868 lines
30 KiB
Python
868 lines
30 KiB
Python
"""
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The :mod:`sklearn.metrics.scorer` submodule implements a flexible
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interface for model selection and evaluation using
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arbitrary score functions.
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A scorer object is a callable that can be passed to
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:class:`~sklearn.model_selection.GridSearchCV` or
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:func:`sklearn.model_selection.cross_val_score` as the ``scoring``
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parameter, to specify how a model should be evaluated.
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The signature of the call is ``(estimator, X, y)`` where ``estimator``
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is the model to be evaluated, ``X`` is the test data and ``y`` is the
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ground truth labeling (or ``None`` in the case of unsupervised models).
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"""
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# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
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# Lars Buitinck
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# Arnaud Joly <arnaud.v.joly@gmail.com>
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# License: Simplified BSD
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from collections.abc import Iterable
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from functools import partial
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from collections import Counter
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from traceback import format_exc
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import numpy as np
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import copy
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import warnings
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from . import (
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r2_score,
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median_absolute_error,
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max_error,
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mean_absolute_error,
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mean_squared_error,
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mean_squared_log_error,
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mean_poisson_deviance,
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mean_gamma_deviance,
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accuracy_score,
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top_k_accuracy_score,
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f1_score,
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roc_auc_score,
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average_precision_score,
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precision_score,
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recall_score,
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log_loss,
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balanced_accuracy_score,
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explained_variance_score,
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brier_score_loss,
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jaccard_score,
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mean_absolute_percentage_error,
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matthews_corrcoef,
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class_likelihood_ratios,
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)
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from .cluster import adjusted_rand_score
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from .cluster import rand_score
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from .cluster import homogeneity_score
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from .cluster import completeness_score
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from .cluster import v_measure_score
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from .cluster import mutual_info_score
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from .cluster import adjusted_mutual_info_score
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from .cluster import normalized_mutual_info_score
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from .cluster import fowlkes_mallows_score
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from ..utils.multiclass import type_of_target
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from ..base import is_regressor
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def _cached_call(cache, estimator, method, *args, **kwargs):
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"""Call estimator with method and args and kwargs."""
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if cache is None:
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return getattr(estimator, method)(*args, **kwargs)
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try:
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return cache[method]
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except KeyError:
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result = getattr(estimator, method)(*args, **kwargs)
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cache[method] = result
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return result
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class _MultimetricScorer:
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"""Callable for multimetric scoring used to avoid repeated calls
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to `predict_proba`, `predict`, and `decision_function`.
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`_MultimetricScorer` will return a dictionary of scores corresponding to
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the scorers in the dictionary. Note that `_MultimetricScorer` can be
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created with a dictionary with one key (i.e. only one actual scorer).
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Parameters
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----------
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scorers : dict
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Dictionary mapping names to callable scorers.
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raise_exc : bool, default=True
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Whether to raise the exception in `__call__` or not. If set to `False`
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a formatted string of the exception details is passed as result of
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the failing scorer.
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"""
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def __init__(self, *, scorers, raise_exc=True):
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self._scorers = scorers
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self._raise_exc = raise_exc
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def __call__(self, estimator, *args, **kwargs):
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"""Evaluate predicted target values."""
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scores = {}
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cache = {} if self._use_cache(estimator) else None
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cached_call = partial(_cached_call, cache)
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for name, scorer in self._scorers.items():
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try:
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if isinstance(scorer, _BaseScorer):
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score = scorer._score(cached_call, estimator, *args, **kwargs)
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else:
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score = scorer(estimator, *args, **kwargs)
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scores[name] = score
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except Exception as e:
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if self._raise_exc:
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raise e
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else:
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scores[name] = format_exc()
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return scores
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def _use_cache(self, estimator):
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"""Return True if using a cache is beneficial.
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Caching may be beneficial when one of these conditions holds:
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- `_ProbaScorer` will be called twice.
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- `_PredictScorer` will be called twice.
