune.searcher.suggestion","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"","p":525},{"i":528,"t":"class Searcher() Copy Abstract class for wrapping suggesting algorithms. Custom algorithms can extend this class easily by overriding the suggest method provide generated parameters for the trials. Any subclass that implements __init__ must also call the constructor of this class: super(Subclass, self).__init__(...). To track suggestions and their corresponding evaluations, the method suggest will be passed a trial_id, which will be used in subsequent notifications. Not all implementations support multi objectives. Arguments: metric str or list - The training result objective value attribute. If list then list of training result objective value attributes mode str or list - If string One of {min, max}. If list then list of max and min, determines whether objective is minimizing or maximizing the metric attribute. Must match type of metric. class ExampleSearch(Searcher): def __init__(self, metric=\"mean_loss\", mode=\"min\", **kwargs): super(ExampleSearch, self).__init__( metric=metric, mode=mode, **kwargs) self.optimizer = Optimizer() self.configurations = {} def suggest(self, trial_id): configuration = self.optimizer.query() self.configurations[trial_id] = configuration def on_trial_complete(self, trial_id, result, **kwargs): configuration = self.configurations[trial_id] if result and self.metric in result: self.optimizer.update(configuration, result[self.metric])tune.run(trainable_function, search_alg=ExampleSearch()) Copy set_search_properties​ def set_search_properties(metric: Optional[str], mode: Optional[str], config: Dict) -> bool Copy Pass search properties to searcher. This method acts as an alternative to instantiating search algorithms with their own specific search spaces. Instead they can accept a Tune config through this method. A searcher should return True if setting the config was successful, or False if it was unsuccessful, e.g. when the search space has already been set. Arguments: metric str - Metric to optimize mode str - One of [\"min\", \"max\"]. Direction to optimize. config dict - Tune config dict. on_trial_result​ def on_trial_result(trial_id: str, result: Dict) Copy Optional notification for result during training. Note that by default, the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. Arguments: trial_id str - A unique string ID for the trial. result dict - Dictionary of metrics for current training progress. Note that the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. metric​ @propertydef metric() -> str Copy The training result objective value attribute. mode​ @propertydef mode() -> str Copy Specifies if minimizing or maximizing the metric.","s":"Searcher Objects","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"#searcher-objects","p":525},{"i":530,"t":"class ConcurrencyLimiter(Searcher) Copy A wrapper algorithm for limiting the number of concurrent trials. Arguments: searcher Searcher - Searcher object that the ConcurrencyLimiter will manage. max_concurrent int - Maximum concurrent samples from the underlying searcher. batch bool - Whether to wait for all concurrent samples to finish before updating the underlying searcher. Example: from ray.tune.suggest import ConcurrencyLimiter # ray version < 2search_alg = HyperOptSearch(metric=\"accuracy\")search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2)tune.run(trainable, search_alg=search_alg) Copy validate_warmstart​ def validate_warmstart(parameter_names: List[str], points_to_evaluate: List[Union[List, Dict]], evaluated_rewards: List, validate_point_name_lengths: bool = True) Copy Generic validation of a Searcher's warm start functionality. Raises exceptions in case of type and length mismatches between parameters. If validate_point_name_lengths is False, the equality of lengths between points_to_evaluate and parameter_names will not be validated.","s":"ConcurrencyLimiter Objects","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"#concurrencylimiter-objects","p":525},{"i":532,"t":"class OptunaSearch(Searcher) Copy A wrapper around Optuna to provide trial suggestions. Optuna <https://optuna.org/>_ is a hyperparameter optimization library. In contrast to other libraries, it employs define-by-run style hyperparameter definitions. This Searcher is a thin wrapper around Optuna's search algorithms. You can pass any Optuna sampler, which will be used to generate hyperparameter suggestions. Multi-objective optimization is supported. Arguments: space - Hyperparameter search space definition for Optuna's sampler. This can be either a dict with parameter names as keys and optuna.distributions as values, or a Callable - in which case, it should be a define-by-run function using optuna.trial to obtain the hyperparameter values. The function should return either a dict of constant values with names as keys, or None. For more information, see https://optuna.readthedocs.io\\ /en/stable/tutorial/10_key_features/002_configurations.html. Warning - No actual computation should take place in the define-by-run function. Instead, put the training logic inside the function or class trainable passed to tune.run. metric - The training result objective value attribute. If None but a mode was passed, the anonymous metric _metric will be used per default. Can be a list of metrics for multi-objective optimization. mode - One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. Can be a list of modes for multi-objective optimization (corresponding to metric). points_to_evaluate - Initial parameter suggestions to be run first. This is for when you already have some good parameters you want to run first to help the algorithm make better suggestions for future parameters. Needs to be a list of dicts containing the configurations. sampler - Optuna sampler used to draw hyperparameter configurations. Defaults to MOTPESampler for multi-objective optimization with Optuna<2.9.0, and TPESampler in every other case. Warning - Please note that with Optuna 2.10.0 and earlier default MOTPESampler/TPESampler suffer from performance issues when dealing with a large number of completed trials (approx. >100). This will manifest as a delay when suggesting new configurations. This is an Optuna issue and may be fixed in a future Optuna release. seed - Seed to initialize sampler with. This parameter is only used when sampler=None. In all other cases, the sampler you pass should be initialized with the seed already. evaluated_rewards - If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same length as points_to_evaluate. Warning - When using evaluated_rewards, the search space space must be provided as a dict with parameter names as keys and optuna.distributions instances as values. The define-by-run search space definition is not yet supported with this functionality. Tune automatically converts search spaces to Optuna's format: from ray.tune.suggest.optuna import OptunaSearchconfig = { Copy \"a\" - tune.uniform(6, 8) \"b\" - tune.loguniform(1e-4, 1e-2) } optuna_search = OptunaSearch( metric=\"loss\", mode=\"min\") tune.run(trainable, config=config, search_alg=optuna_search) If you would like to pass the search space manually, the code wouldlook like this:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Equivalent Optuna define-by-run function approach: def define_search_space(trial: optuna.Trial): trial.suggest_float(\"a\", 6, 8) trial.suggest_float(\"b\", 1e-4, 1e-2, log=True) training logic goes into trainable, this is just for search space definition optuna_search = OptunaSearch( define_search_space, metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Multi-objective optimization is supported:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } Note you have to specify metric and mode here instead of in tune.run optuna_search = OptunaSearch( space, metric=[\"loss1\", \"loss2\"], mode=[\"min\", \"max\"]) Do not specify metric and mode here! tune.run( trainable, search_alg=optuna_search ) You can pass configs that will be evaluated first using``points_to_evaluate``:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, points_to_evaluate=[{\"a\" - 6.5, \"b\": 5e-4}, {\"a\": 7.5, \"b\": 1e-3}] metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Avoid re-running evaluated trials by passing the rewards together with`points_to_evaluate`:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, points_to_evaluate=[{\"a\" - 6.5, \"b\": 5e-4}, {\"a\": 7.5, \"b\": 1e-3}] evaluated_rewards=[0.89, 0.42] metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Copy","s":"OptunaSearch Objects","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"#optunasearch-objects","p":525},{"i":540,"t":"On this page","s":"tune.space","u":"/FLAML/docs/reference/tune/space","h":"","p":539},{"i":542,"t":"On this page","s":"tune.trial","u":"/FLAML/docs/reference/tune/trial","h":"","p":541},{"i":544,"t":"class Trial() Copy A trial object holds the state for one model training run. Trials are themselves managed by the TrialRunner class, which implements the event loop for submitting trial runs to a Ray cluster. Trials start in the PENDING state, and transition to RUNNING once started. On error it transitions to ERROR, otherwise TERMINATED on success. Attributes: trainable_name str - Name of the trainable object to be executed. config dict - Provided configuration dictionary with evaluated params. trial_id str - Unique identifier for the trial. local_dir str - Local_dir as passed to tune.run. logdir str - Directory where the trial logs are saved. evaluated_params dict - Evaluated parameters by search algorithm, experiment_tag str - Identifying trial name to show in the console. resources Resources - Amount of resources that this trial will use. status str - One of PENDING, RUNNING, PAUSED, TERMINATED, ERROR/ error_file str - Path to the errors that this trial has raised. set_status​ def set_status(status) Copy Sets the status of the trial.","s":"Trial Objects","u":"/FLAML/docs/reference/tune/trial","h":"#trial-objects","p":541},{"i":546,"t":"On this page","s":"tune.searcher.variant_generator","u":"/FLAML/docs/reference/tune/searcher/variant_generator","h":"","p":545},{"i":548,"t":"class TuneError(Exception) Copy General error class raised by ray.tune. generate_variants​ def generate_variants(unresolved_spec: Dict, constant_grid_search: bool = False, random_state: \"RandomState\" = None) -> Generator[Tuple[Dict, Dict], None, None] Copy Generates variants from a spec (dict) with unresolved values. There are two types of unresolved values: Grid search: These define a grid search over values. For example, the following grid search values in a spec will produce six distinct variants in combination: \"activation\": grid_search([\"relu\", \"tanh\"]) \"learning_rate\": grid_search([1e-3, 1e-4, 1e-5]) Lambda functions: These are evaluated to produce a concrete value, and can express dependencies or conditional distributions between values. They can also be used to express random search (e.g., by calling into the random or np module). \"cpu\": lambda spec: spec.config.num_workers \"batch_size\": lambda spec: random.uniform(1, 1000) Finally, to support defining specs in plain JSON / YAML, grid search and lambda functions can also be defined alternatively as follows: \"activation\": {\"grid_search\": [\"relu\", \"tanh\"]} \"cpu\": {\"eval\": \"spec.config.num_workers\"} Use format_vars to format the returned dict of hyperparameters. Yields: (Dict of resolved variables, Spec object) grid_search​ def grid_search(values: List) -> Dict[str, List] Copy Convenience method for specifying grid search over a value. Arguments: values - An iterable whose parameters will be gridded.","s":"TuneError Objects","u":"/FLAML/docs/reference/tune/searcher/variant_generator","h":"#tuneerror-objects","p":545},{"i":550,"t":"On this page","s":"tune.spark.utils","u":"/FLAML/docs/reference/tune/spark/utils","h":"","p":549},{"i":552,"t":"class PySparkOvertimeMonitor() Copy A context manager class to monitor if the PySpark job is overtime. Example: with PySparkOvertimeMonitor(time_start, time_budget_s, force_cancel, parallel=parallel): results = parallel( delayed(evaluation_function)(trial_to_run.config) for trial_to_run in trials_to_run ) Copy __init__​ def __init__(start_time, time_budget_s, force_cancel=False, cancel_func=None, parallel=None, sc=None) Copy Constructor. Specify the time budget and start time of the PySpark job, and specify how to cancel them. Arguments: Args relate to monitoring: start_time - float | The start time of the PySpark job. time_budget_s - float | The time budget of the PySpark job in seconds. force_cancel - boolean, default=False | Whether to forcely cancel the PySpark job if overtime. Args relate to how to cancel the PySpark job: (Only one of the following args will work. Priorities from top to bottom) cancel_func - function | A function to cancel the PySpark job. parallel - joblib.parallel.Parallel | Specify this if using joblib_spark as a parallel backend. It will call parallel._backend.terminate() to cancel the jobs. sc - pyspark.SparkContext object | You can pass a specific SparkContext. If all three args is None, the monitor will call pyspark.SparkContext.getOrCreate().cancelAllJobs() to cancel the jobs. __enter__​ def __enter__() Copy Enter the context manager. This will start a monitor thread if spark is available and force_cancel is True. __exit__​ def __exit__(exc_type, exc_value, exc_traceback) Copy Exit the context manager. This will wait for the monitor thread to nicely exit.","s":"PySparkOvertimeMonitor Objects","u":"/FLAML/docs/reference/tune/spark/utils","h":"#pysparkovertimemonitor-objects","p":549},{"i":554,"t":"On this page","s":"tune.tune","u":"/FLAML/docs/reference/tune/tune","h":"","p":553},{"i":556,"t":"class ExperimentAnalysis(EA) Copy Class for storing the experiment results. report​ def report(_metric=None, **kwargs) Copy A function called by the HPO application to report final or intermediate results. Example: import timefrom flaml import tunedef compute_with_config(config): current_time = time.time() metric2minimize = (round(config['x'])-95000)**2 time2eval = time.time() - current_time tune.report(metric2minimize=metric2minimize, time2eval=time2eval)analysis = tune.run( compute_with_config, config={ 'x': tune.lograndint(lower=1, upper=1000000), 'y': tune.randint(lower=1, upper=1000000) }, metric='metric2minimize', mode='min', num_samples=1000000, time_budget_s=60, use_ray=False)print(analysis.trials[-1].last_result) Copy Arguments: _metric - Optional default anonymous metric for tune.report(value). (For compatibility with ray.tune.report) **kwargs - Any key value pair to be reported. Raises: StopIteration (when not using ray, i.e., _use_ray=False): A StopIteration exception is raised if the trial has been signaled to stop. SystemExit (when using ray): A SystemExit exception is raised if the trial has been signaled to stop by ray. run​ def run(evaluation_function, config: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, metric: Optional[str] = None, mode: Optional[str] = None, time_budget_s: Union[int, float] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, scheduler=None, search_alg=None, verbose: Optional[int] = 2, local_dir: Optional[str] = None, num_samples: Optional[int] = 1, resources_per_trial: Optional[dict] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, max_failure: Optional[int] = 100, use_ray: Optional[bool] = False, use_spark: Optional[bool] = False, use_incumbent_result_in_evaluation: Optional[bool] = None, log_file_name: Optional[str] = None, lexico_objectives: Optional[dict] = None, force_cancel: Optional[bool] = False, n_concurrent_trials: Optional[int] = 0, **ray_args, ,) Copy The function-based way of performing HPO. Example: import timefrom flaml import tunedef compute_with_config(config): current_time = time.time() metric2minimize = (round(config['x'])-95000)**2 time2eval = time.time() - current_time tune.report(metric2minimize=metric2minimize, time2eval=time2eval) # if the evaluation fails unexpectedly and the exception is caught, # and it doesn't inform the goodness of the config, # return {} # if the failure indicates a config is bad, # report a bad metric value like np.inf or -np.inf # depending on metric mode being min or maxanalysis = tune.run( compute_with_config, config={ 'x': tune.lograndint(lower=1, upper=1000000), 'y': tune.randint(lower=1, upper=1000000) }, metric='metric2minimize', mode='min', num_samples=-1, time_budget_s=60, use_ray=False)print(analysis.trials[-1].last_result) Copy Arguments: evaluation_function - A user-defined evaluation function. It takes a configuration as input, outputs a evaluation result (can be a numerical value or a dictionary of string and numerical value pairs) for the input configuration. For machine learning tasks, it usually involves training and scoring a machine learning model, e.g., through validation loss. config - A dictionary to specify the search space. low_cost_partial_config - A dictionary from a subset of controlled dimensions to the initial low-cost values. e.g., {'n_estimators': 4, 'max_leaves': 4} cat_hp_cost - A dictionary from a subset of categorical dimensions to the relative cost of each choice. e.g., {'tree_method': [1, 1, 2]} i.e., the relative cost of the three choices of 'tree_method' is 1, 1 and 2 respectively metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. time_budget_s - int or float | The time budget in seconds. points_to_evaluate - A list of initial hyperparameter configurations to run first. evaluated_rewards list - If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same or shorter length than points_to_evaluate. e.g., points_to_evaluate = [ {\"b\": .99, \"cost_related\": {\"a\": 3}}, {\"b\": .99, \"cost_related\": {\"a\": 2}},]evaluated_rewards = [3.0] Copy means that you know the reward for the first config in points_to_evaluate is 3.0 and want to inform run(). resource_attr - A string to specify the resource dimension used by the scheduler via \"scheduler\". min_resource - A float of the minimal resource to use for the resource_attr. max_resource - A float of the maximal resource to use for the resource_attr. reduction_factor - A float of the reduction factor used for incremental pruning. scheduler - A scheduler for executing the experiment. Can be None, 'flaml', 'asha' (or 'async_hyperband', 'asynchyperband') or a custom instance of the TrialScheduler class. Default is None: in this case when resource_attr is provided, the 'flaml' scheduler will be used, otherwise no scheduler will be used. When set 'flaml', an authentic scheduler implemented in FLAML will be used. It does not require users to report intermediate results in evaluation_function. Find more details about this scheduler in this paper https://arxiv.org/pdf/1911.04706.pdf). When set 'asha', the input for arguments \"resource_attr\", \"min_resource\", \"max_resource\" and \"reduction_factor\" will be passed to ASHA's \"time_attr\", \"max_t\", \"grace_period\" and \"reduction_factor\" respectively. You can also provide a self-defined scheduler instance of the TrialScheduler class. When 'asha' or self-defined scheduler is used, you usually need to report intermediate results in the evaluation function via 'tune.report()'. If you would like to do some cleanup opearation when the trial is stopped by the scheduler, you can catch the StopIteration (when not using ray) or SystemExit (when using ray) exception explicitly, as shown in the following example. Please find more examples using different types of schedulers and how to set up the corresponding evaluation functions in test/tune/test_scheduler.py, and test/tune/example_scheduler.py. def easy_objective(config): width, height = config[\"width\"], config[\"height\"] for step in range(config[\"steps\"]): intermediate_score = evaluation_fn(step, width, height) try: tune.report(iterations=step, mean_loss=intermediate_score) except (StopIteration, SystemExit): # do cleanup operation here return Copy search_alg - An instance/string of the search algorithm to be used. The same instance can be used for iterative tuning. e.g., from flaml import BlendSearchalgo = BlendSearch(metric='val_loss', mode='min', space=search_space, low_cost_partial_config=low_cost_partial_config)for i in range(10): analysis = tune.run(compute_with_config, search_alg=algo, use_ray=False) print(analysis.trials[-1].last_result) Copy verbose - 0, 1, 2, or 3. If ray or spark backend is used, their verbosity will be affected by this argument. 0 = silent, 1 = only status updates, 2 = status and brief trial results, 3 = status and detailed trial results. Defaults to 2. local_dir - A string of the local dir to save ray logs if ray backend is used; or a local dir to save the tuning log. num_samples - An integer of the number of configs to try. Defaults to 1. resources_per_trial - A dictionary of the hardware resources to allocate per trial, e.g., {'cpu': 1}. It is only valid when using ray backend (by setting 'use_ray = True'). It shall be used when you need to do parallel tuning. config_constraints - A list of config constraints to be satisfied. e.g., config_constraints = [(mem_size, '<=', 1024**3)] mem_size is a function which produces a float number for the bytes needed for a config. It is used to skip configs which do not fit in memory. metric_constraints - A list of metric constraints to be satisfied. e.g., ['precision', '>=', 0.9]. The sign can be \">=\" or \"<=\". max_failure - int | the maximal consecutive number of failures to sample a trial before the tuning is terminated. use_ray - A boolean of whether to use ray as the backend. use_spark - A boolean of whether to use spark as the backend. log_file_name - A string of the log file name. Default to None. When set to None: if local_dir is not given, no log file is created; if local_dir is given, the log file name will be autogenerated under local_dir. Only valid when verbose > 0 or use_ray is True. lexico_objectives - dict, default=None | It specifics information needed to perform multi-objective optimization with lexicographic preferences. When lexico_objectives is not None, the arguments metric, mode, will be invalid, and flaml's tune uses CFO as the search_alg, which makes the input (if provided) `search_alg' invalid. This dictionary shall contain the following fields of key-value pairs: \"metrics\": a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. \"modes\" (optional): a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives. \"targets\" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. \"tolerances\" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. E.g., lexico_objectives = { \"metrics\": [\"error_rate\", \"pred_time\"], \"modes\": [\"min\", \"min\"], \"tolerances\": {\"error_rate\": 0.01, \"pred_time\": 0.0}, \"targets\": {\"error_rate\": 0.0},} Copy We also support percentage tolerance. E.g., lexico_objectives = { \"metrics\": [\"error_rate\", \"pred_time\"], \"modes\": [\"min\", \"min\"], \"tolerances\": {\"error_rate\": \"5%\", \"pred_time\": \"0%\"}, \"targets\": {\"error_rate\": 0.0},} Copy force_cancel - boolean, default=False | Whether to forcely cancel the PySpark job if overtime. n_concurrent_trials - int, default=0 | The number of concurrent trials when perform hyperparameter tuning with Spark. Only valid when use_spark=True and spark is required: pip install flaml[spark]. Please check here for more details about installing Spark. When tune.run() is called from AutoML, it will be overwritten by the value of n_concurrent_trials in AutoML. When <= 0, the concurrent trials will be set to the number of executors. **ray_args - keyword arguments to pass to ray.tune.run(). Only valid when use_ray=True.","s":"ExperimentAnalysis Objects","u":"/FLAML/docs/reference/tune/tune","h":"#experimentanalysis-objects","p":553},{"i":558,"t":"class Tuner() Copy Tuner is the class-based way of launching hyperparameter tuning jobs compatible with Ray Tune 2. Arguments: trainable - A user-defined evaluation function. It takes a configuration as input, outputs a evaluation result (can be a numerical value or a dictionary of string and numerical value pairs) for the input configuration. For machine learning tasks, it usually involves training and scoring a machine learning model, e.g., through validation loss. param_space - Search space of the tuning job. One thing to note is that both preprocessor and dataset can be tuned here. tune_config - Tuning algorithm specific configs. Refer to ray.tune.tune_config.TuneConfig for more info. run_config - Runtime configuration that is specific to individual trials. If passed, this will overwrite the run config passed to the Trainer, if applicable. Refer to ray.air.config.RunConfig for more info. Usage pattern: .. code-block:: python from sklearn.datasets import load_breast_cancer from ray import tune from ray.data import from_pandas from ray.air.config import RunConfig, ScalingConfig from ray.train.xgboost import XGBoostTrainer from ray.tune.tuner import Tuner def get_dataset(): data_raw = load_breast_cancer(as_frame=True) dataset_df = data_raw[\"data\"] dataset_df[\"target\"] = data_raw[\"target\"] dataset = from_pandas(dataset_df) return dataset trainer = XGBoostTrainer( label_column=\"target\", params={}, datasets={\"train\" - get_dataset()}, ) param_space = { \"scaling_config\" - ScalingConfig( num_workers=tune.grid_search([2, 4]), resources_per_worker={ \"CPU\" - tune.grid_search([1, 2]), }, ), You can even grid search various datasets in Tune. \"datasets\": { \"train\": tune.grid_search( [ds1, ds2] ), }, \"params\" - { \"objective\" - \"binary:logistic\", \"tree_method\" - \"approx\", \"eval_metric\" - [\"logloss\", \"error\"], \"eta\" - tune.loguniform(1e-4, 1e-1), \"subsample\" - tune.uniform(0.5, 1.0), \"max_depth\" - tune.randint(1, 9), }, } tuner = Tuner(trainable=trainer, param_space=param_space, run_config=RunConfig(name=\"my_tune_run\")) analysis = tuner.fit() To retry a failed tune run, you can then do .. code-block:: python tuner = Tuner.restore(experiment_checkpoint_dir) tuner.fit() experiment_checkpoint_dir can be easily located near the end of the console output of your first failed run.","s":"Tuner Objects","u":"/FLAML/docs/reference/tune/tune","h":"#tuner-objects","p":553},{"i":566,"t":"On this page","s":"tune.utils","u":"/FLAML/docs/reference/tune/utils","h":"","p":565},{"i":568,"t":"Research For technical details, please check our research publications. FLAML: A Fast and Lightweight AutoML Library. Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021. @inproceedings{wang2021flaml, title={FLAML: A Fast and Lightweight AutoML Library}, author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu}, year={2021}, booktitle={MLSys},} Copy Frugal Optimization for Cost-related Hyperparameters. Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021. @inproceedings{wu2021cfo, title={Frugal Optimization for Cost-related Hyperparameters}, author={Qingyun Wu and Chi Wang and Silu Huang}, year={2021}, booktitle={AAAI},} Copy Economical Hyperparameter Optimization With Blended Search Strategy. Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021. @inproceedings{wang2021blendsearch, title={Economical Hyperparameter Optimization With Blended Search Strategy}, author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied}, year={2021}, booktitle={ICLR},} Copy An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models. Susan Xueqing Liu, Chi Wang. ACL 2021. @inproceedings{liuwang2021hpolm, title={An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models}, author={Susan Xueqing Liu and Chi Wang}, year={2021}, booktitle={ACL},} Copy ChaCha for Online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021. @inproceedings{wu2021chacha, title={ChaCha for Online AutoML}, author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi}, year={2021}, booktitle={ICML},} Copy Fair AutoML. Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021). @inproceedings{wuwang2021fairautoml, title={Fair AutoML}, author={Qingyun Wu and Chi Wang}, year={2021}, booktitle={ArXiv preprint arXiv:2111.06495},} Copy Mining Robust Default Configurations for Resource-constrained AutoML. Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022). @inproceedings{kayaliwang2022default, title={Mining Robust Default Configurations for Resource-constrained AutoML}, author={Moe Kayali and Chi Wang}, year={2022}, booktitle={ArXiv preprint arXiv:2202.09927},} Copy Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives. Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%). @inproceedings{zhang2023targeted, title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives}, author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu}, booktitle={International Conference on Learning Representations}, year={2023}, url={https://openreview.net/forum?id=0Ij9_q567Ma},} Copy Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference. Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023). @inproceedings{wang2023EcoOptiGen, title={Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference}, author={Chi Wang and Susan Xueqing Liu and Ahmed H. Awadallah}, year={2023}, booktitle={ArXiv preprint arXiv:2303.04673},} Copy An Empirical Study on Challenging Math Problem Solving with GPT-4. Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023). @inproceedings{wu2023empirical, title={An Empirical Study on Challenging Math Problem Solving with GPT-4}, author={Yiran Wu and Feiran Jia and Shaokun Zhang and Hangyu Li and Erkang Zhu and Yue Wang and Yin Tat Lee and Richard Peng and Qingyun Wu and Chi Wang}, year={2023}, booktitle={ArXiv preprint arXiv:2306.01337},} Copy","s":"Research","u":"/FLAML/docs/Research","h":"","p":567},{"i":570,"t":"AutoGen for Large Language Models Please refer to https://microsoft.github.io/autogen/.","s":"AutoGen for Large Language Models","u":"/FLAML/docs/Use-Cases/Autogen","h":"","p":569},{"i":572,"t":"On this page","s":"Task Oriented AutoML","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"","p":571},{"i":574,"t":"flaml.AutoML is a class for task-oriented AutoML. It can be used as a scikit-learn style estimator with the standard fit and predict functions. The minimal inputs from users are the training data and the task type. Training data: numpy array. When the input data are stored in numpy array, they are passed to fit() as X_train and y_train. pandas dataframe. When the input data are stored in pandas dataframe, they are passed to fit() either as X_train and y_train, or as dataframe and label. Tasks (specified via task): 'classification': classification with tabular data. 'regression': regression with tabular data. 'ts_forecast': time series forecasting. 'ts_forecast_classification': time series forecasting for classification. 'ts_forecast_panel': time series forecasting for panel datasets (multiple time series). 'rank': learning to rank. 'seq-classification': sequence classification. 'seq-regression': sequence regression. 'summarization': text summarization. 'token-classification': token classification. 'multichoice-classification': multichoice classification. Two optional inputs are time_budget and max_iter for searching models and hyperparameters. When both are unspecified, only one model per estimator will be trained (using our zero-shot technique). When time_budget is provided, there can be randomness in the result due to runtime variance. A typical way to use flaml.AutoML: # Prepare training data# ...from flaml import AutoMLautoml = AutoML()automl.fit(X_train, y_train, task=\"regression\", time_budget=60, **other_settings)# Save the modelwith open(\"automl.pkl\", \"wb\") as f: pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)# At prediction timewith open(\"automl.pkl\", \"rb\") as f: automl = pickle.load(f)pred = automl.predict(X_test) Copy If users provide the minimal inputs only, AutoML uses the default settings for optimization metric, estimator list etc.","s":"Overview","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#overview","p":571},{"i":577,"t":"The optimization metric is specified via the metric argument. It can be either a string which refers to a built-in metric, or a user-defined function. Built-in metric. 'accuracy': 1 - accuracy as the corresponding metric to minimize. 'log_loss': default metric for multiclass classification. 'r2': 1 - r2_score as the corresponding metric to minimize. Default metric for regression. 'rmse': root mean squared error. 'mse': mean squared error. 'mae': mean absolute error. 'mape': mean absolute percentage error. 'roc_auc': minimize 1 - roc_auc_score. Default metric for binary classification. 'roc_auc_ovr': minimize 1 - roc_auc_score with multi_class=\"ovr\". 'roc_auc_ovo': minimize 1 - roc_auc_score with multi_class=\"ovo\". 'roc_auc_weighted': minimize 1 - roc_auc_score with average=\"weighted\". 'roc_auc_ovr_weighted': minimize 1 - roc_auc_score with multi_class=\"ovr\" and average=\"weighted\". 'roc_auc_ovo_weighted': minimize 1 - roc_auc_score with multi_class=\"ovo\" and average=\"weighted\". 'f1': minimize 1 - f1_score. 'micro_f1': minimize 1 - f1_score with average=\"micro\". 'macro_f1': minimize 1 - f1_score with average=\"macro\". 'ap': minimize 1 - average_precision_score. 'ndcg': minimize 1 - ndcg_score. 'ndcg@k': minimize 1 - ndcg_score@k. k is an integer. User-defined function. A customized metric function that requires the following (input) signature, and returns the input config’s value in terms of the metric you want to minimize, and a dictionary of auxiliary information at your choice: def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, config=None, groups_val=None, groups_train=None,): return metric_to_minimize, metrics_to_log Copy For example, def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args,): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { \"val_loss\": val_loss, \"train_loss\": train_loss, \"pred_time\": pred_time, } Copy It returns the validation loss penalized by the gap between validation and training loss as the metric to minimize, and three metrics to log: val_loss, train_loss and pred_time. The arguments config, groups_val and groups_train are not used in the function.","s":"Optimization metric","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#optimization-metric","p":571},{"i":579,"t":"The estimator list can contain one or more estimator names, each corresponding to a built-in estimator or a custom estimator. Each estimator has a search space for hyperparameter configurations. FLAML supports both classical machine learning models and deep neural networks. Estimator​ Built-in estimator. 'lgbm': LGBMEstimator for task \"classification\", \"regression\", \"rank\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, num_leaves, min_child_samples, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), colsample_bytree, reg_alpha, reg_lambda. 'xgboost': XGBoostSkLearnEstimator for task \"classification\", \"regression\", \"rank\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_leaves, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda. 'xgb_limitdepth': XGBoostLimitDepthEstimator for task \"classification\", \"regression\", \"rank\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_depth, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda. 'rf': RandomForestEstimator for task \"classification\", \"regression\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only). Starting from v1.1.0, it uses a fixed random_state by default. 'extra_tree': ExtraTreesEstimator for task \"classification\", \"regression\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only). Starting from v1.1.0, it uses a fixed random_state by default. 'histgb': HistGradientBoostingEstimator for task \"classification\", \"regression\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_leaves, min_samples_leaf, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), l2_regularization. It uses a fixed random_state by default. 'lrl1': LRL1Classifier (sklearn.LogisticRegression with L1 regularization) for task \"classification\". Hyperparameters: C. 'lrl2': LRL2Classifier (sklearn.LogisticRegression with L2 regularization) for task \"classification\". Hyperparameters: C. 'catboost': CatBoostEstimator for task \"classification\" and \"regression\". Hyperparameters: early_stopping_rounds, learning_rate, n_estimators. 'kneighbor': KNeighborsEstimator for task \"classification\" and \"regression\". Hyperparameters: n_neighbors. 'prophet': Prophet for task \"ts_forecast\". Hyperparameters: changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode. 'arima': ARIMA for task \"ts_forecast\". Hyperparameters: p, d, q. 'sarimax': SARIMAX for task \"ts_forecast\". Hyperparameters: p, d, q, P, D, Q, s. 'holt-winters': Holt-Winters (triple exponential smoothing) model for task \"ts_forecast\". Hyperparameters: seasonal_perdiods, seasonal, use_boxcox, trend, damped_trend. 'transformer': Huggingface transformer models for task \"seq-classification\", \"seq-regression\", \"multichoice-classification\", \"token-classification\" and \"summarization\". Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed. 'temporal_fusion_transformer': TemporalFusionTransformerEstimator for task \"ts_forecast_panel\". Hyperparameters: gradient_clip_val, hidden_size, hidden_continuous_size, attention_head_size, dropout, learning_rate. There is a known issue with pytorch-forecast logging. Custom estimator. Use custom estimator for: tuning an estimator that is not built-in; customizing search space for a built-in estimator. Guidelines on tuning a custom estimator​ To tune a custom estimator that is not built-in, you need to: Build a custom estimator by inheritting flaml.automl.model.BaseEstimator or a derived class. For example, if you have a estimator class with scikit-learn style fit() and predict() functions, you only need to set self.estimator_class to be that class in your constructor. from flaml.automl.model import SKLearnEstimator# SKLearnEstimator is derived from BaseEstimatorimport rgfclass MyRegularizedGreedyForest(SKLearnEstimator): def __init__(self, task=\"binary\", **config): super().__init__(task, **config) if task in CLASSIFICATION: from rgf.sklearn import RGFClassifier self.estimator_class = RGFClassifier else: from rgf.sklearn import RGFRegressor self.estimator_class = RGFRegressor @classmethod def search_space(cls, data_size, task): space = { \"max_leaf\": { \"domain\": tune.lograndint(lower=4, upper=data_size), \"low_cost_init_value\": 4, }, \"n_iter\": { \"domain\": tune.lograndint(lower=1, upper=data_size), \"low_cost_init_value\": 1, }, \"learning_rate\": {\"domain\": tune.loguniform(lower=0.01, upper=20.0)}, \"min_samples_leaf\": { \"domain\": tune.lograndint(lower=1, upper=20), \"init_value\": 20, }, } return space Copy In the constructor, we set self.estimator_class as RGFClassifier or RGFRegressor according to the task type. If the estimator you want to tune does not have a scikit-learn style fit() and predict() API, you can override the fit() and predict() function of flaml.automl.model.BaseEstimator, like XGBoostEstimator. Importantly, we also add the task=\"binary\" parameter in the signature of __init__ so that it doesn't get grouped together with the **config kwargs that determines the parameters with which the underlying estimator (self.estimator_class) is constructed. If your estimator doesn't use one of the parameters that it is passed, for example some regressors in scikit-learn don't use the n_jobs parameter, it is enough to add n_jobs=None to the signature so that it is ignored by the **config dict. Give the custom estimator a name and add it in AutoML. E.g., from flaml import AutoMLautoml = AutoML()automl.add_learner(\"rgf\", MyRegularizedGreedyForest) Copy This registers the MyRegularizedGreedyForest class in AutoML, with the name \"rgf\". Tune the newly added custom estimator in either of the following two ways depending on your needs: tune rgf alone: automl.fit(..., estimator_list=[\"rgf\"]); or mix it with other built-in learners: automl.fit(..., estimator_list=[\"rgf\", \"lgbm\", \"xgboost\", \"rf\"]). Search space​ Each estimator class, built-in or not, must have a search_space function. In the search_space function, we return a dictionary about the hyperparameters, the keys of which are the names of the hyperparameters to tune, and each value is a set of detailed search configurations about the corresponding hyperparameters represented in a dictionary. A search configuration dictionary includes the following fields: domain, which specifies the possible values of the hyperparameter and their distribution. Please refer to more details about the search space domain. init_value (optional), which specifies the initial value of the hyperparameter. low_cost_init_value(optional), which specifies the value of the hyperparameter that is associated with low computation cost. See cost related hyperparameters or FAQ for more details. In the example above, we tune four hyperparameters, three integers and one float. They all follow a log-uniform distribution. \"max_leaf\" and \"n_iter\" have \"low_cost_init_value\" specified as their values heavily influence the training cost. To customize the search space for a built-in estimator, use a similar approach to define a class that inherits the existing estimator. For example, from flaml.automl.model import XGBoostEstimatordef logregobj(preds, dtrain): labels = dtrain.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight grad = preds - labels hess = preds * (1.0 - preds) return grad, hessclass MyXGB1(XGBoostEstimator): \"\"\"XGBoostEstimator with logregobj as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=logregobj, **config) Copy We override the constructor and set the training objective as a custom function logregobj. The hyperparameters and their search range do not change. For another example, class XGBoost2D(XGBoostSklearnEstimator): @classmethod def search_space(cls, data_size, task): upper = min(32768, int(data_size)) return { \"n_estimators\": { \"domain\": tune.lograndint(lower=4, upper=upper), \"low_cost_init_value\": 4, }, \"max_leaves\": { \"domain\": tune.lograndint(lower=4, upper=upper), \"low_cost_init_value\": 4, }, } Copy We override the search_space function to tune two hyperparameters only, \"n_estimators\" and \"max_leaves\". They are both random integers in the log space, ranging from 4 to data-dependent upper bound. The lower bound for each corresponds to low training cost, hence the \"low_cost_init_value\" for each is set to 4. A shortcut to override the search space​ One can use the custom_hp argument in AutoML.fit() to override the search space for an existing estimator quickly. For example, if you would like to temporarily change the search range of \"n_estimators\" of xgboost, disable searching \"max_leaves\" in random forest, and add \"subsample\" in the search space of lightgbm, you can set: custom_hp = { \"xgboost\": { \"n_estimators\": { \"domain\": tune.lograndint(lower=new_lower, upper=new_upper), \"low_cost_init_value\": new_lower, }, }, \"rf\": { \"max_leaves\": { \"domain\": None, # disable search }, }, \"lgbm\": { \"subsample\": { \"domain\": tune.uniform(lower=0.1, upper=1.0), \"init_value\": 1.0, }, \"subsample_freq\": { \"domain\": 1, # subsample_freq must > 0 to enable subsample }, },} Copy","s":"Estimator and search space","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#estimator-and-search-space","p":571},{"i":581,"t":"There are several types of constraints you can impose. Constraints on the AutoML process. time_budget: constrains the wall-clock time (seconds) used by the AutoML process. We provide some tips on how to set time budget. max_iter: constrains the maximal number of models to try in the AutoML process. Constraints on the constructor arguments of the estimators. Some constraints on the estimator can be implemented via the custom learner. For example, class MonotonicXGBoostEstimator(XGBoostSklearnEstimator): @classmethod def search_space(**args): space = super().search_space(**args) space.update({\"monotone_constraints\": {\"domain\": \"(1, -1)\"}}) return space Copy It adds a monotonicity constraint to XGBoost. This approach can be used to set any constraint that is an argument in the underlying estimator's constructor. A shortcut to do this is to use the custom_hp argument: custom_hp = { \"xgboost\": { \"monotone_constraints\": {\"domain\": \"(1, -1)\"} # fix the domain as a constant }} Copy Constraints on the models tried in AutoML. Users can set constraints such as the maximal number of models to try, limit on training time and prediction time per model. train_time_limit: training time in seconds. pred_time_limit: prediction time per instance in seconds. For example, automl.fit(X_train, y_train, max_iter=100, train_time_limit=1, pred_time_limit=1e-3) Copy Constraints on the metrics of the ML model tried in AutoML. When users provide a custom metric function, which returns a primary optimization metric and a dictionary of additional metrics (typically also about the model) to log, users can also specify constraints on one or more of the metrics in the dictionary of additional metrics. Users need to provide a list of such constraints in the following format: Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from \">=\" and \"\\<=\", and the third element is the constraint value. E.g., ('val_loss', '<=', 0.1). For example, metric_constraints = [(\"train_loss\", \"<=\", 0.1), (\"val_loss\", \"<=\", 0.1)]automl.fit( X_train, y_train, max_iter=100, train_time_limit=1, metric_constraints=metric_constraints,) Copy","s":"Constraint","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#constraint","p":571},{"i":583,"t":"To use stacked ensemble after the model search, set ensemble=True or a dict. When ensemble=True, the final estimator and passthrough in the stacker will be automatically chosen. You can specify customized final estimator or passthrough option: \"final_estimator\": an instance of the final estimator in the stacker. \"passthrough\": True (default) or False, whether to pass the original features to the stacker. For example, automl.fit( X_train, y_train, task=\"classification\", \"ensemble\": { \"final_estimator\": LogisticRegression(), \"passthrough\": False, },) Copy","s":"Ensemble","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#ensemble","p":571},{"i":585,"t":"By default, flaml decides the resampling automatically according to the data size and the time budget. If you would like to enforce a certain resampling strategy, you can set eval_method to be \"holdout\" or \"cv\" for holdout or cross-validation. For holdout, you can also set: split_ratio: the fraction for validation data, 0.1 by default. X_val, y_val: a separate validation dataset. When they are passed, the validation metrics will be computed against this given validation dataset. If they are not passed, then a validation dataset will be split from the training data and held out from training during the model search. After the model search, flaml will retrain the model with best configuration on the full training data. You can setretrain_full to be False to skip the final retraining or \"budget\" to ask flaml to do its best to retrain within the time budget. For cross validation, you can also set n_splits of the number of folds. By default it is 5. Data split method​ flaml relies on the provided task type to infer the default splitting strategy: stratified split for classification; uniform split for regression; time-based split for time series forecasting; group-based split for learning to rank. The data split method for classification can be changed into uniform split by setting split_type=\"uniform\". The data are shuffled when split_type in (\"uniform\", \"stratified\"). For both classification and regression tasks more advanced split configurations are possible: time-based split can be enforced if the data are sorted by timestamps, by setting split_type=\"time\", group-based splits can be set by using split_type=\"group\" while providing the group identifier for each sample through the groups argument. This is also shown in an example notebook. More in general, split_type can also be set as a custom splitter object, when eval_method=\"cv\". It needs to be an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. To disable shuffling, the splitter instance must contain the attribute shuffle=False.","s":"Resampling strategy","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#resampling-strategy","p":571},{"i":587,"t":"When you have parallel resources, you can either spend them in training and keep the model search sequential, or perform parallel search. Following scikit-learn, the parameter n_jobs specifies how many CPU cores to use for each training job. The number of parallel trials is specified via the parameter n_concurrent_trials. By default, n_jobs=-1, n_concurrent_trials=1. That is, all the CPU cores (in a single compute node) are used for training a single model and the search is sequential. When you have more resources than what each single training job needs, you can consider increasing n_concurrent_trials. FLAML now support two backends for parallel tuning, i.e., Ray and Spark. You can use either of them, but not both for one tuning job. Parallel tuning with Ray​ To do parallel tuning with Ray, install the ray and blendsearch options: pip install flaml[ray,blendsearch] Copy ray is used to manage the resources. For example, ray.init(num_cpus=16) Copy allocates 16 CPU cores. Then, when you run: automl.fit(X_train, y_train, n_jobs=4, n_concurrent_trials=4) Copy flaml will perform 4 trials in parallel, each consuming 4 CPU cores. The parallel tuning uses the BlendSearch algorithm. Parallel tuning with Spark​ To do parallel tuning with Spark, install the spark and blendsearch options: Spark support is added in v1.1.0 pip install flaml[spark,blendsearch]>=1.1.0 Copy For more details about installing Spark, please refer to Installation. An example of using Spark for parallel tuning is: automl.fit(X_train, y_train, n_concurrent_trials=4, use_spark=True) Copy Details about parallel tuning with Spark could be found here. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable FLAML_MAX_CONCURRENT to override the detected num_executors. The final number of concurrent trials will be the minimum of n_concurrent_trials and num_executors. Also, GPU training is not supported yet when use_spark is True. Guidelines on parallel vs sequential tuning​ (1) Considerations on wall-clock time. One common motivation for parallel tuning is to save wall-clock time. When sequential tuning and parallel tuning achieve a similar wall-clock time, sequential tuning should be preferred. This is a rule of thumb when the HPO algorithm is sequential by nature (e.g., Bayesian Optimization and FLAML's HPO algorithms CFO and BS). Sequential tuning allows the HPO algorithms to take advantage of the historical trial results. Then the question is How to estimate the wall-clock-time needed by parallel tuning and sequential tuning? You can use the following way to roughly estimate the wall-clock time in parallel tuning and sequential tuning: To finish NNN trials of hyperparameter tuning, i.e., run NNN hyperparameter configurations, the total wall-clock time needed is N/k\\*(SingleTrialTime+Overhead)N/k\\*(SingleTrialTime + Overhead)N/k\\*(SingleTrialTime+Overhead), in which SingleTrialTimeSingleTrialTimeSingleTrialTime is the trial time to evaluate a particular hyperparameter configuration, kkk is the scale of parallelism, e.g., the number of parallel CPU/GPU cores, and OverheadOverheadOverhead is the computation overhead. In sequential tuning, k=1k=1k=1, and in parallel tuning k>1k>1k>1. This may suggest that parallel tuning has a shorter wall-clock time. But it is not always the case considering the other two factors SingleTrialTimeSingleTrialTimeSingleTrialTime, and OverheadOverheadOverhead: The OverheadOverheadOverhead in sequential tuning is typically negligible; while in parallel tuning, it is relatively large. You can also try to reduce the SingleTrialTimeSingleTrialTimeSingleTrialTime to reduce the wall-clock time in sequential tuning: For example, by increasing the resource consumed by a single trial (distributed or multi-thread training), you can reduce SingleTrialTimeSingleTrialTimeSingleTrialTime. One concrete example is to use the n_jobs parameter that sets the number of threads the fitting process can use in many scikit-learn style algorithms. (2) Considerations on randomness. Potential reasons that cause randomness: Parallel tuning: In the case of parallel tuning, the order of trials' finishing time is no longer deterministic. This non-deterministic order, combined with sequential HPO algorithms, leads to a non-deterministic hyperparameter tuning trajectory. Distributed or multi-thread training: Distributed/multi-thread training may introduce randomness in model training, i.e., the trained model with the same hyperparameter may be different because of such randomness. This model-level randomness may be undesirable in some cases.","s":"Parallel tuning","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#parallel-tuning","p":571},{"i":589,"t":"We can warm start the AutoML by providing starting points of hyperparameter configurstions for each estimator. For example, if you have run AutoML for one hour, after checking the results, you would like to run it for another two hours, then you can use the best configurations found for each estimator as the starting points for the new run. automl1 = AutoML()automl1.fit(X_train, y_train, time_budget=3600)automl2 = AutoML()automl2.fit( X_train, y_train, time_budget=7200, starting_points=automl1.best_config_per_estimator,) Copy starting_points is a dictionary or a str to specify the starting hyperparameter config. (1) When it is a dictionary, the keys are the estimator names. If you do not need to specify starting points for an estimator, exclude its name from the dictionary. The value for each key can be either a dictionary of a list of dictionaries, corresponding to one hyperparameter configuration, or multiple hyperparameter configurations, respectively. (2) When it is a str: if \"data\", use data-dependent defaults; if \"data:path\", use data-dependent defaults which are stored at path; if \"static\", use data-independent defaults. Please find more details about data-dependent defaults in zero shot AutoML.","s":"Warm start","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#warm-start","p":571},{"i":591,"t":"The trials are logged in a file if a log_file_name is passed. Each trial is logged as a json record in one line. The best trial's id is logged in the last line. For example, {\"record_id\": 0, \"iter_per_learner\": 1, \"logged_metric\": null, \"trial_time\": 0.12717914581298828, \"wall_clock_time\": 0.1728971004486084, \"validation_loss\": 0.07333333333333332, \"config\": {\"n_estimators\": 4, \"num_leaves\": 4, \"min_child_samples\": 20, \"learning_rate\": 0.09999999999999995, \"log_max_bin\": 8, \"colsample_bytree\": 1.0, \"reg_alpha\": 0.0009765625, \"reg_lambda\": 1.0}, \"learner\": \"lgbm\", \"sample_size\": 150}{\"record_id\": 1, \"iter_per_learner\": 3, \"logged_metric\": null, \"trial_time\": 0.07027268409729004, \"wall_clock_time\": 0.3756711483001709, \"validation_loss\": 0.05333333333333332, \"config\": {\"n_estimators\": 4, \"num_leaves\": 4, \"min_child_samples\": 12, \"learning_rate\": 0.2677050123105203, \"log_max_bin\": 7, \"colsample_bytree\": 1.0, \"reg_alpha\": 0.001348364934537134, \"reg_lambda\": 1.4442580148221913}, \"learner\": \"lgbm\", \"sample_size\": 150}{\"curr_best_record_id\": 1} Copy iter_per_learner means how many models have been tried for each learner. The reason you see records like iter_per_learner=3 for record_id=1 is that flaml only logs better configs than the previous iters by default, i.e., log_type='better'. If you use log_type='all' instead, all the trials will be logged. trial_time means the time taken to train and evaluate one config in that trial. total_search_time is the total time spent from the beginning of fit(). flaml will adjust the n_estimators for lightgbm etc. according to the remaining budget and check the time budget constraint and stop in several places. Most of the time that makes fit() stops before the given budget. Occasionally it may run over the time budget slightly. But the log file always contains the best config info and you can recover the best model until any time point using retrain_from_log(). We can also use mlflow for logging: mlflow.set_experiment(\"flaml\")with mlflow.start_run(): automl.fit(X_train=X_train, y_train=y_train, **settings) Copy To disable mlflow logging pre-configured in FLAML, set mlflow_logging=False: automl = AutoML(mlflow_logging=False) Copy or automl.fit(X_train=X_train, y_train=y_train, mlflow_logging=False, **settings) Copy Setting mlflow_logging=False in the constructor will disable mlflow logging for all the fit() calls. Setting mlflow_logging=False in fit() will disable mlflow logging for that fit() call only.","s":"Log the trials","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#log-the-trials","p":571},{"i":593,"t":"Extra fit arguments that are needed by the estimators can be passed to AutoML.fit(). For example, if there is a weight associated with each training example, they can be passed via sample_weight. For another example, period can be passed for time series forecaster. For any extra keywork argument passed to AutoML.fit() which has not been explicitly listed in the function signature, it will be passed to the underlying estimators' fit() as is. For another example, you can set the number of gpus used by each trial with the gpu_per_trial argument, which is only used by TransformersEstimator and XGBoostSklearnEstimator. In addition, you can specify the different arguments needed by different estimators using the fit_kwargs_by_estimator argument. For example, you can set the custom arguments for a Transformers model: from flaml.automl.data import load_openml_datasetfrom flaml import AutoMLX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir=\"./\")automl = AutoML()automl_settings = { \"task\": \"classification\", \"time_budget\": 10, \"estimator_list\": [\"catboost\", \"rf\"], \"fit_kwargs_by_estimator\": { \"catboost\": { \"verbose\": True, # setting the verbosity of catboost to True } },}automl.fit(X_train=X_train, y_train=y_train, **automl_settings) Copy","s":"Extra fit arguments","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#extra-fit-arguments","p":571},{"i":596,"t":"The best model can be obtained by the model property of an AutoML instance. For example, automl.fit(X_train, y_train, task=\"regression\")print(automl.model)# <flaml.automl.model.LGBMEstimator object at 0x7f9b502c4550> Copy flaml.automl.model.LGBMEstimator is a wrapper class for LightGBM models. To access the underlying model, use the estimator property of the flaml.automl.model.LGBMEstimator instance. print(automl.model.estimator)\"\"\"LGBMRegressor(colsample_bytree=0.7610534336273627, learning_rate=0.41929025492645006, max_bin=255, min_child_samples=4, n_estimators=45, num_leaves=4, reg_alpha=0.0009765625, reg_lambda=0.009280655005879943, verbose=-1)\"\"\" Copy Just like a normal LightGBM model, we can inspect it. For example, we can plot the feature importance: import matplotlib.pyplot as pltplt.barh( automl.model.estimator.feature_name_, automl.model.estimator.feature_importances_) Copy","s":"Get best model","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#get-best-model","p":571},{"i":598,"t":"We can find the best estimator's name and best configuration by: print(automl.best_estimator)# lgbmprint(automl.best_config)# {'n_estimators': 148, 'num_leaves': 18, 'min_child_samples': 3, 'learning_rate': 0.17402065726724145, 'log_max_bin': 8, 'colsample_bytree': 0.6649148062238498, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0067613624509965} Copy We can also find the best configuration per estimator. print(automl.best_config_per_estimator)# {'lgbm': {'n_estimators': 148, 'num_leaves': 18, 'min_child_samples': 3, 'learning_rate': 0.17402065726724145, 'log_max_bin': 8, 'colsample_bytree': 0.6649148062238498, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0067613624509965}, 'rf': None, 'catboost': None, 'xgboost': {'n_estimators': 4, 'max_leaves': 4, 'min_child_weight': 1.8630223791106992, 'learning_rate': 1.0, 'subsample': 0.8513627344387318, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.946138073111236, 'reg_alpha': 0.0018311776973217073, 'reg_lambda': 0.27901659190538414}, 'extra_tree': {'n_estimators': 4, 'max_features': 1.0, 'max_leaves': 4}} Copy The None value corresponds to the estimators which have not been tried. Other useful information: print(automl.best_config_train_time)# 0.24841618537902832print(automl.best_iteration)# 10print(automl.best_loss)# 0.15448622217577546print(automl.time_to_find_best_model)# 0.4167296886444092print(automl.config_history)# {0: ('lgbm', {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0}, 1.2300517559051514)}# Meaning: at iteration 0, the config tried is {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0} for lgbm, and the wallclock time is 1.23s when this trial is finished. Copy","s":"Get best configuration","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#get-best-configuration","p":571},{"i":600,"t":"To plot how the loss is improved over time during the model search, first load the search history from the log file: from flaml.automl.data import get_output_from_logtime_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = get_output_from_log(filename=settings[\"log_file_name\"], time_budget=120) Copy Then, assuming the optimization metric is \"accuracy\", we can plot the accuracy versus wallclock time: import matplotlib.pyplot as pltimport numpy as npplt.title(\"Learning Curve\")plt.xlabel(\"Wall Clock Time (s)\")plt.ylabel(\"Validation Accuracy\")plt.step(time_history, 1 - np.array(best_valid_loss_history), where=\"post\")plt.show() Copy The curve suggests that increasing the time budget may further improve the accuracy.","s":"Plot learning curve","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#plot-learning-curve","p":571},{"i":602,"t":"If you have an exact constraint for the total search time, set it as the time budget. If you have flexible time constraints, for example, your desirable time budget is t1=60s, and the longest time budget you can tolerate is t2=3600s, you can try the following two ways: set t1 as the time budget, and check the message in the console log in the end. If the budget is too small, you will see a warning like WARNING - Time taken to find the best model is 91% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. set t2 as the time budget, and also set early_stop=True. If the early stopping is triggered, you will see a warning like WARNING - All estimator hyperparameters local search has converged at least once, and the total search time exceeds 10 times the time taken to find the best model. WARNING - Stopping search as early_stop is set to True.","s":"How to set time budget","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#how-to-set-time-budget","p":571},{"i":604,"t":"If you want to get a sense of how much time is needed to find the best model, you can use max_iter=2 to perform two trials first. The message will be like: INFO - iteration 0, current learner lgbm INFO - Estimated sufficient time budget=145194s. Estimated necessary time budget=2118s. INFO - at 2.6s, estimator lgbm's best error=0.4459, best estimator lgbm's best error=0.4459 You will see that the time to finish the first and cheapest trial is 2.6 seconds. The estimated necessary time budget is 2118 seconds, and the estimated sufficient time budget is 145194 seconds. Note that this is only an estimated range to help you decide your budget. When the time budget is set too low, it can happen that no estimator is trained at all within the budget. In this case, it is recommanded to use max_iter instead of time_budget. This ensures that you have enough time to train a model without worring about variance of the execution time for the code before starting a trainning.","s":"How much time is needed to find the best model","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#how-much-time-is-needed-to-find-the-best-model","p":571},{"i":606,"t":"On this page","s":"Zero Shot AutoML","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"","p":605},{"i":608,"t":"The easiest way to leverage this technique is to import a \"flamlized\" learner of your favorite choice and use it just as how you use the learner before. The automation is done behind the scene and you are not required to change your code. For example, if you are currently using: from lightgbm import LGBMRegressorestimator = LGBMRegressor()estimator.fit(X_train, y_train)estimator.predict(X_test) Copy Simply replace the first line with: from flaml.default import LGBMRegressor Copy All the other code remains the same. And you are expected to get a equal or better model in most cases. The current list of \"flamlized\" learners are: LGBMClassifier, LGBMRegressor. XGBClassifier, XGBRegressor. RandomForestClassifier, RandomForestRegressor. ExtraTreesClassifier, ExtraTreesRegressor.","s":"How to Use at Runtime","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#how-to-use-at-runtime","p":605},{"i":610,"t":"flaml.default.LGBMRegressor inherits lightgbm.LGBMRegressor, so all the APIs in lightgbm.LGBMRegressor are still valid in flaml.default.LGBMRegressor. The difference is, flaml.default.LGBMRegressor decides the hyperparameter configurations based on the training data. It would use a different configuration if it is predicted to outperform the original data-independent default. If you inspect the params of the fitted estimator, you can find what configuration is used. If the original default configuration is used, then it is equivalent to the original estimator. The recommendation of which configuration should be used is based on offline AutoML run results. Information about the training dataset, such as the size of the dataset will be used to recommend a data-dependent configuration. The recommendation is done instantly in negligible time. The training can be faster or slower than using the original default configuration depending on the recommended configuration. Note that there is no tuning involved. Only one model is trained.","s":"What's the magic behind the scene?","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#whats-the-magic-behind-the-scene","p":605},{"i":612,"t":"Yes. You can use suggest_hyperparams() to find the suggested configuration. For example, from flaml.default import LGBMRegressorestimator = LGBMRegressor()( hyperparams, estimator_name, X_transformed, y_transformed,) = estimator.suggest_hyperparams(X_train, y_train)print(hyperparams) Copy If you would like more control over the training, use an equivalent, open-box way for zero-shot AutoML. For example, from flaml.default import preprocess_and_suggest_hyperparamsX, y = load_iris(return_X_y=True, as_frame=True)X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)( hyperparams, estimator_class, X_transformed, y_transformed, feature_transformer, label_transformer,) = preprocess_and_suggest_hyperparams(\"classification\", X_train, y_train, \"lgbm\")model = estimator_class(**hyperparams) # estimator_class is lightgbm.LGBMClassifiermodel.fit(X_transformed, y_train) # LGBMClassifier can handle raw labelsX_test = feature_transformer.transform(X_test) # preprocess test datay_pred = model.predict(X_test) Copy Note that some classifiers like XGBClassifier require the labels to be integers, while others do not. So you can decide whether to use the transformed labels y_transformed and the label transformer label_transformer. Also, each estimator may require specific preprocessing of the data. X_transformed is the preprocessed data, and feature_transformer is the preprocessor. It needs to be applied to the test data before prediction. These are automated when you use the \"flamlized\" learner. When you use the open-box way, pay attention to them.","s":"Can I check the configuration before training?","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#can-i-check-the-configuration-before-training","p":605},{"i":614,"t":"Zero Shot AutoML is fast. If tuning from the recommended data-dependent configuration is required, you can use flaml.AutoML.fit() and set starting_points=\"data\". For example, from flaml import AutoMLautoml = AutoML()automl_settings = { \"task\": \"classification\", \"starting_points\": \"data\", \"estimator_list\": [\"lgbm\"], \"time_budget\": 600, \"max_iter\": 50,}automl.fit(X_train, y_train, **automl_settings) Copy Note that if you set max_iter=0 and time_budget=None, you are effectively using zero-shot AutoML. When estimator_list is omitted, the estimator together with its hyperparameter configuration will be decided in a zero-shot manner.","s":"Combine zero shot AutoML and hyperparameter tuning","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#combine-zero-shot-automl-and-hyperparameter-tuning","p":605},{"i":616,"t":"To use your own meta-learned defaults, specify the path containing the meta-learned defaults. For example, estimator = flaml.default.LGBMRegressor(default_location=\"location_for_defaults\") Copy Or, preprocess_and_suggest_hyperparams( \"classification\", X_train, y_train, \"lgbm\", location=\"location_for_defaults\") Copy Or, X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)automl = AutoML()automl_settings = { \"task\": \"classification\", \"log_file_name\": \"test/iris.log\", \"starting_points\": \"data:location_for_defaults\", \"estimator_list\": [\"lgbm\", \"xgb_limitdepth\", \"rf\"] \"max_iter\": 0,}automl.fit(X_train, y_train, **automl_settings) Copy Since this is a multiclass task, it will look for the following files under {location_for_defaults}/: all/multiclass.json. {learner_name}/multiclass.json for every learner_name in the estimator_list. Read the next section to understand how to generate these files if you would like to meta-learn the defaults yourself.","s":"Use your own meta-learned defaults","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#use-your-own-meta-learned-defaults","p":605},{"i":618,"t":"This section is intended for: AutoML providers for a particular domain. Data scientists or engineers who need to repeatedly train models for similar tasks with varying training data. Instead of running full hyperparameter tuning from scratch every time, one can leverage the tuning experiences in similar tasks before. While we have offered the meta-learned defaults from tuning experiences of several popular learners on benchmark datasets for classification and regression, you can customize the defaults for your own tasks/learners/metrics based on your own tuning experiences.","s":"How to Prepare Offline","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#how-to-prepare-offline","p":605},{"i":620,"t":"Collect a diverse set of training tasks. For each task, extract its meta feature and save in a .csv file. For example, test/default/all/metafeatures.csv: Dataset,NumberOfInstances,NumberOfFeatures,NumberOfClasses,PercentageOfNumericFeatures2dplanes,36691,10,0,1.0adult,43957,14,2,0.42857142857142855Airlines,485444,7,2,0.42857142857142855Albert,382716,78,2,0.3333333333333333Amazon_employee_access,29492,9,2,0.0bng_breastTumor,104976,9,0,0.1111111111111111bng_pbc,900000,18,0,0.5555555555555556car,1555,6,4,0.0connect-4,60801,42,3,0.0dilbert,9000,2000,5,1.0Dionis,374569,60,355,1.0poker,922509,10,0,1.0 Copy The first column is the dataset name, and the latter four are meta features.","s":"Prepare a collection of training tasks","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#prepare-a-collection-of-training-tasks","p":605},{"i":622,"t":"You can extract the best configurations for each task in your collection of training tasks by running flaml on each of them with a long enough budget. Save the best configuration in a .json file under {location_for_defaults}/{learner_name}/{task_name}.json. For example, X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)automl.fit(X_train, y_train, estimator_list=[\"lgbm\"], **settings)automl.save_best_config(\"test/default/lgbm/iris.json\") Copy","s":"Prepare the candidate configurations","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#prepare-the-candidate-configurations","p":605},{"i":624,"t":"Save the evaluation results in a .csv file. For example, save the evaluation results for lgbm under test/default/lgbm/results.csv: task,fold,type,result,params2dplanes,0,regression,0.946366,{'_modeljson': 'lgbm/2dplanes.json'}2dplanes,0,regression,0.907774,{'_modeljson': 'lgbm/adult.json'}2dplanes,0,regression,0.901643,{'_modeljson': 'lgbm/Airlines.json'}2dplanes,0,regression,0.915098,{'_modeljson': 'lgbm/Albert.json'}2dplanes,0,regression,0.302328,{'_modeljson': 'lgbm/Amazon_employee_access.json'}2dplanes,0,regression,0.94523,{'_modeljson': 'lgbm/bng_breastTumor.json'}2dplanes,0,regression,0.945698,{'_modeljson': 'lgbm/bng_pbc.json'}2dplanes,0,regression,0.946194,{'_modeljson': 'lgbm/car.json'}2dplanes,0,regression,0.945549,{'_modeljson': 'lgbm/connect-4.json'}2dplanes,0,regression,0.946232,{'_modeljson': 'lgbm/default.json'}2dplanes,0,regression,0.945594,{'_modeljson': 'lgbm/dilbert.json'}2dplanes,0,regression,0.836996,{'_modeljson': 'lgbm/Dionis.json'}2dplanes,0,regression,0.917152,{'_modeljson': 'lgbm/poker.json'}adult,0,binary,0.927203,{'_modeljson': 'lgbm/2dplanes.json'}adult,0,binary,0.932072,{'_modeljson': 'lgbm/adult.json'}adult,0,binary,0.926563,{'_modeljson': 'lgbm/Airlines.json'}adult,0,binary,0.928604,{'_modeljson': 'lgbm/Albert.json'}adult,0,binary,0.911171,{'_modeljson': 'lgbm/Amazon_employee_access.json'}adult,0,binary,0.930645,{'_modeljson': 'lgbm/bng_breastTumor.json'}adult,0,binary,0.928603,{'_modeljson': 'lgbm/bng_pbc.json'}adult,0,binary,0.915825,{'_modeljson': 'lgbm/car.json'}adult,0,binary,0.919499,{'_modeljson': 'lgbm/connect-4.json'}adult,0,binary,0.930109,{'_modeljson': 'lgbm/default.json'}adult,0,binary,0.932453,{'_modeljson': 'lgbm/dilbert.json'}adult,0,binary,0.921959,{'_modeljson': 'lgbm/Dionis.json'}adult,0,binary,0.910763,{'_modeljson': 'lgbm/poker.json'}... Copy The type column indicates the type of the task, such as regression, binary or multiclass. The result column stores the evaluation result, assumed the large the better. The params column indicates which json config is used. For example 'lgbm/2dplanes.json' indicates that the best lgbm configuration extracted from 2dplanes is used. Different types of tasks can appear in the same file, as long as any json config file can be used in all the tasks. For example, 'lgbm/2dplanes.json' is extracted from a regression task, and it can be applied to binary and multiclass tasks as well.","s":"Evaluate each candidate configuration on each task","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#evaluate-each-candidate-configuration-on-each-task","p":605},{"i":626,"t":"To recap, the inputs required for meta-learning are: Metafeatures: e.g., {location}/all/metafeatures.csv. Configurations: {location}/{learner_name}/{task_name}.json. Evaluation results: {location}/{learner_name}/results.csv. For example, if the input location is \"test/default\", learners are lgbm, xgb_limitdepth and rf, the following command learns data-dependent defaults for binary classification tasks. python portfolio.py --output test/default --input test/default --metafeatures test/default/all/metafeatures.csv --task binary --estimator lgbm xgb_limitdepth rf Copy In a few seconds, it will produce the following files as output: test/default/lgbm/binary.json: the learned defaults for lgbm. test/default/xgb_limitdepth/binary.json: the learned defaults for xgb_limitdepth. test/default/rf/binary.json: the learned defaults for rf. test/default/all/binary.json: the learned defaults for lgbm, xgb_limitdepth and rf together. Change \"binary\" into \"multiclass\" or \"regression\", or your own types in your \"results.csv\" for the other types of tasks. To update the learned defaults when more experiences are available, simply update your input files and rerun the learning command.","s":"Learn data-dependent defaults","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#learn-data-dependent-defaults","p":605},{"i":628,"t":"You have now effectively built your own zero-shot AutoML solution. Congratulations! Optionally, you can \"flamlize\" a learner using flaml.default.flamlize_estimator for easy dissemination. For example, import sklearn.ensemble as ensemblefrom flaml.default import flamlize_estimatorExtraTreesClassifier = flamlize_estimator( ensemble.ExtraTreesClassifier, \"extra_tree\", \"classification\") Copy Then, you can share this \"flamlized\" ExtraTreesClassifier together with the location of your learned defaults with others (or the future yourself). They will benefit from your past experience. Your group can also share experiences in a central place and update the learned defaults continuously. Over time, your organization gets better collectively.","s":"\"Flamlize\" a learner","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#flamlize-a-learner","p":605},{"i":630,"t":"On this page","s":"tune.trial_runner","u":"/FLAML/docs/reference/tune/trial_runner","h":"","p":629},{"i":632,"t":"class Nologger() Copy Logger without logging.","s":"Nologger Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#nologger-objects","p":629},{"i":634,"t":"class SimpleTrial(Trial) Copy A simple trial class.","s":"SimpleTrial Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#simpletrial-objects","p":629},{"i":636,"t":"class BaseTrialRunner() Copy Implementation of a simple trial runner. Note that the caller usually should not mutate trial state directly. get_trials​ def get_trials() Copy Returns the list of trials managed by this TrialRunner. Note that the caller usually should not mutate trial state directly. add_trial​ def add_trial(trial) Copy Adds a new trial to this TrialRunner. Trials may be added at any time. Arguments: trial Trial - Trial to queue. stop_trial​ def stop_trial(trial) Copy Stops trial.","s":"BaseTrialRunner Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#basetrialrunner-objects","p":629},{"i":638,"t":"class SequentialTrialRunner(BaseTrialRunner) Copy Implementation of the sequential trial runner. step​ def step() -> Trial Copy Runs one step of the trial event loop. Callers should typically run this method repeatedly in a loop. They may inspect or modify the runner's state in between calls to step(). Returns: a trial to run.","s":"SequentialTrialRunner Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#sequentialtrialrunner-objects","p":629},{"i":640,"t":"class SparkTrialRunner(BaseTrialRunner) Copy Implementation of the spark trial runner. step​ def step() -> Trial Copy Runs one step of the trial event loop. Callers should typically run this method repeatedly in a loop. They may inspect or modify the runner's state in between calls to step(). Returns: a trial to run.","s":"SparkTrialRunner Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#sparktrialrunner-objects","p":629},{"i":642,"t":"On this page","s":"Tune User Defined Function","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"","p":641},{"i":644,"t":"There are three essential steps (assuming the knowledge of the set of hyperparameters to tune) to use flaml.tune to finish a basic tuning task: Specify the tuning objective with respect to the hyperparameters. Specify a search space of the hyperparameters. Specify tuning constraints, including constraints on the resource budget to do the tuning, constraints on the configurations, or/and constraints on a (or multiple) particular metric(s). With these steps, you can perform a basic tuning task accordingly.","s":"Basic Tuning Procedure","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#basic-tuning-procedure","p":641},{"i":646,"t":"Related arguments: evaluation_function: A user-defined evaluation function. metric: A string of the metric name to optimize for. mode: A string in ['min', 'max'] to specify the objective as minimization or maximization. The first step is to specify your tuning objective. To do it, you should first specify your evaluation procedure (e.g., perform a machine learning model training and validation) with respect to the hyperparameters in a user-defined function evaluation_function. The function requires a hyperparameter configuration as input, and can simply return a metric value in a scalar or return a dictionary of metric name and metric value pairs. In the following code, we define an evaluation function with respect to two hyperparameters named x and y according to obj:=(x−85000)2−x/yobj := (x-85000)^2 - x/yobj:=(x−85000)2−x/y. Note that we use this toy example here for more accessible demonstration purposes. In real use cases, the evaluation function usually cannot be written in this closed form, but instead involves a black-box and expensive evaluation procedure. Please check out Tune HuggingFace, Tune PyTorch and Tune LightGBM for real examples of tuning tasks. import timedef evaluate_config(config: dict): \"\"\"evaluate a hyperparameter configuration\"\"\" score = (config[\"x\"] - 85000) ** 2 - config[\"x\"] / config[\"y\"] # usually the evaluation takes an non-neglible cost # and the cost could be related to certain hyperparameters # here we simulate this cost by calling the time.sleep() function # here we assume the cost is proportional to x faked_evaluation_cost = config[\"x\"] / 100000 time.sleep(faked_evaluation_cost) # we can return a single float as a score on the input config: # return score # or, we can return a dictionary that maps metric name to metric value: return { \"score\": score, \"evaluation_cost\": faked_evaluation_cost, \"constraint_metric\": config[\"x\"] * config[\"y\"], } Copy When the evaluation function returns a dictionary of metrics, you need to specify the name of the metric to optimize via the argument metric (this can be skipped when the function is just returning a scalar). In addition, you need to specify a mode of your optimization/tuning task (maximization or minimization) via the argument mode by choosing from \"min\" or \"max\". For example, flaml.tune.run(evaluation_function=evaluate_config, metric=\"score\", mode=\"min\", ...) Copy","s":"Tuning objective","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#tuning-objective","p":641},{"i":648,"t":"Related arguments: config: A dictionary to specify the search space. low_cost_partial_config (optional): A dictionary from a subset of controlled dimensions to the initial low-cost values. cat_hp_cost (optional): A dictionary from a subset of categorical dimensions to the relative cost of each choice. The second step is to specify a search space of the hyperparameters through the argument config. In the search space, you need to specify valid values for your hyperparameters and can specify how these values are sampled (e.g., from a uniform distribution or a log-uniform distribution). In the following code example, we include a search space for the two hyperparameters x and y as introduced above. The valid values for both are integers in the range of [1, 100000]. The values for x are sampled uniformly in the specified range (using tune.randint(lower=1, upper=100000)), and the values for y are sampled uniformly in logarithmic space of the specified range (using tune.lograndit(lower=1, upper=100000)). from flaml import tune# construct a search space for the hyperparameters x and y.config_search_space = { \"x\": tune.lograndint(lower=1, upper=100000), \"y\": tune.randint(lower=1, upper=100000),}# provide the search space to tune.runtune.run(..., config=config_search_space, ...) Copy Details and guidelines on hyperparameter search space​ The corresponding value of a particular hyperparameter in the search space dictionary is called a domain, for example, tune.randint(lower=1, upper=100000) is the domain for the hyperparameter y. The domain specifies a type and valid range to sample parameters from. Supported types include float, integer, and categorical. Categorical hyperparameter If it is a categorical hyperparameter, then you should use tune.choice(possible_choices) in which possible_choices is the list of possible categorical values of the hyperparameter. For example, if you are tuning the optimizer used in model training, and the candidate optimizers are \"sgd\" and \"adam\", you should specify the search space in the following way: { \"optimizer\": tune.choice([\"sgd\", \"adam\"]),} Copy Numerical hyperparameter If it is a numerical hyperparameter, you need to know whether it takes integer values or float values. In addition, you need to know: The range of valid values, i.e., what are the lower limit and upper limit of the hyperparameter value? Do you want to sample in linear scale or log scale? It is a common practice to sample in the log scale if the valid value range is large and the evaluation function changes more regularly with respect to the log domain, as shown in the following example for learning rate tuning. In this code example, we set the lower limit and the upper limit of the learning rate to be 1/1024 and 1.0, respectively. We sample in the log space because model performance changes more regularly in the log scale with respect to the learning rate within such a large search range. { \"learning_rate\": tune.loguniform(lower=1 / 1024, upper=1.0),} Copy When the search range of learning rate is small, it is more common to sample in the linear scale as shown in the following example, { \"learning_rate\": tune.uniform(lower=0.1, upper=0.2),} Copy Do you have quantization granularity requirements? When you have a desired quantization granularity for the hyperparameter change, you can use tune.qlograndint or tune.qloguniform to realize the quantization requirement. The following code example helps you realize the need for sampling uniformly in the range of 0.1 and 0.2 with increments of 0.02, i.e., the sampled learning rate can only take values in {0.1, 0.12, 0.14, 0.16, ..., 0.2}, { \"learning_rate\": tune.quniform(lower=0.1, upper=0.2, q=0.02),} Copy You can find the corresponding search space choice in the table below once you have answers to the aforementioned three questions. Integer Float linear scale tune.randint(lower: int, upper: int) tune.uniform(lower: float, upper: float) log scale tune.lograndint(lower: int, upper: int, base: float = 10 tune.loguniform(lower: float, upper: float, base: float = 10) linear scale with quantization tune.qrandint(lower: int, upper: int, q: int = 1) tune.quniform(lower: float, upper: float, q: float = 1) log scale with quantization tune.qlograndint(lower: int, upper, q: int = 1, base: float = 10) tune.qloguniform(lower: float, upper, q: float = 1, base: float = 10) See the example below for the commonly used types of domains. config = { # Sample a float uniformly between -5.0 and -1.0 \"uniform\": tune.uniform(-5, -1), # Sample a float uniformly between 3.2 and 5.4, # rounding to increments of 0.2 \"quniform\": tune.quniform(3.2, 5.4, 0.2), # Sample a float uniformly between 0.0001 and 0.01, while # sampling in log space \"loguniform\": tune.loguniform(1e-4, 1e-2), # Sample a float uniformly between 0.0001 and 0.1, while # sampling in log space and rounding to increments of 0.00005 \"qloguniform\": tune.qloguniform(1e-4, 1e-1, 5e-5), # Sample a random float from a normal distribution with # mean=10 and sd=2 \"randn\": tune.randn(10, 2), # Sample a random float from a normal distribution with # mean=10 and sd=2, rounding to increments of 0.2 \"qrandn\": tune.qrandn(10, 2, 0.2), # Sample a integer uniformly between -9 (inclusive) and 15 (exclusive) \"randint\": tune.randint(-9, 15), # Sample a random uniformly between -21 (inclusive) and 12 (inclusive (!)) # rounding to increments of 3 (includes 12) \"qrandint\": tune.qrandint(-21, 12, 3), # Sample a integer uniformly between 1 (inclusive) and 10 (exclusive), # while sampling in log space \"lograndint\": tune.lograndint(1, 10), # Sample a integer uniformly between 2 (inclusive) and 10 (inclusive (!)), # while sampling in log space and rounding to increments of 2 \"qlograndint\": tune.qlograndint(2, 10, 2), # Sample an option uniformly from the specified choices \"choice\": tune.choice([\"a\", \"b\", \"c\"]),} Copy Cost-related hyperparameters​ Cost-related hyperparameters are a subset of the hyperparameters which directly affect the computation cost incurred in the evaluation of any hyperparameter configuration. For example, the number of estimators (n_estimators) and the maximum number of leaves (max_leaves) are known to affect the training cost of tree-based learners. So they are cost-related hyperparameters for tree-based learners. When cost-related hyperparameters exist, the evaluation cost in the search space is heterogeneous. In this case, designing a search space with proper ranges of the hyperparameter values is highly non-trivial. Classical tuning algorithms such as Bayesian optimization and random search are typically sensitive to such ranges. It may take them a very high cost to find a good choice if the ranges are too large. And if the ranges are too small, the optimal choice(s) may not be included and thus not possible to be found. With our method, you can use a search space with larger ranges in the case of heterogeneous cost. Our search algorithms are designed to finish the tuning process at a low total cost when the evaluation cost in the search space is heterogeneous. So in such scenarios, if you are aware of low-cost configurations for the cost-related hyperparameters, you are encouraged to set them as the low_cost_partial_config, which is a dictionary of a subset of the hyperparameter coordinates whose value corresponds to a configuration with known low cost. Using the example of the tree-based methods again, since we know that small n_estimators and max_leaves generally correspond to simpler models and thus lower cost, we set {'n_estimators': 4, 'max_leaves': 4} as the low_cost_partial_config by default (note that 4 is the lower bound of search space for these two hyperparameters), e.g., in LGBM. Please find more details on how the algorithm works here. In addition, if you are aware of the cost relationship between different categorical hyperparameter choices, you are encouraged to provide this information through cat_hp_cost. It also helps the search algorithm to reduce the total cost.","s":"Search space","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#search-space","p":641},{"i":650,"t":"Related arguments: time_budget_s: The time budget in seconds. num_samples: An integer of the number of configs to try. config_constraints (optional): A list of config constraints to be satisfied. metric_constraints (optional): A list of metric constraints to be satisfied. e.g., ['precision', '>=', 0.9]. The third step is to specify constraints of the tuning task. One notable property of flaml.tune is that it is able to finish the tuning process (obtaining good results) within a required resource constraint. A user can either provide the resource constraint in terms of wall-clock time (in seconds) through the argument time_budget_s, or in terms of the number of trials through the argument num_samples. The following example shows three use cases: # Set a resource constraint of 60 seconds wall-clock time for the tuning.flaml.tune.run(..., time_budget_s=60, ...)# Set a resource constraint of 100 trials for the tuning.flaml.tune.run(..., num_samples=100, ...)# Use at most 60 seconds and at most 100 trials for the tuning.flaml.tune.run(..., time_budget_s=60, num_samples=100, ...) Copy Optionally, you can provide a list of config constraints to be satisfied through the argument config_constraints and provide a list of metric constraints to be satisfied through the argument metric_constraints. We provide more details about related use cases in the Advanced Tuning Options section.","s":"Tuning constraints","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#tuning-constraints","p":641},{"i":652,"t":"After the aforementioned key steps, one is ready to perform a tuning task by calling flaml.tune.run(). Below is a quick sequential tuning example using the pre-defined search space config_search_space and a minimization (mode='min') objective for the score metric evaluated in evaluate_config, using the default serach algorithm in flaml. The time budget is 10 seconds (time_budget_s=10). # require: pip install flaml[blendsearch]analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds) Copy","s":"Put together","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#put-together","p":641},{"i":654,"t":"Once the tuning process finishes, it returns an ExperimentAnalysis object, which provides methods to analyze the tuning. In the following code example, we retrieve the best configuration found during the tuning, and retrieve the best trial's result from the returned analysis. analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds)print(analysis.best_config) # the best configprint(analysis.best_trial.last_result) # the best trial's result Copy","s":"Result analysis","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#result-analysis","p":641},{"i":656,"t":"There are several advanced tuning options worth mentioning.","s":"Advanced Tuning Options","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#advanced-tuning-options","p":641},{"i":658,"t":"A user can specify constraints on the configurations to be satisfied via the argument config_constraints. The config_constraints receives a list of such constraints to be satisfied. Specifically, each constraint is a tuple that consists of (1) a function that takes a configuration as input and returns a numerical value; (2) an operation chosen from \"\\<=\", \">=\", \"\\<\" or \">\"; (3) a numerical threshold. In the following code example, we constrain the output of area, which takes a configuration as input and outputs a numerical value, to be no larger than 1000. def my_model_size(config): return config[\"n_estimators\"] * config[\"max_leaves\"]analysis = tune.run( ..., config_constraints=[(my_model_size, \"<=\", 40)],) Copy You can also specify a list of metric constraints to be satisfied via the argument metric_constraints. Each element in the metric_constraints list is a tuple that consists of (1) a string specifying the name of the metric (the metric name must be defined and returned in the user-defined evaluation_function); (2) an operation chosen from \"\\<=\" or \">=\"; (3) a numerical threshold. In the following code example, we constrain the metric training_cost to be no larger than 1 second. analysis = (tune.run(..., metric_constraints=[(\"training_cost\", \"<=\", 1)]),) Copy config_constraints vs metric_constraints:​ The key difference between these two types of constraints is that the calculation of constraints in config_constraints does not rely on the computation procedure in the evaluation function, i.e., in evaluation_function. For example, when a constraint only depends on the config itself, as shown in the code example. Due to this independency, constraints in config_constraints will be checked before evaluation. So configurations that do not satisfy config_constraints will not be evaluated.","s":"More constraints on the tuning","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#more-constraints-on-the-tuning","p":641},{"i":660,"t":"Related arguments: use_ray: A boolean of whether to use ray as the backend. use_spark: A boolean of whether to use spark as the backend. resources_per_trial: A dictionary of the hardware resources to allocate per trial, e.g., {'cpu': 1}. Only valid when using ray backend. Details about parallel tuning with Spark could be found here. You can perform parallel tuning by specifying use_ray=True (requiring flaml[ray] option installed) or use_spark=True (requiring flaml[spark] option installed). You can also limit the amount of resources allocated per trial by specifying resources_per_trial, e.g., resources_per_trial={'cpu': 2} when use_ray=True. # require: pip install flaml[ray]analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds use_ray=True, resources_per_trial={\"cpu\": 2}, # limit resources allocated per trial)print(analysis.best_trial.last_result) # the best trial's resultprint(analysis.best_config) # the best config Copy # require: pip install flaml[spark]analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds use_spark=True,)print(analysis.best_trial.last_result) # the best trial's resultprint(analysis.best_config) # the best config Copy A headsup about computation overhead. When parallel tuning is used, there will be a certain amount of computation overhead in each trial. In case each trial's original cost is much smaller than the overhead, parallel tuning can underperform sequential tuning. Sequential tuning is recommended when compute resource is limited, and each trial can consume all the resources.","s":"Parallel tuning","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#parallel-tuning","p":641},{"i":662,"t":"Related arguments: scheduler: A scheduler for executing the trials. resource_attr: A string to specify the resource dimension used by the scheduler. min_resource: A float of the minimal resource to use for the resource_attr. max_resource: A float of the maximal resource to use for the resource_attr. reduction_factor: A float of the reduction factor used for incremental pruning. A scheduler can help manage the trials' execution. It can be used to perform multi-fiedlity evalution, or/and early stopping. You can use two different types of schedulers in flaml.tune via scheduler. 1. An authentic scheduler implemented in FLAML (scheduler='flaml').​ This scheduler is authentic to the new search algorithms provided by FLAML. In a nutshell, it starts the search with the minimum resource. It switches between HPO with the current resource and increasing the resource for evaluation depending on which leads to faster improvement. If this scheduler is used, you need to Specify a resource dimension. Conceptually a 'resource dimension' is a factor that affects the cost of the evaluation (e.g., sample size, the number of epochs). You need to specify the name of the resource dimension via resource_attr. For example, if resource_attr=\"sample_size\", then the config dict passed to the evaluation_function would contain a key \"sample_size\" and its value suggested by the search algorithm. That value should be used in the evaluation function to control the compute cost. The larger is the value, the more expensive the evaluation is. Provide the lower and upper limit of the resource dimension via min_resource and max_resource, and optionally provide reduction_factor, which determines the magnitude of resource (multiplicative) increase when we decide to increase the resource. In the following code example, we consider the sample size as the resource dimension. It determines how much data is used to perform training as reflected in the evaluation_function. We set the min_resource and max_resource to 1000 and the size of the full training dataset, respectively. from flaml import tunefrom functools import partialfrom flaml.automl.data import load_openml_taskdef obj_from_resource_attr(resource_attr, X_train, X_test, y_train, y_test, config): from lightgbm import LGBMClassifier from sklearn.metrics import accuracy_score # in this example sample size is our resource dimension resource = int(config[resource_attr]) sampled_X_train = X_train.iloc[:resource] sampled_y_train = y_train[:resource] # construct a LGBM model from the config # note that you need to first remove the resource_attr field # from the config as it is not part of the original search space model_config = config.copy() del model_config[resource_attr] model = LGBMClassifier(**model_config) model.fit(sampled_X_train, sampled_y_train) y_test_predict = model.predict(X_test) test_loss = 1.0 - accuracy_score(y_test, y_test_predict) return {resource_attr: resource, \"loss\": test_loss}X_train, X_test, y_train, y_test = load_openml_task(task_id=7592, data_dir=\"test/\")max_resource = len(y_train)resource_attr = \"sample_size\"min_resource = 1000analysis = tune.run( partial(obj_from_resource_attr, resource_attr, X_train, X_test, y_train, y_test), config={ \"n_estimators\": tune.lograndint(lower=4, upper=32768), \"max_leaves\": tune.lograndint(lower=4, upper=32768), \"learning_rate\": tune.loguniform(lower=1 / 1024, upper=1.0), }, metric=\"loss\", mode=\"min\", resource_attr=resource_attr, scheduler=\"flaml\", max_resource=max_resource, min_resource=min_resource, reduction_factor=2, time_budget_s=10, num_samples=-1,) Copy You can find more details about this scheduler in this paper. 2. A scheduler of the TrialScheduler class from ray.tune.​ There is a handful of schedulers of this type implemented in ray.tune, for example, ASHA, HyperBand, BOHB, etc. To use this type of scheduler you can either (1) set scheduler='asha', which will automatically create an ASHAScheduler instance using the provided inputs (resource_attr, min_resource, max_resource, and reduction_factor); or (2) create an instance by yourself and provided it via scheduler, as shown in the following code example, # require: pip install flaml[ray]from ray.tune.schedulers import HyperBandSchedulermy_scheduler = HyperBandScheduler(time_attr=\"sample_size\", max_t=max_resource, reduction_factor=2)tune.run(.., scheduler=my_scheduler, ...) Copy Similar to the case where the flaml scheduler is used, you need to specify the resource dimension, use the resource dimension accordingly in your evaluation_function, and provide the necessary information needed for scheduling, such as min_resource, max_resource and reduction_factor (depending on the requirements of the specific scheduler). Different from the case when the flaml scheduler is used, the amount of resources to use at each iteration is not suggested by the search algorithm through the resource_attr in a configuration. You need to specify the evaluation schedule explicitly by yourself in the evaluation_function and report intermediate results (using tune.report()) accordingly. In the following code example, we use the ASHA scheduler by setting scheduler=\"asha\". We specify resource_attr, min_resource, min_resource and reduction_factor the same way as in the previous example (when \"flaml\" is used as the scheduler). We perform the evaluation in a customized schedule. Use ray backend or not? You can choose to use ray backend or not by specifying use_ray=True or use_ray=False. When ray backend is not used, i.e., use_ray=False, you also need to stop the evaluation function by explicitly catching the StopIteration exception, as shown in the end of the evaluation function obj_w_intermediate_report() in the following code example. def obj_w_intermediate_report( resource_attr, X_train, X_test, y_train, y_test, min_resource, max_resource, config): from lightgbm import LGBMClassifier from sklearn.metrics import accuracy_score # a customized schedule to perform the evaluation eval_schedule = [res for res in range(min_resource, max_resource, 5000)] + [ max_resource ] for resource in eval_schedule: sampled_X_train = X_train.iloc[:resource] sampled_y_train = y_train[:resource] # construct a LGBM model from the config model = LGBMClassifier(**config) model.fit(sampled_X_train, sampled_y_train) y_test_predict = model.predict(X_test) test_loss = 1.0 - accuracy_score(y_test, y_test_predict) # need to report the resource attribute used and the corresponding intermediate results try: tune.report(sample_size=resource, loss=test_loss) except (StopIteration, SystemExit): # do cleanup operation here returnresource_attr = \"sample_size\"min_resource = 1000max_resource = len(y_train)analysis = tune.run( partial( obj_w_intermediate_report, resource_attr, X_train, X_test, y_train, y_test, min_resource, max_resource, ), config={ \"n_estimators\": tune.lograndint(lower=4, upper=32768), \"learning_rate\": tune.loguniform(lower=1 / 1024, upper=1.0), }, metric=\"loss\", mode=\"min\", resource_attr=resource_attr, scheduler=\"asha\", max_resource=max_resource, min_resource=min_resource, reduction_factor=2, time_budget_s=10, num_samples=-1,) Copy If you would like to do some cleanup opearation when the trial is stopped by the scheduler, you can do it when you catch the StopIteration (when not using ray) or SystemExit (when using ray) exception explicitly.","s":"Trial scheduling","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#trial-scheduling","p":641},{"i":664,"t":"Related arguments: points_to_evaluate: A list of initial hyperparameter configurations to run first. evaluated_rewards: If you have previously evaluated the parameters passed in as points_to_evaluate , you can avoid re-running those trials by passing in the reward attributes as a list so the optimizer can be told the results without needing to re-compute the trial. Must be the same length or shorter length than points_to_evaluate. If you are aware of some good hyperparameter configurations, you are encouraged to provide them via points_to_evaluate. The search algorithm will try them first and use them to bootstrap the search. You can use previously evaluated configurations to warm-start your tuning. For example, the following code means that you know the reward for the two configs in points_to_evaluate are 3.99 and 1.99, respectively, and want to inform tune.run(). def simple_obj(config): return config[\"a\"] + config[\"b\"]from flaml import tuneconfig_search_space = { \"a\": tune.uniform(lower=0, upper=0.99), \"b\": tune.uniform(lower=0, upper=3),}points_to_evaluate = [ {\"b\": 0.99, \"a\": 3}, {\"b\": 0.99, \"a\": 2}, {\"b\": 0.80, \"a\": 3}, {\"b\": 0.80, \"a\": 2},]evaluated_rewards = [3.99, 2.99]analysis = tune.run( simple_obj, config=config_search_space, mode=\"max\", points_to_evaluate=points_to_evaluate, evaluated_rewards=evaluated_rewards, time_budget_s=10, num_samples=-1,) Copy","s":"Warm start","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#warm-start","p":641},{"i":666,"t":"By default, there is randomness in our tuning process (for versions \\<= 0.9.1). If reproducibility is desired, you could manually set a random seed before calling tune.run(). For example, in the following code, we call np.random.seed(100) to set the random seed. With this random seed, running the following code multiple times will generate exactly the same search trajectory. The reproducibility can only be guaranteed in sequential tuning. import numpy as npnp.random.seed(100) # This line is not needed starting from version v0.9.2.analysis = tune.run( simple_obj, config=config_search_space, mode=\"max\", num_samples=10,) Copy","s":"Reproducibility","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#reproducibility","p":641},{"i":668,"t":"We support tuning multiple objectives with lexicographic preference by providing argument lexico_objectives for tune.run(). lexico_objectives is a dictionary that contains the following fields of key-value pairs: metrics: a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. modes: (optional) a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives. tolerances: (optional) a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. targets: (optional) a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. In the following example, we want to minimize val_loss and pred_time of the model where val_loss has high priority. The tolerances for val_loss and pre_time are 0.02 and 0 respectively. We do not set targets for these two objectives and we set them to -inf for both objectives. lexico_objectives = {}lexico_objectives[\"metrics\"] = [\"val_loss\", \"pred_time\"]lexico_objectives[\"modes\"] = [\"min\", \"min\"]lexico_objectives[\"tolerances\"] = {\"val_loss\": 0.02, \"pred_time\": 0.0}lexico_objectives[\"targets\"] = {\"val_loss\": -float(\"inf\"), \"pred_time\": -float(\"inf\")}# provide the lexico_objectives to tune.runtune.run(..., search_alg=None, lexico_objectives=lexico_objectives) Copy We also supports providing percentage tolerance as shown below. lexico_objectives[\"tolerances\"] = {\"val_loss\": \"10%\", \"pred_time\": \"0%\"} Copy NOTE: When lexico_objectives is not None, the arguments metric, mode, will be invalid, and flaml's tune uses CFO as the search_alg, which makes the input (if provided) search_alg invalid. This is a new feature that will be released in version 1.1.0 and is subject to change in the future version.","s":"Lexicographic Objectives","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#lexicographic-objectives","p":641},{"i":670,"t":"To tune the hyperparameters toward your objective, you will want to use a hyperparameter optimization algorithm which can help suggest hyperparameters with better performance (regarding your objective). flaml offers two HPO methods: CFO and BlendSearch. flaml.tune uses BlendSearch by default when the option [blendsearch] is installed.","s":"Hyperparameter Optimization Algorithm","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#hyperparameter-optimization-algorithm","p":641},{"i":672,"t":"CFO uses the randomized direct search method FLOW2 with adaptive stepsize and random restart. It requires a low-cost initial point as input if such point exists. The search begins with the low-cost initial point and gradually move to high cost region if needed. The local search method has a provable convergence rate and bounded cost. About FLOW2: FLOW2 is a simple yet effective randomized direct search method. It is an iterative optimization method that can optimize for black-box functions. FLOW2 only requires pairwise comparisons between function values to perform iterative update. Comparing to existing HPO methods, FLOW2 has the following appealing properties: It is applicable to general black-box functions with a good convergence rate in terms of loss. It provides theoretical guarantees on the total evaluation cost incurred. The GIFs attached below demonstrate an example search trajectory of FLOW2 shown in the loss and evaluation cost (i.e., the training time ) space respectively. FLOW2 is used in tuning the # of leaves and the # of trees for XGBoost. The two background heatmaps show the loss and cost distribution of all configurations. The black dots are the points evaluated in FLOW2. Black dots connected by lines are points that yield better loss performance when evaluated. From the demonstration, we can see that (1) FLOW2 can quickly move toward the low-loss region, showing good convergence property and (2) FLOW2 tends to avoid exploring the high-cost region until necessary. Example: from flaml import CFOtune.run(... search_alg=CFO(low_cost_partial_config=low_cost_partial_config),) Copy Recommended scenario: There exist cost-related hyperparameters and a low-cost initial point is known before optimization. If the search space is complex and CFO gets trapped into local optima, consider using BlendSearch.","s":"CFO: Frugal Optimization for Cost-related Hyperparameters","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#cfo-frugal-optimization-for-cost-related-hyperparameters","p":641},{"i":674,"t":"BlendSearch combines local search with global search. It leverages the frugality of CFO and the space exploration ability of global search methods such as Bayesian optimization. Like CFO, BlendSearch requires a low-cost initial point as input if such point exists, and starts the search from there. Different from CFO, BlendSearch will not wait for the local search to fully converge before trying new start points. The new start points are suggested by the global search method and filtered based on their distance to the existing points in the cost-related dimensions. BlendSearch still gradually increases the trial cost. It prioritizes among the global search thread and multiple local search threads based on optimism in face of uncertainty. Example: # require: pip install flaml[blendsearch]from flaml import BlendSearchtune.run(... search_alg=BlendSearch(low_cost_partial_config=low_cost_partial_config),) Copy Recommended scenario: Cost-related hyperparameters exist, a low-cost initial point is known, and the search space is complex such that local search is prone to be stuck at local optima. Suggestion about using larger search space in BlendSearch. In hyperparameter optimization, a larger search space is desirable because it is more likely to include the optimal configuration (or one of the optimal configurations) in hindsight. However the performance (especially anytime performance) of most existing HPO methods is undesirable if the cost of the configurations in the search space has a large variation. Thus hand-crafted small search spaces (with relatively homogeneous cost) are often used in practice for these methods, which is subject to idiosyncrasy. BlendSearch combines the benefits of local search and global search, which enables a smart (economical) way of deciding where to explore in the search space even though it is larger than necessary. This allows users to specify a larger search space in BlendSearch, which is often easier and a better practice than narrowing down the search space by hand. For more technical details, please check our papers. Frugal Optimization for Cost-related Hyperparameters. Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021. @inproceedings{wu2021cfo, title={Frugal Optimization for Cost-related Hyperparameters}, author={Qingyun Wu and Chi Wang and Silu Huang}, year={2021}, booktitle={AAAI'21},} Copy Economical Hyperparameter Optimization With Blended Search Strategy. Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021. @inproceedings{wang2021blendsearch, title={Economical Hyperparameter Optimization With Blended Search Strategy}, author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied}, year={2021}, booktitle={ICLR'21},} Copy Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives. Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%). @inproceedings{zhang2023targeted, title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives}, author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu}, booktitle={International Conference on Learning Representations}, year={2023}, url={https://openreview.net/forum?id=0Ij9_q567Ma}} Copy","s":"BlendSearch: Economical Hyperparameter Optimization With Blended Search 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Just by tuning the inference parameters like model, number of responses, temperature etc. without changing any model weights or prompt, the baseline accuracy of untuned gpt-4 can be improved by 20% in high school math competition problems. For easy problems, the tuned gpt-3.5-turbo model vastly outperformed untuned gpt-4 in accuracy (e.g., 90% vs. 70%) and cost efficiency. For hard problems, the tuned gpt-4 is much more accurate (e.g., 35% vs. 20%) and less expensive than untuned gpt-4. FLAML can help with model selection, parameter tuning, and cost-saving in LLM applications. Large language models (LLMs) are powerful tools that can generate natural language texts for various applications, such as chatbots, summarization, translation, and more. GPT-4 is currently the state of the art LLM in the world. Is model selection irrelevant? What about inference parameters? In this blog post, we will explore how model and inference parameter matter in LLM applications, using a case study for MATH, a benchmark for evaluating LLMs on advanced mathematical problem solving. MATH consists of 12K math competition problems from AMC-10, AMC-12 and AIME. Each problem is accompanied by a step-by-step solution. We will use the new subpackage flaml.autogen to automatically find the best model and inference parameter for LLMs on a given task and dataset given an inference budget, using a novel low-cost search & pruning strategy. FLAML currently supports all the LLMs from OpenAI, such as GPT-3.5 and GPT-4. We will use FLAML to perform model selection and inference parameter tuning. Then we compare the performance and inference cost on solving algebra problems with the untuned gpt-4. We will also analyze how different difficulty levels affect the results.","s":"Does Model and Inference Parameter Matter in LLM Applications? - A Case Study for MATH","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"","p":1},{"i":4,"t":"We use FLAML to select between the following models with a target inference budget $0.02 per instance: gpt-3.5-turbo, a relatively cheap model that powers the popular ChatGPT app gpt-4, the state of the art LLM that costs more than 10 times of gpt-3.5-turbo We adapt the models using 20 examples in the train set, using the problem statement as the input and generating the solution as the output. We use the following inference parameters: temperature: The parameter that controls the randomness of the output text. A higher temperature means more diversity but less coherence. We search for the optimal temperature in the range of [0, 1]. top_p: The parameter that controls the probability mass of the output tokens. Only tokens with a cumulative probability less than or equal to top-p are considered. A lower top-p means more diversity but less coherence. We search for the optimal top-p in the range of [0, 1]. max_tokens: The maximum number of tokens that can be generated for each output. We search for the optimal max length in the range of [50, 1000]. n: The number of responses to generate. We search for the optimal n in the range of [1, 100]. prompt: We use the template: \"{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed{{}}.\" where {problem} will be replaced by the math problem instance. In this experiment, when n > 1, we find the answer with highest votes among all the responses and then select it as the final answer to compare with the ground truth. For example, if n = 5 and 3 of the responses contain a final answer 301 while 2 of the responses contain a final answer 159, we choose 301 as the final answer. This can help with resolving potential errors due to randomness. We use the average accuracy and average inference cost as the metric to evaluate the performance over a dataset. The inference cost of a particular instance is measured by the price per 1K tokens and the number of tokens consumed.","s":"Experiment Setup","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#experiment-setup","p":1},{"i":6,"t":"The first figure in this blog post shows the average accuracy and average inference cost of each configuration on the level 2 Algebra test set. Surprisingly, the tuned gpt-3.5-turbo model is selected as a better model and it vastly outperforms untuned gpt-4 in accuracy (92% vs. 70%) with equal or 2.5 times higher inference budget. The same observation can be obtained on the level 3 Algebra test set. However, the selected model changes on level 4 Algebra. This time gpt-4 is selected as the best model. The tuned gpt-4 achieves much higher accuracy (56% vs. 44%) and lower cost than the untuned gpt-4. On level 5 the result is similar. We can see that FLAML has found different optimal model and inference parameters for each subset of a particular level, which shows that these parameters matter in cost-sensitive LLM applications and need to be carefully tuned or adapted. An example notebook to run these experiments can be found at: https://github.com/microsoft/FLAML/blob/v1.2.1/notebook/autogen_chatgpt.ipynb","s":"Experiment Results","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#experiment-results","p":1},{"i":8,"t":"While gpt-3.5-turbo demonstrates competitive accuracy with voted answers in relatively easy algebra problems under the same inference budget, gpt-4 is a better choice for the most difficult problems. In general, through parameter tuning and model selection, we can identify the opportunity to save the expensive model for more challenging tasks, and improve the overall effectiveness of a budget-constrained system. There are many other alternative ways of solving math problems, which we have not covered in this blog post. When there are choices beyond the inference parameters, they can be generally tuned via flaml.tune. The need for model selection, parameter tuning and cost saving is not specific to the math problems. The Auto-GPT project is an example where high cost can easily prevent a generic complex task to be accomplished as it needs many LLM inference calls.","s":"Analysis and Discussion","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#analysis-and-discussion","p":1},{"i":10,"t":"Research paper about the tuning technique Documentation about flaml.autogen Do you have any experience to share about LLM applications? Do you like to see more support or research of LLM optimization or automation? Please join our Discord server for discussion.","s":"For Further Reading","u":"/FLAML/blog/2023/04/21/LLM-tuning-math","h":"#for-further-reading","p":1},{"i":12,"t":"TL;DR: Celebrating FLAML's milestone: 1 million downloads Introducing Large Language Model (LLM) support in the upcoming FLAML v2 This week, FLAML has reached a significant milestone: 1 million downloads. Originating as an intern research project within Microsoft Research, FLAML has grown into an open-source library used widely across the industry and supported by an active community. As we celebrate this milestone, we want to recognize the passionate contributors and users who have played an essential role in molding FLAML into the flourishing project it is today. Our heartfelt gratitude goes out to each of you for your unwavering support, constructive feedback, and innovative contributions that have driven FLAML to new heights. A big shoutout to our industrial collaborators from Azure Core, Azure Machine Learning, Azure Synapse Analytics, Microsoft 365, ML.NET, Vowpal Wabbit, Anyscale, Databricks, and Wise; and academic collaborators from MIT, Penn State University, Stevens Institute of Technology, Tel Aviv University, Texas A & M University, University of Manchester, University of Washington, and The Chinese University of Hong Kong etc. We'd also like to take the opportunity to reflect on FLAML's past achievements and its future roadmap, with a particular focus on large language models (LLM) and LLMOps.","s":"Surpassing 1 Million Downloads - A Retrospective and a Look into the Future","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"","p":11},{"i":15,"t":"FLAML offers an off-the-shelf AutoML solution that enables users to quickly discover high-quality models or configurations for common ML/AI tasks. By automatically selecting models and hyperparameters for training or inference, FLAML saves users time and effort. FLAML has significantly reduced development time for developers and data scientists alike, while also providing a convenient way to integrate new algorithms into the pipeline, enabling easy extensions and large-scale parallel tuning. These features make FLAML a valuable tool in R&D efforts for many enterprise users. FLAML is capable of handling a variety of common ML tasks, such as classification, regression, time series forecasting, NLP tasks, and generative tasks, providing a comprehensive solution for various applications.","s":"Bring AutoML to One's Fingertips","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#bring-automl-to-ones-fingertips","p":11},{"i":17,"t":"What sets FLAML apart from other AutoML libraries is its exceptional efficiency, thanks to the economical and efficient hyperparameter optimization and model selection methods developed in our research. FLAML is also capable of handling large search spaces with heterogeneous evaluation costs, complex constraints, guidance, and early stopping. The zero-shot AutoML option further reduces the cost of AutoML, making FLAML an even more attractive solution for a wide range of applications with low resources.","s":"Speed and Efficiency: The FLAML Advantage","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#speed-and-efficiency-the-flaml-advantage","p":11},{"i":19,"t":"FLAML is designed for easy extensibility and customization, allowing users to add custom learners, metrics, search space, etc. For example, the support of hierarchical search spaces allows one to first choose an ML learner and then sampling from the hyperparameter space specific to that learner. The level of customization ranges from minimal (providing only training data and task type as input) to full (tuning a user-defined function). This flexibility and support for easy customization have led to FLAML's adoption in various domains, including security, finance, marketing, engineering, supply chain, insurance, and healthcare, delivering highly accurate results.","s":"Easy Customization and Extensibility","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#easy-customization-and-extensibility","p":11},{"i":21,"t":"As large language models continue to reshape the AI ecosystem, FLAML is poised to adapt and grow alongside these advancements. Recognizing the importance of large language models, we have recently incorporated an autogen package into FLAML, and are committed to focusing our collective efforts on addressing the unique challenges that arise in LLMOps (Large Language Model Operations). In its current iteration, FLAML offers support for model selection and inference parameter tuning for large language models. We are actively working on the development of new features, such as low-level inference API with caching, templating, filtering, and higher-level components like LLM-based coding and interactive agents, to enable more effective and economical usage of LLM. We are eagerly preparing for the launch of FLAML v2, where we will place special emphasis on incorporating and enhancing features specifically tailored for large language models (LLMs), further expanding FLAML's capabilities. We invite contributions from anyone interested in this topic and look forward to collaborating with the community as we shape the future of FLAML and LLMOps together.","s":"Embracing Large Language Models in FLAML v2","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#embracing-large-language-models-in-flaml-v2","p":11},{"i":23,"t":"Documentation about flaml.autogen Code Example: Tune chatGPT for Math Problem Solving with FLAML Do you have any experience to share about LLM applications? Do you like to see more support or research of LLMOps? Please join our Discord server for discussion.","s":"For Further Reading","u":"/FLAML/blog/2023/05/07/1M-milestone","h":"#for-further-reading","p":11},{"i":25,"t":"TL;DR: We demonstrate how to use flaml.autogen for local LLM application. As an example, we will initiate an endpoint using FastChat and perform inference on ChatGLMv2-6b.","s":"Use flaml.autogen for Local LLMs","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"","p":24},{"i":28,"t":"FastChat provides OpenAI-compatible APIs for its supported models, so you can use FastChat as a local drop-in replacement for OpenAI APIs. However, its code needs minor modification in order to function properly. git clone https://github.com/lm-sys/FastChat.gitcd FastChat Copy","s":"Clone FastChat","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#clone-fastchat","p":24},{"i":30,"t":"ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters. ChatGLM2-6B is its second-generation version. Before downloading from HuggingFace Hub, you need to have Git LFS installed. git clone https://huggingface.co/THUDM/chatglm2-6b Copy","s":"Download checkpoint","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#download-checkpoint","p":24},{"i":32,"t":"First, launch the controller python -m fastchat.serve.controller Copy Then, launch the model worker(s) python -m fastchat.serve.model_worker --model-path chatglm2-6b Copy Finally, launch the RESTful API server python -m fastchat.serve.openai_api_server --host localhost --port 8000 Copy Normally this will work. However, if you encounter error like this, commenting out all the lines containing finish_reason in fastchat/protocol/api_protocal.py and fastchat/protocol/openai_api_protocol.py will fix the problem. The modified code looks like: class CompletionResponseChoice(BaseModel): index: int text: str logprobs: Optional[int] = None # finish_reason: Optional[Literal[\"stop\", \"length\"]]class CompletionResponseStreamChoice(BaseModel): index: int text: str logprobs: Optional[float] = None # finish_reason: Optional[Literal[\"stop\", \"length\"]] = None Copy","s":"Initiate server","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#initiate-server","p":24},{"i":34,"t":"Now the models can be directly accessed through openai-python library as well as flaml.oai.Completion and flaml.oai.ChatCompletion. from flaml import oai# create a text completion requestresponse = oai.Completion.create( config_list=[ { \"model\": \"chatglm2-6b\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", # just a placeholder } ], prompt=\"Hi\",)print(response)# create a chat completion requestresponse = oai.ChatCompletion.create( config_list=[ { \"model\": \"chatglm2-6b\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", } ], messages=[{\"role\": \"user\", \"content\": \"Hi\"}])print(response) Copy If you would like to switch to different models, download their checkpoints and specify model path when launching model worker(s).","s":"Interact with model using oai.Completion","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#interact-with-model-using-oaicompletion","p":24},{"i":36,"t":"If you would like to interact with multiple LLMs on your local machine, replace the model_worker step above with a multi model variant: python -m fastchat.serve.multi_model_worker \\ --model-path lmsys/vicuna-7b-v1.3 \\ --model-names vicuna-7b-v1.3 \\ --model-path chatglm2-6b \\ --model-names chatglm2-6b Copy The inference code would be: from flaml import oai# create a chat completion requestresponse = oai.ChatCompletion.create( config_list=[ { \"model\": \"chatglm2-6b\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", }, { \"model\": \"vicuna-7b-v1.3\", \"api_base\": \"http://localhost:8000/v1\", \"api_type\": \"open_ai\", \"api_key\": \"NULL\", } ], messages=[{\"role\": \"user\", \"content\": \"Hi\"}])print(response) Copy","s":"interacting with multiple local LLMs","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#interacting-with-multiple-local-llms","p":24},{"i":38,"t":"Documentation about flaml.autogen Documentation about FastChat.","s":"For Further Reading","u":"/FLAML/blog/2023/07/14/Local-LLMs","h":"#for-further-reading","p":24},{"i":40,"t":"TL;DR: We introduce MathChat, a conversational framework leveraging Large Language Models (LLMs), specifically GPT-4, to solve advanced mathematical problems. MathChat improves LLM's performance on challenging math problem-solving, outperforming basic prompting and other strategies by about 6%. The improvement was especially notable in the Algebra category, with a 15% increase in accuracy. Despite the advancement, GPT-4 still struggles to solve very challenging math problems, even with effective prompting strategies. Further improvements are needed, such as the development of more specific assistant models or the integration of new tools and prompts. Recent Large Language Models (LLMs) like GTP-3.5 and GPT-4 have demonstrated astonishing abilities over previous models on various tasks, such as text generation, question answering, and code generation. Moreover, these models can communicate with humans through conversations and remember previous contexts, making it easier for humans to interact with them. These models play an increasingly important role in our daily lives assisting people with different tasks, such as writing emails, summarizing documents, and writing code. In this blog post, we probe into the problem-solving capabilities of LLMs. Specifically, we are interested in their capabilities to solve advanced math problems, which could be representative of a broader class of problems that require precise reasoning and also have deterministic solutions. We introduce MathChat, a conversational framework designed for solving challenging math problems with LLMs. This framework takes advantage of the chat-optimized feature of state-of-the-art LLMs, where a user proxy agent and an LLM assistant work together to tackle math problems. We also test previous prompting techniques for comparison.","s":"MathChat - An Conversational Framework to Solve Math Problems","u":"/FLAML/blog/2023/06/28/MathChat","h":"","p":39},{"i":42,"t":"MathChat simulates a conversation between the LLM assistant and a user proxy agent. As the name indicates, the user proxy agent acts as a proxy for the user, which is responsible for communicating with the LLM assistant and continuing the conversation in a desired manner. The proxy agent first presents a math problem to the LLM assistant, framed by an initial prompt that guides the assistant in how it should work collaboratively to solve the problem. We incorporate existing prompting methods such as CoT (chain-of-thought) and tool-using are incorporated in the initial prompt. Key components of the prompt include: Tool-using Prompt: This guides the LLM assistant to use Python code in the correct format to address the problem, allowing the user proxy agent to parse the code and provide the appropriate results. Problem-Solving Strategy Selection Prompt: The assistant is instructed to choose one of three potential problem-solving strategies, including: Writing a Python program to solve the problem directly. Solving the problem directly without using Python, which exercises LLMs' inherent reasoning capabilities. Solving the problem step by step with Python. This should be used when the first two strategies aren't suitable. Final Answer Encapsulation Prompt: This part instructs the assistant to put the final answer in \\boxed{}. The prompt design in MathChat is flexible and can easily be refined to include other tools, such as Wolfram Alpha. Let's take a look at an example between the User Proxy Agent and the LLM Assistant (GPT-4). The conversation focuses on how to solve inequality using Python. (The conversation is modified for readability.) Participant Dialogue User Proxy Agent (Initial Prompt: Let's solve a math problem with Python...) Problem: Find all x that satisfy the inequality (2x+10)(x+3) < (3x+9)(x+8). Express your answer in interval notation. LLM assistant The key idea to solve this problem is to find the critical points, which are the points where the inequality is equal. Then, test the intervals between the critical points to find the interval for which the inequality holds. LLM assistant (continued) Here's Python code to find the critical points: from sympy import symbols, Eq, solve; x = symbols(\"x\"); lhs = (2*x + 10)*(x + 3); rhs = (3*x + 9)*(x + 8); equation = Eq(lhs, rhs); critical_points = solve(equation, x); critical_points User Proxy Agent The critical points are: [-14, -3] LLM assistant We found the critical points to be x = -14 and x = -3. Now we'll test the intervals between these critical points as well as points before and after the critical points. The intervals are: x < -14, -14 < x < -3, x > -3. LLM assistant (continued) Here's Python code to test the inequality for each interval: from sympy import symbols; x = symbols(\"x\"); inequality = (2*x + 10)*(x + 3) < (3*x + 9)*(x + 8); test_points = [-15, -5, 0]; intervals = []; for point in test_points: if inequality.subs(x, point): intervals.append(point); intervals User Proxy Agent The intervals where the inequality holds true are for test points: [-15, 0] LLM assistant We have tested the intervals and found that the inequality holds for: 1. x < -14 (test point: -15) 2. x > -3 (test point: 0). Therefore, the solution to the inequality (2x+10)(x+3) < (3x+9)(x+8) is x ∈ (-∞, -14) ∪ (-3, ∞)","s":"The MathChat Framework","u":"/FLAML/blog/2023/06/28/MathChat","h":"#the-mathchat-framework","p":39},{"i":44,"t":"We evaluate the improvement brought by MathChat. For the experiment, we focus on the level-5 problems from the MATH dataset, which are composed of high school competition problems. These problems include the application of theorems and complex equation derivation and are challenging even for undergraduate students. We evaluate 6 of 7 categories from the dataset (excluding Geometry): Prealgebra, Algebra, Number Theory, Counting and Probability, Intermediate Algebra, and Precalculus. We evaluate GPT-4 and use the default configuration of the OpenAI API. To access the final performance, we manually compare the final answer with the correct answer. For the vanilla prompt, Program Synthesis, and MathChat, we have GPT-4 enclose the final answer in \\boxed{}, and we take the return of the function in PoT as the final answer. We also evaluate the following methods for comparison: Vanilla prompting: Evaluates GPT-4's direct problem-solving capability. The prompt used is: \" Solve the problem carefully. Put the final answer in \\boxed{}\". Program of Thoughts (PoT): Uses a zero-shot PoT prompt that requests the model to create a Solver function to solve the problem and return the final answer. Program Synthesis (PS) prompting: Like PoT, it prompts the model to write a program to solve the problem. The prompt used is: \"Write a program that answers the following question: {Problem}\".","s":"Experiment Setup","u":"/FLAML/blog/2023/06/28/MathChat","h":"#experiment-setup","p":39},{"i":46,"t":"The accuracy on all the problems with difficulty level-5 from different categories of the MATH dataset with different methods is shown below: We found that compared to basic prompting, which demonstrates the innate capabilities of GPT-4, utilizing Python within the context of PoT or PS strategy improved the overall accuracy by about 10%. This increase was mostly seen in categories involving more numerical manipulations, such as Counting & Probability and Number Theory, and in more complex categories like Intermediate Algebra and Precalculus. For categories like Algebra and Prealgebra, PoT and PS showed little improvement, and in some instances, even led to a decrease in accuracy. However, MathChat was able to enhance total accuracy by around 6% compared to PoT and PS, showing competitive performance across all categories. Remarkably, MathChat improved accuracy in the Algebra category by about 15% over other methods. Note that categories like Intermediate Algebra and Precalculus remained challenging for all methods, with only about 20% of problems solved accurately. The code for experiments can be found at this repository. We now provide an implementation of MathChat using the interactive agents in FLAML. See this notebook for example usage.","s":"Experiment Results","u":"/FLAML/blog/2023/06/28/MathChat","h":"#experiment-results","p":39},{"i":48,"t":"Despite MathChat's improvements over previous methods, the results show that complex math problem is still challenging for recent powerful LLMs, like GPT-4, even with help from external tools. Further work can be done to enhance this framework or math problem-solving in general: Although enabling the model to use tools like Python can reduce calculation errors, LLMs are still prone to logic errors. Methods like self-consistency (Sample several solutions and take a major vote on the final answer), or self-verification (use another LLM instance to check whether an answer is correct) might improve the performance. Sometimes, whether the LLM can solve the problem depends on the plan it uses. Some plans require less computation and logical reasoning, leaving less room for mistakes. MathChat has the potential to be adapted into a copilot system, which could assist users with math problems. This system could allow users to be more involved in the problem-solving process, potentially enhancing learning.","s":"Future Directions","u":"/FLAML/blog/2023/06/28/MathChat","h":"#future-directions","p":39},{"i":50,"t":"Research paper of MathChat Documentation about flaml.autogen Are you working on applications that involve math problem-solving? Would you appreciate additional research or support on the application of LLM-based agents for math problem-solving? Please join our Discord server for discussion.","s":"For Further Reading","u":"/FLAML/blog/2023/06/28/MathChat","h":"#for-further-reading","p":39},{"i":53,"t":"On this page","s":"Contributing","u":"/FLAML/docs/Contribute","h":"","p":52},{"i":55,"t":"When you submit an issue to GitHub, please do your best to follow these guidelines! This will make it a lot easier to provide you with good feedback: The ideal bug report contains a short reproducible code snippet. This way anyone can try to reproduce the bug easily (see this for more details). If your snippet is longer than around 50 lines, please link to a gist or a GitHub repo. If an exception is raised, please provide the full traceback. Please include your operating system type and version number, as well as your Python, flaml, scikit-learn versions. The version of flaml can be found by running the following code snippet: import flamlprint(flaml.