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.

../../_images/tune-sklearn1.png

Scikit-Learn has an existing module for model selection, 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 documentation.

Overview

tune-sklearn is a module that integrates Ray Tune’s hyperparameter tuning and scikit-learn’s Classifier API. tune-sklearn has two APIs: TuneSearchCV, and TuneGridSearchCV. 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.

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 Tune’s documentation on 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.

Further Reading

If you’re using scikit-learn for other tasks, take a look at Ray’s replacement for joblib, which allows users to parallelize scikit learn jobs over multiple nodes.

Total running time of the script: ( 0 minutes 0.000 seconds)

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