315 lines
12 KiB
Python
315 lines
12 KiB
Python
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"""Utilities to get the response values of a classifier or a regressor.
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It allows to make uniform checks and validation.
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"""
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import numpy as np
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from ..base import is_classifier
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from .multiclass import type_of_target
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from .validation import _check_response_method, check_is_fitted
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def _process_predict_proba(*, y_pred, target_type, classes, pos_label):
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"""Get the response values when the response method is `predict_proba`.
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This function process the `y_pred` array in the binary and multi-label cases.
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In the binary case, it selects the column corresponding to the positive
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class. In the multi-label case, it stacks the predictions if they are not
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in the "compressed" format `(n_samples, n_outputs)`.
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Parameters
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----------
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y_pred : ndarray
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Output of `estimator.predict_proba`. The shape depends on the target type:
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- for binary classification, it is a 2d array of shape `(n_samples, 2)`;
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- for multiclass classification, it is a 2d array of shape
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`(n_samples, n_classes)`;
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- for multilabel classification, it is either a list of 2d arrays of shape
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`(n_samples, 2)` (e.g. `RandomForestClassifier` or `KNeighborsClassifier`) or
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an array of shape `(n_samples, n_outputs)` (e.g. `MLPClassifier` or
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`RidgeClassifier`).
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target_type : {"binary", "multiclass", "multilabel-indicator"}
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Type of the target.
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classes : ndarray of shape (n_classes,) or list of such arrays
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Class labels as reported by `estimator.classes_`.
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pos_label : int, float, bool or str
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Only used with binary and multiclass targets.
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Returns
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-------
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y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or \
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(n_samples, n_output)
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Compressed predictions format as requested by the metrics.
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"""
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if target_type == "binary" and y_pred.shape[1] < 2:
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# We don't handle classifiers trained on a single class.
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raise ValueError(
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f"Got predict_proba of shape {y_pred.shape}, but need "
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"classifier with two classes."
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)
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if target_type == "binary":
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col_idx = np.flatnonzero(classes == pos_label)[0]
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return y_pred[:, col_idx]
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elif target_type == "multilabel-indicator":
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# Use a compress format of shape `(n_samples, n_output)`.
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# Only `MLPClassifier` and `RidgeClassifier` return an array of shape
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# `(n_samples, n_outputs)`.
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if isinstance(y_pred, list):
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# list of arrays of shape `(n_samples, 2)`
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return np.vstack([p[:, -1] for p in y_pred]).T
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else:
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# array of shape `(n_samples, n_outputs)`
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return y_pred
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return y_pred
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def _process_decision_function(*, y_pred, target_type, classes, pos_label):
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"""Get the response values when the response method is `decision_function`.
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This function process the `y_pred` array in the binary and multi-label cases.
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In the binary case, it inverts the sign of the score if the positive label
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is not `classes[1]`. In the multi-label case, it stacks the predictions if
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they are not in the "compressed" format `(n_samples, n_outputs)`.
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Parameters
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----------
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y_pred : ndarray
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Output of `estimator.predict_proba`. The shape depends on the target type:
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- for binary classification, it is a 1d array of shape `(n_samples,)` where the
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sign is assuming that `classes[1]` is the positive class;
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- for multiclass classification, it is a 2d array of shape
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`(n_samples, n_classes)`;
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- for multilabel classification, it is a 2d array of shape `(n_samples,
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n_outputs)`.
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target_type : {"binary", "multiclass", "multilabel-indicator"}
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Type of the target.
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classes : ndarray of shape (n_classes,) or list of such arrays
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Class labels as reported by `estimator.classes_`.
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pos_label : int, float, bool or str
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Only used with binary and multiclass targets.
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Returns
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-------
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y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or \
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(n_samples, n_output)
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Compressed predictions format as requested by the metrics.
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"""
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if target_type == "binary" and pos_label == classes[0]:
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return -1 * y_pred
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return y_pred
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def _get_response_values(
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estimator,
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X,
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response_method,
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pos_label=None,
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return_response_method_used=False,
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):
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"""Compute the response values of a classifier, an outlier detector, or a regressor.
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The response values are predictions such that it follows the following shape:
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- for binary classification, it is a 1d array of shape `(n_samples,)`;
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- for multiclass classification, it is a 2d array of shape `(n_samples, n_classes)`;
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- for multilabel classification, it is a 2d array of shape `(n_samples, n_outputs)`;
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- for outlier detection, it is a 1d array of shape `(n_samples,)`;
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- for regression, it is a 1d array of shape `(n_samples,)`.
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If `estimator` is a binary classifier, also return the label for the
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effective positive class.
