"""
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 <amueller@ais.uni-bonn.de>
# Lars Buitinck
# Arnaud Joly <arnaud.v.joly@gmail.com>
# License: Simplified BSD
from collections.abc import Iterable
from functools import partial
from collections import Counter
from traceback import format_exc
import numpy as np
import copy
import warnings
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,
matthews_corrcoef,
class_likelihood_ratios,
)
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 ..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.
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)
for name, scorer in self._scorers.items():
try:
if isinstance(scorer, _BaseScorer):
score = scorer._score(cached_call, estimator, *args, **kwargs)
else:
score = scorer(estimator, *args, **kwargs)
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
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__} "
"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`
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"
[docs]def get_scorer(scoring):
"""Get a scorer from string.
Read more in the :ref:`User Guide <scoring_parameter>`.
:func:`~sklearn.metrics.get_scorer_names` can be used to retrieve the names
of all available scorers.
Parameters
----------
scoring : str or callable
Scoring method as string. If callable it is returned as is.
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
def _passthrough_scorer(estimator, *args, **kwargs):
"""Function that wraps estimator.score"""
return estimator.score(*args, **kwargs)
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 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
Strategy to evaluate the performance of the cross-validated model on
the test set.
The possibilities are:
- a list or tuple of unique strings;
- a callable returning a dictionary where they keys are the metric
names and the values are the metric scores;
- a dictionary with metric names as keys and callables a values.
See :ref:`multimetric_grid_search` for an example.
Returns
-------
scorers_dict : dict
A dict mapping each scorer name to its validated scorer.
"""
err_msg_generic = (
f"scoring is invalid (got {scoring!r}). Refer to the "
"scoring glossary for details: "
"https://scikit-learn.org/stable/glossary.html#term-scoring"
)
if isinstance(scoring, (list, tuple, set)):
err_msg = (
"The list/tuple elements must be unique strings of predefined scorers. "
)
try:
keys = set(scoring)
except TypeError as e:
raise ValueError(err_msg) from e
if len(keys) != len(scoring):
raise ValueError(
f"{err_msg} Duplicate elements were found in"
f" the given list. {scoring!r}"
)
elif len(keys) > 0:
if not all(isinstance(k, str) for k in keys):
if any(callable(k) for k in keys):
raise ValueError(
f"{err_msg} One or more of the elements "
"were callables. Use a dict of score "
"name mapped to the scorer callable. "
f"Got {scoring!r}"
)
else:
raise ValueError(
f"{err_msg} Non-string types were found "
f"in the given list. Got {scoring!r}"
)
scorers = {
scorer: check_scoring(estimator, scoring=scorer) for scorer in scoring
}
else:
raise ValueError(f"{err_msg} Empty list was given. {scoring!r}")
elif isinstance(scoring, dict):
keys = set(scoring)
if not all(isinstance(k, str) for k in keys):
raise ValueError(
"Non-string types were found in the keys of "
f"the given dict. scoring={scoring!r}"
)
if len(keys) == 0:
raise ValueError(f"An empty dict was passed. {scoring!r}")
scorers = {
key: check_scoring(estimator, scoring=scorer)
for key, scorer in scoring.items()
}
else:
raise ValueError(err_msg_generic)
return scorers
def make_scorer(
score_func,
*,
greater_is_better=True,
needs_proba=False,
needs_threshold=False,
**kwargs,
):
"""Make a scorer from a performance metric or loss function.
This factory function wraps scoring functions for use in
:class:`~sklearn.model_selection.GridSearchCV` and
:func:`~sklearn.model_selection.cross_val_score`.
It takes a score function, such as :func:`~sklearn.metrics.accuracy_score`,
:func:`~sklearn.metrics.mean_squared_error`,
:func:`~sklearn.metrics.adjusted_rand_score` or
:func:`~sklearn.metrics.average_precision_score`
and returns a callable that scores an estimator's output.
The signature of the call is `(estimator, X, y)` where `estimator`
is the model to be evaluated, `X` is the data and `y` is the
ground truth labeling (or `None` in the case of unsupervised models).
Read more in the :ref:`User Guide <scoring>`.
Parameters
----------
score_func : callable
Score function (or loss function) with signature
`score_func(y, y_pred, **kwargs)`.
greater_is_better : bool, default=True
Whether `score_func` is a score function (default), meaning high is
good, or a loss function, meaning low is good. In the latter case, the
scorer object will sign-flip the outcome of the `score_func`.
needs_proba : bool, default=False
Whether `score_func` requires `predict_proba` to get probability
estimates out of a classifier.
