From 8c3f03a93ecf6b53f663143af17d365b9905f13b Mon Sep 17 00:00:00 2001 From: Di Wu Date: Mon, 10 May 2021 14:56:33 +0900 Subject: [PATCH 01/31] Copy nni package from the original repository --- nni/algorithms/hpo/benchmark/nni/README.md | 16 +++ .../benchmark/nni/benchmarks/nnivalid.yaml | 43 ++++++ nni/algorithms/hpo/benchmark/nni/config.yaml | 33 +++++ .../NNI/.marker_setup_safe_to_delete | 0 .../benchmark/nni/extensions/NNI/__init__.py | 4 + .../NNI/architectures/run_random_forest.py | 66 +++++++++ .../hpo/benchmark/nni/extensions/NNI/exec.py | 34 +++++ .../nni/extensions/NNI/run_experiment.py | 12 ++ .../benchmark/nni/extensions/NNI/tuners.py | 136 ++++++++++++++++++ .../hpo/benchmark/nni/frameworks.yaml | 68 +++++++++ .../hpo/benchmark/parse_result_csv.py | 29 ++++ .../hpo/benchmark/runbenchmark_nni.sh | 11 ++ nni/algorithms/hpo/benchmark/setup.sh | 0 13 files changed, 452 insertions(+) create mode 100644 nni/algorithms/hpo/benchmark/nni/README.md create mode 100644 nni/algorithms/hpo/benchmark/nni/benchmarks/nnivalid.yaml create mode 100644 nni/algorithms/hpo/benchmark/nni/config.yaml create mode 100644 nni/algorithms/hpo/benchmark/nni/extensions/NNI/.marker_setup_safe_to_delete create mode 100644 nni/algorithms/hpo/benchmark/nni/extensions/NNI/__init__.py create mode 100644 nni/algorithms/hpo/benchmark/nni/extensions/NNI/architectures/run_random_forest.py create mode 100644 nni/algorithms/hpo/benchmark/nni/extensions/NNI/exec.py create mode 100644 nni/algorithms/hpo/benchmark/nni/extensions/NNI/run_experiment.py create mode 100644 nni/algorithms/hpo/benchmark/nni/extensions/NNI/tuners.py create mode 100644 nni/algorithms/hpo/benchmark/nni/frameworks.yaml create mode 100644 nni/algorithms/hpo/benchmark/parse_result_csv.py create mode 100755 nni/algorithms/hpo/benchmark/runbenchmark_nni.sh create mode 100755 nni/algorithms/hpo/benchmark/setup.sh diff --git a/nni/algorithms/hpo/benchmark/nni/README.md b/nni/algorithms/hpo/benchmark/nni/README.md new file mode 100644 index 0000000000..6146c101f7 --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/README.md @@ -0,0 +1,16 @@ +NNI Custom benchmarks: + +This folder contains a customized framework with several NNI Hyperparameter Tuners, along with testing benchmarks. + +**To experiment with an existing tuner:** + +1. Select tuner type and architecture type in frameworks.yaml. +2. Find or define a benchmark in the "benchmarks" folder with name .yaml. +3. Go to the project root folder and run the following command. + +```bash +python runbenchmark.py NNI -u nni +``` + +**Note:** + you can also copy those files in automlbenchmark user config directory `~/.config/automlbenchmark` to be able to use them without having to specify the `-u examples/custom` argument. diff --git a/nni/algorithms/hpo/benchmark/nni/benchmarks/nnivalid.yaml b/nni/algorithms/hpo/benchmark/nni/benchmarks/nnivalid.yaml new file mode 100644 index 0000000000..ffc4d3d8ee --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/benchmarks/nnivalid.yaml @@ -0,0 +1,43 @@ +--- +#for doc purpose using syntax when it applies. + +#FORMAT: global defaults are defined in config.yaml +- name: __dummy-task + enabled: false # actual default is `true` of course... + openml_task_id: 0 + metric: # the first metric in the task list will be optimized against and used for the main result, the other ones are optional and purely informative. Only the metrics annotated with (*) can be used as a performance metric. + - # classification + - acc # (*) accuracy + - auc # (*) array under curve + - logloss # (*) log loss + - f1 # F1 score + - # regression + - mae # (*) mean absolute error + - mse # (*) mean squared error + - rmse # root mean squared error + - rmsle # root mean squared log error + - r2 # R^2 score + folds: 1 + max_runtime_seconds: 600 + cores: 1 + max_mem_size_mb: -1 + ec2_instance_type: m5.large + + +# local defaults (applying only to tasks defined in this file) can be defined in a task named "__defaults__" +- name: __defaults__ + folds: 2 + cores: 2 + max_runtime_seconds: 180 + +- name: kc2 + openml_task_id: 3913 + description: "binary test dataset" + +- name: iris + openml_task_id: 59 + description: "multiclass test dataset" + +- name: cholesterol + openml_task_id: 2295 + description: "regression test dataset" diff --git a/nni/algorithms/hpo/benchmark/nni/config.yaml b/nni/algorithms/hpo/benchmark/nni/config.yaml new file mode 100644 index 0000000000..6d8cef39bc --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/config.yaml @@ -0,0 +1,33 @@ +# put this file in custom input dir or in ~/.config/automlbenchmark directory +# in order to have a custom setup, allowing to add custom frameworks definitions (in our case, creating a benchmark for various H2O versions) +--- +project_repository: https://github.com/openml/automlbenchmark + +input_dir: '{user}/data' # change the default input directory (where data files are loaded and/or downloaded). + +frameworks: + definition_file: # this allows to add custom framework definitions (in {user}/frameworks.yaml) on top of the default ones. + - '{root}/resources/frameworks.yaml' + - '{user}/frameworks.yaml' + +benchmarks: + definition_dir: # this allows to add custom benchmark definitions (under {user}/benchmarks) to the default ones. + - '{user}/benchmarks' + - '{root}/resources/benchmarks' + constraints_file: # this allows to add custom constraint definitions (in {user}/constraints.yaml) on top of the default ones. + - '{root}/resources/constraints.yaml' + - '{user}/constraints.yaml' + +aws: + resource_files: # this allows to automatically upload custom config + frameworks to the running instance (benchmark files are always uploaded). + - '{user}/config.yaml' + - '{user}/frameworks.yaml' + - '{user}/constraints.yaml' + - '{user}/benchmarks' + - '{user}/extensions' + + ec2: + instance_type: + series: t3 + use_docker: false # you can decide to always use the prebuilt docker images on AWS. + diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/.marker_setup_safe_to_delete b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/.marker_setup_safe_to_delete new file mode 100644 index 0000000000..e69de29bb2 diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/__init__.py b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/__init__.py new file mode 100644 index 0000000000..3a9a0dc12c --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/__init__.py @@ -0,0 +1,4 @@ + +def run(*args, **kwargs): + from .exec import run + return run(*args, **kwargs) diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/architectures/run_random_forest.py b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/architectures/run_random_forest.py new file mode 100644 index 0000000000..e3fd04ed58 --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -0,0 +1,66 @@ +import logging +import sklearn +import time +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.model_selection import cross_val_score + +from amlb.benchmark import TaskConfig +from amlb.data import Dataset +from amlb.datautils import impute +from amlb.utils import Timer + + + +SEARCH_SPACE = { + "n_estimators": {"_type":"choice", "_value": [128, 256, 512, 1024, 2048, 4096]}, + "max_depth": {"_type":"choice", "_value": [5, 10, 25, 50, 100]}, +} + + +def run_random_forest(dataset, config, tuner, log): + is_classification = config.type == 'classification' + + X_train, X_test = impute(dataset.train.X, dataset.test.X) + y_train, y_test = dataset.train.y, dataset.test.y + + estimator = RandomForestClassifier if is_classification else RandomForestRegressor + + best_score, best_params, best_model = None, None, None + score_higher_better = True + + tuner.update_search_space(SEARCH_SPACE) + start_time = time.time() + while True: + try: + param_idx, cur_params = tuner.generate_parameters() + if 'TRIAL_BUDGET' in cur_params: + cur_params.pop('TRIAL_BUDGET') + cur_model = estimator(random_state=config.seed, **cur_params) + # Here score is the output of score() from the estimator + cur_score = cross_val_score(cur_model, X_train, y_train) + cur_score = sum(cur_score) / float(len(cur_score)) + if best_score is None or (score_higher_better and cur_score > best_score) or (not score_higher_better and cur_score < best_score): + best_score, best_params, best_model = cur_score, cur_params, cur_model + + log.info("Trial {}: \n{}\nScore: {}\n".format(param_idx, cur_params, cur_score)) + tuner.receive_trial_result(param_idx, cur_params, cur_score) + + current_time = time.time() + elapsed_time = current_time - start_time + if elapsed_time > config.max_runtime_seconds: + break + except: + break + + # This line is required to fully terminate some advisors + tuner.handle_terminate() + + log.info("Tuning done, the best parameters are:\n{}\n".format(best_params)) + + # retrain on the whole dataset + with Timer() as training: + best_model.fit(X_train, y_train) + predictions = best_model.predict(X_test) + probabilities = best_model.predict_proba(X_test) if is_classification else None + + return probabilities, predictions, training, y_test diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/exec.py b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/exec.py new file mode 100644 index 0000000000..3fd7cd6fcd --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/exec.py @@ -0,0 +1,34 @@ +import logging + +from .tuners import NNITuner +from .run_experiment import * + +from amlb.benchmark import TaskConfig +from amlb.data import Dataset +from amlb.results import save_predictions_to_file +from amlb.utils import Timer + + +log = logging.getLogger(__name__) + + +def run(dataset: Dataset, config: TaskConfig): + if 'tuner_type' not in config.framework_params: + raise RuntimeError('framework.yaml does not have a "tuner_type" field.') + + tuner = NNITuner(config) + log.info("Tuning {} with NNI {} with a maximum time of {}s\n" + .format(config.framework_params['arch_type'], tuner.description, config.max_runtime_seconds)) + + probabilities, predictions, train_timer, y_test = run_experiment(dataset, config, tuner, log) + + save_predictions_to_file(dataset=dataset, + output_file=config.output_predictions_file, + probabilities=probabilities, + predictions=predictions, + truth=y_test) + + return dict( + models_count=1, + training_duration=train_timer.duration + ) diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/run_experiment.py b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/run_experiment.py new file mode 100644 index 0000000000..2fa0c1f173 --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/run_experiment.py @@ -0,0 +1,12 @@ +from .architectures.run_random_forest import * + + +def run_experiment(dataset, config, tuner, log): + if 'arch_type' not in config.framework_params: + raise RuntimeError('framework.yaml does not have a "arch_type" field.') + + if config.framework_params['arch_type'] == 'random_forest': + return run_random_forest(dataset, config, tuner, log) + + else: + raise RuntimeError('The requested arch type in framework.yaml is unavailable.') diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/tuners.py b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/tuners.py new file mode 100644 index 0000000000..b86795666a --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/extensions/NNI/tuners.py @@ -0,0 +1,136 @@ +from amlb.benchmark import TaskConfig + +import nni + +from nni.utils import MetricType + +from nni.algorithms.hpo.hyperopt_tuner import HyperoptTuner +from nni.algorithms.hpo.evolution_tuner import EvolutionTuner +from nni.algorithms.hpo.smac_tuner.smac_tuner import SMACTuner +from nni.algorithms.hpo.gp_tuner.gp_tuner import GPTuner +from nni.algorithms.hpo.metis_tuner.metis_tuner import MetisTuner +from nni.algorithms.hpo.hyperband_advisor import Hyperband +from nni.algorithms.hpo.bohb_advisor.bohb_advisor import BOHB +from nni.algorithms.hpo.bohb_advisor.config_generator import CG_BOHB + + +def get_tuner(config: TaskConfig): + # Users may add their customized Tuners here + if config.framework_params['tuner_type'] == 'tpe': + return HyperoptTuner('tpe'), 'TPE Tuner' + + elif config.framework_params['tuner_type'] == 'random_search': + return HyperoptTuner('random_search'), 'Random Search Tuner' + + elif config.framework_params['tuner_type'] == 'anneal': + return HyperoptTuner('anneal'), 'Annealing Tuner' + + elif config.framework_params['tuner_type'] == 'evolution': + return EvolutionTuner(), 'Evolution Tuner' + + elif config.framework_params['tuner_type'] == 'smac': + return SMACTuner(), 'SMAC Tuner' + + elif config.framework_params['tuner_type'] == 'gp': + return GPTuner(), 'GP Tuner' + + elif config.framework_params['tuner_type'] == 'metis': + return MetisTuner(), 'Metis Tuner' + + elif config.framework_params['tuner_type'] == 'hyperband': + if 'max_resource' in config.framework_params: + tuner = Hyperband(R=config.framework_params['max_resource']) + else: + tuner = Hyperband() + return tuner, 'Hyperband Advisor' + + elif config.framework_params['tuner_type'] == 'bohb': + if 'max_resource' in config.framework_params: + tuner = BOHB(max_budget=config.framework_params['max_resource']) + else: + tuner = BOHB(max_budget=60) + return tuner, 'BOHB Advisor' + + else: + raise RuntimeError('The requested tuner type in framework.yaml is unavailable.') + + +class NNITuner: + ''' + A specialized wrapper for the automlbenchmark framework. + Abstracts the different behaviors of tuners and advisors into a tuner API. + ''' + def __init__(self, config: TaskConfig): + self.config = config + self.core, self.description = get_tuner(config) + + # 'tuner' or 'advisor' + self.core_type = None + if isinstance(self.core, nni.tuner.Tuner): + self.core_type = 'tuner' + elif isinstance(self.core, nni.runtime.msg_dispatcher_base.MsgDispatcherBase): + self.core_type = 'advisor' + else: + raise RuntimeError('Unsupported tuner or advisor type') + + # note: tuners and advisors use this variable differently + self.cur_param_id = 0 + + + def __del__(self): + self.handle_terminate() + + + def update_search_space(self, search_space): + if self.core_type == 'tuner': + self.core.update_search_space(search_space) + + elif self.core_type == 'advisor': + self.core.handle_update_search_space(search_space) + # special initializations for BOHB Advisor + if isinstance(self.core, BOHB): + self.core.cg = CG_BOHB(configspace=self.core.search_space, + min_points_in_model=self.core.min_points_in_model, + top_n_percent=self.core.top_n_percent, + num_samples=self.core.num_samples, + random_fraction=self.core.random_fraction, + bandwidth_factor=self.core.bandwidth_factor, + min_bandwidth=self.core.min_bandwidth) + self.core.generate_new_bracket() + + + def generate_parameters(self): + self.cur_param_id += 1 + if self.core_type == 'tuner': + self.cur_param = self.core.generate_parameters(self.cur_param_id-1) + return self.cur_param_id-1, self.cur_param + + elif self.core_type == 'advisor': + self.cur_param = self.core._get_one_trial_job() + hyperparams = self.cur_param['parameters'].copy() + #hyperparams.pop('TRIAL_BUDGET') + return self.cur_param['parameter_id'], hyperparams + + + def receive_trial_result(self, parameter_id, parameters, value): + if self.core_type == 'tuner': + return self.core.receive_trial_result(parameter_id, parameters, value) + + elif self.core_type == 'advisor': + metric_report = {} + metric_report['parameter_id'] = parameter_id + metric_report['trial_job_id'] = self.cur_param_id + metric_report['type'] = MetricType.FINAL + metric_report['value'] = str(value) + metric_report['sequence'] = self.cur_param_id + return self.core.handle_report_metric_data(metric_report) + + + def handle_terminate(self): + if self.core_type == 'tuner': + pass + + elif self.core_type == 'advisor': + self.core.stopping = True + + diff --git a/nni/algorithms/hpo/benchmark/nni/frameworks.yaml b/nni/algorithms/hpo/benchmark/nni/frameworks.yaml new file mode 100644 index 0000000000..49755b7a4a --- /dev/null +++ b/nni/algorithms/hpo/benchmark/nni/frameworks.yaml @@ -0,0 +1,68 @@ +--- + +NNI: + module: extensions.NNI + version: '2.2' + project: https://github.com/microsoft/nni + params: + tuner_type: 'bohb' + arch_type: 'random forest' + +# tuner type in ['tpe', 'random_search', 'anneal', 'evolution', 'smac', 'gp', 'metis', 'hyperband', 'bohb'] +# arch_type in ['random_forest'] + +NNI_TPE: + extends: NNI + params: + tuner_type: 'tpe' + arch_type: 'random_forest' + +NNI_RANDOM_SEARCH: + extends: NNI + params: + tuner_type: 'random_search' + arch_type: 'random_forest' + +NNI_ANNEAL: + extends: NNI + params: + tuner_type: 'anneal' + arch_type: 'random_forest' + +NNI_EVOLUTION: + extends: NNI + params: + tuner_type: 'evolution' + arch_type: 'random_forest' + +NNI_SMAC: + extends: NNI + params: + tuner_type: 'smac' + arch_type: 'random_forest' + +NNI_GP: + extends: NNI + params: + tuner_type: 'gp' + arch_type: 'random_forest' + +NNI_METIS: + extends: NNI + params: + tuner_type: 'metis' + arch_type: 'random_forest' + +NNI_HYPERBAND: + extends: NNI + params: + tuner_type: 'hyperband' + arch_type: 'random_forest' + max_resource: 60 + +NNI_BOHB: + extends: NNI + params: + tuner_type: 'bohb' + arch_type: 'random_forest' + max_resource: 60 \ No newline at end of file diff --git a/nni/algorithms/hpo/benchmark/parse_result_csv.py b/nni/algorithms/hpo/benchmark/parse_result_csv.py new file mode 100644 index 0000000000..b3189f84e6 --- /dev/null +++ b/nni/algorithms/hpo/benchmark/parse_result_csv.py @@ -0,0 +1,29 @@ +import pandas as pd +import sys + + +if len(sys.argv) != 2: + print("Usage: python parse_result_csv.py ") + exit(0) + +result_file = sys.argv[1] +result = pd.read_csv(result_file) + +task_ids = result['id'].unique() + +with open(result_file + '.parsed', 'w') as out_f: + for task_id in task_ids: + task_results = result[result['id'] == task_id] + task_name = task_results.task.unique()[0] + out_f.write("====================================================\n") + out_f.write("Task ID: {}\n".format(task_id)) + out_f.write("Task Name: {}\n".format(task_name)) + folds = task_results['fold'].unique() + for fold in folds: + out_f.write("Fold {}:\n".format(fold)) + keep_parameters = ['framework', 'constraint', 'result', 'metric', 'params', 'utc', 'duration', 'acc', 'auc', 'logloss', 'r2', 'rmse'] + res = task_results[task_results['fold'] == fold][keep_parameters] + out_f.write(res.to_string()) + out_f.write('\n') + + out_f.write('\n') diff --git a/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh b/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh new file mode 100755 index 0000000000..180e9c1dac --- /dev/null +++ b/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh @@ -0,0 +1,11 @@ +#!