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[MRG] Improve multi-metric scorer speed #10979

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20fcae2
[DOC] Fix _PredicScorer parameter description
gamazeps Apr 13, 2018
4469067
Improve multi-metric scoring computation.
gamazeps Apr 14, 2018
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82 changes: 67 additions & 15 deletions sklearn/metrics/scorer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,8 +26,9 @@
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,
balanced_accuracy_score, explained_variance_score,
brier_score_loss)

from .cluster import adjusted_rand_score
from .cluster import homogeneity_score
Expand Down Expand Up @@ -79,7 +80,7 @@ def __call__(self, estimator, X, y_true, sample_weight=None):
Parameters
----------
estimator : object
Trained estimator to use for scoring. Must have a predict_proba
Trained estimator to use for scoring. Must have a predict
method; the output of that is used to compute the score.

X : array-like or sparse matrix
Expand All @@ -96,9 +97,32 @@ def __call__(self, estimator, X, y_true, sample_weight=None):
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)
return self.score_predictions(y_true, y_pred, sample_weight)

def score_predictions(self, y_true, y_pred, sample_weight=None):
"""Evaluate predicted target values y_pred relative to y_true.

Parameters
----------
y_pred : array-like
Prodicted values for y.

y_true : array-like
Gold standard target values for y.

sample_weight : array-like, optional (default=None)
Sample weights.

Returns
-------
score : float
Score function applied to prediction of estimator on X.
"""
# We call __call__ with no arguments as it only serves to show
# deprecation warnings.
super(_PredictScorer, self).__call__(None, None, None,
sample_weight=sample_weight)
if sample_weight is not None:
return self._sign * self._score_func(y_true, y_pred,
sample_weight=sample_weight,
Expand All @@ -109,7 +133,7 @@ def __call__(self, estimator, X, y_true, sample_weight=None):


class _ProbaScorer(_BaseScorer):
def __call__(self, clf, X, y, sample_weight=None):
def __call__(self, clf, X, y_true, sample_weight=None):
"""Evaluate predicted probabilities for X relative to y_true.

Parameters
Expand All @@ -121,7 +145,7 @@ def __call__(self, clf, X, y, sample_weight=None):
X : array-like or sparse matrix
Test data that will be fed to clf.predict_proba.

y : array-like
y_true : array-like
Gold standard target values for X. These must be class labels,
not probabilities.

Expand All @@ -133,21 +157,49 @@ def __call__(self, clf, X, y, sample_weight=None):
score : float
Score function applied to prediction of estimator on X.
"""
super(_ProbaScorer, self).__call__(clf, X, y,
sample_weight=sample_weight)
y_type = type_of_target(y)
y_pred = clf.predict_proba(X)

return self.score_predictions(y_true, y_pred, sample_weight)

def _factory_args(self):
return ", needs_proba=True"

def score_predictions(self, y_true, y_pred, sample_weight=None):
"""Evaluate predicted y_pred relative to y_true.

Parameters
----------
y_pred : array-like
Predicted values for y by a classifier. These must be class labels,
not probabilities.

y_true : array-like
Gold standard target values for y. 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.
"""
# We call __call__ with no arguments as it only serves to show
# deprecation warnings.
super(_ProbaScorer, self).__call__(None, None, None,
sample_weight=sample_weight)
y_type = type_of_target(y_true)
if y_type == "binary":
y_pred = y_pred[:, 1]
if sample_weight is not None:
return self._sign * self._score_func(y, y_pred,
return self._sign * self._score_func(y_true, 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"
return self._sign * self._score_func(y_true,
y_pred,
**self._kwargs)


class _ThresholdScorer(_BaseScorer):
Expand Down
45 changes: 39 additions & 6 deletions sklearn/model_selection/_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,8 @@
from ..utils.metaestimators import _safe_split
from ..externals.joblib import Parallel, delayed, logger
from ..externals.six.moves import zip
from ..metrics.scorer import check_scoring, _check_multimetric_scoring
from ..metrics.scorer import (check_scoring, _check_multimetric_scoring,
_PredictScorer, _ProbaScorer)
from ..exceptions import FitFailedWarning
from ._split import check_cv
from ..preprocessing import LabelEncoder
Expand Down Expand Up @@ -577,14 +578,46 @@ def _score(estimator, X_test, y_test, scorer, is_multimetric=False):

def _multimetric_score(estimator, X_test, y_test, scorers):
"""Return a dict of score for multimetric scoring"""
scores = {}

for name, scorer in scorers.items():
def _is_proba(x):
return isinstance(x, _ProbaScorer)

def _is_predict(x):
return isinstance(x, _PredictScorer)

tmp_scores = {}

# The following two are special cases where we want to compute
# the `predict` and `predict_proba` only once.
# This is ugly but gives a good performance boost, see #10802
# for more details.
predict_scorers = [
(name, sc) for name, sc in scorers.items()
if _is_predict(sc)]
if predict_scorers:
y_pred = estimator.predict(X_test)
for (name, scorer) in predict_scorers:
tmp_scores[name] = scorer.score_predictions(y_test, y_pred)

proba_scorers = [
(name, sc) for name, sc in scorers.items()
if _is_proba(sc)]
if proba_scorers:
y_pred = estimator.predict_proba(X_test)
for (name, scorer) in proba_scorers:
tmp_scores[name] = scorer.score_predictions(y_test, y_pred)

other_scorers = [
(name, sc) for name, sc in scorers.items()
if not (_is_proba(sc) or _is_predict(sc))]
for name, scorer in other_scorers:
if y_test is None:
score = scorer(estimator, X_test)
tmp_scores[name] = scorer(estimator, X_test)
else:
score = scorer(estimator, X_test, y_test)
tmp_scores[name] = scorer(estimator, X_test, y_test)

scores = {}
for name in scorers:
score = tmp_scores[name]
if hasattr(score, 'item'):
try:
# e.g. unwrap memmapped scalars
Expand Down