Active Learning Playground

Introduction

This is a python module for experimenting with different active learning algorithms. There are a few key components to running active learning experiments:

• Main experiment script is run_experiment.py with many flags for different run options.

• Supported datasets can be downloaded to a specified directory by running utils/create_data.py.

• Supported active learning methods are in sampling_methods.

Below I will go into each component in more detail.

DISCLAIMER: This is not an official Google product.

Setup

The dependencies are in requirements.txt. Please make sure these packages are installed before running experiments. If GPU capable tensorflow is desired, please follow instructions here.

It is highly suggested that you install all dependencies into a separate virtualenv for easy package management.

Getting benchmark datasets

By default the datasets are saved to /tmp/data. You can specify another directory via the --save_dir flag.

Redownloading all the datasets will be very time consuming so please be patient. You can specify a subset of the data to download by passing in a comma separated string of datasets via the --datasets flag.

Running experiments

There are a few key flags for run_experiment.py:

• dataset: name of the dataset, must match the save name used in create_data.py. Must also exist in the data_dir.

• sampling_method: active learning method to use. Must be specified in sampling_methods/constants.py.

• warmstart_size: initial batch of uniformly sampled examples to use as seed data. Float indicates percentage of total training data and integer indicates raw size.

• batch_size: number of datapoints to request in each batch. Float indicates percentage of total training data and integer indicates raw size.

• score_method: model to use to evaluate the performance of the sampling method. Must be in get_model method of utils/utils.py.

• data_dir: directory with saved datasets.

• save_dir: directory to save results.

This is just a subset of all the flags. There are also options for preprocessing, introducing labeling noise, dataset subsampling, and using a different model to select than to score/evaluate.

Available active learning methods

All named active learning methods are in sampling_methods/constants.py.

You can also specify a mixture of active learning methods by following the pattern of [sampling_method]-[mixture_weight] separated by dashes; i.e. mixture_of_samplers-margin-0.33-informative_diverse-0.33-uniform-0.34.

Some supported sampling methods include:

• Uniform: samples are selected via uniform sampling.

• Margin: uncertainty based sampling method.

• Informative and diverse: margin and cluster based sampling method.

• k-center greedy: representative strategy that greedily forms a batch of points to minimize maximum distance from a labeled point.

• Graph density: representative strategy that selects points in dense regions of pool.

• Exp3 bandit: meta-active learning method that tries to learns optimal sampling method using a popular multi-armed bandit algorithm.

Adding new active learning methods

Implement either a base sampler that inherits from SamplingMethod or a meta-sampler that calls base samplers which inherits from WrapperSamplingMethod.

The only method that must be implemented by any sampler is select_batch_, which can have arbitrary named arguments. The only restriction is that the name for the same input must be consistent across all the samplers (i.e. the indices for already selected examples all have the same name across samplers). Adding a new named argument that hasn't been used in other sampling methods will require feeding that into the select_batch call in run_experiment.py.

After implementing your sampler, be sure to add it to constants.py so that it can be called from run_experiment.py.

Available models

All available models are in the get_model method of utils/utils.py.

Supported methods:

• Linear SVM: scikit method with grid search wrapper for regularization parameter.

• Kernel SVM: scikit method with grid search wrapper for regularization parameter.

• Logistc Regression: scikit method with grid search wrapper for regularization parameter.

• Small CNN: 4 layer CNN optimized using rmsprop implemented in Keras with tensorflow backend.

• Kernel Least Squares Classification: block gradient descient solver that can use multiple cores so is often faster than scikit Kernel SVM.

Adding new models

New models must follow the scikit learn api and implement the following methods

• fit(X, y[, sample_weight]): fit the model to the input features and target.

• predict(X): predict the value of the input features.

• score(X, y): returns target metric given test features and test targets.

• decision_function(X) (optional): return class probabilities, distance to decision boundaries, or other metric that can be used by margin sampler as a measure of uncertainty.

See small_cnn.py for an example.

After implementing your new model, be sure to add it to get_model method of utils/utils.py.

Currently models must be added on a one-off basis and not all scikit-learn classifiers are supported due to the need for user input on whether and how to tune the hyperparameters of the model. However, it is very easy to add a scikit-learn model with hyperparameter search wrapped around as a supported model.

Collecting results and charting

The utils/chart_data.py script handles processing of data and charting for a specified dataset and source directory.