projektAI/venv/Lib/site-packages/mlxtend/classifier/_base_classification.py

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2021-06-06 22:13:05 +02:00
import numpy as np
from scipy import sparse
from sklearn.base import ClassifierMixin
from ..externals.estimator_checks import check_is_fitted
class _BaseStackingClassifier(ClassifierMixin):
"""Base class of stacking classifiers
"""
def _do_predict(self, X, predict_fn):
meta_features = self.predict_meta_features(X)
if not self.use_features_in_secondary:
return predict_fn(meta_features)
elif sparse.issparse(X):
return predict_fn(sparse.hstack((X, meta_features)))
else:
return predict_fn(np.hstack((X, meta_features)))
def predict(self, X):
""" Predict target values for X.
Parameters
----------
X : numpy array, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Returns
----------
labels : array-like, shape = [n_samples]
Predicted class labels.
"""
check_is_fitted(self, ['clfs_', 'meta_clf_'])
return self._do_predict(X, self.meta_clf_.predict)
def predict_proba(self, X):
""" Predict class probabilities for X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Returns
----------
proba : array-like, shape = [n_samples, n_classes] or a list of \
n_outputs of such arrays if n_outputs > 1.
Probability for each class per sample.
"""
check_is_fitted(self, ['clfs_', 'meta_clf_'])
return self._do_predict(X, self.meta_clf_.predict_proba)
def decision_function(self, X):
""" Predict class confidence scores for X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Returns
----------
scores : shape=(n_samples,) if n_classes == 2 else \
(n_samples, n_classes).
Confidence scores per (sample, class) combination. In the binary
case, confidence score for self.classes_[1] where >0 means this
class would be predicted.
"""
check_is_fitted(self, ['clfs_', 'meta_clf_'])
return self._do_predict(X, self.meta_clf_.decision_function)