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scorer.py
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scorer.py
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"""
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 abc import ABCMeta
import numpy as np
from . import (r2_score, median_absolute_error, mean_absolute_error,
mean_squared_error, mean_squared_log_error, 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)
from .cluster import adjusted_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.fixes import _Iterable as Iterable
from ..externals import six
from ..base import is_regressor
class _BaseScorer(six.with_metaclass(ABCMeta, object)):
def __init__(self, score_func, sign, kwargs):
self._kwargs = kwargs
self._score_func = score_func
self._sign = sign
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 _factory_args(self):
"""Return non-default make_scorer arguments for repr."""
return ""
class _PredictScorer(_BaseScorer):
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 or 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, optional (default=None)
Sample weights.
Returns
-------
score : float
Score function applied to prediction of estimator on X.
"""
y_pred = 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 __call__(self, clf, X, y, sample_weight=None):
"""Evaluate predicted probabilities for X relative to y_true.
Parameters
----------
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 or 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, optional (default=None)
Sample weights.
Returns
-------
score : float
Score function applied to prediction of estimator on X.
"""
y_type = type_of_target(y)
y_pred = clf.predict_proba(X)
if y_type == "binary":
y_pred = y_pred[:, 1]
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 __call__(self, clf, X, y, sample_weight=None):
"""Evaluate decision function output for X relative to y_true.
Parameters
----------
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 or 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, optional (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 = clf.predict(X)
else:
try:
y_pred = clf.decision_function(X)
# For multi-output multi-class estimator
if isinstance(y_pred, list):
y_pred = np.vstack(p for p in y_pred).T
except (NotImplementedError, AttributeError):
y_pred = clf.predict_proba(X)
if y_type == "binary":
y_pred = y_pred[:, 1]
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
Parameters
----------
scoring : str | callable
scoring method as string. If callable it is returned as is.
Returns
-------
scorer : callable
The scorer.
"""
if isinstance(scoring, six.string_types):
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)
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 : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
allow_none : boolean, optional, 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, six.string_types):
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=None):
"""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 : string, callable, list/tuple, dict or None, default: None
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.
NOTE that when using custom scorers, each scorer should return a single
value. Metric functions returning a list/array of values can be wrapped
into multiple scorers that return one value each.
See :ref:`multimetric_grid_search` for an example.
If None the estimator's default scorer (if available) is used.
The return value in that case will be ``{'score': <default_scorer>}``.
If the estimator's default scorer is not available, a ``TypeError``
is raised.
Returns
-------
scorers_dict : dict
A dict mapping each scorer name to its validated scorer.
is_multimetric : bool
True if scorer is a list/tuple or dict of callables
False if scorer is None/str/callable
"""
if callable(scoring) or scoring is None or isinstance(scoring,
six.string_types):
scorers = {"score": check_scoring(estimator, scoring=scoring)}
return scorers, False
else:
err_msg_generic = ("scoring should either be a single string or "
"callable for single metric evaluation or a "
"list/tuple of strings or a dict of scorer name "
"mapped to the callable for multiple metric "
"evaluation. Got %s of type %s"
% (repr(scoring), type(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(err_msg + "Duplicate elements were found in"
" the given list. %r" % repr(scoring))
elif len(keys) > 0:
if not all(isinstance(k, six.string_types) for k in keys):
if any(callable(k) for k in keys):
raise ValueError(err_msg +
"One or more of the elements were "
"callables. Use a dict of score name "
"mapped to the scorer callable. "
"Got %r" % repr(scoring))
else:
raise ValueError(err_msg +
"Non-string types were found in "
"the given list. Got %r"
% repr(scoring))
scorers = {scorer: check_scoring(estimator, scoring=scorer)
for scorer in scoring}
else:
raise ValueError(err_msg +
"Empty list was given. %r" % repr(scoring))
elif isinstance(scoring, dict):
keys = set(scoring)
if not all(isinstance(k, six.string_types) for k in keys):
raise ValueError("Non-string types were found in the keys of "
"the given dict. scoring=%r" % repr(scoring))
if len(keys) == 0:
raise ValueError("An empty dict was passed. %r"
% repr(scoring))
scorers = {key: check_scoring(estimator, scoring=scorer)
for key, scorer in scoring.items()}
else:
raise ValueError(err_msg_generic)
return scorers, 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 GridSearchCV
and cross_val_score. It takes a score function, such as ``accuracy_score``,
``mean_squared_error``, ``adjusted_rand_index`` or ``average_precision``
and returns a callable that scores an estimator's output.
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 : boolean, 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 : boolean, default=False
Whether score_func requires predict_proba to get probability estimates
out of a classifier.
needs_threshold : boolean, 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.
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)
"""
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)
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_median_absolute_error_scorer = make_scorer(median_absolute_error,
greater_is_better=False)
# Standard Classification Scores
accuracy_scorer = make_scorer(accuracy_score)
f1_scorer = make_scorer(f1_score)
balanced_accuracy_scorer = make_scorer(balanced_accuracy_score)
# Score functions that need decision values
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)
precision_scorer = make_scorer(precision_score)
recall_scorer = make_scorer(recall_score)
# Score function for probabilistic classification
neg_log_loss_scorer = make_scorer(log_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)
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,
neg_median_absolute_error=neg_median_absolute_error_scorer,
neg_mean_absolute_error=neg_mean_absolute_error_scorer,
neg_mean_squared_error=neg_mean_squared_error_scorer,
neg_mean_squared_log_error=neg_mean_squared_log_error_scorer,
accuracy=accuracy_scorer, roc_auc=roc_auc_scorer,
balanced_accuracy=balanced_accuracy_scorer,
average_precision=average_precision_scorer,
neg_log_loss=neg_log_loss_scorer,
brier_score_loss=brier_score_loss_scorer,
# Cluster metrics that use supervised evaluation
adjusted_rand_score=adjusted_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)]:
SCORERS[name] = make_scorer(metric)
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)