""" 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 import copy import warnings from collections import Counter from functools import partial from inspect import signature from traceback import format_exc import numpy as np from ..base import is_regressor from ..utils import Bunch from ..utils._param_validation import HasMethods, StrOptions, validate_params from ..utils._response import _get_response_values from ..utils.metadata_routing import ( MetadataRequest, MetadataRouter, _MetadataRequester, _routing_enabled, get_routing_for_object, process_routing, ) from ..utils.multiclass import type_of_target from . import ( accuracy_score, average_precision_score, balanced_accuracy_score, brier_score_loss, class_likelihood_ratios, explained_variance_score, f1_score, jaccard_score, log_loss, matthews_corrcoef, max_error, mean_absolute_error, mean_absolute_percentage_error, mean_gamma_deviance, mean_poisson_deviance, mean_squared_error, mean_squared_log_error, median_absolute_error, precision_score, r2_score, recall_score, roc_auc_score, top_k_accuracy_score, ) from .cluster import ( adjusted_mutual_info_score, adjusted_rand_score, completeness_score, fowlkes_mallows_score, homogeneity_score, mutual_info_score, normalized_mutual_info_score, rand_score, v_measure_score, ) def _cached_call(cache, estimator, response_method, *args, **kwargs): """Call estimator with method and args and kwargs.""" if cache is not None and response_method in cache: return cache[response_method] result, _ = _get_response_values( estimator, *args, response_method=response_method, **kwargs ) if cache is not None: cache[response_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. raise_exc : bool, default=True Whether to raise the exception in `__call__` or not. If set to `False` a formatted string of the exception details is passed as result of the failing scorer. """ def __init__(self, *, scorers, raise_exc=True): self._scorers = scorers self._raise_exc = raise_exc 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) if _routing_enabled(): routed_params = process_routing(self, "score", kwargs) else: # they all get the same args, and they all get them all routed_params = Bunch( **{name: Bunch(score=kwargs) for name in self._scorers} ) for name, scorer in self._scorers.items(): try: if isinstance(scorer, _BaseScorer): score = scorer._score( cached_call, estimator, *args, **routed_params.get(name).score ) else: score = scorer(estimator, *args, **routed_params.get(name).score) scores[name] = score except Exception as e: if self._raise_exc: raise e else: scores[name] = format_exc() 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 def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.3 Returns ------- routing : MetadataRouter A :class:`~utils.metadata_routing.MetadataRouter` encapsulating routing information. """ return MetadataRouter(owner=self.__class__.__name__).add( **self._scorers, method_mapping="score" ) class _BaseScorer(_MetadataRequester): def __init__(self, score_func, sign, kwargs): self._kwargs = kwargs self._score_func = score_func self._sign = sign def _get_pos_label(self): if "pos_label" in self._kwargs: return self._kwargs["pos_label"] score_func_params = signature(self._score_func).parameters if "pos_label" in score_func_params: return score_func_params["pos_label"].default return None 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, **kwargs): """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. **kwargs : dict Other parameters passed to the scorer. Refer to :func:`set_score_request` for more details. Only available if `enable_metadata_routing=True`. See the :ref:`User Guide `. .. versionadded:: 1.3 Returns ------- score : float Score function applied to prediction of estimator on X. """ if kwargs and not _routing_enabled(): raise ValueError( "kwargs is only supported if enable_metadata_routing=True. See" " the User Guide for more information." ) _kwargs = copy.deepcopy(kwargs) if sample_weight is not None: _kwargs["sample_weight"] = sample_weight return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs) def _factory_args(self): """Return non-default make_scorer arguments for repr.""" return "" def _warn_overlap(self, message, kwargs): """Warn if there is any overlap between ``self._kwargs`` and ``kwargs``. This method is intended to be used to check for overlap between ``self._kwargs`` and ``kwargs`` passed as metadata. """ _kwargs = set() if self._kwargs is None else set(self._kwargs.keys()) overlap = _kwargs.intersection(kwargs.keys()) if overlap: warnings.warn( f"{message} Overlapping parameters are: {overlap}", UserWarning ) def set_score_request(self, **kwargs): """Set requested parameters by the scorer. Please see :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.3 Parameters ---------- kwargs : dict Arguments should be of the form ``param_name=alias``, and `alias` can be one of ``{True, False, None, str}``. """ self._warn_overlap( message=( "You are setting metadata request for parameters which are " "already set as kwargs for this metric. These set values will be " "overridden by passed metadata if provided. Please pass them either " "as metadata or kwargs to `make_scorer`." ), kwargs=kwargs, ) self._metadata_request = MetadataRequest(owner=self.__class__.