"""
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
import copy
import warnings
from collections import Counter
from functools import partial
from inspect import signature
from traceback import format_exc
from ..base import is_regressor
from ..utils import Bunch
from ..utils._param_validation import HasMethods, Hidden, StrOptions, validate_params
from ..utils._response import _get_response_values
from ..utils.metadata_routing import (
MetadataRequest,
MetadataRouter,
_MetadataRequester,
_raise_for_params,
_routing_enabled,
get_routing_for_object,
process_routing,
)
from ..utils.validation import _check_response_method
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,
root_mean_squared_error,
root_mean_squared_log_error,
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, thus when a response method will
be called several time.
"""
if len(self._scorers) == 1: # Only one scorer
return False
counter = Counter(
[
_check_response_method(estimator, scorer._response_method).__name__
for scorer in self._scorers.values()
if isinstance(scorer, _BaseScorer)
]
)
if any(val > 1 for val in counter.values()):
# The exact same response method or iterable of response methods
# will be called more than once.
return True
return False
def get_metadata_routing(self):
"""Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` 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, response_method="predict"):
self._score_func = score_func
self._sign = sign
self._kwargs = kwargs
self._response_method = response_method
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):
sign_string = "" if self._sign > 0 else ", greater_is_better=False"
response_method_string = f", response_method={self._response_method!r}"
kwargs_string = "".join([f", {k}={v}" for k, v in self._kwargs.items()])
return (
f"make_scorer({self._score_func.__name__}{sign_string}"
f"{response_method_string}{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 <metadata_routing>`.
.. versionadded:: 1.3
Returns
-------
score : float
Score function applied to prediction of estimator on X.
"""
_raise_for_params(kwargs, self, None)
_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 _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 <metadata_routing>` 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}``.
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
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 _Scorer(_BaseScorer):
def _score(self, method_caller, estimator, X, y_true, **kwargs):
"""Evaluate the response method of `estimator` on `X` and `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.
X : {array-like, sparse matrix}
Test data that will be fed to clf.decision_function or
clf.predict_proba.
y_true : 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.
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,
)
pos_label = None if is_regressor(estimator) else self._get_pos_label()
response_method = _check_response_method(estimator, self._response_method)
y_pred = method_caller(
estimator, response_method.__name__, X, pos_label=pos_label
)
scoring_kwargs = {**self._kwargs, **kwargs}
return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)
[docs]
@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 <scoring_parameter>`.
: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.
Examples
--------
>>> import numpy as np
>>> from sklearn.dummy import DummyClassifier
>>> from sklearn.metrics import get_scorer
>>> X = np.reshape([0, 1, -1, -0.5, 2], (-1, 1))
>>> y = np.array([0, 1, 1, 0, 1])
>>> classifier = DummyClassifier(strategy="constant", constant=0).fit(X, y)
>>> accuracy = get_scorer("accuracy")
>>> accuracy(classifier, X, y)
0.4
"""
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 <metadata_routing>` 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
# requests of the given estimator. This object behaves as a consumer
# rather than a router. Ideally it only exposes the score requests to
# the parent object; however, that requires computing the routing for
# meta-estimators, which would be more time consuming than simply
# returning the child object's requests.
return get_routing_for_object(self._estimator)
def _check_multimetric_scoring(estimator, scoring):
"""Check the scoring parameter in cases when multiple metrics are allowed.
In addition, multimetric scoring leverages a caching mechanism to not call the same
estimator response method multiple times. Hence, the scorer is modified to only use
a single response method given a list of response methods and the estimator.
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 _get_response_method(response_method, needs_threshold, needs_proba):
"""Handles deprecation of `needs_threshold` and `needs_proba` parameters in
favor of `response_method`.
"""
needs_threshold_provided = needs_threshold != "deprecated"
needs_proba_provided = needs_proba != "deprecated"
response_method_provided = response_method is not None
needs_threshold = False if needs_threshold == "deprecated" else needs_threshold
needs_proba = False if needs_proba == "deprecated" else needs_proba
if response_method_provided and (needs_proba_provided or needs_threshold_provided):
raise ValueError(
"You cannot set both `response_method` and `needs_proba` or "
"`needs_threshold` at the same time. Only use `response_method` since "
"the other two are deprecated in version 1.4 and will be removed in 1.6."
)
if needs_proba_provided or needs_threshold_provided:
warnings.warn(
(
"The `needs_threshold` and `needs_proba` parameter are deprecated in "
"version 1.4 and will be removed in 1.6. You can either let "
"`response_method` be `None` or set it to `predict` to preserve the "
"same behaviour."
),
FutureWarning,
)
if response_method_provided:
return response_method
if needs_proba is True and needs_threshold is True:
raise ValueError(
"You cannot set both `needs_proba` and `needs_threshold` at the same "
"time. Use `response_method` instead since the other two are deprecated "
"in version 1.4 and will be removed in 1.6."
