def score(self, X, y):
"""
Generates the Precision-Recall curve on the specified test data.
Returns
-------
score_ : float
Average precision, a summary of the plot as a weighted mean of
precision at each threshold, weighted by the increase in recall from
the previous threshold.
"""
# If we don't do this check, then it is possible that OneVsRestClassifier
# has not correctly been fitted for multi-class targets.
if not hasattr(self, "target_type_"):
raise NotFitted.from_estimator(self, "score")
# Must perform label binarization before calling super
if self.target_type_ == MULTICLASS:
# Use label_binarize to create multi-label output for OneVsRestClassifier
y = label_binarize(y, classes=self._target_labels)
# Call super to check if fitted and to compute classes_
# Note that self.score_ computed in super will be overridden below
super(PrecisionRecallCurve, self).score(X, y)
# Compute the prediction/threshold scores
y_scores = self._get_y_scores(X)
# Handle binary and multiclass cases to create correct data structure
if self.target_type_ == BINARY:
self.precision_, self.recall_, _ = sk_precision_recall_curve(
y, y_scores)
self.score_ = average_precision_score(y, y_scores)
else:
self.precision_, self.recall_, self.score_ = {}, {}, {}
# Compute PRCurve for all classes
for i, class_i in enumerate(self.classes_):
self.precision_[class_i], self.recall_[
class_i], _ = sk_precision_recall_curve(
y[:, i], y_scores[:, i])
self.score_[class_i] = average_precision_score(
y[:, i], y_scores[:, i])
# Compute micro average PR curve
self.precision_[MICRO], self.recall_[
MICRO], _ = sk_precision_recall_curve(y.ravel(),
y_scores.ravel())
self.score_[MICRO] = average_precision_score(y,
y_scores,
average=MICRO)
# Draw the figure
self.draw()
# Return a score between 0 and 1
if self.target_type_ == BINARY:
return self.score_
return self.score_[MICRO]
def score(self, X, y=None):
"""
Generates the Precision-Recall curve on the specified test data.
Returns
-------
score_ : float
Average precision, a summary of the plot as a weighted mean of
precision at each threshold, weighted by the increase in recall from
the previous threshold.
"""
# If we don't do this check, then it is possible that OneVsRestClassifier
# has not correctly been fitted for multi-class targets.
if not hasattr(self, "target_type_"):
raise NotFitted((
"{} cannot wrap an already fitted estimator"
).format(
self.__class__.__name__
))
# Compute the prediction/threshold scores
y_scores = self._get_y_scores(X)
# Handle binary and multiclass cases to create correct data structure
if self.target_type_ == BINARY:
self.precision_, self.recall_, _ = sk_precision_recall_curve(y, y_scores)
self.score_ = average_precision_score(y, y_scores)
else:
# Use label_binarize to create multi-label ouptut for OneVsRestClassifier
Y = label_binarize(y, classes=self.classes_)
self.precision_, self.recall_, self.score_ = {}, {}, {}
# Compute PRCurve for all classes
for i, class_i in enumerate(self.classes_):
self.precision_[class_i], self.recall_[class_i], _ = sk_precision_recall_curve(Y[:,i], y_scores[:,i])
self.score_[class_i] = average_precision_score(Y[:,i], y_scores[:,i])
# Compute micro average PR curve
self.precision_[MICRO], self.recall_[MICRO], _ = sk_precision_recall_curve(
Y.ravel(), y_scores.ravel()
)
self.score_[MICRO] = average_precision_score(Y, y_scores, average=MICRO)
# Draw the figure
self.draw()
# Return a score between 0 and 1
if self.target_type_ == BINARY:
return self.score_
return self.score_[MICRO]
def _sk_precision_recall_curve(y_true, probas_pred, num_classes=1):
""" Adjusted comparison function that can also handles multiclass """
if num_classes == 1:
return sk_precision_recall_curve(y_true, probas_pred)
precision, recall, thresholds = [], [], []
for i in range(num_classes):
y_true_temp = np.zeros_like(y_true)
y_true_temp[y_true == i] = 1
res = sk_precision_recall_curve(y_true_temp, probas_pred[:, i])
precision.append(res[0])
recall.append(res[1])
thresholds.append(res[2])
return precision, recall, thresholds