560 lines
22 KiB
Python
560 lines
22 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 abc import ABCMeta, abstractmethod
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import warnings
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import numpy as np
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from . import (r2_score, median_absolute_error, mean_absolute_error,
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mean_squared_error, mean_squared_log_error, accuracy_score,
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f1_score, roc_auc_score, average_precision_score,
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precision_score, recall_score, log_loss,
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explained_variance_score)
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from .cluster import adjusted_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 ..externals import six
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from ..base import is_regressor
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class _BaseScorer(six.with_metaclass(ABCMeta, object)):
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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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# XXX After removing the deprecated scorers (v0.20) remove the
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# XXX deprecation_msg property again and remove __call__'s body again
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self._deprecation_msg = None
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@abstractmethod
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def __call__(self, estimator, X, y, sample_weight=None):
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if self._deprecation_msg is not None:
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warnings.warn(self._deprecation_msg,
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category=DeprecationWarning,
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stacklevel=2)
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def __repr__(self):
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kwargs_string = "".join([", %s=%s" % (str(k), str(v))
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for k, v in self._kwargs.items()])
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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(), kwargs_string))
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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 __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 or 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, optional (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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super(_PredictScorer, self).__call__(estimator, X, y_true,
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sample_weight=sample_weight)
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y_pred = estimator.predict(X)
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if sample_weight is not None:
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return self._sign * self._score_func(y_true, y_pred,
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sample_weight=sample_weight,
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**self._kwargs)
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else:
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return self._sign * self._score_func(y_true, y_pred,
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**self._kwargs)
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class _ProbaScorer(_BaseScorer):
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def __call__(self, 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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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 or 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, optional (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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super(_ProbaScorer, self).__call__(clf, X, y,
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sample_weight=sample_weight)
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y_pred = clf.predict_proba(X)
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if sample_weight is not None:
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return self._sign * self._score_func(y, y_pred,
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sample_weight=sample_weight,
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**self._kwargs)
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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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class _ThresholdScorer(_BaseScorer):
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def __call__(self, 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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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
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that is used to compute the score.
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X : array-like or 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,
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not decision function values.
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sample_weight : array-like, optional (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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super(_ThresholdScorer, self).__call__(clf, X, y,
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sample_weight=sample_weight)
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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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if is_regressor(clf):
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y_pred = clf.predict(X)
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else:
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try:
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y_pred = clf.decision_function(X)
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# For multi-output multi-class estimator
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if isinstance(y_pred, list):
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y_pred = np.vstack(p for p in y_pred).T
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except (NotImplementedError, AttributeError):
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y_pred = clf.predict_proba(X)
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if y_type == "binary":
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y_pred = y_pred[:, 1]
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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(y, y_pred,
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sample_weight=sample_weight,
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**self._kwargs)
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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_threshold=True"
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def get_scorer(scoring):
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"""Get a scorer from string
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Parameters
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----------
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scoring : str | 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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"""
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valid = True
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if isinstance(scoring, six.string_types):
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try:
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scorer = SCORERS[scoring]
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except KeyError:
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scorers = [scorer for scorer in SCORERS
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if SCORERS[scorer]._deprecation_msg is None]
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valid = False # Don't raise here to make the error message elegant
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if not valid:
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raise ValueError('%r is not a valid scoring value. '
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'Valid options are %s'
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% (scoring, sorted(scorers)))
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else:
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scorer = scoring
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return scorer
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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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def check_scoring(estimator, scoring=None, allow_none=False):
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"""Determine scorer from user options.
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A TypeError will be thrown if the estimator cannot be scored.
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Parameters
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----------
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estimator : estimator object implementing 'fit'
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The object to use to fit the data.
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scoring : string, callable or None, optional, 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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allow_none : boolean, optional, default: False
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If no scoring is specified and the estimator has no score function, we
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can either return None or raise an exception.
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Returns
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-------
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scoring : callable
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A scorer callable object / function with signature
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``scorer(estimator, X, y)``.
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"""
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if not hasattr(estimator, 'fit'):
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raise TypeError("estimator should be an estimator implementing "
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"'fit' method, %r was passed" % estimator)
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if isinstance(scoring, six.string_types):
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return get_scorer(scoring)
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elif callable(scoring):
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# Heuristic to ensure user has not passed a metric
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module = getattr(scoring, '__module__', None)
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if hasattr(module, 'startswith') and \
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module.startswith('sklearn.metrics.') and \
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not module.startswith('sklearn.metrics.scorer') and \
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not module.startswith('sklearn.metrics.tests.'):
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raise ValueError('scoring value %r looks like it is a metric '
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'function rather than a scorer. A scorer should '
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'require an estimator as its first parameter. '
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'Please use `make_scorer` to convert a metric '
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'to a scorer.' % scoring)
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return get_scorer(scoring)
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elif scoring is None:
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if hasattr(estimator, 'score'):
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return _passthrough_scorer
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elif allow_none:
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return None
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else:
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raise TypeError(
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"If no scoring is specified, the estimator passed should "
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"have a 'score' method. The estimator %r does not."