__version__) Copy Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks for more details.","s":"How to make a good bug report","u":"/FLAML/docs/Contribute","h":"#how-to-make-a-good-bug-report","p":52},{"i":57,"t":"There is currently no formal reviewer solicitation process. Current reviewers identify reviewers from active contributors. If you are willing to become a reviewer, you are welcome to let us know on discord.","s":"Becoming a Reviewer","u":"/FLAML/docs/Contribute","h":"#becoming-a-reviewer","p":52},{"i":60,"t":"git clone https://github.com/microsoft/FLAML.gitpip install -e FLAML[notebook,autogen] Copy In case the pip install command fails, try escaping the brackets such as pip install -e FLAML\\[notebook,autogen\\].","s":"Setup","u":"/FLAML/docs/Contribute","h":"#setup","p":52},{"i":62,"t":"We provide a simple Dockerfile. docker build https://github.com/microsoft/FLAML.git#main -t flaml-devdocker run -it flaml-dev Copy","s":"Docker","u":"/FLAML/docs/Contribute","h":"#docker","p":52},{"i":64,"t":"If you use vscode, you can open the FLAML folder in a Container. We have provided the configuration in devcontainer.","s":"Develop in Remote Container","u":"/FLAML/docs/Contribute","h":"#develop-in-remote-container","p":52},{"i":66,"t":"Run pre-commit install to install pre-commit into your git hooks. Before you commit, run pre-commit run to check if you meet the pre-commit requirements. If you use Windows (without WSL) and can't commit after installing pre-commit, you can run pre-commit uninstall to uninstall the hook. In WSL or Linux this is supposed to work.","s":"Pre-commit","u":"/FLAML/docs/Contribute","h":"#pre-commit","p":52},{"i":68,"t":"Any code you commit should not decrease coverage. To run all unit tests, install the [test] option under FLAML/: pip install -e.\"[test]\"coverage run -m pytest test Copy Then you can see the coverage report by coverage report -m or coverage html.","s":"Coverage","u":"/FLAML/docs/Contribute","h":"#coverage","p":52},{"i":70,"t":"To build and test documentation locally, install Node.js. For example, nvm install --lts Copy Then: npm install --global yarn # skip if you use the dev container we providedpip install pydoc-markdown==4.5.0 # skip if you use the dev container we providedcd websiteyarn install --frozen-lockfile --ignore-enginespydoc-markdownyarn start Copy The last command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. Note: some tips in this guide are based off the contributor guide from ray, scikit-learn, or hummingbird.","s":"Documentation","u":"/FLAML/docs/Contribute","h":"#documentation","p":52},{"i":72,"t":"AutoGen - Automated Multi Agent Chat Please refer to https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat.","s":"AutoGen - Automated Multi Agent Chat","u":"/FLAML/docs/Examples/AutoGen-AgentChat","h":"","p":71},{"i":74,"t":"TL;DR: A case study using the HumanEval benchmark shows that an adaptive way of using multiple GPT models can achieve both much higher accuracy (from 68% to 90%) and lower inference cost (by 18%) than using GPT-4 for coding. GPT-4 is a big upgrade of foundation model capability, e.g., in code and math, accompanied by a much higher (more than 10x) price per token to use over GPT-3.5-Turbo. On a code completion benchmark, HumanEval, developed by OpenAI, GPT-4 can successfully solve 68% tasks while GPT-3.5-Turbo does 46%. It is possible to increase the success rate of GPT-4 further by generating multiple responses or making multiple calls. However, that will further increase the cost, which is already nearly 20 times of using GPT-3.5-Turbo and with more restricted API call rate limit. Can we achieve more with less? In this blog post, we will explore a creative, adaptive way of using GPT models which leads to a big leap forward.","s":"Achieve More, Pay Less - Use GPT-4 Smartly","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"","p":73},{"i":76,"t":"GPT-3.5-Turbo can alrady solve 40%-50% tasks. For these tasks if we never use GPT-4, we can save nearly 40-50% cost. If we use the saved cost to generate more responses with GPT-4 for the remaining unsolved tasks, it is possible to solve some more of them while keeping the amortized cost down. The obstacle of leveraging these observations is that we do not know a priori which tasks can be solved by the cheaper model, which tasks can be solved by the expensive model, and which tasks can be solved by paying even more to the expensive model. To overcome that obstacle, one may want to predict which task requires what model to solve and how many responses are required for each task. Let's look at one example code completion task: def vowels_count(s): \"\"\"Write a function vowels_count which takes a string representing a word as input and returns the number of vowels in the string. Vowels in this case are 'a', 'e', 'i', 'o', 'u'. Here, 'y' is also a vowel, but only when it is at the end of the given word. Example: >>> vowels_count(\"abcde\") 2 >>> vowels_count(\"ACEDY\") 3 \"\"\" Copy Can we predict whether GPT-3.5-Turbo can solve this task or do we need to use GPT-4? My first guess is that GPT-3.5-Turbo can get it right because the instruction is fairly straightforward. Yet, it turns out that GPT-3.5-Turbo does not consistently get it right, if we only give it one chance. It's not obvious (but an interesting research question!) how to predict the performance without actually trying. What else can we do? We notice that: It's \"easier\" to verify a given solution than finding a correct solution from scratch. Some simple example test cases are provided in the docstr. If we already have a response generated by a model, we can use those test cases to filter wrong implementations, and either use a more powerful model or generate more responses, until the result passes the example test cases. Moreover, this step can be automated by asking GPT-3.5-Turbo to generate assertion statements from the examples given in the docstr (a simpler task where we can place our bet) and executing the code.","s":"Observations","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#observations","p":73},{"i":78,"t":"Combining these observations, we can design a solution with two intuitive ideas: Make use of auto-generated feedback, i.e., code execution results, to filter responses. Try inference configurations one by one, until one response can pass the filter. This solution works adaptively without knowing or predicting which task fits which configuration. It simply tries multiple configurations one by one, starting from the cheapest configuration. Note that one configuration can generate multiple responses (by setting the inference parameter n larger than 1). And different configurations can use the same model and different inference parameters such as n and temperature. Only one response is returned and evaluated per task. An implementation of this solution is provided in flaml.autogen. It uses the following sequence of configurations: GPT-3.5-Turbo, n=1, temperature=0 GPT-3.5-Turbo, n=7, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"] GPT-4, n=1, temperature=0 GPT-4, n=2, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"] GPT-4, n=1, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]","s":"Solution","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#solution","p":73},{"i":80,"t":"The first figure in this blog post shows the success rate and average inference cost of the adaptive solution compared with default GPT-4. The inference cost includes the cost for generating the assertions in our solution. The generated assertions are not always correct, and programs that pass/fail the generated assertions are not always right/wrong. Despite of that, the adaptive solution can increase the success rate (referred to as pass@1 in the literature) from 68% to 90%, while reducing the cost by 18%. Here are a few examples of function definitions which are solved by different configurations in the portfolio. Solved by GPT-3.5-Turbo, n=1, temperature=0 def compare(game,guess): \"\"\"I think we all remember that feeling when the result of some long-awaited event is finally known. The feelings and thoughts you have at that moment are definitely worth noting down and comparing. Your task is to determine if a person correctly guessed the results of a number of matches. You are given two arrays of scores and guesses of equal length, where each index shows a match. Return an array of the same length denoting how far off each guess was. If they have guessed correctly, the value is 0, and if not, the value is the absolute difference between the guess and the score. example: compare([1,2,3,4,5,1],[1,2,3,4,2,-2]) -> [0,0,0,0,3,3] compare([0,5,0,0,0,4],[4,1,1,0,0,-2]) -> [4,4,1,0,0,6] \"\"\" Copy Solved by GPT-3.5-Turbo, n=7, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]: the vowels_count function presented earlier. Solved by GPT-4, n=1, temperature=0: def string_xor(a: str, b: str) -> str: \"\"\" Input are two strings a and b consisting only of 1s and 0s. Perform binary XOR on these inputs and return result also as a string. >>> string_xor('010', '110') '100' \"\"\" Copy Solved by GPT-4, n=2, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]: def is_palindrome(string: str) -> bool: \"\"\" Test if given string is a palindrome \"\"\" return string == string[::-1]def make_palindrome(string: str) -> str: \"\"\" Find the shortest palindrome that begins with a supplied string. Algorithm idea is simple: - Find the longest postfix of supplied string that is a palindrome. - Append to the end of the string reverse of a string prefix that comes before the palindromic suffix. >>> make_palindrome('') '' >>> make_palindrome('cat') 'catac' >>> make_palindrome('cata') 'catac' \"\"\" Copy Solved by GPT-4, n=1, temperature=1, stop=[\"\\nclass\", \"\\ndef\", \"\\nif\", \"\\nprint\"]: def sort_array(arr): \"\"\" In this Kata, you have to sort an array of non-negative integers according to number of ones in their binary representation in ascending order. For similar number of ones, sort based on decimal value. It must be implemented like this: >>> sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5] >>> sort_array([-2, -3, -4, -5, -6]) == [-6, -5, -4, -3, -2] >>> sort_array([1, 0, 2, 3, 4]) [0, 1, 2, 3, 4] \"\"\" Copy The last problem is an example with wrong example test cases in the original definition. It misleads the adaptive solution because a correct implementation is regarded as wrong and more trials are made. The last configuration in the sequence returns the right implementation, even though it does not pass the auto-generated assertions. This example demonstrates that: Our adaptive solution has a certain degree of fault tolerance. The success rate and inference cost for the adaptive solution can be further improved if correct example test cases are used. It is worth noting that the reduced inference cost is the amortized cost over all the tasks. For each individual task, the cost can be either larger or smaller than directly using GPT-4. This is the nature of the adaptive solution: The cost is in general larger for difficult tasks than that for easy tasks. An example notebook to run this experiment can be found at: https://github.com/microsoft/FLAML/blob/v1.2.1/notebook/research/autogen_code.ipynb","s":"Experiment Results","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#experiment-results","p":73},{"i":82,"t":"Our solution is quite simple to implement using a generic interface offered in flaml.autogen, yet the result is quite encouraging. While the specific way of generating assertions is application-specific, the main ideas are general in LLM operations: Generate multiple responses to select - especially useful when selecting a good response is relatively easier than generating a good response at one shot. Consider multiple configurations to generate responses - especially useful when: Model and other inference parameter choice affect the utility-cost tradeoff; or Different configurations have complementary effect. A previous blog post provides evidence that these ideas are relevant in solving math problems too. flaml.autogen uses a technique EcoOptiGen to support inference parameter tuning and model selection. There are many directions of extensions in research and development: Generalize the way to provide feedback. Automate the process of optimizing the configurations. Build adaptive agents for different applications. Do you find this approach applicable to your use case? Do you have any other challenge to share about LLM applications? Do you like to see more support or research of LLM optimization or automation? Please join our Discord server for discussion.","s":"Discussion","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#discussion","p":73},{"i":84,"t":"Documentation about flaml.autogen and Research paper. Blog post about a related study for math.","s":"For Further Reading","u":"/FLAML/blog/2023/05/18/GPT-adaptive-humaneval","h":"#for-further-reading","p":73},{"i":86,"t":"AutoGen - Tune GPT Models flaml.autogen offers a cost-effective hyperparameter optimization technique EcoOptiGen for tuning Large Language Models. The research study finds that tuning hyperparameters can significantly improve the utility of them. Please find documentation about this feature here. Links to notebook examples: Optimize for Code Generation | Open in colab Optimize for Math | Open in colab","s":"AutoGen - Tune GPT Models","u":"/FLAML/docs/Examples/AutoGen-OpenAI","h":"","p":85},{"i":88,"t":"On this page","s":"AutoML - Classification","u":"/FLAML/docs/Examples/AutoML-Classification","h":"","p":87},{"i":90,"t":"Install the [automl] option. pip install \"flaml[automl]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Classification","h":"#prerequisites","p":87},{"i":92,"t":"from flaml import AutoMLfrom sklearn.datasets import load_iris# Initialize an AutoML instanceautoml = AutoML()# Specify automl goal and constraintautoml_settings = { \"time_budget\": 1, # in seconds \"metric\": \"accuracy\", \"task\": \"classification\", \"log_file_name\": \"iris.log\",}X_train, y_train = load_iris(return_X_y=True)# Train with labeled input dataautoml.fit(X_train=X_train, y_train=y_train, **automl_settings)# Predictprint(automl.predict_proba(X_train))# Print the best modelprint(automl.model.estimator) Copy Sample of output​ [flaml.automl: 11-12 18:21:44] {1485} INFO - Data split method: stratified[flaml.automl: 11-12 18:21:44] {1489} INFO - Evaluation method: cv[flaml.automl: 11-12 18:21:44] {1540} INFO - Minimizing error metric: 1-accuracy[flaml.automl: 11-12 18:21:44] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1'][flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-12 18:21:44] {1944} INFO - Estimated sufficient time budget=1285s. Estimated necessary time budget=23s.[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.2s, estimator lgbm's best error=0.0733, best estimator lgbm's best error=0.0733[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.3s, estimator lgbm's best error=0.0733, best estimator lgbm's best error=0.0733[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.4s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 4, current learner lgbm[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 5, current learner xgboost[flaml.automl: 11-12 18:21:45] {2029} INFO - at 0.9s, estimator xgboost's best error=0.0600, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-12 18:21:45] {2029} INFO - at 1.0s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 7, current learner extra_tree[flaml.automl: 11-12 18:21:45] {2029} INFO - at 1.1s, estimator extra_tree's best error=0.0667, best estimator lgbm's best error=0.0533[flaml.automl: 11-12 18:21:45] {2242} INFO - retrain lgbm for 0.0s[flaml.automl: 11-12 18:21:45] {2247} INFO - retrained model: LGBMClassifier(learning_rate=0.2677050123105203, max_bin=127, min_child_samples=12, n_estimators=4, num_leaves=4, reg_alpha=0.001348364934537134, reg_lambda=1.4442580148221913, verbose=-1)[flaml.automl: 11-12 18:21:45] {1608} INFO - fit succeeded[flaml.automl: 11-12 18:21:45] {1610} INFO - Time taken to find the best model: 0.3756711483001709 Copy","s":"A basic classification example","u":"/FLAML/docs/Examples/AutoML-Classification","h":"#a-basic-classification-example","p":87},{"i":94,"t":"Link to notebook | Open in colab","s":"A more advanced example including custom learner and metric","u":"/FLAML/docs/Examples/AutoML-Classification","h":"#a-more-advanced-example-including-custom-learner-and-metric","p":87},{"i":96,"t":"On this page","s":"AutoML - Rank","u":"/FLAML/docs/Examples/AutoML-Rank","h":"","p":95},{"i":98,"t":"Install the [automl] option. pip install \"flaml[automl]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Rank","h":"#prerequisites","p":95},{"i":100,"t":"from sklearn.datasets import fetch_openmlfrom flaml import AutoMLX_train, y_train = fetch_openml(name=\"credit-g\", return_X_y=True, as_frame=False)y_train = y_train.cat.codes# not a real learning to rank dataasetgroups = [200] * 4 + [100] * 2 # group countsautoml = AutoML()automl.fit( X_train, y_train, groups=groups, task=\"rank\", time_budget=10, # in seconds) Copy Sample output​ [flaml.automl: 11-15 07:14:30] {1485} INFO - Data split method: group[flaml.automl: 11-15 07:14:30] {1489} INFO - Evaluation method: holdout[flaml.automl: 11-15 07:14:30] {1540} INFO - Minimizing error metric: 1-ndcg[flaml.automl: 11-15 07:14:30] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'xgboost'][flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-15 07:14:30] {1944} INFO - Estimated sufficient time budget=679s. Estimated necessary time budget=1s.[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.1s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.1s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 4, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 5, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.2s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.3s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 7, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.3s, estimator lgbm's best error=0.0248, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 8, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 9, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0315, best estimator lgbm's best error=0.0248[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 10, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 11, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 12, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 13, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.4s, estimator xgboost's best error=0.0233, best estimator xgboost's best error=0.0233[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 14, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.5s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 15, current learner xgboost[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.5s, estimator xgboost's best error=0.0233, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 16, current learner lgbm[flaml.automl: 11-15 07:14:30] {2029} INFO - at 0.5s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:30] {1826} INFO - iteration 17, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.5s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 18, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0225, best estimator lgbm's best error=0.0225[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 19, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 20, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 21, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.7s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 22, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.7s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 23, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.8s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 24, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.8s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 25, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.8s, estimator lgbm's best error=0.0201, best estimator lgbm's best error=0.0201[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 26, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.9s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 27, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 0.9s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 28, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 1.0s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {1826} INFO - iteration 29, current learner lgbm[flaml.automl: 11-15 07:14:31] {2029} INFO - at 1.0s, estimator lgbm's best error=0.0197, best estimator lgbm's best error=0.0197[flaml.automl: 11-15 07:14:31] {2242} INFO - retrain lgbm for 0.0s[flaml.automl: 11-15 07:14:31] {2247} INFO - retrained model: LGBMRanker(colsample_bytree=0.9852774042640857, learning_rate=0.034918421933217675, max_bin=1023, min_child_samples=22, n_estimators=6, num_leaves=23, reg_alpha=0.0009765625, reg_lambda=21.505295697527654, verbose=-1)[flaml.automl: 11-15 07:14:31] {1608} INFO - fit succeeded[flaml.automl: 11-15 07:14:31] {1610} INFO - Time taken to find the best model: 0.8846545219421387[flaml.automl: 11-15 07:14:31] {1624} WARNING - Time taken to find the best model is 88% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy","s":"A simple learning-to-rank example","u":"/FLAML/docs/Examples/AutoML-Rank","h":"#a-simple-learning-to-rank-example","p":95},{"i":102,"t":"On this page","s":"AutoML - NLP","u":"/FLAML/docs/Examples/AutoML-NLP","h":"","p":101},{"i":104,"t":"This example requires GPU. Install the [automl,hf] option: pip install \"flaml[automl,hf]\" Copy","s":"Requirements","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#requirements","p":101},{"i":106,"t":"from flaml import AutoMLfrom datasets import load_datasettrain_dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\").to_pandas()dev_dataset = load_dataset(\"glue\", \"mrpc\", split=\"validation\").to_pandas()test_dataset = load_dataset(\"glue\", \"mrpc\", split=\"test\").to_pandas()custom_sent_keys = [\"sentence1\", \"sentence2\"]label_key = \"label\"X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]X_test = test_dataset[custom_sent_keys]automl = AutoML()automl_settings = { \"time_budget\": 100, \"task\": \"seq-classification\", \"fit_kwargs_by_estimator\": { \"transformer\": { \"output_dir\": \"data/output/\" # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base } }, # setting the huggingface arguments: output directory \"gpu_per_trial\": 1, # set to 0 if no GPU is available}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)automl.predict(X_test) Copy Notice that after you run automl.fit, the intermediate checkpoints are saved under the specified output_dir data/output. You can use the following code to clean these outputs if they consume a large storage space: if os.path.exists(\"data/output/\"): shutil.rmtree(\"data/output/\") Copy Sample output​ [flaml.automl: 12-06 08:21:39] {1943} INFO - task = seq-classification[flaml.automl: 12-06 08:21:39] {1945} INFO - Data split method: stratified[flaml.automl: 12-06 08:21:39] {1949} INFO - Evaluation method: holdout[flaml.automl: 12-06 08:21:39] {2019} INFO - Minimizing error metric: 1-accuracy[flaml.automl: 12-06 08:21:39] {2071} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 12-06 08:21:39] {2311} INFO - iteration 0, current learner transformer{'data/output/train_2021-12-06_08-21-53/train_8947b1b2_1_n=1e-06,s=9223372036854775807,e=1e-05,s=-1,s=0.45765,e=32,d=42,o=0.0,y=0.0_2021-12-06_08-21-53/checkpoint-53': 53}[flaml.automl: 12-06 08:22:56] {2424} INFO - Estimated sufficient time budget=766860s. Estimated necessary time budget=767s.[flaml.automl: 12-06 08:22:56] {2499} INFO - at 76.7s, estimator transformer's best error=0.1740, best estimator transformer's best error=0.1740[flaml.automl: 12-06 08:22:56] {2606} INFO - selected model: <flaml.nlp.huggingface.trainer.TrainerForAuto object at 0x7f49ea8414f0>[flaml.automl: 12-06 08:22:56] {2100} INFO - fit succeeded[flaml.automl: 12-06 08:22:56] {2101} INFO - Time taken to find the best model: 76.69802761077881[flaml.automl: 12-06 08:22:56] {2112} WARNING - Time taken to find the best model is 77% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy","s":"A simple sequence classification example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-sequence-classification-example","p":101},{"i":108,"t":"from flaml import AutoMLfrom datasets import load_datasettrain_dataset = load_dataset(\"glue\", \"stsb\", split=\"train\").to_pandas()dev_dataset = load_dataset(\"glue\", \"stsb\", split=\"train\").to_pandas()custom_sent_keys = [\"sentence1\", \"sentence2\"]label_key = \"label\"X_train = train_dataset[custom_sent_keys]y_train = train_dataset[label_key]X_val = dev_dataset[custom_sent_keys]y_val = dev_dataset[label_key]automl = AutoML()automl_settings = { \"gpu_per_trial\": 0, \"time_budget\": 20, \"task\": \"seq-regression\", \"metric\": \"rmse\",}automl_settings[\"fit_kwargs_by_estimator\"] = { # setting the huggingface arguments \"transformer\": { \"model_path\": \"google/electra-small-discriminator\", # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base \"output_dir\": \"data/output/\", # setting the output directory \"fp16\": False, } # setting whether to use FP16}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings) Copy Sample output​ [flaml.automl: 12-20 11:47:28] {1965} INFO - task = seq-regression[flaml.automl: 12-20 11:47:28] {1967} INFO - Data split method: uniform[flaml.automl: 12-20 11:47:28] {1971} INFO - Evaluation method: holdout[flaml.automl: 12-20 11:47:28] {2063} INFO - Minimizing error metric: rmse[flaml.automl: 12-20 11:47:28] {2115} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 12-20 11:47:28] {2355} INFO - iteration 0, current learner transformer Copy","s":"A simple sequence regression example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-sequence-regression-example","p":101},{"i":110,"t":"from flaml import AutoMLfrom datasets import load_datasettrain_dataset = load_dataset(\"xsum\", split=\"train\").to_pandas()dev_dataset = load_dataset(\"xsum\", split=\"validation\").to_pandas()custom_sent_keys = [\"document\"]label_key = \"summary\"X_train = train_dataset[custom_sent_keys]y_train = train_dataset[label_key]X_val = dev_dataset[custom_sent_keys]y_val = dev_dataset[label_key]automl = AutoML()automl_settings = { \"gpu_per_trial\": 1, \"time_budget\": 20, \"task\": \"summarization\", \"metric\": \"rouge1\",}automl_settings[\"fit_kwargs_by_estimator\"] = { # setting the huggingface arguments \"transformer\": { \"model_path\": \"t5-small\", # if model_path is not set, the default model is t5-small: https://huggingface.co/t5-small \"output_dir\": \"data/output/\", # setting the output directory \"fp16\": False, } # setting whether to use FP16}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings) Copy Sample Output​ [flaml.automl: 12-20 11:44:03] {1965} INFO - task = summarization[flaml.automl: 12-20 11:44:03] {1967} INFO - Data split method: uniform[flaml.automl: 12-20 11:44:03] {1971} INFO - Evaluation method: holdout[flaml.automl: 12-20 11:44:03] {2063} INFO - Minimizing error metric: -rouge[flaml.automl: 12-20 11:44:03] {2115} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 12-20 11:44:03] {2355} INFO - iteration 0, current learner transformerloading configuration file https://huggingface.co/t5-small/resolve/main/config.json from cache at /home/xliu127/.cache/huggingface/transformers/fe501e8fd6425b8ec93df37767fcce78ce626e34cc5edc859c662350cf712e41.406701565c0afd9899544c1cb8b93185a76f00b31e5ce7f6e18bbaef02241985Model config T5Config { \"_name_or_path\": \"t5-small\", \"architectures\": [ \"T5WithLMHeadModel\" ], \"d_ff\": 2048, \"d_kv\": 64, \"d_model\": 512, \"decoder_start_token_id\": 0, \"dropout_rate\": 0.1, \"eos_token_id\": 1, \"feed_forward_proj\": \"relu\", \"initializer_factor\": 1.0, \"is_encoder_decoder\": true, \"layer_norm_epsilon\": 1e-06, \"model_type\": \"t5\", \"n_positions\": 512, \"num_decoder_layers\": 6, \"num_heads\": 8, \"num_layers\": 6, \"output_past\": true, \"pad_token_id\": 0, \"relative_attention_num_buckets\": 32, \"task_specific_params\": { \"summarization\": { \"early_stopping\": true, \"length_penalty\": 2.0, \"max_length\": 200, \"min_length\": 30, \"no_repeat_ngram_size\": 3, \"num_beams\": 4, \"prefix\": \"summarize: \" }, \"translation_en_to_de\": { \"early_stopping\": true, \"max_length\": 300, \"num_beams\": 4, \"prefix\": \"translate English to German: \" }, \"translation_en_to_fr\": { \"early_stopping\": true, \"max_length\": 300, \"num_beams\": 4, \"prefix\": \"translate English to French: \" }, \"translation_en_to_ro\": { \"early_stopping\": true, \"max_length\": 300, \"num_beams\": 4, \"prefix\": \"translate English to Romanian: \" } }, \"transformers_version\": \"4.14.1\", \"use_cache\": true, \"vocab_size\": 32128} Copy","s":"A simple summarization example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-summarization-example","p":101},{"i":112,"t":"There are two ways to define the label for a token classification task. The first is to define the token labels: from flaml import AutoMLimport pandas as pdtrain_dataset = { \"id\": [\"0\", \"1\"], \"ner_tags\": [ [\"B-ORG\", \"O\", \"B-MISC\", \"O\", \"O\", \"O\", \"B-MISC\", \"O\", \"O\"], [\"B-PER\", \"I-PER\"], ], \"tokens\": [ [ \"EU\", \"rejects\", \"German\", \"call\", \"to\", \"boycott\", \"British\", \"lamb\", \".\", ], [\"Peter\", \"Blackburn\"], ],}dev_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [\"O\"], ], \"tokens\": [[\"1996-08-22\"]],}test_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [\"O\"], ], \"tokens\": [[\".\"]],}custom_sent_keys = [\"tokens\"]label_key = \"ner_tags\"train_dataset = pd.DataFrame(train_dataset)dev_dataset = pd.DataFrame(dev_dataset)test_dataset = pd.DataFrame(test_dataset)X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]X_test = test_dataset[custom_sent_keys]automl = AutoML()automl_settings = { \"time_budget\": 10, \"task\": \"token-classification\", \"fit_kwargs_by_estimator\": { \"transformer\": { \"output_dir\": \"data/output/\" # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base } }, # setting the huggingface arguments: output directory \"gpu_per_trial\": 1, # set to 0 if no GPU is available \"metric\": \"seqeval:overall_f1\",}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)automl.predict(X_test) Copy The second is to define the id labels + a token label list: from flaml import AutoMLimport pandas as pdtrain_dataset = { \"id\": [\"0\", \"1\"], \"ner_tags\": [ [3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], ], \"tokens\": [ [ \"EU\", \"rejects\", \"German\", \"call\", \"to\", \"boycott\", \"British\", \"lamb\", \".\", ], [\"Peter\", \"Blackburn\"], ],}dev_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [0], ], \"tokens\": [[\"1996-08-22\"]],}test_dataset = { \"id\": [\"0\"], \"ner_tags\": [ [0], ], \"tokens\": [[\".\"]],}custom_sent_keys = [\"tokens\"]label_key = \"ner_tags\"train_dataset = pd.DataFrame(train_dataset)dev_dataset = pd.DataFrame(dev_dataset)test_dataset = pd.DataFrame(test_dataset)X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]X_test = test_dataset[custom_sent_keys]automl = AutoML()automl_settings = { \"time_budget\": 10, \"task\": \"token-classification\", \"fit_kwargs_by_estimator\": { \"transformer\": { \"output_dir\": \"data/output/\", # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base \"label_list\": [ \"O\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\", \"B-MISC\", \"I-MISC\", ], } }, # setting the huggingface arguments: output directory \"gpu_per_trial\": 1, # set to 0 if no GPU is available \"metric\": \"seqeval:overall_f1\",}automl.fit( X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)automl.predict(X_test) Copy Sample Output​ [flaml.automl: 06-30 03:10:02] {2423} INFO - task = token-classification[flaml.automl: 06-30 03:10:02] {2425} INFO - Data split method: stratified[flaml.automl: 06-30 03:10:02] {2428} INFO - Evaluation method: holdout[flaml.automl: 06-30 03:10:02] {2497} INFO - Minimizing error metric: seqeval:overall_f1[flaml.automl: 06-30 03:10:02] {2637} INFO - List of ML learners in AutoML Run: ['transformer'][flaml.automl: 06-30 03:10:02] {2929} INFO - iteration 0, current learner transformer Copy For tasks that are not currently supported, use flaml.tune for customized tuning.","s":"A simple token classification example","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#a-simple-token-classification-example","p":101},{"i":114,"t":"To run more examples, especially examples using Ray Tune, please go to: Link to notebook | Open in colab","s":"Link to Jupyter notebook","u":"/FLAML/docs/Examples/AutoML-NLP","h":"#link-to-jupyter-notebook","p":101},{"i":116,"t":"On this page","s":"AutoML for XGBoost","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"","p":115},{"i":118,"t":"Install the [automl] option. pip install \"flaml[automl] matplotlib openml\" Copy","s":"Prerequisites for this example","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"#prerequisites-for-this-example","p":115},{"i":120,"t":"from flaml import AutoMLfrom flaml.automl.data import load_openml_dataset# Download [houses dataset](https://www.openml.org/d/537) from OpenML. The task is to predict median price of the house in the region based on demographic composition and a state of housing market in the region.X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")automl = AutoML()settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"r2\", # primary metrics for regression can be chosen from: ['mae','mse','r2'] \"estimator_list\": [ \"xgboost\" ], # list of ML learners; we tune XGBoost in this example \"task\": \"regression\", # task type \"log_file_name\": \"houses_experiment.log\", # flaml log file \"seed\": 7654321, # random seed}automl.fit(X_train=X_train, y_train=y_train, **settings) Copy Sample output​ [flaml.automl: 09-29 23:06:46] {1446} INFO - Data split method: uniform[flaml.automl: 09-29 23:06:46] {1450} INFO - Evaluation method: cv[flaml.automl: 09-29 23:06:46] {1496} INFO - Minimizing error metric: 1-r2[flaml.automl: 09-29 23:06:46] {1533} INFO - List of ML learners in AutoML Run: ['xgboost'][flaml.automl: 09-29 23:06:46] {1763} INFO - iteration 0, current learner xgboost[flaml.automl: 09-29 23:06:47] {1880} INFO - Estimated sufficient time budget=2621s. Estimated necessary time budget=3s.[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.3s, estimator xgboost's best error=2.1267, best estimator xgboost's best error=2.1267[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 1, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.5s, estimator xgboost's best error=2.1267, best estimator xgboost's best error=2.1267[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 2, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.6s, estimator xgboost's best error=0.8485, best estimator xgboost's best error=0.8485[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 3, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 0.8s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 4, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 1.0s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 5, current learner xgboost[flaml.automl: 09-29 23:06:47] {1952} INFO - at 1.2s, estimator xgboost's best error=0.3799, best estimator xgboost's best error=0.3799[flaml.automl: 09-29 23:06:47] {1763} INFO - iteration 6, current learner xgboost[flaml.automl: 09-29 23:06:48] {1952} INFO - at 1.5s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992[flaml.automl: 09-29 23:06:48] {1763} INFO - iteration 7, current learner xgboost[flaml.automl: 09-29 23:06:48] {1952} INFO - at 1.9s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992[flaml.automl: 09-29 23:06:48] {1763} INFO - iteration 8, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.2s, estimator xgboost's best error=0.2992, best estimator xgboost's best error=0.2992[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 9, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.5s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 10, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 2.8s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 11, current learner xgboost[flaml.automl: 09-29 23:06:49] {1952} INFO - at 3.0s, estimator xgboost's best error=0.2513, best estimator xgboost's best error=0.2513[flaml.automl: 09-29 23:06:49] {1763} INFO - iteration 12, current learner xgboost[flaml.automl: 09-29 23:06:50] {1952} INFO - at 3.3s, estimator xgboost's best error=0.2113, best estimator xgboost's best error=0.2113[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 13, current learner xgboost[flaml.automl: 09-29 23:06:50] {1952} INFO - at 3.5s, estimator xgboost's best error=0.2113, best estimator xgboost's best error=0.2113[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 14, current learner xgboost[flaml.automl: 09-29 23:06:50] {1952} INFO - at 4.0s, estimator xgboost's best error=0.2090, best estimator xgboost's best error=0.2090[flaml.automl: 09-29 23:06:50] {1763} INFO - iteration 15, current learner xgboost[flaml.automl: 09-29 23:06:51] {1952} INFO - at 4.5s, estimator xgboost's best error=0.2090, best estimator xgboost's best error=0.2090[flaml.automl: 09-29 23:06:51] {1763} INFO - iteration 16, current learner xgboost[flaml.automl: 09-29 23:06:51] {1952} INFO - at 5.2s, estimator xgboost's best error=0.1919, best estimator xgboost's best error=0.1919[flaml.automl: 09-29 23:06:51] {1763} INFO - iteration 17, current learner xgboost[flaml.automl: 09-29 23:06:52] {1952} INFO - at 5.5s, estimator xgboost's best error=0.1919, best estimator xgboost's best error=0.1919[flaml.automl: 09-29 23:06:52] {1763} INFO - iteration 18, current learner xgboost[flaml.automl: 09-29 23:06:54] {1952} INFO - at 8.0s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:06:54] {1763} INFO - iteration 19, current learner xgboost[flaml.automl: 09-29 23:06:55] {1952} INFO - at 9.0s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:06:55] {1763} INFO - iteration 20, current learner xgboost[flaml.automl: 09-29 23:07:08] {1952} INFO - at 21.8s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:07:08] {1763} INFO - iteration 21, current learner xgboost[flaml.automl: 09-29 23:07:11] {1952} INFO - at 24.4s, estimator xgboost's best error=0.1797, best estimator xgboost's best error=0.1797[flaml.automl: 09-29 23:07:11] {1763} INFO - iteration 22, current learner xgboost[flaml.automl: 09-29 23:07:16] {1952} INFO - at 30.0s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:16] {1763} INFO - iteration 23, current learner xgboost[flaml.automl: 09-29 23:07:20] {1952} INFO - at 33.5s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:20] {1763} INFO - iteration 24, current learner xgboost[flaml.automl: 09-29 23:07:29] {1952} INFO - at 42.3s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:29] {1763} INFO - iteration 25, current learner xgboost[flaml.automl: 09-29 23:07:30] {1952} INFO - at 43.2s, estimator xgboost's best error=0.1782, best estimator xgboost's best error=0.1782[flaml.automl: 09-29 23:07:30] {1763} INFO - iteration 26, current learner xgboost[flaml.automl: 09-29 23:07:50] {1952} INFO - at 63.4s, estimator xgboost's best error=0.1663, best estimator xgboost's best error=0.1663[flaml.automl: 09-29 23:07:50] {2059} INFO - selected model: <xgboost.core.Booster object at 0x7f6399005910>[flaml.automl: 09-29 23:07:55] {2122} INFO - retrain xgboost for 5.4s[flaml.automl: 09-29 23:07:55] {2128} INFO - retrained model: <xgboost.core.Booster object at 0x7f6398fc0eb0>[flaml.automl: 09-29 23:07:55] {1557} INFO - fit succeeded[flaml.automl: 09-29 23:07:55] {1558} INFO - Time taken to find the best model: 63.427649974823[flaml.automl: 09-29 23:07:55] {1569} WARNING - Time taken to find the best model is 106% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy Retrieve best config​ print(\"Best hyperparmeter config:\", automl.best_config)print(\"Best r2 on validation data: {0:.4g}\".format(1 - automl.best_loss))print(\"Training duration of best run: {0:.4g} s\".format(automl.best_config_train_time))print(automl.model.estimator)# Best hyperparmeter config: {'n_estimators': 473, 'max_leaves': 35, 'max_depth': 0, 'min_child_weight': 0.001, 'learning_rate': 0.26865031351923346, 'subsample': 0.9718245679598786, 'colsample_bylevel': 0.7421362469066445, 'colsample_bytree': 1.0, 'reg_alpha': 0.06824336834995245, 'reg_lambda': 250.9654222583276}# Best r2 on validation data: 0.8384# Training duration of best run: 2.194 s# XGBRegressor(base_score=0.5, booster='gbtree',# colsample_bylevel=0.7421362469066445, colsample_bynode=1,# colsample_bytree=1.0, gamma=0, gpu_id=-1, grow_policy='lossguide',# importance_type='gain', interaction_constraints='',# learning_rate=0.26865031351923346, max_delta_step=0, max_depth=0,# max_leaves=35, min_child_weight=0.001, missing=nan,# monotone_constraints='()', n_estimators=473, n_jobs=-1,# num_parallel_tree=1, random_state=0, reg_alpha=0.06824336834995245,# reg_lambda=250.9654222583276, scale_pos_weight=1,# subsample=0.9718245679598786, tree_method='hist',# use_label_encoder=False, validate_parameters=1, verbosity=0) Copy Plot feature importance​ import matplotlib.pyplot as pltplt.barh(automl.feature_names_in_, automl.feature_importances_) Copy Compute predictions of testing dataset​ y_pred = automl.predict(X_test)print(\"Predicted labels\", y_pred)# Predicted labels [139062.95 237622. 140522.03 ... 182125.5 252156.36 264884.5 ] Copy Compute different metric values on testing dataset​ from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))print(\"mse\", \"=\", sklearn_metric_loss_score(\"mse\", y_pred, y_test))print(\"mae\", \"=\", sklearn_metric_loss_score(\"mae\", y_pred, y_test))# r2 = 0.8456494234135888# mse = 2040284106.2781258# mae = 30212.830996680445 Copy Compare with untuned XGBoost​ from xgboost import XGBRegressorxgb = XGBRegressor()xgb.fit(X_train, y_train)y_pred = xgb.predict(X_test)from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"default xgboost r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))# default xgboost r2 = 0.8265451174596482 Copy Plot learning curve​ How does the model accuracy improve as we search for different hyperparameter configurations? from flaml.automl.data import get_output_from_logimport numpy as nptime_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = get_output_from_log(filename=settings['log_file_name'], time_budget=60)plt.title('Learning Curve')plt.xlabel('Wall Clock Time (s)')plt.ylabel('Validation r2')plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')plt.show() Copy","s":"Use built-in XGBoostSklearnEstimator","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"#use-built-in-xgboostsklearnestimator","p":115},{"i":122,"t":"You can easily enable a custom objective function by adding a customized XGBoost learner (inherit XGBoostEstimator or XGBoostSklearnEstimator) in FLAML. In the following example, we show how to add such a customized XGBoost learner with a custom objective function. import numpy as np# define your customized objective functiondef logregobj(preds, dtrain): labels = dtrain.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight grad = preds - labels hess = preds * (1.0 - preds) return grad, hessfrom flaml.automl.model import XGBoostEstimatorclass MyXGB1(XGBoostEstimator): \"\"\"XGBoostEstimator with the logregobj function as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=logregobj, **config)class MyXGB2(XGBoostEstimator): \"\"\"XGBoostEstimator with 'reg:squarederror' as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=\"reg:gamma\", **config) Copy Add the customized learners and tune them​ automl = AutoML()automl.add_learner(learner_name=\"my_xgb1\", learner_class=MyXGB1)automl.add_learner(learner_name=\"my_xgb2\", learner_class=MyXGB2)settings[\"estimator_list\"] = [\"my_xgb1\", \"my_xgb2\"] # change the estimator listautoml.fit(X_train=X_train, y_train=y_train, **settings) Copy Link to notebook | Open in colab","s":"Use a customized XGBoost learner","u":"/FLAML/docs/Examples/AutoML-for-XGBoost","h":"#use-a-customized-xgboost-learner","p":115},{"i":124,"t":"On this page","s":"AutoML - Regression","u":"/FLAML/docs/Examples/AutoML-Regression","h":"","p":123},{"i":126,"t":"Install the [automl] option. pip install \"flaml[automl]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Regression","h":"#prerequisites","p":123},{"i":128,"t":"from flaml import AutoMLfrom sklearn.datasets import fetch_california_housing# Initialize an AutoML instanceautoml = AutoML()# Specify automl goal and constraintautoml_settings = { \"time_budget\": 1, # in seconds \"metric\": \"r2\", \"task\": \"regression\", \"log_file_name\": \"california.log\",}X_train, y_train = fetch_california_housing(return_X_y=True)# Train with labeled input dataautoml.fit(X_train=X_train, y_train=y_train, **automl_settings)# Predictprint(automl.predict(X_train))# Print the best modelprint(automl.model.estimator) Copy Sample output​ [flaml.automl: 11-15 07:08:19] {1485} INFO - Data split method: uniform[flaml.automl: 11-15 07:08:19] {1489} INFO - Evaluation method: holdout[flaml.automl: 11-15 07:08:19] {1540} INFO - Minimizing error metric: 1-r2[flaml.automl: 11-15 07:08:19] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree'][flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-15 07:08:19] {1944} INFO - Estimated sufficient time budget=846s. Estimated necessary time budget=2s.[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.2s, estimator lgbm's best error=0.7393, best estimator lgbm's best error=0.7393[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.3s, estimator lgbm's best error=0.7393, best estimator lgbm's best error=0.7393[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.3s, estimator lgbm's best error=0.5446, best estimator lgbm's best error=0.5446[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.4s, estimator lgbm's best error=0.2807, best estimator lgbm's best error=0.2807[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 4, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.5s, estimator lgbm's best error=0.2712, best estimator lgbm's best error=0.2712[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 5, current learner lgbm[flaml.automl: 11-15 07:08:19] {2029} INFO - at 0.5s, estimator lgbm's best error=0.2712, best estimator lgbm's best error=0.2712[flaml.automl: 11-15 07:08:19] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.6s, estimator lgbm's best error=0.2712, best estimator lgbm's best error=0.2712[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 7, current learner lgbm[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.7s, estimator lgbm's best error=0.2197, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 8, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.8s, estimator xgboost's best error=1.4958, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 9, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.8s, estimator xgboost's best error=1.4958, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 10, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.9s, estimator xgboost's best error=0.7052, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 11, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.9s, estimator xgboost's best error=0.3619, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 12, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 0.9s, estimator xgboost's best error=0.3619, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 13, current learner xgboost[flaml.automl: 11-15 07:08:20] {2029} INFO - at 1.0s, estimator xgboost's best error=0.3619, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {1826} INFO - iteration 14, current learner extra_tree[flaml.automl: 11-15 07:08:20] {2029} INFO - at 1.1s, estimator extra_tree's best error=0.7197, best estimator lgbm's best error=0.2197[flaml.automl: 11-15 07:08:20] {2242} INFO - retrain lgbm for 0.0s[flaml.automl: 11-15 07:08:20] {2247} INFO - retrained model: LGBMRegressor(colsample_bytree=0.7610534336273627, learning_rate=0.41929025492645006, max_bin=255, min_child_samples=4, n_estimators=45, num_leaves=4, reg_alpha=0.0009765625, reg_lambda=0.009280655005879943, verbose=-1)[flaml.automl: 11-15 07:08:20] {1608} INFO - fit succeeded[flaml.automl: 11-15 07:08:20] {1610} INFO - Time taken to find the best model: 0.7289648056030273[flaml.automl: 11-15 07:08:20] {1624} WARNING - Time taken to find the best model is 73% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy","s":"A basic regression example","u":"/FLAML/docs/Examples/AutoML-Regression","h":"#a-basic-regression-example","p":123},{"i":130,"t":"We can combine sklearn.MultiOutputRegressor and flaml.AutoML to do AutoML for multi-output regression. from flaml import AutoMLfrom sklearn.datasets import make_regressionfrom sklearn.model_selection import train_test_splitfrom sklearn.multioutput import MultiOutputRegressor# create regression dataX, y = make_regression(n_targets=3)# split into train and test dataX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.30, random_state=42)# train the modelmodel = MultiOutputRegressor(AutoML(task=\"regression\", time_budget=60))model.fit(X_train, y_train)# predictprint(model.predict(X_test)) Copy It will perform AutoML for each target, each taking 60 seconds.","s":"Multi-output regression","u":"/FLAML/docs/Examples/AutoML-Regression","h":"#multi-output-regression","p":123},{"i":132,"t":"On this page","s":"Integrate - Scikit-learn Pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"","p":131},{"i":134,"t":"Install the [automl] option. pip install \"flaml[automl] openml\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#prerequisites","p":131},{"i":136,"t":"from flaml.automl.data import load_openml_dataset# Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.X_train, X_test, y_train, y_test = load_openml_dataset( dataset_id=1169, data_dir=\"./\", random_state=1234, dataset_format=\"array\") Copy","s":"Load data","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#load-data","p":131},{"i":138,"t":"from sklearn import set_configfrom sklearn.pipeline import Pipelinefrom sklearn.impute import SimpleImputerfrom sklearn.preprocessing import StandardScalerfrom flaml import AutoMLset_config(display=\"diagram\")imputer = SimpleImputer()standardizer = StandardScaler()automl = AutoML()automl_pipeline = Pipeline( [(\"imputuer\", imputer), (\"standardizer\", standardizer), (\"automl\", automl)])automl_pipeline Copy","s":"Create a pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#create-a-pipeline","p":131},{"i":140,"t":"automl_settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"accuracy\", # primary metrics can be chosen from: ['accuracy', 'roc_auc', 'roc_auc_weighted', 'roc_auc_ovr', 'roc_auc_ovo', 'f1', 'log_loss', 'mae', 'mse', 'r2'] Check the documentation for more details (https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#optimization-metric) \"task\": \"classification\", # task type \"estimator_list\": [\"xgboost\", \"catboost\", \"lgbm\"], \"log_file_name\": \"airlines_experiment.log\", # flaml log file}pipeline_settings = {f\"automl__{key}\": value for key, value in automl_settings.items()}automl_pipeline.fit(X_train, y_train, **pipeline_settings) Copy","s":"Run AutoML in the pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#run-automl-in-the-pipeline","p":131},{"i":142,"t":"automl = automl_pipeline.steps[2][1]# Get the best config and best learnerprint(\"Best ML leaner:\", automl.best_estimator)print(\"Best hyperparmeter config:\", automl.best_config)print(\"Best accuracy on validation data: {0:.4g}\".format(1 - automl.best_loss))print(\"Training duration of best run: {0:.4g} s\".format(automl.best_config_train_time)) Copy Link to notebook | Open in colab","s":"Get the automl object from the pipeline","u":"/FLAML/docs/Examples/Integrate - Scikit-learn Pipeline","h":"#get-the-automl-object-from-the-pipeline","p":131},{"i":144,"t":"On this page","s":"Integrate - Spark","u":"/FLAML/docs/Examples/Integrate - Spark","h":"","p":143},{"i":146,"t":"FLAML integrates estimators based on Spark ML models. These models are trained in parallel using Spark, so we called them Spark estimators. To use these models, you first need to organize your data in the required format.","s":"Spark ML Estimators","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#spark-ml-estimators","p":143},{"i":148,"t":"For Spark estimators, AutoML only consumes Spark data. FLAML provides a convenient function to_pandas_on_spark in the flaml.automl.spark.utils module to convert your data into a pandas-on-spark (pyspark.pandas) dataframe/series, which Spark estimators require. This utility function takes data in the form of a pandas.Dataframe or pyspark.sql.Dataframe and converts it into a pandas-on-spark dataframe. It also takes pandas.Series or pyspark.sql.Dataframe and converts it into a pandas-on-spark series. If you pass in a pyspark.pandas.Dataframe, it will not make any changes. This function also accepts optional arguments index_col and default_index_type. index_col is the column name to use as the index, default is None. default_index_type is the default index type, default is \"distributed-sequence\". More info about default index type could be found on Spark official documentation Here is an example code snippet for Spark Data: import pandas as pdfrom flaml.automl.spark.utils import to_pandas_on_spark# Creating a dictionarydata = { \"Square_Feet\": [800, 1200, 1800, 1500, 850], \"Age_Years\": [20, 15, 10, 7, 25], \"Price\": [100000, 200000, 300000, 240000, 120000],}# Creating a pandas DataFramedataframe = pd.DataFrame(data)label = \"Price\"# Convert to pandas-on-spark dataframepsdf = to_pandas_on_spark(dataframe) Copy To use Spark ML models you need to format your data appropriately. Specifically, use VectorAssembler to merge all feature columns into a single vector column. Here is an example of how to use it: from pyspark.ml.feature import VectorAssemblercolumns = psdf.columnsfeature_cols = [col for col in columns if col != label]featurizer = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")psdf = featurizer.transform(psdf.to_spark(index_col=\"index\"))[\"index\", \"features\"] Copy Later in conducting the experiment, use your pandas-on-spark data like non-spark data and pass them using X_train, y_train or dataframe, label.","s":"Data","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#data","p":143},{"i":150,"t":"Model List​ lgbm_spark: The class for fine-tuning Spark version LightGBM models, using SynapseML API. Usage​ First, prepare your data in the required format as described in the previous section. By including the models you intend to try in the estimators_list argument to flaml.automl, FLAML will start trying configurations for these models. If your input is Spark data, FLAML will also use estimators with the _spark postfix by default, even if you haven't specified them. Here is an example code snippet using SparkML models in AutoML: import flaml# prepare your data in pandas-on-spark format as we previously mentionedautoml = flaml.AutoML()settings = { \"time_budget\": 30, \"metric\": \"r2\", \"estimator_list\": [\"lgbm_spark\"], # this setting is optional \"task\": \"regression\",}automl.fit( dataframe=psdf, label=label, **settings,) Copy Link to notebook | Open in colab","s":"Estimators","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#estimators","p":143},{"i":152,"t":"You can activate Spark as the parallel backend during parallel tuning in both AutoML and Hyperparameter Tuning, by setting the use_spark to true. FLAML will dispatch your job to the distributed Spark backend using joblib-spark. Please note that you should not set use_spark to true when applying AutoML and Tuning for Spark Data. This is because only SparkML models will be used for Spark Data in AutoML and Tuning. As SparkML models run in parallel, there is no need to distribute them with use_spark again. All the Spark-related arguments are stated below. These arguments are available in both Hyperparameter Tuning and AutoML: use_spark: boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable FLAML_MAX_CONCURRENT to override the detected num_executors. The final number of concurrent trials will be the minimum of n_concurrent_trials and num_executors. n_concurrent_trials: int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, FLAML performes parallel tuning. force_cancel: boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. Spark jobs include parallel tuning jobs and Spark-based model training jobs. An example code snippet for using parallel Spark jobs: import flamlautoml_experiment = flaml.AutoML()automl_settings = { \"time_budget\": 30, \"metric\": \"r2\", \"task\": \"regression\", \"n_concurrent_trials\": 2, \"use_spark\": True, \"force_cancel\": True, # Activating the force_cancel option can immediately halt Spark jobs once they exceed the allocated time_budget.}automl.fit( dataframe=dataframe, label=label, **automl_settings,) Copy Link to notebook | Open in colab","s":"Parallel Spark Jobs","u":"/FLAML/docs/Examples/Integrate - Spark","h":"#parallel-spark-jobs","p":143},{"i":154,"t":"On this page","s":"Default - Flamlized Estimator","u":"/FLAML/docs/Examples/Default-Flamlized","h":"","p":153},{"i":157,"t":"This example requires the [autozero] option. pip install flaml[autozero] lightgbm openml Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#prerequisites","p":153},{"i":159,"t":"from flaml.automl.data import load_openml_datasetfrom flaml.default import LGBMRegressorfrom flaml.automl.ml import sklearn_metric_loss_scoreX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")lgbm = LGBMRegressor()lgbm.fit(X_train, y_train)y_pred = lgbm.predict(X_test)print(\"flamlized lgbm r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))print(lgbm) Copy Sample output​ load dataset from ./openml_ds537.pklDataset name: housesX_train.shape: (15480, 8), y_train.shape: (15480,);X_test.shape: (5160, 8), y_test.shape: (5160,)flamlized lgbm r2 = 0.8537444671194614LGBMRegressor(colsample_bytree=0.7019911744574896, learning_rate=0.022635758411078528, max_bin=511, min_child_samples=2, n_estimators=4797, num_leaves=122, reg_alpha=0.004252223402511765, reg_lambda=0.11288241427227624, verbose=-1) Copy","s":"Zero-shot AutoML","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#zero-shot-automl","p":153},{"i":161,"t":"from flaml.automl.data import load_openml_datasetfrom flaml.default import LGBMRegressorfrom flaml.ml import sklearn_metric_loss_scoreX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")lgbm = LGBMRegressor()hyperparams, estimator_name, X_transformed, y_transformed = lgbm.suggest_hyperparams(X_train, y_train)print(hyperparams) Copy Sample output​ load dataset from ./openml_ds537.pklDataset name: housesX_train.shape: (15480, 8), y_train.shape: (15480,);X_test.shape: (5160, 8), y_test.shape: (5160,){'n_estimators': 4797, 'num_leaves': 122, 'min_child_samples': 2, 'learning_rate': 0.022635758411078528, 'colsample_bytree': 0.7019911744574896, 'reg_alpha': 0.004252223402511765, 'reg_lambda': 0.11288241427227624, 'max_bin': 511, 'verbose': -1} Copy Link to notebook | Open in colab","s":"Suggest hyperparameters without training","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#suggest-hyperparameters-without-training","p":153},{"i":164,"t":"This example requires xgboost, sklearn, openml==0.10.2.","s":"Prerequisites","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#prerequisites-1","p":153},{"i":166,"t":"from flaml.automl.data import load_openml_datasetfrom flaml.default import XGBClassifierfrom flaml.automl.ml import sklearn_metric_loss_scoreX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir=\"./\")xgb = XGBClassifier()xgb.fit(X_train, y_train)y_pred = xgb.predict(X_test)print( \"flamlized xgb accuracy\", \"=\", 1 - sklearn_metric_loss_score(\"accuracy\", y_pred, y_test),)print(xgb) Copy Sample output​ load dataset from ./openml_ds1169.pklDataset name: airlinesX_train.shape: (404537, 7), y_train.shape: (404537,);X_test.shape: (134846, 7), y_test.shape: (134846,)flamlized xgb accuracy = 0.6729009388487608XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.4601573737792679, colsample_bynode=1, colsample_bytree=1.0, gamma=0, gpu_id=-1, grow_policy='lossguide', importance_type='gain', interaction_constraints='', learning_rate=0.04039771837785377, max_delta_step=0, max_depth=0, max_leaves=159, min_child_weight=0.3396294979905001, missing=nan, monotone_constraints='()', n_estimators=540, n_jobs=4, num_parallel_tree=1, random_state=0, reg_alpha=0.0012362430984376035, reg_lambda=3.093428791531145, scale_pos_weight=1, subsample=1.0, tree_method='hist', use_label_encoder=False, validate_parameters=1, verbosity=0) Copy","s":"Zero-shot AutoML","u":"/FLAML/docs/Examples/Default-Flamlized","h":"#zero-shot-automl-1","p":153},{"i":168,"t":"On this page","s":"Integrate - AzureML","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"","p":167},{"i":170,"t":"Install the [automl,azureml] option. pip install \"flaml[automl,azureml]\" Copy Setup a AzureML workspace: from azureml.core import Workspacews = Workspace.create( name=\"myworkspace\", subscription_id=\"<azure-subscription-id>\", resource_group=\"myresourcegroup\",) Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#prerequisites","p":167},{"i":172,"t":"import mlflowfrom azureml.core import Workspacews = Workspace.from_config()mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri()) Copy","s":"Enable mlflow in AzureML workspace","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#enable-mlflow-in-azureml-workspace","p":167},{"i":174,"t":"from flaml.automl.data import load_openml_datasetfrom flaml import AutoML# Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir=\"./\")automl = AutoML()settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"accuracy\", # metric to optimize \"task\": \"classification\", # task type \"log_file_name\": \"airlines_experiment.log\", # flaml log file}experiment = mlflow.set_experiment(\"flaml\") # the experiment name in AzureML workspacewith mlflow.start_run() as run: # create a mlflow run automl.fit(X_train=X_train, y_train=y_train, **settings) mlflow.sklearn.log_model(automl, \"automl\") Copy The metrics in the run will be automatically logged in an experiment named \"flaml\" in your AzureML workspace. They can be retrieved by mlflow.search_runs: mlflow.search_runs( experiment_ids=[experiment.experiment_id], filter_string=\"params.learner = 'xgboost'\",) Copy The logged model can be loaded and used to make predictions: automl = mlflow.sklearn.load_model(f\"{run.info.artifact_uri}/automl\")print(automl.predict(X_test)) Copy Link to notebook | Open in colab","s":"Start an AutoML run","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#start-an-automl-run","p":167},{"i":176,"t":"When you have a compute cluster in AzureML, you can distribute flaml.AutoML or flaml.tune with ray. Build a ray environment in AzureML​ Create a docker file such as .Docker/Dockerfile-cpu. Make sure RUN pip install flaml[blendsearch,ray] is included in the docker file. Then build a AzureML environment in the workspace ws. ray_environment_name = \"aml-ray-cpu\"ray_environment_dockerfile_path = \"./Docker/Dockerfile-cpu\"# Build CPU image for Rayray_cpu_env = Environment.from_dockerfile( name=ray_environment_name, dockerfile=ray_environment_dockerfile_path)ray_cpu_env.register(workspace=ws)ray_cpu_build_details = ray_cpu_env.build(workspace=ws)import timewhile ray_cpu_build_details.status not in [\"Succeeded\", \"Failed\"]: print( f\"Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}\" ) time.sleep(10) Copy You only need to do this step once for one workspace. Create a compute cluster with multiple nodes​ from azureml.core.compute import AmlCompute, ComputeTargetcompute_target_name = \"cpucluster\"node_count = 2# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6compute_target_size = \"STANDARD_D2_V2\"if compute_target_name in ws.compute_targets: compute_target = ws.compute_targets[compute_target_name] if compute_target and type(compute_target) is AmlCompute: if compute_target.provisioning_state == \"Succeeded\": print(\"Found compute target; using it:\", compute_target_name) else: raise Exception( \"Found compute target but it is in state\", compute_target.provisioning_state, )else: print(\"creating a new compute target...