使用KFold进行训练集和验证集的拆分，使用准确率和召回率来挑选合适的阈值(threshold) 1.KFold(进行交叉验证) 2.np.logical_and(两bool数组都是正即为正) 3.np.logical_not(bool数组为正即为反，为反即为正)

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1. k_fold = KFold(n_split, shuffle) 构造KFold的索引切割器

k_fold.split(indices) 对索引进行切割。

参数说明：n_split表示切割的份数，假设切割的份数为10，那么有9份是训练集有1份是测试集，shuffle是否进行清洗，indices表示需要进行切割的索引值

import numpy as np
from sklearn.model_selection import KFold
 
indices = np.arange(20)
k_fold = KFold(n_splits=10, shuffle=False)
train_test_set = k_fold.split(indices)
for (train_set, test_set) in train_test_set:
    print(train_set)
    print(test_set)

2.np.logical_and(pred_issame, test_issame) # 如果pred_issame中的元素和test_issame都是True, 返回的也是True，否者返回的是False

参数说明：pred_issame输入的bool数组，test_issame输入的bool数组

import numpy as np
pred_issame = np.array([True, True, False, False])
actual_issame = np.array([False, True, False, False])
print(np.logical_and(pred_issame, actual_issame))
# [False  True False False]

3. np.logical_not(pred_issame)  # 将输入的True转换为False，False转换为Train

参数说明: pred_issame 表示输入的bool数组

import numpy as np
pred_issame = np.array([True, True, False, False])
print(np.logical_not(pred_issame))
# [False False  True  True]

第一步：构造indices的索引值，使用KFold对incides进行train_set和test_set的生成

第二步: 使用np.arange(0, 4, 0.4)  构造threshold的列表，循环threshold列表

第三步:

第一步: 使用np.less(dist, threshold) 来获得预测结果

第二步:

tp = np.logical_and(pred_issame, actual_issame)  # 正样本被判定为正样本

fp = np.logical_and(pre_issame, np.logical_not(actual_issame)) # 负样本被判断为正样本

tn = np.logical_and(np.logical_not(pre_issame), np.logical_not(actual_issame)) # 负样本判断为负样本

fn = np.logical_and(np.logical_not(pre_issame), actual_issame) # 正样本被判断为负样本

tpr = 0 if tp + fn == 0 else float(tp) / float(tp + fn)  # 召回率

fpr = 0 if fp + tn == 0 else float(tn) / float(fp + tn)

accur = (tp + tn) / (tp+fp+fn+tn)

第四步：使用threshold_max = np.argmax(accur) # 获得准确率最大的索引值，即为thresholds最好的索引值

def calculate_roc(thresh, dist, actual_issame):
    pre_issame = np.less(dist, thresh)
    tp = np.sum(np.logical_and(pre_issame, actual_issame)) # 正样本被预测为正样本
    fp = np.sum(np.logical_and(pre_issame, np.logical_not(actual_issame))) # 负样本被预测为正样本
    tn = np.sum(np.logical_and(np.logical_not(pre_issame), np.logical_not(actual_issame))) # 负样本被预测为负样本
    fn = np.sum(np.logical_and(np.logical_not(pre_issame),  actual_issame)) # 正样本被预测为负样本
 
    tpr = 0 if tp + tn == 0 else float(tp) / float(tp + fn)
    fpr = 0 if tp + fn == 0 else float(tn) / float(fp + tn)
    accur = ((tp + tn) / dist.size)
    return tpr, fpr, accur
#
import numpy as np
from sklearn.model_selection import KFold
distance = np.array([0.1, 0.2, 0.3, 0.25, 0.33, 0.20, 0.18, 0.24])
actual_issame = np.array([True, True, False, False, False, True, True, False])
k_fold = KFold(n_splits=4, shuffle=False)
indices = np.arange(len(distance))
for k_num, (train_set, test_set) in enumerate(k_fold.split(indices)):
    thresholds = np.arange(0, 1, 0.04)
    accuracy = np.zeros(len(thresholds))
    for threshold_index, threshold in enumerate(thresholds):
        _, _, accuracy[threshold_index] = calculate_roc(threshold, distance[train_set], actual_issame[train_set])
 
    max_threshold = np.argmax(accuracy)
    print(thresholds[max_threshold])

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