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% estimator)
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else:
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raise ValueError("scoring value should either be a callable, string or"
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" None. %r was passed" % scoring)
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def _check_multimetric_scoring(estimator, scoring=None):
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"""Check the scoring parameter in cases when multiple metrics are allowed
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Parameters
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----------
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estimator : sklearn estimator instance
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The estimator for which the scoring will be applied.
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scoring : string, callable, list/tuple, dict or None, default: None
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A single string (see :ref:`scoring_parameter`) or a callable
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(see :ref:`scoring`) to evaluate the predictions on the test set.
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For evaluating multiple metrics, either give a list of (unique) strings
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or a dict with names as keys and callables as values.
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NOTE that when using custom scorers, each scorer should return a single
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value. Metric functions returning a list/array of values can be wrapped
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into multiple scorers that return one value each.
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See :ref:`multimetric_grid_search` for an example.
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If None the estimator's default scorer (if available) is used.
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The return value in that case will be ``{'score': <default_scorer>}``.
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If the estimator's default scorer is not available, a ``TypeError``
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is raised.
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Returns
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-------
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scorers_dict : dict
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A dict mapping each scorer name to its validated scorer.
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is_multimetric : bool
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True if scorer is a list/tuple or dict of callables
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False if scorer is None/str/callable
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"""
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if callable(scoring) or scoring is None or isinstance(scoring,
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six.string_types):
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scorers = {"score": check_scoring(estimator, scoring=scoring)}
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return scorers, False
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else:
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err_msg_generic = ("scoring should either be a single string or "
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"callable for single metric evaluation or a "
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"list/tuple of strings or a dict of scorer name "
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"mapped to the callable for multiple metric "
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"evaluation. Got %s of type %s"
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% (repr(scoring), type(scoring)))
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if isinstance(scoring, (list, tuple, set)):
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err_msg = ("The list/tuple elements must be unique "
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"strings of predefined scorers. ")
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invalid = False
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try:
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keys = set(scoring)
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except TypeError:
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invalid = True
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if invalid:
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raise ValueError(err_msg)
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if len(keys) != len(scoring):
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raise ValueError(err_msg + "Duplicate elements were found in"
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" the given list. %r" % repr(scoring))
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elif len(keys) > 0:
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if not all(isinstance(k, six.string_types) for k in keys):
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if any(callable(k) for k in keys):
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raise ValueError(err_msg +
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"One or more of the elements were "
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"callables. Use a dict of score name "
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"mapped to the scorer callable. "
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"Got %r" % repr(scoring))
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else:
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raise ValueError(err_msg +
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"Non-string types were found in "
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"the given list. Got %r"
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% repr(scoring))
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scorers = {scorer: check_scoring(estimator, scoring=scorer)
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for scorer in scoring}
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else:
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raise ValueError(err_msg +
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"Empty list was given. %r" % repr(scoring))
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elif isinstance(scoring, dict):
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keys = set(scoring)
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if not all(isinstance(k, six.string_types) for k in keys):
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raise ValueError("Non-string types were found in the keys of "
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"the given dict. scoring=%r" % repr(scoring))
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if len(keys) == 0:
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raise ValueError("An empty dict was passed. %r"
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% repr(scoring))
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scorers = {key: check_scoring(estimator, scoring=scorer)
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for key, scorer in scoring.items()}
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else:
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raise ValueError(err_msg_generic)
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return scorers, True
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def make_scorer(score_func, greater_is_better=True, needs_proba=False,
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needs_threshold=False, **kwargs):
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"""Make a scorer from a performance metric or loss function.
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This factory function wraps scoring functions for use in GridSearchCV
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and cross_val_score. It takes a score function, such as ``accuracy_score``,
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``mean_squared_error``, ``adjusted_rand_index`` or ``average_precision``
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and returns a callable that scores an estimator's output.
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Read more in the :ref:`User Guide <scoring>`.
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Parameters
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----------
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score_func : callable,
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Score function (or loss function) with signature
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``score_func(y, y_pred, **kwargs)``.
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greater_is_better : boolean, default=True
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Whether score_func is a score function (default), meaning high is good,
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or a loss function, meaning low is good. In the latter case, the
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scorer object will sign-flip the outcome of the score_func.
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needs_proba : boolean, default=False
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Whether score_func requires predict_proba to get probability estimates
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out of a classifier.
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needs_threshold : boolean, default=False
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Whether score_func takes a continuous decision certainty.
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This only works for binary classification using estimators that
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have either a decision_function or predict_proba method.
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For example ``average_precision`` or the area under the roc curve
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can not be computed using discrete predictions alone.
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**kwargs : additional arguments
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Additional parameters to be passed to score_func.
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Returns
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-------
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scorer : callable
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Callable object that returns a scalar score; greater is better.
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Examples
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--------
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>>> from sklearn.metrics import fbeta_score, make_scorer
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>>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
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>>> ftwo_scorer
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make_scorer(fbeta_score, beta=2)
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>>> from sklearn.model_selection import GridSearchCV
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>>> from sklearn.svm import LinearSVC
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>>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
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... scoring=ftwo_scorer)
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"""
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sign = 1 if greater_is_better else -1
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if needs_proba and needs_threshold:
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raise ValueError("Set either needs_proba or needs_threshold to True,"
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" but not both.")
