Multi-label to achieve the precision and recall rate

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
    @File : recall.py
    @Author : 郭凯锋
    @Time : 2020/1/12 17:57
    @Software : PyCharm
    @Git-Hub : daguonice
    @博客园: https://www.cnblogs.com/daguonice/
    
"""
import pandas as pd
import numpy as np
from sklearn.metrics import recall_score


def right_or_wrong(ypred_ele, ytrue_ele):
    if ypred_ele in ytrue_ele:
        return True
    else:
        return False


def get_single_score(y_true, y_pred):
    TP = np.sum(np.logical_and(np.equal(y_true, 1), np.equal(y_pred, 1)))
    FP = np.sum(np.logical_and(np.equal(y_true, 0), np.equal(y_pred, 1)))
    TN = np.sum(np.logical_and(np.equal(y_true, 1), np.equal(y_pred, 0)))
    FN = np.sum(np.logical_and(np.equal(y_true, 0), np.equal(y_pred, 0)))
    recall = [TP, (TP + FN)]
    accuracy = [(TP + TN), len(y_pred)]
    return accuracy, recall


def get_multi_score(y_true, y_pred):
    list_score = []
    for ele in ['相互宝', '健康险', '保险线', '花呗']:
        y_true_temp = np.zeros(len(y_true))
        y_pred_temp = np.zeros(len(y_pred))
        for idx in range(len(y_true)):
            if y_pred[idx] == ele:
                y_pred_temp[idx] = 1.
            if right_or_wrong(ele, y_true[idx]):
                y_true_temp[idx] = 1.
        accuracy, recall = get_single_score (y_true_temp, y_pred_temp) # becomes percentile 
        list_score.append ([Accuracy, Recall]) 
    return list_score # returns a list of three-dimensional, are mutually Po, fractional precision and recall of the health insurance, insurance lines 


fraction2decimal DEF (Result): 
    RES = [] 
    for ELE in Result: 
        list_ele = [] 
        for Lab in ELE: 
            list_lab = [] 
            for Score in Lab: 
                IF Score [. 1] == 0: 
                    list_lab.append (0.0) 
                the else: 
                    list_lab.append (round (Score [0] / Score [. 1], 2)) 
            list_ele.append (list_lab) 
        res.append (list_ele) 
    return RES 


DEF FUNC (y_true, y_pred):
    # y_true是一个一维列表， y_pred是一个二维列表
    if len(y_true) != len(y_pred):
        raise Exception("The two input lengths are inconsistent")
    length = len(y_true)
    list_score = get_multi_score(y_true, y_pred)
    result = []
    temp_list = list_score
    for idx in range(length):
        list_score = temp_list
        if len(y_true[idx]) == 1 and y_pred[idx] not in y_true[idx]:
            list_score[1][1][1] += 1
            list_score[0][0][1] += 1
            result.append(list_score)
        elif len(y_true[idx]) == 2 and y_pred[idx] in y_true[idx]:
            list_score[0][0][0] += 1
            list_score[0][0][1] += 1
            list_score [0] [. 1] [0] + =. 1 
            list_score [0] [. 1] [. 1] + =. 1 
            result.append (list_score) 
        elif len (y_true [IDX]) == 2 and y_pred [IDX] Not in y_true [IDX]: 
            list_score [. 1] [. 1] [. 1] + =. 1 
            list_score [2] [. 1] [. 1] + =. 1 
            list_score [0] [0] [. 1] + =. 1 
            result.append (list_score) 
        the else: 
            result.append (list_score) 
    Result = fraction2decimal (Result) 
    return Result 


IF the __name__ == '__main__': 
    alist = [ 'are Po', 'are Po', 'are Po', 'health insurance' 'line of insurance '' flower chanting '] 
    blist = [' health insurance ', [' health insurance ',' mutual Po '], [' health insurance ',' safety line '], [' mutual Po '], [' Insurance line '],[ 'Flower chanting']] 
    RES = FUNC (for blist, alist)
    # Res = pd.DataFrame (res, columns = [ ' mutual treasure', 'health insurance', 'safety line', 'flower chanting']) 
    # pd.set_option ( 'display.max_columns', None) 
    # pd.set_option ( 'display.max_rows', None) 
    # pd.set_option ( 'display.width', 100000) 
    # pd.set_option ( 'display.unicode.east_asian_width', True) 
    Print () 
    Print (RES)