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‎CONTRIBUTING.md

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# How to Contribute
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We'd love to accept your patches and contributions to this project. There are
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just a few small guidelines you need to follow.
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## Contributor License Agreement
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Contributions to this project must be accompanied by a Contributor License
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Agreement. You (or your employer) retain the copyright to your contribution,
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this simply gives us permission to use and redistribute your contributions as
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part of the project. Head over to <https://cla.developers.google.com/> to see
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your current agreements on file or to sign a new one.
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You generally only need to submit a CLA once, so if you've already submitted one
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(even if it was for a different project), you probably don't need to do it
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again.
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## Code reviews
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All submissions, including submissions by project members, require review. We
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use GitHub pull requests for this purpose. Consult
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[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
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information on using pull requests.

‎LICENSE

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Apache License
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‎README.md

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# Active Learning Playground
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## Introduction
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This is a python module for experimenting with different active learning
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algorithms. There are a few key components to running active learning
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experiments:
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* Main experiment script is
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[`run_experiment.py`](run_experiment.py)
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with many flags for different run options.
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* Supported datasets can be downloaded to a specified directory by running
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[`utils/create_data.py`](utils/create_data.py).
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* Supported active learning methods are in
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[`sampling_methods`](sampling_methods/).
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Below I will go into each component in more detail.
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DISCLAIMER: This is not an official Google product.
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## Setup
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The dependencies are in [`requirements.txt`](requirements.txt). Please make sure these packages are
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installed before running experiments. If GPU capable `tensorflow` is desired, please follow
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instructions [here](https://www.tensorflow.org/install/).
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It is highly suggested that you install all dependencies into a separate `virtualenv` for
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easy package management.
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## Getting benchmark datasets
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By default the datasets are saved to `/tmp/data`. You can specify another directory via the
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`--save_dir` flag.
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Redownloading all the datasets will be very time consuming so please be patient.
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You can specify a subset of the data to download by passing in a comma separated
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string of datasets via the `--datasets` flag.
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## Running experiments
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There are a few key flags for
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[`run_experiment.py`](run_experiment.py):
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* `dataset`: name of the dataset, must match the save name used in
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`create_data.py`. Must also exist in the data_dir.
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* `sampling_method`: active learning method to use. Must be specified in
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[`sampling_methods/constants.py`](sampling_methods/constants.py).
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* `warmstart_size`: initial batch of uniformly sampled examples to use as seed
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data. Float indicates percentage of total training data and integer
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indicates raw size.
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* `batch_size`: number of datapoints to request in each batch. Float indicates
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percentage of total training data and integer indicates raw size.
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* `score_method`: model to use to evaluate the performance of the sampling
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method. Must be in `get_model` method of
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[`utils/utils.py`](utils/utils.py).
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* `data_dir`: directory with saved datasets.
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* `save_dir`: directory to save results.
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This is just a subset of all the flags. There are also options for
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preprocessing, introducing labeling noise, dataset subsampling, and using a
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different model to select than to score/evaluate.
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## Available active learning methods
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All named active learning methods are in
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[`sampling_methods/constants.py`](sampling_methods/constants.py).
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You can also specify a mixture of active learning methods by following the
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pattern of `[sampling_method]-[mixture_weight]` separated by dashes; i.e.
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`mixture_of_samplers-margin-0.33-informative_diverse-0.33-uniform-0.34`.
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Some supported sampling methods include:
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* Uniform: samples are selected via uniform sampling.
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* Margin: uncertainty based sampling method.
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* Informative and diverse: margin and cluster based sampling method.
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* k-center greedy: representative strategy that greedily forms a batch of
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points to minimize maximum distance from a labeled point.
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* Graph density: representative strategy that selects points in dense regions
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of pool.
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* Exp3 bandit: meta-active learning method that tries to learns optimal
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sampling method using a popular multi-armed bandit algorithm.
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### Adding new active learning methods
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Implement either a base sampler that inherits from
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[`SamplingMethod`](sampling_methods/sampling_def.py)
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or a meta-sampler that calls base samplers which inherits from
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[`WrapperSamplingMethod`](sampling_methods/wrapper_sampler_def.py).
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The only method that must be implemented by any sampler is `select_batch_`,
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which can have arbitrary named arguments. The only restriction is that the name
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for the same input must be consistent across all the samplers (i.e. the indices
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for already selected examples all have the same name across samplers). Adding a
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new named argument that hasn't been used in other sampling methods will require
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feeding that into the `select_batch` call in
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[`run_experiment.py`](run_experiment.py).
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After implementing your sampler, be sure to add it to
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[`constants.py`](sampling_methods/constants.py)
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so that it can be called from
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[`run_experiment.py`](run_experiment.py).
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## Available models
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All available models are in the `get_model` method of
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[`utils/utils.py`](utils/utils.py).
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Supported methods:
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* Linear SVM: scikit method with grid search wrapper for regularization
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parameter.
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* Kernel SVM: scikit method with grid search wrapper for regularization
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parameter.
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* Logistc Regression: scikit method with grid search wrapper for
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regularization parameter.
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* Small CNN: 4 layer CNN optimized using rmsprop implemented in Keras with
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tensorflow backend.
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* Kernel Least Squares Classification: block gradient descient solver that can
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use multiple cores so is often faster than scikit Kernel SVM.
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### Adding new models
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New models must follow the scikit learn api and implement the following methods
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* `fit(X, y[, sample_weight])`: fit the model to the input features and
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target.
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* `predict(X)`: predict the value of the input features.
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* `score(X, y)`: returns target metric given test features and test targets.
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* `decision_function(X)` (optional): return class probabilities, distance to
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decision boundaries, or other metric that can be used by margin sampler as a
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measure of uncertainty.
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See
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[`small_cnn.py`](utils/small_cnn.py)
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for an example.
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After implementing your new model, be sure to add it to `get_model` method of
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[`utils/utils.py`](utils/utils.py).
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Currently models must be added on a one-off basis and not all scikit-learn
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classifiers are supported due to the need for user input on whether and how to
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tune the hyperparameters of the model. However, it is very easy to add a
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scikit-learn model with hyperparameter search wrapped around as a supported
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model.
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## Collecting results and charting
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The
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[`utils/chart_data.py`](utils/chart_data.py)
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script handles processing of data and charting for a specified dataset and
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source directory.

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