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if needs_proba:
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cls = _ProbaScorer
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elif needs_threshold:
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cls = _ThresholdScorer
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else:
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cls = _PredictScorer
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return cls(score_func, sign, kwargs)
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# Standard regression scores
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explained_variance_scorer = make_scorer(explained_variance_score)
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r2_scorer = make_scorer(r2_score)
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neg_mean_squared_error_scorer = make_scorer(mean_squared_error,
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greater_is_better=False)
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deprecation_msg = ('Scoring method mean_squared_error was renamed to '
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'neg_mean_squared_error in version 0.18 and will '
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'be removed in 0.20.')
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mean_squared_error_scorer = make_scorer(mean_squared_error,
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greater_is_better=False)
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mean_squared_error_scorer._deprecation_msg = deprecation_msg
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neg_mean_squared_log_error_scorer = make_scorer(mean_squared_log_error,
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greater_is_better=False)
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neg_mean_absolute_error_scorer = make_scorer(mean_absolute_error,
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greater_is_better=False)
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deprecation_msg = ('Scoring method mean_absolute_error was renamed to '
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'neg_mean_absolute_error in version 0.18 and will '
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'be removed in 0.20.')
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mean_absolute_error_scorer = make_scorer(mean_absolute_error,
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greater_is_better=False)
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mean_absolute_error_scorer._deprecation_msg = deprecation_msg
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neg_median_absolute_error_scorer = make_scorer(median_absolute_error,
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greater_is_better=False)
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deprecation_msg = ('Scoring method median_absolute_error was renamed to '
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'neg_median_absolute_error in version 0.18 and will '
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'be removed in 0.20.')
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median_absolute_error_scorer = make_scorer(median_absolute_error,
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greater_is_better=False)
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median_absolute_error_scorer._deprecation_msg = deprecation_msg
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# Standard Classification Scores
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accuracy_scorer = make_scorer(accuracy_score)
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f1_scorer = make_scorer(f1_score)
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# Score functions that need decision values
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roc_auc_scorer = make_scorer(roc_auc_score, greater_is_better=True,
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needs_threshold=True)
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average_precision_scorer = make_scorer(average_precision_score,
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needs_threshold=True)
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precision_scorer = make_scorer(precision_score)
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recall_scorer = make_scorer(recall_score)
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# Score function for probabilistic classification
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neg_log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
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needs_proba=True)
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deprecation_msg = ('Scoring method log_loss was renamed to '
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'neg_log_loss in version 0.18 and will be removed in 0.20.')
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log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
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needs_proba=True)
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log_loss_scorer._deprecation_msg = deprecation_msg
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|
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# Clustering scores
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adjusted_rand_scorer = make_scorer(adjusted_rand_score)
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homogeneity_scorer = make_scorer(homogeneity_score)
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completeness_scorer = make_scorer(completeness_score)
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v_measure_scorer = make_scorer(v_measure_score)
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mutual_info_scorer = make_scorer(mutual_info_score)
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adjusted_mutual_info_scorer = make_scorer(adjusted_mutual_info_score)
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normalized_mutual_info_scorer = make_scorer(normalized_mutual_info_score)
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fowlkes_mallows_scorer = make_scorer(fowlkes_mallows_score)
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|
|
|
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SCORERS = dict(explained_variance=explained_variance_scorer,
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r2=r2_scorer,
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neg_median_absolute_error=neg_median_absolute_error_scorer,
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neg_mean_absolute_error=neg_mean_absolute_error_scorer,
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neg_mean_squared_error=neg_mean_squared_error_scorer,
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neg_mean_squared_log_error=neg_mean_squared_log_error_scorer,
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median_absolute_error=median_absolute_error_scorer,
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mean_absolute_error=mean_absolute_error_scorer,
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mean_squared_error=mean_squared_error_scorer,
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accuracy=accuracy_scorer, roc_auc=roc_auc_scorer,
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average_precision=average_precision_scorer,
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log_loss=log_loss_scorer,
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neg_log_loss=neg_log_loss_scorer,
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# Cluster metrics that use supervised evaluation
|
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adjusted_rand_score=adjusted_rand_scorer,
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|
homogeneity_score=homogeneity_scorer,
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|
completeness_score=completeness_scorer,
|
|
v_measure_score=v_measure_scorer,
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mutual_info_score=mutual_info_scorer,
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|
adjusted_mutual_info_score=adjusted_mutual_info_scorer,
|
|
normalized_mutual_info_score=normalized_mutual_info_scorer,
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fowlkes_mallows_score=fowlkes_mallows_scorer)
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|
|
|
|
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for name, metric in [('precision', precision_score),
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|
('recall', recall_score), ('f1', f1_score)]:
|
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SCORERS[name] = make_scorer(metric)
|
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for average in ['macro', 'micro', 'samples', 'weighted']:
|
|
qualified_name = '{0}_{1}'.format(name, average)
|
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SCORERS[qualified_name] = make_scorer(metric, pos_label=None,
|
|
average=average)
|