/usr/bin/bash + +outdir='results_nni' +benchmark='nnivalid' + +for tuner in 'NNI_TPE' 'NNI_RANDOM_SEARCH' 'NNI_ANNEAL' 'NNI_EVOLUTION' 'NNI_SMAC' 'NNI_GP' 'NNI_METIS' 'NNI_HYPERBAND' 'NNI_BOHB'; do + echo "python runbenchmark.py $tuner $benchmark -o $outdir -u nni" + python runbenchmark.py $tuner $benchmark -o $outdir -u nni +done + +python parse_result_csv.py "$outdir/results.csv" diff --git a/nni/algorithms/hpo/benchmark/setup.sh b/nni/algorithms/hpo/benchmark/setup.sh new file mode 100755 index 0000000000..e69de29bb2 From bc317ccf0c774aecec7d36a157ca8955ce55622b Mon Sep 17 00:00:00 2001 From: Di Wu Date: Mon, 10 May 2021 15:50:16 +0900 Subject: [PATCH 02/31] Add initialization code; change hpo benchmark autorun code --- nni/algorithms/hpo/benchmark/runbenchmark_nni.sh | 10 ++++++---- nni/algorithms/hpo/benchmark/setup.sh | 5 +++++ 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh b/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh index 180e9c1dac..e837137c88 100755 --- a/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh +++ b/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh @@ -1,11 +1,13 @@ -#!/usr/bin/bash +#!/bin/bash -outdir='results_nni' +time=$(date "+%Y%m%d%H%M%S") +installation='automlbenchmark' +outdir="results_$time" benchmark='nnivalid' for tuner in 'NNI_TPE' 'NNI_RANDOM_SEARCH' 'NNI_ANNEAL' 'NNI_EVOLUTION' 'NNI_SMAC' 'NNI_GP' 'NNI_METIS' 'NNI_HYPERBAND' 'NNI_BOHB'; do - echo "python runbenchmark.py $tuner $benchmark -o $outdir -u nni" - python runbenchmark.py $tuner $benchmark -o $outdir -u nni + echo "python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni" + python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni done python parse_result_csv.py "$outdir/results.csv" diff --git a/nni/algorithms/hpo/benchmark/setup.sh b/nni/algorithms/hpo/benchmark/setup.sh index e69de29bb2..d25203b08e 100755 --- a/nni/algorithms/hpo/benchmark/setup.sh +++ b/nni/algorithms/hpo/benchmark/setup.sh @@ -0,0 +1,5 @@ +#!/bin/bash + + +# setup code +git clone https://github.com/openml/automlbenchmark.git --branch stable --depth 1 From 9e463c2aa1b16ce0fdc2f33e61d09606a6c2fc2c Mon Sep 17 00:00:00 2001 From: Di Wu Date: Mon, 10 May 2021 17:03:07 +0900 Subject: [PATCH 03/31] HPO Benchmark README v1 --- .gitignore | 1 + nni/algorithms/hpo/benchmark/README.md | 32 ++++++++++++++++++++++++++ 2 files changed, 33 insertions(+) create mode 100644 nni/algorithms/hpo/benchmark/README.md diff --git a/.gitignore b/.gitignore index 482d04e8c9..e15a772368 100644 --- a/.gitignore +++ b/.gitignore @@ -23,6 +23,7 @@ pids *.pid *.seed *.pid.lock +/nni/algorithms/hpo/benchmark/nni/data # Build package dist/ diff --git a/nni/algorithms/hpo/benchmark/README.md b/nni/algorithms/hpo/benchmark/README.md new file mode 100644 index 0000000000..d58b7ab943 --- /dev/null +++ b/nni/algorithms/hpo/benchmark/README.md @@ -0,0 +1,32 @@ +# Benchmark for Tuners + +This folder contains a benchmark tool that enables comparisons between the performances of tuners provided by NNI (and users' custom tuners) on different tasks. The implementation of this tool is based on the automlbenchmark repository (https://github.com/openml/automlbenchmark), which provides services of running different *frameworks* against different *benchmarks* consisting of multiple *tasks*. + +### Terminology: + +* task: a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). +* benchmark: a benchmark is a set of tasks, along with other external constraints such as time and resource. +* framework: given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. +* tuner: a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. +* architecture: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. + +### Setup +```bash +./setup.sh +``` + +### Run predefined benchmarks on existing tuners +```bash +./runbenchmark_nni.sh +``` +This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the "results.csv.parsed" file in the result folder associated with current time. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. + +### Run customized benchmarks on existing tuners +To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nni/benchmarks`, and change the `benchmark` variable in `runbenchmark_nni.sh`. See `./automlbenchmark/resources/benchmarks/` for some examples of defining a custom benchmark. + +### Run benchmarks on custom tuners +To use custom tuners, first make sure that the tuner inherits from `nni.tuner.Tuner` and correctly implements the required APIs. Next, perform the following steps: +1. Copy the implementation into the folder `./nni/extensionsions/NNI`. +1. In `tuners.py` (under the same folder), import the custom tuner, and change the `get_tuner` function to include the new option. +1. In `./nni/frameworks.yaml`, add a new framework extending the base framework NNI. Make sure that the parameter `tuner_type` corresponds to the name in step 2. +1. (Optional) Include the new framework in the last step into `runbenchmark_nni.sh`. From 9b08605c9326cf58ad7fcda55f096500be47edff Mon Sep 17 00:00:00 2001 From: Di Wu Date: Mon, 10 May 2021 17:05:32 +0900 Subject: [PATCH 04/31] HPO Benchmark README V1.1 --- nni/algorithms/hpo/benchmark/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/nni/algorithms/hpo/benchmark/README.md b/nni/algorithms/hpo/benchmark/README.md index d58b7ab943..d631ec1191 100644 --- a/nni/algorithms/hpo/benchmark/README.md +++ b/nni/algorithms/hpo/benchmark/README.md @@ -2,13 +2,13 @@ This folder contains a benchmark tool that enables comparisons between the performances of tuners provided by NNI (and users' custom tuners) on different tasks. The implementation of this tool is based on the automlbenchmark repository (https://github.com/openml/automlbenchmark), which provides services of running different *frameworks* against different *benchmarks* consisting of multiple *tasks*. -### Terminology: +### Terminology -* task: a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). +* #task: a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). * benchmark: a benchmark is a set of tasks, along with other external constraints such as time and resource. -* framework: given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. -* tuner: a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. -* architecture: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. +* #framework: given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. +* #tuner: a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. +* #architecture: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. ### Setup ```bash From 13705fa332e65f671d939109da823979e4a9ca8a Mon Sep 17 00:00:00 2001 From: Di Wu Date: Mon, 10 May 2021 17:07:11 +0900 Subject: [PATCH 05/31] HPO Benchmark README V1.2 --- nni/algorithms/hpo/benchmark/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/nni/algorithms/hpo/benchmark/README.md b/nni/algorithms/hpo/benchmark/README.md index d631ec1191..ed9a6e2b6e 100644 --- a/nni/algorithms/hpo/benchmark/README.md +++ b/nni/algorithms/hpo/benchmark/README.md @@ -4,11 +4,11 @@ This folder contains a benchmark tool that enables comparisons between the perfo ### Terminology -* #task: a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). -* benchmark: a benchmark is a set of tasks, along with other external constraints such as time and resource. -* #framework: given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. -* #tuner: a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. -* #architecture: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. +* **task**: a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). +* **benchmark**: a benchmark is a set of tasks, along with other external constraints such as time and resource. +* **framework**: given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. +* **tuner**: a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. +* **architecture**: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. ### Setup ```bash From aa4f22310b1d723f34d9d15ebe491503624a4303 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 11 May 2021 15:51:15 +0900 Subject: [PATCH 06/31] Move HPO Benchmark to examples directory --- .../trials/benchmarking/automlbenchmark}/README.md | 0 .../trials/benchmarking/automlbenchmark}/nni/README.md | 0 .../benchmarking/automlbenchmark}/nni/benchmarks/nnivalid.yaml | 0 .../trials/benchmarking/automlbenchmark}/nni/config.yaml | 0 .../nni/extensions/NNI/.marker_setup_safe_to_delete | 0 .../benchmarking/automlbenchmark}/nni/extensions/NNI/__init__.py | 0 .../nni/extensions/NNI/architectures/run_random_forest.py | 0 .../benchmarking/automlbenchmark}/nni/extensions/NNI/exec.py | 0 .../automlbenchmark}/nni/extensions/NNI/run_experiment.py | 0 .../benchmarking/automlbenchmark}/nni/extensions/NNI/tuners.py | 0 .../trials/benchmarking/automlbenchmark}/nni/frameworks.yaml | 0 .../trials/benchmarking/automlbenchmark}/parse_result_csv.py | 0 .../trials/benchmarking/automlbenchmark}/runbenchmark_nni.sh | 0 .../trials/benchmarking/automlbenchmark}/setup.sh | 0 examples/trials/benchmarking/{ => hyperband}/config_hyperband.yml | 0 examples/trials/benchmarking/{ => hyperband}/main.py | 0 examples/trials/benchmarking/{ => hyperband}/search_space.json | 0 17 files changed, 0 insertions(+), 0 deletions(-) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/README.md (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/README.md (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/benchmarks/nnivalid.yaml (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/config.yaml (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/extensions/NNI/.marker_setup_safe_to_delete (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/extensions/NNI/__init__.py (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/extensions/NNI/architectures/run_random_forest.py (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/extensions/NNI/exec.py (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/extensions/NNI/run_experiment.py (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/extensions/NNI/tuners.py (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/nni/frameworks.yaml (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/parse_result_csv.py (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/runbenchmark_nni.sh (100%) rename {nni/algorithms/hpo/benchmark => examples/trials/benchmarking/automlbenchmark}/setup.sh (100%) rename examples/trials/benchmarking/{ => hyperband}/config_hyperband.yml (100%) rename examples/trials/benchmarking/{ => hyperband}/main.py (100%) rename examples/trials/benchmarking/{ => hyperband}/search_space.json (100%) diff --git a/nni/algorithms/hpo/benchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md similarity index 100% rename from nni/algorithms/hpo/benchmark/README.md rename to examples/trials/benchmarking/automlbenchmark/README.md diff --git a/nni/algorithms/hpo/benchmark/nni/README.md b/examples/trials/benchmarking/automlbenchmark/nni/README.md similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/README.md rename to examples/trials/benchmarking/automlbenchmark/nni/README.md diff --git a/nni/algorithms/hpo/benchmark/nni/benchmarks/nnivalid.yaml b/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnivalid.yaml similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/benchmarks/nnivalid.yaml rename to examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnivalid.yaml diff --git a/nni/algorithms/hpo/benchmark/nni/config.yaml b/examples/trials/benchmarking/automlbenchmark/nni/config.yaml similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/config.yaml rename to examples/trials/benchmarking/automlbenchmark/nni/config.yaml diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/.marker_setup_safe_to_delete b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/.marker_setup_safe_to_delete similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/extensions/NNI/.marker_setup_safe_to_delete rename to examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/.marker_setup_safe_to_delete diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/__init__.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/__init__.py similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/extensions/NNI/__init__.py rename to examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/__init__.py diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/extensions/NNI/architectures/run_random_forest.py rename to examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/exec.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/extensions/NNI/exec.py rename to examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/run_experiment.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/run_experiment.py similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/extensions/NNI/run_experiment.py rename to examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/run_experiment.py diff --git a/nni/algorithms/hpo/benchmark/nni/extensions/NNI/tuners.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/extensions/NNI/tuners.py rename to examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py diff --git a/nni/algorithms/hpo/benchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml similarity index 100% rename from nni/algorithms/hpo/benchmark/nni/frameworks.yaml rename to examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml diff --git a/nni/algorithms/hpo/benchmark/parse_result_csv.py b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py similarity index 100% rename from nni/algorithms/hpo/benchmark/parse_result_csv.py rename to examples/trials/benchmarking/automlbenchmark/parse_result_csv.py diff --git a/nni/algorithms/hpo/benchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh similarity index 100% rename from nni/algorithms/hpo/benchmark/runbenchmark_nni.sh rename to examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh diff --git a/nni/algorithms/hpo/benchmark/setup.sh b/examples/trials/benchmarking/automlbenchmark/setup.sh similarity index 100% rename from nni/algorithms/hpo/benchmark/setup.sh rename to examples/trials/benchmarking/automlbenchmark/setup.sh diff --git a/examples/trials/benchmarking/config_hyperband.yml b/examples/trials/benchmarking/hyperband/config_hyperband.yml similarity index 100% rename from examples/trials/benchmarking/config_hyperband.yml rename to examples/trials/benchmarking/hyperband/config_hyperband.yml diff --git a/examples/trials/benchmarking/main.py b/examples/trials/benchmarking/hyperband/main.py similarity index 100% rename from examples/trials/benchmarking/main.py rename to examples/trials/benchmarking/hyperband/main.py diff --git a/examples/trials/benchmarking/search_space.json b/examples/trials/benchmarking/hyperband/search_space.json similarity index 100% rename from examples/trials/benchmarking/search_space.json rename to examples/trials/benchmarking/hyperband/search_space.json From 34016ed6e65d6d968e758db5698265fc3ad1281c Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 11 May 2021 17:11:30 +0900 Subject: [PATCH 07/31] Change HPO Benchmark tuner import logic --- .../benchmarking/automlbenchmark/README.md | 5 +- .../nni/extensions/NNI/tuners.py | 101 ++++++++++-------- .../automlbenchmark/nni/frameworks.yaml | 24 ++--- 3 files changed, 71 insertions(+), 59 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index ed9a6e2b6e..ff770156cb 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -26,7 +26,6 @@ To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nn ### Run benchmarks on custom tuners To use custom tuners, first make sure that the tuner inherits from `nni.tuner.Tuner` and correctly implements the required APIs. Next, perform the following steps: -1. Copy the implementation into the folder `./nni/extensionsions/NNI`. -1. In `tuners.py` (under the same folder), import the custom tuner, and change the `get_tuner` function to include the new option. -1. In `./nni/frameworks.yaml`, add a new framework extending the base framework NNI. Make sure that the parameter `tuner_type` corresponds to the name in step 2. +1. Install the custom tuner with command `nnictl algo register`. Check [this page](https://nni.readthedocs.io/en/stable/Tutorial/Nnictl.html) for details. +1. In `./nni/frameworks.yaml`, add a new framework extending the base framework NNI. Make sure that the parameter `tuner_type` corresponds to the "builtinName" of tuner installed in step 1. 1. (Optional) Include the new framework in the last step into `runbenchmark_nni.sh`. diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py index b86795666a..7e748b905e 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py @@ -1,58 +1,71 @@ -from amlb.benchmark import TaskConfig +import os +import yaml +import importlib import nni - +from nni.runtime.config import get_config_file from nni.utils import MetricType - -from nni.algorithms.hpo.hyperopt_tuner import HyperoptTuner -from nni.algorithms.hpo.evolution_tuner import EvolutionTuner -from nni.algorithms.hpo.smac_tuner.smac_tuner import SMACTuner -from nni.algorithms.hpo.gp_tuner.gp_tuner import GPTuner -from nni.algorithms.hpo.metis_tuner.metis_tuner import MetisTuner -from nni.algorithms.hpo.hyperband_advisor import Hyperband +from nni.tuner import Tuner +from nni.runtime.msg_dispatcher_base import MsgDispatcherBase from nni.algorithms.hpo.bohb_advisor.bohb_advisor import BOHB from nni.algorithms.hpo.bohb_advisor.config_generator import CG_BOHB +from amlb.benchmark import TaskConfig -def get_tuner(config: TaskConfig): - # Users may add their customized Tuners here - if config.framework_params['tuner_type'] == 'tpe': - return HyperoptTuner('tpe'), 'TPE Tuner' - elif config.framework_params['tuner_type'] == 'random_search': - return HyperoptTuner('random_search'), 'Random Search Tuner' +def get_tuner_class_dict(): + config_file = str(get_config_file('registered_algorithms.yml')) + if os.path.exists(config_file): + with open(config_file, 'r') as f: + config = yaml.load(f, Loader=yaml.SafeLoader) + else: + config = defaultdict(list) + ret = {} + for t in ['tuners', 'advisors']: + for entry in config[t]: + ret[entry['builtinName']] = entry['className'] + return ret - elif config.framework_params['tuner_type'] == 'anneal': - return HyperoptTuner('anneal'), 'Annealing Tuner' - - elif config.framework_params['tuner_type'] == 'evolution': - return EvolutionTuner(), 'Evolution Tuner' - elif config.framework_params['tuner_type'] == 'smac': - return SMACTuner(), 'SMAC Tuner' +def get_tuner(config: TaskConfig): + name2tuner = get_tuner_class_dict() + if config.framework_params['tuner_type'] not in name2tuner: + raise RuntimeError('The requested tuner type is unavailable.') + else: + module_name = name2tuner[config.framework_params['tuner_type']] + tuner_name = module_name.split('.')