""" The :mod:`sklearn.metrics.scorer` submodule implements a flexible interface for model selection and evaluation using arbitrary score functions. A scorer object is a callable that can be passed to :class:`~sklearn.model_selection.GridSearchCV` or :func:`sklearn.model_selection.cross_val_score` as the ``scoring`` parameter, to specify how a model should be evaluated. The signature of the call is ``(estimator, X, y)`` where ``estimator`` is the model to be evaluated, ``X`` is the test data and ``y`` is the ground truth labeling (or ``None`` in the case of unsupervised models). """ # Authors: Andreas Mueller # Lars Buitinck # Arnaud Joly # License: Simplified BSD from collections.abc import Iterable from functools import partial from collections import Counter import numpy as np from . import (r2_score, median_absolute_error, max_error, mean_absolute_error, mean_squared_error, mean_squared_log_error, mean_poisson_deviance, mean_gamma_deviance, accuracy_score, top_k_accuracy_score, f1_score, roc_auc_score, average_precision_score, precision_score, recall_score, log_loss, balanced_accuracy_score, explained_variance_score, brier_score_loss, jaccard_score, mean_absolute_percentage_error) from .cluster import adjusted_rand_score from .cluster import rand_score from .cluster import homogeneity_score from .cluster import completeness_score from .cluster import v_measure_score from .cluster import mutual_info_score from .cluster import adjusted_mutual_info_score from .cluster import normalized_mutual_info_score from .cluster import fowlkes_mallows_score from ..utils.multiclass import type_of_target from ..utils.validation import _deprecate_positional_args from ..base import is_regressor def _cached_call(cache, estimator, method, *args, **kwargs): """Call estimator with method and args and kwargs.""" if cache is None: return getattr(estimator, method)(*args, **kwargs) try: return cache[method] except KeyError: result = getattr(estimator, method)(*args, **kwargs) cache[method] = result return result class _MultimetricScorer: """Callable for multimetric scoring used to avoid repeated calls to `predict_proba`, `predict`, and `decision_function`. `_MultimetricScorer` will return a dictionary of scores corresponding to the scorers in the dictionary. Note that `_MultimetricScorer` can be created with a dictionary with one key (i.e. only one actual scorer). Parameters ---------- scorers : dict Dictionary mapping names to callable scorers. """ def __init__(self, **scorers): self._scorers = scorers def __call__(self, estimator, *args, **kwargs): """Evaluate predicted target values.""" scores = {} cache = {} if self._use_cache(estimator) else None cached_call = partial(_cached_call, cache) for name, scorer in self._scorers.items(): if isinstance(scorer, _BaseScorer): score = scorer._score(cached_call, estimator, *args, **kwargs) else: score = scorer(estimator, *args, **kwargs) scores[name] = score return scores def _use_cache(self, estimator): """Return True if using a cache is beneficial. Caching may be beneficial when one of these conditions holds: - `_ProbaScorer` will be called twice. - `_PredictScorer` will be called twice. - `_ThresholdScorer` will be called twice. - `_ThresholdScorer` and `_PredictScorer` are called and estimator is a regressor. - `_ThresholdScorer` and `_ProbaScorer` are called and estimator does not have a `decision_function` attribute. """ if len(self._scorers) == 1: # Only one scorer return False counter = Counter([type(v) for v in self._scorers.values()]) if any(counter[known_type] > 1 for known_type in [_PredictScorer, _ProbaScorer, _ThresholdScorer]): return True if counter[_ThresholdScorer]: if is_regressor(estimator) and counter[_PredictScorer]: return True elif (counter[_ProbaScorer] and not hasattr(estimator, "decision_function")): return True return False class _BaseScorer: def __init__(self, score_func, sign, kwargs): self._kwargs = kwargs self._score_func = score_func self._sign = sign @staticmethod def _check_pos_label(pos_label, classes): if pos_label not in list(classes): raise ValueError( f"pos_label={pos_label} is not a valid label: {classes}" ) def _select_proba_binary(self, y_pred, classes): """Select the column of the positive label in `y_pred` when probabilities are provided. Parameters ---------- y_pred : ndarray of shape (n_samples, n_classes) The prediction given by `predict_proba`. classes : ndarray of shape (n_classes,) The class labels for the estimator. Returns ------- y_pred : ndarray of shape (n_samples,) Probability predictions of the positive class. """ if y_pred.shape[1] == 2: pos_label = self._kwargs.get("pos_label", classes[1]) self._check_pos_label(pos_label, classes) col_idx = np.flatnonzero(classes == pos_label)[0] return y_pred[:, col_idx] err_msg = ( f"Got predict_proba of shape {y_pred.shape}, but need " f"classifier with two classes for {self._score_func.__name__} " f"scoring" ) raise ValueError(err_msg) def __repr__(self): kwargs_string = "".join([", %s=%s" % (str(k), str(v)) for k, v in self._kwargs.items()]) return ("make_scorer(%s%s%s%s)" % (self._score_func.__name__, "" if self._sign > 0 else ", greater_is_better=False", self._factory_args(), kwargs_string)) def __call__(self, estimator, X, y_true, sample_weight=None): """Evaluate predicted target values for X relative to y_true. Parameters ---------- estimator : object Trained estimator to use for scoring. Must have a predict_proba method; the output of that is used to compute the score. X : {array-like, sparse matrix} Test data that will be fed to estimator.predict. y_true : array-like Gold standard target values for X. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float Score function applied to prediction of estimator on X. """ return self._score(partial(_cached_call, None), estimator, X, y_true, sample_weight=sample_weight) def _factory_args(self): """Return non-default make_scorer arguments for repr.""" return "" class _PredictScorer(_BaseScorer): def _score(self, method_caller, estimator, X, y_true, sample_weight=None): """Evaluate predicted target values for X relative to y_true. Parameters ---------- method_caller : callable Returns predictions given an estimator, method name, and other arguments, potentially caching results. estimator : object Trained estimator to use for scoring. Must have a predict_proba method; the output of that is used to compute the score. X : {array-like, sparse matrix} Test data that will be fed to estimator.predict. y_true : array-like Gold standard target values for X. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float Score function applied to prediction of estimator on X. """ y_pred = method_caller(estimator, "predict", X) if sample_weight is not None: return self._sign * self._score_func(y_true, y_pred, sample_weight=sample_weight, **self._kwargs) else: return self._sign * self._score_func(y_true, y_pred, **self._kwargs) class _ProbaScorer(_BaseScorer): def _score(self, method_caller, clf, X, y, sample_weight=None): """Evaluate predicted probabilities for X relative to y_true. Parameters ---------- method_caller : callable Returns predictions given an estimator, method name, and other arguments, potentially caching results. clf : object Trained classifier to use for scoring. Must have a predict_proba method; the output of that is used to compute the score. X : {array-like, sparse matrix} Test data that will be fed to clf.predict_proba. y : array-like Gold standard target values for X. These must be class labels, not probabilities. sample_weight : array-like, default=None Sample weights. Returns ------- score : float Score function applied to prediction of estimator on X. """ y_type = type_of_target(y) y_pred = method_caller(clf, "predict_proba", X) if y_type == "binary" and y_pred.shape[1] <= 2: # `y_type` could be equal to "binary" even in a multi-class # problem: (when only 2 class are given to `y_true` during scoring) # Thus, we need to check for the shape of `y_pred`. y_pred = self._select_proba_binary(y_pred, clf.classes_) if sample_weight is not None: return self._sign * self._score_func(y, y_pred, sample_weight=sample_weight, **self._kwargs) else: return self._sign * self._score_func(y, y_pred, **self._kwargs) def _factory_args(self): return ", needs_proba=True" class _ThresholdScorer(_BaseScorer): def _score(self, method_caller, clf, X, y, sample_weight=None): """Evaluate decision function output for X relative to y_true. Parameters ---------- method_caller : callable Returns predictions given an estimator, method name, and other arguments, potentially caching results. clf : object Trained classifier to use for scoring. Must have either a decision_function method or a predict_proba method; the output of that is used to compute the score. X : {array-like, sparse matrix} Test data that will be fed to clf.decision_function or clf.predict_proba. y : array-like Gold standard target values for X. These must be class labels, not decision function values. sample_weight : array-like, default=None Sample weights. Returns ------- score : float Score function applied to prediction of estimator on X. """ y_type = type_of_target(y) if y_type not in ("binary", "multilabel-indicator"): raise ValueError("{0} format is not supported".format(y_type)) if is_regressor(clf): y_pred = method_caller(clf, "predict", X) else: try: y_pred = method_caller(clf, "decision_function", X) if isinstance(y_pred, list): # For multi-output multi-class estimator y_pred = np.vstack([p for p in y_pred]).T elif y_type == "binary" and "pos_label" in self._kwargs: self._check_pos_label( self._kwargs["pos_label"], clf.classes_ ) if self._kwargs["pos_label"] == clf.classes_[0]: # The implicit positive class of the binary classifier # does not match `pos_label`: we need to invert the # predictions y_pred *= -1 except (NotImplementedError, AttributeError): y_pred = method_caller(clf, "predict_proba", X) if y_type == "binary": y_pred = self._select_proba_binary(y_pred, clf.classes_) elif isinstance(y_pred, list): y_pred = np.vstack([p[:, -1] for p in y_pred]).T if sample_weight is not None: return self._sign * self._score_func(y, y_pred, sample_weight=sample_weight, **self._kwargs) else: return self._sign * self._score_func(y, y_pred, **self._kwargs) def _factory_args(self): return ", needs_threshold=True" def get_scorer(scoring): """Get a scorer from string. Read more in the :ref:`User Guide `. Parameters ---------- scoring : str or callable Scoring method as string. If callable it is returned as is. Returns ------- scorer : callable The scorer. """ if isinstance(scoring, str): try: scorer = SCORERS[scoring] except KeyError: raise ValueError('%r is not a valid scoring value. ' 'Use sorted(sklearn.metrics.SCORERS.keys()) ' 'to get valid options.' % scoring) else: scorer = scoring return scorer def _passthrough_scorer(estimator, *args, **kwargs): """Function that wraps estimator.score""" return estimator.score(*args, **kwargs) @_deprecate_positional_args def check_scoring(estimator, scoring=None, *, allow_none=False): """Determine scorer from user options. A TypeError will be thrown if the estimator cannot be scored. Parameters ---------- estimator : estimator object implementing 'fit' The object to use to fit the data. scoring : str or callable, default=None A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``. 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 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 A single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`) to evaluate the predictions on the test set. For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as 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. ") invalid = False try: keys = set(scoring) except TypeError: invalid = True if invalid: raise ValueError(err_msg) 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 @_deprecate_positional_args 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_index` or :func:`~sklearn.metrics.average_precision` 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 `. 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. 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) 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`. """ 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) # 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) SCORERS = dict(explained_variance=explained_variance_scorer, r2=r2_scorer, max_error=max_error_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, # 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) 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)