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- `_ThresholdScorer` will be called twice.
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- `_ThresholdScorer` and `_PredictScorer` are called and
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estimator is a regressor.
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- `_ThresholdScorer` and `_ProbaScorer` are called and
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estimator does not have a `decision_function` attribute.
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"""
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if len(self._scorers) == 1: # Only one scorer
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return False
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counter = Counter([type(v) for v in self._scorers.values()])
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if any(
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counter[known_type] > 1
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for known_type in [_PredictScorer, _ProbaScorer, _ThresholdScorer]
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):
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return True
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if counter[_ThresholdScorer]:
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if is_regressor(estimator) and counter[_PredictScorer]:
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return True
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elif counter[_ProbaScorer] and not hasattr(estimator, "decision_function"):
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return True
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return False
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class _BaseScorer:
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def __init__(self, score_func, sign, kwargs):
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self._kwargs = kwargs
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self._score_func = score_func
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self._sign = sign
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@staticmethod
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def _check_pos_label(pos_label, classes):
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if pos_label not in list(classes):
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raise ValueError(f"pos_label={pos_label} is not a valid label: {classes}")
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def _select_proba_binary(self, y_pred, classes):
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"""Select the column of the positive label in `y_pred` when
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probabilities are provided.
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Parameters
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----------
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y_pred : ndarray of shape (n_samples, n_classes)
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The prediction given by `predict_proba`.
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classes : ndarray of shape (n_classes,)
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The class labels for the estimator.
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Returns
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-------
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y_pred : ndarray of shape (n_samples,)
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Probability predictions of the positive class.
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"""
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if y_pred.shape[1] == 2:
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pos_label = self._kwargs.get("pos_label", classes[1])
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self._check_pos_label(pos_label, classes)
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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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err_msg = (
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f"Got predict_proba of shape {y_pred.shape}, but need "
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f"classifier with two classes for {self._score_func.__name__} "
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"scoring"
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)
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raise ValueError(err_msg)
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def __repr__(self):
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kwargs_string = "".join(
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[", %s=%s" % (str(k), str(v)) for k, v in self._kwargs.items()]
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)
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return "make_scorer(%s%s%s%s)" % (
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self._score_func.__name__,
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"" if self._sign > 0 else ", greater_is_better=False",
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self._factory_args(),
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kwargs_string,
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)
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def __call__(self, estimator, X, y_true, sample_weight=None):
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"""Evaluate predicted target values for X relative to y_true.
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Parameters
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----------
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estimator : object
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Trained estimator to use for scoring. Must have a predict_proba
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method; the output of that is used to compute the score.
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X : {array-like, sparse matrix}
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Test data that will be fed to estimator.predict.
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y_true : array-like
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Gold standard target values for X.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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Returns
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-------
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score : float
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Score function applied to prediction of estimator on X.
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"""
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return self._score(
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partial(_cached_call, None),
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estimator,
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X,
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y_true,
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sample_weight=sample_weight,
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)
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def _factory_args(self):
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"""Return non-default make_scorer arguments for repr."""
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return ""
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class _PredictScorer(_BaseScorer):
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def _score(self, method_caller, estimator, X, y_true, sample_weight=None):
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"""Evaluate predicted target values for X relative to y_true.
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Parameters
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----------
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method_caller : callable
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Returns predictions given an estimator, method name, and other
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arguments, potentially caching results.
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estimator : object
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Trained estimator to use for scoring. Must have a `predict`
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method; the output of that is used to compute the score.
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X : {array-like, sparse matrix}
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Test data that will be fed to estimator.predict.
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y_true : array-like
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Gold standard target values for X.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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Returns
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-------
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score : float
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Score function applied to prediction of estimator on X.