[-1] + module_name = '.'.join(module_name.split('.')[:-1]) + tuner_type = getattr(importlib.import_module(module_name), tuner_name) + + # special handlings for tuner initialization + tuner = None + if config.framework_params['tuner_type'] == 'TPE': + tuner = tuner_type('tpe') + + elif config.framework_params['tuner_type'] == 'Random': + tuner = tuner_type('random_search') + + elif config.framework_params['tuner_type'] == 'Anneal': + tuner = tuner_type('anneal') + + elif config.framework_params['tuner_type'] == 'Hyperband': + if 'max_resource' in config.framework_params: + tuner = tuner_type(R=config.framework_params['max_resource']) + else: + tuner = tuner_type() + + elif config.framework_params['tuner_type'] == 'BOHB': + if 'max_resource' in config.framework_params: + tuner = tuner_type(max_budget=config.framework_params['max_resource']) + else: + tuner = tuner_type(max_budget=60) - elif config.framework_params['tuner_type'] == 'gp': - return GPTuner(), 'GP Tuner' + else: + tuner = tuner_type() - elif config.framework_params['tuner_type'] == 'metis': - return MetisTuner(), 'Metis Tuner' + assert(tuner is not None) - elif config.framework_params['tuner_type'] == 'hyperband': - if 'max_resource' in config.framework_params: - tuner = Hyperband(R=config.framework_params['max_resource']) - else: - tuner = Hyperband() - return tuner, 'Hyperband Advisor' - - elif config.framework_params['tuner_type'] == 'bohb': - if 'max_resource' in config.framework_params: - tuner = BOHB(max_budget=config.framework_params['max_resource']) - else: - tuner = BOHB(max_budget=60) - return tuner, 'BOHB Advisor' - - else: - raise RuntimeError('The requested tuner type in framework.yaml is unavailable.') + return tuner, config.framework_params['tuner_type'] class NNITuner: @@ -66,9 +79,9 @@ def __init__(self, config: TaskConfig): # 'tuner' or 'advisor' self.core_type = None - if isinstance(self.core, nni.tuner.Tuner): + if isinstance(self.core, Tuner): self.core_type = 'tuner' - elif isinstance(self.core, nni.runtime.msg_dispatcher_base.MsgDispatcherBase): + elif isinstance(self.core, MsgDispatcherBase): self.core_type = 'advisor' else: raise RuntimeError('Unsupported tuner or advisor type') diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml index 49755b7a4a..4f3ee033b8 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml @@ -5,64 +5,64 @@ NNI: version: '2.2' project: https://github.com/microsoft/nni params: - tuner_type: 'bohb' - arch_type: 'random forest' + tuner_type: 'Random' + arch_type: 'random_forest' -# tuner type in ['tpe', 'random_search', 'anneal', 'evolution', 'smac', 'gp', 'metis', 'hyperband', 'bohb'] +# tuner type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'Hyperband', 'BOHB'] # arch_type in ['random_forest'] NNI_TPE: extends: NNI params: - tuner_type: 'tpe' + tuner_type: 'TPE' arch_type: 'random_forest' NNI_RANDOM_SEARCH: extends: NNI params: - tuner_type: 'random_search' + tuner_type: 'Random' arch_type: 'random_forest' NNI_ANNEAL: extends: NNI params: - tuner_type: 'anneal' + tuner_type: 'Anneal' arch_type: 'random_forest' NNI_EVOLUTION: extends: NNI params: - tuner_type: 'evolution' + tuner_type: 'Evolution' arch_type: 'random_forest' NNI_SMAC: extends: NNI params: - tuner_type: 'smac' + tuner_type: 'SMAC' arch_type: 'random_forest' NNI_GP: extends: NNI params: - tuner_type: 'gp' + tuner_type: 'GPTuner' arch_type: 'random_forest' NNI_METIS: extends: NNI params: - tuner_type: 'metis' + tuner_type: 'MetisTuner' arch_type: 'random_forest' NNI_HYPERBAND: extends: NNI params: - tuner_type: 'hyperband' + tuner_type: 'Hyperband' arch_type: 'random_forest' max_resource: 60 NNI_BOHB: extends: NNI params: - tuner_type: 'bohb' + tuner_type: 'BOHB' arch_type: 'random_forest' max_resource: 60 \ No newline at end of file From c6ebe4a0c8ec006bb719ceb4f26976da31f4aeb9 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 11 May 2021 17:28:03 +0900 Subject: [PATCH 08/31] Change HPO Benchmark running script logic; Modified README file --- .../trials/benchmarking/automlbenchmark/README.md | 11 ++++++++--- .../benchmarking/automlbenchmark/runbenchmark_nni.sh | 8 +++++++- 2 files changed, 15 insertions(+), 4 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index ff770156cb..fc769649ce 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -17,15 +17,20 @@ This folder contains a benchmark tool that enables comparisons between the perfo ### Run predefined benchmarks on existing tuners ```bash -./runbenchmark_nni.sh +./runbenchmark_nni.sh \[tuner-names\] ``` This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the "results.csv.parsed" file in the result folder associated with current time. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. +By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in \[tuner-names\], it only runs the tuners in the list. + ### Run customized benchmarks on existing tuners To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nni/benchmarks`, and change the `benchmark` variable in `runbenchmark_nni.sh`. See `./automlbenchmark/resources/benchmarks/` for some examples of defining a custom benchmark. ### Run benchmarks on custom tuners -To use custom tuners, first make sure that the tuner inherits from `nni.tuner.Tuner` and correctly implements the required APIs. Next, perform the following steps: +To use custom tuners, first make sure that the tuner inherits from `nni.tuner.Tuner` and correctly implements the required APIs. For more information on implementing a custom tuner, please refer to [here](https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html). Next, perform the following steps: 1. Install the custom tuner with command `nnictl algo register`. Check [this page](https://nni.readthedocs.io/en/stable/Tutorial/Nnictl.html) for details. 1. In `./nni/frameworks.yaml`, add a new framework extending the base framework NNI. Make sure that the parameter `tuner_type` corresponds to the "builtinName" of tuner installed in step 1. -1. (Optional) Include the new framework in the last step into `runbenchmark_nni.sh`. +1. Run the following command: +```bash +./runbenchmark_nni.sh \ +``` diff --git a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh index e837137c88..7b4cea762a 100755 --- a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh +++ b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh @@ -5,7 +5,13 @@ installation='automlbenchmark' outdir="results_$time" benchmark='nnivalid' -for tuner in 'NNI_TPE' 'NNI_RANDOM_SEARCH' 'NNI_ANNEAL' 'NNI_EVOLUTION' 'NNI_SMAC' 'NNI_GP' 'NNI_METIS' 'NNI_HYPERBAND' 'NNI_BOHB'; do +if [ "$#" -eq 0 ]; then + tuner_array=('NNI_TPE' 'NNI_RANDOM_SEARCH' 'NNI_ANNEAL' 'NNI_EVOLUTION' 'NNI_SMAC' 'NNI_GP' 'NNI_METIS' 'NNI_HYPERBAND' 'NNI_BOHB') +else + tuner_array=( "$@" ) +fi + +for tuner in ${tuner_array[*]}; do echo "python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni" python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni done From cf129d6add266e4baaa16174a4c4b8314b1acaea Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 11 May 2021 17:30:12 +0900 Subject: [PATCH 09/31] README fix --- examples/trials/benchmarking/automlbenchmark/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index fc769649ce..663e8eac07 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -17,11 +17,11 @@ This folder contains a benchmark tool that enables comparisons between the perfo ### Run predefined benchmarks on existing tuners ```bash -./runbenchmark_nni.sh \[tuner-names\] +./runbenchmark_nni.sh [tuner-names] ``` This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the "results.csv.parsed" file in the result folder associated with current time. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. -By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in \[tuner-names\], it only runs the tuners in the list. +By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. ### Run customized benchmarks on existing tuners To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nni/benchmarks`, and change the `benchmark` variable in `runbenchmark_nni.sh`. See `./automlbenchmark/resources/benchmarks/` for some examples of defining a custom benchmark. @@ -32,5 +32,5 @@ To use custom tuners, first make sure that the tuner inherits from `nni.tuner.Tu 1. In `./nni/frameworks.yaml`, add a new framework extending the base framework NNI. Make sure that the parameter `tuner_type` corresponds to the "builtinName" of tuner installed in step 1. 1. Run the following command: ```bash -./runbenchmark_nni.sh \ +./runbenchmark_nni.sh new-tuner-builtinName ``` From b9f4e3889e58bd9e126448559af5d39d562cc3e3 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 11 May 2021 17:46:06 +0900 Subject: [PATCH 10/31] README fix --- examples/trials/benchmarking/automlbenchmark/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index 663e8eac07..6298ed6706 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -21,7 +21,7 @@ This folder contains a benchmark tool that enables comparisons between the perfo ``` This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the "results.csv.parsed" file in the result folder associated with current time. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. -By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. +By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. ### Run customized benchmarks on existing tuners To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nni/benchmarks`, and change the `benchmark` variable in `runbenchmark_nni.sh`. See `./automlbenchmark/resources/benchmarks/` for some examples of defining a custom benchmark. From 50caa4f30fc00f6a18619d8b1306047195d4d3b3 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Wed, 12 May 2021 18:37:45 +0900 Subject: [PATCH 11/31] Summarize dependencies in a requirement.txt file; update the documentation on this change --- .../benchmarking/automlbenchmark/README.md | 11 ++++ .../automlbenchmark/nni/benchmarks/small.yaml | 53 +++++++++++++++++++ .../NNI/architectures/run_random_forest.py | 10 +++- .../automlbenchmark/requirements.txt | 5 ++ 4 files changed, 77 insertions(+), 2 deletions(-) create mode 100644 examples/trials/benchmarking/automlbenchmark/nni/benchmarks/small.yaml create mode 100644 examples/trials/benchmarking/automlbenchmark/requirements.txt diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index 6298ed6706..861df8978c 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -11,9 +11,20 @@ This folder contains a benchmark tool that enables comparisons between the perfo * **architecture**: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. ### Setup +Due to some incompatibilities between automlbenchmark and python 3.8, python 3.7 is recommended for running experiments contained in this folder. First, run the following shell script to clone the automlbenchmark repository. ```bash ./setup.sh ``` +Then, install the dependencies required by automlbenchmark using the following commands. +```bash +cd automlbenchmark +pip3 install -r requirements.txt +``` +Finally, install additional dependencies using the following commands. +```bash +cd .. +pip3 install -r requirements.txt +``` ### Run predefined benchmarks on existing tuners ```bash diff --git a/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/small.yaml b/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/small.yaml new file mode 100644 index 0000000000..8654f41287 --- /dev/null +++ b/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/small.yaml @@ -0,0 +1,53 @@ +--- +- name: __defaults__ + folds: 2 + cores: 2 + max_runtime_seconds: 60 + +- name: Australian + openml_task_id: 146818 + +- name: blood-transfusion + openml_task_id: 10101 + +- name: car + openml_task_id: 146821 + +- name: christine + openml_task_id: 168908 + +- name: cnae-9 + openml_task_id: 9981 + +- name: credit-g + openml_task_id: 31 + +- name: dilbert + openml_task_id: 168909 + +- name: fabert + openml_task_id: 168910 + +- name: jasmine + openml_task_id: 168911 + +- name: kc1 + openml_task_id: 3917 + +- name: kr-vs-kp + openml_task_id: 3 + +- name: mfeat-factors + openml_task_id: 12 + +- name: phoneme + openml_task_id: 9952 + +- name: segment + openml_task_id: 146822 + +- name: sylvine + openml_task_id: 168912 + +- name: vehicle + openml_task_id: 53 diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py index e3fd04ed58..ed1a8ffca4 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -14,6 +14,9 @@ SEARCH_SPACE = { "n_estimators": {"_type":"choice", "_value": [128, 256, 512, 1024, 2048, 4096]}, "max_depth": {"_type":"choice", "_value": [5, 10, 25, 50, 100]}, + "min_samples_leaf": {"_type":"choice", "_value": [1, 2, 4, 8]}, + "min_samples_split": {"_type":"choice", "_value": [2, 4, 8, 16]}, + "max_leaf_nodes": {"_type":"choice", "_value": [0, 8, 64, 512]} # 0 for None } @@ -33,9 +36,12 @@ def run_random_forest(dataset, config, tuner, log): while True: try: param_idx, cur_params = tuner.generate_parameters() + train_params = cur_params.copy() if 'TRIAL_BUDGET' in cur_params: - cur_params.pop('TRIAL_BUDGET') - cur_model = estimator(random_state=config.seed, **cur_params) + train_params.pop('TRIAL_BUDGET') + if cur_params['max_leaf_nodes'] == 0: + train_params.pop('max_leaf_nodes') + cur_model = estimator(random_state=config.seed, **train_params) # Here score is the output of score() from the estimator cur_score = cross_val_score(cur_model, X_train, y_train) cur_score = sum(cur_score) / float(len(cur_score)) diff --git a/examples/trials/benchmarking/automlbenchmark/requirements.txt b/examples/trials/benchmarking/automlbenchmark/requirements.txt new file mode 100644 index 0000000000..20d362b600 --- /dev/null +++ b/examples/trials/benchmarking/automlbenchmark/requirements.txt @@ -0,0 +1,5 @@ +pandas>=1.2.0 +statsmodels>=0.12.2 +pyyaml>=5.4.1 +ConfigSpace>=0.4.18 +nni[smac] From f1b06519caf5e3a4be4470114cfaad0178a70315 Mon Sep 17 00:00:00 2001 From: Bill Wu <43978113+xiaowu0162@users.noreply.github.com> Date: Wed, 12 May 2021 17:44:02 +0800 Subject: [PATCH 12/31] Update README.md --- examples/trials/benchmarking/automlbenchmark/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index 861df8978c..7262d10df2 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -15,7 +15,7 @@ Due to some incompatibilities between automlbenchmark and python 3.8, python 3.7 ```bash ./setup.sh ``` -Then, install the dependencies required by automlbenchmark using the following commands. +Then, install the dependencies required by automlbenchmark using the following commands. It is recommended to perform the following steps in a separate virtual environment. ```bash cd automlbenchmark pip3 install -r requirements.txt From 6d621d04fd27879962621a4ae75ea995325a7650 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Wed, 12 May 2021 19:05:22 +0900 Subject: [PATCH 13/31] Updated requirement for HPO benchmark --- examples/trials/benchmarking/automlbenchmark/README.md | 1 + examples/trials/benchmarking/automlbenchmark/requirements.txt | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index 861df8978c..d4c1504a02 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -33,6 +33,7 @@ pip3 install -r requirements.txt This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the "results.csv.parsed" file in the result folder associated with current time. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. +Note: the SMAC tuner has to be manually installed before any experiments can be run on it. Please refer to [this page](https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni%5Bsmac%5D#smac) for more details on installing SMAC. ### Run customized benchmarks on existing tuners To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nni/benchmarks`, and change the `benchmark` variable in `runbenchmark_nni.sh`. See `./automlbenchmark/resources/benchmarks/` for some examples of defining a custom benchmark. diff --git a/examples/trials/benchmarking/automlbenchmark/requirements.txt b/examples/trials/benchmarking/automlbenchmark/requirements.txt index 20d362b600..30d06d4c12 100644 --- a/examples/trials/benchmarking/automlbenchmark/requirements.txt +++ b/examples/trials/benchmarking/automlbenchmark/requirements.txt @@ -2,4 +2,3 @@ pandas>=1.2.0 statsmodels>=0.12.2 pyyaml>=5.4.1 ConfigSpace>=0.4.18 -nni[smac] From 40c8a42cbf86fc96df69c0db787708176d03f486 Mon Sep 17 00:00:00 2001 From: Bill Wu <43978113+xiaowu0162@users.noreply.github.com> Date: Wed, 12 May 2021 18:09:10 +0800 Subject: [PATCH 14/31] Update README.md --- examples/trials/benchmarking/automlbenchmark/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index 47126d17cb..86e5fb5b14 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -33,6 +33,7 @@ pip3 install -r requirements.txt This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the "results.csv.parsed" file in the result folder associated with current time. