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# copyright: hyperactive developers, MIT License (see LICENSE file) |
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from collections.abc import Callable |
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from typing import Union |
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from sklearn.base import BaseEstimator, clone |
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from sklearn.utils.validation import indexable, _check_method_params |
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from hyperactive.experiment.integrations.sklearn_cv import SklearnCvExperiment |
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from hyperactive.integrations.sklearn.best_estimator import ( |
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BestEstimator as _BestEstimator_ |
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) |
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from hyperactive.integrations.sklearn.checks import Checks |
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class OptCV(BaseEstimator, _BestEstimator_, Checks): |
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"""Tuning via any optimizer in the hyperactive API. |
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Parameters |
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---------- |
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estimator : SklearnBaseEstimator |
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The estimator to be tuned. |
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optimizer : hyperactive BaseOptimizer |
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The optimizer to be used for hyperparameter search. |
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estimator : sklearn estimator |
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The estimator to be used for the experiment. |
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cv : int or cross-validation generator, default = KFold(n_splits=3, shuffle=True) |
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The number of folds or cross-validation strategy to be used. |
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If int, the cross-validation used is KFold(n_splits=cv, shuffle=True). |
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scoring : callable or str, default = accuracy_score or mean_squared_error |
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sklearn scoring function or metric to evaluate the model's performance. |
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Default is determined by the type of estimator: |
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``accuracy_score`` for classifiers, and |
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``mean_squared_error`` for regressors, as per sklearn convention |
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through the default ``score`` method of the estimator. |
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Example |
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------- |
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Tuning sklearn SVC via grid search |
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1. defining the tuned estimator: |
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>>> from sklearn.svm import SVC |
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>>> from hyperactive.integrations.sklearn import OptCV |
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>>> from hyperactive.opt import GridSearch |
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>>> |
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>>> param_grid = {"kernel": ["linear", "rbf"], "C": [1, 10]} |
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>>> tuned_svc = OptCV(SVC(), GridSearch(param_grid)) |
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2. fitting the tuned estimator: |
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>>> from sklearn.datasets import load_iris |
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>>> from sklearn.model_selection import train_test_split |
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>>> X, y = load_iris(return_X_y=True) |
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>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) |
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>>> |
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>>> tuned_svc.fit(X_train, y_train) |
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OptCV(...) |
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>>> y_pred = tuned_svc.predict(X_test) |
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3. obtaining best parameters and best estimator |
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>>> best_params = tuned_svc.best_params_ |
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>>> best_estimator = tuned_svc.best_estimator_ |
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""" |
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_required_parameters = ["estimator", "optimizer"] |
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def __init__( |
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self, |
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estimator, |
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optimizer, |
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*, |
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scoring: Union[Callable, str, None] = None, |
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cv=None, |
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): |
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super().__init__() |
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self.estimator = estimator |
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self.optimizer = optimizer |
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self.scoring = scoring |
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self.cv = cv |
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def _refit(self, X, y=None, **fit_params): |
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self.best_estimator_ = clone(self.estimator).set_params( |
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**clone(self.best_params_, safe=False) |
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) |
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self.best_estimator_.fit(X, y, **fit_params) |
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return self |
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def _check_data(self, X, y): |
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X, y = indexable(X, y) |
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if hasattr(self, "_validate_data"): |
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validate_data = self._validate_data |
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else: |
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from sklearn.utils.validation import validate_data |
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return validate_data(X, y) |
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@Checks.verify_fit |
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def fit(self, X, y, **fit_params): |
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"""Fit the model. |
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Parameters |
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---------- |
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X : {array-like, sparse matrix} of shape (n_samples, n_features) |
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Training data. |
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y : array-like of shape (n_samples,) or (n_samples, n_targets) |
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Target values. Will be cast to X's dtype if necessary. |
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Returns |
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------- |
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self : object |
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Fitted Estimator. |
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""" |
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X, y = self._check_data(X, y) |
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fit_params = _check_method_params(X, params=fit_params) |
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experiment = SklearnCvExperiment( |
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estimator=self.estimator, |
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scoring=self.scoring, |
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cv=self.cv, |
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X=X, |
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y=y, |
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) |
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self.scorer_ = experiment.scorer_ |
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optimizer = self.optimizer.clone() |
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optimizer.set_params(experiment=experiment) |
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best_params = optimizer.run() |
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self.best_params_ = best_params |
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self.best_estimator_ = clone(self.estimator).set_params(**best_params) |
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if self.refit: |
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self._refit(X, y, **fit_params) |
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return self |
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def score(self, X, y=None, **params): |
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"""Return the score on the given data, if the estimator has been refit. |
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This uses the score defined by ``scoring`` where provided, and the |
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``best_estimator_.score`` method otherwise. |
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Parameters |
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---------- |
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X : array-like of shape (n_samples, n_features) |
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Input data, where `n_samples` is the number of samples and |
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`n_features` is the number of features. |
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y : array-like of shape (n_samples, n_output) \ |
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or (n_samples,), default=None |
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Target relative to X for classification or regression; |
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None for unsupervised learning. |
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**params : dict |
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Parameters to be passed to the underlying scorer(s). |
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Returns |
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------- |
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score : float |
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The score defined by ``scoring`` if provided, and the |
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``best_estimator_.score`` method otherwise. |
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""" |
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return self.scorer_(self.best_estimator_, X, y, **params) |
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@property |
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def fit_successful(self): |
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self._fit_successful |
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SimonBlanke /
Hyperactive