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"""
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y_pred = method_caller(estimator, "predict", X)
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if sample_weight is not None:
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return self._sign * self._score_func(
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y_true, y_pred, sample_weight=sample_weight, **self._kwargs
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)
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else:
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return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
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class _ProbaScorer(_BaseScorer):
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def _score(self, method_caller, clf, X, y, sample_weight=None):
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"""Evaluate predicted probabilities for X relative to y_true.
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Parameters
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----------
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method_caller : callable
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Returns predictions given an estimator, method name, and other
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arguments, potentially caching results.
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clf : object
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Trained classifier to use for scoring. Must have a `predict_proba`
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method; the output of that is used to compute the score.
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X : {array-like, sparse matrix}
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Test data that will be fed to clf.predict_proba.
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y : array-like
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Gold standard target values for X. These must be class labels,
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not probabilities.
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sample_weight : array-like, default=None
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Sample weights.
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Returns
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-------
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score : float
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Score function applied to prediction of estimator on X.
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"""
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y_type = type_of_target(y)
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y_pred = method_caller(clf, "predict_proba", X)
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if y_type == "binary" and y_pred.shape[1] <= 2:
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# `y_type` could be equal to "binary" even in a multi-class
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# problem: (when only 2 class are given to `y_true` during scoring)
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# Thus, we need to check for the shape of `y_pred`.
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y_pred = self._select_proba_binary(y_pred, clf.classes_)
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if sample_weight is not None:
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return self._sign * self._score_func(
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y, y_pred, sample_weight=sample_weight, **self._kwargs
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)
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else:
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return self._sign * self._score_func(y, y_pred, **self._kwargs)
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def _factory_args(self):
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return ", needs_proba=True"
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|
|
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class _ThresholdScorer(_BaseScorer):
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def _score(self, method_caller, clf, X, y, sample_weight=None):
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"""Evaluate decision function output for X relative to y_true.
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|
Parameters
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----------
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method_caller : callable
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Returns predictions given an estimator, method name, and other
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arguments, potentially caching results.
|
|
|
|
clf : object
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|
Trained classifier to use for scoring. Must have either a
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decision_function method or a predict_proba method; the output of
|
|
that is used to compute the score.
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|
|
|
X : {array-like, sparse matrix}
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Test data that will be fed to clf.decision_function or
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clf.predict_proba.
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|
|
|
y : array-like
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|
Gold standard target values for X. These must be class labels,
|
|
not decision function values.
|
|
|
|
sample_weight : array-like, default=None
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Sample weights.
|
|
|
|
Returns
|
|
-------
|
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score : float
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|
Score function applied to prediction of estimator on X.
|
|
"""
|
|
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y_type = type_of_target(y)
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if y_type not in ("binary", "multilabel-indicator"):
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raise ValueError("{0} format is not supported".format(y_type))
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|
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if is_regressor(clf):
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y_pred = method_caller(clf, "predict", X)
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else:
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try:
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y_pred = method_caller(clf, "decision_function", X)
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if isinstance(y_pred, list):
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# For multi-output multi-class estimator
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y_pred = np.vstack([p for p in y_pred]).T
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elif y_type == "binary" and "pos_label" in self._kwargs:
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self._check_pos_label(self._kwargs["pos_label"], clf.classes_)
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if self._kwargs["pos_label"] == clf.classes_[0]:
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# The implicit positive class of the binary classifier
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# does not match `pos_label`: we need to invert the
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# predictions
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y_pred *= -1
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except (NotImplementedError, AttributeError):
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y_pred = method_caller(clf, "predict_proba", X)
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if y_type == "binary":
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y_pred = self._select_proba_binary(y_pred, clf.classes_)
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elif isinstance(y_pred, list):
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y_pred = np.vstack([p[:, -1] for p in y_pred]).T
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if sample_weight is not None:
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return self._sign * self._score_func(
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y, y_pred, sample_weight=sample_weight, **self._kwargs
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)
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else:
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return self._sign * self._score_func(y, y_pred, **self._kwargs)
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|
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def _factory_args(self):
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return ", needs_threshold=True"
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|
|
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def get_scorer(scoring):
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"""Get a scorer from string.