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. + Note: the SMAC tuner has to be manually installed before any experiments can be run on it. Please refer to [this page](https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni%5Bsmac%5D#smac) for more details on installing SMAC. ### Run customized benchmarks on existing tuners From bcd8758251010899b432e55f598ffec415807981 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Thu, 13 May 2021 17:59:14 +0900 Subject: [PATCH 15/31] 20210513 HPO Benchmark feature updates: * add a new benchmark "nnismall" with binary classification, multi-class classification, and regression tasks * re-implement a correct data preprocessing pipeline for random forest * re-organize dependencies and update setup.sh --- .gitignore | 2 +- .../benchmarking/automlbenchmark/README.md | 14 +- .../benchmarks/{small.yaml => nnismall.yaml} | 28 +++- .../nni/benchmarks/nnismall_description.txt | 152 ++++++++++++++++++ .../NNI/architectures/run_random_forest.py | 69 +++++++- .../nni/extensions/NNI/tuners.py | 26 +-- .../automlbenchmark/nni/frameworks.yaml | 18 +-- .../automlbenchmark/parse_result_csv.py | 3 +- .../automlbenchmark/requirements.txt | 2 - .../automlbenchmark/runbenchmark_nni.sh | 4 +- .../benchmarking/automlbenchmark/setup.sh | 10 +- 11 files changed, 278 insertions(+), 50 deletions(-) rename examples/trials/benchmarking/automlbenchmark/nni/benchmarks/{small.yaml => nnismall.yaml} (65%) create mode 100644 examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt diff --git a/.gitignore b/.gitignore index e15a772368..918caf8b55 100644 --- a/.gitignore +++ b/.gitignore @@ -23,7 +23,7 @@ pids *.pid *.seed *.pid.lock -/nni/algorithms/hpo/benchmark/nni/data +/examples/trials/benchmarking/automlbenchmark/nni/data # Build package dist/ diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index 86e5fb5b14..e232ab5d23 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -11,20 +11,10 @@ This folder contains a benchmark tool that enables comparisons between the perfo * **architecture**: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. ### Setup -Due to some incompatibilities between automlbenchmark and python 3.8, python 3.7 is recommended for running experiments contained in this folder. First, run the following shell script to clone the automlbenchmark repository. +Due to some incompatibilities between automlbenchmark and python 3.8, python 3.7 is recommended for running experiments contained in this folder. First, run the following shell script to clone the automlbenchmark repository. Note: it is recommended to perform the following steps in a separate virtual environment, as the setup code may install several packages. ```bash ./setup.sh ``` -Then, install the dependencies required by automlbenchmark using the following commands. It is recommended to perform the following steps in a separate virtual environment. -```bash -cd automlbenchmark -pip3 install -r requirements.txt -``` -Finally, install additional dependencies using the following commands. -```bash -cd .. -pip3 install -r requirements.txt -``` ### Run predefined benchmarks on existing tuners ```bash @@ -34,7 +24,7 @@ This script runs the benchmark 'nnivalid', which consists of a regression task, By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. -Note: the SMAC tuner has to be manually installed before any experiments can be run on it. Please refer to [this page](https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni%5Bsmac%5D#smac) for more details on installing SMAC. +Note: the SMAC tuner and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to [this page](https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni) for more details on installing SMAC and BOHB. ### Run customized benchmarks on existing tuners To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nni/benchmarks`, and change the `benchmark` variable in `runbenchmark_nni.sh`. See `./automlbenchmark/resources/benchmarks/` for some examples of defining a custom benchmark. diff --git a/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/small.yaml b/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall.yaml similarity index 65% rename from examples/trials/benchmarking/automlbenchmark/nni/benchmarks/small.yaml rename to examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall.yaml index 8654f41287..5b68dc898d 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/small.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall.yaml @@ -2,8 +2,32 @@ - name: __defaults__ folds: 2 cores: 2 - max_runtime_seconds: 60 - + max_runtime_seconds: 300 + +- name: cholesterol + openml_task_id: 2295 + +- name: liver-disorders + openml_task_id: 52948 + +- name: kin8nm + openml_task_id: 2280 + +- name: cpu_small + openml_task_id: 4883 + +- name: titanic_2 + openml_task_id: 211993 + +- name: boston + openml_task_id: 4857 + +- name: stock + openml_task_id: 2311 + +- name: space_ga + openml_task_id: 4835 + - name: Australian openml_task_id: 146818 diff --git a/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt b/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt new file mode 100644 index 0000000000..d8d0aa0ec4 --- /dev/null +++ b/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt @@ -0,0 +1,152 @@ +nnismall: +This benchmark contains 24 tasks: 8 tasks each for binary classfication, multi-class classification, and regression. + +Binary Classification: +- name: Australian + openml_task_id: 146818 + Introduction: Australian Credit Approval dataset, originating from the StatLog project. It concerns credit card applications. + Features: 6 numerical and 8 categorical features, all normalized to [-1,1]. + Number of instances: 690 + +- name: blood-transfusion + openml_task_id: 10101 + Introduction: Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The target attribute is a binary variable representing whether he/she donated blood in March 2007 (2 stands for donating blood; 1 stands for not donating blood). + Features: 4 numerical features. + Number of instances: 748 + +- name: christine + openml_task_id: 168908 + Introduction: An Openml challenge dataset on classification. The identity of the datasets and the type of data is concealed. + Features: 1599 numerical features and 38 categorical features + Number of instances: 5418 + +- name: credit-g + openml_task_id: 31 + Introduction: This dataset classifies people described by a set of attributes as good or bad credit risks. + Features: 7 numerical features and 13 categorical features + Number of instances: 1000 + +- name: kc1 + openml_task_id: 3917 + Introduction: One of the NASA Metrics Data Program defect data sets. Data from software for storage management for receiving and processing ground data. + Features: 21 numerical features + Number of instances: 2109 + +- name: kr-vs-kp + openml_task_id: 3 + Introduction: Given a board configuration, predict whether white can win or not. + Features: 37 categorical features + Number of instances: 3196 + +- name: phoneme + openml_task_id: 9952 + Introduction: The aim of this dataset is to distinguish between nasal (class 0) and oral sounds (class 1). + Features: 5 numerical features + Number of instances: 5404 + +- name: sylvine + openml_task_id: 168912 + Introduction: An Openml challenge dataset on classification. The identity of the datasets and the type of data is concealed. + Features: 20 numerical features + Number of instances: 5124 + + + +Multi-class Classification +- name: car + openml_task_id: 146821 + Introduction: The model evaluates cars using six intermediate concepts. + Features: 6 categorical features + Number of instances: 1728 + +- name: cnae-9 + openml_task_id: 9981 + Introduction: This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a subset of 9 categories. + Features: 856 numerical features (word frequency) + Number of instances: 1080 + +- name: dilbert + openml_task_id: 168909 + Introduction: An Openml challenge dataset on classification. The identity of the datasets and the type of data is concealed. + Features: 2000 numerical features + Number of instances: 10000 + +- name: fabert + openml_task_id: 168910 + Introduction: An Openml challenge dataset on classification. The identity of the datasets and the type of data is concealed. + Features: 800 numerical features + Number of instances: 8237 + +- name: jasmine + openml_task_id: 168911 + Introduction: An Openml challenge dataset on classification. The identity of the datasets and the type of data is concealed. + Features: 8 numerical features and 137 categorical features + Number of instances: 2984 + +- name: mfeat-factors + openml_task_id: 12 + Introduction: Hand-written numeral classification. + Features: 216 numerical features(corresponding to binarized image) + Number of instances: 2000 + +- name: segment + openml_task_id: 146822 + Introduction: segmentation of outdoor images into 7 classes + Features: 19 numerical features + Number of instances: 2310 (3x3 patches from 7 images) + +- name: vehicle + openml_task_id: 53 + Introduction: Classify a given silhouette as one of four types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles. + Features: 18 numerical features + Number of instances: 846 + + +Regression +- name: cholesterol + openml_task_id: 2295 + Introduction: Predict the cholesterol level of patients. + Features: 6 numerical features and 7 categorical features + Number of instances: 303 + +- name: liver-disorders + openml_task_id: 52948 + Introduction: Predict alcohol assumption based on blood test results. + Features: 5 numerical features + Number of instances: 345 + +- name: kin8nm + openml_task_id: 2280 + Introduction: This dataset is concerned with the forward kinematics of an 8 link robot arm. + Features: 8 numerical features + Number of instances: 8192 + +- name: cpu_small + openml_task_id: 4883 + Introduction: Predict the portion of time that cpus run in user mode. + Features: 12 numerical features + Number of instances: 8192 + +- name: titanic_2 + openml_task_id: 211993 + Introduction: Predict probability of survival + Features: 7 numerical features + Number of instances: 891 + +- name: boston + openml_task_id: 4857 + Introduction: Boston house price. + Features: 11 numerical features and 2 categorical features + Number of instances: 506 + +- name: stock + openml_task_id: 2311 + Introduction: This is a dataset obtained from the StatLib repository. The data provided are daily stock prices from January 1988 through October 1991, for ten aerospace companies. + Features: 11 numerical features + Number of instances: 950 + +- name: space_ga + openml_task_id: 4835 + Introduction: Predict the log of the proportion of votes cast for both candidates in the 1980 presidential election. + Features: 6 numerical attributes + Number of instances: 3107 diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py index ed1a8ffca4..4498bdb328 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -1,6 +1,12 @@ import logging import sklearn import time +import numpy as np + +from sklearn.impute import SimpleImputer +from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import OrdinalEncoder +from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from sklearn.model_selection import cross_val_score @@ -10,22 +16,66 @@ from amlb.utils import Timer - SEARCH_SPACE = { "n_estimators": {"_type":"choice", "_value": [128, 256, 512, 1024, 2048, 4096]}, "max_depth": {"_type":"choice", "_value": [5, 10, 25, 50, 100]}, "min_samples_leaf": {"_type":"choice", "_value": [1, 2, 4, 8]}, "min_samples_split": {"_type":"choice", "_value": [2, 4, 8, 16]}, - "max_leaf_nodes": {"_type":"choice", "_value": [0, 8, 64, 512]} # 0 for None + "max_leaf_nodes": {"_type":"choice", "_value": [8, 64, 512, 0]} # 0 for None +} + +SEARCH_SPACE_SIMPLE = { + "n_estimators": {"_type":"choice", "_value": [128]}, + "max_depth": {"_type":"choice", "_value": [5]}, + "min_samples_leaf": {"_type":"choice", "_value": [8]}, + "min_samples_split": {"_type":"choice", "_value": [16]}, + "max_leaf_nodes": {"_type":"choice", "_value": [64]} } + +def preprocess_random_forest(dataset, log): + ''' + For random forest: + - Do nothing for numerical features except null imputation. + - For categorical features, use ordinal encoding to map them into integers. + ''' + cat_columns, num_columns = [], [] + shift_amount = 0 + for i, f in enumerate(dataset.features): + if f.is_target: + shift_amount += 1 + continue + elif f.is_categorical(): + cat_columns.append(i - shift_amount) + else: + num_columns.append(i - shift_amount) + + cat_pipeline = Pipeline([('imputer', SimpleImputer(strategy='most_frequent')), + ('ordinal_encoder', OrdinalEncoder()), + ]) -def run_random_forest(dataset, config, tuner, log): - is_classification = config.type == 'classification' + num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean')), + ]) + + data_pipeline = ColumnTransformer([ + ('categorical', cat_pipeline, cat_columns), + ('numerical', num_pipeline, num_columns), + ]) - X_train, X_test = impute(dataset.train.X, dataset.test.X) + data_pipeline.fit(np.concatenate([dataset.train.X, dataset.test.X], axis=0)) + + X_train = data_pipeline.transform(dataset.train.X) + X_test = data_pipeline.transform(dataset.test.X) + + return X_train, X_test + + +def run_random_forest(dataset, config, tuner, log): + #X_train, X_test = impute(dataset.train.X, dataset.test.X, strategy='mean') + X_train, X_test = preprocess_random_forest(dataset, log) y_train, y_test = dataset.train.y, dataset.test.y + is_classification = config.type == 'classification' estimator = RandomForestClassifier if is_classification else RandomForestRegressor best_score, best_params, best_model = None, None, None @@ -41,14 +91,17 @@ def run_random_forest(dataset, config, tuner, log): train_params.pop('TRIAL_BUDGET') if cur_params['max_leaf_nodes'] == 0: train_params.pop('max_leaf_nodes') + log.info("Trial {}: \n{}\n".format(param_idx, cur_params)) + cur_model = estimator(random_state=config.seed, **train_params) + # Here score is the output of score() from the estimator cur_score = cross_val_score(cur_model, X_train, y_train) cur_score = sum(cur_score) / float(len(cur_score)) + log.info("Score: {}\n".format(cur_score)) if best_score is None or (score_higher_better and cur_score > best_score) or (not score_higher_better and cur_score < best_score): - best_score, best_params, best_model = cur_score, cur_params, cur_model - - log.info("Trial {}: \n{}\nScore: {}\n".format(param_idx, cur_params, cur_score)) + best_score, best_params, best_model = cur_score, cur_params, cur_model + tuner.receive_trial_result(param_idx, cur_params, cur_score) current_time = time.time() diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py index 7e748b905e..3e02a6109d 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py @@ -7,8 +7,6 @@ from nni.utils import MetricType from nni.tuner import Tuner from nni.runtime.msg_dispatcher_base import MsgDispatcherBase -from nni.algorithms.hpo.bohb_advisor.bohb_advisor import BOHB -from nni.algorithms.hpo.bohb_advisor.config_generator import CG_BOHB from amlb.benchmark import TaskConfig @@ -101,15 +99,21 @@ def update_search_space(self, search_space): elif self.core_type == 'advisor': self.core.handle_update_search_space(search_space) # special initializations for BOHB Advisor - if isinstance(self.core, BOHB): - self.core.cg = CG_BOHB(configspace=self.core.search_space, - min_points_in_model=self.core.min_points_in_model, - top_n_percent=self.core.top_n_percent, - num_samples=self.core.num_samples, - random_fraction=self.core.random_fraction, - bandwidth_factor=self.core.bandwidth_factor, - min_bandwidth=self.core.min_bandwidth) - self.core.generate_new_bracket() + from nni.algorithms.hpo.hyperband_advisor import Hyperband + if isinstance(self.core, Hyperband): + pass + else: + from nni.algorithms.hpo.bohb_advisor.bohb_advisor import BOHB + from nni.algorithms.hpo.bohb_advisor.config_generator import CG_BOHB + if isinstance(self.core, BOHB): + self.core.cg = CG_BOHB(configspace=self.core.search_space, + min_points_in_model=self.core.min_points_in_model, + top_n_percent=self.core.top_n_percent, + num_samples=self.core.num_samples, + random_fraction=self.core.random_fraction, + bandwidth_factor=self.core.bandwidth_factor, + min_bandwidth=self.core.min_bandwidth) + self.core.generate_new_bracket() def generate_parameters(self): diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml index 4f3ee033b8..4bdd84b055 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml @@ -11,56 +11,56 @@ NNI: # tuner type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'Hyperband', 'BOHB'] # arch_type in ['random_forest'] -NNI_TPE: +TPE: extends: NNI params: tuner_type: 'TPE' arch_type: 'random_forest' -NNI_RANDOM_SEARCH: +Random: extends: NNI params: tuner_type: 'Random' arch_type: 'random_forest' -NNI_ANNEAL: +Anneal: extends: NNI params: tuner_type: 'Anneal' arch_type: 'random_forest' -NNI_EVOLUTION: +Evolution: extends: NNI params: tuner_type: 'Evolution' arch_type: 'random_forest' -NNI_SMAC: +SMAC: extends: NNI params: tuner_type: 'SMAC' arch_type: 'random_forest' -NNI_GP: +GPTuner: extends: NNI params: tuner_type: 'GPTuner' arch_type: 'random_forest' -NNI_METIS: +MetisTuner: extends: NNI params: tuner_type: 'MetisTuner' arch_type: 'random_forest' -NNI_HYPERBAND: +Hyperband: extends: NNI params: tuner_type: 'Hyperband' arch_type: 'random_forest' max_resource: 60 -NNI_BOHB: +BOHB: extends: NNI params: tuner_type: 'BOHB' diff --git a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py index b3189f84e6..cb94377f85 100644 --- a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py +++ b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py @@ -9,6 +9,8 @@ result_file = sys.argv[1] result = pd.read_csv(result_file) +keep_parameters = ['framework', 'constraint', 'result', 'metric', 'params', 'utc', 'duration'] + list(result.columns[16:]) + task_ids = result['id'].unique() with open(result_file + '.parsed', 'w') as out_f: @@ -21,7 +23,6 @@ folds = task_results['fold'].unique() for fold in folds: out_f.write("Fold {}:\n".format(fold)) - keep_parameters = ['framework', 'constraint', 'result', 'metric', 'params', 'utc', 'duration', 'acc', 'auc', 'logloss', 'r2', 'rmse'] res = task_results[task_results['fold'] == fold][keep_parameters] out_f.write(res.to_string()) out_f.write('\n') diff --git a/examples/trials/benchmarking/automlbenchmark/requirements.txt b/examples/trials/benchmarking/automlbenchmark/requirements.txt index 30d06d4c12..d68c776ee4 100644 --- a/examples/trials/benchmarking/automlbenchmark/requirements.txt +++ b/examples/trials/benchmarking/automlbenchmark/requirements.txt @@ -1,4 +1,2 @@ pandas>=1.2.0 -statsmodels>=0.12.2 pyyaml>=5.4.1 -ConfigSpace>=0.4.18 diff --git a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh index 7b4cea762a..9b1b9bf8b7 100755 --- a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh +++ b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh @@ -3,10 +3,10 @@ time=$(date "+%Y%m%d%H%M%S") installation='automlbenchmark' outdir="results_$time" -benchmark='nnivalid' +benchmark='nnismall' if [ "$#" -eq 0 ]; then - tuner_array=('NNI_TPE' 'NNI_RANDOM_SEARCH' 'NNI_ANNEAL' 'NNI_EVOLUTION' 'NNI_SMAC' 'NNI_GP' 'NNI_METIS' 'NNI_HYPERBAND' 'NNI_BOHB') + tuner_array=('TPE' 'Random' 'Anneal' 'Evolution' 'GPTuner' 'MetisTuner' 'Hyperband') else tuner_array=( "$@" ) fi diff --git a/examples/trials/benchmarking/automlbenchmark/setup.sh b/examples/trials/benchmarking/automlbenchmark/setup.sh index d25203b08e..6fe091fad6 100755 --- a/examples/trials/benchmarking/automlbenchmark/setup.sh +++ b/examples/trials/benchmarking/automlbenchmark/setup.sh @@ -1,5 +1,11 @@ #!