\") provisioning_config = AmlCompute.provisioning_configuration( vm_size=compute_target_size, min_nodes=0, max_nodes=node_count ) # Create the cluster compute_target = ComputeTarget.create(ws, compute_target_name, provisioning_config) # Can poll for a minimum number of nodes and for a specific timeout. # If no min node count is provided it will use the scale settings for the cluster compute_target.wait_for_completion( show_output=True, min_node_count=None, timeout_in_minutes=20 ) # For a more detailed view of current AmlCompute status, use get_status() print(compute_target.get_status().serialize()) Copy If the computer target \"cpucluster\" already exists, it will not be recreated. Run distributed AutoML job​ Assuming you have an automl script like ray/distribute_automl.py. It uses n_concurrent_trials=k to inform AutoML.fit() to perform k concurrent trials in parallel. Submit an AzureML job as the following: from azureml.core import Workspace, Experiment, ScriptRunConfig, Environmentfrom azureml.core.runconfig import RunConfiguration, DockerConfigurationcommand = [\"python distribute_automl.py\"]ray_environment_name = \"aml-ray-cpu\"env = Environment.get(workspace=ws, name=ray_environment_name)aml_run_config = RunConfiguration(communicator=\"OpenMpi\")aml_run_config.target = compute_targetaml_run_config.docker = DockerConfiguration(use_docker=True)aml_run_config.environment = envaml_run_config.node_count = 2config = ScriptRunConfig( source_directory=\"ray/\", command=command, run_config=aml_run_config,)exp = Experiment(ws, \"distribute-automl\")run = exp.submit(config)print(run.get_portal_url()) # link to ml.azure.comrun.wait_for_completion(show_output=True) Copy Run distributed tune job​ Prepare a script like ray/distribute_tune.py. Replace the command in the above eample with: command = [\"python distribute_tune.py\"] Copy Everything else is the same.","s":"Use ray to distribute across a cluster","u":"/FLAML/docs/Examples/Integrate - AzureML","h":"#use-ray-to-distribute-across-a-cluster","p":167},{"i":178,"t":"On this page","s":"AutoML - Time Series Forecast","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"","p":177},{"i":180,"t":"Install the [automl,ts_forecast] option. pip install \"flaml[automl,ts_forecast]\" Copy","s":"Prerequisites","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#prerequisites","p":177},{"i":182,"t":"import numpy as npfrom flaml import AutoMLX_train = np.arange(\"2014-01\", \"2022-01\", dtype=\"datetime64[M]\")y_train = np.random.random(size=84)automl = AutoML()automl.fit( X_train=X_train[:84], # a single column of timestamp y_train=y_train, # value for each timestamp period=12, # time horizon to forecast, e.g., 12 months task=\"ts_forecast\", time_budget=15, # time budget in seconds log_file_name=\"ts_forecast.log\", eval_method=\"holdout\",)print(automl.predict(X_train[84:])) Copy Sample output​ [flaml.automl: 01-21 08:01:20] {2018} INFO - task = ts_forecast[flaml.automl: 01-21 08:01:20] {2020} INFO - Data split method: time[flaml.automl: 01-21 08:01:20] {2024} INFO - Evaluation method: holdout[flaml.automl: 01-21 08:01:20] {2124} INFO - Minimizing error metric: mape[flaml.automl: 01-21 08:01:21] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'][flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 0, current learner lgbm[flaml.automl: 01-21 08:01:21] {2547} INFO - Estimated sufficient time budget=1429s. Estimated necessary time budget=1s.[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 1, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 2, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 3, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 4, current learner lgbm[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811[flaml.automl: 01-21 08:01:21] {2434} INFO - 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iteration 100, current learner xgb_limitdepth[flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.3s, estimator xgb_limitdepth's best error=0.9683, best estimator sarimax's best error=0.5600 Copy","s":"Simple NumPy Example","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#simple-numpy-example","p":177},{"i":184,"t":"import statsmodels.api as smdata = sm.datasets.co2.load_pandas().data# data is given in weeks, but the task is to predict monthly, so use monthly averages insteaddata = data[\"co2\"].resample(\"MS\").mean()data = data.bfill().ffill() # makes sure there are no missing valuesdata = data.to_frame().reset_index()num_samples = data.shape[0]time_horizon = 12split_idx = num_samples - time_horizontrain_df = data[ :split_idx] # train_df is a dataframe with two columns: timestamp and labelX_test = data[split_idx:][ \"index\"].to_frame() # X_test is a dataframe with dates for predictiony_test = data[split_idx:][ \"co2\"] # y_test is a series of the values corresponding to the dates for predictionfrom flaml import AutoMLautoml = AutoML()settings = { \"time_budget\": 10, # total running time in seconds \"metric\": \"mape\", # primary metric for validation: 'mape' is generally used for forecast tasks \"task\": \"ts_forecast\", # task type \"log_file_name\": \"CO2_forecast.log\", # flaml log file \"eval_method\": \"holdout\", # validation method can be chosen from ['auto', 'holdout', 'cv'] \"seed\": 7654321, # random seed}automl.fit( dataframe=train_df, # training data label=\"co2\", # label column period=time_horizon, # key word argument 'period' must be included for forecast task) **settings) Copy Sample output​ [flaml.automl: 01-21 07:54:04] {2018} INFO - task = ts_forecast[flaml.automl: 01-21 07:54:04] {2020} INFO - Data split method: time[flaml.automl: 01-21 07:54:04] {2024} INFO - Evaluation method: holdout[flaml.automl: 01-21 07:54:04] {2124} INFO - Minimizing error metric: mapeImporting plotly failed. Interactive plots will not work.[flaml.automl: 01-21 07:54:04] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'][flaml.automl: 01-21 07:54:04] {2434} INFO - iteration 0, current learner lgbm[flaml.automl: 01-21 07:54:05] {2547} INFO - Estimated sufficient time budget=2145s. 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fit succeeded[flaml.automl: 01-21 07:54:14] {2211} INFO - Time taken to find the best model: 9.339771270751953[flaml.automl: 01-21 07:54:14] {2222} WARNING - Time taken to find the best model is 93% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy Compute and plot predictions​ The example plotting code requires matplotlib. flaml_y_pred = automl.predict(X_test)import matplotlib.pyplot as pltplt.plot(X_test, y_test, label=\"Actual level\")plt.plot(X_test, flaml_y_pred, label=\"FLAML forecast\")plt.xlabel(\"Date\")plt.ylabel(\"CO2 Levels\")plt.legend() Copy","s":"Univariate time series","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#univariate-time-series","p":177},{"i":186,"t":"import pandas as pd# pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)multi_df = pd.read_csv( \"https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/nyc_energy_consumption.csv\")# preprocessing datamulti_df[\"timeStamp\"] = pd.to_datetime(multi_df[\"timeStamp\"])multi_df = multi_df.set_index(\"timeStamp\")multi_df = multi_df.resample(\"D\").mean()multi_df[\"temp\"] = multi_df[\"temp\"].fillna(method=\"ffill\")multi_df[\"precip\"] = multi_df[\"precip\"].fillna(method=\"ffill\")multi_df = multi_df[:-2] # last two rows are NaN for 'demand' column so remove themmulti_df = multi_df.reset_index()# Using temperature values create categorical values# where 1 denotes daily tempurature is above monthly average and 0 is below.def get_monthly_avg(data): data[\"month\"] = data[\"timeStamp\"].dt.month data = data[[\"month\", \"temp\"]].groupby(\"month\") data = data.agg({\"temp\": \"mean\"}) return datamonthly_avg = get_monthly_avg(multi_df).to_dict().get(\"temp\")def above_monthly_avg(date, temp): month = date.month if temp > monthly_avg.get(month): return 1 else: return 0multi_df[\"temp_above_monthly_avg\"] = multi_df.apply( lambda x: above_monthly_avg(x[\"timeStamp\"], x[\"temp\"]), axis=1)del multi_df[\"month\"] # remove temperature column to reduce redundancy# split data into train and testnum_samples = multi_df.shape[0]multi_time_horizon = 180split_idx = num_samples - multi_time_horizonmulti_train_df = multi_df[:split_idx]multi_test_df = multi_df[split_idx:]multi_X_test = multi_test_df[ [\"timeStamp\", \"precip\", \"temp\", \"temp_above_monthly_avg\"]] # test dataframe must contain values for the regressors / multivariate variablesmulti_y_test = multi_test_df[\"demand\"]# initialize AutoML instanceautoml = AutoML()# configure AutoML settingssettings = { \"time_budget\": 10, # total running time in seconds \"metric\": \"mape\", # primary metric \"task\": \"ts_forecast\", # task type \"log_file_name\": \"energy_forecast_categorical.log\", # flaml log file \"eval_method\": \"holdout\", \"log_type\": \"all\", \"label\": \"demand\",}# train the modelautoml.fit(dataframe=df, **settings, period=time_horizon)# predictionsprint(automl.predict(multi_X_test)) Copy Sample Output​ [flaml.automl: 08-13 01:03:11] {2540} INFO - task = ts_forecast[flaml.automl: 08-13 01:03:11] {2542} INFO - Data split method: time[flaml.automl: 08-13 01:03:11] {2545} INFO - Evaluation method: holdout[flaml.automl: 08-13 01:03:11] {2664} INFO - Minimizing error metric: mape[flaml.automl: 08-13 01:03:12] {2806} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'][flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 0, current learner lgbm[flaml.automl: 08-13 01:03:12] {3241} INFO - Estimated sufficient time budget=7681s. Estimated necessary time budget=8s.[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.8s, estimator lgbm's best error=0.0854, best estimator lgbm's best error=0.0854[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 1, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0854, best estimator lgbm's best error=0.0854[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 2, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0525, best estimator lgbm's best error=0.0525[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 3, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0525, best estimator lgbm's best error=0.0525[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 4, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 5, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 6, current learner lgbm[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 7, current learner lgbm[flaml.automl: 08-13 01:03:13] {3288} INFO - at 1.1s, estimator lgbm's best error=0.0393, best estimator lgbm's best error=0.0393[flaml.automl: 08-13 01:03:13] {3108} INFO - iteration 8, current learner lgbm[flaml.automl: 08-13 01:03:13] {3288} INFO - at 1.1s, estimator lgbm's best error=0.0393, best estimator lgbm's best error=0.0393[flaml.automl: 08-13 01:03:13] {3108} INFO - iteration 9, current learner lgbm... silent=True, subsample=1.0, subsample_for_bin=200000, subsample_freq=0, verbose=-1)[flaml.automl: 08-13 01:03:22] {2837} INFO - fit succeeded[flaml.automl: 08-13 01:03:22] {2838} INFO - Time taken to find the best model: 3.4941744804382324 Copy","s":"Multivariate Time Series (Forecasting with Exogenous Variables)","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#multivariate-time-series-forecasting-with-exogenous-variables","p":177},{"i":188,"t":"from hcrystalball.utils import get_sales_dataimport numpy as npfrom flaml import AutoMLtime_horizon = 30df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)df = df[[\"Sales\", \"Open\", \"Promo\", \"Promo2\"]]# feature engineering - create a discrete value column# 1 denotes above mean and 0 denotes below meandf[\"above_mean_sales\"] = np.where(df[\"Sales\"] > df[\"Sales\"].mean(), 1, 0)df.reset_index(inplace=True)# train-test splitdiscrete_train_df = df[:-time_horizon]discrete_test_df = df[-time_horizon:]discrete_X_train, discrete_X_test = ( discrete_train_df[[\"Date\", \"Open\", \"Promo\", \"Promo2\"]], discrete_test_df[[\"Date\", \"Open\", \"Promo\", \"Promo2\"]],)discrete_y_train, discrete_y_test = ( discrete_train_df[\"above_mean_sales\"], discrete_test_df[\"above_mean_sales\"],)# initialize AutoML instanceautoml = AutoML()# configure the settingssettings = { \"time_budget\": 15, # total running time in seconds \"metric\": \"accuracy\", # primary metric \"task\": \"ts_forecast_classification\", # task type \"log_file_name\": \"sales_classification_forecast.log\", # flaml log file \"eval_method\": \"holdout\",}# train the modelautoml.fit( X_train=discrete_X_train, y_train=discrete_y_train, **settings, period=time_horizon)# make predictionsdiscrete_y_pred = automl.predict(discrete_X_test)print(\"Predicted label\", discrete_y_pred)print(\"True label\", discrete_y_test) Copy Sample Output​ [flaml.automl: 02-28 21:53:03] {2060} INFO - task = ts_forecast_classification[flaml.automl: 02-28 21:53:03] {2062} INFO - Data split method: time[flaml.automl: 02-28 21:53:03] {2066} INFO - Evaluation method: holdout[flaml.automl: 02-28 21:53:03] {2147} INFO - Minimizing error metric: 1-accuracy[flaml.automl: 02-28 21:53:03] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth'][flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 0, current learner lgbm[flaml.automl: 02-28 21:53:03] {2573} INFO - Estimated sufficient time budget=269s. 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iteration 25, current learner xgb_limitdepth[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 26, current learner xgb_limitdepth[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 27, current learner xgboost[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgboost's best error=0.0333, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 28, current learner extra_tree[flaml.automl: 02-28 21:53:04] {2620} INFO - at 1.0s, estimator extra_tree's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 29, current learner xgb_limitdepth[flaml.automl: 02-28 21:53:04] {2620} INFO - at 1.0s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333[flaml.automl: 02-28 21:53:04] {2850} INFO - retrain xgboost for 0.0s[flaml.automl: 02-28 21:53:04] {2857} INFO - retrained model: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.9826753651836615, colsample_bynode=1, colsample_bytree=0.9725493834064914, gamma=0, gpu_id=-1, grow_policy='lossguide', importance_type='gain', interaction_constraints='', learning_rate=0.1665803484560213, max_delta_step=0, max_depth=0, max_leaves=4, min_child_weight=0.5649012460525115, missing=nan, monotone_constraints='()', n_estimators=4, n_jobs=-1, num_parallel_tree=1, objective='binary:logistic', random_state=0, reg_alpha=0.009638363373006869, reg_lambda=0.143703802530408, scale_pos_weight=1, subsample=0.9643606787051899, tree_method='hist', use_label_encoder=False, validate_parameters=1, verbosity=0)[flaml.automl: 02-28 21:53:04] {2234} INFO - fit succeeded[flaml.automl: 02-28 21:53:04] {2235} INFO - Time taken to find the best model: 0.8547139167785645 Copy","s":"Forecasting Discrete Variables","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#forecasting-discrete-variables","p":177},{"i":190,"t":"Panel time series datasets involves multiple individual time series. For example, see Stallion demand dataset from PyTorch Forecasting, orginally from Kaggle. def get_stalliion_data(): from pytorch_forecasting.data.examples import get_stallion_data data = get_stallion_data() # add time index - For datasets with no missing values, FLAML will automate this process data[\"time_idx\"] = data[\"date\"].dt.year * 12 + data[\"date\"].dt.month data[\"time_idx\"] -= data[\"time_idx\"].min() # add additional features data[\"month\"] = data.date.dt.month.astype(str).astype( \"category\" ) # categories have be strings data[\"log_volume\"] = np.log(data.volume + 1e-8) data[\"avg_volume_by_sku\"] = data.groupby( [\"time_idx\", \"sku\"], observed=True ).volume.transform(\"mean\") data[\"avg_volume_by_agency\"] = data.groupby( [\"time_idx\", \"agency\"], observed=True ).volume.transform(\"mean\") # we want to encode special days as one variable and thus need to first reverse one-hot encoding special_days = [ \"easter_day\", \"good_friday\", \"new_year\", \"christmas\", \"labor_day\", \"independence_day\", \"revolution_day_memorial\", \"regional_games\", \"beer_capital\", \"music_fest\", ] data[special_days] = ( data[special_days] .apply(lambda x: x.map({0: \"-\", 1: x.name})) .astype(\"category\") ) return data, special_daysdata, special_days = get_stalliion_data()time_horizon = 6 # predict six monthstraining_cutoff = data[\"time_idx\"].max() - time_horizondata[\"time_idx\"] = data[\"time_idx\"].astype(\"int\")ts_col = data.pop(\"date\")data.insert(0, \"date\", ts_col)# FLAML assumes input is not sorted, but we sort here for comparison purposes with y_testdata = data.sort_values([\"agency\", \"sku\", \"date\"])X_train = data[lambda x: x.time_idx <= training_cutoff]X_test = data[lambda x: x.time_idx > training_cutoff]y_train = X_train.pop(\"volume\")y_test = X_test.pop(\"volume\")automl = AutoML()# Configure settings for FLAML modelsettings = { \"time_budget\": budget, # total running time in seconds \"metric\": \"mape\", # primary metric \"task\": \"ts_forecast_panel\", # task type \"log_file_name\": \"test/stallion_forecast.log\", # flaml log file \"eval_method\": \"holdout\",}# Specify kwargs for TimeSeriesDataSet used by TemporalFusionTransformerEstimatorfit_kwargs_by_estimator = { \"tft\": { \"max_encoder_length\": 24, \"static_categoricals\": [\"agency\", \"sku\"], \"static_reals\": [\"avg_population_2017\", \"avg_yearly_household_income_2017\"], \"time_varying_known_categoricals\": [\"special_days\", \"month\"], \"variable_groups\": { \"special_days\": special_days }, # group of categorical variables can be treated as one variable \"time_varying_known_reals\": [ \"time_idx\", \"price_regular\", \"discount_in_percent\", ], \"time_varying_unknown_categoricals\": [], \"time_varying_unknown_reals\": [ \"y\", # always need a 'y' column for the target column \"log_volume\", \"industry_volume\", \"soda_volume\", \"avg_max_temp\", \"avg_volume_by_agency\", \"avg_volume_by_sku\", ], \"batch_size\": 256, \"max_epochs\": 1, \"gpu_per_trial\": -1, }}# Train the modelautoml.fit( X_train=X_train, y_train=y_train, **settings, period=time_horizon, group_ids=[\"agency\", \"sku\"], fit_kwargs_by_estimator=fit_kwargs_by_estimator,)# Compute predictions of testing datasety_pred = automl.predict(X_test)print(y_test)print(y_pred)# best modelprint(automl.model.estimator) Copy Sample Output​ [flaml.automl: 07-28 21:26:03] {2478} INFO - task = ts_forecast_panel[flaml.automl: 07-28 21:26:03] {2480} INFO - Data split method: time[flaml.automl: 07-28 21:26:03] {2483} INFO - Evaluation method: holdout[flaml.automl: 07-28 21:26:03] {2552} INFO - Minimizing error metric: mape[flaml.automl: 07-28 21:26:03] {2694} INFO - List of ML learners in AutoML Run: ['tft'][flaml.automl: 07-28 21:26:03] {2986} INFO - iteration 0, current learner tftGPU available: False, used: FalseTPU available: False, using: 0 TPU coresIPU available: False, using: 0 IPUs | Name | Type | Params----------------------------------------------------------------------------------------0 | loss | QuantileLoss | 01 | logging_metrics | ModuleList | 02 | input_embeddings | MultiEmbedding | 1.3 K3 | prescalers | ModuleDict | 2564 | static_variable_selection | VariableSelectionNetwork | 3.4 K5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K10 | static_context_enrichment | GatedResidualNetwork | 1.1 K11 | lstm_encoder | LSTM | 4.4 K12 | lstm_decoder | LSTM | 4.4 K13 | post_lstm_gate_encoder | GatedLinearUnit | 54414 | post_lstm_add_norm_encoder | AddNorm | 3215 | static_enrichment | GatedResidualNetwork | 1.4 K16 | multihead_attn | InterpretableMultiHeadAttention | 67617 | post_attn_gate_norm | GateAddNorm | 57618 | pos_wise_ff | GatedResidualNetwork | 1.1 K19 | pre_output_gate_norm | GateAddNorm | 57620 | output_layer | Linear | 119----------------------------------------------------------------------------------------33.6 K Trainable params0 Non-trainable params33.6 K Total params0.135 Total estimated model params size (MB)Epoch 19: 100%|██████████| 129/129 [00:56<00:00, 2.27it/s, loss=45.9, v_num=2, train_loss_step=43.00, val_loss=65.20, train_loss_epoch=46.50][flaml.automl: 07-28 21:46:46] {3114} INFO - Estimated sufficient time budget=12424212s. Estimated necessary time budget=12424s.[flaml.automl: 07-28 21:46:46] {3161} INFO - at 1242.6s,\\testimator tft's best error=1324290483134574.7500,\\tbest estimator tft's best error=1324290483134574.7500GPU available: False, used: FalseTPU available: False, using: 0 TPU coresIPU available: False, using: 0 IPUs | Name | Type | Params----------------------------------------------------------------------------------------0 | loss | QuantileLoss | 01 | logging_metrics | ModuleList | 02 | input_embeddings | MultiEmbedding | 1.3 K3 | prescalers | ModuleDict | 2564 | static_variable_selection | VariableSelectionNetwork | 3.4 K5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K10 | static_context_enrichment | GatedResidualNetwork | 1.1 K11 | lstm_encoder | LSTM | 4.4 K12 | lstm_decoder | LSTM | 4.4 K13 | post_lstm_gate_encoder | GatedLinearUnit | 54414 | post_lstm_add_norm_encoder | AddNorm | 3215 | static_enrichment | GatedResidualNetwork | 1.4 K16 | multihead_attn | InterpretableMultiHeadAttention | 67617 | post_attn_gate_norm | GateAddNorm | 57618 | pos_wise_ff | GatedResidualNetwork | 1.1 K19 | pre_output_gate_norm | GateAddNorm | 57620 | output_layer | Linear | 119----------------------------------------------------------------------------------------33.6 K Trainable params0 Non-trainable params33.6 K Total params0.135 Total estimated model params size (MB)Epoch 19: 100%|██████████| 145/145 [01:03<00:00, 2.28it/s, loss=45.2, v_num=3, train_loss_step=46.30, val_loss=67.60, train_loss_epoch=48.10][flaml.automl: 07-28 22:08:05] {3425} INFO - retrain tft for 1279.6s[flaml.automl: 07-28 22:08:05] {3432} INFO - retrained model: TemporalFusionTransformer( (loss): QuantileLoss() (logging_metrics): ModuleList( (0): SMAPE() (1): MAE() (2): RMSE() (3): MAPE() ) (input_embeddings): MultiEmbedding( (embeddings): ModuleDict( (agency): Embedding(58, 16) (sku): Embedding(25, 10) (special_days): TimeDistributedEmbeddingBag(11, 6, mode=sum) (month): Embedding(12, 6) ) ) (prescalers): ModuleDict( (avg_population_2017): Linear(in_features=1, out_features=8, bias=True) (avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True) (encoder_length): Linear(in_features=1, out_features=8, bias=True) (y_center): Linear(in_features=1, out_features=8, bias=True) (y_scale): Linear(in_features=1, out_features=8, bias=True) (time_idx): Linear(in_features=1, out_features=8, bias=True) (price_regular): Linear(in_features=1, out_features=8, bias=True) (discount_in_percent): Linear(in_features=1, out_features=8, bias=True) (relative_time_idx): Linear(in_features=1, out_features=8, bias=True) (y): Linear(in_features=1, out_features=8, bias=True) (log_volume): Linear(in_features=1, out_features=8, bias=True) (industry_volume): Linear(in_features=1, out_features=8, bias=True) (soda_volume): Linear(in_features=1, out_features=8, bias=True) (avg_max_temp): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True) ) (static_variable_selection): VariableSelectionNetwork( (flattened_grn): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=66, out_features=7, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=7, out_features=7, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=7, out_features=14, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True) ) ) ) (single_variable_grns): ModuleDict( (agency): ResampleNorm( (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (sku): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (avg_population_2017): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_yearly_household_income_2017): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (encoder_length): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (y_center): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (y_scale): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) ) (prescalers): ModuleDict( (avg_population_2017): Linear(in_features=1, out_features=8, bias=True) (avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True) (encoder_length): Linear(in_features=1, out_features=8, bias=True) (y_center): Linear(in_features=1, out_features=8, bias=True) (y_scale): Linear(in_features=1, out_features=8, bias=True) ) (softmax): Softmax(dim=-1) ) (encoder_variable_selection): VariableSelectionNetwork( (flattened_grn): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=100, out_features=13, bias=True) (elu): ELU(alpha=1.0) (context): Linear(in_features=16, out_features=13, bias=False) (fc2): Linear(in_features=13, out_features=13, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=13, out_features=26, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True) ) ) ) (single_variable_grns): ModuleDict( (special_days): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (month): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (price_regular): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (discount_in_percent): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (relative_time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (y): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (log_volume): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (industry_volume): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (soda_volume): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_max_temp): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_volume_by_agency): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (avg_volume_by_sku): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) ) (prescalers): ModuleDict( (time_idx): Linear(in_features=1, out_features=8, bias=True) (price_regular): Linear(in_features=1, out_features=8, bias=True) (discount_in_percent): Linear(in_features=1, out_features=8, bias=True) (relative_time_idx): Linear(in_features=1, out_features=8, bias=True) (y): Linear(in_features=1, out_features=8, bias=True) (log_volume): Linear(in_features=1, out_features=8, bias=True) (industry_volume): Linear(in_features=1, out_features=8, bias=True) (soda_volume): Linear(in_features=1, out_features=8, bias=True) (avg_max_temp): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True) (avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True) ) (softmax): Softmax(dim=-1) ) (decoder_variable_selection): VariableSelectionNetwork( (flattened_grn): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=44, out_features=6, bias=True) (elu): ELU(alpha=1.0) (context): Linear(in_features=16, out_features=6, bias=False) (fc2): Linear(in_features=6, out_features=6, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=6, out_features=12, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True) ) ) ) (single_variable_grns): ModuleDict( (special_days): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (month): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (price_regular): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (discount_in_percent): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (relative_time_idx): GatedResidualNetwork( (resample_norm): ResampleNorm( (resample): TimeDistributedInterpolation() (gate): Sigmoid() (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (fc1): Linear(in_features=8, out_features=8, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=8, out_features=8, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=8, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) ) (prescalers): ModuleDict( (time_idx): Linear(in_features=1, out_features=8, bias=True) (price_regular): Linear(in_features=1, out_features=8, bias=True) (discount_in_percent): Linear(in_features=1, out_features=8, bias=True) (relative_time_idx): Linear(in_features=1, out_features=8, bias=True) ) (softmax): Softmax(dim=-1) ) (static_context_variable_selection): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (static_context_initial_hidden_lstm): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (static_context_initial_cell_lstm): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (static_context_enrichment): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (lstm_encoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1) (lstm_decoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1) (post_lstm_gate_encoder): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (post_lstm_gate_decoder): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (post_lstm_add_norm_encoder): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (post_lstm_add_norm_decoder): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) (static_enrichment): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (context): Linear(in_features=16, out_features=16, bias=False) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (multihead_attn): InterpretableMultiHeadAttention( (dropout): Dropout(p=0.1, inplace=False) (v_layer): Linear(in_features=16, out_features=4, bias=True) (q_layers): ModuleList( (0): Linear(in_features=16, out_features=4, bias=True) (1): Linear(in_features=16, out_features=4, bias=True) (2): Linear(in_features=16, out_features=4, bias=True) (3): Linear(in_features=16, out_features=4, bias=True) ) (k_layers): ModuleList( (0): Linear(in_features=16, out_features=4, bias=True) (1): Linear(in_features=16, out_features=4, bias=True) (2): Linear(in_features=16, out_features=4, bias=True) (3): Linear(in_features=16, out_features=4, bias=True) ) (attention): ScaledDotProductAttention( (softmax): Softmax(dim=2) ) (w_h): Linear(in_features=4, out_features=16, bias=False) ) (post_attn_gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) (pos_wise_ff): GatedResidualNetwork( (fc1): Linear(in_features=16, out_features=16, bias=True) (elu): ELU(alpha=1.0) (fc2): Linear(in_features=16, out_features=16, bias=True) (gate_norm): GateAddNorm( (glu): GatedLinearUnit( (dropout): Dropout(p=0.1, inplace=False) (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) ) (pre_output_gate_norm): GateAddNorm( (glu): GatedLinearUnit( (fc): Linear(in_features=16, out_features=32, bias=True) ) (add_norm): AddNorm( (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) ) ) (output_layer): Linear(in_features=16, out_features=7, bias=True))[flaml.automl: 07-28 22:08:05] {2725} INFO - fit succeeded[flaml.automl: 07-28 22:08:05] {2726} INFO - Time taken to find the best model: 1242.6435902118683[flaml.automl: 07-28 22:08:05] {2737} WARNING - Time taken to find the best model is 414% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\\n\" ] } ], Copy Link to notebook | Open in colab","s":"Forecasting with Panel Datasets","u":"/FLAML/docs/Examples/AutoML-Time series forecast","h":"#forecasting-with-panel-datasets","p":177},{"i":192,"t":"On this page","s":"Tune - AzureML pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"","p":191},{"i":195,"t":"We recommend using conda or venv to create a virtual env to install the dependencies. # set up new conda environmentconda create -n pipeline_tune python=3.8 pip=20.2 -yconda activate pipeline_tune# install azureml packages for runnig AzureML pipelinespip install azureml-core==1.39.0pip install azure-ml-component[notebooks]==0.9.10.post1pip install azureml-dataset-runtime==1.39.0# install hydra-core for passing AzureML pipeline parameterspip install hydra-core==1.1.1# install flamlpip install flaml[blendsearch,ray]==1.0.9 Copy","s":"Requirements","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#requirements","p":191},{"i":197,"t":"Before we are ready for tuning, we must first have an Azure ML pipeline. In this example, we use the following toy pipeline for illustration. The pipeline consists of two steps: (1) data preparation and (2) model training. . The code example discussed in the page is included in test/pipeline_tuning_example/. We will use the relative path in the rest of the page.","s":"Azure ML training pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#azure-ml-training-pipeline","p":191},{"i":199,"t":"The example data exsits in data/data.csv. It will be uploaded to AzureML workspace to be consumed by the training pipeline using the following code. Dataset.File.upload_directory( src_dir=to_absolute_path(LOCAL_DIR / \"data\"), target=(datastore, \"classification_data\"), overwrite=True,)dataset = Dataset.File.from_files(path=(datastore, \"classification_data\")) Copy","s":"Data","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#data","p":191},{"i":201,"t":"The pipeline configuration is defined in configs/train_config.yaml. hydra: searchpath: - file://.aml_config: workspace_name: your_workspace_name resource_group: your_resource_group subscription_id: your_subscription_id cpu_target: cpuclustertrain_config: exp_name: sklearn_breast_cancer_classification test_train_ratio: 0.4 learning_rate: 0.05 n_estimators: 50 Copy","s":"Configurations for the pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#configurations-for-the-pipeline","p":191},{"i":203,"t":"The pipeline was defined in submit_train_pipeline.py. To submit the pipeline, please specify your AzureML resources in the configs/train_config.yaml and run cd test/pipeline_tuning_examplepython submit_train_pipeline.py Copy To get the pipeline ready for HPO, in the training step, we need to log the metrics of interest to AzureML using run.log(f\"{data_name}_{eval_name}\", result) Copy","s":"Define and submit the pipeline","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#define-and-submit-the-pipeline","p":191},{"i":205,"t":"We are now ready to set up the HPO job for the AzureML pipeline, including: config the HPO job, set up the interaction between the HPO job and the training job. These two steps are done in tuner/tuner_func.py.","s":"Hyperparameter Optimization","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#hyperparameter-optimization","p":191},{"i":207,"t":"tuner_func.tune_pipeline sets up the search space, metric to optimize, mode, etc. def tune_pipeline(concurrent_run=1): start_time = time.time() # config the HPO job search_space = { \"train_config.n_estimators\": flaml.tune.randint(50, 200), \"train_config.learning_rate\": flaml.tune.uniform(0.01, 0.5), } hp_metric = \"eval_binary_error\" mode = \"max\" num_samples = 2 if concurrent_run > 1: import ray # For parallel tuning ray.init(num_cpus=concurrent_run) use_ray = True else: use_ray = False # launch the HPO job analysis = flaml.tune.run( run_with_config, config=search_space, metric=hp_metric, mode=mode, num_samples=num_samples, # number of trials use_ray=use_ray, ) # get the best config best_trial = analysis.get_best_trial(hp_metric, mode, \"all\") metric = best_trial.metric_analysis[hp_metric][mode] print(f\"n_trials={len(analysis.trials)}\") print(f\"time={time.time()-start_time}\") print(f\"Best {hp_metric}: {metric:.4f}\") print(f\"Best coonfiguration: {best_trial.config}\") Copy","s":"Set up the tune job","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#set-up-the-tune-job","p":191},{"i":209,"t":"The interaction between FLAML and AzureML pipeline jobs is in tuner_func.run_with_config. def run_with_config(config: dict): \"\"\"Run the pipeline with a given config dict\"\"\" # pass the hyperparameters to AzureML jobs by overwriting the config file. overrides = [f\"{key}={value}\" for key, value in config.items()] print(overrides) run = submit_train_pipeline.build_and_submit_aml_pipeline(overrides) print(run.get_portal_url()) # retrieving the metrics to optimize before the job completes. stop = False while not stop: # get status status = run._core_run.get_status() print(f\"status: {status}\") # get metrics metrics = run._core_run.get_metrics(recursive=True) if metrics: run_metrics = list(metrics.values()) new_metric = run_metrics[0][\"eval_binary_error\"] if type(new_metric) == list: new_metric = new_metric[-1] print(f\"eval_binary_error: {new_metric}\") tune.report(eval_binary_error=new_metric) time.sleep(5) if status == \"FAILED\" or status == \"Completed\": stop = True print(\"The run is terminated.\") print(status) return Copy Overall, to tune the hyperparameters of the AzureML pipeline, run: # the training job will run remotely as an AzureML job in both choices# run the tuning job locallypython submit_tune.py --local# run the tuning job remotelypython submit_tune.py --remote --subscription_id <your subscription_id> --resource_group <your resource_group> --workspace <your workspace> Copy The local option runs the tuner/tuner_func.py in your local machine. The remote option wraps up the tuner/tuner_func.py as an AzureML component and starts another AzureML job to tune the AzureML pipeline.","s":"Interact with AzureML pipeline jobs","u":"/FLAML/docs/Examples/Tune-AzureML-pipeline","h":"#interact-with-azureml-pipeline-jobs","p":191},{"i":211,"t":"On this page","s":"Tune - Lexicographic Objectives","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"","p":210},{"i":213,"t":"pip install \"flaml>=1.1.0\" thop torchvision torch Copy Tuning multiple objectives with Lexicographic preference is a new feature added in version 1.1.0 and is subject to change in future versions.","s":"Requirements","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#requirements","p":210},{"i":216,"t":"import torchimport thopimport torch.nn as nnfrom flaml import tuneimport torch.nn.functional as Fimport torchvisionimport numpy as npimport osDEVICE = torch.device(\"cpu\")BATCHSIZE = 128N_TRAIN_EXAMPLES = BATCHSIZE * 30N_VALID_EXAMPLES = BATCHSIZE * 10data_dir = os.path.abspath(\"data\")train_dataset = torchvision.datasets.FashionMNIST( data_dir, train=True, download=True, transform=torchvision.transforms.ToTensor(),)train_loader = torch.utils.data.DataLoader( torch.utils.data.Subset(train_dataset, list(range(N_TRAIN_EXAMPLES))), batch_size=BATCHSIZE, shuffle=True,)val_dataset = torchvision.datasets.FashionMNIST( data_dir, train=False, transform=torchvision.transforms.ToTensor())val_loader = torch.utils.data.DataLoader( torch.utils.data.Subset(val_dataset, list(range(N_VALID_EXAMPLES))), batch_size=BATCHSIZE, shuffle=True, Copy","s":"Data","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#data","p":210},{"i":218,"t":"def define_model(configuration): n_layers = configuration[\"n_layers\"] layers = [] in_features = 28 * 28 for i in range(n_layers): out_features = configuration[\"n_units_l{}\".format(i)] layers.append(nn.Linear(in_features, out_features)) layers.append(nn.ReLU()) p = configuration[\"dropout_{}\".format(i)] layers.append(nn.Dropout(p)) in_features = out_features layers.append(nn.Linear(in_features, 10)) layers.append(nn.LogSoftmax(dim=1)) return nn.Sequential(*layers) Copy","s":"Specific the model","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#specific-the-model","p":210},{"i":220,"t":"def train_model(model, optimizer, train_loader): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE) optimizer.zero_grad() F.nll_loss(model(data), target).backward() optimizer.step() Copy","s":"Train","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#train","p":210},{"i":222,"t":"def eval_model(model, valid_loader): model.eval() correct = 0 with torch.no_grad(): for batch_idx, (data, target) in enumerate(valid_loader): data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE) pred = model(data).argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() accuracy = correct / N_VALID_EXAMPLES flops, params = thop.profile( model, inputs=(torch.randn(1, 28 * 28).to(DEVICE),), verbose=False ) return np.log2(flops), 1 - accuracy, params Copy","s":"Metrics","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#metrics","p":210},{"i":224,"t":"def evaluate_function(configuration): model = define_model(configuration).to(DEVICE) optimizer = torch.optim.Adam(model.parameters(), configuration[\"lr\"]) n_epoch = configuration[\"n_epoch\"] for epoch in range(n_epoch): train_model(model, optimizer, train_loader) flops, error_rate, params = eval_model(model, val_loader) return {\"error_rate\": error_rate, \"flops\": flops, \"params\": params} Copy","s":"Evaluation function","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#evaluation-function","p":210},{"i":226,"t":"search_space = { \"n_layers\": tune.randint(lower=1, upper=3), \"n_units_l0\": tune.randint(lower=4, upper=128), \"n_units_l1\": tune.randint(lower=4, upper=128), \"n_units_l2\": tune.randint(lower=4, upper=128), \"dropout_0\": tune.uniform(lower=0.2, upper=0.5), \"dropout_1\": tune.uniform(lower=0.2, upper=0.5), \"dropout_2\": tune.uniform(lower=0.2, upper=0.5), \"lr\": tune.loguniform(lower=1e-5, upper=1e-1), \"n_epoch\": tune.randint(lower=1, upper=20),} Copy","s":"Search space","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#search-space","p":210},{"i":228,"t":"# Low cost initial pointlow_cost_partial_config = { \"n_layers\": 1, \"n_units_l0\": 4, \"n_units_l1\": 4, \"n_units_l2\": 4, \"n_epoch\": 1,}# Specific lexicographic preferencelexico_objectives = {}lexico_objectives[\"metrics\"] = [\"error_rate\", \"flops\"]lexico_objectives[\"tolerances\"] = {\"error_rate\": 0.02, \"flops\": 0.0}lexico_objectives[\"targets\"] = {\"error_rate\": 0.0, \"flops\": 0.0}lexico_objectives[\"modes\"] = [\"min\", \"min\"]# launch the tuning processanalysis = tune.run( evaluate_function, num_samples=-1, time_budget_s=100, config=search_space, # search space of NN use_ray=False, lexico_objectives=lexico_objectives, low_cost_partial_config=low_cost_partial_config, # low cost initial point) Copy We also support providing percentage tolerance as shown below. lexico_objectives[\"tolerances\"] = {\"error_rate\": \"5%\", \"flops\": \"0%\"} Copy Link to notebook | Open in colab","s":"Launch the tuning process","u":"/FLAML/docs/Examples/Tune-Lexicographic-objectives","h":"#launch-the-tuning-process","p":210},{"i":230,"t":"On this page","s":"Tune - HuggingFace","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"","p":229},{"i":232,"t":"This example requires GPU. Install dependencies: pip install torch transformers datasets \"flaml[blendsearch,ray]\" Copy","s":"Requirements","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#requirements","p":229},{"i":234,"t":"Tokenizer​ from transformers import AutoTokenizerMODEL_NAME = \"distilbert-base-uncased\"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)COLUMN_NAME = \"sentence\"def tokenize(examples): return tokenizer(examples[COLUMN_NAME], truncation=True) Copy Define training method​ import flamlimport datasetsfrom transformers import AutoModelForSequenceClassificationTASK = \"cola\"NUM_LABELS = 2def train_distilbert(config: dict): # Load CoLA dataset and apply tokenizer cola_raw = datasets.load_dataset(\"glue\", TASK) cola_encoded = cola_raw.map(tokenize, batched=True) train_dataset, eval_dataset = cola_encoded[\"train\"], cola_encoded[\"validation\"] model = AutoModelForSequenceClassification.from_pretrained( MODEL_NAME, num_labels=NUM_LABELS ) metric = datasets.load_metric(\"glue\", TASK) def compute_metrics(eval_pred): predictions, labels = eval_pred predictions = np.argmax(predictions, axis=1) return metric.compute(predictions=predictions, references=labels) training_args = TrainingArguments( output_dir=\".\", do_eval=False, disable_tqdm=True, logging_steps=20000, save_total_limit=0, **config, ) trainer = Trainer( model, training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=tokenizer, compute_metrics=compute_metrics, ) # train model trainer.train() # evaluate model eval_output = trainer.evaluate() # report the metric to optimize & the metric to log flaml.tune.report( loss=eval_output[\"eval_loss\"], matthews_correlation=eval_output[\"eval_matthews_correlation\"], ) Copy","s":"Prepare for tuning","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#prepare-for-tuning","p":229},{"i":236,"t":"We are now ready to define our search. This includes: The search_space for our hyperparameters The metric and the mode ('max' or 'min') for optimization The constraints (n_cpus, n_gpus, num_samples, and time_budget_s) max_num_epoch = 64search_space = { # You can mix constants with search space objects. \"num_train_epochs\": flaml.tune.loguniform(1, max_num_epoch), \"learning_rate\": flaml.tune.loguniform(1e-6, 1e-4), \"adam_epsilon\": flaml.tune.loguniform(1e-9, 1e-7), \"adam_beta1\": flaml.tune.uniform(0.8, 0.99), \"adam_beta2\": flaml.tune.loguniform(98e-2, 9999e-4),}# optimization objectiveHP_METRIC, MODE = \"matthews_correlation\", \"max\"# resourcesnum_cpus = 4num_gpus = 4 # change according to your GPU resources# constraintsnum_samples = -1 # number of trials, -1 means unlimitedtime_budget_s = 3600 # time budget in seconds Copy","s":"Define the search","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#define-the-search","p":229},{"i":238,"t":"We are now ready to launch the tuning using flaml.tune.run: import rayray.init(num_cpus=num_cpus, num_gpus=num_gpus)print(\"Tuning started...\")analysis = flaml.tune.run( train_distilbert, search_alg=flaml.CFO( space=search_space, metric=HP_METRIC, mode=MODE, low_cost_partial_config={\"num_train_epochs\": 1}, ), resources_per_trial={\"gpu\": num_gpus, \"cpu\": num_cpus}, local_dir=\"logs/\", num_samples=num_samples, time_budget_s=time_budget_s, use_ray=True,) Copy This will run tuning for one hour. At the end we will see a summary. == Status ==Memory usage on this node: 32.0/251.6 GiBUsing FIFO scheduling algorithm.Resources requested: 0/4 CPUs, 0/4 GPUs, 0.0/150.39 GiB heap, 0.0/47.22 GiB objects (0/1.0 accelerator_type:V100)Result logdir: /home/chiw/FLAML/notebook/logs/train_distilbert_2021-05-07_02-35-58Number of trials: 22/infinite (22 TERMINATED)Trial name status loc adam_beta1 adam_beta2 adam_epsilon learning_rate num_train_epochs iter total time (s) loss matthews_correlationtrain_distilbert_a0c303d0 TERMINATED 0.939079 0.991865 7.96945e-08 5.61152e-06 1 1 55.6909 0.587986 0train_distilbert_a0c303d1 TERMINATED 0.811036 0.997214 2.05111e-09 2.05134e-06 1.44427 1 71.7663 0.603018 0train_distilbert_c39b2ef0 TERMINATED 0.909395 0.993715 1e-07 5.26543e-06 1 1 53.7619 0.586518 0train_distilbert_f00776e2 TERMINATED 0.968763 0.990019 4.38943e-08 5.98035e-06 1.02723 1 56.8382 0.581313 0train_distilbert_11ab3900 TERMINATED 0.962198 0.991838 7.09296e-08 5.06608e-06 1 1 54.0231 0.585576 0train_distilbert_353025b6 TERMINATED 0.91596 0.991892 8.95426e-08 6.21568e-06 2.15443 1 98.3233 0.531632 0.388893train_distilbert_5728a1de TERMINATED 0.926933 0.993146 1e-07 1.00902e-05 1 1 55.3726 0.538505 0.280558train_distilbert_9394c2e2 TERMINATED 0.928106 0.990614 4.49975e-08 3.45674e-06 2.72935 1 121.388 0.539177 0.327295train_distilbert_b6543fec TERMINATED 0.876896 0.992098 1e-07 7.01176e-06 1.59538 1 76.0244 0.527516 0.379177train_distilbert_0071f998 TERMINATED 0.955024 0.991687 7.39776e-08 5.50998e-06 2.90939 1 126.871 0.516225 0.417157train_distilbert_2f830be6 TERMINATED 0.886931 0.989628 7.6127e-08 4.37646e-06 1.53338 1 73.8934 0.551629 0.0655887train_distilbert_7ce03f12 TERMINATED 0.984053 0.993956 8.70144e-08 7.82557e-06 4.08775 1 174.027 0.523732 0.453549train_distilbert_aaab0508 TERMINATED 0.940707 0.993946 1e-07 8.91979e-06 3.40243 1 146.249 0.511288 0.45085train_distilbert_14262454 TERMINATED 0.99 0.991696 4.60093e-08 4.83405e-06 3.4954 1 152.008 0.53506 0.400851train_distilbert_6d211fe6 TERMINATED 0.959277 0.994556 5.40791e-08 1.17333e-05 6.64995 1 271.444 0.609851 0.526802train_distilbert_c980bae4 TERMINATED 0.99 0.993355 1e-07 5.21929e-06 2.51275 1 111.799 0.542276 0.324968train_distilbert_6d0d29d6 TERMINATED 0.965773 0.995182 9.9752e-08 1.15549e-05 13.694 1 527.944 0.923802 0.549474train_distilbert_b16ea82a TERMINATED 0.952781 0.993931 2.93182e-08 1.19145e-05 3.2293 1 139.844 0.533466 0.451307train_distilbert_eddf7cc0 TERMINATED 0.99 0.997109 8.13498e-08 1.28515e-05 15.5807 1 614.789 0.983285 0.56993train_distilbert_43008974 TERMINATED 0.929089 0.993258 1e-07 1.03892e-05 12.0357 1 474.387 0.857461 0.520022train_distilbert_b3408a4e TERMINATED 0.99 0.993809 4.67441e-08 1.10418e-05 11.9165 1 474.126 0.828205 0.526164train_distilbert_cfbfb220 TERMINATED 0.979454 0.9999 1e-07 1.49578e-05 20.3715 Copy","s":"Launch the tuning","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#launch-the-tuning","p":229},{"i":240,"t":"best_trial = analysis.get_best_trial(HP_METRIC, MODE, \"all\")metric = best_trial.metric_analysis[HP_METRIC][MODE]print(f\"n_trials={len(analysis.trials)}\")print(f\"time={time.time()-start_time}\")print(f\"Best model eval {HP_METRIC}: {metric:.4f}\")print(f\"Best model parameters: {best_trial.config}\")# n_trials=22# time=3999.769361972809# Best model eval matthews_correlation: 0.5699# Best model parameters: {'num_train_epochs': 15.580684188655825, 'learning_rate': 1.2851507818900338e-05, 'adam_epsilon': 8.134982521948352e-08, 'adam_beta1': 0.99, 'adam_beta2': 0.9971094424784387} Copy Link to notebook | Open in colab","s":"Retrieve the results","u":"/FLAML/docs/Examples/Tune-HuggingFace","h":"#retrieve-the-results","p":229},{"i":242,"t":"On this page","s":"Tune - PyTorch","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"","p":241},{"i":245,"t":"pip install torchvision \"flaml[blendsearch,ray]\" Copy Before we are ready for tuning, we first need to define the neural network that we would like to tune.","s":"Requirements","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#requirements","p":241},{"i":247,"t":"import torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torch.utils.data import random_splitimport torchvisionimport torchvision.transforms as transformsclass Net(nn.Module): def __init__(self, l1=120, l2=84): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, l1) self.fc2 = nn.Linear(l1, l2) self.fc3 = nn.Linear(l2, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x Copy","s":"Network Specification","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#network-specification","p":241},{"i":249,"t":"def load_data(data_dir=\"data\"): transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] ) trainset = torchvision.datasets.CIFAR10( root=data_dir, train=True, download=True, transform=transform ) testset = torchvision.datasets.CIFAR10( root=data_dir, train=False, download=True, transform=transform ) return trainset, testset Copy","s":"Data","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#data","p":241},{"i":251,"t":"from ray import tunedef train_cifar(config, checkpoint_dir=None, data_dir=None): if \"l1\" not in config: logger.warning(config) net = Net(2 ** config[\"l1\"], 2 ** config[\"l2\"]) device = \"cpu\" if torch.cuda.is_available(): device = \"cuda:0\" if torch.cuda.device_count() > 1: net = nn.DataParallel(net) net.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=config[\"lr\"], momentum=0.9) # The `checkpoint_dir` parameter gets passed by Ray Tune when a checkpoint # should be restored. if checkpoint_dir: checkpoint = os.path.join(checkpoint_dir, \"checkpoint\") model_state, optimizer_state = torch.load(checkpoint) net.load_state_dict(model_state) optimizer.load_state_dict(optimizer_state) trainset, testset = load_data(data_dir) test_abs = int(len(trainset) * 0.8) train_subset, val_subset = random_split( trainset, [test_abs, len(trainset) - test_abs] ) trainloader = torch.utils.data.DataLoader( train_subset, batch_size=int(2 ** config[\"batch_size\"]), shuffle=True, num_workers=4, ) valloader = torch.utils.data.DataLoader( val_subset, batch_size=int(2 ** config[\"batch_size\"]), shuffle=True, num_workers=4, ) for epoch in range( int(round(config[\"num_epochs\"])) ): # loop over the dataset multiple times running_loss = 0.0 epoch_steps = 0 for i, data in enumerate(trainloader, 0): # get the inputs; data is a list of [inputs, labels] inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics running_loss += loss.item() epoch_steps += 1 if i % 2000 == 1999: # print every 2000 mini-batches print( \"[%d, %5d] loss: %.3f\" % (epoch + 1, i + 1, running_loss / epoch_steps) ) running_loss = 0.0 # Validation loss val_loss = 0.0 val_steps = 0 total = 0 correct = 0 for i, data in enumerate(valloader, 0): with torch.no_grad(): inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) outputs = net(inputs) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() loss = criterion(outputs, labels) val_loss += loss.cpu().numpy() val_steps += 1 # Here we save a checkpoint. It is automatically registered with # Ray Tune and will potentially be passed as the `checkpoint_dir` # parameter in future iterations. with tune.checkpoint_dir(step=epoch) as checkpoint_dir: path = os.path.join(checkpoint_dir, \"checkpoint\") torch.save((net.state_dict(), optimizer.state_dict()), path) tune.report(loss=(val_loss / val_steps), accuracy=correct / total) print(\"Finished Training\") Copy","s":"Training","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#training","p":241},{"i":253,"t":"def _test_accuracy(net, device=\"cpu\"): trainset, testset = load_data() testloader = torch.utils.data.DataLoader( testset, batch_size=4, shuffle=False, num_workers=2 ) correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels = data images, labels = images.to(device), labels.to(device) outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return correct / total Copy","s":"Test Accuracy","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#test-accuracy","p":241},{"i":255,"t":"import numpy as npimport flamlimport osdata_dir = os.path.abspath(\"data\")load_data(data_dir) # Download data for all trials before starting the run Copy","s":"Hyperparameter Optimization","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#hyperparameter-optimization","p":241},{"i":257,"t":"max_num_epoch = 100config = { \"l1\": tune.randint(2, 9), # log transformed with base 2 \"l2\": tune.randint(2, 9), # log transformed with base 2 \"lr\": tune.loguniform(1e-4, 1e-1), \"num_epochs\": tune.loguniform(1, max_num_epoch), \"batch_size\": tune.randint(1, 5), # log transformed with base 2} Copy","s":"Search space","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#search-space","p":241},{"i":259,"t":"time_budget_s = 600 # time budget in secondsgpus_per_trial = ( 0.5 # number of gpus for each trial; 0.5 means two training jobs can share one gpu)num_samples = 500 # maximal number of trialsnp.random.seed(7654321) Copy","s":"Budget and resource constraints","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#budget-and-resource-constraints","p":241},{"i":261,"t":"import timestart_time = time.time()result = flaml.tune.run( tune.with_parameters(train_cifar, data_dir=data_dir), config=config, metric=\"loss\", mode=\"min\", low_cost_partial_config={\"num_epochs\": 1}, max_resource=max_num_epoch, min_resource=1, scheduler=\"asha\", # Use asha scheduler to perform early stopping based on intermediate results reported resources_per_trial={\"cpu\": 1, \"gpu\": gpus_per_trial}, local_dir=\"logs/\", num_samples=num_samples, time_budget_s=time_budget_s, use_ray=True,) Copy","s":"Launch the tuning","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#launch-the-tuning","p":241},{"i":263,"t":"print(f\"#trials={len(result.trials)}\")print(f\"time={time.time()-start_time}\")best_trial = result.get_best_trial(\"loss\", \"min\", \"all\")print(\"Best trial config: {}\".format(best_trial.config))print( \"Best trial final validation loss: {}\".format( best_trial.metric_analysis[\"loss\"][\"min\"] ))print( \"Best trial final validation accuracy: {}\".format( best_trial.metric_analysis[\"accuracy\"][\"max\"] ))best_trained_model = Net(2 ** best_trial.config[\"l1\"], 2 ** best_trial.config[\"l2\"])device = \"cpu\"if torch.cuda.is_available(): device = \"cuda:0\" if gpus_per_trial > 1: best_trained_model = nn.DataParallel(best_trained_model)best_trained_model.to(device)checkpoint_value = ( getattr(best_trial.checkpoint, \"dir_or_data\", None) or best_trial.checkpoint.value)checkpoint_path = os.path.join(checkpoint_value, \"checkpoint\")model_state, optimizer_state = torch.load(checkpoint_path)best_trained_model.load_state_dict(model_state)test_acc = _test_accuracy(best_trained_model, device)print(\"Best trial test set accuracy: {}\".format(test_acc)) Copy","s":"Check the result","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#check-the-result","p":241},{"i":265,"t":"#trials=44time=1193.913584947586Best trial config: {'l1': 8, 'l2': 8, 'lr': 0.0008818671030627281, 'num_epochs': 55.9513429004283, 'batch_size': 3}Best trial final validation loss: 1.0694482081472874Best trial final validation accuracy: 0.6389Files already downloaded and verifiedFiles already downloaded and verifiedBest trial test set accuracy: 0.6294 Copy Link to notebook | Open in colab","s":"Sample of output","u":"/FLAML/docs/Examples/Tune-PyTorch","h":"#sample-of-output","p":241},{"i":267,"t":"On this page","s":"Frequently Asked Questions","u":"/FLAML/docs/FAQ","h":"","p":266},{"i":272,"t":"Definition and purpose: The low_cost_partial_config is a dictionary of subset of the hyperparameter coordinates whose value corresponds to a configuration with known low-cost (i.e., low computation cost for training the corresponding model). The concept of low/high-cost is meaningful in the case where a subset of the hyperparameters to tune directly affects the computation cost for training the model. For example, n_estimators and max_leaves are known to affect the training cost of tree-based learners. We call this subset of hyperparameters, cost-related hyperparameters. In such scenarios, if you are aware of low-cost configurations for the cost-related hyperparameters, you are recommended to set them as the low_cost_partial_config. Using the tree-based method example again, since we know that small n_estimators and max_leaves generally correspond to simpler models and thus lower cost, we set {'n_estimators': 4, 'max_leaves': 4} as the low_cost_partial_config by default (note that 4 is the lower bound of search space for these two hyperparameters), e.g., in LGBM. Configuring low_cost_partial_config helps the search algorithms make more cost-efficient choices. In AutoML, the low_cost_init_value in search_space() function for each estimator serves the same role. Usage in practice: It is recommended to configure it if there are cost-related hyperparameters in your tuning task and you happen to know the low-cost values for them, but it is not required (It is fine to leave it the default value, i.e., None). How does it work: low_cost_partial_config if configured, will be used as an initial point of the search. It also affects the search trajectory. For more details about how does it play a role in the search algorithms, please refer to the papers about the search algorithms used: Section 2 of Frugal Optimization for Cost-related Hyperparameters (CFO) and Section 3 of Economical Hyperparameter Optimization with Blended Search Strategy (BlendSearch).","s":"About low_cost_partial_config in tune.","u":"/FLAML/docs/FAQ","h":"#about-low_cost_partial_config-in-tune","p":266},{"i":274,"t":"Currently FLAML does several things for imbalanced data. When a class contains fewer than 20 examples, we repeatedly add these examples to the training data until the count is at least 20. We use stratified sampling when doing holdout and kf. We make sure no class is empty in both training and holdout data. We allow users to pass sample_weight to AutoML.fit(). User can customize the weight of each class by setting the custom_hp or fit_kwargs_by_estimator arguments. For example, the following code sets the weight for pos vs. neg as 2:1 for the RandomForest estimator: from flaml import AutoMLfrom sklearn.datasets import load_irisX_train, y_train = load_iris(return_X_y=True)automl = AutoML()automl_settings = { \"time_budget\": 2, \"task\": \"classification\", \"log_file_name\": \"test/iris.log\", \"estimator_list\": [\"rf\", \"xgboost\"],}automl_settings[\"custom_hp\"] = { \"xgboost\": { \"scale_pos_weight\": { \"domain\": 0.5, \"init_value\": 0.5, } }, \"rf\": {\"class_weight\": {\"domain\": \"balanced\", \"init_value\": \"balanced\"}},}print(automl.model) Copy","s":"How does FLAML handle imbalanced data (unequal distribution of target classes in classification task)?","u":"/FLAML/docs/FAQ","h":"#how-does-flaml-handle-imbalanced-data-unequal-distribution-of-target-classes-in-classification-task","p":266},{"i":276,"t":"You can use automl.model.estimator.feature_importances_ to get the feature_importances_ for the best model found by automl. See an example. Packages such as azureml-interpret and sklearn.inspection.permutation_importance can be used on automl.model.estimator to explain the selected model. Model explanation is frequently asked and adding a native support may be a good feature. Suggestions/contributions are welcome. Optimization history can be checked from the log. You can also retrieve the log and plot the learning curve.","s":"How to interpret model performance? Is it possible for me to visualize feature importance, SHAP values, optimization history?","u":"/FLAML/docs/FAQ","h":"#how-to-interpret-model-performance-is-it-possible-for-me-to-visualize-feature-importance-shap-values-optimization-history","p":266},{"i":278,"t":"Set free_mem_ratio a float between 0 and 1. For example, 0.2 means try to keep free memory above 20% of total memory. Training may be early stopped for memory consumption reason when this is set. Set model_history False. If your data are already preprocessed, set skip_transform False. If you can preprocess the data before the fit starts, this setting can save memory needed for preprocessing in fit. If the OOM error only happens for some particular trials: set use_ray True. This will increase the overhead per trial but can keep the AutoML process running when a single trial fails due to OOM error. provide a more accurate size function for the memory bytes consumption of each config for the estimator causing this error. modify the search space for the estimators causing this error. or remove this estimator from the estimator_list. If the OOM error happens when ensembling, consider disabling ensemble, or use a cheaper ensemble option. (Example).","s":"How to resolve out-of-memory error in AutoML.fit()","u":"/FLAML/docs/FAQ","h":"#how-to-resolve-out-of-memory-error-in-automlfit","p":266},{"i":280,"t":"On this page","s":"Getting Started","u":"/FLAML/docs/Getting-Started","h":"","p":279},{"i":282,"t":"FLAML enables building next-gen GPT-X applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation and optimization of a complex GPT-X workflow. It maximizes the performance of GPT-X models and augments their weakness. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It is easy to customize or extend. It supports fast and economical automatic tuning, capable of handling large search space with heterogeneous evaluation cost and complex constraints/guidance/early stopping. FLAML is powered by a series of research studies from Microsoft Research and collaborators such as Penn State University, Stevens Institute of Technology, University of Washington, and University of Waterloo.","s":"Main Features","u":"/FLAML/docs/Getting-Started","h":"#main-features","p":279},{"i":284,"t":"Install FLAML from pip: pip install flaml. Find more options in Installation. There are several ways of using flaml: (New) AutoGen​ Autogen enables the next-gen GPT-X applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools and human. By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example, from flaml import autogenassistant = autogen.AssistantAgent(\"assistant\")user_proxy = autogen.UserProxyAgent(\"user_proxy\")user_proxy.initiate_chat( assistant, message=\"Show me the YTD gain of 10 largest technology companies as of today.