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Read more in the :ref:`User Guide <scoring_parameter>`.
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:func:`~sklearn.metrics.get_scorer_names` can be used to retrieve the names
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of all available scorers.
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Parameters
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----------
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scoring : str or callable
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Scoring method as string. If callable it is returned as is.
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Returns
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-------
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scorer : callable
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The scorer.
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Notes
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-----
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When passed a string, this function always returns a copy of the scorer
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object. Calling `get_scorer` twice for the same scorer results in two
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separate scorer objects.
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"""
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if isinstance(scoring, str):
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try:
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scorer = copy.deepcopy(_SCORERS[scoring])
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except KeyError:
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raise ValueError(
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"%r is not a valid scoring value. "
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"Use sklearn.metrics.get_scorer_names() "
|
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"to get valid options." % scoring
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)
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else:
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scorer = scoring
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return scorer
|
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|
|
|
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def _passthrough_scorer(estimator, *args, **kwargs):
|
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"""Function that wraps estimator.score"""
|
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return estimator.score(*args, **kwargs)
|
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|
|
|
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def check_scoring(estimator, scoring=None, *, allow_none=False):
|
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"""Determine scorer from user options.
|
|
|
|
A TypeError will be thrown if the estimator cannot be scored.
|
|
|
|
Parameters
|
|
----------
|
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estimator : estimator object implementing 'fit'
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The object to use to fit the data.
|
|
|
|
scoring : str or callable, default=None
|
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A string (see model evaluation documentation) or
|
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a scorer callable object / function with signature
|
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``scorer(estimator, X, y)``.
|
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If None, the provided estimator object's `score` method is used.
|
|
|
|
allow_none : bool, default=False
|
|
If no scoring is specified and the estimator has no score function, we
|
|
can either return None or raise an exception.
|
|
|
|
Returns
|
|
-------
|
|
scoring : callable
|
|
A scorer callable object / function with signature
|
|
``scorer(estimator, X, y)``.
|
|
"""
|
|
if not hasattr(estimator, "fit"):
|
|
raise TypeError(
|
|
"estimator should be an estimator implementing 'fit' method, %r was passed"
|
|
% estimator
|
|
)
|
|
if isinstance(scoring, str):
|
|
return get_scorer(scoring)
|
|
elif callable(scoring):
|
|
# Heuristic to ensure user has not passed a metric
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|
module = getattr(scoring, "__module__", None)
|
|
if (
|
|
hasattr(module, "startswith")
|
|
and module.startswith("sklearn.metrics.")
|
|
and not module.startswith("sklearn.metrics._scorer")
|
|
and not module.startswith("sklearn.metrics.tests.")
|
|
):
|
|
raise ValueError(
|
|
"scoring value %r looks like it is a metric "
|
|
"function rather than a scorer. A scorer should "
|
|
"require an estimator as its first parameter. "
|
|
"Please use `make_scorer` to convert a metric "
|
|
"to a scorer." % scoring
|
|
)
|
|
return get_scorer(scoring)
|
|
elif scoring is None:
|
|
if hasattr(estimator, "score"):
|
|
return _passthrough_scorer
|
|
elif allow_none:
|
|
return None
|
|
else:
|
|
raise TypeError(
|
|
"If no scoring is specified, the estimator passed should "
|
|
"have a 'score' method. The estimator %r does not." % estimator
|
|
)
|
|
elif isinstance(scoring, Iterable):
|
|
raise ValueError(
|
|
"For evaluating multiple scores, use "
|
|
"sklearn.model_selection.cross_validate instead. "
|
|
"{0} was passed.".format(scoring)
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"scoring value should either be a callable, string or None. %r was passed"
|
|
% scoring
|
|
)
|
|
|
|
|
|
def _check_multimetric_scoring(estimator, scoring):
|
|
"""Check the scoring parameter in cases when multiple metrics are allowed.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : sklearn estimator instance
|
|
The estimator for which the scoring will be applied.