/bin/bash -# setup code -git clone https://github.com/openml/automlbenchmark.git --branch stable --depth 1 +# download automlbenchmark repository +if [ ! -d './automlbenchmark' ] ; then + git clone https://github.com/openml/automlbenchmark.git --branch stable --depth 1 +fi + +# install dependencies +pip3 install -r automlbenchmark/requirements.txt +pip3 install -r requirements.txt --ignore-installed From 7136f3138ef6ec6f19b304e74c10bd6ad9723a86 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Fri, 14 May 2021 15:27:05 +0900 Subject: [PATCH 16/31] debug --- .../benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py index 3e02a6109d..9d0b00c655 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py @@ -17,7 +17,7 @@ def get_tuner_class_dict(): with open(config_file, 'r') as f: config = yaml.load(f, Loader=yaml.SafeLoader) else: - config = defaultdict(list) + config = {} ret = {} for t in ['tuners', 'advisors']: for entry in config[t]: From 70559915e72cbbfdafa3fc2ee7a3b24a7325e30a Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 18 May 2021 16:08:22 +0900 Subject: [PATCH 17/31] Support either "time" or "ntrials" as benchmark constraints --- .../NNI/architectures/run_random_forest.py | 51 ++++++++++++++----- .../nni/extensions/NNI/exec.py | 27 ++++++++-- .../automlbenchmark/nni/frameworks.yaml | 22 +++++++- .../automlbenchmark/runbenchmark_nni.sh | 2 +- 4 files changed, 84 insertions(+), 18 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py index 4498bdb328..d292567ff4 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -16,20 +16,35 @@ from amlb.utils import Timer +import sys +import warnings + +if not sys.warnoptions: + warnings.simplefilter("ignore") + + SEARCH_SPACE = { - "n_estimators": {"_type":"choice", "_value": [128, 256, 512, 1024, 2048, 4096]}, - "max_depth": {"_type":"choice", "_value": [5, 10, 25, 50, 100]}, + "n_estimators": {"_type":"randint", "_value": [8, 512]}, + "max_depth": {"_type":"choice", "_value": [4, 8, 16, 32, 64, 128, 256, 0]}, # 0 for None + "min_samples_leaf": {"_type":"randint", "_value": [1, 8]}, + "min_samples_split": {"_type":"randint", "_value": [2, 16]}, + "max_leaf_nodes": {"_type":"randint", "_value": [0, 4096]} # 0 for None +} + +SEARCH_SPACE_CHOICE = { + "n_estimators": {"_type":"choice", "_value": [8, 16, 32, 64, 128, 256, 512]}, + "max_depth": {"_type":"choice", "_value": [4, 8, 16, 32, 64, 128, 0]}, # 0 for None "min_samples_leaf": {"_type":"choice", "_value": [1, 2, 4, 8]}, "min_samples_split": {"_type":"choice", "_value": [2, 4, 8, 16]}, - "max_leaf_nodes": {"_type":"choice", "_value": [8, 64, 512, 0]} # 0 for None + "max_leaf_nodes": {"_type":"choice", "_value": [8, 32, 128, 512, 0]} # 0 for None } SEARCH_SPACE_SIMPLE = { - "n_estimators": {"_type":"choice", "_value": [128]}, + "n_estimators": {"_type":"choice", "_value": [10]}, "max_depth": {"_type":"choice", "_value": [5]}, "min_samples_leaf": {"_type":"choice", "_value": [8]}, "min_samples_split": {"_type":"choice", "_value": [16]}, - "max_leaf_nodes": {"_type":"choice", "_value": [64]} + "max_leaf_nodes": {"_type":"choice", "_value": [64]} } @@ -40,7 +55,7 @@ def preprocess_random_forest(dataset, log): - For categorical features, use ordinal encoding to map them into integers. ''' cat_columns, num_columns = [], [] - shift_amount = 0 + shift_amount = 0 for i, f in enumerate(dataset.features): if f.is_target: shift_amount += 1 @@ -49,7 +64,7 @@ def preprocess_random_forest(dataset, log): cat_columns.append(i - shift_amount) else: num_columns.append(i - shift_amount) - + cat_pipeline = Pipeline([('imputer', SimpleImputer(strategy='most_frequent')), ('ordinal_encoder', OrdinalEncoder()), ]) @@ -71,7 +86,8 @@ def preprocess_random_forest(dataset, log): def run_random_forest(dataset, config, tuner, log): - #X_train, X_test = impute(dataset.train.X, dataset.test.X, strategy='mean') + limit_type, limit = config.framework_params['limit_type'], int(config.framework_params['limit']) + X_train, X_test = preprocess_random_forest(dataset, log) y_train, y_test = dataset.train.y, dataset.test.y @@ -82,15 +98,22 @@ def run_random_forest(dataset, config, tuner, log): score_higher_better = True tuner.update_search_space(SEARCH_SPACE) + + start_time = time.time() + trial_count = 0 + while True: try: + trial_count += 1 param_idx, cur_params = tuner.generate_parameters() train_params = cur_params.copy() if 'TRIAL_BUDGET' in cur_params: train_params.pop('TRIAL_BUDGET') if cur_params['max_leaf_nodes'] == 0: train_params.pop('max_leaf_nodes') + if cur_params['max_depth'] == 0: + train_params.pop('max_depth') log.info("Trial {}: \n{}\n".format(param_idx, cur_params)) cur_model = estimator(random_state=config.seed, **train_params) @@ -104,10 +127,14 @@ def run_random_forest(dataset, config, tuner, log): tuner.receive_trial_result(param_idx, cur_params, cur_score) - current_time = time.time() - elapsed_time = current_time - start_time - if elapsed_time > config.max_runtime_seconds: - break + if limit_type == 'time': + current_time = time.time() + elapsed_time = current_time - start_time + if elapsed_time >= limit: # config.max_runtime_seconds: + break + elif limit_type == 'ntrials': + if trial_count >= limit: + break except: break diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py index 3fd7cd6fcd..a0cdccdeb0 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py @@ -11,14 +11,33 @@ log = logging.getLogger(__name__) - -def run(dataset: Dataset, config: TaskConfig): + +def validate_config(config: TaskConfig): if 'tuner_type' not in config.framework_params: raise RuntimeError('framework.yaml does not have a "tuner_type" field.') + if 'limit_type' not in config.framework_params: + raise RuntimeError('framework.yaml does not have a "limit_type" field.') + if config.framework_params['limit_type'] not in ['time', 'ntrials']: + raise RuntimeError('"limit_type" field must be "time" or "ntrials".') + if 'limit' not in config.framework_params: + raise RuntimeError('framework.yaml does not have a "limit" field.') + else: + try: + _ = int(config.framework_params['limit']) + except: + raise RuntimeError('"limit" field must be an integer.') + +def run(dataset: Dataset, config: TaskConfig): + validate_config(config) tuner = NNITuner(config) - log.info("Tuning {} with NNI {} with a maximum time of {}s\n" - .format(config.framework_params['arch_type'], tuner.description, config.max_runtime_seconds)) + if config.framework_params['limit_type'] == 'time': + log.info("Tuning {} with NNI {} with a maximum time of {}s\n" + .format(config.framework_params['arch_type'], tuner.description, config.framework_params['limit'])) + elif config.framework_params['limit_type'] == 'ntrials': + log.info("Tuning {} with NNI {} with a maximum number of trials of {}\n" + .format(config.framework_params['arch_type'], tuner.description, config.framework_params['limit'])) + log.info("Note: any time constraints are ignored.") probabilities, predictions, train_timer, y_test = run_experiment(dataset, config, tuner, log) diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml index 4bdd84b055..19880eed1b 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml @@ -10,48 +10,64 @@ NNI: # tuner type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'Hyperband', 'BOHB'] # arch_type in ['random_forest'] +# limit_type in ['time', 'ntrials'] +# limit must be an integer TPE: extends: NNI params: tuner_type: 'TPE' arch_type: 'random_forest' + limit_type: 'ntrials' + limit: 5 Random: extends: NNI params: tuner_type: 'Random' arch_type: 'random_forest' + limit_type: 'ntrials' + limit: 5 Anneal: extends: NNI params: tuner_type: 'Anneal' arch_type: 'random_forest' + limit_type: 'ntrials' + limit: 5 Evolution: extends: NNI params: tuner_type: 'Evolution' arch_type: 'random_forest' + limit_type: 'ntrials' + limit: 5 SMAC: extends: NNI params: tuner_type: 'SMAC' arch_type: 'random_forest' + limit_type: 'ntrials' + limit: 5 GPTuner: extends: NNI params: tuner_type: 'GPTuner' arch_type: 'random_forest' + limit_type: 'ntrials' + limit: 5 MetisTuner: extends: NNI params: tuner_type: 'MetisTuner' arch_type: 'random_forest' + limit_type: 'ntrials' + limit: 5 Hyperband: extends: NNI @@ -59,10 +75,14 @@ Hyperband: tuner_type: 'Hyperband' arch_type: 'random_forest' max_resource: 60 + limit_type: 'ntrials' + limit: 5 BOHB: extends: NNI params: tuner_type: 'BOHB' arch_type: 'random_forest' - max_resource: 60 \ No newline at end of file + max_resource: 60 + limit_type: 'ntrials' + limit: 5 diff --git a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh index 9b1b9bf8b7..519594bc23 100755 --- a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh +++ b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh @@ -3,7 +3,7 @@ time=$(date "+%Y%m%d%H%M%S") installation='automlbenchmark' outdir="results_$time" -benchmark='nnismall' +benchmark='nnivalid' if [ "$#" -eq 0 ]; then tuner_array=('TPE' 'Random' 'Anneal' 'Evolution' 'GPTuner' 'MetisTuner' 'Hyperband') From 4bb2bd78fb4e58b53457a6c008d09d23602cb4d7 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 18 May 2021 18:41:18 +0900 Subject: [PATCH 18/31] Add graphical presentation of benchmark results --- .../NNI/architectures/run_random_forest.py | 24 ++++--- .../nni/extensions/NNI/exec.py | 37 ++++++---- .../automlbenchmark/nni/frameworks.yaml | 18 ++--- .../automlbenchmark/parse_result_csv.py | 70 ++++++++++++++----- .../automlbenchmark/requirements.txt | 1 + .../automlbenchmark/runbenchmark_nni.sh | 2 + 6 files changed, 104 insertions(+), 48 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py index d292567ff4..47c10bcecf 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -14,21 +14,24 @@ from amlb.data import Dataset from amlb.datautils import impute from amlb.utils import Timer +from amlb.results import save_predictions_to_file +# for future use +''' import sys import warnings if not sys.warnoptions: warnings.simplefilter("ignore") - +''' SEARCH_SPACE = { "n_estimators": {"_type":"randint", "_value": [8, 512]}, "max_depth": {"_type":"choice", "_value": [4, 8, 16, 32, 64, 128, 256, 0]}, # 0 for None "min_samples_leaf": {"_type":"randint", "_value": [1, 8]}, "min_samples_split": {"_type":"randint", "_value": [2, 16]}, - "max_leaf_nodes": {"_type":"randint", "_value": [0, 4096]} # 0 for None + "max_leaf_nodes": {"_type":"randint", "_value": [0, 4096]} # 0 for None } SEARCH_SPACE_CHOICE = { @@ -86,7 +89,9 @@ def preprocess_random_forest(dataset, log): def run_random_forest(dataset, config, tuner, log): - limit_type, limit = config.framework_params['limit_type'], int(config.framework_params['limit']) + limit_type, trial_limit = config.framework_params['limit_type'], None + if limit_type == 'ntrials': + trial_limit = int(config.framework_params['trial_limit']) X_train, X_test = preprocess_random_forest(dataset, log) y_train, y_test = dataset.train.y, dataset.test.y @@ -97,12 +102,12 @@ def run_random_forest(dataset, config, tuner, log): best_score, best_params, best_model = None, None, None score_higher_better = True - tuner.update_search_space(SEARCH_SPACE) - + tuner.update_search_space(SEARCH_SPACE) start_time = time.time() trial_count = 0 - + intermediate_best_scores = [] # should be monotone increasing + while True: try: trial_count += 1 @@ -125,15 +130,16 @@ def run_random_forest(dataset, config, tuner, log): if best_score is None or (score_higher_better and cur_score > best_score) or (not score_higher_better and cur_score < best_score): best_score, best_params, best_model = cur_score, cur_params, cur_model + intermediate_best_scores.append(best_score) tuner.receive_trial_result(param_idx, cur_params, cur_score) if limit_type == 'time': current_time = time.time() elapsed_time = current_time - start_time - if elapsed_time >= limit: # config.max_runtime_seconds: + if elapsed_time >= config.max_runtime_seconds: break elif limit_type == 'ntrials': - if trial_count >= limit: + if trial_count >= trial_limit: break except: break @@ -149,4 +155,4 @@ def run_random_forest(dataset, config, tuner, log): predictions = best_model.predict(X_test) probabilities = best_model.predict_proba(X_test) if is_classification else None - return probabilities, predictions, training, y_test + return probabilities, predictions, training, y_test, intermediate_best_scores diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py index a0cdccdeb0..70bf8681a9 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py @@ -18,35 +18,46 @@ def validate_config(config: TaskConfig): if 'limit_type' not in config.framework_params: raise RuntimeError('framework.yaml does not have a "limit_type" field.') if config.framework_params['limit_type'] not in ['time', 'ntrials']: - raise RuntimeError('"limit_type" field must be "time" or "ntrials".') - if 'limit' not in config.framework_params: - raise RuntimeError('framework.yaml does not have a "limit" field.') - else: - try: - _ = int(config.framework_params['limit']) - except: - raise RuntimeError('"limit" field must be an integer.') - + raise RuntimeError('"limit_type" field must be "time" or "ntrials".') + if config.framework_params['limit_type'] == 'ntrials': + if 'trial_limit' not in config.framework_params: + raise RuntimeError('framework.yaml does not have a "limit" field.') + else: + try: + _ = int(config.framework_params['trial_limit']) + except: + raise RuntimeError('"trial_limit" field must be an integer.') + + +def save_scores_to_file(scores, out_file): + with open(out_file, 'w') as f: + f.write('ntrials,best_score\n') + for i, score in enumerate(scores): + f.write('{},{}\n'.format(i+1, score)) + def run(dataset: Dataset, config: TaskConfig): validate_config(config) tuner = NNITuner(config) if config.framework_params['limit_type'] == 'time': log.info("Tuning {} with NNI {} with a maximum time of {}s\n" - .format(config.framework_params['arch_type'], tuner.description, config.framework_params['limit'])) + .format(config.framework_params['arch_type'], tuner.description, config.max_runtime_seconds)) elif config.framework_params['limit_type'] == 'ntrials': log.info("Tuning {} with NNI {} with a maximum number of trials of {}\n" - .format(config.framework_params['arch_type'], tuner.description, config.framework_params['limit'])) + .format(config.framework_params['arch_type'], tuner.description, config.framework_params['trial_limit'])) log.info("Note: any time constraints are ignored.") - probabilities, predictions, train_timer, y_test = run_experiment(dataset, config, tuner, log) - + probabilities, predictions, train_timer, y_test, intermediate_best_scores = run_experiment(dataset, config, tuner, log) + save_predictions_to_file(dataset=dataset, output_file=config.output_predictions_file, probabilities=probabilities, predictions=predictions, truth=y_test) + scores_file = '/'.join(config.output_predictions_file.split('/')[:-3]) + '/scorelogs/' + config.output_predictions_file.split('/')[-1] + save_scores_to_file(intermediate_best_scores, scores_file) + return dict( models_count=1, training_duration=train_timer.duration diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml index 19880eed1b..9496913dbd 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml @@ -19,7 +19,7 @@ TPE: tuner_type: 'TPE' arch_type: 'random_forest' limit_type: 'ntrials' - limit: 5 + trial_limit: 30 Random: extends: NNI @@ -27,7 +27,7 @@ Random: tuner_type: 'Random' arch_type: 'random_forest' limit_type: 'ntrials' - limit: 5 + trial_limit: 30 Anneal: extends: NNI @@ -35,7 +35,7 @@ Anneal: tuner_type: 'Anneal' arch_type: 'random_forest' limit_type: 'ntrials' - limit: 5 + trial_limit: 30 Evolution: extends: NNI @@ -43,7 +43,7 @@ Evolution: tuner_type: 'Evolution' arch_type: 'random_forest' limit_type: 'ntrials' - limit: 5 + trial_limit: 30 SMAC: extends: NNI @@ -51,7 +51,7 @@ SMAC: tuner_type: 'SMAC' arch_type: 'random_forest' limit_type: 'ntrials' - limit: 5 + trial_limit: 30 GPTuner: extends: NNI @@ -59,7 +59,7 @@ GPTuner: tuner_type: 'GPTuner' arch_type: 'random_forest' limit_type: 'ntrials' - limit: 5 + trial_limit: 30 MetisTuner: extends: NNI @@ -67,7 +67,7 @@ MetisTuner: tuner_type: 'MetisTuner' arch_type: 'random_forest' limit_type: 'ntrials' - limit: 5 + trial_limit: 30 Hyperband: extends: NNI @@ -76,7 +76,7 @@ Hyperband: arch_type: 'random_forest' max_resource: 60 limit_type: 'ntrials' - limit: 5 + trial_limit: 30 BOHB: extends: NNI @@ -85,4 +85,4 @@ BOHB: arch_type: 'random_forest' max_resource: 60 limit_type: 'ntrials' - limit: 5 + trial_limit: 30 diff --git a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py index cb94377f85..f0a16dd10b 100644 --- a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py +++ b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py @@ -1,30 +1,66 @@ import pandas as pd import sys +import matplotlib.pyplot as plt -if len(sys.argv) != 2: - print("Usage: python parse_result_csv.py ") - exit(0) +def generate_perf_report(result_file_name): + result = pd.read_csv(result_file_name) + task_ids = result['id'].unique() + keep_parameters = ['framework', 'constraint', 'result', 'metric', 'params', 'utc', 'duration'] + list(result.columns[16:]) -result_file = sys.argv[1] -result = pd.read_csv(result_file) + with open(result_file_name.replace('results.csv', 'reports/performances.txt'), 'w') as out_f: + for task_id in task_ids: + task_results = result[result['id'] == task_id] + task_name = task_results.task.unique()[0] + out_f.write("====================================================\n") + out_f.write("Task ID: {}\n".format(task_id)) + out_f.write("Task Name: {}\n".format(task_name)) + folds = task_results['fold'].unique() + for fold in folds: + out_f.write("Fold {}:\n".format(fold)) + res = task_results[task_results['fold'] == fold][keep_parameters] + out_f.write(res.to_string()) + out_f.write('\n') -keep_parameters = ['framework', 'constraint', 'result', 'metric', 'params', 'utc', 'duration'] + list(result.columns[16:]) + out_f.write('\n') -task_ids = result['id'].unique() -with open(result_file + '.parsed', 'w') as out_f: +def generate_convergence_report(result_file_name): + result = pd.read_csv(result_file_name) + scorelog_dir = result_file_name.replace('results.csv', 'scorelogs/') + output_dir = result_file_name.replace('results.csv', 'reports/') + task_ids = result['id'].unique() for task_id in task_ids: task_results = result[result['id'] == task_id] task_name = task_results.task.unique()[0] - out_f.write("====================================================\n") - out_f.write("Task ID: {}\n".format(task_id)) - out_f.write("Task Name: {}\n".format(task_name)) folds = task_results['fold'].unique() - for fold in folds: - out_f.write("Fold {}:\n".format(fold)) - res = task_results[task_results['fold'] == fold][keep_parameters] - out_f.write(res.to_string()) - out_f.write('\n') + # load scorelog files + scores = [] + for fold in folds: + tuners = list(task_results[task_results.fold == fold]['framework'].unique()) + for tuner in tuners: + scorelog_name = '{}_{}_{}.csv'.format(tuner.lower(), task_name, fold) + intermediate_scores = pd.read_csv(scorelog_dir + scorelog_name) + scores.append([tuner, fold, list(intermediate_scores['best_score'])]) - out_f.write('\n') + # generate a graph + for tuner, fold, score in scores: + plt.plot(score, label='{} Fold {}'.format(tuner, fold)) + plt.title(task_name) + plt.xlabel("Number of Trials") + plt.ylabel("Best Cross Validation Score") + plt.legend() + plt.savefig(output_dir + '{}.jpg'.format(task_name)) + plt.close() + + +def main(): + if len(sys.argv) != 2: + print("Usage: python parse_result_csv.py ") + exit(0) + generate_perf_report(sys.argv[1]) + generate_convergence_report(sys.argv[1]) + + +if __name__ == '__main__': + main() diff --git a/examples/trials/benchmarking/automlbenchmark/requirements.txt b/examples/trials/benchmarking/automlbenchmark/requirements.txt index d68c776ee4..9a7b00059d 100644 --- a/examples/trials/benchmarking/automlbenchmark/requirements.txt +++ b/examples/trials/benchmarking/automlbenchmark/requirements.txt @@ -1,2 +1,3 @@ pandas>=1.2.0 pyyaml>=5.4.1 +matplotlib>=3.4.1 diff --git a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh index 519594bc23..9ee307355e 100755 --- a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh +++ b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh @@ -5,6 +5,8 @@ installation='automlbenchmark' outdir="results_$time" benchmark='nnivalid' +mkdir $outdir $outdir/scorelogs $outdir/reports + if [ "$#" -eq 0 ]; then tuner_array=('TPE' 'Random' 'Anneal' 'Evolution' 'GPTuner' 'MetisTuner' 'Hyperband') else From 469596279fe634d425bb8f097368c584b067dfe7 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Wed, 19 May 2021 12:17:41 +0900 Subject: [PATCH 19/31] Finalize HPO Benchmark graphical reports --- .../NNI/architectures/run_random_forest.py | 13 +++------- .../nni/extensions/NNI/exec.py | 13 +++++----- .../automlbenchmark/nni/frameworks.yaml | 18 ++++++------- .../automlbenchmark/parse_result_csv.py | 25 +++++++++++++------ 4 files changed, 37 insertions(+), 32 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py index 47c10bcecf..0a7554d948 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -17,15 +17,6 @@ from amlb.results import save_predictions_to_file -# for future use -''' -import sys -import warnings - -if not sys.warnoptions: - warnings.simplefilter("ignore") -''' - SEARCH_SPACE = { "n_estimators": {"_type":"randint", "_value": [8, 512]}, "max_depth": {"_type":"choice", "_value": [4, 8, 16, 32, 64, 128, 256, 0]}, # 0 for None @@ -106,6 +97,7 @@ def run_random_forest(dataset, config, tuner, log): start_time = time.time() trial_count = 0 + intermediate_scores = [] intermediate_best_scores = [] # should be monotone increasing while True: @@ -130,6 +122,7 @@ def run_random_forest(dataset, config, tuner, log): if best_score is None or (score_higher_better and cur_score > best_score) or (not score_higher_better and cur_score < best_score): best_score, best_params, best_model = cur_score, cur_params, cur_model + intermediate_scores.append(cur_score) intermediate_best_scores.append(best_score) tuner.receive_trial_result(param_idx, cur_params, cur_score) @@ -155,4 +148,4 @@ def run_random_forest(dataset, config, tuner, log): predictions = best_model.predict(X_test) probabilities = best_model.predict_proba(X_test) if is_classification else None - return probabilities, predictions, training, y_test, intermediate_best_scores + return probabilities, predictions, training, y_test, intermediate_scores, intermediate_best_scores diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py index 70bf8681a9..461f2e620b 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py @@ -29,11 +29,11 @@ def validate_config(config: TaskConfig): raise RuntimeError('"trial_limit" field must be an integer.') -def save_scores_to_file(scores, out_file): +def save_scores_to_file(intermediate_scores, intermediate_best_scores, out_file): with open(out_file, 'w') as f: - f.write('ntrials,best_score\n') - for i, score in enumerate(scores): - f.write('{},{}\n'.format(i+1, score)) + f.write('ntrials,trial_score,best_score\n') + for i, (trial_score, best_score) in enumerate(zip(intermediate_scores, intermediate_best_scores)): + f.write('{},{},{}\n'.format(i+1, trial_score, best_score)) def run(dataset: Dataset, config: TaskConfig): @@ -47,7 +47,7 @@ def run(dataset: Dataset, config: TaskConfig): .format(config.framework_params['arch_type'], tuner.description, config.framework_params['trial_limit'])) log.info("Note: any time constraints are ignored.") - probabilities, predictions, train_timer, y_test, intermediate_best_scores = run_experiment(dataset, config, tuner, log) + probabilities, predictions, train_timer, y_test, intermediate_scores, intermediate_best_scores = run_experiment(dataset, config, tuner, log) save_predictions_to_file(dataset=dataset, output_file=config.output_predictions_file, @@ -56,7 +56,8 @@ def run(dataset: Dataset, config: TaskConfig): truth=y_test) scores_file = '/'.join(config.output_predictions_file.split('/')[:-3]) + '/scorelogs/' + config.output_predictions_file.split('/')[-1] - save_scores_to_file(intermediate_best_scores, scores_file) + assert(len(intermediate_scores) == len(intermediate_best_scores)) + save_scores_to_file(intermediate_scores, intermediate_best_scores, scores_file) return dict( models_count=1, diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml index 9496913dbd..2651f74afa 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml @@ -19,7 +19,7 @@ TPE: tuner_type: 'TPE' arch_type: 'random_forest' limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 Random: extends: NNI @@ -27,7 +27,7 @@ Random: tuner_type: 'Random' arch_type: 'random_forest' limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 Anneal: extends: NNI @@ -35,7 +35,7 @@ Anneal: tuner_type: 'Anneal' arch_type: 'random_forest' limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 Evolution: extends: NNI @@ -43,7 +43,7 @@ Evolution: tuner_type: 'Evolution' arch_type: 'random_forest' limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 SMAC: extends: NNI @@ -51,7 +51,7 @@ SMAC: tuner_type: 'SMAC' arch_type: 'random_forest' limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 GPTuner: extends: NNI @@ -59,7 +59,7 @@ GPTuner: tuner_type: 'GPTuner' arch_type: 'random_forest' limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 MetisTuner: extends: NNI @@ -67,7 +67,7 @@ MetisTuner: tuner_type: 'MetisTuner' arch_type: 'random_forest' limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 Hyperband: extends: NNI @@ -76,7 +76,7 @@ Hyperband: arch_type: 'random_forest' max_resource: 60 limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 BOHB: extends: NNI @@ -85,4 +85,4 @@ BOHB: arch_type: 'random_forest' max_resource: 60 limit_type: 'ntrials' - trial_limit: 30 + trial_limit: 10 diff --git a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py index f0a16dd10b..e15bc20299 100644 --- a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py +++ b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py @@ -25,7 +25,7 @@ def generate_perf_report(result_file_name): out_f.write('\n') -def generate_convergence_report(result_file_name): +def generate_graphs(result_file_name): result = pd.read_csv(result_file_name) scorelog_dir = result_file_name.replace('results.csv', 'scorelogs/') output_dir = result_file_name.replace('results.csv', 'reports/') @@ -35,22 +35,33 @@ def generate_convergence_report(result_file_name): task_name = task_results.task.unique()[0] folds = task_results['fold'].unique() # load scorelog files - scores = [] + trial_scores, best_scores = [], [] for fold in folds: tuners = list(task_results[task_results.fold == fold]['framework'].unique()) for tuner in tuners: scorelog_name = '{}_{}_{}.csv'.format(tuner.lower(), task_name, fold) intermediate_scores = pd.read_csv(scorelog_dir + scorelog_name) - scores.append([tuner, fold, list(intermediate_scores['best_score'])]) + best_scores.append([tuner, fold, list(intermediate_scores['best_score'])]) + trial_scores.append([tuner, fold, list(intermediate_scores['trial_score'])]) - # generate a graph - for tuner, fold, score in scores: + # generate the best score graph + for tuner, fold, score in best_scores: plt.plot(score, label='{} Fold {}'.format(tuner, fold)) plt.title(task_name) plt.xlabel("Number of Trials") plt.ylabel("Best Cross Validation Score") plt.legend() - plt.savefig(output_dir + '{}.jpg'.format(task_name)) + plt.savefig(output_dir + '{}_1.jpg'.format(task_name)) + plt.close() + + # generate the trial score graph + for tuner, fold, score in trial_scores: + plt.plot(score, label='{} Fold {}'.format(tuner, fold)) + plt.title(task_name) + plt.xlabel("Trial Number") + plt.ylabel("Trial Cross Validation Score") + plt.legend() + plt.savefig(output_dir + '{}_2.jpg'.format(task_name)) plt.close() @@ -59,7 +70,7 @@ def main(): print("Usage: python parse_result_csv.py ") exit(0) generate_perf_report(sys.argv[1]) - generate_convergence_report(sys.argv[1]) + generate_graphs(sys.argv[1]) if __name__ == '__main__': From c5cc749e75bc0f58ba024a2c369cd5246bc7aade Mon Sep 17 00:00:00 2001 From: Di Wu Date: Wed, 19 May 2021 12:53:44 +0900 Subject: [PATCH 20/31] Add HPO Benchmark code comments and documentation --- .../benchmarking/automlbenchmark/README.md | 2 +- .../nni/extensions/NNI/__init__.py | 2 ++ .../NNI/architectures/run_random_forest.py | 11 +++++++++- .../nni/extensions/NNI/exec.py | 6 ++++++ .../nni/extensions/NNI/run_experiment.py | 3 +++ .../nni/extensions/NNI/tuners.py | 3 +++ .../automlbenchmark/parse_result_csv.py | 21 +++++++++++++++++-- .../automlbenchmark/runbenchmark_nni.sh | 5 ++++- .../benchmarking/automlbenchmark/setup.sh | 4 +++- 9 files changed, 51 insertions(+), 6 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md index e232ab5d23..7ea17c4b04 100644 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ b/examples/trials/benchmarking/automlbenchmark/README.md @@ -20,7 +20,7 @@ Due to some incompatibilities between automlbenchmark and python 3.8, python 3.7 ```bash ./runbenchmark_nni.sh [tuner-names] ``` -This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the "results.csv.parsed" file in the result folder associated with current time. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. +This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classfication tasks, and 8 multi-class classification tasks. By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/__init__.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/__init__.py index 3a9a0dc12c..5f5a2aa16a 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/__init__.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/__init__.py @@ -1,3 +1,5 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. def run(*args, **kwargs): from .exec import run diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py index 0a7554d948..c9ceba186b 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -1,3 +1,6 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + import logging import sklearn import time @@ -80,6 +83,12 @@ def preprocess_random_forest(dataset, log): def run_random_forest(dataset, config, tuner, log): + """ + Using the given tuner, tune a random forest within the given time constraint. + This function uses cross validation score as the feedback score to the tuner. + The search space on which tuners search on is defined above empirically as a global variable. + """ + limit_type, trial_limit = config.framework_params['limit_type'], None if limit_type == 'ntrials': trial_limit = int(config.framework_params['trial_limit']) @@ -98,7 +107,7 @@ def run_random_forest(dataset, config, tuner, log): start_time = time.time() trial_count = 0 intermediate_scores = [] - intermediate_best_scores = [] # should be monotone increasing + intermediate_best_scores = [] # should be monotonically increasing while True: try: diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py index 461f2e620b..a79e0d3b5c 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/exec.py @@ -1,3 +1,6 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + import logging from .tuners import NNITuner @@ -30,6 +33,9 @@ def validate_config(config: TaskConfig): def save_scores_to_file(intermediate_scores, intermediate_best_scores, out_file): + """ + Save statistics of every trial to a log file for generating reports. + """ with open(out_file, 'w') as f: f.write('ntrials,trial_score,best_score\n') for i, (trial_score, best_score) in enumerate(zip(intermediate_scores, intermediate_best_scores)): diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/run_experiment.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/run_experiment.py index 2fa0c1f173..ae85c909e3 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/run_experiment.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/run_experiment.py @@ -1,3 +1,6 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + from .architectures.run_random_forest import * diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py index 9d0b00c655..4016aae6e5 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py @@ -1,3 +1,6 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + import os import yaml import importlib diff --git a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py index e15bc20299..00cbc4810f 100644 --- a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py +++ b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py @@ -1,9 +1,18 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + import pandas as pd import sys import matplotlib.pyplot as plt def generate_perf_report(result_file_name): + """ + Generate a performance report. + The input result_file_name should be the path of the "results.csv" generated by automlbenchmark. + This function outputs a formatted report named "performances.txt" in the "reports/" directory + located in the same parent directory as "results.csv". + """ result = pd.read_csv(result_file_name) task_ids = result['id'].unique() keep_parameters = ['framework', 'constraint', 'result', 'metric', 'params', 'utc', 'duration'] + list(result.columns[16:]) @@ -26,6 +35,14 @@ def generate_perf_report(result_file_name): def generate_graphs(result_file_name): + """ + Generate graphs describing performance statistics. + The input result_file_name should be the path of the "results.csv" generated by automlbenchmark. + For each task, this function outputs two graphs in the "reports/" directory located in the same + parent directory as "results.csv". + The graph named task_1.jpg summarizes the best score each tuner gets after n trials. + The graph named task_2.jpg summarizes the score each tuner gets in each trial. + """ result = pd.read_csv(result_file_name) scorelog_dir = result_file_name.replace('results.csv', 'scorelogs/') output_dir = result_file_name.replace('results.csv', 'reports/') @@ -49,7 +66,7 @@ def generate_graphs(result_file_name): plt.plot(score, label='{} Fold {}'.format(tuner, fold)) plt.title(task_name) plt.xlabel("Number of Trials") - plt.ylabel("Best Cross Validation Score") + plt.ylabel("Best Score") plt.legend() plt.savefig(output_dir + '{}_1.jpg'.format(task_name)) plt.close() @@ -59,7 +76,7 @@ def generate_graphs(result_file_name): plt.plot(score, label='{} Fold {}'.format(tuner, fold)) plt.title(task_name) plt.xlabel("Trial Number") - plt.ylabel("Trial Cross Validation Score") + plt.ylabel("Trial Score") plt.legend() plt.savefig(output_dir + '{}_2.jpg'.format(task_name)) plt.close() diff --git a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh index 9ee307355e..f41c900721 100755 --- a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh +++ b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh @@ -1,9 +1,12 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + #!/bin/bash time=$(date "+%Y%m%d%H%M%S") installation='automlbenchmark' outdir="results_$time" -benchmark='nnivalid' +benchmark='nnivalid' # 'nnismall' mkdir $outdir $outdir/scorelogs $outdir/reports diff --git a/examples/trials/benchmarking/automlbenchmark/setup.sh b/examples/trials/benchmarking/automlbenchmark/setup.sh index 6fe091fad6..d87aea1b66 100755 --- a/examples/trials/benchmarking/automlbenchmark/setup.sh +++ b/examples/trials/benchmarking/automlbenchmark/setup.sh @@ -1,5 +1,7 @@ -#!/bin/bash +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. +#!/bin/bash # download automlbenchmark repository if [ ! -d './automlbenchmark' ] ; then From b66168e17be11aa22be9f9da77e1e85eaec2f482 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Fri, 21 May 2021 10:53:29 +0900 Subject: [PATCH 21/31] Refactor HPO benchmark documentation and configs --- .gitignore | 1 - docs/en_US/hpo_benchmark.rst | 55 +++++++++++++++++++ .../benchmarking/automlbenchmark/.gitignore | 13 +++++ .../benchmarking/automlbenchmark/README.md | 39 ------------- .../automlbenchmark/nni/README.md | 16 ------ .../automlbenchmark/nni/config.yaml | 29 ++-------- .../automlbenchmark/nni/frameworks.yaml | 7 +-- 7 files changed, 76 insertions(+), 84 deletions(-) create mode 100644 docs/en_US/hpo_benchmark.rst create mode 100644 examples/trials/benchmarking/automlbenchmark/.gitignore delete mode 100644 examples/trials/benchmarking/automlbenchmark/README.md delete mode 100644 examples/trials/benchmarking/automlbenchmark/nni/README.md diff --git a/.gitignore b/.gitignore index 918caf8b55..482d04e8c9 100644 --- a/.gitignore +++ b/.gitignore @@ -23,7 +23,6 @@ pids *.pid *.seed *.pid.lock -/examples/trials/benchmarking/automlbenchmark/nni/data # Build package dist/ diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst new file mode 100644 index 0000000000..8d5850781b --- /dev/null +++ b/docs/en_US/hpo_benchmark.rst @@ -0,0 +1,55 @@ + +Benchmark for Tuners +==================== + +We provide a benchmarking tool to compare the performances of tuners provided by NNI (and users' custom tuners) on different tasks. The implementation of this tool is based on the automlbenchmark repository (https://github.com/openml/automlbenchmark), which provides services of running different *frameworks* against different *benchmarks* consisting of multiple *tasks*. The tool is located in ``examples/trials/benchmarking/automlbenchmark``. This document provides a brief introduction to the tool and its usage. + +Terminology +^^^^^^^^^^^ + + +* **task**\ : a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). +* **benchmark**\ : a benchmark is a set of tasks, along with other external constraints such as time and resource. +* **framework**\ : given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. +* **tuner**\ : a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. +* **architecture**\ : an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See ``./nni/extensions/NNI/architectures`` for examples. + +Setup +^^^^^ + +Due to some incompatibilities between automlbenchmark and python 3.8, python 3.7 is recommended for running experiments contained in this folder. First, run the following shell script to clone the automlbenchmark repository. Note: it is recommended to perform the following steps in a separate virtual environment, as the setup code may install several packages. + +.. code-block:: bash + + ./setup.sh + +Run predefined benchmarks on existing tuners +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: bash + + ./runbenchmark_nni.sh [tuner-names] + +This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the ``benchmark`` variable in ``runbenchmark_nni.sh``. Some benchmarks are defined in ``./nni/benchmarks``\ , and others are defined in ``./automlbenchmark/resources/benchmarks/``. