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https://github.com/vale981/ray
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160 lines
7.5 KiB
Python
160 lines
7.5 KiB
Python
# flake8: noqa
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"""
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Tune's Scikit Learn Adapters
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============================
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Scikit-Learn is one of the most widely used tools in the ML community for working with data, offering dozens of easy-to-use machine learning algorithms. However, to achieve high performance for these algorithms, you often need to perform **model selection**.
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.. image:: /images/tune-sklearn.png
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:align: center
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:width: 50%
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Scikit-Learn `has an existing module for model selection <https://scikit-learn.org/stable/modules/grid_search.html>`_, but the algorithms offered (Grid Search/``GridSearchCV`` and Random Search/``RandomizedSearchCV``) are often considered inefficient. In this tutorial, we'll cover ``tune-sklearn``, a drop-in replacement for Scikit-Learn's model selection module with state-of-the-art optimization features such as early stopping and Bayesian Optimization.
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.. tip:: Check out the `tune-sklearn code`_ and :ref:`documentation <tune-sklearn-docs>`.
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.. _`tune-sklearn code`: https://github.com/ray-project/tune-sklearn
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Overview
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--------
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``tune-sklearn`` is a module that integrates Ray Tune's hyperparameter tuning and scikit-learn's Classifier API. ``tune-sklearn`` has two APIs: :ref:`TuneSearchCV <tunesearchcv-docs>`, and :ref:`TuneGridSearchCV <tunegridsearchcv-docs>`. They are drop-in replacements for Scikit-learn's RandomizedSearchCV and GridSearchCV, so you only need to change less than 5 lines in a standard Scikit-Learn script to use the API.
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Ray Tune's Scikit-learn APIs allows you to easily leverage Bayesian Optimization, HyperBand, and other cutting edge tuning techniques by simply toggling a few parameters. It also supports and provides examples for many other frameworks with Scikit-Learn wrappers such as Skorch (Pytorch), KerasClassifiers (Keras), and XGBoostClassifiers (XGBoost).
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Run ``pip install "ray[tune]" tune-sklearn`` to get started.
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Walkthrough
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-----------
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Let's compare Tune's Scikit-Learn APIs to the standard scikit-learn GridSearchCV. For this example, we'll be using ``TuneGridSearchCV`` with a `SGDClassifier`_.
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.. _`digits dataset`: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html
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.. _`SGDClassifier`: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
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To start out, change the import statement to get tune-scikit-learn’s grid search cross validation interface:
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"""
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# Keep this here for https://github.com/ray-project/ray/issues/11547
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from sklearn.model_selection import GridSearchCV
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# Replace above line with:
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from ray.tune.sklearn import TuneGridSearchCV
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#######################################################################
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# And from there, we would proceed just like how we would in Scikit-Learn’s interface!
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#
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# The `SGDClassifier`_ has a ``partial_fit`` API, which enables it to stop fitting to the data for a certain hyperparameter configuration.
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# If the estimator does not support early stopping, we would fall back to a parallel grid search.
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# Other imports
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import SGDClassifier
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from sklearn.datasets import make_classification
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import numpy as np
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# Create dataset
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X, y = make_classification(
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n_samples=11000,
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n_features=1000,
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n_informative=50,
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n_redundant=0,
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n_classes=10,
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class_sep=2.5)
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x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=1000)
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# Example parameters to tune from SGDClassifier
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parameter_grid = {"alpha": [1e-4, 1e-1, 1], "epsilon": [0.01, 0.1]}
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#######################################################################
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# As you can see, the setup here is exactly how you would do it for Scikit-Learn. Now, let's try fitting a model.
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tune_search = TuneGridSearchCV(
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SGDClassifier(), parameter_grid, early_stopping=True, max_iters=10)
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import time # Just to compare fit times
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start = time.time()
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tune_search.fit(x_train, y_train)
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end = time.time()
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print("Tune GridSearch Fit Time:", end - start)
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# Tune GridSearch Fit Time: 15.436315774917603 (for an 8 core laptop)
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#######################################################################
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# Note the slight differences we introduced above:
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#
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# * a `early_stopping`, and
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# * a specification of `max_iters` parameter
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#
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# The ``early_stopping`` parameter allows us to terminate unpromising configurations. If ``early_stopping=True``,
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# TuneGridSearchCV will default to using Tune's ASHAScheduler. You can pass in a custom
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# algorithm - see :ref:`Tune's documentation on schedulers <tune-schedulers>` here for a full list to choose from.
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# ``max_iters`` is the maximum number of iterations a given hyperparameter set could run for; it may run for fewer iterations if it is early stopped.
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#
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# Try running this compared to the GridSearchCV equivalent, and see the speedup for yourself!
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from sklearn.model_selection import GridSearchCV
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# n_jobs=-1 enables use of all cores like Tune does
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sklearn_search = GridSearchCV(SGDClassifier(), parameter_grid, n_jobs=-1)
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start = time.time()
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sklearn_search.fit(x_train, y_train)
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end = time.time()
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print("Sklearn Fit Time:", end - start)
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# Sklearn Fit Time: 47.48055911064148 (for an 8 core laptop)
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###################################################################
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# Using Bayesian Optimization
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# ---------------------------
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#
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# In addition to the grid search interface, tune-sklearn also provides an interface, TuneSearchCV, for sampling from **distributions of hyperparameters**.
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#
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# In addition, you can easily enable Bayesian optimization over the distributions in only 2 lines of code:
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# First run `pip install bayesian-optimization`
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from ray.tune.sklearn import TuneSearchCV
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from sklearn.linear_model import SGDClassifier
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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import numpy as np
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digits = datasets.load_digits()
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x = digits.data
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y = digits.target
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2)
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clf = SGDClassifier()
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parameter_grid = {"alpha": (1e-4, 1), "epsilon": (0.01, 0.1)}
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tune_search = TuneSearchCV(
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clf,
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parameter_grid,
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search_optimization="bayesian",
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n_trials=3,
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early_stopping=True,
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max_iters=10,
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)
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tune_search.fit(x_train, y_train)
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print(tune_search.best_params_)
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# {'alpha': 0.37460266483547777, 'epsilon': 0.09556428757689246}
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################################################################
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# As you can see, it’s very simple to integrate tune-sklearn into existing code. Distributed execution is also easy - you can simply run ``ray.init(address="auto")`` before
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# TuneSearchCV to connect to the Ray cluster and parallelize tuning across multiple nodes, as you would in any other Ray Tune script.
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#
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#
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# Code Examples
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# -------------
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#
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# Check out more detailed examples and get started with tune-sklearn!
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#
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# * `Skorch with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/torch_nn.py>`_
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# * `Scikit-Learn Pipelines with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/sklearn_pipeline.py>`_
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# * `XGBoost with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/xgbclassifier.py>`_
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# * `KerasClassifier with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/keras_example.py>`_
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# * `LightGBM with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/lgbm.py>`_
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#
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#
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# Further Reading
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# ---------------
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#
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# If you're using scikit-learn for other tasks, take a look at Ray’s :ref:`replacement for joblib <ray-joblib>`, which allows users to parallelize scikit learn jobs over multiple nodes.
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