\",)# This initiates an automated chat between the two agents to solve the task Copy Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of openai.Completion or openai.ChatCompletion with powerful functionalites like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets. # perform tuningconfig, analysis = autogen.Completion.tune( data=tune_data, metric=\"success\", mode=\"max\", eval_func=eval_func, inference_budget=0.05, optimization_budget=3, num_samples=-1,)# perform inference for a test instanceresponse = autogen.Completion.create(context=test_instance, **config) Copy Task-oriented AutoML​ With three lines of code, you can start using this economical and fast AutoML engine as a scikit-learn style estimator. from flaml import AutoMLautoml = AutoML()automl.fit(X_train, y_train, task=\"classification\", time_budget=60) Copy It automatically tunes the hyperparameters and selects the best model from default learners such as LightGBM, XGBoost, random forest etc. for the specified time budget 60 seconds. Customizing the optimization metrics, learners and search spaces etc. is very easy. For example, automl.add_learner(\"mylgbm\", MyLGBMEstimator)automl.fit( X_train, y_train, task=\"classification\", metric=custom_metric, estimator_list=[\"mylgbm\"], time_budget=60,) Copy Tune user-defined function​ You can run generic hyperparameter tuning for a custom function (machine learning or beyond). For example, from flaml import tunefrom flaml.automl.model import LGBMEstimatordef train_lgbm(config: dict) -> dict: # convert config dict to lgbm params params = LGBMEstimator(**config).params # train the model train_set = lightgbm.Dataset(csv_file_name) model = lightgbm.train(params, train_set) # evaluate the model pred = model.predict(X_test) mse = mean_squared_error(y_test, pred) # return eval results as a dictionary return {\"mse\": mse}# load a built-in search space from flamlflaml_lgbm_search_space = LGBMEstimator.search_space(X_train.shape)# specify the search space as a dict from hp name to domain; you can define your own search space same wayconfig_search_space = { hp: space[\"domain\"] for hp, space in flaml_lgbm_search_space.items()}# give guidance about hp values corresponding to low training cost, i.e., {\"n_estimators\": 4, \"num_leaves\": 4}low_cost_partial_config = { hp: space[\"low_cost_init_value\"] for hp, space in flaml_lgbm_search_space.items() if \"low_cost_init_value\" in space}# run the tuning, minimizing mse, with total time budget 3 secondsanalysis = tune.run( train_lgbm, metric=\"mse\", mode=\"min\", config=config_search_space, low_cost_partial_config=low_cost_partial_config, time_budget_s=3, num_samples=-1,) Copy Please see this script for the complete version of the above example. Zero-shot AutoML​ FLAML offers a unique, seamless and effortless way to leverage AutoML for the commonly used classifiers and regressors such as LightGBM and XGBoost. For example, if you are using lightgbm.LGBMClassifier as your current learner, all you need to do is to replace from lightgbm import LGBMClassifier by: from flaml.default import LGBMClassifier Copy Then, you can use it just like you use the original LGMBClassifier. Your other code can remain unchanged. When you call the fit() function from flaml.default.LGBMClassifier, it will automatically instantiate a good data-dependent hyperparameter configuration for your dataset, which is expected to work better than the default configuration.","s":"Quickstart","u":"/FLAML/docs/Getting-Started","h":"#quickstart","p":279},{"i":286,"t":"Understand the use cases for AutoGen, Task-oriented AutoML, Tune user-defined function and Zero-shot AutoML. Find code examples under \"Examples\": from AutoGen - AgentChat to Tune - PyTorch. Learn about research around FLAML and check blogposts. Chat on Discord. If you like our project, please give it a star on GitHub. If you are interested in contributing, please read Contributor's Guide.","s":"Where to Go Next?","u":"/FLAML/docs/Getting-Started","h":"#where-to-go-next","p":279},{"i":288,"t":"On this page","s":"Installation","u":"/FLAML/docs/Installation","h":"","p":287},{"i":290,"t":"FLAML requires Python version >= 3.7. It can be installed from pip: pip install flaml Copy or conda: conda install flaml -c conda-forge Copy","s":"Python","u":"/FLAML/docs/Installation","h":"#python","p":287},{"i":292,"t":"Autogen​ pip install \"flaml[autogen]\" Copy Task-oriented AutoML​ pip install \"flaml[automl]\" Copy Extra learners/models​ openai models pip install \"flaml[openai]\" Copy catboost pip install \"flaml[catboost]\" Copy vowpal wabbit pip install \"flaml[vw]\" Copy time series forecaster: prophet, statsmodels pip install \"flaml[forecast]\" Copy huggingface transformers pip install \"flaml[hf]\" Copy Notebook​ To run the notebook examples, install flaml with the [notebook] option: pip install \"flaml[notebook]\" Copy Distributed tuning​ ray pip install \"flaml[ray]\" Copy spark Spark support is added in v1.1.0 pip install \"flaml[spark]>=1.1.0\" Copy For cloud platforms such as Azure Synapse, Spark clusters are provided. But you may also need to install Spark manually when setting up your own environment. For latest Ubuntu system, you can install Spark 3.3.0 standalone version with below script. For more details of installing Spark, please refer to Spark Doc. sudo apt-get update && sudo apt-get install -y --allow-downgrades --allow-change-held-packages --no-install-recommends \\ ca-certificates-java ca-certificates openjdk-17-jdk-headless \\ && sudo apt-get clean && sudo rm -rf /var/lib/apt/lists/*wget --progress=dot:giga \"https://www.apache.org/dyn/closer.lua/spark/spark-3.3.0/spark-3.3.0-bin-hadoop2.tgz?action=download\" \\ -O - | tar -xzC /tmp; archive=$(basename \"spark-3.3.0/spark-3.3.0-bin-hadoop2.tgz\") \\ bash -c \"sudo mv -v /tmp/\\${archive/%.tgz/} /spark\"export SPARK_HOME=/sparkexport PYTHONPATH=/spark/python/lib/py4j-0.10.9.5-src.zip:/spark/pythonexport PATH=$PATH:$SPARK_HOME/bin Copy nni pip install \"flaml[nni]\" Copy blendsearch pip install \"flaml[blendsearch]\" Copy synapse To install flaml in Azure Synapse and similar cloud platform pip install flaml[synapse] Copy Test and Benchmark​ test pip install flaml[test] Copy benchmark pip install flaml[benchmark] Copy","s":"Optional Dependencies","u":"/FLAML/docs/Installation","h":"#optional-dependencies","p":287},{"i":294,"t":"FLAML has a .NET implementation in ML.NET, an open-source, cross-platform machine learning framework for .NET. You can use FLAML in .NET in the following ways: Low-code Model Builder - A Visual Studio extension for training ML models using FLAML. For more information on how to install, see the install Model Builder guide. ML.NET CLI - A dotnet CLI tool for training machine learning models using FLAML on Windows, MacOS, and Linux. For more information on how to install the ML.NET CLI, see the install the ML.NET CLI guide. Code-first Microsoft.ML.AutoML - NuGet package that provides direct access to the FLAML AutoML APIs that power low-code solutions like Model Builder and the ML.NET CLI. For more information on installing NuGet packages, see the install and use a NuGet package in Visual Studio or dotnet CLI guides. To get started with the ML.NET API and AutoML, see the csharp-notebooks.","s":".NET","u":"/FLAML/docs/Installation","h":"#net","p":287},{"i":296,"t":"On this page","s":"autogen.agentchat.agent","u":"/FLAML/docs/reference/autogen/agentchat/agent","h":"","p":295},{"i":298,"t":"class Agent() Copy (In preview) An abstract class for AI agent. An agent can communicate with other agents and perform actions. Different agents can differ in what actions they perform in the receive method. __init__​ def __init__(name: str) Copy Arguments: name str - name of the agent. name​ @propertydef name() Copy Get the name of the agent. send​ def send(message: Union[Dict, str], recipient: \"Agent\", request_reply: Optional[bool] = None) Copy (Aabstract method) Send a message to another agent. a_send​ async def a_send(message: Union[Dict, str], recipient: \"Agent\", request_reply: Optional[bool] = None) Copy (Aabstract async method) Send a message to another agent. receive​ def receive(message: Union[Dict, str], sender: \"Agent\", request_reply: Optional[bool] = None) Copy (Abstract method) Receive a message from another agent. a_receive​ async def a_receive(message: Union[Dict, str], sender: \"Agent\", request_reply: Optional[bool] = None) Copy (Abstract async method) Receive a message from another agent. reset​ def reset() Copy (Abstract method) Reset the agent. generate_reply​ def generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[\"Agent\"] = None, **kwargs, ,) -> Union[str, Dict, None] Copy (Abstract method) Generate a reply based on the received messages. Arguments: messages list[dict] - a list of messages received. sender - sender of an Agent instance. Returns: str or dict or None: the generated reply. If None, no reply is generated. a_generate_reply​ async def a_generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[\"Agent\"] = None, **kwargs, ,) -> Union[str, Dict, None] Copy (Abstract async method) Generate a reply based on the received messages. Arguments: messages list[dict] - a list of messages received. sender - sender of an Agent instance. Returns: str or dict or None: the generated reply. If None, no reply is generated.","s":"Agent Objects","u":"/FLAML/docs/reference/autogen/agentchat/agent","h":"#agent-objects","p":295},{"i":300,"t":"On this page","s":"autogen.agentchat.assistant_agent","u":"/FLAML/docs/reference/autogen/agentchat/assistant_agent","h":"","p":299},{"i":302,"t":"class AssistantAgent(ConversableAgent) Copy (In preview) Assistant agent, designed to solve a task with LLM. AssistantAgent is a subclass of ConversableAgent configured with a default system message. The default system message is designed to solve a task with LLM, including suggesting python code blocks and debugging. human_input_mode is default to \"NEVER\" and code_execution_config is default to False. This agent doesn't execute code by default, and expects the user to execute the code. __init__​ def __init__(name: str, system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE, llm_config: Optional[Union[Dict, bool]] = None, is_termination_msg: Optional[Callable[[Dict], bool]] = None, max_consecutive_auto_reply: Optional[int] = None, human_input_mode: Optional[str] = \"NEVER\", code_execution_config: Optional[Union[Dict, bool]] = False, **kwargs, ,) Copy Arguments: name str - agent name. system_message str - system message for the ChatCompletion inference. Please override this attribute if you want to reprogram the agent. llm_config dict - llm inference configuration. Please refer to autogen.Completion.create for available options. is_termination_msg function - a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. The dict can contain the following keys: \"content\", \"role\", \"name\", \"function_call\". max_consecutive_auto_reply int - the maximum number of consecutive auto replies. default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). The limit only plays a role when human_input_mode is not \"ALWAYS\". **kwargs dict - Please refer to other kwargs in ConversableAgent.","s":"AssistantAgent Objects","u":"/FLAML/docs/reference/autogen/agentchat/assistant_agent","h":"#assistantagent-objects","p":299},{"i":304,"t":"On this page","s":"autogen.agentchat.groupchat","u":"/FLAML/docs/reference/autogen/agentchat/groupchat","h":"","p":303},{"i":306,"t":"@dataclassclass GroupChat() Copy A group chat class that contains a list of agents and the maximum number of rounds. agent_names​ @propertydef agent_names() -> List[str] Copy Return the names of the agents in the group chat. reset​ def reset() Copy Reset the group chat. agent_by_name​ def agent_by_name(name: str) -> Agent Copy Find the next speaker based on the message. next_agent​ def next_agent(agent: Agent) -> Agent Copy Return the next agent in the list. select_speaker_msg​ def select_speaker_msg() Copy Return the message for selecting the next speaker. select_speaker​ def select_speaker(last_speaker: Agent, selector: ConversableAgent) Copy Select the next speaker.","s":"GroupChat Objects","u":"/FLAML/docs/reference/autogen/agentchat/groupchat","h":"#groupchat-objects","p":303},{"i":308,"t":"class GroupChatManager(ConversableAgent) Copy (In preview) A chat manager agent that can manage a group chat of multiple agents. run_chat​ def run_chat(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[GroupChat] = None) -> Union[str, Dict, None] Copy Run a group chat.","s":"GroupChatManager Objects","u":"/FLAML/docs/reference/autogen/agentchat/groupchat","h":"#groupchatmanager-objects","p":303},{"i":310,"t":"On this page","s":"autogen.math_utils","u":"/FLAML/docs/reference/autogen/math_utils","h":"","p":309},{"i":312,"t":"On this page","s":"autogen.agentchat.conversable_agent","u":"/FLAML/docs/reference/autogen/agentchat/conversable_agent","h":"","p":311},{"i":314,"t":"class ConversableAgent(Agent) Copy (In preview) A class for generic conversable agents which can be configured as assistant or user proxy. After receiving each message, the agent will send a reply to the sender unless the msg is a termination msg. For example, AssistantAgent and UserProxyAgent are subclasses of this class, configured with different default settings. To modify auto reply, override generate_reply method. To disable/enable human response in every turn, set human_input_mode to \"NEVER\" or \"ALWAYS\". To modify the way to get human input, override get_human_input method. To modify the way to execute code blocks, single code block, or function call, override execute_code_blocks, run_code, and execute_function methods respectively. To customize the initial message when a conversation starts, override generate_init_message method. __init__​ def __init__(name: str, system_message: Optional[str] = \"You are a helpful AI Assistant.\", is_termination_msg: Optional[Callable[[Dict], bool]] = None, max_consecutive_auto_reply: Optional[int] = None, human_input_mode: Optional[str] = \"TERMINATE\", function_map: Optional[Dict[str, Callable]] = None, code_execution_config: Optional[Union[Dict, bool]] = None, llm_config: Optional[Union[Dict, bool]] = None, default_auto_reply: Optional[Union[str, Dict, None]] = \"\") Copy Arguments: name str - name of the agent. system_message str - system message for the ChatCompletion inference. is_termination_msg function - a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. The dict can contain the following keys: \"content\", \"role\", \"name\", \"function_call\". max_consecutive_auto_reply int - the maximum number of consecutive auto replies. default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). When set to 0, no auto reply will be generated. human_input_mode str - whether to ask for human inputs every time a message is received. Possible values are \"ALWAYS\", \"TERMINATE\", \"NEVER\". (1) When \"ALWAYS\", the agent prompts for human input every time a message is received. Under this mode, the conversation stops when the human input is \"exit\", or when is_termination_msg is True and there is no human input. (2) When \"TERMINATE\", the agent only prompts for human input only when a termination message is received or the number of auto reply reaches the max_consecutive_auto_reply. (3) When \"NEVER\", the agent will never prompt for human input. Under this mode, the conversation stops when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. function_map dict[str, callable] - Mapping function names (passed to openai) to callable functions. code_execution_config dict or False - config for the code execution. To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: work_dir (Optional, str): The working directory for the code execution. If None, a default working directory will be used. The default working directory is the \"extensions\" directory under \"path_to_flaml/autogen\". use_docker (Optional, list, str or bool): The docker image to use for code execution. If a list or a str of image name(s) is provided, the code will be executed in a docker container with the first image successfully pulled. If None, False or empty, the code will be executed in the current environment. Default is True, which will be converted into a list. If the code is executed in the current environment, the code must be trusted. timeout (Optional, int): The maximum execution time in seconds. last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. llm_config dict or False - llm inference configuration. Please refer to autogen.Completion.create for available options. To disable llm-based auto reply, set to False. default_auto_reply str or dict or None - default auto reply when no code execution or llm-based reply is generated. register_reply​ def register_reply(trigger: Union[Type[Agent], str, Agent, Callable[[Agent], bool], List], reply_func: Callable, position: Optional[int] = 0, config: Optional[Any] = None, reset_config: Optional[Callable] = None) Copy Register a reply function. The reply function will be called when the trigger matches the sender. The function registered later will be checked earlier by default. To change the order, set the position to a positive integer. Arguments: trigger Agent class, str, Agent instance, callable, or list - the trigger. If a class is provided, the reply function will be called when the sender is an instance of the class. If a string is provided, the reply function will be called when the sender's name matches the string. If an agent instance is provided, the reply function will be called when the sender is the agent instance. If a callable is provided, the reply function will be called when the callable returns True. If a list is provided, the reply function will be called when any of the triggers in the list is activated. If None is provided, the reply function will be called only when the sender is None. Note - Be sure to register None as a trigger if you would like to trigger an auto-reply function with non-empty messages and sender=None. reply_func Callable - the reply function. The function takes a recipient agent, a list of messages, a sender agent and a config as input and returns a reply message. def reply_func( recipient: ConversableAgent, messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None,) -> Union[str, Dict, None]: Copy position int - the position of the reply function in the reply function list. The function registered later will be checked earlier by default. To change the order, set the position to a positive integer. config Any - the config to be passed to the reply function. When an agent is reset, the config will be reset to the original value. reset_config Callable - the function to reset the config. The function returns None. Signature: def reset_config(config: Any) system_message​ @propertydef system_message() Copy Return the system message. update_system_message​ def update_system_message(system_message: str) Copy Update the system message. Arguments: system_message str - system message for the ChatCompletion inference. update_max_consecutive_auto_reply​ def update_max_consecutive_auto_reply(value: int, sender: Optional[Agent] = None) Copy Update the maximum number of consecutive auto replies. Arguments: value int - the maximum number of consecutive auto replies. sender Agent - when the sender is provided, only update the max_consecutive_auto_reply for that sender. max_consecutive_auto_reply​ def max_consecutive_auto_reply(sender: Optional[Agent] = None) -> int Copy The maximum number of consecutive auto replies. chat_messages​ @propertydef chat_messages() -> Dict[str, List[Dict]] Copy A dictionary of conversations from name to list of ChatCompletion messages. last_message​ def last_message(agent: Optional[Agent] = None) -> Dict Copy The last message exchanged with the agent. Arguments: agent Agent - The agent in the conversation. If None and more than one agent's conversations are found, an error will be raised. If None and only one conversation is found, the last message of the only conversation will be returned. Returns: The last message exchanged with the agent. use_docker​ @propertydef use_docker() -> Union[bool, str, None] Copy Bool value of whether to use docker to execute the code, or str value of the docker image name to use, or None when code execution is disabled. send​ def send(message: Union[Dict, str], recipient: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) -> bool Copy Send a message to another agent. Arguments: message dict or str - message to be sent. The message could contain the following fields (either content or function_call must be provided): content (str): the content of the message. function_call (str): the name of the function to be called. name (str): the name of the function to be called. role (str): the role of the message, any role that is not \"function\" will be modified to \"assistant\". context (dict): the context of the message, which will be passed to autogen.Completion.create. For example, one agent can send a message A as: { \"content\": lambda context: context[\"use_tool_msg\"], \"context\": { \"use_tool_msg\": \"Use tool X if they are relevant.\" }} Copy Next time, one agent can send a message B with a different \"use_tool_msg\". Then the content of message A will be refreshed to the new \"use_tool_msg\". So effectively, this provides a way for an agent to send a \"link\" and modify the content of the \"link\" later. recipient Agent - the recipient of the message. request_reply bool or None - whether to request a reply from the recipient. silent bool or None - (Experimental) whether to print the message sent. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. a_send​ async def a_send(message: Union[Dict, str], recipient: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) -> bool Copy (async) Send a message to another agent. Arguments: message dict or str - message to be sent. The message could contain the following fields (either content or function_call must be provided): content (str): the content of the message. function_call (str): the name of the function to be called. name (str): the name of the function to be called. role (str): the role of the message, any role that is not \"function\" will be modified to \"assistant\". context (dict): the context of the message, which will be passed to autogen.Completion.create. For example, one agent can send a message A as: { \"content\": lambda context: context[\"use_tool_msg\"], \"context\": { \"use_tool_msg\": \"Use tool X if they are relevant.\" }} Copy Next time, one agent can send a message B with a different \"use_tool_msg\". Then the content of message A will be refreshed to the new \"use_tool_msg\". So effectively, this provides a way for an agent to send a \"link\" and modify the content of the \"link\" later. recipient Agent - the recipient of the message. request_reply bool or None - whether to request a reply from the recipient. silent bool or None - (Experimental) whether to print the message sent. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. receive​ def receive(message: Union[Dict, str], sender: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) Copy Receive a message from another agent. Once a message is received, this function sends a reply to the sender or stop. The reply can be generated automatically or entered manually by a human. Arguments: message dict or str - message from the sender. If the type is dict, it may contain the following reserved fields (either content or function_call need to be provided). \"content\": content of the message, can be None. \"function_call\": a dictionary containing the function name and arguments. \"role\": role of the message, can be \"assistant\", \"user\", \"function\". This field is only needed to distinguish between \"function\" or \"assistant\"/\"user\". \"name\": In most cases, this field is not needed. When the role is \"function\", this field is needed to indicate the function name. \"context\" (dict): the context of the message, which will be passed to autogen.Completion.create. sender - sender of an Agent instance. request_reply bool or None - whether a reply is requested from the sender. If None, the value is determined by self.reply_at_receive[sender]. silent bool or None - (Experimental) whether to print the message received. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. a_receive​ async def a_receive(message: Union[Dict, str], sender: Agent, request_reply: Optional[bool] = None, silent: Optional[bool] = False) Copy (async) Receive a message from another agent. Once a message is received, this function sends a reply to the sender or stop. The reply can be generated automatically or entered manually by a human. Arguments: message dict or str - message from the sender. If the type is dict, it may contain the following reserved fields (either content or function_call need to be provided). \"content\": content of the message, can be None. \"function_call\": a dictionary containing the function name and arguments. \"role\": role of the message, can be \"assistant\", \"user\", \"function\". This field is only needed to distinguish between \"function\" or \"assistant\"/\"user\". \"name\": In most cases, this field is not needed. When the role is \"function\", this field is needed to indicate the function name. \"context\" (dict): the context of the message, which will be passed to autogen.Completion.create. sender - sender of an Agent instance. request_reply bool or None - whether a reply is requested from the sender. If None, the value is determined by self.reply_at_receive[sender]. silent bool or None - (Experimental) whether to print the message received. Raises: ValueError - if the message can't be converted into a valid ChatCompletion message. initiate_chat​ def initiate_chat(recipient: \"ConversableAgent\", clear_history: Optional[bool] = True, silent: Optional[bool] = False, **context, ,) Copy Initiate a chat with the recipient agent. Reset the consecutive auto reply counter. If clear_history is True, the chat history with the recipient agent will be cleared. generate_init_message is called to generate the initial message for the agent. Arguments: recipient - the recipient agent. clear_history bool - whether to clear the chat history with the agent. silent bool or None - (Experimental) whether to print the messages for this conversation. **context - any context information. \"message\" needs to be provided if the generate_init_message method is not overridden. a_initiate_chat​ async def a_initiate_chat(recipient: \"ConversableAgent\", clear_history: Optional[bool] = True, silent: Optional[bool] = False, **context, ,) Copy (async) Initiate a chat with the recipient agent. Reset the consecutive auto reply counter. If clear_history is True, the chat history with the recipient agent will be cleared. generate_init_message is called to generate the initial message for the agent. Arguments: recipient - the recipient agent. clear_history bool - whether to clear the chat history with the agent. silent bool or None - (Experimental) whether to print the messages for this conversation. **context - any context information. \"message\" needs to be provided if the generate_init_message method is not overridden. reset​ def reset() Copy Reset the agent. stop_reply_at_receive​ def stop_reply_at_receive(sender: Optional[Agent] = None) Copy Reset the reply_at_receive of the sender. reset_consecutive_auto_reply_counter​ def reset_consecutive_auto_reply_counter(sender: Optional[Agent] = None) Copy Reset the consecutive_auto_reply_counter of the sender. clear_history​ def clear_history(agent: Optional[Agent] = None) Copy Clear the chat history of the agent. Arguments: agent - the agent with whom the chat history to clear. If None, clear the chat history with all agents. generate_oai_reply​ def generate_oai_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) -> Tuple[bool, Union[str, Dict, None]] Copy Generate a reply using autogen.oai. generate_code_execution_reply​ def generate_code_execution_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) Copy Generate a reply using code execution. generate_function_call_reply​ def generate_function_call_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) Copy Generate a reply using function call. check_termination_and_human_reply​ def check_termination_and_human_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, config: Optional[Any] = None) -> Tuple[bool, Union[str, Dict, None]] Copy Check if the conversation should be terminated, and if human reply is provided. generate_reply​ def generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, exclude: Optional[List[Callable]] = None) -> Union[str, Dict, None] Copy Reply based on the conversation history and the sender. Either messages or sender must be provided. Register a reply_func with None as one trigger for it to be activated when messages is non-empty and sender is None. Use registered auto reply functions to generate replies. By default, the following functions are checked in order: check_termination_and_human_reply generate_function_call_reply generate_code_execution_reply generate_oai_reply Every function returns a tuple (final, reply). When a function returns final=False, the next function will be checked. So by default, termination and human reply will be checked first. If not terminating and human reply is skipped, execute function or code and return the result. AI replies are generated only when no code execution is performed. Arguments: messages - a list of messages in the conversation history. default_reply str or dict - default reply. sender - sender of an Agent instance. exclude - a list of functions to exclude. Returns: str or dict or None: reply. None if no reply is generated. a_generate_reply​ async def a_generate_reply(messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None, exclude: Optional[List[Callable]] = None) -> Union[str, Dict, None] Copy (async) Reply based on the conversation history and the sender. Either messages or sender must be provided. Register a reply_func with None as one trigger for it to be activated when messages is non-empty and sender is None. Use registered auto reply functions to generate replies. By default, the following functions are checked in order: check_termination_and_human_reply generate_function_call_reply generate_code_execution_reply generate_oai_reply Every function returns a tuple (final, reply). When a function returns final=False, the next function will be checked. So by default, termination and human reply will be checked first. If not terminating and human reply is skipped, execute function or code and return the result. AI replies are generated only when no code execution is performed. Arguments: messages - a list of messages in the conversation history. default_reply str or dict - default reply. sender - sender of an Agent instance. exclude - a list of functions to exclude. Returns: str or dict or None: reply. None if no reply is generated. get_human_input​ def get_human_input(prompt: str) -> str Copy Get human input. Override this method to customize the way to get human input. Arguments: prompt str - prompt for the human input. Returns: str - human input. run_code​ def run_code(code, **kwargs) Copy Run the code and return the result. Override this function to modify the way to run the code. Arguments: code str - the code to be executed. **kwargs - other keyword arguments. Returns: A tuple of (exitcode, logs, image). exitcode int - the exit code of the code execution. logs str - the logs of the code execution. image str or None - the docker image used for the code execution. execute_code_blocks​ def execute_code_blocks(code_blocks) Copy Execute the code blocks and return the result. execute_function​ def execute_function(func_call) Copy Execute a function call and return the result. Override this function to modify the way to execute a function call. Arguments: func_call - a dictionary extracted from openai message at key \"function_call\" with keys \"name\" and \"arguments\". Returns: A tuple of (is_exec_success, result_dict). is_exec_success boolean - whether the execution is successful. result_dict - a dictionary with keys \"name\", \"role\", and \"content\". Value of \"role\" is \"function\". generate_init_message​ def generate_init_message(**context) -> Union[str, Dict] Copy Generate the initial message for the agent. Override this function to customize the initial message based on user's request. If not overriden, \"message\" needs to be provided in the context. register_function​ def register_function(function_map: Dict[str, Callable]) Copy Register functions to the agent. Arguments: function_map - a dictionary mapping function names to functions.","s":"ConversableAgent Objects","u":"/FLAML/docs/reference/autogen/agentchat/conversable_agent","h":"#conversableagent-objects","p":311},{"i":316,"t":"On this page","s":"autogen.oai.completion","u":"/FLAML/docs/reference/autogen/oai/completion","h":"","p":315},{"i":318,"t":"class Completion(openai_Completion) Copy A class for OpenAI completion API. It also supports: ChatCompletion, Azure OpenAI API. set_cache​ @classmethoddef set_cache(cls, seed: Optional[int] = 41, cache_path_root: Optional[str] = \".cache\") Copy Set cache path. Arguments: seed int, Optional - The integer identifier for the pseudo seed. Results corresponding to different seeds will be cached in different places. cache_path str, Optional - The root path for the cache. The complete cache path will be {cache_path}/{seed}. clear_cache​ @classmethoddef clear_cache(cls, seed: Optional[int] = None, cache_path_root: Optional[str] = \".cache\") Copy Clear cache. Arguments: seed int, Optional - The integer identifier for the pseudo seed. If omitted, all caches under cache_path_root will be cleared. cache_path str, Optional - The root path for the cache. The complete cache path will be {cache_path}/{seed}. tune​ @classmethoddef tune(cls, data: List[Dict], metric: str, mode: str, eval_func: Callable, log_file_name: Optional[str] = None, inference_budget: Optional[float] = None, optimization_budget: Optional[float] = None, num_samples: Optional[int] = 1, logging_level: Optional[int] = logging.WARNING, **config, ,) Copy Tune the parameters for the OpenAI API call. TODO: support parallel tuning with ray or spark. TODO: support agg_method as in test Arguments: data list - The list of data points. metric str - The metric to optimize. mode str - The optimization mode, \"min\" or \"max. eval_func Callable - The evaluation function for responses. The function should take a list of responses and a data point as input, and return a dict of metrics. For example, def eval_func(responses, **data): solution = data[\"solution\"] success_list = [] n = len(responses) for i in range(n): response = responses[i] succeed = is_equiv_chain_of_thought(response, solution) success_list.append(succeed) return { \"expected_success\": 1 - pow(1 - sum(success_list) / n, n), \"success\": any(s for s in success_list), } Copy log_file_name str, optional - The log file. inference_budget float, optional - The inference budget, dollar per instance. optimization_budget float, optional - The optimization budget, dollar in total. num_samples int, optional - The number of samples to evaluate. -1 means no hard restriction in the number of trials and the actual number is decided by optimization_budget. Defaults to 1. logging_level optional - logging level. Defaults to logging.WARNING. **config dict - The search space to update over the default search. For prompt, please provide a string/Callable or a list of strings/Callables. If prompt is provided for chat models, it will be converted to messages under role \"user\". Do not provide both prompt and messages for chat models, but provide either of them. A string template will be used to generate a prompt for each data instance using prompt.format(**data). A callable template will be used to generate a prompt for each data instance using prompt(data). For stop, please provide a string, a list of strings, or a list of lists of strings. For messages (chat models only), please provide a list of messages (for a single chat prefix) or a list of lists of messages (for multiple choices of chat prefix to choose from). Each message should be a dict with keys \"role\" and \"content\". The value of \"content\" can be a string/Callable template. Returns: dict - The optimized hyperparameter setting. tune.ExperimentAnalysis - The tuning results. create​ @classmethoddef create(cls, context: Optional[Dict] = None, use_cache: Optional[bool] = True, config_list: Optional[List[Dict]] = None, filter_func: Optional[Callable[[Dict, Dict, Dict], bool]] = None, raise_on_ratelimit_or_timeout: Optional[bool] = True, allow_format_str_template: Optional[bool] = False, **config, ,) Copy Make a completion for a given context. Arguments: context Dict, Optional - The context to instantiate the prompt. It needs to contain keys that are used by the prompt template or the filter function. E.g., prompt=\"Complete the following sentence: {prefix}, context={\"prefix\": \"Today I feel\"}. The actual prompt will be: \"Complete the following sentence: Today I feel\". More examples can be found at templating. use_cache bool, Optional - Whether to use cached responses. config_list List, Optional - List of configurations for the completion to try. The first one that does not raise an error will be used. Only the differences from the default config need to be provided. E.g., response = oai.Completion.create( config_list=[ { \"model\": \"gpt-4\", \"api_key\": os.environ.get(\"AZURE_OPENAI_API_KEY\"), \"api_type\": \"azure\", \"api_base\": os.environ.get(\"AZURE_OPENAI_API_BASE\"), \"api_version\": \"2023-03-15-preview\", }, { \"model\": \"gpt-3.5-turbo\", \"api_key\": os.environ.get(\"OPENAI_API_KEY\"), \"api_type\": \"open_ai\", \"api_base\": \"https://api.openai.com/v1\", }, { \"model\": \"llama-7B\", \"api_base\": \"http://127.0.0.1:8080\", \"api_type\": \"open_ai\", } ], prompt=\"Hi\",) Copy filter_func Callable, Optional - A function that takes in the context, the config and the response and returns a boolean to indicate whether the response is valid. E.g., def yes_or_no_filter(context, config, response): return context.get(\"yes_or_no_choice\", False) is False or any( text in [\"Yes.\", \"No.\"] for text in oai.Completion.extract_text(response) ) Copy raise_on_ratelimit_or_timeout bool, Optional - Whether to raise RateLimitError or Timeout when all configs fail. When set to False, -1 will be returned when all configs fail. allow_format_str_template bool, Optional - Whether to allow format string template in the config. **config - Configuration for the openai API call. This is used as parameters for calling openai API. Besides the parameters for the openai API call, it can also contain a seed (int) for the cache. This is useful when implementing \"controlled randomness\" for the completion. Also, the \"prompt\" or \"messages\" parameter can contain a template (str or Callable) which will be instantiated with the context. Returns: Responses from OpenAI API, with additional fields. cost: the total cost. When config_list is provided, the response will contain a few more fields: config_id: the index of the config in the config_list that is used to generate the response. pass_filter: whether the response passes the filter function. None if no filter is provided. test​ @classmethoddef test(cls, data, eval_func=None, use_cache=True, agg_method=\"avg\", return_responses_and_per_instance_result=False, logging_level=logging.WARNING, **config, ,) Copy Evaluate the responses created with the config for the OpenAI API call. Arguments: data list - The list of test data points. eval_func Callable - The evaluation function for responses per data instance. The function should take a list of responses and a data point as input, and return a dict of metrics. You need to either provide a valid callable eval_func; or do not provide one (set None) but call the test function after calling the tune function in which a eval_func is provided. In the latter case we will use the eval_func provided via tune function. Defaults to None. def eval_func(responses, **data): solution = data[\"solution\"] success_list = [] n = len(responses) for i in range(n): response = responses[i] succeed = is_equiv_chain_of_thought(response, solution) success_list.append(succeed) return { \"expected_success\": 1 - pow(1 - sum(success_list) / n, n), \"success\": any(s for s in success_list), } Copy use_cache bool, Optional - Whether to use cached responses. Defaults to True. agg_method str, Callable or a dict of Callable - Result aggregation method (across multiple instances) for each of the metrics. Defaults to 'avg'. An example agg_method in str: agg_method = 'median' Copy An example agg_method in a Callable: agg_method = np.median Copy An example agg_method in a dict of Callable: agg_method={'median_success': np.median, 'avg_success': np.mean} Copy return_responses_and_per_instance_result bool - Whether to also return responses and per instance results in addition to the aggregated results. logging_level optional - logging level. Defaults to logging.WARNING. **config dict - parametes passed to the openai api call create(). Returns: None when no valid eval_func is provided in either test or tune; Otherwise, a dict of aggregated results, responses and per instance results if return_responses_and_per_instance_result is True; Otherwise, a dict of aggregated results (responses and per instance results are not returned). cost​ @classmethoddef cost(cls, response: dict) Copy Compute the cost of an API call. Arguments: response dict - The response from OpenAI API. Returns: The cost in USD. 0 if the model is not supported. extract_text​ @classmethoddef extract_text(cls, response: dict) -> List[str] Copy Extract the text from a completion or chat response. Arguments: response dict - The response from OpenAI API. Returns: A list of text in the responses. extract_text_or_function_call​ @classmethoddef extract_text_or_function_call(cls, response: dict) -> List[str] Copy Extract the text or function calls from a completion or chat response. Arguments: response dict - The response from OpenAI API. Returns: A list of text or function calls in the responses. logged_history​ @classmethod@propertydef logged_history(cls) -> Dict Copy Return the book keeping dictionary. start_logging​ @classmethoddef start_logging(cls, history_dict: Optional[Dict] = None, compact: Optional[bool] = True, reset_counter: Optional[bool] = True) Copy Start book keeping. Arguments: history_dict Dict - A dictionary for book keeping. If no provided, a new one will be created. compact bool - Whether to keep the history dictionary compact. Compact history contains one key per conversation, and the value is a dictionary like: { \"create_at\": [0, 1], \"cost\": [0.1, 0.2],} Copy where \"created_at\" is the index of API calls indicating the order of all the calls, and \"cost\" is the cost of each call. This example shows that the conversation is based on two API calls. The compact format is useful for condensing the history of a conversation. If compact is False, the history dictionary will contain all the API calls: the key is the index of the API call, and the value is a dictionary like: { \"request\": request_dict, \"response\": response_dict,} Copy where request_dict is the request sent to OpenAI API, and response_dict is the response. For a conversation containing two API calls, the non-compact history dictionary will be like: { 0: { \"request\": request_dict_0, \"response\": response_dict_0, }, 1: { \"request\": request_dict_1, \"response\": response_dict_1, }, Copy The first request's messages plus the response is equal to the second request's messages. For a conversation with many turns, the non-compact history dictionary has a quadratic size while the compact history dict has a linear size. reset_counter bool - whether to reset the counter of the number of API calls. stop_logging​ @classmethoddef stop_logging(cls) Copy End book keeping.","s":"Completion Objects","u":"/FLAML/docs/reference/autogen/oai/completion","h":"#completion-objects","p":315},{"i":320,"t":"class ChatCompletion(Completion) Copy A class for OpenAI API ChatCompletion.","s":"ChatCompletion Objects","u":"/FLAML/docs/reference/autogen/oai/completion","h":"#chatcompletion-objects","p":315},{"i":322,"t":"On this page","s":"autogen.code_utils","u":"/FLAML/docs/reference/autogen/code_utils","h":"","p":321},{"i":324,"t":"class PassAssertionFilter() Copy pass_assertions​ def pass_assertions(context, response, **_) Copy Check if the response passes the assertions. implement​ def implement(definition: str, configs: Optional[List[Dict]] = None, assertions: Optional[Union[str, Callable[[str], Tuple[str, float]]]] = generate_assertions) -> Tuple[str, float] Copy Implement a function from a definition. Arguments: definition str - The function definition, including the signature and docstr. configs list - The list of configurations for completion. assertions Optional, str or Callable - The assertion code which serves as a filter of the responses, or an assertion generator. Returns: str - The implementation. float - The cost of the implementation. int - The index of the configuration which generates the implementation.","s":"PassAssertionFilter Objects","u":"/FLAML/docs/reference/autogen/code_utils","h":"#passassertionfilter-objects","p":321},{"i":326,"t":"On this page","s":"autogen.oai.openai_utils","u":"/FLAML/docs/reference/autogen/oai/openai_utils","h":"","p":325},{"i":328,"t":"On this page","s":"AutoML for LightGBM","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"","p":327},{"i":330,"t":"Install the [automl] option. pip install \"flaml[automl] matplotlib openml\" Copy","s":"Prerequisites for this example","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"#prerequisites-for-this-example","p":327},{"i":332,"t":"from flaml import AutoMLfrom flaml.automl.data import load_openml_dataset# Download [houses dataset](https://www.openml.org/d/537) from OpenML. The task is to predict median price of the house in the region based on demographic composition and a state of housing market in the region.X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir=\"./\")automl = AutoML()settings = { \"time_budget\": 60, # total running time in seconds \"metric\": \"r2\", # primary metrics for regression can be chosen from: ['mae','mse','r2'] \"estimator_list\": [\"lgbm\"], # list of ML learners; we tune lightgbm in this example \"task\": \"regression\", # task type \"log_file_name\": \"houses_experiment.log\", # flaml log file \"seed\": 7654321, # random seed}automl.fit(X_train=X_train, y_train=y_train, **settings) Copy Sample output​ [flaml.automl: 11-15 19:46:44] {1485} INFO - Data split method: uniform[flaml.automl: 11-15 19:46:44] {1489} INFO - Evaluation method: cv[flaml.automl: 11-15 19:46:44] {1540} INFO - Minimizing error metric: 1-r2[flaml.automl: 11-15 19:46:44] {1577} INFO - List of ML learners in AutoML Run: ['lgbm'][flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 0, current learner lgbm[flaml.automl: 11-15 19:46:44] {1944} INFO - Estimated sufficient time budget=3232s. Estimated necessary time budget=3s.[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.5s, estimator lgbm's best error=0.7383, best estimator lgbm's best error=0.7383[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 1, current learner lgbm[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.4774, best estimator lgbm's best error=0.4774[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 2, current learner lgbm[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.7s, estimator lgbm's best error=0.4774, best estimator lgbm's best error=0.4774[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 3, current learner lgbm[flaml.automl: 11-15 19:46:44] {2029} INFO - at 0.9s, estimator lgbm's best error=0.2985, best estimator lgbm's best error=0.2985[flaml.automl: 11-15 19:46:44] {1826} INFO - iteration 4, current learner lgbm[flaml.automl: 11-15 19:46:45] {2029} INFO - at 1.3s, estimator lgbm's best error=0.2337, best estimator lgbm's best error=0.2337[flaml.automl: 11-15 19:46:45] {1826} INFO - iteration 5, current learner lgbm[flaml.automl: 11-15 19:46:45] {2029} INFO - at 1.4s, estimator lgbm's best error=0.2337, best estimator lgbm's best error=0.2337[flaml.automl: 11-15 19:46:45] {1826} INFO - iteration 6, current learner lgbm[flaml.automl: 11-15 19:46:46] {2029} INFO - at 2.5s, estimator lgbm's best error=0.2219, best estimator lgbm's best error=0.2219[flaml.automl: 11-15 19:46:46] {1826} INFO - iteration 7, current learner lgbm[flaml.automl: 11-15 19:46:46] {2029} INFO - at 2.9s, estimator lgbm's best error=0.2219, best estimator lgbm's best error=0.2219[flaml.automl: 11-15 19:46:46] {1826} INFO - iteration 8, current learner lgbm[flaml.automl: 11-15 19:46:48] {2029} INFO - at 4.5s, estimator lgbm's best error=0.1764, best estimator lgbm's best error=0.1764[flaml.automl: 11-15 19:46:48] {1826} INFO - iteration 9, current learner lgbm[flaml.automl: 11-15 19:46:54] {2029} INFO - at 10.5s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:46:54] {1826} INFO - iteration 10, current learner lgbm[flaml.automl: 11-15 19:46:56] {2029} INFO - at 12.4s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:46:56] {1826} INFO - iteration 11, current learner lgbm[flaml.automl: 11-15 19:47:13] {2029} INFO - at 29.0s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:47:13] {1826} INFO - iteration 12, current learner lgbm[flaml.automl: 11-15 19:47:15] {2029} INFO - at 31.1s, estimator lgbm's best error=0.1630, best estimator lgbm's best error=0.1630[flaml.automl: 11-15 19:47:15] {1826} INFO - iteration 13, current learner lgbm[flaml.automl: 11-15 19:47:29] {2029} INFO - at 45.8s, estimator lgbm's best error=0.1564, best estimator lgbm's best error=0.1564[flaml.automl: 11-15 19:47:33] {2242} INFO - retrain lgbm for 3.2s[flaml.automl: 11-15 19:47:33] {2247} INFO - retrained model: LGBMRegressor(colsample_bytree=0.8025848209352517, learning_rate=0.09100963138990374, max_bin=255, min_child_samples=42, n_estimators=363, num_leaves=216, reg_alpha=0.001113000336715291, reg_lambda=76.50614276906414, verbose=-1)[flaml.automl: 11-15 19:47:33] {1608} INFO - fit succeeded[flaml.automl: 11-15 19:47:33] {1610} INFO - Time taken to find the best model: 45.75616669654846[flaml.automl: 11-15 19:47:33] {1624} WARNING - Time taken to find the best model is 76% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. Copy Retrieve best config​ print(\"Best hyperparmeter config:\", automl.best_config)print(\"Best r2 on validation data: {0:.4g}\".format(1 - automl.best_loss))print(\"Training duration of best run: {0:.4g} s\".format(automl.best_config_train_time))print(automl.model.estimator)# Best hyperparmeter config: {'n_estimators': 363, 'num_leaves': 216, 'min_child_samples': 42, 'learning_rate': 0.09100963138990374, 'log_max_bin': 8, 'colsample_bytree': 0.8025848209352517, 'reg_alpha': 0.001113000336715291, 'reg_lambda': 76.50614276906414}# Best r2 on validation data: 0.8436# Training duration of best run: 3.229 s# LGBMRegressor(colsample_bytree=0.8025848209352517,# learning_rate=0.09100963138990374, max_bin=255,# min_child_samples=42, n_estimators=363, num_leaves=216,# reg_alpha=0.001113000336715291, reg_lambda=76.50614276906414,# verbose=-1) Copy Plot feature importance​ import matplotlib.pyplot as pltplt.barh(automl.feature_names_in_, automl.feature_importances_) Copy Compute predictions of testing dataset​ y_pred = automl.predict(X_test)print(\"Predicted labels\", y_pred)# Predicted labels [143391.65036562 245535.13731811 153171.44071629 ... 184354.52735963# 235510.49470445 282617.22858956] Copy Compute different metric values on testing dataset​ from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))print(\"mse\", \"=\", sklearn_metric_loss_score(\"mse\", y_pred, y_test))print(\"mae\", \"=\", sklearn_metric_loss_score(\"mae\", y_pred, y_test))# r2 = 0.8505434326526395# mse = 1975592613.138005# mae = 29471.536046068788 Copy Compare with untuned LightGBM​ from lightgbm import LGBMRegressorlgbm = LGBMRegressor()lgbm.fit(X_train, y_train)y_pred = lgbm.predict(X_test)from flaml.automl.ml import sklearn_metric_loss_scoreprint(\"default lgbm r2\", \"=\", 1 - sklearn_metric_loss_score(\"r2\", y_pred, y_test))# default lgbm r2 = 0.8296179648694404 Copy Plot learning curve​ How does the model accuracy improve as we search for different hyperparameter configurations? from flaml.automl.data import get_output_from_logimport numpy as nptime_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = get_output_from_log(filename=settings['log_file_name'], time_budget=60)plt.title('Learning Curve')plt.xlabel('Wall Clock Time (s)')plt.ylabel('Validation r2')plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')plt.show() Copy","s":"Use built-in LGBMEstimator","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"#use-built-in-lgbmestimator","p":327},{"i":334,"t":"The native API of LightGBM allows one to specify a custom objective function in the model constructor. You can easily enable it by adding a customized LightGBM learner in FLAML. In the following example, we show how to add such a customized LightGBM learner with a custom objective function. Create a customized LightGBM learner with a custom objective function​ import numpy as np# define your customized objective functiondef my_loss_obj(y_true, y_pred): c = 0.5 residual = y_pred - y_true grad = c * residual / (np.abs(residual) + c) hess = c ** 2 / (np.abs(residual) + c) ** 2 # rmse grad and hess grad_rmse = residual hess_rmse = 1.0 # mae grad and hess grad_mae = np.array(residual) grad_mae[grad_mae > 0] = 1. grad_mae[grad_mae <= 0] = -1. hess_mae = 1.0 coef = [0.4, 0.3, 0.3] return coef[0] * grad + coef[1] * grad_rmse + coef[2] * grad_mae, coef[0] * hess + coef[1] * hess_rmse + coef[2] * hess_maefrom flaml.automl.model import LGBMEstimatorclass MyLGBM(LGBMEstimator): \"\"\"LGBMEstimator with my_loss_obj as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=my_loss_obj, **config) Copy Add the customized learner and tune it​ automl = AutoML()automl.add_learner(learner_name=\"my_lgbm\", learner_class=MyLGBM)settings[\"estimator_list\"] = [\"my_lgbm\"] # change the estimator listautoml.fit(X_train=X_train, y_train=y_train, **settings) Copy Link to notebook | Open in colab","s":"Use a customized LightGBM learner","u":"/FLAML/docs/Examples/AutoML-for-LightGBM","h":"#use-a-customized-lightgbm-learner","p":327},{"i":336,"t":"On this page","s":"autogen.agentchat.user_proxy_agent","u":"/FLAML/docs/reference/autogen/agentchat/user_proxy_agent","h":"","p":335},{"i":338,"t":"class UserProxyAgent(ConversableAgent) Copy (In preview) A proxy agent for the user, that can execute code and provide feedback to the other agents. UserProxyAgent is a subclass of ConversableAgent configured with human_input_mode to ALWAYS and llm_config to False. By default, the agent will prompt for human input every time a message is received. Code execution is enabled by default. LLM-based auto reply is disabled by default. To modify auto reply, register a method with (register_reply)[conversable_agent#register_reply]. To modify the way to get human input, override get_human_input method. To modify the way to execute code blocks, single code block, or function call, override execute_code_blocks, run_code, and execute_function methods respectively. To customize the initial message when a conversation starts, override generate_init_message method. __init__​ def __init__(name: str, is_termination_msg: Optional[Callable[[Dict], bool]] = None, max_consecutive_auto_reply: Optional[int] = None, human_input_mode: Optional[str] = \"ALWAYS\", function_map: Optional[Dict[str, Callable]] = None, code_execution_config: Optional[Union[Dict, bool]] = None, default_auto_reply: Optional[Union[str, Dict, None]] = \"\", llm_config: Optional[Union[Dict, bool]] = False, system_message: Optional[str] = \"\") Copy Arguments: name str - name of the agent. is_termination_msg function - a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. The dict can contain the following keys: \"content\", \"role\", \"name\", \"function_call\". max_consecutive_auto_reply int - the maximum number of consecutive auto replies. default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). The limit only plays a role when human_input_mode is not \"ALWAYS\". human_input_mode str - whether to ask for human inputs every time a message is received. Possible values are \"ALWAYS\", \"TERMINATE\", \"NEVER\". (1) When \"ALWAYS\", the agent prompts for human input every time a message is received. Under this mode, the conversation stops when the human input is \"exit\", or when is_termination_msg is True and there is no human input. (2) When \"TERMINATE\", the agent only prompts for human input only when a termination message is received or the number of auto reply reaches the max_consecutive_auto_reply. (3) When \"NEVER\", the agent will never prompt for human input. Under this mode, the conversation stops when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. function_map dict[str, callable] - Mapping function names (passed to openai) to callable functions. code_execution_config dict or False - config for the code execution. To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: work_dir (Optional, str): The working directory for the code execution. If None, a default working directory will be used. The default working directory is the \"extensions\" directory under \"path_to_flaml/autogen\". use_docker (Optional, list, str or bool): The docker image to use for code execution. If a list or a str of image name(s) is provided, the code will be executed in a docker container with the first image successfully pulled. If None, False or empty, the code will be executed in the current environment. Default is True, which will be converted into a list. If the code is executed in the current environment, the code must be trusted. timeout (Optional, int): The maximum execution time in seconds. last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. default_auto_reply str or dict or None - the default auto reply message when no code execution or llm based reply is generated. llm_config dict or False - llm inference configuration. Please refer to autogen.Completion.create for available options. Default to false, which disables llm-based auto reply. system_message str - system message for ChatCompletion inference. Only used when llm_config is not False. Use it to reprogram the agent.","s":"UserProxyAgent Objects","u":"/FLAML/docs/reference/autogen/agentchat/user_proxy_agent","h":"#userproxyagent-objects","p":335},{"i":340,"t":"On this page","s":"autogen.retrieve_utils","u":"/FLAML/docs/reference/autogen/retrieve_utils","h":"","p":339},{"i":342,"t":"On this page","s":"automl.contrib.histgb","u":"/FLAML/docs/reference/automl/contrib/histgb","h":"","p":341},{"i":344,"t":"class HistGradientBoostingEstimator(SKLearnEstimator) Copy The class for tuning Histogram Gradient Boosting.","s":"HistGradientBoostingEstimator Objects","u":"/FLAML/docs/reference/automl/contrib/histgb","h":"#histgradientboostingestimator-objects","p":341},{"i":346,"t":"On this page","s":"automl.automl","u":"/FLAML/docs/reference/automl/automl","h":"","p":345},{"i":348,"t":"class AutoML(BaseEstimator) Copy The AutoML class. Example: automl = AutoML()automl_settings = { \"time_budget\": 60, \"metric\": 'accuracy', \"task\": 'classification', \"log_file_name\": 'mylog.log',}automl.fit(X_train = X_train, y_train = y_train, **automl_settings) Copy __init__​ def __init__(**settings) Copy Constructor. Many settings in fit() can be passed to the constructor too. If an argument in fit() is provided, it will override the setting passed to the constructor. If an argument in fit() is not provided but provided in the constructor, the value passed to the constructor will be used. Arguments: metric - A string of the metric name or a function, e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted', 'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If passing a customized metric function, the function needs to have the following input arguments: def custom_metric( X_test, y_test, estimator, labels, X_train, y_train, weight_test=None, weight_train=None, config=None, groups_test=None, groups_train=None,): return metric_to_minimize, metrics_to_log Copy which returns a float number as the minimization objective, and a dictionary as the metrics to log. E.g., def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args,): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { \"val_loss\": val_loss, \"train_loss\": train_loss, \"pred_time\": pred_time, } Copy task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression', 'summarization', or an instance of the Task class. n_jobs - An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. log_file_name - A string of the log file name | default=\"\". To disable logging, set it to be an empty string \"\". estimator_list - A list of strings for estimator names, or 'auto'. e.g., ['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']. time_budget - A float number of the time budget in seconds. Use -1 if no time limit. max_iter - An integer of the maximal number of iterations. sample - A boolean of whether to sample the training data during search. ensemble - boolean or dict | default=False. Whether to perform ensemble after search. Can be a dict with keys 'passthrough' and 'final_estimator' to specify the passthrough and final_estimator in the stacker. The dict can also contain 'n_jobs' as the key to specify the number of jobs for the stacker. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio - A float of the valiation data percentage for holdout. n_splits - An integer of the number of folds for cross - validation. log_type - A string of the log type, one of ['better', 'all']. 'better' only logs configs with better loss than previos iters 'all' logs all the tried configs. model_history - A boolean of whether to keep the best model per estimator. Make sure memory is large enough if setting to True. log_training_metric - A boolean of whether to log the training metric for each model. mem_thres - A float of the memory size constraint in bytes. pred_time_limit - A float of the prediction latency constraint in seconds. It refers to the average prediction time per row in validation data. train_time_limit - A float of the training time constraint in seconds. verbose - int, default=3 | Controls the verbosity, higher means more messages. retrain_full - bool or str, default=True | whether to retrain the selected model on the full training data when using holdout. True - retrain only after search finishes; False - no retraining; 'budget' - do best effort to retrain without violating the time budget. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. hpo_method - str, default=\"auto\" | The hyperparameter optimization method. By default, CFO is used for sequential search and BlendSearch is used for parallel search. No need to set when using flaml's default search space or using a simple customized search space. When set to 'bs', BlendSearch is used. BlendSearch can be tried when the search space is complex, for example, containing multiple disjoint, discontinuous subspaces. When set to 'random', random search is used. starting_points - A dictionary or a str to specify the starting hyperparameter config for the estimators | default=\"static\". If str: if \"data\", use data-dependent defaults; if \"data:path\" use data-dependent defaults which are stored at path; if \"static\", use data-independent defaults. If dict, keys are the name of the estimators, and values are the starting hyperparameter configurations for the corresponding estimators. The value can be a single hyperparameter configuration dict or a list of hyperparameter configuration dicts. In the following code example, we get starting_points from the automl object and use them in the new_automl object. e.g., from flaml import AutoMLautoml = AutoML()X_train, y_train = load_iris(return_X_y=True)automl.fit(X_train, y_train)starting_points = automl.best_config_per_estimatornew_automl = AutoML()new_automl.fit(X_train, y_train, starting_points=starting_points) Copy seed - int or None, default=None | The random seed for hpo. n_concurrent_trials - [In preview] int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, flaml performes parallel tuning and installation of ray or spark is required: pip install flaml[ray] or pip install flaml[spark]. Please check here for more details about installing Spark. keep_search_state - boolean, default=False | Whether to keep data needed for model search after fit(). By default the state is deleted for space saving. preserve_checkpoint - boolean, default=True | Whether to preserve the saved checkpoint on disk when deleting automl. By default the checkpoint is preserved. early_stop - boolean, default=False | Whether to stop early if the search is considered to converge. force_cancel - boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. append_log - boolean, default=False | Whether to directly append the log records to the input log file if it exists. auto_augment - boolean, default=True | Whether to automatically augment rare classes. min_sample_size - int, default=MIN_SAMPLE_TRAIN | the minimal sample size when sample=True. use_ray - boolean or dict. If boolean: default=False | Whether to use ray to run the training in separate processes. This can be used to prevent OOM for large datasets, but will incur more overhead in time. If dict: the dict contains the keywords arguments to be passed to ray.tune.run. use_spark - boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable FLAML_MAX_CONCURRENT to override the detected num_executors. The final number of concurrent trials will be the minimum of n_concurrent_trials and num_executors. free_mem_ratio - float between 0 and 1, default=0. The free memory ratio to keep during training. metric_constraints - list, default=[] | The list of metric constraints. Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from \">=\" and \"<=\", and the third element is the constraint value. E.g., ('val_loss', '<=', 0.1). Note that all the metric names in metric_constraints need to be reported via the metrics_to_log dictionary returned by a customized metric function. The customized metric function shall be provided via the metric key word argument of the fit() function or the automl constructor. Find an example in the 4th constraint type in this doc. If pred_time_limit is provided as one of keyword arguments to fit() function or the automl constructor, flaml will automatically (and under the hood) add it as an additional element in the metric_constraints. Essentially 'pred_time_limit' specifies a constraint about the prediction latency constraint in seconds. custom_hp - dict, default=None | The custom search space specified by user. It is a nested dict with keys being the estimator names, and values being dicts per estimator search space. In the per estimator search space dict, the keys are the hyperparameter names, and values are dicts of info (\"domain\", \"init_value\", and \"low_cost_init_value\") about the search space associated with the hyperparameter (i.e., per hyperparameter search space dict). When custom_hp is provided, the built-in search space which is also a nested dict of per estimator search space dict, will be updated with custom_hp. Note that during this nested dict update, the per hyperparameter search space dicts will be replaced (instead of updated) by the ones provided in custom_hp. Note that the value for \"domain\" can either be a constant or a sample.Domain object. e.g., custom_hp = { \"transformer_ms\": { \"model_path\": { \"domain\": \"albert-base-v2\", }, \"learning_rate\": { \"domain\": tune.choice([1e-4, 1e-5]), } } } Copy skip_transform - boolean, default=False | Whether to pre-process data prior to modeling. fit_kwargs_by_estimator - dict, default=None | The user specified keywords arguments, grouped by estimator name. e.g., fit_kwargs_by_estimator = { \"transformer\": { \"output_dir\": \"test/data/output/\", \"fp16\": False, }} Copy mlflow_logging - boolean, default=True | Whether to log the training results to mlflow. This requires mlflow to be installed and to have an active mlflow run. FLAML will create nested runs. config_history​ @propertydef config_history() -> dict Copy A dictionary of iter->(estimator, config, time), storing the best estimator, config, and the time when the best model is updated each time. model​ @propertydef model() Copy An object with predict() and predict_proba() method (for classification), storing the best trained model. best_model_for_estimator​ def best_model_for_estimator(estimator_name: str) Copy Return the best model found for a particular estimator. Arguments: estimator_name - a str of the estimator's name. Returns: An object storing the best model for estimator_name. If model_history was set to False during fit(), then the returned model is untrained unless estimator_name is the best estimator. If model_history was set to True, then the returned model is trained. best_estimator​ @propertydef best_estimator() Copy A string indicating the best estimator found. best_iteration​ @propertydef best_iteration() Copy An integer of the iteration number where the best config is found. best_config​ @propertydef best_config() Copy A dictionary of the best configuration. best_config_per_estimator​ @propertydef best_config_per_estimator() Copy A dictionary of all estimators' best configuration. best_loss_per_estimator​ @propertydef best_loss_per_estimator() Copy A dictionary of all estimators' best loss. best_loss​ @propertydef best_loss() Copy A float of the best loss found. best_result​ @propertydef best_result() Copy Result dictionary for model trained with the best config. metrics_for_best_config​ @propertydef metrics_for_best_config() Copy Returns a float of the best loss, and a dictionary of the auxiliary metrics to log associated with the best config. These two objects correspond to the returned objects by the customized metric function for the config with the best loss. best_config_train_time​ @propertydef best_config_train_time() Copy A float of the seconds taken by training the best config. feature_transformer​ @propertydef feature_transformer() Copy Returns feature transformer which is used to preprocess data before applying training or inference. label_transformer​ @propertydef label_transformer() Copy Returns label transformer which is used to preprocess labels before scoring, and inverse transform labels after inference. classes_​ @propertydef classes_() Copy A numpy array of shape (n_classes,) for class labels. time_to_find_best_model​ @propertydef time_to_find_best_model() -> float Copy Time taken to find best model in seconds. predict​ def predict(X: Union[np.array, DataFrame, List[str], List[List[str]], psDataFrame], **pred_kwargs, ,) Copy Predict label from features. Arguments: X - A numpy array or pandas dataframe or pyspark.pandas dataframe of featurized instances, shape n * m, or for time series forcast tasks: a pandas dataframe with the first column containing timestamp values (datetime type) or an integer n for the predict steps (only valid when the estimator is arima or sarimax). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). **pred_kwargs - Other key word arguments to pass to predict() function of the searched learners, such as per_device_eval_batch_size. multivariate_X_test = DataFrame({ 'timeStamp': pd.date_range(start='1/1/2022', end='1/07/2022'), 'categorical_col': ['yes', 'yes', 'no', 'no', 'yes', 'no', 'yes'], 'continuous_col': [105, 107, 120, 118, 110, 112, 115]})model.predict(multivariate_X_test) Copy Returns: A array-like of shape n * 1: each element is a predicted label for an instance. predict_proba​ def predict_proba(X, **pred_kwargs) Copy Predict the probability of each class from features, only works for classification problems. Arguments: X - A numpy array of featurized instances, shape n * m. **pred_kwargs - Other key word arguments to pass to predict_proba() function of the searched learners, such as per_device_eval_batch_size. Returns: A numpy array of shape n * c. c is the # classes. Each element at (i, j) is the probability for instance i to be in class j. add_learner​ def add_learner(learner_name, learner_class) Copy Add a customized learner. Arguments: learner_name - A string of the learner's name. learner_class - A subclass of flaml.automl.model.BaseEstimator. get_estimator_from_log​ def get_estimator_from_log(log_file_name: str, record_id: int, task: Union[str, Task]) Copy Get the estimator from log file. Arguments: log_file_name - A string of the log file name. record_id - An integer of the record ID in the file, 0 corresponds to the first trial. task - A string of the task type, 'binary', 'multiclass', 'regression', 'ts_forecast', 'rank', or an instance of the Task class. Returns: An estimator object for the given configuration. retrain_from_log​ def retrain_from_log(log_file_name, X_train=None, y_train=None, dataframe=None, label=None, time_budget=np.inf, task: Optional[Union[str, Task]] = None, eval_method=None, split_ratio=None, n_splits=None, split_type=None, groups=None, n_jobs=-1, train_best=True, train_full=False, record_id=-1, auto_augment=None, custom_hp=None, skip_transform=None, preserve_checkpoint=True, fit_kwargs_by_estimator=None, **fit_kwargs, ,) Copy Retrain from log file. This function is intended to retrain the logged configurations. NOTE: In some rare case, the last config is early stopped to meet time_budget and it's the best config. But the logged config's ITER_HP (e.g., n_estimators) is not reduced. Arguments: log_file_name - A string of the log file name. X_train - A numpy array or dataframe of training data in shape n*m. For time series forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). y_train - A numpy array or series of labels in shape n*1. dataframe - A dataframe of training data including label column. For time series forecast tasks, dataframe must be specified and should have at least two columns: timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). label - A str of the label column name, e.g., 'label'; Note - If X_train and y_train are provided, dataframe and label are ignored; If not, dataframe and label must be provided. time_budget - A float number of the time budget in seconds. task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression', 'summarization', or an instance of Task class. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio - A float of the validation data percentage for holdout. n_splits - An integer of the number of folds for cross-validation. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. groups - None or array-like | Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. n_jobs - An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. train_best - A boolean of whether to train the best config in the time budget; if false, train the last config in the budget. train_full - A boolean of whether to train on the full data. If true, eval_method and sample_size in the log file will be ignored. record_id - the ID of the training log record from which the model will be retrained. By default record_id = -1 which means this will be ignored. record_id = 0 corresponds to the first trial, and when record_id >= 0, time_budget will be ignored. auto_augment - boolean, default=True | Whether to automatically augment rare classes. custom_hp - dict, default=None | The custom search space specified by user Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the domain of the custom search space can either be a value or a sample.Domain object. custom_hp = { \"transformer_ms\": { \"model_path\": { \"domain\": \"albert-base-v2\", }, \"learning_rate\": { \"domain\": tune.choice([1e-4, 1e-5]), } }} Copy fit_kwargs_by_estimator - dict, default=None | The user specified keywords arguments, grouped by estimator name. e.g., fit_kwargs_by_estimator = { \"transformer\": { \"output_dir\": \"test/data/output/\", \"fp16\": False, }} Copy **fit_kwargs - Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. Below are a few examples of estimator-specific parameters: period - int | forecast horizon for all time series forecast tasks. gpu_per_trial - float, default = 0 | A float of the number of gpus per trial, only used by TransformersEstimator, XGBoostSklearnEstimator, and TemporalFusionTransformerEstimator. group_ids - list of strings of column names identifying a time series, only used by TemporalFusionTransformerEstimator, required for 'ts_forecast_panel' task. group_ids is a parameter for TimeSeriesDataSet object from PyTorchForecasting. For other parameters to describe your dataset, refer to TimeSeriesDataSet PyTorchForecasting. To specify your variables, use static_categoricals, static_reals, time_varying_known_categoricals, time_varying_known_reals, time_varying_unknown_categoricals, time_varying_unknown_reals, variable_groups. To provide more information on your data, use max_encoder_length, min_encoder_length, lags. log_dir - str, default = \"lightning_logs\" | Folder into which to log results for tensorboard, only used by TemporalFusionTransformerEstimator. max_epochs - int, default = 20 | Maximum number of epochs to run training, only used by TemporalFusionTransformerEstimator. batch_size - int, default = 64 | Batch size for training model, only used by TemporalFusionTransformerEstimator. search_space​ @propertydef search_space() -> dict Copy Search space. Must be called after fit(...) (use max_iter=0 and retrain_final=False to prevent actual fitting). Returns: A dict of the search space. low_cost_partial_config​ @propertydef low_cost_partial_config() -> dict Copy Low cost partial config. Returns: A dict. (a) if there is only one estimator in estimator_list, each key is a hyperparameter name. (b) otherwise, it is a nested dict with 'ml' as the key, and a list of the low_cost_partial_configs as the value, corresponding to each learner's low_cost_partial_config; the estimator index as an integer corresponding to the cheapest learner is appended to the list at the end. cat_hp_cost​ @propertydef cat_hp_cost() -> dict Copy Categorical hyperparameter cost Returns: A dict. (a) if there is only one estimator in estimator_list, each key is a hyperparameter name. (b) otherwise, it is a nested dict with 'ml' as the key, and a list of the cat_hp_cost's as the value, corresponding to each learner's cat_hp_cost; the cost relative to lgbm for each learner (as a list itself) is appended to the list at the end. points_to_evaluate​ @propertydef points_to_evaluate() -> dict Copy Initial points to evaluate. Returns: A list of dicts. Each dict is the initial point for each learner. resource_attr​ @propertydef resource_attr() -> Optional[str] Copy Attribute of the resource dimension. Returns: A string for the sample size attribute (the resource attribute in AutoML) or None. min_resource​ @propertydef min_resource() -> Optional[float] Copy Attribute for pruning. Returns: A float for the minimal sample size or None. max_resource​ @propertydef max_resource() -> Optional[float] Copy Attribute for pruning. Returns: A float for the maximal sample size or None. trainable​ @propertydef trainable() -> Callable[[dict], Optional[float]] Copy Training function. Returns: A function that evaluates each config and returns the loss. metric_constraints​ @propertydef metric_constraints() -> list Copy Metric constraints. Returns: A list of the metric constraints. fit​ def fit(X_train=None, y_train=None, dataframe=None, label=None, metric=None, task: Optional[Union[str, Task]] = None, n_jobs=None, log_file_name=None, estimator_list=None, time_budget=None, max_iter=None, sample=None, ensemble=None, eval_method=None, log_type=None, model_history=None, split_ratio=None, n_splits=None, log_training_metric=None, mem_thres=None, pred_time_limit=None, train_time_limit=None, X_val=None, y_val=None, sample_weight_val=None, groups_val=None, groups=None, verbose=None, retrain_full=None, split_type=None, learner_selector=None, hpo_method=None, starting_points=None, seed=None, n_concurrent_trials=None, keep_search_state=None, preserve_checkpoint=True, early_stop=None, force_cancel=None, append_log=None, auto_augment=None, min_sample_size=None, use_ray=None, use_spark=None, free_mem_ratio=0, metric_constraints=None, custom_hp=None, time_col=None, cv_score_agg_func=None, skip_transform=None, mlflow_logging=None, fit_kwargs_by_estimator=None, **fit_kwargs, ,) Copy Find a model for a given task. Arguments: X_train - A numpy array or a pandas dataframe of training data in shape (n, m). For time series forecsat tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). When using ray, X_train can be a ray.ObjectRef. y_train - A numpy array or a pandas series of labels in shape (n, ). dataframe - A dataframe of training data including label column. For time series forecast tasks, dataframe must be specified and must have at least two columns, timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). When using ray, dataframe can be a ray.ObjectRef. label - A str of the label column name for, e.g., 'label'; Note - If X_train and y_train are provided, dataframe and label are ignored; If not, dataframe and label must be provided. metric - A string of the metric name or a function, e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_weighted', 'roc_auc_ovo_weighted', 'roc_auc_ovr_weighted', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If passing a customized metric function, the function needs to have the following input arguments: def custom_metric( X_test, y_test, estimator, labels, X_train, y_train, weight_test=None, weight_train=None, config=None, groups_test=None, groups_train=None,): return metric_to_minimize, metrics_to_log Copy which returns a float number as the minimization objective, and a dictionary as the metrics to log. E.g., def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args,): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { \"val_loss\": val_loss, \"train_loss\": train_loss, \"pred_time\": pred_time, } Copy task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast_regression', 'ts_forecast_classification', 'rank', 'seq-classification', 'seq-regression', 'summarization', or an instance of Task class n_jobs - An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. log_file_name - A string of the log file name | default=\"\". To disable logging, set it to be an empty string \"\". estimator_list - A list of strings for estimator names, or 'auto'. e.g., ['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']. time_budget - A float number of the time budget in seconds. Use -1 if no time limit. max_iter - An integer of the maximal number of iterations. NOTE - when both time_budget and max_iter are unspecified, only one model will be trained per estimator. sample - A boolean of whether to sample the training data during search. ensemble - boolean or dict | default=False. Whether to perform ensemble after search. Can be a dict with keys 'passthrough' and 'final_estimator' to specify the passthrough and final_estimator in the stacker. The dict can also contain 'n_jobs' as the key to specify the number of jobs for the stacker. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio - A float of the valiation data percentage for holdout. n_splits - An integer of the number of folds for cross - validation. log_type - A string of the log type, one of ['better', 'all']. 'better' only logs configs with better loss than previos iters 'all' logs all the tried configs. model_history - A boolean of whether to keep the trained best model per estimator. Make sure memory is large enough if setting to True. Default value is False: best_model_for_estimator would return a untrained model for non-best learner. log_training_metric - A boolean of whether to log the training metric for each model. mem_thres - A float of the memory size constraint in bytes. pred_time_limit - A float of the prediction latency constraint in seconds. It refers to the average prediction time per row in validation data. train_time_limit - None or a float of the training time constraint in seconds. X_val - None or a numpy array or a pandas dataframe of validation data. y_val - None or a numpy array or a pandas series of validation labels. sample_weight_val - None or a numpy array of the sample weight of validation data of the same shape as y_val. groups_val - None or array-like | group labels (with matching length to y_val) or group counts (with sum equal to length of y_val) for validation data. Need to be consistent with groups. groups - None or array-like | Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. verbose - int, default=3 | Controls the verbosity, higher means more messages. retrain_full - bool or str, default=True | whether to retrain the selected model on the full training data when using holdout. True - retrain only after search finishes; False - no retraining; 'budget' - do best effort to retrain without violating the time budget. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. hpo_method - str, default=\"auto\" | The hyperparameter optimization method. By default, CFO is used for sequential search and BlendSearch is used for parallel search. No need to set when using flaml's default search space or using a simple customized search space. When set to 'bs', BlendSearch is used. BlendSearch can be tried when the search space is complex, for example, containing multiple disjoint, discontinuous subspaces. When set to 'random', random search is used. starting_points - A dictionary or a str to specify the starting hyperparameter config for the estimators | default=\"data\". If str: if \"data\", use data-dependent defaults; if \"data:path\" use data-dependent defaults which are stored at path; if \"static\", use data-independent defaults. If dict, keys are the name of the estimators, and values are the starting hyperparameter configurations for the corresponding estimators. The value can be a single hyperparameter configuration dict or a list of hyperparameter configuration dicts. In the following code example, we get starting_points from the automl object and use them in the new_automl object. e.g., from flaml import AutoMLautoml = AutoML()X_train, y_train = load_iris(return_X_y=True)automl.fit(X_train, y_train)starting_points = automl.best_config_per_estimatornew_automl = AutoML()new_automl.fit(X_train, y_train, starting_points=starting_points) Copy seed - int or None, default=None | The random seed for hpo. n_concurrent_trials - [In preview] int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, flaml performes parallel tuning and installation of ray or spark is required: pip install flaml[ray] or pip install flaml[spark]. Please check here for more details about installing Spark. keep_search_state - boolean, default=False | Whether to keep data needed for model search after fit(). By default the state is deleted for space saving. preserve_checkpoint - boolean, default=True | Whether to preserve the saved checkpoint on disk when deleting automl. By default the checkpoint is preserved. early_stop - boolean, default=False | Whether to stop early if the search is considered to converge. force_cancel - boolean, default=False | Whether to forcely cancel the PySpark job if overtime. append_log - boolean, default=False | Whether to directly append the log records to the input log file if it exists. auto_augment - boolean, default=True | Whether to automatically augment rare classes. min_sample_size - int, default=MIN_SAMPLE_TRAIN | the minimal sample size when sample=True. use_ray - boolean or dict. If boolean: default=False | Whether to use ray to run the training in separate processes. This can be used to prevent OOM for large datasets, but will incur more overhead in time. If dict: the dict contains the keywords arguments to be passed to ray.tune.run. use_spark - boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. free_mem_ratio - float between 0 and 1, default=0. The free memory ratio to keep during training. metric_constraints - list, default=[] | The list of metric constraints. Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from \">=\" and \"<=\", and the third element is the constraint value. E.g., ('precision', '>=', 0.9). Note that all the metric names in metric_constraints need to be reported via the metrics_to_log dictionary returned by a customized metric function. The customized metric function shall be provided via the metric key word argument of the fit() function or the automl constructor. Find examples in this test. If pred_time_limit is provided as one of keyword arguments to fit() function or the automl constructor, flaml will automatically (and under the hood) add it as an additional element in the metric_constraints. Essentially 'pred_time_limit' specifies a constraint about the prediction latency constraint in seconds. custom_hp - dict, default=None | The custom search space specified by user Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the domain of the custom search space can either be a value of a sample.Domain object. custom_hp = { \"transformer_ms\": { \"model_path\": { \"domain\": \"albert-base-v2\", }, \"learning_rate\": { \"domain\": tune.choice([1e-4, 1e-5]), } }} Copy time_col - for a time series task, name of the column containing the timestamps. If not provided, defaults to the first column of X_train/X_val cv_score_agg_func - customized cross-validation scores aggregate function. Default to average metrics across folds. If specificed, this function needs to have the following input arguments: val_loss_folds: list of floats, the loss scores of each fold; log_metrics_folds: list of dicts/floats, the metrics of each fold to log. This function should return the final aggregate result of all folds. A float number of the minimization objective, and a dictionary as the metrics to log or None. E.g., def cv_score_agg_func(val_loss_folds, log_metrics_folds): metric_to_minimize = sum(val_loss_folds)/len(val_loss_folds) metrics_to_log = None for single_fold in log_metrics_folds: if metrics_to_log is None: metrics_to_log = single_fold elif isinstance(metrics_to_log, dict): metrics_to_log = {k: metrics_to_log[k] + v for k, v in single_fold.items()} else: metrics_to_log += single_fold if metrics_to_log: n = len(val_loss_folds) metrics_to_log = ( {k: v / n for k, v in metrics_to_log.items()} if isinstance(metrics_to_log, dict) else metrics_to_log / n ) return metric_to_minimize, metrics_to_log Copy skip_transform - boolean, default=False | Whether to pre-process data prior to modeling. mlflow_logging - boolean, default=None | Whether to log the training results to mlflow. Default value is None, which means the logging decision is made based on AutoML.init's mlflow_logging argument. This requires mlflow to be installed and to have an active mlflow run. FLAML will create nested runs. fit_kwargs_by_estimator - dict, default=None | The user specified keywords arguments, grouped by estimator name. For TransformersEstimator, available fit_kwargs can be found from TrainingArgumentsForAuto. e.g., fit_kwargs_by_estimator = { \"transformer\": { \"output_dir\": \"test/data/output/\", \"fp16\": False, }, \"tft\": { \"max_encoder_length\": 1, \"min_encoder_length\": 1, \"static_categoricals\": [], \"static_reals\": [], \"time_varying_known_categoricals\": [], \"time_varying_known_reals\": [], \"time_varying_unknown_categoricals\": [], \"time_varying_unknown_reals\": [], \"variable_groups\": {}, \"lags\": {}, }} Copy **fit_kwargs - Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. Below are a few examples of estimator-specific parameters: period - int | forecast horizon for all time series forecast tasks. gpu_per_trial - float, default = 0 | A float of the number of gpus per trial, only used by TransformersEstimator, XGBoostSklearnEstimator, and TemporalFusionTransformerEstimator. group_ids - list of strings of column names identifying a time series, only used by TemporalFusionTransformerEstimator, required for 'ts_forecast_panel' task. group_ids is a parameter for TimeSeriesDataSet object from PyTorchForecasting. For other parameters to describe your dataset, refer to TimeSeriesDataSet PyTorchForecasting. To specify your variables, use static_categoricals, static_reals, time_varying_known_categoricals, time_varying_known_reals, time_varying_unknown_categoricals, time_varying_unknown_reals, variable_groups. To provide more information on your data, use max_encoder_length, min_encoder_length, lags. log_dir - str, default = \"lightning_logs\" | Folder into which to log results for tensorboard, only used by TemporalFusionTransformerEstimator. max_epochs - int, default = 20 | Maximum number of epochs to run training, only used by TemporalFusionTransformerEstimator. batch_size - int, default = 64 | Batch size for training model, only used by TemporalFusionTransformerEstimator.","s":"AutoML Objects","u":"/FLAML/docs/reference/automl/automl","h":"#automl-objects","p":345},{"i":350,"t":"On this page","s":"automl.ml","u":"/FLAML/docs/reference/automl/ml","h":"","p":349},{"i":352,"t":"On this page","s":"automl.nlp.huggingface.trainer","u":"/FLAML/docs/reference/automl/nlp/huggingface/trainer","h":"","p":351},{"i":354,"t":"class TrainerForAuto(Seq2SeqTrainer) Copy evaluate​ def evaluate(eval_dataset=None, ignore_keys=None, metric_key_prefix=\"eval\") Copy Overriding transformers.Trainer.evaluate by saving metrics and checkpoint path.","s":"TrainerForAuto Objects","u":"/FLAML/docs/reference/automl/nlp/huggingface/trainer","h":"#trainerforauto-objects","p":351},{"i":356,"t":"On this page","s":"automl.nlp.huggingface.utils","u":"/FLAML/docs/reference/automl/nlp/huggingface/utils","h":"","p":355},{"i":358,"t":"On this page","s":"automl.model","u":"/FLAML/docs/reference/automl/model","h":"","p":357},{"i":360,"t":"class BaseEstimator() Copy The abstract class for all learners. Typical examples: XGBoostEstimator: for regression. XGBoostSklearnEstimator: for classification. LGBMEstimator, RandomForestEstimator, LRL1Classifier, LRL2Classifier: for both regression and classification. __init__​ def __init__(task=\"binary\", **config) Copy Constructor. Arguments: task - A string of the task type, one of 'binary', 'multiclass', 'regression', 'rank', 'seq-classification', 'seq-regression', 'token-classification', 'multichoice-classification', 'summarization', 'ts_forecast', 'ts_forecast_classification'. config - A dictionary containing the hyperparameter names, 'n_jobs' as keys. n_jobs is the number of parallel threads. model​ @propertydef model() Copy Trained model after fit() is called, or None before fit() is called. estimator​ @propertydef estimator() Copy Trained model after fit() is called, or None before fit() is called. feature_names_in_​ @propertydef feature_names_in_() Copy if self.model has attribute feature_names_in, return it. otherwise, if self.model has attribute feature_name, return it. otherwise, if self._model has attribute feature_names, return it. otherwise, if self._model has method get_booster, return the feature names. otherwise, return None. feature_importances_​ @propertydef feature_importances_() Copy if self.model has attribute feature_importances, return it. otherwise, if self.model has attribute coef, return it. otherwise, return None. fit​ def fit(X_train, y_train, budget=None, free_mem_ratio=0, **kwargs) Copy Train the model from given training data. Arguments: X_train - A numpy array or a dataframe of training data in shape n*m. y_train - A numpy array or a series of labels in shape n*1. budget - A float of the time budget in seconds. free_mem_ratio - A float between 0 and 1 for the free memory ratio to keep during training. Returns: train_time - A float of the training time in seconds. predict​ def predict(X, **kwargs) Copy Predict label from features. Arguments: X - A numpy array or a dataframe of featurized instances, shape n*m. Returns: A numpy array of shape n*1. Each element is the label for a instance. predict_proba​ def predict_proba(X, **kwargs) Copy Predict the probability of each class from features. Only works for classification problems Arguments: X - A numpy array of featurized instances, shape n*m. Returns: A numpy array of shape n*c. c is the # classes. Each element at (i,j) is the probability for instance i to be in class j. score​ def score(X_val: DataFrame, y_val: Series, **kwargs) Copy Report the evaluation score of a trained estimator. Arguments: X_val - A pandas dataframe of the validation input data. y_val - A pandas series of the validation label. kwargs - keyword argument of the evaluation function, for example: metric: A string of the metric name or a function e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If metric is given, the score will report the user specified metric. If metric is not given, the metric is set to accuracy for classification and r2 for regression. You can also pass a customized metric function, for examples on how to pass a customized metric function, please check test/nlp/test_autohf_custom_metric.py and test/automl/test_multiclass.py. Returns: The evaluation score on the validation dataset. search_space​ @classmethoddef search_space(cls, data_size, task, **params) Copy [required method] search space. Arguments: data_size - A tuple of two integers, number of rows and columns. task - A str of the task type, e.g., \"binary\", \"multiclass\", \"regression\". Returns: A dictionary of the search space. Each key is the name of a hyperparameter, and value is a dict with its domain (required) and low_cost_init_value, init_value, cat_hp_cost (if applicable). e.g., {'domain': tune.randint(lower=1, upper=10), 'init_value': 1}. size​ @classmethoddef size(cls, config: dict) -> float Copy [optional method] memory size of the estimator in bytes. Arguments: config - A dict of the hyperparameter config. Returns: A float of the memory size required by the estimator to train the given config. cost_relative2lgbm​ @classmethoddef cost_relative2lgbm(cls) -> float Copy [optional method] relative cost compared to lightgbm. init​ @classmethoddef init(cls) Copy [optional method] initialize the class. config2params​ def config2params(config: dict) -> dict Copy [optional method] config dict to params dict Arguments: config - A dict of the hyperparameter config. Returns: A dict that will be passed to self.estimator_class's constructor.","s":"BaseEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#baseestimator-objects","p":357},{"i":362,"t":"class SparkEstimator(BaseEstimator) Copy The base class for fine-tuning spark models, using pyspark.ml and SynapseML API. fit​ def fit(X_train: psDataFrame, y_train: psSeries = None, budget=None, free_mem_ratio=0, index_col: str = \"tmp_index_col\", **kwargs, ,) Copy Train the model from given training data. Arguments: X_train - A pyspark.pandas DataFrame of training data in shape n*m. y_train - A pyspark.pandas Series in shape n*1. None if X_train is a pyspark.pandas Dataframe contains y_train. budget - A float of the time budget in seconds. free_mem_ratio - A float between 0 and 1 for the free memory ratio to keep during training. Returns: train_time - A float of the training time in seconds. predict​ def predict(X, index_col=\"tmp_index_col\", return_all=False, **kwargs) Copy Predict label from features. Arguments: X - A pyspark or pyspark.pandas dataframe of featurized instances, shape n*m. index_col - A str of the index column name. Default to \"tmp_index_col\". return_all - A bool of whether to return all the prediction results. Default to False. Returns: A pyspark.pandas series of shape n*1 if return_all is False. Otherwise, a pyspark.pandas dataframe. predict_proba​ def predict_proba(X, index_col=\"tmp_index_col\", return_all=False, **kwargs) Copy Predict the probability of each class from features. Only works for classification problems Arguments: X - A pyspark or pyspark.pandas dataframe of featurized instances, shape n*m. index_col - A str of the index column name. Default to \"tmp_index_col\". return_all - A bool of whether to return all the prediction results. Default to False. Returns: A pyspark.pandas dataframe of shape n*c. c is the # classes. Each element at (i,j) is the probability for instance i to be in class j.","s":"SparkEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#sparkestimator-objects","p":357},{"i":364,"t":"class SparkLGBMEstimator(SparkEstimator) Copy The class for fine-tuning spark version lightgbm models, using SynapseML API.","s":"SparkLGBMEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#sparklgbmestimator-objects","p":357},{"i":366,"t":"class TransformersEstimator(BaseEstimator) Copy The class for fine-tuning language models, using huggingface transformers API.","s":"TransformersEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#transformersestimator-objects","p":357},{"i":368,"t":"class SKLearnEstimator(BaseEstimator) Copy The base class for tuning scikit-learn estimators. Subclasses can modify the function signature of __init__ to ignore the values in config that are not relevant to the constructor of their underlying estimator. For example, some regressors in scikit-learn don't accept the n_jobs parameter contained in config. For these, one can add n_jobs=None, before **config to make sure config doesn't contain an n_jobs key.","s":"SKLearnEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#sklearnestimator-objects","p":357},{"i":370,"t":"class LGBMEstimator(BaseEstimator) Copy The class for tuning LGBM, using sklearn API.","s":"LGBMEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#lgbmestimator-objects","p":357},{"i":372,"t":"class XGBoostEstimator(SKLearnEstimator) Copy The class for tuning XGBoost regressor, not using sklearn API.","s":"XGBoostEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#xgboostestimator-objects","p":357},{"i":374,"t":"class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator) Copy The class for tuning XGBoost with unlimited depth, using sklearn API.","s":"XGBoostSklearnEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#xgboostsklearnestimator-objects","p":357},{"i":376,"t":"class XGBoostLimitDepthEstimator(XGBoostSklearnEstimator) Copy The class for tuning XGBoost with limited depth, using sklearn API.","s":"XGBoostLimitDepthEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#xgboostlimitdepthestimator-objects","p":357},{"i":378,"t":"class RandomForestEstimator(SKLearnEstimator, LGBMEstimator) Copy The class for tuning Random Forest.","s":"RandomForestEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#randomforestestimator-objects","p":357},{"i":380,"t":"class ExtraTreesEstimator(RandomForestEstimator) Copy The class for tuning Extra Trees.","s":"ExtraTreesEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#extratreesestimator-objects","p":357},{"i":382,"t":"class LRL1Classifier(SKLearnEstimator) Copy The class for tuning Logistic Regression with L1 regularization.","s":"LRL1Classifier Objects","u":"/FLAML/docs/reference/automl/model","h":"#lrl1classifier-objects","p":357},{"i":384,"t":"class LRL2Classifier(SKLearnEstimator) Copy The class for tuning Logistic Regression with L2 regularization.","s":"LRL2Classifier Objects","u":"/FLAML/docs/reference/automl/model","h":"#lrl2classifier-objects","p":357},{"i":386,"t":"class CatBoostEstimator(BaseEstimator) Copy The class for tuning CatBoost.","s":"CatBoostEstimator Objects","u":"/FLAML/docs/reference/automl/model","h":"#catboostestimator-objects","p":357},{"i":388,"t":"On this page","s":"automl.spark.metrics","u":"/FLAML/docs/reference/automl/spark/metrics","h":"","p":387},{"i":390,"t":"On this page","s":"automl.data","u":"/FLAML/docs/reference/automl/data","h":"","p":389},{"i":392,"t":"class DataTransformer() Copy Transform input training data. fit_transform​ def fit_transform(X: Union[DataFrame, np.ndarray], y, task: Union[str, \"Task\"]) Copy Fit transformer and process the input training data according to the task type. Arguments: X - A numpy array or a pandas dataframe of training data. y - A numpy array or a pandas series of labels. task - An instance of type Task, or a str such as 'classification', 'regression'. Returns: X - Processed numpy array or pandas dataframe of training data. y - Processed numpy array or pandas series of labels. transform​ def transform(X: Union[DataFrame, np.array]) Copy Process data using fit transformer. Arguments: X - A numpy array or a pandas dataframe of training data. Returns: X - Processed numpy array or pandas dataframe of training data.","s":"DataTransformer Objects","u":"/FLAML/docs/reference/automl/data","h":"#datatransformer-objects","p":389},{"i":394,"t":"On this page","s":"automl.nlp.utils","u":"/FLAML/docs/reference/automl/nlp/utils","h":"","p":393},{"i":396,"t":"On this page","s":"automl.spark.utils","u":"/FLAML/docs/reference/automl/spark/utils","h":"","p":395},{"i":398,"t":"On this page","s":"automl.state","u":"/FLAML/docs/reference/automl/state","h":"","p":397},{"i":400,"t":"class AutoMLState() Copy sanitize​ @classmethoddef sanitize(cls, config: dict) -> dict Copy Make a config ready for passing to estimator.","s":"AutoMLState Objects","u":"/FLAML/docs/reference/automl/state","h":"#automlstate-objects","p":397},{"i":402,"t":"On this page","s":"automl.nlp.huggingface.training_args","u":"/FLAML/docs/reference/automl/nlp/huggingface/training_args","h":"","p":401},{"i":404,"t":"@dataclassclass TrainingArgumentsForAuto(TrainingArguments) Copy FLAML custom TrainingArguments. Arguments: task str - the task name for NLP tasks, e.g., seq-classification, token-classification output_dir str - data root directory for outputing the log, etc. model_path str, optional, defaults to \"facebook/muppet-roberta-base\" - A string, the path of the language model file, either a path from huggingface model card huggingface.co/models, or a local path for the model. fp16 bool, optional, defaults to \"False\" - A bool, whether to use FP16. max_seq_length int, optional, defaults to 128 - An integer, the max length of the sequence. For token classification task, this argument will be ineffective. pad_to_max_length (bool, optional, defaults to \"False\"): whether to pad all samples to model maximum sentence length. If False, will pad the samples dynamically when batching to the maximum length in the batch. per_device_eval_batch_size int, optional, defaults to 1 - An integer, the per gpu evaluation batch size. label_list List[str], optional, defaults to None - A list of string, the string list of the label names. When the task is sequence labeling/token classification, there are two formats of the labels: (1) The token labels, i.e., [B-PER, I-PER, B-LOC]; (2) Id labels. For (2), need to pass the label_list (e.g., [B-PER, I-PER, B-LOC]) to convert the Id to token labels when computing the metric with metric_loss_score. See the example in a simple token classification example.","s":"TrainingArgumentsForAuto Objects","u":"/FLAML/docs/reference/automl/nlp/huggingface/training_args","h":"#trainingargumentsforauto-objects","p":401},{"i":406,"t":"On this page","s":"automl.time_series.sklearn","u":"/FLAML/docs/reference/automl/time_series/sklearn","h":"","p":405},{"i":408,"t":"X : pandas.DataFrame Input features. y : array_like, (1d) Target vector. horizon : int length of X for predict method","s":"Parameters","u":"/FLAML/docs/reference/automl/time_series/sklearn","h":"#parameters","p":405},{"i":410,"t":"pandas.DataFrame shifted dataframe with lags columns","s":"Returns","u":"/FLAML/docs/reference/automl/time_series/sklearn","h":"#returns","p":405},{"i":412,"t":"On this page","s":"automl.task.task","u":"/FLAML/docs/reference/automl/task/task","h":"","p":411},{"i":414,"t":"class Task(ABC) Copy Abstract base class for a machine learning task. Class definitions should implement abstract methods and provide a non-empty dictionary of estimator classes. A Task can be suitable to be used for multiple machine-learning tasks (e.g. classification or regression) or be implemented specifically for a single one depending on the generality of data validation and model evaluation methods implemented. The implementation of a Task may optionally use the training data and labels to determine data and task specific details, such as in determining if a problem is single-label or multi-label. FLAML evaluates at runtime how to behave exactly, relying on the task instance to provide implementations of operations which vary between tasks. __init__​ def __init__(task_name: str, X_train: Optional[Union[np.ndarray, DataFrame, psDataFrame]] = None, y_train: Optional[Union[np.ndarray, DataFrame, Series, psSeries]] = None) Copy Constructor. Arguments: task_name - String name for this type of task. Used when the Task can be generic and implement a number of types of sub-task. X_train - Optional. Some Task types may use the data shape or features to determine details of their usage, such as in binary vs multilabel classification. y_train - Optional. Some Task types may use the data shape or features to determine details of their usage, such as in binary vs multilabel classification. __str__​ def __str__() -> str Copy Name of this task type. evaluate_model_CV​ @abstractmethoddef evaluate_model_CV(config: dict, estimator: \"flaml.automl.ml.BaseEstimator\", X_train_all: Union[np.ndarray, DataFrame, psDataFrame], y_train_all: Union[np.ndarray, DataFrame, Series, psSeries], budget: int, kf, eval_metric: str, best_val_loss: float, log_training_metric: bool = False, fit_kwargs: Optional[dict] = {}) -> Tuple[float, float, float, float] Copy Evaluate the model using cross-validation. Arguments: config - configuration used in the evaluation of the metric. estimator - Estimator class of the model. X_train_all - Complete training feature data. y_train_all - Complete training target data. budget - Training time budget. kf - Cross-validation index generator. eval_metric - Metric name to be used for evaluation. best_val_loss - Best current validation-set loss. log_training_metric - Bool defaults False. Enables logging of the training metric. fit_kwargs - Additional kwargs passed to the estimator's fit method. Returns: validation loss, metric value, train time, prediction time validate_data​ @abstractmethoddef validate_data(automl: \"flaml.automl.automl.AutoML\", state: \"flaml.automl.state.AutoMLState\", X_train_all: Union[np.ndarray, DataFrame, psDataFrame, None], y_train_all: Union[np.ndarray, DataFrame, Series, psSeries, None], dataframe: Union[DataFrame, None], label: str, X_val: Optional[Union[np.ndarray, DataFrame, psDataFrame]] = None, y_val: Optional[Union[np.ndarray, DataFrame, Series, psSeries]] = None, groups_val: Optional[List[str]] = None, groups: Optional[List[str]] = None) Copy Validate that the data is suitable for this task type. Arguments: automl - The AutoML instance from which this task has been constructed. state - The AutoMLState instance for this run. X_train_all - The complete data set or None if dataframe is supplied. y_train_all - The complete target set or None if dataframe is supplied. dataframe - A dataframe constaining the complete data set with targets. label - The name of the target column in dataframe. X_val - Optional. A data set for validation. y_val - Optional. A target vector corresponding to X_val for validation. groups_val - Group labels (with matching length to y_val) or group counts (with sum equal to length of y_val) for validation data. Need to be consistent with groups. groups - Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. Raises: AssertionError - The data provided is invalid for this task type and configuration. prepare_data​ @abstractmethoddef prepare_data(state: \"flaml.automl.state.AutoMLState\", X_train_all: Union[np.ndarray, DataFrame, psDataFrame], y_train_all: Union[np.ndarray, DataFrame, Series, psSeries, None], auto_augment: bool, eval_method: str, split_type: str, split_ratio: float, n_splits: int, data_is_df: bool, sample_weight_full: Optional[List[float]] = None) Copy Prepare the data for fitting or inference. Arguments: automl - The AutoML instance from which this task has been constructed. state - The AutoMLState instance for this run. X_train_all - The complete data set or None if dataframe is supplied. Must contain the target if y_train_all is None y_train_all - The complete target set or None if supplied in X_train_all. auto_augment - If true, task-specific data augmentations will be applied. eval_method - A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_type - str or splitter object, default=\"auto\" | the data split type. A valid splitter object is an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. Set eval_method to \"cv\" to use the splitter object. Valid str options depend on different tasks. For classification tasks, valid choices are [\"auto\", 'stratified', 'uniform', 'time', 'group']. \"auto\" -> stratified. For regression tasks, valid choices are [\"auto\", 'uniform', 'time']. \"auto\" -> uniform. For time series forecast tasks, must be \"auto\" or 'time'. For ranking task, must be \"auto\" or 'group'. split_ratio - A float of the valiation data percentage for holdout. n_splits - An integer of the number of folds for cross - validation. data_is_df - True if the data was provided as a DataFrame else False. sample_weight_full - A 1d arraylike of the sample weight. Raises: AssertionError - The configuration provided is invalid for this task type and data. decide_split_type​ @abstractmethoddef decide_split_type(split_type: str, y_train_all: Union[np.ndarray, DataFrame, Series, psSeries, None], fit_kwargs: dict, groups: Optional[List[str]] = None) -> str Copy Choose an appropriate data split type for this data and task. If split_type is 'auto' then this is determined based on the task type and data. If a specific split_type is requested then the choice is validated to be appropriate. Arguments: split_type - Either 'auto' or a task appropriate split type. y_train_all - The complete set of targets. fit_kwargs - Additional kwargs passed to the estimator's fit method. groups - Optional. Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. Returns: The determined appropriate split type. Raises: AssertionError - The requested split_type is invalid for this task, configuration and data. preprocess​ @abstractmethoddef preprocess(X: Union[np.ndarray, DataFrame, psDataFrame], transformer: Optional[\"flaml.automl.data.DataTransformer\"] = None) -> Union[np.ndarray, DataFrame] Copy Preprocess the data ready for fitting or inference with this task type. Arguments: X - The data set to process. transformer - A DataTransformer instance to be used in processing. Returns: The preprocessed data set having the same type as the input. default_estimator_list​ @abstractmethoddef default_estimator_list(estimator_list: Union[List[str], str] = \"auto\", is_spark_dataframe: bool = False) -> List[str] Copy Return the list of default estimators registered for this task type. If 'auto' is provided then the default list is returned, else the provided list will be validated given this task type. Arguments: estimator_list - Either 'auto' or a list of estimator names to be validated. is_spark_dataframe - True if the data is a spark dataframe. Returns: A list of valid estimator names for this task type. default_metric​ @abstractmethoddef default_metric(metric: str) -> str Copy Return the default metric for this task type. If 'auto' is provided then the default metric for this task will be returned. Otherwise, the provided metric name is validated for this task type. Arguments: metric - The name of a metric to be used in evaluation of models during fitting or validation. Returns: The default metric, or the provided metric if it is valid for this task type. __eq__​ def __eq__(other: str) -> bool Copy For backward compatibility with all the string comparisons to task estimator_class_from_str​ def estimator_class_from_str(estimator_name: str) -> \"flaml.automl.ml.BaseEstimator\" Copy Determine the estimator class corresponding to the provided name. Arguments: estimator_name - Name of the desired estimator. Returns: The estimator class corresponding to the provided name. Raises: ValueError - The provided estimator_name has not been registered for this task type.","s":"Task Objects","u":"/FLAML/docs/reference/automl/task/task","h":"#task-objects","p":411},{"i":416,"t":"On this page","s":"automl.task.time_series_task","u":"/FLAML/docs/reference/automl/task/time_series_task","h":"","p":415},{"i":418,"t":"On this page","s":"automl.time_series.tft","u":"/FLAML/docs/reference/automl/time_series/tft","h":"","p":417},{"i":420,"t":"class TemporalFusionTransformerEstimator(TimeSeriesEstimator) Copy The class for tuning Temporal Fusion Transformer","s":"TemporalFusionTransformerEstimator Objects","u":"/FLAML/docs/reference/automl/time_series/tft","h":"#temporalfusiontransformerestimator-objects","p":417},{"i":422,"t":"On this page","s":"automl.time_series.ts_data","u":"/FLAML/docs/reference/automl/time_series/ts_data","h":"","p":421},{"i":424,"t":"@dataclassclass TimeSeriesDataset() Copy to_univariate​ def to_univariate() -> Dict[str, \"TimeSeriesDataset\"] Copy Convert a multivariate TrainingData to a dict of univariate ones @param df: @return: fourier_series​ def fourier_series(feature: pd.Series, name: str) Copy Assume feature goes from 0 to 1 cyclically, transform that into Fourier @param feature: input feature @return: sin(2pifeature), cos(2pifeature)","s":"TimeSeriesDataset Objects","u":"/FLAML/docs/reference/automl/time_series/ts_data","h":"#timeseriesdataset-objects","p":421},{"i":426,"t":"class DataTransformerTS() Copy Transform input time series training data. fit​ def fit(X: Union[DataFrame, np.array], y) Copy Fit transformer. Arguments: X - A numpy array or a pandas dataframe of training data. y - A numpy array or a pandas series of labels. Returns: X - Processed numpy array or pandas dataframe of training data. y - Processed numpy array or pandas series of labels.","s":"DataTransformerTS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_data","h":"#datatransformerts-objects","p":421},{"i":428,"t":"On this page","s":"automl.time_series.ts_model","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"","p":427},{"i":430,"t":"class Prophet(TimeSeriesEstimator) Copy The class for tuning Prophet.","s":"Prophet Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#prophet-objects","p":427},{"i":432,"t":"class ARIMA(StatsModelsEstimator) Copy The class for tuning ARIMA.","s":"ARIMA Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#arima-objects","p":427},{"i":434,"t":"class SARIMAX(StatsModelsEstimator) Copy The class for tuning SARIMA.","s":"SARIMAX Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#sarimax-objects","p":427},{"i":436,"t":"class HoltWinters(StatsModelsEstimator) Copy The class for tuning Holt Winters model, aka 'Triple Exponential Smoothing'.","s":"HoltWinters Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#holtwinters-objects","p":427},{"i":438,"t":"class TS_SKLearn(TimeSeriesEstimator) Copy The class for tuning SKLearn Regressors for time-series forecasting","s":"TS_SKLearn Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#ts_sklearn-objects","p":427},{"i":440,"t":"class LGBM_TS(TS_SKLearn) Copy The class for tuning LGBM Regressor for time-series forecasting","s":"LGBM_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#lgbm_ts-objects","p":427},{"i":442,"t":"class XGBoost_TS(TS_SKLearn) Copy The class for tuning XGBoost Regressor for time-series forecasting","s":"XGBoost_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#xgboost_ts-objects","p":427},{"i":444,"t":"class RF_TS(TS_SKLearn) Copy The class for tuning Random Forest Regressor for time-series forecasting","s":"RF_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#rf_ts-objects","p":427},{"i":446,"t":"class ExtraTrees_TS(TS_SKLearn) Copy The class for tuning Extra Trees Regressor for time-series forecasting","s":"ExtraTrees_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#extratrees_ts-objects","p":427},{"i":448,"t":"class XGBoostLimitDepth_TS(TS_SKLearn) Copy The class for tuning XGBoost Regressor with unlimited depth for time-series forecasting","s":"XGBoostLimitDepth_TS Objects","u":"/FLAML/docs/reference/automl/time_series/ts_model","h":"#xgboostlimitdepth_ts-objects","p":427},{"i":450,"t":"On this page","s":"default.estimator","u":"/FLAML/docs/reference/default/estimator","h":"","p":449},{"i":452,"t":"On this page","s":"default.greedy","u":"/FLAML/docs/reference/default/greedy","h":"","p":451},{"i":454,"t":"On this page","s":"default.portfolio","u":"/FLAML/docs/reference/default/portfolio","h":"","p":453},{"i":456,"t":"On this page","s":"onlineml.autovw","u":"/FLAML/docs/reference/onlineml/autovw","h":"","p":455},{"i":458,"t":"class AutoVW() Copy Class for the AutoVW algorithm. __init__​ def __init__(max_live_model_num: int, search_space: dict, init_config: Optional[dict] = {}, min_resource_lease: Optional[Union[str, float]] = \"auto\", automl_runner_args: Optional[dict] = {}, scheduler_args: Optional[dict] = {}, model_select_policy: Optional[str] = \"threshold_loss_ucb\", metric: Optional[str] = \"mae_clipped\", random_seed: Optional[int] = None, model_selection_mode: Optional[str] = \"min\", cb_coef: Optional[float] = None) Copy Constructor. Arguments: max_live_model_num - An int to specify the maximum number of 'live' models, which, in other words, is the maximum number of models allowed to update in each learning iteraction. search_space - A dictionary of the search space. This search space includes both hyperparameters we want to tune and fixed hyperparameters. In the latter case, the value is a fixed value. init_config - A dictionary of a partial or full initial config, e.g. {'interactions': set(), 'learning_rate': 0.5} min_resource_lease - string or float | The minimum resource lease assigned to a particular model/trial. If set as 'auto', it will be calculated automatically. automl_runner_args - A dictionary of configuration for the OnlineTrialRunner. If set {}, default values will be used, which is equivalent to using the following configs. Example: automl_runner_args = { \"champion_test_policy\": 'loss_ucb', # the statistic test for a better champion \"remove_worse\": False, # whether to do worse than test} Copy scheduler_args - A dictionary of configuration for the scheduler. If set {}, default values will be used, which is equivalent to using the following config. Example: scheduler_args = { \"keep_challenger_metric\": 'ucb', # what metric to use when deciding the top performing challengers \"keep_challenger_ratio\": 0.5, # denotes the ratio of top performing challengers to keep live \"keep_champion\": True, # specifcies whether to keep the champion always running} Copy model_select_policy - A string in ['threshold_loss_ucb', 'threshold_loss_lcb', 'threshold_loss_avg', 'loss_ucb', 'loss_lcb', 'loss_avg'] to specify how to select one model to do prediction from the live model pool. Default value is 'threshold_loss_ucb'. metric - A string in ['mae_clipped', 'mae', 'mse', 'absolute_clipped', 'absolute', 'squared'] to specify the name of the loss function used for calculating the progressive validation loss in ChaCha. random_seed - An integer of the random seed used in the searcher (more specifically this the random seed for ConfigOracle). model_selection_mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. cb_coef - A float coefficient (optional) used in the sample complexity bound. predict​ def predict(data_sample) Copy Predict on the input data sample. Arguments: data_sample - one data example in vw format. learn​ def learn(data_sample) Copy Perform one online learning step with the given data sample. Arguments: data_sample - one data example in vw format. It will be used to update the vw model. get_ns_feature_dim_from_vw_example​ @staticmethoddef get_ns_feature_dim_from_vw_example(vw_example) -> dict Copy Get a dictionary of feature dimensionality for each namespace singleton.","s":"AutoVW Objects","u":"/FLAML/docs/reference/onlineml/autovw","h":"#autovw-objects","p":455},{"i":460,"t":"On this page","s":"default.suggest","u":"/FLAML/docs/reference/default/suggest","h":"","p":459},{"i":462,"t":"On this page","s":"onlineml.trial","u":"/FLAML/docs/reference/onlineml/trial","h":"","p":461},{"i":464,"t":"class OnlineResult() Copy Class for managing the result statistics of a trial. __init__​ def __init__(result_type_name: str, cb_coef: Optional[float] = None, init_loss: Optional[float] = 0.0, init_cb: Optional[float] = 100.0, mode: Optional[str] = \"min\", sliding_window_size: Optional[int] = 100) Copy Constructor. Arguments: result_type_name - A String to specify the name of the result type. cb_coef - a string to specify the coefficient on the confidence bound. init_loss - a float to specify the inital loss. init_cb - a float to specify the intial confidence bound. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. sliding_window_size - An int to specify the size of the sliding window (for experimental purpose). update_result​ def update_result(new_loss, new_resource_used, data_dimension, bound_of_range=1.0, new_observation_count=1.0) Copy Update result statistics.","s":"OnlineResult Objects","u":"/FLAML/docs/reference/onlineml/trial","h":"#onlineresult-objects","p":461},{"i":466,"t":"class BaseOnlineTrial(Trial) Copy Class for the online trial. __init__​ def __init__(config: dict, min_resource_lease: float, is_champion: Optional[bool] = False, is_checked_under_current_champion: Optional[bool] = True, custom_trial_name: Optional[str] = \"mae\", trial_id: Optional[str] = None) Copy Constructor. Arguments: config - The configuration dictionary. min_resource_lease - A float specifying the minimum resource lease. is_champion - A bool variable indicating whether the trial is champion. is_checked_under_current_champion - A bool indicating whether the trial has been used under the current champion. custom_trial_name - A string of a custom trial name. trial_id - A string for the trial id. set_resource_lease​ def set_resource_lease(resource: float) Copy Sets the resource lease accordingly. set_status​ def set_status(status) Copy Sets the status of the trial and record the start time.","s":"BaseOnlineTrial Objects","u":"/FLAML/docs/reference/onlineml/trial","h":"#baseonlinetrial-objects","p":461},{"i":468,"t":"class VowpalWabbitTrial(BaseOnlineTrial) Copy The class for Vowpal Wabbit online trials. __init__​ def __init__(config: dict, min_resource_lease: float, metric: str = \"mae\", is_champion: Optional[bool] = False, is_checked_under_current_champion: Optional[bool] = True, custom_trial_name: Optional[str] = \"vw_mae_clipped\", trial_id: Optional[str] = None, cb_coef: Optional[float] = None) Copy Constructor. Arguments: config dict - the config of the trial (note that the config is a set because the hyperparameters are). min_resource_lease float - the minimum resource lease. metric str - the loss metric. is_champion bool - indicates whether the trial is the current champion or not. is_checked_under_current_champion bool - indicates whether this trials has been paused under the current champion. trial_id str - id of the trial (if None, it will be generated in the constructor). train_eval_model_online​ def train_eval_model_online(data_sample, y_pred) Copy Train and evaluate model online. predict​ def predict(x) Copy Predict using the model.","s":"VowpalWabbitTrial Objects","u":"/FLAML/docs/reference/onlineml/trial","h":"#vowpalwabbittrial-objects","p":461},{"i":470,"t":"On this page","s":"tune.analysis","u":"/FLAML/docs/reference/tune/analysis","h":"","p":469},{"i":472,"t":"class ExperimentAnalysis() Copy Analyze results from a Tune experiment. best_trial​ @propertydef best_trial() -> Trial Copy Get the best trial of the experiment The best trial is determined by comparing the last trial results using the metric and mode parameters passed to tune.run(). If you didn't pass these parameters, use get_best_trial(metric, mode, scope) instead. best_config​ @propertydef best_config() -> Dict Copy Get the config of the best trial of the experiment The best trial is determined by comparing the last trial results using the metric and mode parameters passed to tune.run(). If you didn't pass these parameters, use get_best_config(metric, mode, scope) instead. results​ @propertydef results() -> Dict[str, Dict] Copy Get the last result of all the trials of the experiment get_best_trial​ def get_best_trial(metric: Optional[str] = None, mode: Optional[str] = None, scope: str = \"last\", filter_nan_and_inf: bool = True) -> Optional[Trial] Copy Retrieve the best trial object. Compares all trials' scores on metric. If metric is not specified, self.default_metric will be used. If mode is not specified, self.default_mode will be used. These values are usually initialized by passing the metric and mode parameters to tune.run(). Arguments: metric str - Key for trial info to order on. Defaults to self.default_metric. mode str - One of [min, max]. Defaults to self.default_mode. scope str - One of [all, last, avg, last-5-avg, last-10-avg]. If scope=last, only look at each trial's final step for metric, and compare across trials based on mode=[min,max]. If scope=avg, consider the simple average over all steps for metric and compare across trials based on mode=[min,max]. If scope=last-5-avg or scope=last-10-avg, consider the simple average over the last 5 or 10 steps for metric and compare across trials based on mode=[min,max]. If scope=all, find each trial's min/max score for metric based on mode, and compare trials based on mode=[min,max]. filter_nan_and_inf bool - If True (default), NaN or infinite values are disregarded and these trials are never selected as the best trial. get_best_config​ def get_best_config(metric: Optional[str] = None, mode: Optional[str] = None, scope: str = \"last\") -> Optional[Dict] Copy Retrieve the best config corresponding to the trial. Compares all trials' scores on metric. If metric is not specified, self.default_metric will be used. If mode is not specified, self.default_mode will be used. These values are usually initialized by passing the metric and mode parameters to tune.run(). Arguments: metric str - Key for trial info to order on. Defaults to self.default_metric. mode str - One of [min, max]. Defaults to self.default_mode. scope str - One of [all, last, avg, last-5-avg, last-10-avg]. If scope=last, only look at each trial's final step for metric, and compare across trials based on mode=[min,max]. If scope=avg, consider the simple average over all steps for metric and compare across trials based on mode=[min,max]. If scope=last-5-avg or scope=last-10-avg, consider the simple average over the last 5 or 10 steps for metric and compare across trials based on mode=[min,max]. If scope=all, find each trial's min/max score for metric based on mode, and compare trials based on mode=[min,max]. best_result​ @propertydef best_result() -> Dict Copy Get the last result of the best trial of the experiment The best trial is determined by comparing the last trial results using the metric and mode parameters passed to tune.run(). If you didn't pass these parameters, use get_best_trial(metric, mode, scope).last_result instead.","s":"ExperimentAnalysis Objects","u":"/FLAML/docs/reference/tune/analysis","h":"#experimentanalysis-objects","p":469},{"i":474,"t":"On this page","s":"onlineml.trial_runner","u":"/FLAML/docs/reference/onlineml/trial_runner","h":"","p":473},{"i":476,"t":"class OnlineTrialRunner() Copy Class for the OnlineTrialRunner. __init__​ def __init__(max_live_model_num: int, searcher=None, scheduler=None, champion_test_policy=\"loss_ucb\", **kwargs) Copy Constructor. Arguments: max_live_model_num - The maximum number of 'live'/running models allowed. searcher - A class for generating Trial objects progressively. The ConfigOracle is implemented in the searcher. scheduler - A class for managing the 'live' trials and allocating the resources for the trials. champion_test_policy - A string to specify what test policy to test for champion. Currently can choose from ['loss_ucb', 'loss_avg', 'loss_lcb', None]. champion_trial​ @propertydef champion_trial() -> Trial Copy The champion trial. running_trials​ @propertydef running_trials() Copy The running/'live' trials. step​ def step(data_sample=None, prediction_trial_tuple=None) Copy Schedule one trial to run each time it is called. Arguments: data_sample - One data example. prediction_trial_tuple - A list of information containing (prediction_made, prediction_trial). get_top_running_trials​ def get_top_running_trials(top_ratio=None, top_metric=\"ucb\") -> list Copy Get a list of trial ids, whose performance is among the top running trials. get_trials​ def get_trials() -> list Copy Return the list of trials managed by this TrialRunner. add_trial​ def add_trial(new_trial) Copy Add a new trial to this TrialRunner. Trials may be added at any time. Arguments: new_trial Trial - Trial to queue. stop_trial​ def stop_trial(trial) Copy Stop a trial: set the status of a trial to be Trial.TERMINATED and perform other subsequent operations. pause_trial​ def pause_trial(trial) Copy Pause a trial: set the status of a trial to be Trial.PAUSED and perform other subsequent operations. run_trial​ def run_trial(trial) Copy Run a trial: set the status of a trial to be Trial.RUNNING and perform other subsequent operations.","s":"OnlineTrialRunner Objects","u":"/FLAML/docs/reference/onlineml/trial_runner","h":"#onlinetrialrunner-objects","p":473},{"i":478,"t":"On this page","s":"tune.scheduler.trial_scheduler","u":"/FLAML/docs/reference/tune/scheduler/trial_scheduler","h":"","p":477},{"i":480,"t":"class TrialScheduler() Copy Interface for implementing a Trial Scheduler class.","s":"TrialScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/trial_scheduler","h":"#trialscheduler-objects","p":477},{"i":482,"t":"On this page","s":"tune.sample","u":"/FLAML/docs/reference/tune/sample","h":"","p":481},{"i":484,"t":"class Domain() Copy Base class to specify a type and valid range to sample parameters from. This base class is implemented by parameter spaces, like float ranges (Float), integer ranges (Integer), or categorical variables (Categorical). The Domain object contains information about valid values (e.g. minimum and maximum values), and exposes methods that allow specification of specific samplers (e.g. uniform() or loguniform()). cast​ def cast(value) Copy Cast value to domain type is_valid​ def is_valid(value: Any) Copy Returns True if value is a valid value in this domain.","s":"Domain Objects","u":"/FLAML/docs/reference/tune/sample","h":"#domain-objects","p":481},{"i":486,"t":"class Grid(Sampler) Copy Dummy sampler used for grid search uniform​ def uniform(lower: float, upper: float) Copy Sample a float value uniformly between lower and upper. Sampling from tune.uniform(1, 10) is equivalent to sampling from np.random.uniform(1, 10)) quniform​ def quniform(lower: float, upper: float, q: float) Copy Sample a quantized float value uniformly between lower and upper. Sampling from tune.uniform(1, 10) is equivalent to sampling from np.random.uniform(1, 10)) The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. loguniform​ def loguniform(lower: float, upper: float, base: float = 10) Copy Sugar for sampling in different orders of magnitude. Arguments: lower float - Lower boundary of the output interval (e.g. 1e-4) upper float - Upper boundary of the output interval (e.g. 1e-2) base int - Base of the log. Defaults to 10. qloguniform​ def qloguniform(lower: float, upper: float, q: float, base: float = 10) Copy Sugar for sampling in different orders of magnitude. The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. Arguments: lower float - Lower boundary of the output interval (e.g. 1e-4) upper float - Upper boundary of the output interval (e.g. 1e-2) q float - Quantization number. The result will be rounded to an integer increment of this value. base int - Base of the log. Defaults to 10. choice​ def choice(categories: Sequence) Copy Sample a categorical value. Sampling from tune.choice([1, 2]) is equivalent to sampling from np.random.choice([1, 2]) randint​ def randint(lower: int, upper: int) Copy Sample an integer value uniformly between lower and upper. lower is inclusive, upper is exclusive. Sampling from tune.randint(10) is equivalent to sampling from np.random.randint(10) lograndint​ def lograndint(lower: int, upper: int, base: float = 10) Copy Sample an integer value log-uniformly between lower and upper, with base being the base of logarithm. lower is inclusive, upper is exclusive. qrandint​ def qrandint(lower: int, upper: int, q: int = 1) Copy Sample an integer value uniformly between lower and upper. lower is inclusive, upper is also inclusive (!). The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. qlograndint​ def qlograndint(lower: int, upper: int, q: int, base: float = 10) Copy Sample an integer value log-uniformly between lower and upper, with base being the base of logarithm. lower is inclusive, upper is also inclusive (!). The value will be quantized, i.e. rounded to an integer increment of q. Quantization makes the upper bound inclusive. randn​ def randn(mean: float = 0.0, sd: float = 1.0) Copy Sample a float value normally with mean and sd. Arguments: mean float - Mean of the normal distribution. Defaults to 0. sd float - SD of the normal distribution. Defaults to 1. qrandn​ def qrandn(mean: float, sd: float, q: float) Copy Sample a float value normally with mean and sd. The value will be quantized, i.e. rounded to an integer increment of q. Arguments: mean - Mean of the normal distribution. sd - SD of the normal distribution. q - Quantization number. The result will be rounded to an integer increment of this value.","s":"Grid Objects","u":"/FLAML/docs/reference/tune/sample","h":"#grid-objects","p":481},{"i":488,"t":"On this page","s":"tune.scheduler.online_scheduler","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"","p":487},{"i":490,"t":"class OnlineScheduler(TrialScheduler) Copy Class for the most basic OnlineScheduler. on_trial_result​ def on_trial_result(trial_runner, trial: Trial, result: Dict) Copy Report result and return a decision on the trial's status. choose_trial_to_run​ def choose_trial_to_run(trial_runner) -> Trial Copy Decide which trial to run next.","s":"OnlineScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"#onlinescheduler-objects","p":487},{"i":492,"t":"class OnlineSuccessiveDoublingScheduler(OnlineScheduler) Copy class for the OnlineSuccessiveDoublingScheduler algorithm. __init__​ def __init__(increase_factor: float = 2.0) Copy Constructor. Arguments: increase_factor - A float of multiplicative factor used to increase resource lease. Default is 2.0. on_trial_result​ def on_trial_result(trial_runner, trial: Trial, result: Dict) Copy Report result and return a decision on the trial's status.","s":"OnlineSuccessiveDoublingScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"#onlinesuccessivedoublingscheduler-objects","p":487},{"i":494,"t":"class ChaChaScheduler(OnlineSuccessiveDoublingScheduler) Copy class for the ChaChaScheduler algorithm. __init__​ def __init__(increase_factor: float = 2.0, **kwargs) Copy Constructor. Arguments: increase_factor - A float of multiplicative factor used to increase resource lease. Default is 2.0. on_trial_result​ def on_trial_result(trial_runner, trial: Trial, result: Dict) Copy Report result and return a decision on the trial's status.","s":"ChaChaScheduler Objects","u":"/FLAML/docs/reference/tune/scheduler/online_scheduler","h":"#chachascheduler-objects","p":487},{"i":496,"t":"On this page","s":"tune.searcher.cfo_cat","u":"/FLAML/docs/reference/tune/searcher/cfo_cat","h":"","p":495},{"i":498,"t":"class FLOW2Cat(FLOW2) Copy Local search algorithm optimized for categorical variables.","s":"FLOW2Cat Objects","u":"/FLAML/docs/reference/tune/searcher/cfo_cat","h":"#flow2cat-objects","p":495},{"i":500,"t":"class CFOCat(CFO) Copy CFO optimized for categorical variables.","s":"CFOCat Objects","u":"/FLAML/docs/reference/tune/searcher/cfo_cat","h":"#cfocat-objects","p":495},{"i":502,"t":"On this page","s":"tune.searcher.blendsearch","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"","p":501},{"i":504,"t":"class BlendSearch(Searcher) Copy class for BlendSearch algorithm. __init__​ def __init__(metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, time_budget_s: Union[int, float] = None, num_samples: Optional[int] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, global_search_alg: Optional[Searcher] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, seed: Optional[int] = 20, cost_attr: Optional[str] = \"auto\", cost_budget: Optional[float] = None, experimental: Optional[bool] = False, lexico_objectives: Optional[dict] = None, use_incumbent_result_in_evaluation=False, allow_empty_config=False) Copy Constructor. Arguments: metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. space - A dictionary to specify the search space. low_cost_partial_config - A dictionary from a subset of controlled dimensions to the initial low-cost values. E.g., {'n_estimators': 4, 'max_leaves': 4}. cat_hp_cost - A dictionary from a subset of categorical dimensions to the relative cost of each choice. E.g., {'tree_method': [1, 1, 2]}. I.e., the relative cost of the three choices of 'tree_method' is 1, 1 and 2 respectively. points_to_evaluate - Initial parameter suggestions to be run first. evaluated_rewards list - If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same or shorter length than points_to_evaluate. When provided, mode must be specified. time_budget_s - int or float | Time budget in seconds. num_samples - int | The number of configs to try. -1 means no limit on the number of configs to try. resource_attr - A string to specify the resource dimension and the best performance is assumed to be at the max_resource. min_resource - A float of the minimal resource to use for the resource_attr. max_resource - A float of the maximal resource to use for the resource_attr. reduction_factor - A float of the reduction factor used for incremental pruning. global_search_alg - A Searcher instance as the global search instance. If omitted, Optuna is used. The following algos have known issues when used as global_search_alg: HyperOptSearch raises exception sometimes TuneBOHB has its own scheduler config_constraints - A list of config constraints to be satisfied. E.g., config_constraints = [(mem_size, '<=', 1024**3)]. mem_size is a function which produces a float number for the bytes needed for a config. It is used to skip configs which do not fit in memory. metric_constraints - A list of metric constraints to be satisfied. E.g., ['precision', '>=', 0.9]. The sign can be \">=\" or \"<=\". seed - An integer of the random seed. cost_attr - None or str to specify the attribute to evaluate the cost of different trials. Default is \"auto\", which means that we will automatically choose the cost attribute to use (depending on the nature of the resource budget). When cost_attr is set to None, cost differences between different trials will be omitted in our search algorithm. When cost_attr is set to a str different from \"auto\" and \"time_total_s\", this cost_attr must be available in the result dict of the trial. cost_budget - A float of the cost budget. Only valid when cost_attr is a str different from \"auto\" and \"time_total_s\". lexico_objectives - dict, default=None | It specifics information needed to perform multi-objective optimization with lexicographic preferences. This is only supported in CFO currently. When lexico_objectives is not None, the arguments metric, mode will be invalid. This dictionary shall contain the following fields of key-value pairs: \"metrics\": a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. \"modes\" (optional): a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives. \"targets\" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. \"tolerances\" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. E.g., lexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": 0.01, \"pred_time\": 0.0}, \"targets\" - {\"error_rate\": 0.0}, } We also support percentage tolerance.E.g.,```pythonlexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": \"5%\", \"pred_time\": \"0%\"}, \"targets\" - {\"error_rate\": 0.0}, } Copy experimental - A bool of whether to use experimental features. save​ def save(checkpoint_path: str) Copy save states to a checkpoint path. restore​ def restore(checkpoint_path: str) Copy restore states from checkpoint. on_trial_complete​ def on_trial_complete(trial_id: str, result: Optional[Dict] = None, error: bool = False) Copy search thread updater and cleaner. on_trial_result​ def on_trial_result(trial_id: str, result: Dict) Copy receive intermediate result. suggest​ def suggest(trial_id: str) -> Optional[Dict] Copy choose thread, suggest a valid config. results​ @propertydef results() -> List[Dict] Copy A list of dicts of results for each evaluated configuration. Each dict has \"config\" and metric names as keys. The returned dict includes the initial results provided via evaluated_reward.","s":"BlendSearch Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#blendsearch-objects","p":501},{"i":506,"t":"class BlendSearchTuner(BlendSearch, NNITuner) Copy Tuner class for NNI. receive_trial_result​ def receive_trial_result(parameter_id, parameters, value, **kwargs) Copy Receive trial's final result. Arguments: parameter_id - int. parameters - object created by generate_parameters(). value - final metrics of the trial, including default metric. generate_parameters​ def generate_parameters(parameter_id, **kwargs) -> Dict Copy Returns a set of trial (hyper-)parameters, as a serializable object. Arguments: parameter_id - int. update_search_space​ def update_search_space(search_space) Copy Required by NNI. Tuners are advised to support updating search space at run-time. If a tuner can only set search space once before generating first hyper-parameters, it should explicitly document this behaviour. Arguments: search_space - JSON object created by experiment owner.","s":"BlendSearchTuner Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#blendsearchtuner-objects","p":501},{"i":508,"t":"class CFO(BlendSearchTuner) Copy class for CFO algorithm.","s":"CFO Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#cfo-objects","p":501},{"i":510,"t":"class RandomSearch(CFO) Copy Class for random search.","s":"RandomSearch Objects","u":"/FLAML/docs/reference/tune/searcher/blendsearch","h":"#randomsearch-objects","p":501},{"i":512,"t":"On this page","s":"tune.searcher.flow2","u":"/FLAML/docs/reference/tune/searcher/flow2","h":"","p":511},{"i":514,"t":"class FLOW2(Searcher) Copy Local search algorithm FLOW2, with adaptive step size. __init__​ def __init__(init_config: dict, metric: Optional[str] = None, mode: Optional[str] = None, space: Optional[dict] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, resource_multiple_factor: Optional[float] = None, cost_attr: Optional[str] = \"time_total_s\", seed: Optional[int] = 20, lexico_objectives=None) Copy Constructor. Arguments: init_config - a dictionary of a partial or full initial config, e.g., from a subset of controlled dimensions to the initial low-cost values. E.g., {'epochs': 1}. metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. space - A dictionary to specify the search space. resource_attr - A string to specify the resource dimension and the best performance is assumed to be at the max_resource. min_resource - A float of the minimal resource to use for the resource_attr. max_resource - A float of the maximal resource to use for the resource_attr. resource_multiple_factor - A float of the multiplicative factor used for increasing resource. cost_attr - A string of the attribute used for cost. seed - An integer of the random seed. lexico_objectives - dict, default=None | It specifics information needed to perform multi-objective optimization with lexicographic preferences. When lexico_objectives is not None, the arguments metric, mode will be invalid. This dictionary shall contain the following fields of key-value pairs: \"metrics\": a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. \"modes\" (optional): a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives \"targets\" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. \"tolerances\" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. E.g., lexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": 0.01, \"pred_time\": 0.0}, \"targets\" - {\"error_rate\": 0.0}, } We also support percentage tolerance.E.g.,```pythonlexico_objectives = { Copy \"metrics\" - [\"error_rate\", \"pred_time\"], \"modes\" - [\"min\", \"min\"], \"tolerances\" - {\"error_rate\": \"5%\", \"pred_time\": \"0%\"}, \"targets\" - {\"error_rate\": 0.0}, } Copy complete_config​ def complete_config(partial_config: Dict, lower: Optional[Dict] = None, upper: Optional[Dict] = None) -> Tuple[Dict, Dict] Copy Generate a complete config from the partial config input. Add minimal resource to config if available. normalize​ def normalize(config, recursive=False) -> Dict Copy normalize each dimension in config to [0,1]. denormalize​ def denormalize(config) Copy denormalize each dimension in config from [0,1]. on_trial_complete​ def on_trial_complete(trial_id: str, result: Optional[Dict] = None, error: bool = False) Copy Compare with incumbent. If better, move, reset num_complete and num_proposed. If not better and num_complete >= 2*dim, num_allowed += 2. on_trial_result​ def on_trial_result(trial_id: str, result: Dict) Copy Early update of incumbent. suggest​ def suggest(trial_id: str) -> Optional[Dict] Copy Suggest a new config, one of the following cases: same incumbent, increase resource. same resource, move from the incumbent to a random direction. same resource, move from the incumbent to the opposite direction. can_suggest​ @propertydef can_suggest() -> bool Copy Can't suggest if 2*dim configs have been proposed for the incumbent while fewer are completed. config_signature​ def config_signature(config, space: Dict = None) -> tuple Copy Return the signature tuple of a config. converged​ @propertydef converged() -> bool Copy Whether the local search has converged. reach​ def reach(other: Searcher) -> bool Copy whether the incumbent can reach the incumbent of other.","s":"FLOW2 Objects","u":"/FLAML/docs/reference/tune/searcher/flow2","h":"#flow2-objects","p":511},{"i":516,"t":"On this page","s":"tune.searcher.online_searcher","u":"/FLAML/docs/reference/tune/searcher/online_searcher","h":"","p":515},{"i":518,"t":"class BaseSearcher() Copy Abstract class for an online searcher.","s":"BaseSearcher Objects","u":"/FLAML/docs/reference/tune/searcher/online_searcher","h":"#basesearcher-objects","p":515},{"i":520,"t":"class ChampionFrontierSearcher(BaseSearcher) Copy The ChampionFrontierSearcher class. NOTE about the correspondence about this code and the research paper: ChaCha for Online AutoML. This class serves the role of ConfigOralce as described in the paper. __init__​ def __init__(init_config: Dict, space: Optional[Dict] = None, metric: Optional[str] = None, mode: Optional[str] = None, random_seed: Optional[int] = 2345, online_trial_args: Optional[Dict] = {}, nonpoly_searcher_name: Optional[str] = \"CFO\") Copy Constructor. Arguments: init_config - A dictionary of initial configuration. space - A dictionary to specify the search space. metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. random_seed - An integer of the random seed. online_trial_args - A dictionary to specify the online trial arguments for experimental purpose. nonpoly_searcher_name - A string to specify the search algorithm for nonpoly hyperparameters. set_search_properties​ def set_search_properties(metric: Optional[str] = None, mode: Optional[str] = None, config: Optional[Dict] = {}, setting: Optional[Dict] = {}, init_call: Optional[bool] = False) Copy Construct search space with the given config, and setup the search. next_trial​ def next_trial() Copy Return a trial from the _challenger_list.","s":"ChampionFrontierSearcher Objects","u":"/FLAML/docs/reference/tune/searcher/online_searcher","h":"#championfrontiersearcher-objects","p":515},{"i":522,"t":"On this page","s":"tune.searcher.search_thread","u":"/FLAML/docs/reference/tune/searcher/search_thread","h":"","p":521},{"i":524,"t":"class SearchThread() Copy Class of global or local search thread. __init__​ def __init__(mode: str = \"min\", search_alg: Optional[Searcher] = None, cost_attr: Optional[str] = TIME_TOTAL_S, eps: Optional[float] = 1.0) Copy When search_alg is omitted, use local search FLOW2. suggest​ def suggest(trial_id: str) -> Optional[Dict] Copy Use the suggest() of the underlying search algorithm. on_trial_complete​ def on_trial_complete(trial_id: str, result: Optional[Dict] = None, error: bool = False) Copy Update the statistics of the thread. reach​ def reach(thread) -> bool Copy Whether the incumbent can reach the incumbent of thread. can_suggest​ @propertydef can_suggest() -> bool Copy Whether the thread can suggest new configs.","s":"SearchThread Objects","u":"/FLAML/docs/reference/tune/searcher/search_thread","h":"#searchthread-objects","p":521},{"i":526,"t":"On this page","s":"tune.searcher.variant_generator","u":"/FLAML/docs/reference/tune/searcher/variant_generator","h":"","p":525},{"i":528,"t":"class TuneError(Exception) Copy General error class raised by ray.tune. generate_variants​ def generate_variants(unresolved_spec: Dict, constant_grid_search: bool = False, random_state: \"RandomState\" = None) -> Generator[Tuple[Dict, Dict], None, None] Copy Generates variants from a spec (dict) with unresolved values. There are two types of unresolved values: Grid search: These define a grid search over values. For example, the following grid search values in a spec will produce six distinct variants in combination: \"activation\": grid_search([\"relu\", \"tanh\"]) \"learning_rate\": grid_search([1e-3, 1e-4, 1e-5]) Lambda functions: These are evaluated to produce a concrete value, and can express dependencies or conditional distributions between values. They can also be used to express random search (e.g., by calling into the random or np module). \"cpu\": lambda spec: spec.config.num_workers \"batch_size\": lambda spec: random.uniform(1, 1000) Finally, to support defining specs in plain JSON / YAML, grid search and lambda functions can also be defined alternatively as follows: \"activation\": {\"grid_search\": [\"relu\", \"tanh\"]} \"cpu\": {\"eval\": \"spec.config.num_workers\"} Use format_vars to format the returned dict of hyperparameters. Yields: (Dict of resolved variables, Spec object) grid_search​ def grid_search(values: List) -> Dict[str, List] Copy Convenience method for specifying grid search over a value. Arguments: values - An iterable whose parameters will be gridded.","s":"TuneError Objects","u":"/FLAML/docs/reference/tune/searcher/variant_generator","h":"#tuneerror-objects","p":525},{"i":530,"t":"On this page","s":"tune.space","u":"/FLAML/docs/reference/tune/space","h":"","p":529},{"i":532,"t":"On this page","s":"tune.spark.utils","u":"/FLAML/docs/reference/tune/spark/utils","h":"","p":531},{"i":534,"t":"class PySparkOvertimeMonitor() Copy A context manager class to monitor if the PySpark job is overtime. Example: with PySparkOvertimeMonitor(time_start, time_budget_s, force_cancel, parallel=parallel): results = parallel( delayed(evaluation_function)(trial_to_run.config) for trial_to_run in trials_to_run ) Copy __init__​ def __init__(start_time, time_budget_s, force_cancel=False, cancel_func=None, parallel=None, sc=None) Copy Constructor. Specify the time budget and start time of the PySpark job, and specify how to cancel them. Arguments: Args relate to monitoring: start_time - float | The start time of the PySpark job. time_budget_s - float | The time budget of the PySpark job in seconds. force_cancel - boolean, default=False | Whether to forcely cancel the PySpark job if overtime. Args relate to how to cancel the PySpark job: (Only one of the following args will work. Priorities from top to bottom) cancel_func - function | A function to cancel the PySpark job. parallel - joblib.parallel.Parallel | Specify this if using joblib_spark as a parallel backend. It will call parallel._backend.terminate() to cancel the jobs. sc - pyspark.SparkContext object | You can pass a specific SparkContext. If all three args is None, the monitor will call pyspark.SparkContext.getOrCreate().cancelAllJobs() to cancel the jobs. __enter__​ def __enter__() Copy Enter the context manager. This will start a monitor thread if spark is available and force_cancel is True. __exit__​ def __exit__(exc_type, exc_value, exc_traceback) Copy Exit the context manager. This will wait for the monitor thread to nicely exit.","s":"PySparkOvertimeMonitor Objects","u":"/FLAML/docs/reference/tune/spark/utils","h":"#pysparkovertimemonitor-objects","p":531},{"i":536,"t":"On this page","s":"tune.trial","u":"/FLAML/docs/reference/tune/trial","h":"","p":535},{"i":538,"t":"class Trial() Copy A trial object holds the state for one model training run. Trials are themselves managed by the TrialRunner class, which implements the event loop for submitting trial runs to a Ray cluster. Trials start in the PENDING state, and transition to RUNNING once started. On error it transitions to ERROR, otherwise TERMINATED on success. Attributes: trainable_name str - Name of the trainable object to be executed. config dict - Provided configuration dictionary with evaluated params. trial_id str - Unique identifier for the trial. local_dir str - Local_dir as passed to tune.run. logdir str - Directory where the trial logs are saved. evaluated_params dict - Evaluated parameters by search algorithm, experiment_tag str - Identifying trial name to show in the console. resources Resources - Amount of resources that this trial will use. status str - One of PENDING, RUNNING, PAUSED, TERMINATED, ERROR/ error_file str - Path to the errors that this trial has raised. set_status​ def set_status(status) Copy Sets the status of the trial.","s":"Trial Objects","u":"/FLAML/docs/reference/tune/trial","h":"#trial-objects","p":535},{"i":540,"t":"On this page","s":"tune.tune","u":"/FLAML/docs/reference/tune/tune","h":"","p":539},{"i":542,"t":"class ExperimentAnalysis(EA) Copy Class for storing the experiment results. report​ def report(_metric=None, **kwargs) Copy A function called by the HPO application to report final or intermediate results. Example: import timefrom flaml import tunedef compute_with_config(config): current_time = time.time() metric2minimize = (round(config['x'])-95000)**2 time2eval = time.time() - current_time tune.report(metric2minimize=metric2minimize, time2eval=time2eval)analysis = tune.run( compute_with_config, config={ 'x': tune.lograndint(lower=1, upper=1000000), 'y': tune.randint(lower=1, upper=1000000) }, metric='metric2minimize', mode='min', num_samples=1000000, time_budget_s=60, use_ray=False)print(analysis.trials[-1].last_result) Copy Arguments: _metric - Optional default anonymous metric for tune.report(value). (For compatibility with ray.tune.report) **kwargs - Any key value pair to be reported. Raises: StopIteration (when not using ray, i.e., _use_ray=False): A StopIteration exception is raised if the trial has been signaled to stop. SystemExit (when using ray): A SystemExit exception is raised if the trial has been signaled to stop by ray. run​ def run(evaluation_function, config: Optional[dict] = None, low_cost_partial_config: Optional[dict] = None, cat_hp_cost: Optional[dict] = None, metric: Optional[str] = None, mode: Optional[str] = None, time_budget_s: Union[int, float] = None, points_to_evaluate: Optional[List[dict]] = None, evaluated_rewards: Optional[List] = None, resource_attr: Optional[str] = None, min_resource: Optional[float] = None, max_resource: Optional[float] = None, reduction_factor: Optional[float] = None, scheduler=None, search_alg=None, verbose: Optional[int] = 2, local_dir: Optional[str] = None, num_samples: Optional[int] = 1, resources_per_trial: Optional[dict] = None, config_constraints: Optional[List[Tuple[Callable[[dict], float], str, float]]] = None, metric_constraints: Optional[List[Tuple[str, str, float]]] = None, max_failure: Optional[int] = 100, use_ray: Optional[bool] = False, use_spark: Optional[bool] = False, use_incumbent_result_in_evaluation: Optional[bool] = None, log_file_name: Optional[str] = None, lexico_objectives: Optional[dict] = None, force_cancel: Optional[bool] = False, n_concurrent_trials: Optional[int] = 0, **ray_args, ,) Copy The function-based way of performing HPO. Example: import timefrom flaml import tunedef compute_with_config(config): current_time = time.time() metric2minimize = (round(config['x'])-95000)**2 time2eval = time.time() - current_time tune.report(metric2minimize=metric2minimize, time2eval=time2eval) # if the evaluation fails unexpectedly and the exception is caught, # and it doesn't inform the goodness of the config, # return {} # if the failure indicates a config is bad, # report a bad metric value like np.inf or -np.inf # depending on metric mode being min or maxanalysis = tune.run( compute_with_config, config={ 'x': tune.lograndint(lower=1, upper=1000000), 'y': tune.randint(lower=1, upper=1000000) }, metric='metric2minimize', mode='min', num_samples=-1, time_budget_s=60, use_ray=False)print(analysis.trials[-1].last_result) Copy Arguments: evaluation_function - A user-defined evaluation function. It takes a configuration as input, outputs a evaluation result (can be a numerical value or a dictionary of string and numerical value pairs) for the input configuration. For machine learning tasks, it usually involves training and scoring a machine learning model, e.g., through validation loss. config - A dictionary to specify the search space. low_cost_partial_config - A dictionary from a subset of controlled dimensions to the initial low-cost values. e.g., {'n_estimators': 4, 'max_leaves': 4} cat_hp_cost - A dictionary from a subset of categorical dimensions to the relative cost of each choice. e.g., {'tree_method': [1, 1, 2]} i.e., the relative cost of the three choices of 'tree_method' is 1, 1 and 2 respectively metric - A string of the metric name to optimize for. mode - A string in ['min', 'max'] to specify the objective as minimization or maximization. time_budget_s - int or float | The time budget in seconds. points_to_evaluate - A list of initial hyperparameter configurations to run first. evaluated_rewards list - If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same or shorter length than points_to_evaluate. e.g., points_to_evaluate = [ {\"b\": .99, \"cost_related\": {\"a\": 3}}, {\"b\": .99, \"cost_related\": {\"a\": 2}},]evaluated_rewards = [3.0] Copy means that you know the reward for the first config in points_to_evaluate is 3.0 and want to inform run(). resource_attr - A string to specify the resource dimension used by the scheduler via \"scheduler\". min_resource - A float of the minimal resource to use for the resource_attr. max_resource - A float of the maximal resource to use for the resource_attr. reduction_factor - A float of the reduction factor used for incremental pruning. scheduler - A scheduler for executing the experiment. Can be None, 'flaml', 'asha' (or 'async_hyperband', 'asynchyperband') or a custom instance of the TrialScheduler class. Default is None: in this case when resource_attr is provided, the 'flaml' scheduler will be used, otherwise no scheduler will be used. When set 'flaml', an authentic scheduler implemented in FLAML will be used. It does not require users to report intermediate results in evaluation_function. Find more details about this scheduler in this paper https://arxiv.org/pdf/1911.04706.pdf). When set 'asha', the input for arguments \"resource_attr\", \"min_resource\", \"max_resource\" and \"reduction_factor\" will be passed to ASHA's \"time_attr\", \"max_t\", \"grace_period\" and \"reduction_factor\" respectively. You can also provide a self-defined scheduler instance of the TrialScheduler class. When 'asha' or self-defined scheduler is used, you usually need to report intermediate results in the evaluation function via 'tune.report()'. If you would like to do some cleanup opearation when the trial is stopped by the scheduler, you can catch the StopIteration (when not using ray) or SystemExit (when using ray) exception explicitly, as shown in the following example. Please find more examples using different types of schedulers and how to set up the corresponding evaluation functions in test/tune/test_scheduler.py, and test/tune/example_scheduler.py. def easy_objective(config): width, height = config[\"width\"], config[\"height\"] for step in range(config[\"steps\"]): intermediate_score = evaluation_fn(step, width, height) try: tune.report(iterations=step, mean_loss=intermediate_score) except (StopIteration, SystemExit): # do cleanup operation here return Copy search_alg - An instance/string of the search algorithm to be used. The same instance can be used for iterative tuning. e.g., from flaml import BlendSearchalgo = BlendSearch(metric='val_loss', mode='min', space=search_space, low_cost_partial_config=low_cost_partial_config)for i in range(10): analysis = tune.run(compute_with_config, search_alg=algo, use_ray=False) print(analysis.trials[-1].last_result) Copy verbose - 0, 1, 2, or 3. If ray or spark backend is used, their verbosity will be affected by this argument. 0 = silent, 1 = only status updates, 2 = status and brief trial results, 3 = status and detailed trial results. Defaults to 2. local_dir - A string of the local dir to save ray logs if ray backend is used; or a local dir to save the tuning log. num_samples - An integer of the number of configs to try. Defaults to 1. resources_per_trial - A dictionary of the hardware resources to allocate per trial, e.g., {'cpu': 1}. It is only valid when using ray backend (by setting 'use_ray = True'). It shall be used when you need to do parallel tuning. config_constraints - A list of config constraints to be satisfied. e.g., config_constraints = [(mem_size, '<=', 1024**3)] mem_size is a function which produces a float number for the bytes needed for a config. It is used to skip configs which do not fit in memory. metric_constraints - A list of metric constraints to be satisfied. e.g., ['precision', '>=', 0.9]. The sign can be \">=\" or \"<=\". max_failure - int | the maximal consecutive number of failures to sample a trial before the tuning is terminated. use_ray - A boolean of whether to use ray as the backend. use_spark - A boolean of whether to use spark as the backend. log_file_name - A string of the log file name. Default to None. When set to None: if local_dir is not given, no log file is created; if local_dir is given, the log file name will be autogenerated under local_dir. Only valid when verbose > 0 or use_ray is True. lexico_objectives - dict, default=None | It specifics information needed to perform multi-objective optimization with lexicographic preferences. When lexico_objectives is not None, the arguments metric, mode, will be invalid, and flaml's tune uses CFO as the search_alg, which makes the input (if provided) `search_alg' invalid. This dictionary shall contain the following fields of key-value pairs: \"metrics\": a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. \"modes\" (optional): a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives. \"targets\" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. \"tolerances\" (optional): a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. E.g., lexico_objectives = { \"metrics\": [\"error_rate\", \"pred_time\"], \"modes\": [\"min\", \"min\"], \"tolerances\": {\"error_rate\": 0.01, \"pred_time\": 0.0}, \"targets\": {\"error_rate\": 0.0},} Copy We also support percentage tolerance. E.g., lexico_objectives = { \"metrics\": [\"error_rate\", \"pred_time\"], \"modes\": [\"min\", \"min\"], \"tolerances\": {\"error_rate\": \"5%\", \"pred_time\": \"0%\"}, \"targets\": {\"error_rate\": 0.0},} Copy force_cancel - boolean, default=False | Whether to forcely cancel the PySpark job if overtime. n_concurrent_trials - int, default=0 | The number of concurrent trials when perform hyperparameter tuning with Spark. Only valid when use_spark=True and spark is required: pip install flaml[spark]. Please check here for more details about installing Spark. When tune.run() is called from AutoML, it will be overwritten by the value of n_concurrent_trials in AutoML. When <= 0, the concurrent trials will be set to the number of executors. **ray_args - keyword arguments to pass to ray.tune.run(). Only valid when use_ray=True.","s":"ExperimentAnalysis Objects","u":"/FLAML/docs/reference/tune/tune","h":"#experimentanalysis-objects","p":539},{"i":544,"t":"class Tuner() Copy Tuner is the class-based way of launching hyperparameter tuning jobs compatible with Ray Tune 2. Arguments: trainable - A user-defined evaluation function. It takes a configuration as input, outputs a evaluation result (can be a numerical value or a dictionary of string and numerical value pairs) for the input configuration. For machine learning tasks, it usually involves training and scoring a machine learning model, e.g., through validation loss. param_space - Search space of the tuning job. One thing to note is that both preprocessor and dataset can be tuned here. tune_config - Tuning algorithm specific configs. Refer to ray.tune.tune_config.TuneConfig for more info. run_config - Runtime configuration that is specific to individual trials. If passed, this will overwrite the run config passed to the Trainer, if applicable. Refer to ray.air.config.RunConfig for more info. Usage pattern: .. code-block:: python from sklearn.datasets import load_breast_cancer from ray import tune from ray.data import from_pandas from ray.air.config import RunConfig, ScalingConfig from ray.train.xgboost import XGBoostTrainer from ray.tune.tuner import Tuner def get_dataset(): data_raw = load_breast_cancer(as_frame=True) dataset_df = data_raw[\"data\"] dataset_df[\"target\"] = data_raw[\"target\"] dataset = from_pandas(dataset_df) return dataset trainer = XGBoostTrainer( label_column=\"target\", params={}, datasets={\"train\" - get_dataset()}, ) param_space = { \"scaling_config\" - ScalingConfig( num_workers=tune.grid_search([2, 4]), resources_per_worker={ \"CPU\" - tune.grid_search([1, 2]), }, ), You can even grid search various datasets in Tune. \"datasets\": { \"train\": tune.grid_search( [ds1, ds2] ), }, \"params\" - { \"objective\" - \"binary:logistic\", \"tree_method\" - \"approx\", \"eval_metric\" - [\"logloss\", \"error\"], \"eta\" - tune.loguniform(1e-4, 1e-1), \"subsample\" - tune.uniform(0.5, 1.0), \"max_depth\" - tune.randint(1, 9), }, } tuner = Tuner(trainable=trainer, param_space=param_space, run_config=RunConfig(name=\"my_tune_run\")) analysis = tuner.fit() To retry a failed tune run, you can then do .. code-block:: python tuner = Tuner.restore(experiment_checkpoint_dir) tuner.fit() experiment_checkpoint_dir can be easily located near the end of the console output of your first failed run.","s":"Tuner Objects","u":"/FLAML/docs/reference/tune/tune","h":"#tuner-objects","p":539},{"i":552,"t":"On this page","s":"tune.trial_runner","u":"/FLAML/docs/reference/tune/trial_runner","h":"","p":551},{"i":554,"t":"class Nologger() Copy Logger without logging.","s":"Nologger Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#nologger-objects","p":551},{"i":556,"t":"class SimpleTrial(Trial) Copy A simple trial class.","s":"SimpleTrial Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#simpletrial-objects","p":551},{"i":558,"t":"class BaseTrialRunner() Copy Implementation of a simple trial runner. Note that the caller usually should not mutate trial state directly. get_trials​ def get_trials() Copy Returns the list of trials managed by this TrialRunner. Note that the caller usually should not mutate trial state directly. add_trial​ def add_trial(trial) Copy Adds a new trial to this TrialRunner. Trials may be added at any time. Arguments: trial Trial - Trial to queue. stop_trial​ def stop_trial(trial) Copy Stops trial.","s":"BaseTrialRunner Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#basetrialrunner-objects","p":551},{"i":560,"t":"class SequentialTrialRunner(BaseTrialRunner) Copy Implementation of the sequential trial runner. step​ def step() -> Trial Copy Runs one step of the trial event loop. Callers should typically run this method repeatedly in a loop. They may inspect or modify the runner's state in between calls to step(). Returns: a trial to run.","s":"SequentialTrialRunner Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#sequentialtrialrunner-objects","p":551},{"i":562,"t":"class SparkTrialRunner(BaseTrialRunner) Copy Implementation of the spark trial runner. step​ def step() -> Trial Copy Runs one step of the trial event loop. Callers should typically run this method repeatedly in a loop. They may inspect or modify the runner's state in between calls to step(). Returns: a trial to run.","s":"SparkTrialRunner Objects","u":"/FLAML/docs/reference/tune/trial_runner","h":"#sparktrialrunner-objects","p":551},{"i":564,"t":"AutoGen for Large Language Models Please refer to https://microsoft.github.io/autogen/.","s":"AutoGen for Large Language Models","u":"/FLAML/docs/Use-Cases/Autogen","h":"","p":563},{"i":566,"t":"Research For technical details, please check our research publications. FLAML: A Fast and Lightweight AutoML Library. Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021. @inproceedings{wang2021flaml, title={FLAML: A Fast and Lightweight AutoML Library}, author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu}, year={2021}, booktitle={MLSys},} Copy Frugal Optimization for Cost-related Hyperparameters. Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021. @inproceedings{wu2021cfo, title={Frugal Optimization for Cost-related Hyperparameters}, author={Qingyun Wu and Chi Wang and Silu Huang}, year={2021}, booktitle={AAAI},} Copy Economical Hyperparameter Optimization With Blended Search Strategy. Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021. @inproceedings{wang2021blendsearch, title={Economical Hyperparameter Optimization With Blended Search Strategy}, author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied}, year={2021}, booktitle={ICLR},} Copy An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models. Susan Xueqing Liu, Chi Wang. ACL 2021. @inproceedings{liuwang2021hpolm, title={An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models}, author={Susan Xueqing Liu and Chi Wang}, year={2021}, booktitle={ACL},} Copy ChaCha for Online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021. @inproceedings{wu2021chacha, title={ChaCha for Online AutoML}, author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi}, year={2021}, booktitle={ICML},} Copy Fair AutoML. Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021). @inproceedings{wuwang2021fairautoml, title={Fair AutoML}, author={Qingyun Wu and Chi Wang}, year={2021}, booktitle={ArXiv preprint arXiv:2111.06495},} Copy Mining Robust Default Configurations for Resource-constrained AutoML. Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022). @inproceedings{kayaliwang2022default, title={Mining Robust Default Configurations for Resource-constrained AutoML}, author={Moe Kayali and Chi Wang}, year={2022}, booktitle={ArXiv preprint arXiv:2202.09927},} Copy Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives. Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%). @inproceedings{zhang2023targeted, title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives}, author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu}, booktitle={International Conference on Learning Representations}, year={2023}, url={https://openreview.net/forum?id=0Ij9_q567Ma},} Copy Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference. Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023). @inproceedings{wang2023EcoOptiGen, title={Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference}, author={Chi Wang and Susan Xueqing Liu and Ahmed H. Awadallah}, year={2023}, booktitle={ArXiv preprint arXiv:2303.04673},} Copy An Empirical Study on Challenging Math Problem Solving with GPT-4. Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023). @inproceedings{wu2023empirical, title={An Empirical Study on Challenging Math Problem Solving with GPT-4}, author={Yiran Wu and Feiran Jia and Shaokun Zhang and Hangyu Li and Erkang Zhu and Yue Wang and Yin Tat Lee and Richard Peng and Qingyun Wu and Chi Wang}, year={2023}, booktitle={ArXiv preprint arXiv:2306.01337},} Copy","s":"Research","u":"/FLAML/docs/Research","h":"","p":565},{"i":568,"t":"On this page","s":"tune.searcher.suggestion","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"","p":567},{"i":570,"t":"class Searcher() Copy Abstract class for wrapping suggesting algorithms. Custom algorithms can extend this class easily by overriding the suggest method provide generated parameters for the trials. Any subclass that implements __init__ must also call the constructor of this class: super(Subclass, self).__init__(...). To track suggestions and their corresponding evaluations, the method suggest will be passed a trial_id, which will be used in subsequent notifications. Not all implementations support multi objectives. Arguments: metric str or list - The training result objective value attribute. If list then list of training result objective value attributes mode str or list - If string One of {min, max}. If list then list of max and min, determines whether objective is minimizing or maximizing the metric attribute. Must match type of metric. class ExampleSearch(Searcher): def __init__(self, metric=\"mean_loss\", mode=\"min\", **kwargs): super(ExampleSearch, self).__init__( metric=metric, mode=mode, **kwargs) self.optimizer = Optimizer() self.configurations = {} def suggest(self, trial_id): configuration = self.optimizer.query() self.configurations[trial_id] = configuration def on_trial_complete(self, trial_id, result, **kwargs): configuration = self.configurations[trial_id] if result and self.metric in result: self.optimizer.update(configuration, result[self.metric])tune.run(trainable_function, search_alg=ExampleSearch()) Copy set_search_properties​ def set_search_properties(metric: Optional[str], mode: Optional[str], config: Dict) -> bool Copy Pass search properties to searcher. This method acts as an alternative to instantiating search algorithms with their own specific search spaces. Instead they can accept a Tune config through this method. A searcher should return True if setting the config was successful, or False if it was unsuccessful, e.g. when the search space has already been set. Arguments: metric str - Metric to optimize mode str - One of [\"min\", \"max\"]. Direction to optimize. config dict - Tune config dict. on_trial_result​ def on_trial_result(trial_id: str, result: Dict) Copy Optional notification for result during training. Note that by default, the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. Arguments: trial_id str - A unique string ID for the trial. result dict - Dictionary of metrics for current training progress. Note that the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. metric​ @propertydef metric() -> str Copy The training result objective value attribute. mode​ @propertydef mode() -> str Copy Specifies if minimizing or maximizing the metric.","s":"Searcher Objects","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"#searcher-objects","p":567},{"i":572,"t":"class ConcurrencyLimiter(Searcher) Copy A wrapper algorithm for limiting the number of concurrent trials. Arguments: searcher Searcher - Searcher object that the ConcurrencyLimiter will manage. max_concurrent int - Maximum concurrent samples from the underlying searcher. batch bool - Whether to wait for all concurrent samples to finish before updating the underlying searcher. Example: from ray.tune.suggest import ConcurrencyLimiter # ray version < 2search_alg = HyperOptSearch(metric=\"accuracy\")search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2)tune.run(trainable, search_alg=search_alg) Copy validate_warmstart​ def validate_warmstart(parameter_names: List[str], points_to_evaluate: List[Union[List, Dict]], evaluated_rewards: List, validate_point_name_lengths: bool = True) Copy Generic validation of a Searcher's warm start functionality. Raises exceptions in case of type and length mismatches between parameters. If validate_point_name_lengths is False, the equality of lengths between points_to_evaluate and parameter_names will not be validated.","s":"ConcurrencyLimiter Objects","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"#concurrencylimiter-objects","p":567},{"i":574,"t":"class OptunaSearch(Searcher) Copy A wrapper around Optuna to provide trial suggestions. Optuna <https://optuna.org/>_ is a hyperparameter optimization library. In contrast to other libraries, it employs define-by-run style hyperparameter definitions. This Searcher is a thin wrapper around Optuna's search algorithms. You can pass any Optuna sampler, which will be used to generate hyperparameter suggestions. Multi-objective optimization is supported. Arguments: space - Hyperparameter search space definition for Optuna's sampler. This can be either a dict with parameter names as keys and optuna.distributions as values, or a Callable - in which case, it should be a define-by-run function using optuna.trial to obtain the hyperparameter values. The function should return either a dict of constant values with names as keys, or None. For more information, see https://optuna.readthedocs.io\\ /en/stable/tutorial/10_key_features/002_configurations.html. Warning - No actual computation should take place in the define-by-run function. Instead, put the training logic inside the function or class trainable passed to tune.run. metric - The training result objective value attribute. If None but a mode was passed, the anonymous metric _metric will be used per default. Can be a list of metrics for multi-objective optimization. mode - One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. Can be a list of modes for multi-objective optimization (corresponding to metric). points_to_evaluate - Initial parameter suggestions to be run first. This is for when you already have some good parameters you want to run first to help the algorithm make better suggestions for future parameters. Needs to be a list of dicts containing the configurations. sampler - Optuna sampler used to draw hyperparameter configurations. Defaults to MOTPESampler for multi-objective optimization with Optuna<2.9.0, and TPESampler in every other case. Warning - Please note that with Optuna 2.10.0 and earlier default MOTPESampler/TPESampler suffer from performance issues when dealing with a large number of completed trials (approx. >100). This will manifest as a delay when suggesting new configurations. This is an Optuna issue and may be fixed in a future Optuna release. seed - Seed to initialize sampler with. This parameter is only used when sampler=None. In all other cases, the sampler you pass should be initialized with the seed already. evaluated_rewards - If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same length as points_to_evaluate. Warning - When using evaluated_rewards, the search space space must be provided as a dict with parameter names as keys and optuna.distributions instances as values. The define-by-run search space definition is not yet supported with this functionality. Tune automatically converts search spaces to Optuna's format: from ray.tune.suggest.optuna import OptunaSearchconfig = { Copy \"a\" - tune.uniform(6, 8) \"b\" - tune.loguniform(1e-4, 1e-2) } optuna_search = OptunaSearch( metric=\"loss\", mode=\"min\") tune.run(trainable, config=config, search_alg=optuna_search) If you would like to pass the search space manually, the code wouldlook like this:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Equivalent Optuna define-by-run function approach: def define_search_space(trial: optuna.Trial): trial.suggest_float(\"a\", 6, 8) trial.suggest_float(\"b\", 1e-4, 1e-2, log=True) training logic goes into trainable, this is just for search space definition optuna_search = OptunaSearch( define_search_space, metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Multi-objective optimization is supported:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } Note you have to specify metric and mode here instead of in tune.run optuna_search = OptunaSearch( space, metric=[\"loss1\", \"loss2\"], mode=[\"min\", \"max\"]) Do not specify metric and mode here! tune.run( trainable, search_alg=optuna_search ) You can pass configs that will be evaluated first using``points_to_evaluate``:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, points_to_evaluate=[{\"a\" - 6.5, \"b\": 5e-4}, {\"a\": 7.5, \"b\": 1e-3}] metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Avoid re-running evaluated trials by passing the rewards together with`points_to_evaluate`:```pythonfrom ray.tune.suggest.optuna import OptunaSearchimport optunaspace = { Copy \"a\" - optuna.distributions.UniformDistribution(6, 8), \"b\" - optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, points_to_evaluate=[{\"a\" - 6.5, \"b\": 5e-4}, {\"a\": 7.5, \"b\": 1e-3}] evaluated_rewards=[0.89, 0.42] metric=\"loss\", mode=\"min\") tune.run(trainable, search_alg=optuna_search) Copy","s":"OptunaSearch Objects","u":"/FLAML/docs/reference/tune/searcher/suggestion","h":"#optunasearch-objects","p":567},{"i":582,"t":"On this page","s":"tune.utils","u":"/FLAML/docs/reference/tune/utils","h":"","p":581},{"i":584,"t":"On this page","s":"Zero Shot AutoML","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"","p":583},{"i":586,"t":"The easiest way to leverage this technique is to import a \"flamlized\" learner of your favorite choice and use it just as how you use the learner before. The automation is done behind the scene and you are not required to change your code. For example, if you are currently using: from lightgbm import LGBMRegressorestimator = LGBMRegressor()estimator.fit(X_train, y_train)estimator.predict(X_test) Copy Simply replace the first line with: from flaml.default import LGBMRegressor Copy All the other code remains the same. And you are expected to get a equal or better model in most cases. The current list of \"flamlized\" learners are: LGBMClassifier, LGBMRegressor. XGBClassifier, XGBRegressor. RandomForestClassifier, RandomForestRegressor. ExtraTreesClassifier, ExtraTreesRegressor.","s":"How to Use at Runtime","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#how-to-use-at-runtime","p":583},{"i":588,"t":"flaml.default.LGBMRegressor inherits lightgbm.LGBMRegressor, so all the APIs in lightgbm.LGBMRegressor are still valid in flaml.default.LGBMRegressor. The difference is, flaml.default.LGBMRegressor decides the hyperparameter configurations based on the training data. It would use a different configuration if it is predicted to outperform the original data-independent default. If you inspect the params of the fitted estimator, you can find what configuration is used. If the original default configuration is used, then it is equivalent to the original estimator. The recommendation of which configuration should be used is based on offline AutoML run results. Information about the training dataset, such as the size of the dataset will be used to recommend a data-dependent configuration. The recommendation is done instantly in negligible time. The training can be faster or slower than using the original default configuration depending on the recommended configuration. Note that there is no tuning involved. Only one model is trained.","s":"What's the magic behind the scene?","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#whats-the-magic-behind-the-scene","p":583},{"i":590,"t":"Yes. You can use suggest_hyperparams() to find the suggested configuration. For example, from flaml.default import LGBMRegressorestimator = LGBMRegressor()( hyperparams, estimator_name, X_transformed, y_transformed,) = estimator.suggest_hyperparams(X_train, y_train)print(hyperparams) Copy If you would like more control over the training, use an equivalent, open-box way for zero-shot AutoML. For example, from flaml.default import preprocess_and_suggest_hyperparamsX, y = load_iris(return_X_y=True, as_frame=True)X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)( hyperparams, estimator_class, X_transformed, y_transformed, feature_transformer, label_transformer,) = preprocess_and_suggest_hyperparams(\"classification\", X_train, y_train, \"lgbm\")model = estimator_class(**hyperparams) # estimator_class is lightgbm.LGBMClassifiermodel.fit(X_transformed, y_train) # LGBMClassifier can handle raw labelsX_test = feature_transformer.transform(X_test) # preprocess test datay_pred = model.predict(X_test) Copy Note that some classifiers like XGBClassifier require the labels to be integers, while others do not. So you can decide whether to use the transformed labels y_transformed and the label transformer label_transformer. Also, each estimator may require specific preprocessing of the data. X_transformed is the preprocessed data, and feature_transformer is the preprocessor. It needs to be applied to the test data before prediction. These are automated when you use the \"flamlized\" learner. When you use the open-box way, pay attention to them.","s":"Can I check the configuration before training?","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#can-i-check-the-configuration-before-training","p":583},{"i":592,"t":"Zero Shot AutoML is fast. If tuning from the recommended data-dependent configuration is required, you can use flaml.AutoML.fit() and set starting_points=\"data\". For example, from flaml import AutoMLautoml = AutoML()automl_settings = { \"task\": \"classification\", \"starting_points\": \"data\", \"estimator_list\": [\"lgbm\"], \"time_budget\": 600, \"max_iter\": 50,}automl.fit(X_train, y_train, **automl_settings) Copy Note that if you set max_iter=0 and time_budget=None, you are effectively using zero-shot AutoML. When estimator_list is omitted, the estimator together with its hyperparameter configuration will be decided in a zero-shot manner.","s":"Combine zero shot AutoML and hyperparameter tuning","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#combine-zero-shot-automl-and-hyperparameter-tuning","p":583},{"i":594,"t":"To use your own meta-learned defaults, specify the path containing the meta-learned defaults. For example, estimator = flaml.default.LGBMRegressor(default_location=\"location_for_defaults\") Copy Or, preprocess_and_suggest_hyperparams( \"classification\", X_train, y_train, \"lgbm\", location=\"location_for_defaults\") Copy Or, X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)automl = AutoML()automl_settings = { \"task\": \"classification\", \"log_file_name\": \"test/iris.log\", \"starting_points\": \"data:location_for_defaults\", \"estimator_list\": [\"lgbm\", \"xgb_limitdepth\", \"rf\"] \"max_iter\": 0,}automl.fit(X_train, y_train, **automl_settings) Copy Since this is a multiclass task, it will look for the following files under {location_for_defaults}/: all/multiclass.json. {learner_name}/multiclass.json for every learner_name in the estimator_list. Read the next section to understand how to generate these files if you would like to meta-learn the defaults yourself.","s":"Use your own meta-learned defaults","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#use-your-own-meta-learned-defaults","p":583},{"i":596,"t":"This section is intended for: AutoML providers for a particular domain. Data scientists or engineers who need to repeatedly train models for similar tasks with varying training data. Instead of running full hyperparameter tuning from scratch every time, one can leverage the tuning experiences in similar tasks before. While we have offered the meta-learned defaults from tuning experiences of several popular learners on benchmark datasets for classification and regression, you can customize the defaults for your own tasks/learners/metrics based on your own tuning experiences.","s":"How to Prepare Offline","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#how-to-prepare-offline","p":583},{"i":598,"t":"Collect a diverse set of training tasks. For each task, extract its meta feature and save in a .csv file. For example, test/default/all/metafeatures.csv: Dataset,NumberOfInstances,NumberOfFeatures,NumberOfClasses,PercentageOfNumericFeatures2dplanes,36691,10,0,1.0adult,43957,14,2,0.42857142857142855Airlines,485444,7,2,0.42857142857142855Albert,382716,78,2,0.3333333333333333Amazon_employee_access,29492,9,2,0.0bng_breastTumor,104976,9,0,0.1111111111111111bng_pbc,900000,18,0,0.5555555555555556car,1555,6,4,0.0connect-4,60801,42,3,0.0dilbert,9000,2000,5,1.0Dionis,374569,60,355,1.0poker,922509,10,0,1.0 Copy The first column is the dataset name, and the latter four are meta features.","s":"Prepare a collection of training tasks","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#prepare-a-collection-of-training-tasks","p":583},{"i":600,"t":"You can extract the best configurations for each task in your collection of training tasks by running flaml on each of them with a long enough budget. Save the best configuration in a .json file under {location_for_defaults}/{learner_name}/{task_name}.json. For example, X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)automl.fit(X_train, y_train, estimator_list=[\"lgbm\"], **settings)automl.save_best_config(\"test/default/lgbm/iris.json\") Copy","s":"Prepare the candidate configurations","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#prepare-the-candidate-configurations","p":583},{"i":602,"t":"Save the evaluation results in a .csv file. For example, save the evaluation results for lgbm under test/default/lgbm/results.csv: task,fold,type,result,params2dplanes,0,regression,0.946366,{'_modeljson': 'lgbm/2dplanes.json'}2dplanes,0,regression,0.907774,{'_modeljson': 'lgbm/adult.json'}2dplanes,0,regression,0.901643,{'_modeljson': 'lgbm/Airlines.json'}2dplanes,0,regression,0.915098,{'_modeljson': 'lgbm/Albert.json'}2dplanes,0,regression,0.302328,{'_modeljson': 'lgbm/Amazon_employee_access.json'}2dplanes,0,regression,0.94523,{'_modeljson': 'lgbm/bng_breastTumor.json'}2dplanes,0,regression,0.945698,{'_modeljson': 'lgbm/bng_pbc.json'}2dplanes,0,regression,0.946194,{'_modeljson': 'lgbm/car.json'}2dplanes,0,regression,0.945549,{'_modeljson': 'lgbm/connect-4.json'}2dplanes,0,regression,0.946232,{'_modeljson': 'lgbm/default.json'}2dplanes,0,regression,0.945594,{'_modeljson': 'lgbm/dilbert.json'}2dplanes,0,regression,0.836996,{'_modeljson': 'lgbm/Dionis.json'}2dplanes,0,regression,0.917152,{'_modeljson': 'lgbm/poker.json'}adult,0,binary,0.927203,{'_modeljson': 'lgbm/2dplanes.json'}adult,0,binary,0.932072,{'_modeljson': 'lgbm/adult.json'}adult,0,binary,0.926563,{'_modeljson': 'lgbm/Airlines.json'}adult,0,binary,0.928604,{'_modeljson': 'lgbm/Albert.json'}adult,0,binary,0.911171,{'_modeljson': 'lgbm/Amazon_employee_access.json'}adult,0,binary,0.930645,{'_modeljson': 'lgbm/bng_breastTumor.json'}adult,0,binary,0.928603,{'_modeljson': 'lgbm/bng_pbc.json'}adult,0,binary,0.915825,{'_modeljson': 'lgbm/car.json'}adult,0,binary,0.919499,{'_modeljson': 'lgbm/connect-4.json'}adult,0,binary,0.930109,{'_modeljson': 'lgbm/default.json'}adult,0,binary,0.932453,{'_modeljson': 'lgbm/dilbert.json'}adult,0,binary,0.921959,{'_modeljson': 'lgbm/Dionis.json'}adult,0,binary,0.910763,{'_modeljson': 'lgbm/poker.json'}... Copy The type column indicates the type of the task, such as regression, binary or multiclass. The result column stores the evaluation result, assumed the large the better. The params column indicates which json config is used. For example 'lgbm/2dplanes.json' indicates that the best lgbm configuration extracted from 2dplanes is used. Different types of tasks can appear in the same file, as long as any json config file can be used in all the tasks. For example, 'lgbm/2dplanes.json' is extracted from a regression task, and it can be applied to binary and multiclass tasks as well.","s":"Evaluate each candidate configuration on each task","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#evaluate-each-candidate-configuration-on-each-task","p":583},{"i":604,"t":"To recap, the inputs required for meta-learning are: Metafeatures: e.g., {location}/all/metafeatures.csv. Configurations: {location}/{learner_name}/{task_name}.json. Evaluation results: {location}/{learner_name}/results.csv. For example, if the input location is \"test/default\", learners are lgbm, xgb_limitdepth and rf, the following command learns data-dependent defaults for binary classification tasks. python portfolio.py --output test/default --input test/default --metafeatures test/default/all/metafeatures.csv --task binary --estimator lgbm xgb_limitdepth rf Copy In a few seconds, it will produce the following files as output: test/default/lgbm/binary.json: the learned defaults for lgbm. test/default/xgb_limitdepth/binary.json: the learned defaults for xgb_limitdepth. test/default/rf/binary.json: the learned defaults for rf. test/default/all/binary.json: the learned defaults for lgbm, xgb_limitdepth and rf together. Change \"binary\" into \"multiclass\" or \"regression\", or your own types in your \"results.csv\" for the other types of tasks. To update the learned defaults when more experiences are available, simply update your input files and rerun the learning command.","s":"Learn data-dependent defaults","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#learn-data-dependent-defaults","p":583},{"i":606,"t":"You have now effectively built your own zero-shot AutoML solution. Congratulations! Optionally, you can \"flamlize\" a learner using flaml.default.flamlize_estimator for easy dissemination. For example, import sklearn.ensemble as ensemblefrom flaml.default import flamlize_estimatorExtraTreesClassifier = flamlize_estimator( ensemble.ExtraTreesClassifier, \"extra_tree\", \"classification\") Copy Then, you can share this \"flamlized\" ExtraTreesClassifier together with the location of your learned defaults with others (or the future yourself). They will benefit from your past experience. Your group can also share experiences in a central place and update the learned defaults continuously. Over time, your organization gets better collectively.","s":"\"Flamlize\" a learner","u":"/FLAML/docs/Use-Cases/Zero-Shot-AutoML","h":"#flamlize-a-learner","p":583},{"i":608,"t":"On this page","s":"Tune User Defined Function","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"","p":607},{"i":610,"t":"There are three essential steps (assuming the knowledge of the set of hyperparameters to tune) to use flaml.tune to finish a basic tuning task: Specify the tuning objective with respect to the hyperparameters. Specify a search space of the hyperparameters. Specify tuning constraints, including constraints on the resource budget to do the tuning, constraints on the configurations, or/and constraints on a (or multiple) particular metric(s). With these steps, you can perform a basic tuning task accordingly.","s":"Basic Tuning Procedure","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#basic-tuning-procedure","p":607},{"i":612,"t":"Related arguments: evaluation_function: A user-defined evaluation function. metric: A string of the metric name to optimize for. mode: A string in ['min', 'max'] to specify the objective as minimization or maximization. The first step is to specify your tuning objective. To do it, you should first specify your evaluation procedure (e.g., perform a machine learning model training and validation) with respect to the hyperparameters in a user-defined function evaluation_function. The function requires a hyperparameter configuration as input, and can simply return a metric value in a scalar or return a dictionary of metric name and metric value pairs. In the following code, we define an evaluation function with respect to two hyperparameters named x and y according to obj:=(x−85000)2−x/yobj := (x-85000)^2 - x/yobj:=(x−85000)2−x/y. Note that we use this toy example here for more accessible demonstration purposes. In real use cases, the evaluation function usually cannot be written in this closed form, but instead involves a black-box and expensive evaluation procedure. Please check out Tune HuggingFace, Tune PyTorch and Tune LightGBM for real examples of tuning tasks. import timedef evaluate_config(config: dict): \"\"\"evaluate a hyperparameter configuration\"\"\" score = (config[\"x\"] - 85000) ** 2 - config[\"x\"] / config[\"y\"] # usually the evaluation takes an non-neglible cost # and the cost could be related to certain hyperparameters # here we simulate this cost by calling the time.sleep() function # here we assume the cost is proportional to x faked_evaluation_cost = config[\"x\"] / 100000 time.sleep(faked_evaluation_cost) # we can return a single float as a score on the input config: # return score # or, we can return a dictionary that maps metric name to metric value: return { \"score\": score, \"evaluation_cost\": faked_evaluation_cost, \"constraint_metric\": config[\"x\"] * config[\"y\"], } Copy When the evaluation function returns a dictionary of metrics, you need to specify the name of the metric to optimize via the argument metric (this can be skipped when the function is just returning a scalar). In addition, you need to specify a mode of your optimization/tuning task (maximization or minimization) via the argument mode by choosing from \"min\" or \"max\". For example, flaml.tune.run(evaluation_function=evaluate_config, metric=\"score\", mode=\"min\", ...) Copy","s":"Tuning objective","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#tuning-objective","p":607},{"i":614,"t":"Related arguments: config: A dictionary to specify the search space. low_cost_partial_config (optional): A dictionary from a subset of controlled dimensions to the initial low-cost values. cat_hp_cost (optional): A dictionary from a subset of categorical dimensions to the relative cost of each choice. The second step is to specify a search space of the hyperparameters through the argument config. In the search space, you need to specify valid values for your hyperparameters and can specify how these values are sampled (e.g., from a uniform distribution or a log-uniform distribution). In the following code example, we include a search space for the two hyperparameters x and y as introduced above. The valid values for both are integers in the range of [1, 100000]. The values for x are sampled uniformly in the specified range (using tune.randint(lower=1, upper=100000)), and the values for y are sampled uniformly in logarithmic space of the specified range (using tune.lograndit(lower=1, upper=100000)). from flaml import tune# construct a search space for the hyperparameters x and y.config_search_space = { \"x\": tune.lograndint(lower=1, upper=100000), \"y\": tune.randint(lower=1, upper=100000),}# provide the search space to tune.runtune.run(..., config=config_search_space, ...) Copy Details and guidelines on hyperparameter search space​ The corresponding value of a particular hyperparameter in the search space dictionary is called a domain, for example, tune.randint(lower=1, upper=100000) is the domain for the hyperparameter y. The domain specifies a type and valid range to sample parameters from. Supported types include float, integer, and categorical. Categorical hyperparameter If it is a categorical hyperparameter, then you should use tune.choice(possible_choices) in which possible_choices is the list of possible categorical values of the hyperparameter. For example, if you are tuning the optimizer used in model training, and the candidate optimizers are \"sgd\" and \"adam\", you should specify the search space in the following way: { \"optimizer\": tune.choice([\"sgd\", \"adam\"]),} Copy Numerical hyperparameter If it is a numerical hyperparameter, you need to know whether it takes integer values or float values. In addition, you need to know: The range of valid values, i.e., what are the lower limit and upper limit of the hyperparameter value? Do you want to sample in linear scale or log scale? It is a common practice to sample in the log scale if the valid value range is large and the evaluation function changes more regularly with respect to the log domain, as shown in the following example for learning rate tuning. In this code example, we set the lower limit and the upper limit of the learning rate to be 1/1024 and 1.0, respectively. We sample in the log space because model performance changes more regularly in the log scale with respect to the learning rate within such a large search range. { \"learning_rate\": tune.loguniform(lower=1 / 1024, upper=1.0),} Copy When the search range of learning rate is small, it is more common to sample in the linear scale as shown in the following example, { \"learning_rate\": tune.uniform(lower=0.1, upper=0.2),} Copy Do you have quantization granularity requirements? When you have a desired quantization granularity for the hyperparameter change, you can use tune.qlograndint or tune.qloguniform to realize the quantization requirement. The following code example helps you realize the need for sampling uniformly in the range of 0.1 and 0.2 with increments of 0.02, i.e., the sampled learning rate can only take values in {0.1, 0.12, 0.14, 0.16, ..., 0.2}, { \"learning_rate\": tune.quniform(lower=0.1, upper=0.2, q=0.02),} Copy You can find the corresponding search space choice in the table below once you have answers to the aforementioned three questions. Integer Float linear scale tune.randint(lower: int, upper: int) tune.uniform(lower: float, upper: float) log scale tune.lograndint(lower: int, upper: int, base: float = 10 tune.loguniform(lower: float, upper: float, base: float = 10) linear scale with quantization tune.qrandint(lower: int, upper: int, q: int = 1) tune.quniform(lower: float, upper: float, q: float = 1) log scale with quantization tune.qlograndint(lower: int, upper, q: int = 1, base: float = 10) tune.qloguniform(lower: float, upper, q: float = 1, base: float = 10) See the example below for the commonly used types of domains. config = { # Sample a float uniformly between -5.0 and -1.0 \"uniform\": tune.uniform(-5, -1), # Sample a float uniformly between 3.2 and 5.4, # rounding to increments of 0.2 \"quniform\": tune.quniform(3.2, 5.4, 0.2), # Sample a float uniformly between 0.0001 and 0.01, while # sampling in log space \"loguniform\": tune.loguniform(1e-4, 1e-2), # Sample a float uniformly between 0.0001 and 0.1, while # sampling in log space and rounding to increments of 0.00005 \"qloguniform\": tune.qloguniform(1e-4, 1e-1, 5e-5), # Sample a random float from a normal distribution with # mean=10 and sd=2 \"randn\": tune.randn(10, 2), # Sample a random float from a normal distribution with # mean=10 and sd=2, rounding to increments of 0.2 \"qrandn\": tune.qrandn(10, 2, 0.2), # Sample a integer uniformly between -9 (inclusive) and 15 (exclusive) \"randint\": tune.randint(-9, 15), # Sample a random uniformly between -21 (inclusive) and 12 (inclusive (!)) # rounding to increments of 3 (includes 12) \"qrandint\": tune.qrandint(-21, 12, 3), # Sample a integer uniformly between 1 (inclusive) and 10 (exclusive), # while sampling in log space \"lograndint\": tune.lograndint(1, 10), # Sample a integer uniformly between 2 (inclusive) and 10 (inclusive (!)), # while sampling in log space and rounding to increments of 2 \"qlograndint\": tune.qlograndint(2, 10, 2), # Sample an option uniformly from the specified choices \"choice\": tune.choice([\"a\", \"b\", \"c\"]),} Copy Cost-related hyperparameters​ Cost-related hyperparameters are a subset of the hyperparameters which directly affect the computation cost incurred in the evaluation of any hyperparameter configuration. For example, the number of estimators (n_estimators) and the maximum number of leaves (max_leaves) are known to affect the training cost of tree-based learners. So they are cost-related hyperparameters for tree-based learners. When cost-related hyperparameters exist, the evaluation cost in the search space is heterogeneous. In this case, designing a search space with proper ranges of the hyperparameter values is highly non-trivial. Classical tuning algorithms such as Bayesian optimization and random search are typically sensitive to such ranges. It may take them a very high cost to find a good choice if the ranges are too large. And if the ranges are too small, the optimal choice(s) may not be included and thus not possible to be found. With our method, you can use a search space with larger ranges in the case of heterogeneous cost. Our search algorithms are designed to finish the tuning process at a low total cost when the evaluation cost in the search space is heterogeneous. So in such scenarios, if you are aware of low-cost configurations for the cost-related hyperparameters, you are encouraged to set them as the low_cost_partial_config, which is a dictionary of a subset of the hyperparameter coordinates whose value corresponds to a configuration with known low cost. Using the example of the tree-based methods again, since we know that small n_estimators and max_leaves generally correspond to simpler models and thus lower cost, we set {'n_estimators': 4, 'max_leaves': 4} as the low_cost_partial_config by default (note that 4 is the lower bound of search space for these two hyperparameters), e.g., in LGBM. Please find more details on how the algorithm works here. In addition, if you are aware of the cost relationship between different categorical hyperparameter choices, you are encouraged to provide this information through cat_hp_cost. It also helps the search algorithm to reduce the total cost.","s":"Search space","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#search-space","p":607},{"i":616,"t":"Related arguments: time_budget_s: The time budget in seconds. num_samples: An integer of the number of configs to try. config_constraints (optional): A list of config constraints to be satisfied. metric_constraints (optional): A list of metric constraints to be satisfied. e.g., ['precision', '>=', 0.9]. The third step is to specify constraints of the tuning task. One notable property of flaml.tune is that it is able to finish the tuning process (obtaining good results) within a required resource constraint. A user can either provide the resource constraint in terms of wall-clock time (in seconds) through the argument time_budget_s, or in terms of the number of trials through the argument num_samples. The following example shows three use cases: # Set a resource constraint of 60 seconds wall-clock time for the tuning.flaml.tune.run(..., time_budget_s=60, ...)