If True, for binary `y_true`, the score function is supposed to accept
a 1D `y_pred` (i.e., probability of the positive class, shape
`(n_samples,)`).
needs_threshold : bool, default=False
Whether `score_func` takes a continuous decision certainty.
This only works for binary classification using estimators that
have either a `decision_function` or `predict_proba` method.
If True, for binary `y_true`, the score function is supposed to accept
a 1D `y_pred` (i.e., probability of the positive class or the decision
function, shape `(n_samples,)`).
For example `average_precision` or the area under the roc curve
can not be computed using discrete predictions alone.
**kwargs : additional arguments
Additional parameters to be passed to `score_func`.
Returns
-------
scorer : callable
Callable object that returns a scalar score; greater is better.
Notes
-----
If `needs_proba=False` and `needs_threshold=False`, the score
function is supposed to accept the output of :term:`predict`. If
`needs_proba=True`, the score function is supposed to accept the
output of :term:`predict_proba` (For binary `y_true`, the score function is
supposed to accept probability of the positive class). If
`needs_threshold=True`, the score function is supposed to accept the
output of :term:`decision_function` or :term:`predict_proba` when
:term:`decision_function` is not present.
Examples
--------
>>> from sklearn.metrics import fbeta_score, make_scorer
>>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
>>> ftwo_scorer
make_scorer(fbeta_score, beta=2)
>>> from sklearn.model_selection import GridSearchCV
>>> from sklearn.svm import LinearSVC
>>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
... scoring=ftwo_scorer)
"""
sign = 1 if greater_is_better else -1
if needs_proba and needs_threshold:
raise ValueError(
"Set either needs_proba or needs_threshold to True, but not both."
)
if needs_proba:
cls = _ProbaScorer
elif needs_threshold:
cls = _ThresholdScorer
else:
cls = _PredictScorer
return cls(score_func, sign, kwargs)
# Standard regression scores
explained_variance_scorer = make_scorer(explained_variance_score)
r2_scorer = make_scorer(r2_score)
max_error_scorer = make_scorer(max_error, greater_is_better=False)
neg_mean_squared_error_scorer = make_scorer(mean_squared_error, greater_is_better=False)
neg_mean_squared_log_error_scorer = make_scorer(
mean_squared_log_error, greater_is_better=False
)
neg_mean_absolute_error_scorer = make_scorer(
mean_absolute_error, greater_is_better=False
)
neg_mean_absolute_percentage_error_scorer = make_scorer(
mean_absolute_percentage_error, greater_is_better=False
)
neg_median_absolute_error_scorer = make_scorer(
median_absolute_error, greater_is_better=False
)
neg_root_mean_squared_error_scorer = make_scorer(
mean_squared_error, greater_is_better=False, squared=False
)
neg_mean_poisson_deviance_scorer = make_scorer(
mean_poisson_deviance, greater_is_better=False
)
neg_mean_gamma_deviance_scorer = make_scorer(
mean_gamma_deviance, greater_is_better=False
)
# Standard Classification Scores
accuracy_scorer = make_scorer(accuracy_score)
balanced_accuracy_scorer = make_scorer(balanced_accuracy_score)
matthews_corrcoef_scorer = make_scorer(matthews_corrcoef)
def positive_likelihood_ratio(y_true, y_pred):
return class_likelihood_ratios(y_true, y_pred)[0]
def negative_likelihood_ratio(y_true, y_pred):
return class_likelihood_ratios(y_true, y_pred)[1]
positive_likelihood_ratio_scorer = make_scorer(positive_likelihood_ratio)
neg_negative_likelihood_ratio_scorer = make_scorer(
negative_likelihood_ratio, greater_is_better=False
)
# Score functions that need decision values
top_k_accuracy_scorer = make_scorer(
top_k_accuracy_score, greater_is_better=True, needs_threshold=True
)
roc_auc_scorer = make_scorer(
roc_auc_score, greater_is_better=True, needs_threshold=True
)
average_precision_scorer = make_scorer(average_precision_score, needs_threshold=True)
roc_auc_ovo_scorer = make_scorer(roc_auc_score, needs_proba=True, multi_class="ovo")
roc_auc_ovo_weighted_scorer = make_scorer(
roc_auc_score, needs_proba=True, multi_class="ovo", average="weighted"
)
roc_auc_ovr_scorer = make_scorer(roc_auc_score, needs_proba=True, multi_class="ovr")
roc_auc_ovr_weighted_scorer = make_scorer(
roc_auc_score, needs_proba=True, multi_class="ovr", average="weighted"
)
# Score function for probabilistic classification
neg_log_loss_scorer = make_scorer(log_loss, greater_is_better=False, needs_proba=True)
neg_brier_score_scorer = make_scorer(
brier_score_loss, greater_is_better=False, needs_proba=True
)
brier_score_loss_scorer = make_scorer(
brier_score_loss, greater_is_better=False, needs_proba=True
)
# Clustering scores
adjusted_rand_scorer = make_scorer(adjusted_rand_score)
rand_scorer = make_scorer(rand_score)
homogeneity_scorer = make_scorer(homogeneity_score)
completeness_scorer = make_scorer(completeness_score)
v_measure_scorer = make_scorer(v_measure_score)
mutual_info_scorer = make_scorer(mutual_info_score)
adjusted_mutual_info_scorer = make_scorer(adjusted_mutual_info_score)
normalized_mutual_info_scorer = make_scorer(normalized_mutual_info_score)
fowlkes_mallows_scorer = make_scorer(fowlkes_mallows_score)
# TODO(1.3) Remove
class _DeprecatedScorers(dict):
"""A temporary class to deprecate SCORERS."""