|
|
|
|
scoring : list, tuple or dict
|
|
Strategy to evaluate the performance of the cross-validated model on
|
|
the test set.
|
|
|
|
The possibilities are:
|
|
|
|
- a list or tuple of unique strings;
|
|
- a callable returning a dictionary where they keys are the metric
|
|
names and the values are the metric scores;
|
|
- a dictionary with metric names as keys and callables a values.
|
|
|
|
See :ref:`multimetric_grid_search` for an example.
|
|
|
|
Returns
|
|
-------
|
|
scorers_dict : dict
|
|
A dict mapping each scorer name to its validated scorer.
|
|
"""
|
|
err_msg_generic = (
|
|
f"scoring is invalid (got {scoring!r}). Refer to the "
|
|
"scoring glossary for details: "
|
|
"https://scikit-learn.org/stable/glossary.html#term-scoring"
|
|
)
|
|
|
|
if isinstance(scoring, (list, tuple, set)):
|
|
err_msg = (
|
|
"The list/tuple elements must be unique strings of predefined scorers. "
|
|
)
|
|
try:
|
|
keys = set(scoring)
|
|
except TypeError as e:
|
|
raise ValueError(err_msg) from e
|
|
|
|
if len(keys) != len(scoring):
|
|
raise ValueError(
|
|
f"{err_msg} Duplicate elements were found in"
|
|
f" the given list. {scoring!r}"
|
|
)
|
|
elif len(keys) > 0:
|
|
if not all(isinstance(k, str) for k in keys):
|
|
if any(callable(k) for k in keys):
|
|
raise ValueError(
|
|
f"{err_msg} One or more of the elements "
|
|
"were callables. Use a dict of score "
|
|
"name mapped to the scorer callable. "
|
|
f"Got {scoring!r}"
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"{err_msg} Non-string types were found "
|
|
f"in the given list. Got {scoring!r}"
|
|
)
|
|
scorers = {
|
|
scorer: check_scoring(estimator, scoring=scorer) for scorer in scoring
|
|
}
|
|
else:
|
|
raise ValueError(f"{err_msg} Empty list was given. {scoring!r}")
|
|
|
|
elif isinstance(scoring, dict):
|
|
keys = set(scoring)
|
|
if not all(isinstance(k, str) for k in keys):
|
|
raise ValueError(
|
|
"Non-string types were found in the keys of "
|
|
f"the given dict. scoring={scoring!r}"
|
|
)
|
|
if len(keys) == 0:
|
|
raise ValueError(f"An empty dict was passed. {scoring!r}")
|
|
scorers = {
|
|
key: check_scoring(estimator, scoring=scorer)
|
|
for key, scorer in scoring.items()
|
|
}
|
|
else:
|
|
raise ValueError(err_msg_generic)
|
|
return scorers
|
|
|
|
|
|
def make_scorer(
|
|
score_func,
|
|
*,
|
|
greater_is_better=True,
|
|
needs_proba=False,
|
|
needs_threshold=False,
|
|
**kwargs,
|
|
):
|
|
"""Make a scorer from a performance metric or loss function.
|
|
|
|
This factory function wraps scoring functions for use in
|
|
:class:`~sklearn.model_selection.GridSearchCV` and
|
|
:func:`~sklearn.model_selection.cross_val_score`.
|
|
It takes a score function, such as :func:`~sklearn.metrics.accuracy_score`,
|
|
:func:`~sklearn.metrics.mean_squared_error`,
|
|
:func:`~sklearn.metrics.adjusted_rand_score` or
|
|
:func:`~sklearn.metrics.average_precision_score`
|
|
and returns a callable that scores an estimator's output.
|
|
The signature of the call is `(estimator, X, y)` where `estimator`
|
|
is the model to be evaluated, `X` is the data and `y` is the
|
|
ground truth labeling (or `None` in the case of unsupervised models).