# Set a resource constraint of 100 trials for the tuning.flaml.tune.run(..., num_samples=100, ...)# Use at most 60 seconds and at most 100 trials for the tuning.flaml.tune.run(..., time_budget_s=60, num_samples=100, ...) Copy Optionally, you can provide a list of config constraints to be satisfied through the argument config_constraints and provide a list of metric constraints to be satisfied through the argument metric_constraints. We provide more details about related use cases in the Advanced Tuning Options section.","s":"Tuning constraints","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#tuning-constraints","p":607},{"i":618,"t":"After the aforementioned key steps, one is ready to perform a tuning task by calling flaml.tune.run(). Below is a quick sequential tuning example using the pre-defined search space config_search_space and a minimization (mode='min') objective for the score metric evaluated in evaluate_config, using the default serach algorithm in flaml. The time budget is 10 seconds (time_budget_s=10). # require: pip install flaml[blendsearch]analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds) Copy","s":"Put together","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#put-together","p":607},{"i":620,"t":"Once the tuning process finishes, it returns an ExperimentAnalysis object, which provides methods to analyze the tuning. In the following code example, we retrieve the best configuration found during the tuning, and retrieve the best trial's result from the returned analysis. analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds)print(analysis.best_config) # the best configprint(analysis.best_trial.last_result) # the best trial's result Copy","s":"Result analysis","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#result-analysis","p":607},{"i":622,"t":"There are several advanced tuning options worth mentioning.","s":"Advanced Tuning Options","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#advanced-tuning-options","p":607},{"i":624,"t":"A user can specify constraints on the configurations to be satisfied via the argument config_constraints. The config_constraints receives a list of such constraints to be satisfied. Specifically, each constraint is a tuple that consists of (1) a function that takes a configuration as input and returns a numerical value; (2) an operation chosen from \"\\<=\", \">=\", \"\\<\" or \">\"; (3) a numerical threshold. In the following code example, we constrain the output of area, which takes a configuration as input and outputs a numerical value, to be no larger than 1000. def my_model_size(config): return config[\"n_estimators\"] * config[\"max_leaves\"]analysis = tune.run( ..., config_constraints=[(my_model_size, \"<=\", 40)],) Copy You can also specify a list of metric constraints to be satisfied via the argument metric_constraints. Each element in the metric_constraints list is a tuple that consists of (1) a string specifying the name of the metric (the metric name must be defined and returned in the user-defined evaluation_function); (2) an operation chosen from \"\\<=\" or \">=\"; (3) a numerical threshold. In the following code example, we constrain the metric training_cost to be no larger than 1 second. analysis = (tune.run(..., metric_constraints=[(\"training_cost\", \"<=\", 1)]),) Copy config_constraints vs metric_constraints:​ The key difference between these two types of constraints is that the calculation of constraints in config_constraints does not rely on the computation procedure in the evaluation function, i.e., in evaluation_function. For example, when a constraint only depends on the config itself, as shown in the code example. Due to this independency, constraints in config_constraints will be checked before evaluation. So configurations that do not satisfy config_constraints will not be evaluated.","s":"More constraints on the tuning","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#more-constraints-on-the-tuning","p":607},{"i":626,"t":"Related arguments: use_ray: A boolean of whether to use ray as the backend. use_spark: A boolean of whether to use spark as the backend. resources_per_trial: A dictionary of the hardware resources to allocate per trial, e.g., {'cpu': 1}. Only valid when using ray backend. Details about parallel tuning with Spark could be found here. You can perform parallel tuning by specifying use_ray=True (requiring flaml[ray] option installed) or use_spark=True (requiring flaml[spark] option installed). You can also limit the amount of resources allocated per trial by specifying resources_per_trial, e.g., resources_per_trial={'cpu': 2} when use_ray=True. # require: pip install flaml[ray]analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds use_ray=True, resources_per_trial={\"cpu\": 2}, # limit resources allocated per trial)print(analysis.best_trial.last_result) # the best trial's resultprint(analysis.best_config) # the best config Copy # require: pip install flaml[spark]analysis = tune.run( evaluate_config, # the function to evaluate a config config=config_search_space, # the search space defined metric=\"score\", mode=\"min\", # the optimization mode, \"min\" or \"max\" num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=10, # the time budget in seconds use_spark=True,)print(analysis.best_trial.last_result) # the best trial's resultprint(analysis.best_config) # the best config Copy A headsup about computation overhead. When parallel tuning is used, there will be a certain amount of computation overhead in each trial. In case each trial's original cost is much smaller than the overhead, parallel tuning can underperform sequential tuning. Sequential tuning is recommended when compute resource is limited, and each trial can consume all the resources.","s":"Parallel tuning","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#parallel-tuning","p":607},{"i":628,"t":"Related arguments: scheduler: A scheduler for executing the trials. resource_attr: A string to specify the resource dimension used by the scheduler. min_resource: A float of the minimal resource to use for the resource_attr. max_resource: A float of the maximal resource to use for the resource_attr. reduction_factor: A float of the reduction factor used for incremental pruning. A scheduler can help manage the trials' execution. It can be used to perform multi-fiedlity evalution, or/and early stopping. You can use two different types of schedulers in flaml.tune via scheduler. 1. An authentic scheduler implemented in FLAML (scheduler='flaml').​ This scheduler is authentic to the new search algorithms provided by FLAML. In a nutshell, it starts the search with the minimum resource. It switches between HPO with the current resource and increasing the resource for evaluation depending on which leads to faster improvement. If this scheduler is used, you need to Specify a resource dimension. Conceptually a 'resource dimension' is a factor that affects the cost of the evaluation (e.g., sample size, the number of epochs). You need to specify the name of the resource dimension via resource_attr. For example, if resource_attr=\"sample_size\", then the config dict passed to the evaluation_function would contain a key \"sample_size\" and its value suggested by the search algorithm. That value should be used in the evaluation function to control the compute cost. The larger is the value, the more expensive the evaluation is. Provide the lower and upper limit of the resource dimension via min_resource and max_resource, and optionally provide reduction_factor, which determines the magnitude of resource (multiplicative) increase when we decide to increase the resource. In the following code example, we consider the sample size as the resource dimension. It determines how much data is used to perform training as reflected in the evaluation_function. We set the min_resource and max_resource to 1000 and the size of the full training dataset, respectively. from flaml import tunefrom functools import partialfrom flaml.automl.data import load_openml_taskdef obj_from_resource_attr(resource_attr, X_train, X_test, y_train, y_test, config): from lightgbm import LGBMClassifier from sklearn.metrics import accuracy_score # in this example sample size is our resource dimension resource = int(config[resource_attr]) sampled_X_train = X_train.iloc[:resource] sampled_y_train = y_train[:resource] # construct a LGBM model from the config # note that you need to first remove the resource_attr field # from the config as it is not part of the original search space model_config = config.copy() del model_config[resource_attr] model = LGBMClassifier(**model_config) model.fit(sampled_X_train, sampled_y_train) y_test_predict = model.predict(X_test) test_loss = 1.0 - accuracy_score(y_test, y_test_predict) return {resource_attr: resource, \"loss\": test_loss}X_train, X_test, y_train, y_test = load_openml_task(task_id=7592, data_dir=\"test/\")max_resource = len(y_train)resource_attr = \"sample_size\"min_resource = 1000analysis = tune.run( partial(obj_from_resource_attr, resource_attr, X_train, X_test, y_train, y_test), config={ \"n_estimators\": tune.lograndint(lower=4, upper=32768), \"max_leaves\": tune.lograndint(lower=4, upper=32768), \"learning_rate\": tune.loguniform(lower=1 / 1024, upper=1.0), }, metric=\"loss\", mode=\"min\", resource_attr=resource_attr, scheduler=\"flaml\", max_resource=max_resource, min_resource=min_resource, reduction_factor=2, time_budget_s=10, num_samples=-1,) Copy You can find more details about this scheduler in this paper. 2. A scheduler of the TrialScheduler class from ray.tune.​ There is a handful of schedulers of this type implemented in ray.tune, for example, ASHA, HyperBand, BOHB, etc. To use this type of scheduler you can either (1) set scheduler='asha', which will automatically create an ASHAScheduler instance using the provided inputs (resource_attr, min_resource, max_resource, and reduction_factor); or (2) create an instance by yourself and provided it via scheduler, as shown in the following code example, # require: pip install flaml[ray]from ray.tune.schedulers import HyperBandSchedulermy_scheduler = HyperBandScheduler(time_attr=\"sample_size\", max_t=max_resource, reduction_factor=2)tune.run(.., scheduler=my_scheduler, ...) Copy Similar to the case where the flaml scheduler is used, you need to specify the resource dimension, use the resource dimension accordingly in your evaluation_function, and provide the necessary information needed for scheduling, such as min_resource, max_resource and reduction_factor (depending on the requirements of the specific scheduler). Different from the case when the flaml scheduler is used, the amount of resources to use at each iteration is not suggested by the search algorithm through the resource_attr in a configuration. You need to specify the evaluation schedule explicitly by yourself in the evaluation_function and report intermediate results (using tune.report()) accordingly. In the following code example, we use the ASHA scheduler by setting scheduler=\"asha\". We specify resource_attr, min_resource, min_resource and reduction_factor the same way as in the previous example (when \"flaml\" is used as the scheduler). We perform the evaluation in a customized schedule. Use ray backend or not? You can choose to use ray backend or not by specifying use_ray=True or use_ray=False. When ray backend is not used, i.e., use_ray=False, you also need to stop the evaluation function by explicitly catching the StopIteration exception, as shown in the end of the evaluation function obj_w_intermediate_report() in the following code example. def obj_w_intermediate_report( resource_attr, X_train, X_test, y_train, y_test, min_resource, max_resource, config): from lightgbm import LGBMClassifier from sklearn.metrics import accuracy_score # a customized schedule to perform the evaluation eval_schedule = [res for res in range(min_resource, max_resource, 5000)] + [ max_resource ] for resource in eval_schedule: sampled_X_train = X_train.iloc[:resource] sampled_y_train = y_train[:resource] # construct a LGBM model from the config model = LGBMClassifier(**config) model.fit(sampled_X_train, sampled_y_train) y_test_predict = model.predict(X_test) test_loss = 1.0 - accuracy_score(y_test, y_test_predict) # need to report the resource attribute used and the corresponding intermediate results try: tune.report(sample_size=resource, loss=test_loss) except (StopIteration, SystemExit): # do cleanup operation here returnresource_attr = \"sample_size\"min_resource = 1000max_resource = len(y_train)analysis = tune.run( partial( obj_w_intermediate_report, resource_attr, X_train, X_test, y_train, y_test, min_resource, max_resource, ), config={ \"n_estimators\": tune.lograndint(lower=4, upper=32768), \"learning_rate\": tune.loguniform(lower=1 / 1024, upper=1.0), }, metric=\"loss\", mode=\"min\", resource_attr=resource_attr, scheduler=\"asha\", max_resource=max_resource, min_resource=min_resource, reduction_factor=2, time_budget_s=10, num_samples=-1,) Copy If you would like to do some cleanup opearation when the trial is stopped by the scheduler, you can do it when you catch the StopIteration (when not using ray) or SystemExit (when using ray) exception explicitly.","s":"Trial scheduling","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#trial-scheduling","p":607},{"i":630,"t":"Related arguments: points_to_evaluate: A list of initial hyperparameter configurations to run first. evaluated_rewards: If you have previously evaluated the parameters passed in as points_to_evaluate , you can avoid re-running those trials by passing in the reward attributes as a list so the optimizer can be told the results without needing to re-compute the trial. Must be the same length or shorter length than points_to_evaluate. If you are aware of some good hyperparameter configurations, you are encouraged to provide them via points_to_evaluate. The search algorithm will try them first and use them to bootstrap the search. You can use previously evaluated configurations to warm-start your tuning. For example, the following code means that you know the reward for the two configs in points_to_evaluate are 3.99 and 1.99, respectively, and want to inform tune.run(). def simple_obj(config): return config[\"a\"] + config[\"b\"]from flaml import tuneconfig_search_space = { \"a\": tune.uniform(lower=0, upper=0.99), \"b\": tune.uniform(lower=0, upper=3),}points_to_evaluate = [ {\"b\": 0.99, \"a\": 3}, {\"b\": 0.99, \"a\": 2}, {\"b\": 0.80, \"a\": 3}, {\"b\": 0.80, \"a\": 2},]evaluated_rewards = [3.99, 2.99]analysis = tune.run( simple_obj, config=config_search_space, mode=\"max\", points_to_evaluate=points_to_evaluate, evaluated_rewards=evaluated_rewards, time_budget_s=10, num_samples=-1,) Copy","s":"Warm start","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#warm-start","p":607},{"i":632,"t":"By default, there is randomness in our tuning process (for versions \\<= 0.9.1). If reproducibility is desired, you could manually set a random seed before calling tune.run(). For example, in the following code, we call np.random.seed(100) to set the random seed. With this random seed, running the following code multiple times will generate exactly the same search trajectory. The reproducibility can only be guaranteed in sequential tuning. import numpy as npnp.random.seed(100) # This line is not needed starting from version v0.9.2.analysis = tune.run( simple_obj, config=config_search_space, mode=\"max\", num_samples=10,) Copy","s":"Reproducibility","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#reproducibility","p":607},{"i":634,"t":"We support tuning multiple objectives with lexicographic preference by providing argument lexico_objectives for tune.run(). lexico_objectives is a dictionary that contains the following fields of key-value pairs: metrics: a list of optimization objectives with the orders reflecting the priorities/preferences of the objectives. modes: (optional) a list of optimization modes (each mode either \"min\" or \"max\") corresponding to the objectives in the metric list. If not provided, we use \"min\" as the default mode for all the objectives. tolerances: (optional) a dictionary to specify the optimality tolerances on objectives. The keys are the metric names (provided in \"metrics\"), and the values are the absolute/percentage tolerance in the form of numeric/string. targets: (optional) a dictionary to specify the optimization targets on the objectives. The keys are the metric names (provided in \"metric\"), and the values are the numerical target values. In the following example, we want to minimize val_loss and pred_time of the model where val_loss has high priority. The tolerances for val_loss and pre_time are 0.02 and 0 respectively. We do not set targets for these two objectives and we set them to -inf for both objectives. lexico_objectives = {}lexico_objectives[\"metrics\"] = [\"val_loss\", \"pred_time\"]lexico_objectives[\"modes\"] = [\"min\", \"min\"]lexico_objectives[\"tolerances\"] = {\"val_loss\": 0.02, \"pred_time\": 0.0}lexico_objectives[\"targets\"] = {\"val_loss\": -float(\"inf\"), \"pred_time\": -float(\"inf\")}# provide the lexico_objectives to tune.runtune.run(..., search_alg=None, lexico_objectives=lexico_objectives) Copy We also supports providing percentage tolerance as shown below. lexico_objectives[\"tolerances\"] = {\"val_loss\": \"10%\", \"pred_time\": \"0%\"} Copy NOTE: When lexico_objectives is not None, the arguments metric, mode, will be invalid, and flaml's tune uses CFO as the search_alg, which makes the input (if provided) search_alg invalid. This is a new feature that will be released in version 1.1.0 and is subject to change in the future version.","s":"Lexicographic Objectives","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#lexicographic-objectives","p":607},{"i":636,"t":"To tune the hyperparameters toward your objective, you will want to use a hyperparameter optimization algorithm which can help suggest hyperparameters with better performance (regarding your objective). flaml offers two HPO methods: CFO and BlendSearch. flaml.tune uses BlendSearch by default when the option [blendsearch] is installed.","s":"Hyperparameter Optimization Algorithm","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#hyperparameter-optimization-algorithm","p":607},{"i":638,"t":"CFO uses the randomized direct search method FLOW2 with adaptive stepsize and random restart. It requires a low-cost initial point as input if such point exists. The search begins with the low-cost initial point and gradually move to high cost region if needed. The local search method has a provable convergence rate and bounded cost. About FLOW2: FLOW2 is a simple yet effective randomized direct search method. It is an iterative optimization method that can optimize for black-box functions. FLOW2 only requires pairwise comparisons between function values to perform iterative update. Comparing to existing HPO methods, FLOW2 has the following appealing properties: It is applicable to general black-box functions with a good convergence rate in terms of loss. It provides theoretical guarantees on the total evaluation cost incurred. The GIFs attached below demonstrate an example search trajectory of FLOW2 shown in the loss and evaluation cost (i.e., the training time ) space respectively. FLOW2 is used in tuning the # of leaves and the # of trees for XGBoost. The two background heatmaps show the loss and cost distribution of all configurations. The black dots are the points evaluated in FLOW2. Black dots connected by lines are points that yield better loss performance when evaluated. From the demonstration, we can see that (1) FLOW2 can quickly move toward the low-loss region, showing good convergence property and (2) FLOW2 tends to avoid exploring the high-cost region until necessary. Example: from flaml import CFOtune.run(... search_alg=CFO(low_cost_partial_config=low_cost_partial_config),) Copy Recommended scenario: There exist cost-related hyperparameters and a low-cost initial point is known before optimization. If the search space is complex and CFO gets trapped into local optima, consider using BlendSearch.","s":"CFO: Frugal Optimization for Cost-related Hyperparameters","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#cfo-frugal-optimization-for-cost-related-hyperparameters","p":607},{"i":640,"t":"BlendSearch combines local search with global search. It leverages the frugality of CFO and the space exploration ability of global search methods such as Bayesian optimization. Like CFO, BlendSearch requires a low-cost initial point as input if such point exists, and starts the search from there. Different from CFO, BlendSearch will not wait for the local search to fully converge before trying new start points. The new start points are suggested by the global search method and filtered based on their distance to the existing points in the cost-related dimensions. BlendSearch still gradually increases the trial cost. It prioritizes among the global search thread and multiple local search threads based on optimism in face of uncertainty. Example: # require: pip install flaml[blendsearch]from flaml import BlendSearchtune.run(... search_alg=BlendSearch(low_cost_partial_config=low_cost_partial_config),) Copy Recommended scenario: Cost-related hyperparameters exist, a low-cost initial point is known, and the search space is complex such that local search is prone to be stuck at local optima. Suggestion about using larger search space in BlendSearch. In hyperparameter optimization, a larger search space is desirable because it is more likely to include the optimal configuration (or one of the optimal configurations) in hindsight. However the performance (especially anytime performance) of most existing HPO methods is undesirable if the cost of the configurations in the search space has a large variation. Thus hand-crafted small search spaces (with relatively homogeneous cost) are often used in practice for these methods, which is subject to idiosyncrasy. BlendSearch combines the benefits of local search and global search, which enables a smart (economical) way of deciding where to explore in the search space even though it is larger than necessary. This allows users to specify a larger search space in BlendSearch, which is often easier and a better practice than narrowing down the search space by hand. For more technical details, please check our papers. Frugal Optimization for Cost-related Hyperparameters. Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021. @inproceedings{wu2021cfo, title={Frugal Optimization for Cost-related Hyperparameters}, author={Qingyun Wu and Chi Wang and Silu Huang}, year={2021}, booktitle={AAAI'21},} Copy Economical Hyperparameter Optimization With Blended Search Strategy. Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021. @inproceedings{wang2021blendsearch, title={Economical Hyperparameter Optimization With Blended Search Strategy}, author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied}, year={2021}, booktitle={ICLR'21},} Copy Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives. Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%). @inproceedings{zhang2023targeted, title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives}, author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu}, booktitle={International Conference on Learning Representations}, year={2023}, url={https://openreview.net/forum?id=0Ij9_q567Ma}} Copy","s":"BlendSearch: Economical Hyperparameter Optimization With Blended Search Strategy","u":"/FLAML/docs/Use-Cases/Tune-User-Defined-Function","h":"#blendsearch-economical-hyperparameter-optimization-with-blended-search-strategy","p":607},{"i":642,"t":"On this page","s":"Task Oriented AutoML","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"","p":641},{"i":644,"t":"flaml.AutoML is a class for task-oriented AutoML. It can be used as a scikit-learn style estimator with the standard fit and predict functions. The minimal inputs from users are the training data and the task type. Training data: numpy array. When the input data are stored in numpy array, they are passed to fit() as X_train and y_train. pandas dataframe. When the input data are stored in pandas dataframe, they are passed to fit() either as X_train and y_train, or as dataframe and label. Tasks (specified via task): 'classification': classification with tabular data. 'regression': regression with tabular data. 'ts_forecast': time series forecasting. 'ts_forecast_classification': time series forecasting for classification. 'ts_forecast_panel': time series forecasting for panel datasets (multiple time series). 'rank': learning to rank. 'seq-classification': sequence classification. 'seq-regression': sequence regression. 'summarization': text summarization. 'token-classification': token classification. 'multichoice-classification': multichoice classification. Two optional inputs are time_budget and max_iter for searching models and hyperparameters. When both are unspecified, only one model per estimator will be trained (using our zero-shot technique). When time_budget is provided, there can be randomness in the result due to runtime variance. A typical way to use flaml.AutoML: # Prepare training data# ...from flaml import AutoMLautoml = AutoML()automl.fit(X_train, y_train, task=\"regression\", time_budget=60, **other_settings)# Save the modelwith open(\"automl.pkl\", \"wb\") as f: pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)# At prediction timewith open(\"automl.pkl\", \"rb\") as f: automl = pickle.load(f)pred = automl.predict(X_test) Copy If users provide the minimal inputs only, AutoML uses the default settings for optimization metric, estimator list etc.","s":"Overview","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#overview","p":641},{"i":647,"t":"The optimization metric is specified via the metric argument. It can be either a string which refers to a built-in metric, or a user-defined function. Built-in metric. 'accuracy': 1 - accuracy as the corresponding metric to minimize. 'log_loss': default metric for multiclass classification. 'r2': 1 - r2_score as the corresponding metric to minimize. Default metric for regression. 'rmse': root mean squared error. 'mse': mean squared error. 'mae': mean absolute error. 'mape': mean absolute percentage error. 'roc_auc': minimize 1 - roc_auc_score. Default metric for binary classification. 'roc_auc_ovr': minimize 1 - roc_auc_score with multi_class=\"ovr\". 'roc_auc_ovo': minimize 1 - roc_auc_score with multi_class=\"ovo\". 'roc_auc_weighted': minimize 1 - roc_auc_score with average=\"weighted\". 'roc_auc_ovr_weighted': minimize 1 - roc_auc_score with multi_class=\"ovr\" and average=\"weighted\". 'roc_auc_ovo_weighted': minimize 1 - roc_auc_score with multi_class=\"ovo\" and average=\"weighted\". 'f1': minimize 1 - f1_score. 'micro_f1': minimize 1 - f1_score with average=\"micro\". 'macro_f1': minimize 1 - f1_score with average=\"macro\". 'ap': minimize 1 - average_precision_score. 'ndcg': minimize 1 - ndcg_score. 'ndcg@k': minimize 1 - ndcg_score@k. k is an integer. User-defined function. A customized metric function that requires the following (input) signature, and returns the input config’s value in terms of the metric you want to minimize, and a dictionary of auxiliary information at your choice: def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, config=None, groups_val=None, groups_train=None,): return metric_to_minimize, metrics_to_log Copy For example, def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args,): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { \"val_loss\": val_loss, \"train_loss\": train_loss, \"pred_time\": pred_time, } Copy It returns the validation loss penalized by the gap between validation and training loss as the metric to minimize, and three metrics to log: val_loss, train_loss and pred_time. The arguments config, groups_val and groups_train are not used in the function.","s":"Optimization metric","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#optimization-metric","p":641},{"i":649,"t":"The estimator list can contain one or more estimator names, each corresponding to a built-in estimator or a custom estimator. Each estimator has a search space for hyperparameter configurations. FLAML supports both classical machine learning models and deep neural networks. Estimator​ Built-in estimator. 'lgbm': LGBMEstimator for task \"classification\", \"regression\", \"rank\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, num_leaves, min_child_samples, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), colsample_bytree, reg_alpha, reg_lambda. 'xgboost': XGBoostSkLearnEstimator for task \"classification\", \"regression\", \"rank\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_leaves, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda. 'xgb_limitdepth': XGBoostLimitDepthEstimator for task \"classification\", \"regression\", \"rank\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_depth, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda. 'rf': RandomForestEstimator for task \"classification\", \"regression\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only). Starting from v1.1.0, it uses a fixed random_state by default. 'extra_tree': ExtraTreesEstimator for task \"classification\", \"regression\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only). Starting from v1.1.0, it uses a fixed random_state by default. 'histgb': HistGradientBoostingEstimator for task \"classification\", \"regression\", \"ts_forecast\" and \"ts_forecast_classification\". Hyperparameters: n_estimators, max_leaves, min_samples_leaf, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), l2_regularization. It uses a fixed random_state by default. 'lrl1': LRL1Classifier (sklearn.LogisticRegression with L1 regularization) for task \"classification\". Hyperparameters: C. 'lrl2': LRL2Classifier (sklearn.LogisticRegression with L2 regularization) for task \"classification\". Hyperparameters: C. 'catboost': CatBoostEstimator for task \"classification\" and \"regression\". Hyperparameters: early_stopping_rounds, learning_rate, n_estimators. 'kneighbor': KNeighborsEstimator for task \"classification\" and \"regression\". Hyperparameters: n_neighbors. 'prophet': Prophet for task \"ts_forecast\". Hyperparameters: changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode. 'arima': ARIMA for task \"ts_forecast\". Hyperparameters: p, d, q. 'sarimax': SARIMAX for task \"ts_forecast\". Hyperparameters: p, d, q, P, D, Q, s. 'holt-winters': Holt-Winters (triple exponential smoothing) model for task \"ts_forecast\". Hyperparameters: seasonal_perdiods, seasonal, use_boxcox, trend, damped_trend. 'transformer': Huggingface transformer models for task \"seq-classification\", \"seq-regression\", \"multichoice-classification\", \"token-classification\" and \"summarization\". Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed. 'temporal_fusion_transformer': TemporalFusionTransformerEstimator for task \"ts_forecast_panel\". Hyperparameters: gradient_clip_val, hidden_size, hidden_continuous_size, attention_head_size, dropout, learning_rate. There is a known issue with pytorch-forecast logging. Custom estimator. Use custom estimator for: tuning an estimator that is not built-in; customizing search space for a built-in estimator. Guidelines on tuning a custom estimator​ To tune a custom estimator that is not built-in, you need to: Build a custom estimator by inheritting flaml.automl.model.BaseEstimator or a derived class. For example, if you have a estimator class with scikit-learn style fit() and predict() functions, you only need to set self.estimator_class to be that class in your constructor. from flaml.automl.model import SKLearnEstimator# SKLearnEstimator is derived from BaseEstimatorimport rgfclass MyRegularizedGreedyForest(SKLearnEstimator): def __init__(self, task=\"binary\", **config): super().__init__(task, **config) if task in CLASSIFICATION: from rgf.sklearn import RGFClassifier self.estimator_class = RGFClassifier else: from rgf.sklearn import RGFRegressor self.estimator_class = RGFRegressor @classmethod def search_space(cls, data_size, task): space = { \"max_leaf\": { \"domain\": tune.lograndint(lower=4, upper=data_size), \"low_cost_init_value\": 4, }, \"n_iter\": { \"domain\": tune.lograndint(lower=1, upper=data_size), \"low_cost_init_value\": 1, }, \"learning_rate\": {\"domain\": tune.loguniform(lower=0.01, upper=20.0)}, \"min_samples_leaf\": { \"domain\": tune.lograndint(lower=1, upper=20), \"init_value\": 20, }, } return space Copy In the constructor, we set self.estimator_class as RGFClassifier or RGFRegressor according to the task type. If the estimator you want to tune does not have a scikit-learn style fit() and predict() API, you can override the fit() and predict() function of flaml.automl.model.BaseEstimator, like XGBoostEstimator. Importantly, we also add the task=\"binary\" parameter in the signature of __init__ so that it doesn't get grouped together with the **config kwargs that determines the parameters with which the underlying estimator (self.estimator_class) is constructed. If your estimator doesn't use one of the parameters that it is passed, for example some regressors in scikit-learn don't use the n_jobs parameter, it is enough to add n_jobs=None to the signature so that it is ignored by the **config dict. Give the custom estimator a name and add it in AutoML. E.g., from flaml import AutoMLautoml = AutoML()automl.add_learner(\"rgf\", MyRegularizedGreedyForest) Copy This registers the MyRegularizedGreedyForest class in AutoML, with the name \"rgf\". Tune the newly added custom estimator in either of the following two ways depending on your needs: tune rgf alone: automl.fit(..., estimator_list=[\"rgf\"]); or mix it with other built-in learners: automl.fit(..., estimator_list=[\"rgf\", \"lgbm\", \"xgboost\", \"rf\"]). Search space​ Each estimator class, built-in or not, must have a search_space function. In the search_space function, we return a dictionary about the hyperparameters, the keys of which are the names of the hyperparameters to tune, and each value is a set of detailed search configurations about the corresponding hyperparameters represented in a dictionary. A search configuration dictionary includes the following fields: domain, which specifies the possible values of the hyperparameter and their distribution. Please refer to more details about the search space domain. init_value (optional), which specifies the initial value of the hyperparameter. low_cost_init_value(optional), which specifies the value of the hyperparameter that is associated with low computation cost. See cost related hyperparameters or FAQ for more details. In the example above, we tune four hyperparameters, three integers and one float. They all follow a log-uniform distribution. \"max_leaf\" and \"n_iter\" have \"low_cost_init_value\" specified as their values heavily influence the training cost. To customize the search space for a built-in estimator, use a similar approach to define a class that inherits the existing estimator. For example, from flaml.automl.model import XGBoostEstimatordef logregobj(preds, dtrain): labels = dtrain.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight grad = preds - labels hess = preds * (1.0 - preds) return grad, hessclass MyXGB1(XGBoostEstimator): \"\"\"XGBoostEstimator with logregobj as the objective function\"\"\" def __init__(self, **config): super().__init__(objective=logregobj, **config) Copy We override the constructor and set the training objective as a custom function logregobj. The hyperparameters and their search range do not change. For another example, class XGBoost2D(XGBoostSklearnEstimator): @classmethod def search_space(cls, data_size, task): upper = min(32768, int(data_size)) return { \"n_estimators\": { \"domain\": tune.lograndint(lower=4, upper=upper), \"low_cost_init_value\": 4, }, \"max_leaves\": { \"domain\": tune.lograndint(lower=4, upper=upper), \"low_cost_init_value\": 4, }, } Copy We override the search_space function to tune two hyperparameters only, \"n_estimators\" and \"max_leaves\". They are both random integers in the log space, ranging from 4 to data-dependent upper bound. The lower bound for each corresponds to low training cost, hence the \"low_cost_init_value\" for each is set to 4. A shortcut to override the search space​ One can use the custom_hp argument in AutoML.fit() to override the search space for an existing estimator quickly. For example, if you would like to temporarily change the search range of \"n_estimators\" of xgboost, disable searching \"max_leaves\" in random forest, and add \"subsample\" in the search space of lightgbm, you can set: custom_hp = { \"xgboost\": { \"n_estimators\": { \"domain\": tune.lograndint(lower=new_lower, upper=new_upper), \"low_cost_init_value\": new_lower, }, }, \"rf\": { \"max_leaves\": { \"domain\": None, # disable search }, }, \"lgbm\": { \"subsample\": { \"domain\": tune.uniform(lower=0.1, upper=1.0), \"init_value\": 1.0, }, \"subsample_freq\": { \"domain\": 1, # subsample_freq must > 0 to enable subsample }, },} Copy","s":"Estimator and search space","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#estimator-and-search-space","p":641},{"i":651,"t":"There are several types of constraints you can impose. Constraints on the AutoML process. time_budget: constrains the wall-clock time (seconds) used by the AutoML process. We provide some tips on how to set time budget. max_iter: constrains the maximal number of models to try in the AutoML process. Constraints on the constructor arguments of the estimators. Some constraints on the estimator can be implemented via the custom learner. For example, class MonotonicXGBoostEstimator(XGBoostSklearnEstimator): @classmethod def search_space(**args): space = super().search_space(**args) space.update({\"monotone_constraints\": {\"domain\": \"(1, -1)\"}}) return space Copy It adds a monotonicity constraint to XGBoost. This approach can be used to set any constraint that is an argument in the underlying estimator's constructor. A shortcut to do this is to use the custom_hp argument: custom_hp = { \"xgboost\": { \"monotone_constraints\": {\"domain\": \"(1, -1)\"} # fix the domain as a constant }} Copy Constraints on the models tried in AutoML. Users can set constraints such as the maximal number of models to try, limit on training time and prediction time per model. train_time_limit: training time in seconds. pred_time_limit: prediction time per instance in seconds. For example, automl.fit(X_train, y_train, max_iter=100, train_time_limit=1, pred_time_limit=1e-3) Copy Constraints on the metrics of the ML model tried in AutoML. When users provide a custom metric function, which returns a primary optimization metric and a dictionary of additional metrics (typically also about the model) to log, users can also specify constraints on one or more of the metrics in the dictionary of additional metrics. Users need to provide a list of such constraints in the following format: Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from \">=\" and \"\\<=\", and the third element is the constraint value. E.g., ('val_loss', '<=', 0.1). For example, metric_constraints = [(\"train_loss\", \"<=\", 0.1), (\"val_loss\", \"<=\", 0.1)]automl.fit( X_train, y_train, max_iter=100, train_time_limit=1, metric_constraints=metric_constraints,) Copy","s":"Constraint","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#constraint","p":641},{"i":653,"t":"To use stacked ensemble after the model search, set ensemble=True or a dict. When ensemble=True, the final estimator and passthrough in the stacker will be automatically chosen. You can specify customized final estimator or passthrough option: \"final_estimator\": an instance of the final estimator in the stacker. \"passthrough\": True (default) or False, whether to pass the original features to the stacker. For example, automl.fit( X_train, y_train, task=\"classification\", \"ensemble\": { \"final_estimator\": LogisticRegression(), \"passthrough\": False, },) Copy","s":"Ensemble","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#ensemble","p":641},{"i":655,"t":"By default, flaml decides the resampling automatically according to the data size and the time budget. If you would like to enforce a certain resampling strategy, you can set eval_method to be \"holdout\" or \"cv\" for holdout or cross-validation. For holdout, you can also set: split_ratio: the fraction for validation data, 0.1 by default. X_val, y_val: a separate validation dataset. When they are passed, the validation metrics will be computed against this given validation dataset. If they are not passed, then a validation dataset will be split from the training data and held out from training during the model search. After the model search, flaml will retrain the model with best configuration on the full training data. You can setretrain_full to be False to skip the final retraining or \"budget\" to ask flaml to do its best to retrain within the time budget. For cross validation, you can also set n_splits of the number of folds. By default it is 5. Data split method​ flaml relies on the provided task type to infer the default splitting strategy: stratified split for classification; uniform split for regression; time-based split for time series forecasting; group-based split for learning to rank. The data split method for classification can be changed into uniform split by setting split_type=\"uniform\". The data are shuffled when split_type in (\"uniform\", \"stratified\"). For both classification and regression tasks more advanced split configurations are possible: time-based split can be enforced if the data are sorted by timestamps, by setting split_type=\"time\", group-based splits can be set by using split_type=\"group\" while providing the group identifier for each sample through the groups argument. This is also shown in an example notebook. More in general, split_type can also be set as a custom splitter object, when eval_method=\"cv\". It needs to be an instance of a derived class of scikit-learn KFold and have split and get_n_splits methods with the same signatures. To disable shuffling, the splitter instance must contain the attribute shuffle=False.","s":"Resampling strategy","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#resampling-strategy","p":641},{"i":657,"t":"When you have parallel resources, you can either spend them in training and keep the model search sequential, or perform parallel search. Following scikit-learn, the parameter n_jobs specifies how many CPU cores to use for each training job. The number of parallel trials is specified via the parameter n_concurrent_trials. By default, n_jobs=-1, n_concurrent_trials=1. That is, all the CPU cores (in a single compute node) are used for training a single model and the search is sequential. When you have more resources than what each single training job needs, you can consider increasing n_concurrent_trials. FLAML now support two backends for parallel tuning, i.e., Ray and Spark. You can use either of them, but not both for one tuning job. Parallel tuning with Ray​ To do parallel tuning with Ray, install the ray and blendsearch options: pip install flaml[ray,blendsearch] Copy ray is used to manage the resources. For example, ray.init(num_cpus=16) Copy allocates 16 CPU cores. Then, when you run: automl.fit(X_train, y_train, n_jobs=4, n_concurrent_trials=4) Copy flaml will perform 4 trials in parallel, each consuming 4 CPU cores. The parallel tuning uses the BlendSearch algorithm. Parallel tuning with Spark​ To do parallel tuning with Spark, install the spark and blendsearch options: Spark support is added in v1.1.0 pip install flaml[spark,blendsearch]>=1.1.0 Copy For more details about installing Spark, please refer to Installation. An example of using Spark for parallel tuning is: automl.fit(X_train, y_train, n_concurrent_trials=4, use_spark=True) Copy Details about parallel tuning with Spark could be found here. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable FLAML_MAX_CONCURRENT to override the detected num_executors. The final number of concurrent trials will be the minimum of n_concurrent_trials and num_executors. Also, GPU training is not supported yet when use_spark is True. Guidelines on parallel vs sequential tuning​ (1) Considerations on wall-clock time. One common motivation for parallel tuning is to save wall-clock time. When sequential tuning and parallel tuning achieve a similar wall-clock time, sequential tuning should be preferred. This is a rule of thumb when the HPO algorithm is sequential by nature (e.g., Bayesian Optimization and FLAML's HPO algorithms CFO and BS). Sequential tuning allows the HPO algorithms to take advantage of the historical trial results. Then the question is How to estimate the wall-clock-time needed by parallel tuning and sequential tuning? You can use the following way to roughly estimate the wall-clock time in parallel tuning and sequential tuning: To finish NNN trials of hyperparameter tuning, i.e., run NNN hyperparameter configurations, the total wall-clock time needed is N/k\\*(SingleTrialTime+Overhead)N/k\\*(SingleTrialTime + Overhead)N/k\\*(SingleTrialTime+Overhead), in which SingleTrialTimeSingleTrialTimeSingleTrialTime is the trial time to evaluate a particular hyperparameter configuration, kkk is the scale of parallelism, e.g., the number of parallel CPU/GPU cores, and OverheadOverheadOverhead is the computation overhead. In sequential tuning, k=1k=1k=1, and in parallel tuning k>1k>1k>1. This may suggest that parallel tuning has a shorter wall-clock time. But it is not always the case considering the other two factors SingleTrialTimeSingleTrialTimeSingleTrialTime, and OverheadOverheadOverhead: The OverheadOverheadOverhead in sequential tuning is typically negligible; while in parallel tuning, it is relatively large. You can also try to reduce the SingleTrialTimeSingleTrialTimeSingleTrialTime to reduce the wall-clock time in sequential tuning: For example, by increasing the resource consumed by a single trial (distributed or multi-thread training), you can reduce SingleTrialTimeSingleTrialTimeSingleTrialTime. One concrete example is to use the n_jobs parameter that sets the number of threads the fitting process can use in many scikit-learn style algorithms. (2) Considerations on randomness. Potential reasons that cause randomness: Parallel tuning: In the case of parallel tuning, the order of trials' finishing time is no longer deterministic. This non-deterministic order, combined with sequential HPO algorithms, leads to a non-deterministic hyperparameter tuning trajectory. Distributed or multi-thread training: Distributed/multi-thread training may introduce randomness in model training, i.e., the trained model with the same hyperparameter may be different because of such randomness. This model-level randomness may be undesirable in some cases.","s":"Parallel tuning","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#parallel-tuning","p":641},{"i":659,"t":"We can warm start the AutoML by providing starting points of hyperparameter configurstions for each estimator. For example, if you have run AutoML for one hour, after checking the results, you would like to run it for another two hours, then you can use the best configurations found for each estimator as the starting points for the new run. automl1 = AutoML()automl1.fit(X_train, y_train, time_budget=3600)automl2 = AutoML()automl2.fit( X_train, y_train, time_budget=7200, starting_points=automl1.best_config_per_estimator,) Copy starting_points is a dictionary or a str to specify the starting hyperparameter config. (1) When it is a dictionary, the keys are the estimator names. If you do not need to specify starting points for an estimator, exclude its name from the dictionary. The value for each key can be either a dictionary of a list of dictionaries, corresponding to one hyperparameter configuration, or multiple hyperparameter configurations, respectively. (2) When it is a str: if \"data\", use data-dependent defaults; if \"data:path\", use data-dependent defaults which are stored at path; if \"static\", use data-independent defaults. Please find more details about data-dependent defaults in zero shot AutoML.","s":"Warm start","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#warm-start","p":641},{"i":661,"t":"The trials are logged in a file if a log_file_name is passed. Each trial is logged as a json record in one line. The best trial's id is logged in the last line. For example, {\"record_id\": 0, \"iter_per_learner\": 1, \"logged_metric\": null, \"trial_time\": 0.12717914581298828, \"wall_clock_time\": 0.1728971004486084, \"validation_loss\": 0.07333333333333332, \"config\": {\"n_estimators\": 4, \"num_leaves\": 4, \"min_child_samples\": 20, \"learning_rate\": 0.09999999999999995, \"log_max_bin\": 8, \"colsample_bytree\": 1.0, \"reg_alpha\": 0.0009765625, \"reg_lambda\": 1.0}, \"learner\": \"lgbm\", \"sample_size\": 150}{\"record_id\": 1, \"iter_per_learner\": 3, \"logged_metric\": null, \"trial_time\": 0.07027268409729004, \"wall_clock_time\": 0.3756711483001709, \"validation_loss\": 0.05333333333333332, \"config\": {\"n_estimators\": 4, \"num_leaves\": 4, \"min_child_samples\": 12, \"learning_rate\": 0.2677050123105203, \"log_max_bin\": 7, \"colsample_bytree\": 1.0, \"reg_alpha\": 0.001348364934537134, \"reg_lambda\": 1.4442580148221913}, \"learner\": \"lgbm\", \"sample_size\": 150}{\"curr_best_record_id\": 1} Copy iter_per_learner means how many models have been tried for each learner. The reason you see records like iter_per_learner=3 for record_id=1 is that flaml only logs better configs than the previous iters by default, i.e., log_type='better'. If you use log_type='all' instead, all the trials will be logged. trial_time means the time taken to train and evaluate one config in that trial. total_search_time is the total time spent from the beginning of fit(). flaml will adjust the n_estimators for lightgbm etc. according to the remaining budget and check the time budget constraint and stop in several places. Most of the time that makes fit() stops before the given budget. Occasionally it may run over the time budget slightly. But the log file always contains the best config info and you can recover the best model until any time point using retrain_from_log(). We can also use mlflow for logging: mlflow.set_experiment(\"flaml\")with mlflow.start_run(): automl.fit(X_train=X_train, y_train=y_train, **settings) Copy To disable mlflow logging pre-configured in FLAML, set mlflow_logging=False: automl = AutoML(mlflow_logging=False) Copy or automl.fit(X_train=X_train, y_train=y_train, mlflow_logging=False, **settings) Copy Setting mlflow_logging=False in the constructor will disable mlflow logging for all the fit() calls. Setting mlflow_logging=False in fit() will disable mlflow logging for that fit() call only.","s":"Log the trials","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#log-the-trials","p":641},{"i":663,"t":"Extra fit arguments that are needed by the estimators can be passed to AutoML.fit(). For example, if there is a weight associated with each training example, they can be passed via sample_weight. For another example, period can be passed for time series forecaster. For any extra keywork argument passed to AutoML.fit() which has not been explicitly listed in the function signature, it will be passed to the underlying estimators' fit() as is. For another example, you can set the number of gpus used by each trial with the gpu_per_trial argument, which is only used by TransformersEstimator and XGBoostSklearnEstimator. In addition, you can specify the different arguments needed by different estimators using the fit_kwargs_by_estimator argument. For example, you can set the custom arguments for a Transformers model: from flaml.automl.data import load_openml_datasetfrom flaml import AutoMLX_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir=\"./\")automl = AutoML()automl_settings = { \"task\": \"classification\", \"time_budget\": 10, \"estimator_list\": [\"catboost\", \"rf\"], \"fit_kwargs_by_estimator\": { \"catboost\": { \"verbose\": True, # setting the verbosity of catboost to True } },}automl.fit(X_train=X_train, y_train=y_train, **automl_settings) Copy","s":"Extra fit arguments","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#extra-fit-arguments","p":641},{"i":666,"t":"The best model can be obtained by the model property of an AutoML instance. For example, automl.fit(X_train, y_train, task=\"regression\")print(automl.model)# <flaml.automl.model.LGBMEstimator object at 0x7f9b502c4550> Copy flaml.automl.model.LGBMEstimator is a wrapper class for LightGBM models. To access the underlying model, use the estimator property of the flaml.automl.model.LGBMEstimator instance. print(automl.model.estimator)\"\"\"LGBMRegressor(colsample_bytree=0.7610534336273627, learning_rate=0.41929025492645006, max_bin=255, min_child_samples=4, n_estimators=45, num_leaves=4, reg_alpha=0.0009765625, reg_lambda=0.009280655005879943, verbose=-1)\"\"\" Copy Just like a normal LightGBM model, we can inspect it. For example, we can plot the feature importance: import matplotlib.pyplot as pltplt.barh( automl.model.estimator.feature_name_, automl.model.estimator.feature_importances_) Copy","s":"Get best model","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#get-best-model","p":641},{"i":668,"t":"We can find the best estimator's name and best configuration by: print(automl.best_estimator)# lgbmprint(automl.best_config)# {'n_estimators': 148, 'num_leaves': 18, 'min_child_samples': 3, 'learning_rate': 0.17402065726724145, 'log_max_bin': 8, 'colsample_bytree': 0.6649148062238498, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0067613624509965} Copy We can also find the best configuration per estimator. print(automl.best_config_per_estimator)# {'lgbm': {'n_estimators': 148, 'num_leaves': 18, 'min_child_samples': 3, 'learning_rate': 0.17402065726724145, 'log_max_bin': 8, 'colsample_bytree': 0.6649148062238498, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0067613624509965}, 'rf': None, 'catboost': None, 'xgboost': {'n_estimators': 4, 'max_leaves': 4, 'min_child_weight': 1.8630223791106992, 'learning_rate': 1.0, 'subsample': 0.8513627344387318, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.946138073111236, 'reg_alpha': 0.0018311776973217073, 'reg_lambda': 0.27901659190538414}, 'extra_tree': {'n_estimators': 4, 'max_features': 1.0, 'max_leaves': 4}} Copy The None value corresponds to the estimators which have not been tried. Other useful information: print(automl.best_config_train_time)# 0.24841618537902832print(automl.best_iteration)# 10print(automl.best_loss)# 0.15448622217577546print(automl.time_to_find_best_model)# 0.4167296886444092print(automl.config_history)# {0: ('lgbm', {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0}, 1.2300517559051514)}# Meaning: at iteration 0, the config tried is {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0} for lgbm, and the wallclock time is 1.23s when this trial is finished. Copy","s":"Get best configuration","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#get-best-configuration","p":641},{"i":670,"t":"To plot how the loss is improved over time during the model search, first load the search history from the log file: from flaml.automl.data import get_output_from_logtime_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = get_output_from_log(filename=settings[\"log_file_name\"], time_budget=120) Copy Then, assuming the optimization metric is \"accuracy\", we can plot the accuracy versus wallclock time: import matplotlib.pyplot as pltimport numpy as npplt.title(\"Learning Curve\")plt.xlabel(\"Wall Clock Time (s)\")plt.ylabel(\"Validation Accuracy\")plt.step(time_history, 1 - np.array(best_valid_loss_history), where=\"post\")plt.show() Copy The curve suggests that increasing the time budget may further improve the accuracy.","s":"Plot learning curve","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#plot-learning-curve","p":641},{"i":672,"t":"If you have an exact constraint for the total search time, set it as the time budget. If you have flexible time constraints, for example, your desirable time budget is t1=60s, and the longest time budget you can tolerate is t2=3600s, you can try the following two ways: set t1 as the time budget, and check the message in the console log in the end. If the budget is too small, you will see a warning like WARNING - Time taken to find the best model is 91% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. set t2 as the time budget, and also set early_stop=True. If the early stopping is triggered, you will see a warning like WARNING - All estimator hyperparameters local search has converged at least once, and the total search time exceeds 10 times the time taken to find the best model. WARNING - Stopping search as early_stop is set to True.","s":"How to set time budget","u":"/FLAML/docs/Use-Cases/Task-Oriented-AutoML","h":"#how-to-set-time-budget","p":641},{"i":674,"t":"If you want to get a sense of how much time is needed to find the best model, you can use max_iter=2 to perform two trials first. The message will be like: INFO - iteration 0, current learner lgbm INFO - Estimated sufficient time budget=145194s. Estimated necessary time budget=2118s. INFO - at 2.6s, estimator lgbm's best error=0.4459, best estimator lgbm's best error=0.4459 You will see that the time to finish the first and cheapest trial is 2.6 seconds. The estimated necessary time budget is 2118 seconds, and the estimated sufficient time budget is 145194 seconds. Note that this is only an estimated range to help you decide your budget. When the time budget is set too low, it can happen that no estimator is trained at all within the budget. In this case, it is recommanded to use max_iter instead of time_budget. This ensures that you have enough time to train a model without worring about variance of the execution time for the code before starting a trainning.","s":"How much time is needed to find the best 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\ No newline at end of file