117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
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from ...base import is_classifier
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def _check_classifier_response_method(estimator, response_method):
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"""Return prediction method from the response_method
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Parameters
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----------
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estimator: object
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Classifier to check
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response_method: {'auto', 'predict_proba', 'decision_function'}
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Specifies whether to use :term:`predict_proba` or
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:term:`decision_function` as the target response. If set to 'auto',
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:term:`predict_proba` is tried first and if it does not exist
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:term:`decision_function` is tried next.
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Returns
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-------
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prediction_method: callable
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prediction method of estimator
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"""
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if response_method not in ("predict_proba", "decision_function", "auto"):
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raise ValueError(
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"response_method must be 'predict_proba', 'decision_function' or 'auto'"
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)
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error_msg = "response method {} is not defined in {}"
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if response_method != "auto":
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prediction_method = getattr(estimator, response_method, None)
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if prediction_method is None:
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raise ValueError(
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error_msg.format(response_method, estimator.__class__.__name__)
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)
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else:
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predict_proba = getattr(estimator, "predict_proba", None)
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decision_function = getattr(estimator, "decision_function", None)
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prediction_method = predict_proba or decision_function
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if prediction_method is None:
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raise ValueError(
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error_msg.format(
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"decision_function or predict_proba", estimator.__class__.__name__
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)
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)
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return prediction_method
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def _get_response(X, estimator, response_method, pos_label=None):
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"""Return response and positive label.
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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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Input values.
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estimator : estimator instance
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Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
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in which the last estimator is a classifier.
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response_method: {'auto', 'predict_proba', 'decision_function'}
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Specifies whether to use :term:`predict_proba` or
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:term:`decision_function` as the target response. If set to 'auto',
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:term:`predict_proba` is tried first and if it does not exist
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:term:`decision_function` is tried next.
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pos_label : str or int, default=None
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The class considered as the positive class when computing
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the metrics. By default, `estimators.classes_[1]` is
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considered as the positive class.
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Returns
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-------
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y_pred: ndarray of shape (n_samples,)
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Target scores calculated from the provided response_method
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and pos_label.
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pos_label: str or int
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The class considered as the positive class when computing
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the metrics.
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"""
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classification_error = (
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"Expected 'estimator' to be a binary classifier, but got"
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f" {estimator.__class__.__name__}"
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)
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if not is_classifier(estimator):
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raise ValueError(classification_error)
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prediction_method = _check_classifier_response_method(estimator, response_method)
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y_pred = prediction_method(X)
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if pos_label is not None:
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try:
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class_idx = estimator.classes_.tolist().index(pos_label)
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except ValueError as e:
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raise ValueError(
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"The class provided by 'pos_label' is unknown. Got "
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f"{pos_label} instead of one of {set(estimator.classes_)}"
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) from e
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else:
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class_idx = 1
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pos_label = estimator.classes_[class_idx]
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if y_pred.ndim != 1: # `predict_proba`
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y_pred_shape = y_pred.shape[1]
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if y_pred_shape != 2:
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raise ValueError(
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f"{classification_error} fit on multiclass ({y_pred_shape} classes)"
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" data"
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)
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y_pred = y_pred[:, class_idx]
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elif pos_label == estimator.classes_[0]: # `decision_function`
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y_pred *= -1
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return y_pred, pos_label
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