.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples\sklearn-gridsearchcv-replacement.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py: ========================================== Scikit-learn hyperparameter search wrapper ========================================== Iaroslav Shcherbatyi, Tim Head and Gilles Louppe. June 2017. Reformatted by Holger Nahrstaedt 2020 .. currentmodule:: skopt Introduction ============ This example assumes basic familiarity with `scikit-learn `_. Search for parameters of machine learning models that results in best cross-validation performance is necessary in almost all practical cases to get a model with best generalization estimate. A standard approach in scikit-learn is to use :obj:`sklearn.model_selection.GridSearchCV` class, which enumerates all combinations of hyperparameters values given as input. This search complexity grows exponentially with the number of parameters. A more scalable approach is to use :obj:`sklearn.model_selection.RandomizedSearchCV`, which however does not take advantage of the structure of a search space. Scikit-optimize provides a drop-in replacement for these two scikit-learn methods. The hyperparameter search is achieved by Bayesian Optimization At each step of the optimization, a surrogate model infers the objective function using observed evluation results as priors. An acquisition function utilizes these predictions to navigate between exploration (sampling unexplored areas) and exploitation (focusing on regions likely containing the global optimum). By balancing these two strategies, Bayesian Optimization identifies probable optimal areas while ensuring comprehensive search coverage. In practice, this method often leads to quicker and better results. Note: for a manual hyperparameter optimization example, see "Hyperparameter Optimization" notebook. .. GENERATED FROM PYTHON SOURCE LINES 42-54 .. code-block:: Python print(__doc__) import numpy as np np.random.seed(123) import matplotlib.pyplot as plt from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.svm import SVC from skopt import BayesSearchCV .. GENERATED FROM PYTHON SOURCE LINES 55-59 Minimal example =============== A minimal example of optimizing hyperparameters of SVC (Support Vector machine Classifier) is given below. .. GENERATED FROM PYTHON SOURCE LINES 59-84 .. code-block:: Python X, y = load_digits(n_class=10, return_X_y=True) X_train, X_test, y_train, y_test = train_test_split( X, y, train_size=0.75, test_size=0.25, random_state=0 ) # log-uniform: understand as search over p = exp(x) by varying x opt = BayesSearchCV( SVC(), { 'C': (1e-6, 1e6, 'log-uniform'), 'gamma': (1e-6, 1e1, 'log-uniform'), 'degree': (1, 8), # integer valued parameter 'kernel': ['linear', 'poly', 'rbf'], # categorical parameter }, n_iter=32, cv=3, ) opt.fit(X_train, y_train) print("val. score: %s" % opt.best_score_) print("test score: %s" % opt.score(X_test, y_test)) .. rst-class:: sphx-glr-script-out .. code-block:: none val. score: 0.9866369710467705 test score: 0.9844444444444445 .. GENERATED FROM PYTHON SOURCE LINES 85-92 Advanced example ================ In practice, one wants to enumerate over multiple predictive model classes, with different search spaces and number of evaluations per class. An example of such search over parameters of Linear SVM, Kernel SVM, and decision trees is given below. .. GENERATED FROM PYTHON SOURCE LINES 92-140 .. code-block:: Python from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.svm import SVC, LinearSVC from skopt import BayesSearchCV from skopt.plots import plot_histogram, plot_objective from skopt.space import Categorical, Integer, Real X, y = load_digits(n_class=10, return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) # pipeline class is used as estimator to enable # search over different model types pipe = Pipeline([('model', SVC())]) # single categorical value of 'model' parameter is # sets the model class # We will get ConvergenceWarnings because the problem is not well-conditioned. # But that's fine, this is just an example. linsvc_search = { 'model': [LinearSVC(max_iter=1000, dual="auto")], 'model__C': (1e-6, 1e6, 'log-uniform'), } # explicit dimension classes can be specified like this svc_search = { 'model': Categorical([SVC()]), 'model__C': Real(1e-6, 1e6, prior='log-uniform'), 'model__gamma': Real(1e-6, 1e1, prior='log-uniform'), 'model__degree': Integer(1, 8), 'model__kernel': Categorical(['linear', 