ray/doc/source/tune/_tutorials/tune-sklearn.py

# flake8: noqa
"""
Tune's Scikit Learn Adapters
============================

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**.


.. image:: /images/tune-sklearn.png
    :align: center
    :width: 50%

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.

.. tip:: Check out the `tune-sklearn code`_ and :ref:`documentation <tune-sklearn-docs>`.

.. _`tune-sklearn code`: https://github.com/ray-project/tune-sklearn

Overview
--------

``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.

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).

Run ``pip install ray[tune] tune-sklearn`` to get started.

Walkthrough
-----------

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`_.

.. _`digits dataset`: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html
.. _`SGDClassifier`: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html

To start out, change the import statement to get tune-scikit-learn’s grid search cross validation interface:

"""
# from sklearn.model_selection import GridSearchCV
from ray.tune.sklearn import TuneGridSearchCV

#######################################################################
# And from there, we would proceed just like how we would in Scikit-Learn’s interface!
#
# The `SGDClassifier`_ has a ``partial_fit`` API, which enables it to stop fitting to the data for a certain hyperparameter configuration.
# If the estimator does not support early stopping, we would fall back to a parallel grid search.

# Other imports
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier
from sklearn.datasets import make_classification
import numpy as np

# Create dataset
X, y = make_classification(
    n_samples=11000,
    n_features=1000,
    n_informative=50,
    n_redundant=0,
    n_classes=10,
    class_sep=2.5)
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=1000)

# Example parameters to tune from SGDClassifier
parameter_grid = {"alpha": [1e-4, 1e-1, 1], "epsilon": [0.01, 0.1]}

#######################################################################
# As you can see, the setup here is exactly how you would do it for Scikit-Learn. Now, let's try fitting a model.

tune_search = TuneGridSearchCV(
    SGDClassifier(), parameter_grid, early_stopping=True, max_iters=10)

import time  # Just to compare fit times
start = time.time()
tune_search.fit(x_train, y_train)
end = time.time()
print("Tune GridSearch Fit Time:", end - start)
# Tune GridSearch Fit Time: 15.436315774917603 (for an 8 core laptop)

#######################################################################
# Note the slight differences we introduced above:
#
#  * a `early_stopping`, and
#  * a specification of `max_iters` parameter
#
# The ``early_stopping`` parameter allows us to terminate unpromising configurations. If ``early_stopping=True``,
# TuneGridSearchCV will default to using Tune's ASHAScheduler. You can pass in a custom
# algorithm - see :ref:`Tune's documentation on schedulers <tune-schedulers>` here for a full list to choose from.
# ``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.
#
# Try running this compared to the GridSearchCV equivalent, and see the speedup for yourself!

from sklearn.model_selection import GridSearchCV
# n_jobs=-1 enables use of all cores like Tune does
sklearn_search = GridSearchCV(SGDClassifier(), parameter_grid, n_jobs=-1)

start = time.time()
sklearn_search.fit(x_train, y_train)
end = time.time()
print("Sklearn Fit Time:", end - start)
# Sklearn Fit Time: 47.48055911064148 (for an 8 core laptop)

###################################################################
# Using Bayesian Optimization
# ---------------------------
#
# In addition to the grid search interface, tune-sklearn also provides an interface, TuneSearchCV, for sampling from **distributions of hyperparameters**.
#
# In addition, you can easily enable Bayesian optimization over the distributions in only 2 lines of code:

# First run `pip install bayesian-optimization`
from ray.tune.sklearn import TuneSearchCV
from sklearn.linear_model import SGDClassifier
from sklearn import datasets
from sklearn.model_selection import train_test_split
import numpy as np

digits = datasets.load_digits()
x = digits.data
y = digits.target
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2)

clf = SGDClassifier()
parameter_grid = {"alpha": (1e-4, 1), "epsilon": (0.01, 0.1)}

tune_search = TuneSearchCV(
    clf,
    parameter_grid,
    search_optimization="bayesian",
    n_trials=3,
    early_stopping=True,
    max_iters=10,
)
tune_search.fit(x_train, y_train)
print(tune_search.best_params_)
# {'alpha': 0.37460266483547777, 'epsilon': 0.09556428757689246}

################################################################
# 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
# TuneSearchCV to connect to the Ray cluster and parallelize tuning across multiple nodes, as you would in any other Ray Tune script.
#
#
# Code Examples
# -------------
#
# Check out more detailed examples and get started with tune-sklearn!
#
#  * `Skorch with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/torch_nn.py>`_
#  * `Scikit-Learn Pipelines with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/sklearn_pipeline.py>`_
#  * `XGBoost with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/xgbclassifier.py>`_
#  * `KerasClassifier with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/keras_example.py>`_
#  * `LightGBM with tune-sklearn <https://github.com/ray-project/tune-sklearn/blob/master/examples/lgbm.py>`_
#
#
# Further Reading
# ---------------
#
# 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.