81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
import numpy as np
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from scipy import sparse
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from sklearn.base import ClassifierMixin
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from ..externals.estimator_checks import check_is_fitted
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class _BaseStackingClassifier(ClassifierMixin):
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"""Base class of stacking classifiers
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"""
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def _do_predict(self, X, predict_fn):
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meta_features = self.predict_meta_features(X)
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if not self.use_features_in_secondary:
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return predict_fn(meta_features)
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elif sparse.issparse(X):
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return predict_fn(sparse.hstack((X, meta_features)))
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else:
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return predict_fn(np.hstack((X, meta_features)))
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def predict(self, X):
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""" Predict target values for X.
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Parameters
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----------
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X : numpy array, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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----------
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labels : array-like, shape = [n_samples]
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Predicted class labels.
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"""
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check_is_fitted(self, ['clfs_', 'meta_clf_'])
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return self._do_predict(X, self.meta_clf_.predict)
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def predict_proba(self, X):
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""" Predict class probabilities for X.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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----------
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proba : array-like, shape = [n_samples, n_classes] or a list of \
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n_outputs of such arrays if n_outputs > 1.
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Probability for each class per sample.
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"""
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check_is_fitted(self, ['clfs_', 'meta_clf_'])
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return self._do_predict(X, self.meta_clf_.predict_proba)
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def decision_function(self, X):
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""" Predict class confidence scores for X.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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----------
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scores : shape=(n_samples,) if n_classes == 2 else \
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(n_samples, n_classes).
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Confidence scores per (sample, class) combination. In the binary
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case, confidence score for self.classes_[1] where >0 means this
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class would be predicted.
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"""
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check_is_fitted(self, ['clfs_', 'meta_clf_'])
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return self._do_predict(X, self.meta_clf_.decision_function)
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