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classfication tasks, and 8 multi-class classification tasks. + +By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. + +Note: the SMAC tuner and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to `this page `_ for more details on installing SMAC and BOHB. + +Run customized benchmarks on existing tuners +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To run customized benchmarks, add a benchmark_name.yaml file in the folder ``./nni/benchmarks``\ , and change the ``benchmark`` variable in ``runbenchmark_nni.sh``. See ``./automlbenchmark/resources/benchmarks/`` for some examples of defining a custom benchmark. + +Run benchmarks on custom tuners +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To use custom tuners, first make sure that the tuner inherits from ``nni.tuner.Tuner`` and correctly implements the required APIs. For more information on implementing a custom tuner, please refer to `here `_. Next, perform the following steps: + + +#. Install the custom tuner with command ``nnictl algo register``. Check `this page `_ for details. +#. In ``./nni/frameworks.yaml``\ , add a new framework extending the base framework NNI. Make sure that the parameter ``tuner_type`` corresponds to the "builtinName" of tuner installed in step 1. +#. Run the following command: + .. code-block:: bash + + ./runbenchmark_nni.sh new-tuner-builtinName diff --git a/examples/trials/benchmarking/automlbenchmark/.gitignore b/examples/trials/benchmarking/automlbenchmark/.gitignore new file mode 100644 index 0000000000..5184f9196f --- /dev/null +++ b/examples/trials/benchmarking/automlbenchmark/.gitignore @@ -0,0 +1,13 @@ +# data files +nni/data/ + +# benchmark repository +automlbenchmark/ + +# all experiment results +results* + +# intermediate outputs of tuners +smac3-output* +param_config_space.pcs +scenario.txt \ No newline at end of file diff --git a/examples/trials/benchmarking/automlbenchmark/README.md b/examples/trials/benchmarking/automlbenchmark/README.md deleted file mode 100644 index 7ea17c4b04..0000000000 --- a/examples/trials/benchmarking/automlbenchmark/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# Benchmark for Tuners - -This folder contains a benchmark tool that enables comparisons between the performances of tuners provided by NNI (and users' custom tuners) on different tasks. The implementation of this tool is based on the automlbenchmark repository (https://github.com/openml/automlbenchmark), which provides services of running different *frameworks* against different *benchmarks* consisting of multiple *tasks*. - -### Terminology - -* **task**: a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). -* **benchmark**: a benchmark is a set of tasks, along with other external constraints such as time and resource. -* **framework**: given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. -* **tuner**: a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. -* **architecture**: an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See `./nni/extensions/NNI/architectures` for examples. - -### Setup -Due to some incompatibilities between automlbenchmark and python 3.8, python 3.7 is recommended for running experiments contained in this folder. First, run the following shell script to clone the automlbenchmark repository. Note: it is recommended to perform the following steps in a separate virtual environment, as the setup code may install several packages. -```bash -./setup.sh -``` - -### Run predefined benchmarks on existing tuners -```bash -./runbenchmark_nni.sh [tuner-names] -``` -This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the `benchmark` variable in `runbenchmark_nni.sh`. Some benchmarks are defined in `./nni/benchmarks`, and others are defined in `./automlbenchmark/resources/benchmarks/`. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classfication tasks, and 8 multi-class classification tasks. - -By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. - -Note: the SMAC tuner and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to [this page](https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni) for more details on installing SMAC and BOHB. - -### Run customized benchmarks on existing tuners -To run customized benchmarks, add a benchmark_name.yaml file in the folder `./nni/benchmarks`, and change the `benchmark` variable in `runbenchmark_nni.sh`. See `./automlbenchmark/resources/benchmarks/` for some examples of defining a custom benchmark. - -### Run benchmarks on custom tuners -To use custom tuners, first make sure that the tuner inherits from `nni.tuner.Tuner` and correctly implements the required APIs. For more information on implementing a custom tuner, please refer to [here](https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html). Next, perform the following steps: -1. Install the custom tuner with command `nnictl algo register`. Check [this page](https://nni.readthedocs.io/en/stable/Tutorial/Nnictl.html) for details. -1. In `./nni/frameworks.yaml`, add a new framework extending the base framework NNI. Make sure that the parameter `tuner_type` corresponds to the "builtinName" of tuner installed in step 1. -1. Run the following command: -```bash -./runbenchmark_nni.sh new-tuner-builtinName -``` diff --git a/examples/trials/benchmarking/automlbenchmark/nni/README.md b/examples/trials/benchmarking/automlbenchmark/nni/README.md deleted file mode 100644 index 6146c101f7..0000000000 --- a/examples/trials/benchmarking/automlbenchmark/nni/README.md +++ /dev/null @@ -1,16 +0,0 @@ -NNI Custom benchmarks: - -This folder contains a customized framework with several NNI Hyperparameter Tuners, along with testing benchmarks. - -**To experiment with an existing tuner:** - -1. Select tuner type and architecture type in frameworks.yaml. -2. Find or define a benchmark in the "benchmarks" folder with name .yaml. -3. Go to the project root folder and run the following command. - -```bash -python runbenchmark.py NNI -u nni -``` - -**Note:** - you can also copy those files in automlbenchmark user config directory `~/.config/automlbenchmark` to be able to use them without having to specify the `-u examples/custom` argument. diff --git a/examples/trials/benchmarking/automlbenchmark/nni/config.yaml b/examples/trials/benchmarking/automlbenchmark/nni/config.yaml index 6d8cef39bc..b4eb5360d5 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/config.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/config.yaml @@ -1,33 +1,16 @@ -# put this file in custom input dir or in ~/.config/automlbenchmark directory -# in order to have a custom setup, allowing to add custom frameworks definitions (in our case, creating a benchmark for various H2O versions) --- -project_repository: https://github.com/openml/automlbenchmark - -input_dir: '{user}/data' # change the default input directory (where data files are loaded and/or downloaded). +input_dir: '{user}/data' frameworks: - definition_file: # this allows to add custom framework definitions (in {user}/frameworks.yaml) on top of the default ones. + definition_file: - '{root}/resources/frameworks.yaml' - '{user}/frameworks.yaml' benchmarks: - definition_dir: # this allows to add custom benchmark definitions (under {user}/benchmarks) to the default ones. + definition_dir: - '{user}/benchmarks' - '{root}/resources/benchmarks' - constraints_file: # this allows to add custom constraint definitions (in {user}/constraints.yaml) on top of the default ones. - - '{root}/resources/constraints.yaml' - - '{user}/constraints.yaml' - -aws: - resource_files: # this allows to automatically upload custom config + frameworks to the running instance (benchmark files are always uploaded). - - '{user}/config.yaml' - - '{user}/frameworks.yaml' + + constraints_file: - '{user}/constraints.yaml' - - '{user}/benchmarks' - - '{user}/extensions' - - ec2: - instance_type: - series: t3 - use_docker: false # you can decide to always use the prebuilt docker images on AWS. - + - '{root}/resources/constraints.yaml' diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml index 2651f74afa..5fa057ac0b 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml +++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml @@ -2,13 +2,10 @@ NNI: module: extensions.NNI - version: '2.2' + version: 'stable' project: https://github.com/microsoft/nni - params: - tuner_type: 'Random' - arch_type: 'random_forest' -# tuner type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'Hyperband', 'BOHB'] +# type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'Hyperband', 'BOHB'] # arch_type in ['random_forest'] # limit_type in ['time', 'ntrials'] # limit must be an integer From 07771ddaf8be8a4c9bfbd0fe0187b240b4121011 Mon Sep 17 00:00:00 2001 From: Bill Wu <43978113+xiaowu0162@users.noreply.github.com> Date: Fri, 21 May 2021 09:55:46 +0800 Subject: [PATCH 22/31] Update hpo_benchmark.rst --- docs/en_US/hpo_benchmark.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst index 8d5850781b..2ff3efecc7 100644 --- a/docs/en_US/hpo_benchmark.rst +++ b/docs/en_US/hpo_benchmark.rst @@ -49,7 +49,7 @@ To use custom tuners, first make sure that the tuner inherits from ``nni.tuner.T #. Install the custom tuner with command ``nnictl algo register``. Check `this page `_ for details. #. In ``./nni/frameworks.yaml``\ , add a new framework extending the base framework NNI. Make sure that the parameter ``tuner_type`` corresponds to the "builtinName" of tuner installed in step 1. -#. Run the following command: +#. Run the following command .. code-block:: bash ./runbenchmark_nni.sh new-tuner-builtinName From b8b6a4e2c312f8a4eb3476ecd74d552bc1026a00 Mon Sep 17 00:00:00 2001 From: Bill Wu <43978113+xiaowu0162@users.noreply.github.com> Date: Fri, 21 May 2021 09:56:21 +0800 Subject: [PATCH 23/31] Update hpo_benchmark.rst --- docs/en_US/hpo_benchmark.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst index 2ff3efecc7..b81b7baeba 100644 --- a/docs/en_US/hpo_benchmark.rst +++ b/docs/en_US/hpo_benchmark.rst @@ -50,6 +50,7 @@ To use custom tuners, first make sure that the tuner inherits from ``nni.tuner.T #. Install the custom tuner with command ``nnictl algo register``. Check `this page `_ for details. #. In ``./nni/frameworks.yaml``\ , add a new framework extending the base framework NNI. Make sure that the parameter ``tuner_type`` corresponds to the "builtinName" of tuner installed in step 1. #. Run the following command - .. code-block:: bash + +.. code-block:: bash ./runbenchmark_nni.sh new-tuner-builtinName From fb097090d1be5ebec00e27109632b77ebdd577df Mon Sep 17 00:00:00 2001 From: Di Wu Date: Fri, 21 May 2021 11:16:13 +0900 Subject: [PATCH 24/31] documentation debug --- docs/en_US/hpo_benchmark.rst | 4 ++-- docs/en_US/hyperparameter_tune.rst | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst index b81b7baeba..e008404bf6 100644 --- a/docs/en_US/hpo_benchmark.rst +++ b/docs/en_US/hpo_benchmark.rst @@ -8,7 +8,7 @@ Terminology ^^^^^^^^^^^ -* **task**\ : a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classfication). +* **task**\ : a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classification). * **benchmark**\ : a benchmark is a set of tasks, along with other external constraints such as time and resource. * **framework**\ : given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. * **tuner**\ : a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. @@ -47,7 +47,7 @@ Run benchmarks on custom tuners To use custom tuners, first make sure that the tuner inherits from ``nni.tuner.Tuner`` and correctly implements the required APIs. For more information on implementing a custom tuner, please refer to `here `_. Next, perform the following steps: -#. Install the custom tuner with command ``nnictl algo register``. Check `this page `_ for details. +#. Install the custom tuner with command ``nnictl algo register``. Check `this document `_ for details. #. In ``./nni/frameworks.yaml``\ , add a new framework extending the base framework NNI. Make sure that the parameter ``tuner_type`` corresponds to the "builtinName" of tuner installed in step 1. #. Run the following command diff --git a/docs/en_US/hyperparameter_tune.rst b/docs/en_US/hyperparameter_tune.rst index 69078abf71..3f37b10c60 100644 --- a/docs/en_US/hyperparameter_tune.rst +++ b/docs/en_US/hyperparameter_tune.rst @@ -24,4 +24,5 @@ according to their needs. Examples WebUI How to Debug - Advanced \ No newline at end of file + Advanced + Benchmark for Tuners From b810804e79bfb9382fe13acdf76a5ca9e84d145a Mon Sep 17 00:00:00 2001 From: Bill Wu <43978113+xiaowu0162@users.noreply.github.com> Date: Fri, 21 May 2021 10:22:34 +0800 Subject: [PATCH 25/31] Update hpo_benchmark.rst --- docs/en_US/hpo_benchmark.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst index e008404bf6..b44af81de3 100644 --- a/docs/en_US/hpo_benchmark.rst +++ b/docs/en_US/hpo_benchmark.rst @@ -30,7 +30,7 @@ Run predefined benchmarks on existing tuners ./runbenchmark_nni.sh [tuner-names] -This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the ``benchmark`` variable in ``runbenchmark_nni.sh``. Some benchmarks are defined in ``./nni/benchmarks``\ , and others are defined in ``./automlbenchmark/resources/benchmarks/``. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classfication tasks, and 8 multi-class classification tasks. +This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the ``benchmark`` variable in ``runbenchmark_nni.sh``. Some benchmarks are defined in ``./nni/benchmarks``\ , and others are defined in ``./automlbenchmark/resources/benchmarks/``. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classification tasks, and 8 multi-class classification tasks. By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. From 60d391cf7c404aee17442ba0cf71cdfc8e8bed0e Mon Sep 17 00:00:00 2001 From: Di Wu Date: Fri, 21 May 2021 11:35:40 +0900 Subject: [PATCH 26/31] benchmark nan temporary fix --- .../nni/extensions/NNI/architectures/run_random_forest.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py index c9ceba186b..35044665be 100644 --- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py +++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py @@ -127,6 +127,9 @@ def run_random_forest(dataset, config, tuner, log): # Here score is the output of score() from the estimator cur_score = cross_val_score(cur_model, X_train, y_train) cur_score = sum(cur_score) / float(len(cur_score)) + if np.isnan(cur_score): + cur_score = 0 + log.info("Score: {}\n".format(cur_score)) if best_score is None or (score_higher_better and cur_score > best_score) or (not score_higher_better and cur_score < best_score): best_score, best_params, best_model = cur_score, cur_params, cur_model From 0619d9c2da3b00537a67a83aaf23d42eae384d33 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Fri, 21 May 2021 11:41:17 +0900 Subject: [PATCH 27/31] hpo benchmark script fix --- .../trials/benchmarking/automlbenchmark/runbenchmark_nni.sh | 3 --- 1 file changed, 3 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh index f41c900721..fceb317e93 100755 --- a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh +++ b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh @@ -1,6 +1,3 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - #!/bin/bash time=$(date "+%Y%m%d%H%M%S") From e0e3348ecd10b73c020ced421e50cd37948e107c Mon Sep 17 00:00:00 2001 From: Di Wu Date: Mon, 24 May 2021 11:39:04 +0900 Subject: [PATCH 28/31] Change result parsing code to generate two graphs per task per fold --- .../automlbenchmark/parse_result_csv.py | 53 ++++++++++--------- 1 file changed, 28 insertions(+), 25 deletions(-) diff --git a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py index 00cbc4810f..bba63220c9 100644 --- a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py +++ b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py @@ -40,8 +40,8 @@ def generate_graphs(result_file_name): The input result_file_name should be the path of the "results.csv" generated by automlbenchmark. For each task, this function outputs two graphs in the "reports/" directory located in the same parent directory as "results.csv". - The graph named task_1.jpg summarizes the best score each tuner gets after n trials. - The graph named task_2.jpg summarizes the score each tuner gets in each trial. + The graph named task_foldx_1.jpg summarizes the best score each tuner gets after n trials. + The graph named task_foldx_2.jpg summarizes the score each tuner gets in each trial. """ result = pd.read_csv(result_file_name) scorelog_dir = result_file_name.replace('results.csv', 'scorelogs/') @@ -50,36 +50,39 @@ def generate_graphs(result_file_name): for task_id in task_ids: task_results = result[result['id'] == task_id] task_name = task_results.task.unique()[0] - folds = task_results['fold'].unique() - # load scorelog files - trial_scores, best_scores = [], [] + folds = task_results['fold'].unique() + for fold in folds: + # load scorelog files + trial_scores, best_scores = [], [] tuners = list(task_results[task_results.fold == fold]['framework'].unique()) for tuner in tuners: scorelog_name = '{}_{}_{}.csv'.format(tuner.lower(), task_name, fold) intermediate_scores = pd.read_csv(scorelog_dir + scorelog_name) - best_scores.append([tuner, fold, list(intermediate_scores['best_score'])]) - trial_scores.append([tuner, fold, list(intermediate_scores['trial_score'])]) + best_scores.append([tuner, list(intermediate_scores['best_score'])]) + trial_scores.append([tuner, list(intermediate_scores['trial_score'])]) - # generate the best score graph - for tuner, fold, score in best_scores: - plt.plot(score, label='{} Fold {}'.format(tuner, fold)) - plt.title(task_name) - plt.xlabel("Number of Trials") - plt.ylabel("Best Score") - plt.legend() - plt.savefig(output_dir + '{}_1.jpg'.format(task_name)) - plt.close() + # generate the best score graph + plt.figure(figsize=(16, 8)) + for tuner, score in best_scores: + plt.plot(score, label=tuner) + plt.title('{} Fold {}'.format(task_name, fold)) + plt.xlabel("Number of Trials") + plt.ylabel("Best Score") + plt.legend() + plt.savefig(output_dir + '{}_fold{}_1.jpg'.format(task_name, fold)) + plt.close() - # generate the trial