def __getitem__(self, item):
warnings.warn(
"sklearn.metrics.SCORERS is deprecated and will be removed in v1.3. "
"Please use sklearn.metrics.get_scorer_names to get a list of available "
"scorers and sklearn.metrics.get_metric to get scorer.",
FutureWarning,
)
return super().__getitem__(item)
_SCORERS = dict(
explained_variance=explained_variance_scorer,
r2=r2_scorer,
max_error=max_error_scorer,
matthews_corrcoef=matthews_corrcoef_scorer,
neg_median_absolute_error=neg_median_absolute_error_scorer,
neg_mean_absolute_error=neg_mean_absolute_error_scorer,
neg_mean_absolute_percentage_error=neg_mean_absolute_percentage_error_scorer, # noqa
neg_mean_squared_error=neg_mean_squared_error_scorer,
neg_mean_squared_log_error=neg_mean_squared_log_error_scorer,
neg_root_mean_squared_error=neg_root_mean_squared_error_scorer,
neg_mean_poisson_deviance=neg_mean_poisson_deviance_scorer,
neg_mean_gamma_deviance=neg_mean_gamma_deviance_scorer,
accuracy=accuracy_scorer,
top_k_accuracy=top_k_accuracy_scorer,
roc_auc=roc_auc_scorer,
roc_auc_ovr=roc_auc_ovr_scorer,
roc_auc_ovo=roc_auc_ovo_scorer,
roc_auc_ovr_weighted=roc_auc_ovr_weighted_scorer,
roc_auc_ovo_weighted=roc_auc_ovo_weighted_scorer,
balanced_accuracy=balanced_accuracy_scorer,
average_precision=average_precision_scorer,
neg_log_loss=neg_log_loss_scorer,
neg_brier_score=neg_brier_score_scorer,
positive_likelihood_ratio=positive_likelihood_ratio_scorer,
neg_negative_likelihood_ratio=neg_negative_likelihood_ratio_scorer,
# Cluster metrics that use supervised evaluation
adjusted_rand_score=adjusted_rand_scorer,
rand_score=rand_scorer,
homogeneity_score=homogeneity_scorer,
completeness_score=completeness_scorer,
v_measure_score=v_measure_scorer,
mutual_info_score=mutual_info_scorer,
adjusted_mutual_info_score=adjusted_mutual_info_scorer,
normalized_mutual_info_score=normalized_mutual_info_scorer,
fowlkes_mallows_score=fowlkes_mallows_scorer,
)
[docs]def get_scorer_names():
"""Get the names of all available scorers.
These names can be passed to :func:`~sklearn.metrics.get_scorer` to
retrieve the scorer object.
Returns
-------
list of str
Names of all available scorers.
"""
return sorted(_SCORERS.keys())
for name, metric in [
("precision", precision_score),
("recall", recall_score),
("f1", f1_score),
("jaccard", jaccard_score),
]:
_SCORERS[name] = make_scorer(metric, average="binary")
for average in ["macro", "micro", "samples", "weighted"]:
qualified_name = "{0}_{1}".format(name, average)
_SCORERS[qualified_name] = make_scorer(metric, pos_label=None, average=average)
SCORERS = _DeprecatedScorers(_SCORERS)