)
if needs_proba is True:
response_method = "predict_proba"
elif needs_threshold is True:
response_method = ("decision_function", "predict_proba")
else:
response_method = "predict"
return response_method
@validate_params(
{
"score_func": [callable],
"response_method": [
None,
list,
tuple,
StrOptions({"predict", "predict_proba", "decision_function"}),
],
"greater_is_better": ["boolean"],
"needs_proba": ["boolean", Hidden(StrOptions({"deprecated"}))],
"needs_threshold": ["boolean", Hidden(StrOptions({"deprecated"}))],
},
prefer_skip_nested_validation=True,
)
def make_scorer(
score_func,
*,
response_method=None,
greater_is_better=True,
needs_proba="deprecated",
needs_threshold="deprecated",
**kwargs,
):
"""Make a scorer from a performance metric or loss function.
A scorer is a wrapper around an arbitrary metric or loss function that is called
with the signature `scorer(estimator, X, y_true, **kwargs)`.
It is accepted in all scikit-learn estimators or functions allowing a `scoring`
parameter.
The parameter `response_method` allows to specify which method of the estimator
should be used to feed the scoring/loss function.
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)``.
response_method : {"predict_proba", "decision_function", "predict"} or \
list/tuple of such str, default=None
Specifies the response method to use get prediction from an estimator
(i.e. :term:`predict_proba`, :term:`decision_function` or
:term:`predict`). Possible choices are:
- if `str`, it corresponds to the name to the method to return;
- if a list or tuple of `str`, it provides the method names in order of
preference. The method returned corresponds to the first method in
the list and which is implemented by `estimator`.
- if `None`, it is equivalent to `"predict"`.
.. versionadded:: 1.4
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,)`).
.. deprecated:: 1.4
`needs_proba` is deprecated in version 1.4 and will be removed in
1.6. Use `response_method="predict_proba"` instead.
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.
.. deprecated:: 1.4
`needs_threshold` is deprecated in version 1.4 and will be removed
in 1.6. Use `response_method=("decision_function", "predict_proba")`
instead to preserve the same behaviour.
**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, response_method='predict', beta=2)
>>> from sklearn.model_selection import GridSearchCV
>>> from sklearn.svm import LinearSVC
>>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
... scoring=ftwo_scorer)
"""
response_method = _get_response_method(
response_method, needs_threshold, needs_proba
)
sign = 1 if greater_is_better else -1
return _Scorer(score_func, sign, kwargs, response_method)
# 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(
root_mean_squared_error, greater_is_better=False
)
neg_root_mean_squared_log_error_scorer = make_scorer(
root_mean_squared_log_error, greater_is_better=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,
response_method=("decision_function", "predict_proba"),
)
roc_auc_scorer = make_scorer(
roc_auc_score,
greater_is_better=True,
response_method=("decision_function", "predict_proba"),
)
average_precision_scorer = make_scorer(
average_precision_score,
response_method=("decision_function", "predict_proba"),
)
roc_auc_ovo_scorer = make_scorer(
roc_auc_score, response_method="predict_proba", multi_class="ovo"
)
roc_auc_ovo_weighted_scorer = make_scorer(
roc_auc_score,
response_method="predict_proba",
multi_class="ovo",
average="weighted",
)
roc_auc_ovr_scorer = make_scorer(
roc_auc_score, response_method="predict_proba", multi_class="ovr"
)
roc_auc_ovr_weighted_scorer = make_scorer(
roc_auc_score,
response_method="predict_proba",
multi_class="ovr",
average="weighted",
)
# Score function for probabilistic classification
neg_log_loss_scorer = make_scorer(
log_loss, greater_is_better=False, response_method="predict_proba"
)
neg_brier_score_scorer = make_scorer(
brier_score_loss, greater_is_better=False, response_method="predict_proba"
)
brier_score_loss_scorer = make_scorer(
brier_score_loss, greater_is_better=False, response_method="predict_proba"
)
# 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,
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_root_mean_squared_log_error=neg_root_mean_squared_log_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.
Examples
--------
>>> from sklearn.metrics import get_scorer_names
>>> all_scorers = get_scorer_names()
>>> type(all_scorers)
<class 'list'>
>>> all_scorers[:3]
['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score']
>>> "roc_auc" in all_scorers
True
"""
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)``.
Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn.metrics import check_scoring
>>> from sklearn.tree import DecisionTreeClassifier
>>> X, y = load_iris(return_X_y=True)
>>> classifier = DecisionTreeClassifier(max_depth=2).fit(X, y)
>>> scorer = check_scoring(classifier, scoring='accuracy')
>>> scorer(classifier, X, y)
0.96...
"""
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
)