score graph - for tuner, fold, score in trial_scores: - plt.plot(score, label='{} Fold {}'.format(tuner, fold)) - plt.title(task_name) - plt.xlabel("Trial Number") - plt.ylabel("Trial Score") - plt.legend() - plt.savefig(output_dir + '{}_2.jpg'.format(task_name)) - plt.close() + # generate the trial score graph + plt.figure(figsize=(16, 8)) + for tuner, score in trial_scores: + plt.plot(score, label=tuner) + plt.title('{} Fold {}'.format(task_name, fold)) + plt.xlabel("Trial Number") + plt.ylabel("Trial Score") + plt.legend() + plt.savefig(output_dir + '{}_fold{}_2.jpg'.format(task_name, fold)) + plt.close() def main(): From ba75e9fcc13cc284c075279de26f5179bdb0e04f Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 25 May 2021 17:54:25 +0900 Subject: [PATCH 29/31] HPO benchmark result article v1 --- docs/en_US/hpo_benchmark.rst | 181 +++++++++++++++++- docs/img/hpo_benchmark/car_fold1_1.jpg | Bin 0 -> 135819 bytes docs/img/hpo_benchmark/car_fold1_2.jpg | Bin 0 -> 303362 bytes docs/img/hpo_benchmark/christine_fold0_1.jpg | Bin 0 -> 156099 bytes docs/img/hpo_benchmark/christine_fold0_2.jpg | Bin 0 -> 224278 bytes docs/img/hpo_benchmark/cnae-9_fold0_1.jpg | Bin 0 -> 126866 bytes docs/img/hpo_benchmark/cnae-9_fold0_2.jpg | Bin 0 -> 248971 bytes docs/img/hpo_benchmark/credit-g_fold1_1.jpg | Bin 0 -> 132929 bytes docs/img/hpo_benchmark/credit-g_fold1_2.jpg | Bin 0 -> 287296 bytes docs/img/hpo_benchmark/performances.png | Bin 0 -> 195148 bytes docs/img/hpo_benchmark/titanic_2_fold1_1.jpg | Bin 0 -> 128197 bytes docs/img/hpo_benchmark/titanic_2_fold1_2.jpg | Bin 0 -> 265584 bytes .../automlbenchmark/parse_result_csv.py | 87 ++++++++- 13 files changed, 258 insertions(+), 10 deletions(-) create mode 100644 docs/img/hpo_benchmark/car_fold1_1.jpg create mode 100644 docs/img/hpo_benchmark/car_fold1_2.jpg create mode 100644 docs/img/hpo_benchmark/christine_fold0_1.jpg create mode 100644 docs/img/hpo_benchmark/christine_fold0_2.jpg create mode 100644 docs/img/hpo_benchmark/cnae-9_fold0_1.jpg create mode 100644 docs/img/hpo_benchmark/cnae-9_fold0_2.jpg create mode 100644 docs/img/hpo_benchmark/credit-g_fold1_1.jpg create mode 100644 docs/img/hpo_benchmark/credit-g_fold1_2.jpg create mode 100644 docs/img/hpo_benchmark/performances.png create mode 100644 docs/img/hpo_benchmark/titanic_2_fold1_1.jpg create mode 100644 docs/img/hpo_benchmark/titanic_2_fold1_2.jpg diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst index b44af81de3..e8c660c5af 100644 --- a/docs/en_US/hpo_benchmark.rst +++ b/docs/en_US/hpo_benchmark.rst @@ -30,7 +30,7 @@ Run predefined benchmarks on existing tuners ./runbenchmark_nni.sh [tuner-names] -This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the ``benchmark`` variable in ``runbenchmark_nni.sh``. Some benchmarks are defined in ``./nni/benchmarks``\ , and others are defined in ``./automlbenchmark/resources/benchmarks/``. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classification tasks, and 8 multi-class classification tasks. +This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the ``benchmark`` variable in ``runbenchmark_nni.sh``. Some benchmarks are defined in ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks``\ , and others are defined in ``/examples/trials/benchmarking/automlbenchmark/automlbenchmark/resources/benchmarks/``. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classification tasks, and 8 multi-class classification tasks. By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. @@ -54,3 +54,182 @@ To use custom tuners, first make sure that the tuner inherits from ``nni.tuner.T .. code-block:: bash ./runbenchmark_nni.sh new-tuner-builtinName + +A Benchmark Example +^^^^^^^^^^^^^^^^^^^ + +As an example, we ran the "nnismall" benchmark on the following 8 tuners: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "DngoTuner". (The DngoTuner is not available as a built-in tuner at the time this article is written.) As some of the tasks contains a considerable amount of training data, it took about 2 days to run the whole benchmark on one tuner using a single CPU core. For a more detailed description of the tasks, please check ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt``. + +After the script finishes, the final scores of each tuner is summarized in the file ``results[time]/reports/performances.txt``. Since the file is large, we only show the following screenshot and summarize other important statistics instead. + +.. image:: ../../img/hpo_benchmark/performances.png + :target: ../../img/hpo_benchmark/performances.png + :alt: + +When the results are parsed, the tuners are ranked based on their final performance. ``results[time]/reports/rankings.txt`` presents a ranking of the tuners for each metric (logloss, rmse, auc), and the rankings of tuners for each metric (another view of the same data). + +Average rankings for metric rmse: + +.. list-table:: + :header-rows: 1 + + * - Tuner Name + - Average Ranking + * - Anneal + - 3.75 + * - Random + - 4.00 + * - Evolution + - 4.44 + * - DNGOTuner + - 4.44 + * - SMAC + - 4.56 + * - TPE + - 4.94 + * - GPTuner + - 4.94 + * - MetisTuner + - 4.94 + +Average rankings for metric auc: + +.. list-table:: + :header-rows: 1 + + * - Tuner Name + - Average Ranking + * - SMAC + - 3.67 + * - GPTuner + - 4.00 + * - Evolution + - 4.22 + * - Anneal + - 4.39 + * - MetisTuner + - 4.39 + * - TPE + - 4.67 + * - Random + - 5.33 + * - DNGOTuner + - 5.33 + +Average rankings for metric logloss: + +.. list-table:: + :header-rows: 1 + + * - Tuner Name + - Average Ranking + * - Random + - 3.36 + * - DNGOTuner + - 3.50 + * - SMAC + - 3.93 + * - GPTuner + - 4.64 + * - TPE + - 4.71 + * - Anneal + - 4.93 + * - Evolution + - 5.00 + * - MetisTuner + - 5.93 + +Average rankings for tuners: + +.. list-table:: + :header-rows: 1 + + * - Tuner Name + - rmse + - auc + - logloss + * - TPE + - 4.94 + - 4.67 + - 4.71 + * - Random + - 4.00 + - 5.33 + - 3.36 + * - Anneal + - 3.75 + - 4.39 + - 4.93 + * - Evolution + - 4.44 + - 4.22 + - 5.00 + * - GPTuner + - 4.94 + - 4.00 + - 4.64 + * - MetisTuner + - 4.94 + - 4.39 + - 5.93 + * - SMAC + - 4.56 + - 3.67 + - 3.93 + * - DNGOTuner + - 4.44 + - 5.33 + - 3.50 + +Besides these reports, our script also generates two graphs for each fold of each task. The first graph presents the best score seen by each tuner until trial x, and the second graph shows the scores of each tuner in trial x. These two graphs can give some information regarding how the tuners are "converging". We found that for "nnismall", tuners on the random forest model with search space defined in ``/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py`` generally converge to the final solution after 40 to 60 trials. As there are too much graphs to incldue in a single report (96 graphs in total), we only present 10 graphs here. + +.. image:: ../img/hpo_benchmark/car_fold1_1.jpg + :target: ../img/hpo_benchmark/car_fold1_1.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/car_fold1_2.jpg + :target: ../img/hpo_benchmark/car_fold1_2.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/christine_fold0_1.jpg + :target: ../img/hpo_benchmark/christine_fold0_1.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/christine_fold0_2.jpg + :target: ../img/hpo_benchmark/christine_fold0_2.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/cnae-9_fold0_1.jpg + :target: ../img/hpo_benchmark/cnae-9_fold0_1.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/cnae-9_fold0_2.jpg + :target: ../img/hpo_benchmark/cnae-9_fold0_2.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/credit-g_fold1_1.jpg + :target: ../img/hpo_benchmark/credit-g_fold1_1.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/credit-g_fold1_2.jpg + :target: ../img/hpo_benchmark/credit-g_fold1_2.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/titanic_2_fold1_1.jpg + :target: ../img/hpo_benchmark/titanic_2_fold1_1.jpg + :alt: + + +.. image:: ../img/hpo_benchmark/titanic_2_fold1_2.jpg + :target: ../img/hpo_benchmark/titanic_2_fold1_2.jpg + :alt: + diff --git a/docs/img/hpo_benchmark/car_fold1_1.jpg b/docs/img/hpo_benchmark/car_fold1_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..db30b6252a8773ad2e897f8679081a4b339281b5 GIT binary patch literal 135819 zcmeFZ2UJtvw?7y}L_z6Ist5>DRhrUbqf3<{H7X)aKsrbW3ermi1e6w~_ehPjM7n^0 zbO|L81f(aF5J(6UzrXjN|69}E^UeI%nwh_sbuMJxbIIBJ?6dc0mvfGP9M1t*^t5!f z05mi-fG5-+!0{B|4&Vg!BMr^(`-$JzN!s6cx|1hqPoAPXb?To+f9CWl`ZM&WPMu~v zeTL!pMg8k6Bg5I>AAbLnf4zEwj+T~=f&LWzKVAOlcKijvdYbmashhMk7Xc?&X=qt# zjynKC000f$Kf|K|{M$uyg7zfcDSGNK7^xlVSg6CNrKJv%{Llp#M!5&4~bNqh&owCw%o3+bt9NM_%j~uf00WaXaZ_ zRqGiMc~czcWA7mbE>VSfG5l}R{-NwYM_AZ@iL(Edu>VdM8gQ1DhB|q)tN5kI!8O*b(|etFqujjIW@|XJnK5gKMq@8B!y6VKy_(J*bva1Q73Fe 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a/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py +++ b/examples/trials/benchmarking/automlbenchmark/parse_result_csv.py @@ -4,19 +4,27 @@ import pandas as pd import sys import matplotlib.pyplot as plt +from matplotlib.lines import Line2D def generate_perf_report(result_file_name): """ Generate a performance report. The input result_file_name should be the path of the "results.csv" generated by automlbenchmark. - This function outputs a formatted report named "performances.txt" in the "reports/" directory - located in the same parent directory as "results.csv". + This function outputs 1) a formatted report named "performances.txt" in the "reports/" directory + located in the same parent directory as "results.csv" and 2) a report named "rankings.txt" in the + same directory ranking the tuners contained in "results.csv". """ result = pd.read_csv(result_file_name) task_ids = result['id'].unique() + tuners = result['framework'].unique() + metric_types = ['rmse', 'auc', 'logloss'] + metric2taskres = {} + for m in metric_types: + metric2taskres[m] = [] keep_parameters = ['framework', 'constraint', 'result', 'metric', 'params', 'utc', 'duration'] + list(result.columns[16:]) + # performance report: one table per task with open(result_file_name.replace('results.csv', 'reports/performances.txt'), 'w') as out_f: for task_id in task_ids: task_results = result[result['id'] == task_id] @@ -30,8 +38,64 @@ def generate_perf_report(result_file_name): res = task_results[task_results['fold'] == fold][keep_parameters] out_f.write(res.to_string()) out_f.write('\n') + # save results for the next step + res_list = [] + for _, row in res.iterrows(): + res_list.append([row['framework'], row['result']]) + metric2taskres[res['metric'].unique()[0]].append(res_list) + out_f.write('\n') - out_f.write('\n') + # rankings report: per task and per tuner + with open(result_file_name.replace('results.csv', 'reports/rankings.txt'), 'w') as out_f: + # generate reports per task + ranking_aggs = {} + for metric_type in metric_types: + sorted_lists = [] + if metric_type in ['auc']: + for l in metric2taskres[metric_type]: + l_sorted = sorted(l, key=(lambda x: x[-1]), reverse=True) + l_sorted = [[x[0], x[1], i+1] for (i, x) in enumerate(l_sorted)] + sorted_lists.append(l_sorted) + elif metric_type in ['rmse', 'logloss']: + for l in metric2taskres[metric_type]: + l_sorted = sorted(l, key=(lambda x: x[-1])) + l_sorted = [[x[0], x[1], i+1] for (i, x) in enumerate(l_sorted)] + sorted_lists.append(l_sorted) + metric2taskres[metric_type] = sorted_lists + + out_f.write("====================================================\n") + out_f.write("Average rankings for metric {}:\n".format(metric_type)) + ranking_agg = [[t, 0] for t in tuners] + for i, tuner in enumerate(tuners): + for trial_res in metric2taskres[metric_type]: + for t, s, r in trial_res: + if t == tuner: + ranking_agg[i][-1] += r + + ranking_agg = [[x[0], x[1]/float(len(metric2taskres[metric_type]))] for x in ranking_agg] + ranking_agg = sorted(ranking_agg, key=(lambda x: x[-1])) + for t, r in ranking_agg: + out_f.write('{:<12} {:.2f}\n'.format(t, r)) + ranking_aggs[metric_type] = ranking_agg + out_f.write('\n') + + # generate reports per tuner + out_f.write("====================================================\n") + out_f.write("Average rankings for tuners:\n") + header_string = '{:<12}' + for _ in metric_types: + header_string += ' {:<12}' + header_string += '\n' + out_f.write(header_string.format("Tuner", *metric_types)) + for tuner in tuners: + tuner_ranks = [] + for m in metric_types: + for t, r in ranking_aggs[m]: + if t == tuner: + tuner_ranks.append('{:.2f}'.format(r)) + break + out_f.write(header_string.format(tuner, *tuner_ranks)) + out_f.write('\n') def generate_graphs(result_file_name): @@ -43,6 +107,7 @@ def generate_graphs(result_file_name): The graph named task_foldx_1.jpg summarizes the best score each tuner gets after n trials. The graph named task_foldx_2.jpg summarizes the score each tuner gets in each trial. """ + markers = list(Line2D.markers.keys()) result = pd.read_csv(result_file_name) scorelog_dir = result_file_name.replace('results.csv', 'scorelogs/') output_dir = result_file_name.replace('results.csv', 'reports/') @@ -59,13 +124,15 @@ def generate_graphs(result_file_name): for tuner in tuners: scorelog_name = '{}_{}_{}.csv'.format(tuner.lower(), task_name, fold) intermediate_scores = pd.read_csv(scorelog_dir + scorelog_name) - best_scores.append([tuner, list(intermediate_scores['best_score'])]) - trial_scores.append([tuner, list(intermediate_scores['trial_score'])]) + bs = list(intermediate_scores['best_score']) + ts = [(i+1, x) for i, x in enumerate(list(intermediate_scores['trial_score'])) if x != 0] + best_scores.append([tuner, bs]) + trial_scores.append([tuner, ts]) # generate the best score graph plt.figure(figsize=(16, 8)) - for tuner, score in best_scores: - plt.plot(score, label=tuner) + for i, (tuner, score) in enumerate(best_scores): + plt.plot(score, label=tuner, marker=markers[i]) plt.title('{} Fold {}'.format(task_name, fold)) plt.xlabel("Number of Trials") plt.ylabel("Best Score") @@ -75,8 +142,10 @@ def generate_graphs(result_file_name): # generate the trial score graph plt.figure(figsize=(16, 8)) - for tuner, score in trial_scores: - plt.plot(score, label=tuner) + for i, (tuner, score) in enumerate(trial_scores): + x = [l[0] for l in score] + y = [l[1] for l in score] + plt.plot(x, y, label=tuner) #, marker=markers[i]) plt.title('{} Fold {}'.format(task_name, fold)) plt.xlabel("Trial Number") plt.ylabel("Trial Score") From 43f1efe275d01265dee0c39332382123e56cbd4a Mon Sep 17 00:00:00 2001 From: Di Wu Date: Tue, 25 May 2021 18:30:49 +0900 Subject: [PATCH 30/31] doc img bug fix --- docs/en_US/hpo_benchmark.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst index e8c660c5af..a3777b184e 100644 --- a/docs/en_US/hpo_benchmark.rst +++ b/docs/en_US/hpo_benchmark.rst @@ -62,8 +62,8 @@ As an example, we ran the "nnismall" benchmark on the following 8 tuners: "TPE", After the script finishes, the final scores of each tuner is summarized in the file ``results[time]/reports/performances.txt``. Since the file is large, we only show the following screenshot and summarize other important statistics instead. -.. image:: ../../img/hpo_benchmark/performances.png - :target: ../../img/hpo_benchmark/performances.png +.. image:: ../img/hpo_benchmark/performances.png + :target: ../img/hpo_benchmark/performances.png :alt: When the results are parsed, the tuners are ranked based on their final performance. ``results[time]/reports/rankings.txt`` presents a ranking of the tuners for each metric (logloss, rmse, auc), and the rankings of tuners for each metric (another view of the same data). From a9f3037b5696ccfb2cfc6c52f4e3a7ee6f4fbef5 Mon Sep 17 00:00:00 2001 From: Di Wu Date: Wed, 26 May 2021 15:06:43 +0900 Subject: [PATCH 31/31] hpo benchmark - add the option of running all experiments in parallel in the background --- docs/en_US/hpo_benchmark.rst | 2 + .../automlbenchmark/runbenchmark_nni.sh | 45 ++++++++++++++++--- 2 files changed, 42 insertions(+), 5 deletions(-) diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst index a3777b184e..a82baab79f 100644 --- a/docs/en_US/hpo_benchmark.rst +++ b/docs/en_US/hpo_benchmark.rst @@ -34,6 +34,8 @@ This script runs the benchmark 'nnivalid', which consists of a regression task, By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. +By default, the script runs the specified tuners against the specified benchmark one by one. To run all the experiments simultaneously in the background, set the "serialize" flag to false in ``runbenchmark_nni.sh``. + Note: the SMAC tuner and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to `this page `_ for more details on installing SMAC and BOHB. Run customized benchmarks on existing tuners diff --git a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh index fceb317e93..d2d740925b 100755 --- a/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh +++ b/examples/trials/benchmarking/automlbenchmark/runbenchmark_nni.sh @@ -4,6 +4,7 @@ time=$(date "+%Y%m%d%H%M%S") installation='automlbenchmark' outdir="results_$time" benchmark='nnivalid' # 'nnismall' +serialize=$true # if false, run all experiments together in background mkdir $outdir $outdir/scorelogs $outdir/reports @@ -13,9 +14,43 @@ else tuner_array=( "$@" ) fi -for tuner in ${tuner_array[*]}; do - echo "python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni" - python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni -done +if [ $serialize ]; then + # run tuners serially + for tuner in ${tuner_array[*]}; do + echo "python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni" + python $installation/runbenchmark.py $tuner $benchmark -o $outdir -u nni + done -python parse_result_csv.py "$outdir/results.csv" + # parse final results + echo "python parse_result_csv.py $outdir/results.csv" + python parse_result_csv.py "$outdir/results.csv" + +else + # run all the tuners in background + for tuner in ${tuner_array[*]}; do + mkdir "$outdir/$tuner" "$outdir/$tuner/scorelogs" + echo "python $installation/runbenchmark.py $tuner $benchmark -o $outdir/$tuner -u nni &" + python $installation/runbenchmark.py $tuner $benchmark -o $outdir/$tuner -u nni & + done + + wait + + # aggregate results + touch "$outdir/results.csv" + let i=0 + for tuner in ${tuner_array[*]}; do + cp "$outdir/$tuner/scorelogs"/* $outdir/scorelogs + if [ $i -eq 0 ]; then + cp "$outdir/$tuner/results.csv" "$outdir/results.csv" + else + let nlines=`cat "$outdir/$tuner/results.csv" | wc -l` + ((nlines=nlines-1)) + tail -n $nlines "$outdir/$tuner/results.csv" >> "$outdir/results.csv" + fi + ((i=i+1)) + done + + # parse final results + echo "python parse_result_csv.py $outdir/results.csv" + python parse_result_csv.py "$outdir/results.csv" +fi

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