|
|
|
|
Read more in the :ref:`User Guide <scoring>`.
|
|
|
|
Parameters
|
|
----------
|
|
score_func : callable
|
|
Score function (or loss function) with signature
|
|
`score_func(y, y_pred, **kwargs)`.
|
|
|
|
greater_is_better : bool, default=True
|
|
Whether `score_func` is a score function (default), meaning high is
|
|
good, or a loss function, meaning low is good. In the latter case, the
|
|
scorer object will sign-flip the outcome of the `score_func`.
|
|
|
|
needs_proba : bool, default=False
|
|
Whether `score_func` requires `predict_proba` to get probability
|
|
estimates out of a classifier.
|
|
|
|
If True, for binary `y_true`, the score function is supposed to accept
|
|
a 1D `y_pred` (i.e., probability of the positive class, shape
|
|
`(n_samples,)`).
|
|
|
|
needs_threshold : bool, default=False
|
|
Whether `score_func` takes a continuous decision certainty.
|
|
This only works for binary classification using estimators that
|
|
have either a `decision_function` or `predict_proba` method.
|
|
|
|
If True, for binary `y_true`, the score function is supposed to accept
|
|
a 1D `y_pred` (i.e., probability of the positive class or the decision
|
|
function, shape `(n_samples,)`).
|
|
|
|
For example `average_precision` or the area under the roc curve
|
|
can not be computed using discrete predictions alone.
|
|
|
|
**kwargs : additional arguments
|
|
Additional parameters to be passed to `score_func`.
|
|
|
|
Returns
|
|
-------
|
|
scorer : callable
|
|
Callable object that returns a scalar score; greater is better.
|
|
|
|
Notes
|
|
-----
|
|
If `needs_proba=False` and `needs_threshold=False`, the score
|
|
function is supposed to accept the output of :term:`predict`. If
|
|
`needs_proba=True`, the score function is supposed to accept the
|
|
output of :term:`predict_proba` (For binary `y_true`, the score function is
|
|
supposed to accept probability of the positive class). If
|
|
`needs_threshold=True`, the score function is supposed to accept the
|
|
output of :term:`decision_function` or :term:`predict_proba` when
|
|
:term:`decision_function` is not present.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.metrics import fbeta_score, make_scorer
|
|
>>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
|
|
>>> ftwo_scorer
|
|
make_scorer(fbeta_score, beta=2)
|
|
>>> from sklearn.model_selection import GridSearchCV
|
|
>>> from sklearn.svm import LinearSVC
|
|
>>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
|
|
... scoring=ftwo_scorer)
|
|
"""
|
|
sign = 1 if greater_is_better else -1
|
|
if needs_proba and needs_threshold:
|
|
raise ValueError(
|
|
"Set either needs_proba or needs_threshold to True, but not both."