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This utility is used primarily in the displays and the scikit-learn scorers.
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.. versionadded:: 1.3
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Parameters
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----------
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estimator : estimator instance
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Fitted classifier, outlier detector, or regressor or a
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fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a
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classifier, an outlier detector, or a regressor.
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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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response_method : {"predict_proba", "predict_log_proba", "decision_function", \
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"predict"} or list of such str
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Specifies the response method to use get prediction from an estimator
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(i.e. :term:`predict_proba`, :term:`predict_log_proba`,
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:term:`decision_function` or :term:`predict`). Possible choices are:
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- if `str`, it corresponds to the name to the method to return;
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- if a list of `str`, it provides the method names in order of
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preference. The method returned corresponds to the first method in
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the list and which is implemented by `estimator`.
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pos_label : int, float, bool or str, default=None
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The class considered as the positive class when computing
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the metrics. If `None` and target is 'binary', `estimators.classes_[1]` is
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considered as the positive class.
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return_response_method_used : bool, default=False
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Whether to return the response method used to compute the response
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values.
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.. versionadded:: 1.4
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Returns
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-------
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y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or \
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(n_samples, n_outputs)
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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 : int, float, bool, str or None
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The class considered as the positive class when computing
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the metrics. Returns `None` if `estimator` is a regressor or an outlier
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detector.
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response_method_used : str
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The response method used to compute the response values. Only returned
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if `return_response_method_used` is `True`.
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.. versionadded:: 1.4
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Raises
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------
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ValueError
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If `pos_label` is not a valid label.
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If the shape of `y_pred` is not consistent for binary classifier.
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If the response method can be applied to a classifier only and
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`estimator` is a regressor.
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"""
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from sklearn.base import is_classifier, is_outlier_detector # noqa
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if is_classifier(estimator):
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prediction_method = _check_response_method(estimator, response_method)
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classes = estimator.classes_
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target_type = type_of_target(classes)
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if target_type in ("binary", "multiclass"):
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if pos_label is not None and pos_label not in classes.tolist():
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raise ValueError(
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f"pos_label={pos_label} is not a valid label: It should be "
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f"one of {classes}"
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)
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elif pos_label is None and target_type == "binary":
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pos_label = classes[-1]
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y_pred = prediction_method(X)
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if prediction_method.__name__ in ("predict_proba", "predict_log_proba"):
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y_pred = _process_predict_proba(
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y_pred=y_pred,
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target_type=target_type,
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classes=classes,
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pos_label=pos_label,
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)
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elif prediction_method.__name__ == "decision_function":
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y_pred = _process_decision_function(
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y_pred=y_pred,
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target_type=target_type,
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classes=classes,
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pos_label=pos_label,
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)
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elif is_outlier_detector(estimator):
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prediction_method = _check_response_method(estimator, response_method)
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y_pred, pos_label = prediction_method(X), None
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else: # estimator is a regressor
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if response_method != "predict":
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raise ValueError(
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f"{estimator.__class__.__name__} should either be a classifier to be "
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f"used with response_method={response_method} or the response_method "
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"should be 'predict'. Got a regressor with response_method="
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f"{response_method} instead."
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)
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prediction_method = estimator.predict
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y_pred, pos_label = prediction_method(X), None
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if return_response_method_used:
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return y_pred, pos_label, prediction_method.__name__
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return y_pred, pos_label
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def _get_response_values_binary(
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estimator, X, response_method, pos_label=None, return_response_method_used=False
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):
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"""Compute the response values of a binary classifier.
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Parameters
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----------
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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 binary classifier.
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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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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 : int, float, bool or str, 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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return_response_method_used : bool, default=False
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Whether to return the response method used to compute the response
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values.
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.. versionadded:: 1.5
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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 : int, float, bool or str
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The class considered as the positive class when computing
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the metrics.
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response_method_used : str
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The response method used to compute the response values. Only returned
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if `return_response_method_used` is `True`.
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.. versionadded:: 1.5
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"""
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classification_error = "Expected 'estimator' to be a binary classifier."
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check_is_fitted(estimator)
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if not is_classifier(estimator):
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raise ValueError(
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classification_error + f" Got {estimator.__class__.__name__} instead."
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)
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elif len(estimator.classes_) != 2:
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raise ValueError(
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classification_error + f" Got {len(estimator.classes_)} classes instead."
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)
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if response_method == "auto":
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response_method = ["predict_proba", "decision_function"]
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return _get_response_values(
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estimator,
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X,
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response_method,
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pos_label=pos_label,
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return_response_method_used=return_response_method_used,
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)
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