__name__) for param, alias in kwargs.items(): self._metadata_request.score.add_request(param=param, alias=alias) return self class _PredictScorer(_BaseScorer): def _score(self, method_caller, estimator, X, y_true, **kwargs): """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` 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. **kwargs : dict Other parameters passed to the scorer. Refer to :func:`set_score_request` for more details. .. versionadded:: 1.3 Returns ------- score : float Score function applied to prediction of estimator on X. """ self._warn_overlap( message=( "There is an overlap between set kwargs of this scorer instance and" " passed metadata. Please pass them either as kwargs to `make_scorer`" " or metadata, but not both." ), kwargs=kwargs, ) y_pred = method_caller(estimator, "predict", X) scoring_kwargs = {**self._kwargs, **kwargs} return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) class _ProbaScorer(_BaseScorer): def _score(self, method_caller, clf, X, y, **kwargs): """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. **kwargs : dict Other parameters passed to the scorer. Refer to :func:`set_score_request` for more details. .. versionadded:: 1.3 Returns ------- score : float Score function applied to prediction of estimator on X. """ self._warn_overlap( message=( "There is an overlap between set kwargs of this scorer instance and" " passed metadata. Please pass them either as kwargs to `make_scorer`" " or metadata, but not both." ), kwargs=kwargs, ) y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label()) scoring_kwargs = {**self._kwargs, **kwargs} return self._sign * self._score_func(y, y_pred, **scoring_kwargs) def _factory_args(self): return ", needs_proba=True" class _ThresholdScorer(_BaseScorer): def _score(self, method_caller, clf, X, y, **kwargs): """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. **kwargs : dict Other parameters passed to the scorer. Refer to :func:`set_score_request` for more details. .. versionadded:: 1.3 Returns ------- score : float Score function applied to prediction of estimator on X. """ self._warn_overlap( message=( "There is an overlap between set kwargs of this scorer instance and" " passed metadata. Please pass them either as kwargs to `make_scorer`" " or metadata, but not both." ), kwargs=kwargs, ) 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: pos_label = self._get_pos_label() try: y_pred = method_caller(clf, "decision_function", X, pos_label=pos_label) if isinstance(y_pred, list): # For multi-output multi-class estimator y_pred = np.vstack([p for p in y_pred]).T except (NotImplementedError, AttributeError): y_pred = method_caller(clf, "predict_proba", X, pos_label=pos_label) if isinstance(y_pred, list): y_pred = np.vstack([p[:, -1] for p in y_pred]).T scoring_kwargs = {**self._kwargs, **kwargs} return self._sign * self._score_func(y, y_pred, **scoring_kwargs) def _factory_args(self): return ", needs_threshold=True" @validate_params( { "scoring": [str, callable, None], }, prefer_skip_nested_validation=True, ) def get_scorer(scoring): """Get a scorer from string. Read more in the :ref:`User Guide `. :func:`~sklearn.metrics.get_scorer_names` can be used to retrieve the names of all available scorers. Parameters ---------- scoring : str, callable or None Scoring method as string. If callable it is returned as is. If None, returns None. Returns ------- scorer : callable The scorer. Notes ----- When passed a string, this function always returns a copy of the scorer object. Calling `get_scorer` twice for the same scorer results in two separate scorer objects. """ if isinstance(scoring, str): try: scorer = copy.deepcopy(_SCORERS[scoring]) except KeyError: raise ValueError( "%r is not a valid scoring value. " "Use sklearn.metrics.get_scorer_names() " "to get valid options." % scoring ) else: scorer = scoring return scorer class _PassthroughScorer: def __init__(self, estimator): self._estimator = estimator def __call__(self, estimator, *args, **kwargs): """Method that wraps estimator.score""" return estimator.score(*args, **kwargs) def get_metadata_routing(self): """Get requested data properties. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.3 Returns ------- routing : MetadataRouter A :class:`~utils.metadata_routing.MetadataRouter` encapsulating routing information. """ # This scorer doesn't do any validation or routing, it only exposes the # score requests to the parent object. This object behaves as a # consumer rather than a router. res = MetadataRequest(owner=self._estimator.__class__.__name__) res.score = get_routing_for_object(self._estimator).score return res 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 @validate_params( { "score_func": [callable], "greater_is_better": ["boolean"], "needs_proba": ["boolean"], "needs_threshold": ["boolean"], }, prefer_skip_nested_validation=True, ) 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 `. 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) _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) @validate_params( { "estimator": [HasMethods("fit")], "scoring": [StrOptions(set(get_scorer_names())), callable, None], "allow_none": ["boolean"], }, prefer_skip_nested_validation=True, ) 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)``. 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 isinstance(scoring, str): return get_scorer(scoring) if 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) if scoring is None: if hasattr(estimator, "score"): return _PassthroughScorer(estimator) 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 )