|
|
)
|
|
if needs_proba:
|
|
cls = _ProbaScorer
|
|
elif needs_threshold:
|
|
cls = _ThresholdScorer
|
|
else:
|
|
cls = _PredictScorer
|
|
return cls(score_func, sign, kwargs)
|
|
|
|
|
|
# Standard regression scores
|
|
explained_variance_scorer = make_scorer(explained_variance_score)
|
|
r2_scorer = make_scorer(r2_score)
|
|
max_error_scorer = make_scorer(max_error, greater_is_better=False)
|
|
neg_mean_squared_error_scorer = make_scorer(mean_squared_error, greater_is_better=False)
|
|
neg_mean_squared_log_error_scorer = make_scorer(
|
|
mean_squared_log_error, greater_is_better=False
|
|
)
|
|
neg_mean_absolute_error_scorer = make_scorer(
|
|
mean_absolute_error, greater_is_better=False
|
|
)
|
|
neg_mean_absolute_percentage_error_scorer = make_scorer(
|
|
mean_absolute_percentage_error, greater_is_better=False
|
|
)
|
|
neg_median_absolute_error_scorer = make_scorer(
|
|
median_absolute_error, greater_is_better=False
|
|
)
|
|
neg_root_mean_squared_error_scorer = make_scorer(
|
|
mean_squared_error, greater_is_better=False, squared=False
|
|
)
|
|
neg_mean_poisson_deviance_scorer = make_scorer(
|
|
mean_poisson_deviance, greater_is_better=False
|
|
)
|
|
|
|
neg_mean_gamma_deviance_scorer = make_scorer(
|
|
mean_gamma_deviance, greater_is_better=False
|
|
)
|
|
|
|
# Standard Classification Scores
|
|
accuracy_scorer = make_scorer(accuracy_score)
|
|
balanced_accuracy_scorer = make_scorer(balanced_accuracy_score)
|
|
matthews_corrcoef_scorer = make_scorer(matthews_corrcoef)
|
|
|
|
|
|
def positive_likelihood_ratio(y_true, y_pred):
|
|
return class_likelihood_ratios(y_true, y_pred)[0]
|
|
|
|
|
|
def negative_likelihood_ratio(y_true, y_pred):
|
|
return class_likelihood_ratios(y_true, y_pred)[1]
|
|
|
|
|
|
positive_likelihood_ratio_scorer = make_scorer(positive_likelihood_ratio)
|
|
neg_negative_likelihood_ratio_scorer = make_scorer(
|
|
negative_likelihood_ratio, greater_is_better=False
|
|
)
|
|
|
|
# Score functions that need decision values
|
|
top_k_accuracy_scorer = make_scorer(
|
|
top_k_accuracy_score, greater_is_better=True, needs_threshold=True
|
|
)
|
|
roc_auc_scorer = make_scorer(
|
|
roc_auc_score, greater_is_better=True, needs_threshold=True
|
|
)
|
|
average_precision_scorer = make_scorer(average_precision_score, needs_threshold=True)
|
|
roc_auc_ovo_scorer = make_scorer(roc_auc_score, needs_proba=True, multi_class="ovo")
|
|
roc_auc_ovo_weighted_scorer = make_scorer(
|
|
roc_auc_score, needs_proba=True, multi_class="ovo", average="weighted"
|
|
)
|
|
roc_auc_ovr_scorer = make_scorer(roc_auc_score, needs_proba=True, multi_class="ovr")
|
|
roc_auc_ovr_weighted_scorer = make_scorer(
|
|
roc_auc_score, needs_proba=True, multi_class="ovr", average="weighted"
|
|
)
|
|
|
|
# Score function for probabilistic classification
|
|
neg_log_loss_scorer = make_scorer(log_loss, greater_is_better=False, needs_proba=True)
|
|
neg_brier_score_scorer = make_scorer(
|
|
brier_score_loss, greater_is_better=False, needs_proba=True
|
|
)
|
|
brier_score_loss_scorer = make_scorer(
|
|
brier_score_loss, greater_is_better=False, needs_proba=True
|
|
)
|
|
|
|
|
|
# Clustering scores
|
|
adjusted_rand_scorer = make_scorer(adjusted_rand_score)
|
|
rand_scorer = make_scorer(rand_score)
|
|
homogeneity_scorer = make_scorer(homogeneity_score)
|
|
completeness_scorer = make_scorer(completeness_score)
|
|
v_measure_scorer = make_scorer(v_measure_score)
|
|
mutual_info_scorer = make_scorer(mutual_info_score)
|
|
adjusted_mutual_info_scorer = make_scorer(adjusted_mutual_info_score)
|
|
normalized_mutual_info_scorer = make_scorer(normalized_mutual_info_score)
|
|
fowlkes_mallows_scorer = make_scorer(fowlkes_mallows_score)
|
|
|
|
|
|
# TODO(1.3) Remove
|
|
class _DeprecatedScorers(dict):
|
|
"""A temporary class to deprecate SCORERS."""
