561 lines
22 KiB
Python
561 lines
22 KiB
Python
|
"""
|
||
|
|
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, abstractmethod
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
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,
|
||
|
|
explained_variance_score)
|
||
|
|
|
||
|
|
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 ..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
|
||
|
|
# XXX After removing the deprecated scorers (v0.20) remove the
|
||
|
|
# XXX deprecation_msg property again and remove __call__'s body again
|
||
|
|
self._deprecation_msg = None
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def __call__(self, estimator, X, y, sample_weight=None):
|
||
|
|
if self._deprecation_msg is not None:
|
||
|
|
warnings.warn(self._deprecation_msg,
|
||
|
|
category=DeprecationWarning,
|
||
|
|
stacklevel=2)
|
||
|
|
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
super(_PredictScorer, self).__call__(estimator, X, y_true,
|
||
|
|
sample_weight=sample_weight)
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
super(_ProbaScorer, self).__call__(clf, X, y,
|
||
|
|
sample_weight=sample_weight)
|
||
|
|
y_pred = clf.predict_proba(X)
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
super(_ThresholdScorer, self).__call__(clf, X, y,
|
||
|
|
sample_weight=sample_weight)
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
valid = True
|
||
|
|
if isinstance(scoring, six.string_types):
|
||
|
|
try:
|
||
|
|
scorer = SCORERS[scoring]
|
||
|
|
except KeyError:
|
||
|
|
scorers = [scorer for scorer in SCORERS
|
||
|
|
if SCORERS[scorer]._deprecation_msg is None]
|
||
|
|
valid = False # Don't raise here to make the error message elegant
|
||
|
|
if not valid:
|
||
|
|
raise ValueError('%r is not a valid scoring value. '
|
||
|
|
'Valid options are %s'
|
||
|
|
% (scoring, sorted(scorers)))
|
||
|
|
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)
|
||
|
|
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)
|
||
|
|
deprecation_msg = ('Scoring method mean_squared_error was renamed to '
|
||
|
|
'neg_mean_squared_error in version 0.18 and will '
|
||
|
|
'be removed in 0.20.')
|
||
|
|
mean_squared_error_scorer = make_scorer(mean_squared_error,
|
||
|
|
greater_is_better=False)
|
||
|
|
mean_squared_error_scorer._deprecation_msg = deprecation_msg
|
||
|
|
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)
|
||
|
|
deprecation_msg = ('Scoring method mean_absolute_error was renamed to '
|
||
|
|
'neg_mean_absolute_error in version 0.18 and will '
|
||
|
|
'be removed in 0.20.')
|
||
|
|
mean_absolute_error_scorer = make_scorer(mean_absolute_error,
|
||
|
|
greater_is_better=False)
|
||
|
|
mean_absolute_error_scorer._deprecation_msg = deprecation_msg
|
||
|
|
neg_median_absolute_error_scorer = make_scorer(median_absolute_error,
|
||
|
|
greater_is_better=False)
|
||
|
|
deprecation_msg = ('Scoring method median_absolute_error was renamed to '
|
||
|
|
'neg_median_absolute_error in version 0.18 and will '
|
||
|
|
'be removed in 0.20.')
|
||
|
|
median_absolute_error_scorer = make_scorer(median_absolute_error,
|
||
|
|
greater_is_better=False)
|
||
|
|
median_absolute_error_scorer._deprecation_msg = deprecation_msg
|
||
|
|
|
||
|
|
|
||
|
|
# Standard Classification Scores
|
||
|
|
accuracy_scorer = make_scorer(accuracy_score)
|
||
|
|
f1_scorer = make_scorer(f1_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)
|
||
|
|
deprecation_msg = ('Scoring method log_loss was renamed to '
|
||
|
|
'neg_log_loss in version 0.18 and will be removed in 0.20.')
|
||
|
|
log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
|
||
|
|
needs_proba=True)
|
||
|
|
log_loss_scorer._deprecation_msg = deprecation_msg
|
||
|
|
|
||
|
|
|
||
|
|
# 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,
|
||
|
|
median_absolute_error=median_absolute_error_scorer,
|
||
|
|
mean_absolute_error=mean_absolute_error_scorer,
|
||
|
|
mean_squared_error=mean_squared_error_scorer,
|
||
|
|
accuracy=accuracy_scorer, roc_auc=roc_auc_scorer,
|
||
|
|
average_precision=average_precision_scorer,
|
||
|
|
log_loss=log_loss_scorer,
|
||
|
|
neg_log_loss=neg_log_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)
|