# -*- coding: utf-8 -*-

"""

Created on Fri Mar 16 12:01:04 2018

@author: User

"""

#相关指标计算

#%% 绘制ROC曲线

from sklearn.metrics import roc_curve

fpr, tpr, thresholds = roc_curve(y_true, pred, pos_label=2)

#%% 计算precision，recall和f1-score

from sklearn.metrics import precision_score, recall_score, f1_score

#数据是list类型

y_true = [0, 1, 1, 0, 1, 0]

y_pred = [1, 1, 1, 0, 0, 1]

p = precision_score(y_true, y_pred, average='binary')

r = recall_score(y_true, y_pred, average='binary')

f1score = f1_score(y_true, y_pred, average='binary')

#数据是ndarray类型

import numpy as np

y_true = np.array([[0, 1, 1],

[0, 1, 0]])

y_pred = np.array([[1, 1, 1],

[0, 0, 1]])

y_true = np.reshape(y_true, [-1])

y_pred = np.reshape(y_pred, [-1])

p = precision_score(y_true, y_pred, average='binary')

r = recall_score(y_true, y_pred, average='binary')

f1score = f1_score(y_true, y_pred, average='binary')

#%% 计算均方差

from sklearn.metrics import mean_squared_error

from math import sqrt

err = sqrt(mean_squared_error(y_true, y_pred))

#%% metircs包括的函数

from sklearn import metrics

dir(metrics)

'''

'accuracy_score',

'auc',

'average_precision_score',

'cluster',

'coverage_error',

'euclidean_distances',

'explained_variance_score',

'f1_score',

'jaccard_similarity_score',

'label_ranking_average_precision_score',

'label_ranking_loss',

'make_scorer',

'matthews_corrcoef',

'mean_absolute_error',

'mean_squared_error',

'mean_squared_log_error',

'median_absolute_error',

'mutual_info_score',

'normalized_mutual_info_score',

'pairwise',

'pairwise_distances',

'pairwise_distances_argmin',

'pairwise_distances_argmin_min',

'precision_recall_curve',

'precision_recall_fscore_support',

'precision_score',

'ranking',

'recall_score',

'regression',

'roc_auc_score',

'roc_curve'

'''