|
|
|
|
def __getitem__(self, item):
|
|
warnings.warn(
|
|
"sklearn.metrics.SCORERS is deprecated and will be removed in v1.3. "
|
|
"Please use sklearn.metrics.get_scorer_names to get a list of available "
|
|
"scorers and sklearn.metrics.get_metric to get scorer.",
|
|
FutureWarning,
|
|
)
|
|
return super().__getitem__(item)
|
|
|
|
|
|
_SCORERS = dict(
|
|
explained_variance=explained_variance_scorer,
|
|
r2=r2_scorer,
|
|
max_error=max_error_scorer,
|
|
matthews_corrcoef=matthews_corrcoef_scorer,
|
|
neg_median_absolute_error=neg_median_absolute_error_scorer,
|
|
neg_mean_absolute_error=neg_mean_absolute_error_scorer,
|
|
neg_mean_absolute_percentage_error=neg_mean_absolute_percentage_error_scorer, # noqa
|
|
neg_mean_squared_error=neg_mean_squared_error_scorer,
|
|
neg_mean_squared_log_error=neg_mean_squared_log_error_scorer,
|
|
neg_root_mean_squared_error=neg_root_mean_squared_error_scorer,
|
|
neg_mean_poisson_deviance=neg_mean_poisson_deviance_scorer,
|
|
neg_mean_gamma_deviance=neg_mean_gamma_deviance_scorer,
|
|
accuracy=accuracy_scorer,
|
|
top_k_accuracy=top_k_accuracy_scorer,
|
|
roc_auc=roc_auc_scorer,
|
|
roc_auc_ovr=roc_auc_ovr_scorer,
|
|
roc_auc_ovo=roc_auc_ovo_scorer,
|
|
roc_auc_ovr_weighted=roc_auc_ovr_weighted_scorer,
|
|
roc_auc_ovo_weighted=roc_auc_ovo_weighted_scorer,
|
|
balanced_accuracy=balanced_accuracy_scorer,
|
|
average_precision=average_precision_scorer,
|
|
neg_log_loss=neg_log_loss_scorer,
|
|
neg_brier_score=neg_brier_score_scorer,
|
|
positive_likelihood_ratio=positive_likelihood_ratio_scorer,
|
|
neg_negative_likelihood_ratio=neg_negative_likelihood_ratio_scorer,
|
|
# Cluster metrics that use supervised evaluation
|
|
adjusted_rand_score=adjusted_rand_scorer,
|
|
rand_score=rand_scorer,
|
|
homogeneity_score=homogeneity_scorer,
|
|
completeness_score=completeness_scorer,
|
|
v_measure_score=v_measure_scorer,
|
|
mutual_info_score=mutual_info_scorer,
|
|
adjusted_mutual_info_score=adjusted_mutual_info_scorer,
|
|
normalized_mutual_info_score=normalized_mutual_info_scorer,
|
|
fowlkes_mallows_score=fowlkes_mallows_scorer,
|
|
)
|
|
|
|
|
|
def get_scorer_names():
|
|
"""Get the names of all available scorers.
|
|
|
|
These names can be passed to :func:`~sklearn.metrics.get_scorer` to
|
|
retrieve the scorer object.
|
|
|
|
Returns
|
|
-------
|
|
list of str
|
|
Names of all available scorers.
|
|
"""
|
|
return sorted(_SCORERS.keys())
|
|
|
|
|
|
for name, metric in [
|
|
("precision", precision_score),
|
|
("recall", recall_score),
|
|
("f1", f1_score),
|
|
("jaccard", jaccard_score),
|
|
]:
|
|
_SCORERS[name] = make_scorer(metric, average="binary")
|
|
for average in ["macro", "micro", "samples", "weighted"]:
|
|
qualified_name = "{0}_{1}".format(name, average)
|
|
_SCORERS[qualified_name] = make_scorer(metric, pos_label=None, average=average)
|
|
|
|
SCORERS = _DeprecatedScorers(_SCORERS)
|