8000 Simplified error message in get_scorer() function in sklearn.metrics.scorer.py file by princejha95 · Pull Request #11062 · scikit-learn/scikit-learn · GitHub
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Simplified error message in get_scorer() function in sklearn.metrics.scorer.py file #11062

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Simplified error message in get_scorer() function in sklearn.metrics.scorer.py file #11062

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3a81e82
Add a list_scorers function to sklearn.metrics
princejha95 May 3, 2018
dea93cb
Add a list_scorers function to sklearn.metrics (#11060)
princejha95 May 3, 2018
cf8a41d
Resolved merge conflict
princejha95 May 3, 2018
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23 changes: 4 additions & 19 deletions sklearn/metrics/scorer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,16 +19,15 @@
# License: Simplified BSD

from abc import ABCMeta, abstractmethod
from collections import Iterable
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, balanced_accuracy_score,
explained_variance_score, brier_score_loss)
precision_score, recall_score, log_loss,
explained_variance_score)

from .cluster import adjusted_rand_score
from .cluster import homogeneity_score
Expand Down Expand Up @@ -136,10 +135,7 @@ def __call__(self, clf, X, y, sample_weight=None):
"""
super(_ProbaScorer, self).__call__(clf, X, y,
sample_weight=sample_weight)
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,
Expand Down Expand Up @@ -212,7 +208,6 @@ def __call__(self, clf, X, y, sample_weight=None):
def _factory_args(self):
return ", needs_threshold=True"


def get_scorer(scoring):
"""Get a scorer from string

Expand All @@ -236,8 +231,8 @@ def get_scorer(scoring):
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)))
'For valid options use sorted(SCORERS.keys())'
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Maybe should make this sklearn.metrics.SCORERS instead of just SCORERS

% (scoring))
else:
scorer = scoring
return scorer
Expand Down Expand Up @@ -301,10 +296,6 @@ def check_scoring(estimator, scoring=None, allow_none=False):
"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)
Expand Down Expand Up @@ -505,7 +496,6 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=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,
Expand All @@ -523,9 +513,6 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
needs_proba=True)
log_loss_scorer._deprecation_msg = deprecation_msg
brier_score_loss_scorer = make_scorer(brier_score_loss,
greater_is_better=False,
needs_proba=True)


# Clustering scores
Expand All @@ -549,11 +536,9 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
mean_absolute_error=mean_absolute_error_scorer,
mean_squared_error=mean_squared_error_scorer,
accuracy=accuracy_scorer, roc_auc=roc_auc_scorer,
balanced_accuracy=balanced_accuracy_scorer,
average_precision=average_precision_scorer,
log_loss=log_loss_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,
Expand Down
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