'poly', 'rbf']), } opt = BayesSearchCV( pipe, # (parameter space, # of evaluations) [(svc_search, 40), (linsvc_search, 16)], cv=3, ) opt.fit(X_train, y_train) print("val. score: %s" % opt.best_score_) print("test score: %s" % opt.score(X_test, y_test)) print("best params: %s" % str(opt.best_params_)) .. rst-class:: sphx-glr-script-out .. code-block:: none val. score: 0.9881217520415739 test score: 0.9888888888888889 best params: OrderedDict([('model', SVC()), ('model__C', 4.580543393203649), ('model__degree', 3), ('model__gamma', 0.0002585937465230229), ('model__kernel', 'poly')]) .. GENERATED FROM PYTHON SOURCE LINES 141-143 Partial Dependence plot of the objective function for SVC .. GENERATED FROM PYTHON SOURCE LINES 143-150 .. code-block:: Python _ = plot_objective( opt.optimizer_results_[0], dimensions=["C", "degree", "gamma", "kernel"], n_minimum_search=int(1e8), ) plt.show() .. image-sg:: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_001.png :alt: sklearn gridsearchcv replacement :srcset: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 151-153 Plot of the histogram for LinearSVC .. GENERATED FROM PYTHON SOURCE LINES 153-156 .. code-block:: Python _ = plot_histogram(opt.optimizer_results_[1], 1) plt.show() .. image-sg:: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_002.png :alt: sklearn gridsearchcv replacement :srcset: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 157-171 Progress monitoring and control using `callback` argument of `fit` method ========================================================================= It is possible to monitor the progress of :class:`BayesSearchCV` with an event handler that is called on every step of subspace exploration. For single job mode, this is called on every evaluation of model configuration, and for parallel mode, this is called when n_jobs model configurations are evaluated in parallel. Additionally, exploration can be stopped if the callback returns `True`. This can be used to stop the exploration early, for instance when the accuracy that you get is sufficiently high. An example usage is shown below. .. GENERATED FROM PYTHON SOURCE LINES 171-198 .. code-block:: Python from sklearn.datasets import load_iris from sklearn.svm import SVC from skopt import BayesSearchCV X, y = load_iris(return_X_y=True) searchcv = BayesSearchCV( SVC(gamma='scale'), search_spaces={'C': (0.01, 100.0, 'log-uniform')}, n_iter=10, cv=3, ) # callback handler def on_step(optim_result): score = -optim_result['fun'] print("best score: %s" % score) if score >= 0.98: print('Interrupting!') return True searchcv.fit(X, y, callback=on_step) .. rst-class:: sphx-glr-script-out .. code-block:: none best score: 0.98 Interrupting! .. raw:: html 
BayesSearchCV(cv=3, estimator=SVC(), n_iter=10,
                  search_spaces={'C': (0.01, 100.0, 'log-uniform')})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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.. GENERATED FROM PYTHON SOURCE LINES 199-207 Counting total iterations that will be used to explore all subspaces ==================================================================== Subspaces in previous examples can further increase in complexity if you add new model subspaces or dimensions for feature extraction pipelines. For monitoring of progress, you would like to know the total number of iterations it will take to explore all subspaces. This can be calculated with `total_iterations` property, as in the code below. .. GENERATED FROM PYTHON SOURCE LINES 207-225 .. code-block:: Python from sklearn.datasets import load_iris from sklearn.svm import SVC from skopt import BayesSearchCV X, y = load_iris(return_X_y=True) searchcv = BayesSearchCV( SVC(), search_spaces=[ ({'C': (0.1, 1.0)}, 19), # 19 iterations for this subspace {'gamma': (0.1, 1.0)}, ], n_iter=10, ) print(searchcv.total_iterations) .. rst-class:: sphx-glr-script-out .. code-block:: none 29 .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 33.880 seconds) .. _sphx_glr_download_auto_examples_sklearn-gridsearchcv-replacement.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/holgern/scikit-optimize/master?urlpath=lab/tree/notebooks/auto_examples/sklearn-gridsearchcv-replacement.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: sklearn-gridsearchcv-replacement.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: sklearn-gridsearchcv-replacement.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_