# -*- coding: utf-8 -*-
from __future__ import absolute_import, division
from functools import wraps
from sklearn_crfsuite.utils import flatten
def _flattens_y(func):
@wraps(func)
def wrapper(y_true, y_pred, *args, **kwargs):
y_true_flat = flatten(y_true)
y_pred_flat = flatten(y_pred)
return func(y_true_flat, y_pred_flat, *args, **kwargs)
return wrapper
[docs]@_flattens_y
def flat_accuracy_score(y_true, y_pred):
"""
Return accuracy score for sequence items.
"""
from sklearn import metrics
return metrics.accuracy_score(y_true, y_pred)
[docs]@_flattens_y
def flat_precision_score(y_true, y_pred, **kwargs):
"""
Return precision score for sequence items.
"""
from sklearn import metrics
return metrics.precision_score(y_true, y_pred, **kwargs)
[docs]@_flattens_y
def flat_recall_score(y_true, y_pred, **kwargs):
"""
Return recall score for sequence items.
"""
from sklearn import metrics
return metrics.recall_score(y_true, y_pred, **kwargs)
[docs]@_flattens_y
def flat_f1_score(y_true, y_pred, **kwargs):
"""
Return F1 score for sequence items.
"""
from sklearn import metrics
return metrics.f1_score(y_true, y_pred, **kwargs)
[docs]@_flattens_y
def flat_fbeta_score(y_true, y_pred, beta, **kwargs):
"""
Return F-beta score for sequence items.
"""
from sklearn import metrics
return metrics.fbeta_score(y_true, y_pred, beta, **kwargs)
[docs]@_flattens_y
def flat_classification_report(y_true, y_pred, labels=None, **kwargs):
"""
Return classification report for sequence items.
"""
from sklearn import metrics
return metrics.classification_report(y_true, y_pred, labels, **kwargs)
[docs]def sequence_accuracy_score(y_true, y_pred):
"""
Return sequence accuracy score. Match is counted only when two sequences
are equal.
"""
total = len(y_true)
if not total:
return 0
matches = sum(1 for yseq_true, yseq_pred in zip(y_true, y_pred)
if yseq_true == yseq_pred)
return matches / total