From 7ccde55b83f5fca18ce7229a8c9745769c0c56c5 Mon Sep 17 00:00:00 2001
From: itlubber <1830611168@qq.com>
Date: Fri, 10 May 2024 16:37:22 +0800
Subject: [PATCH] fix DecisionTreeRuleExtractor methods

---
 ...\347\225\245\346\214\226\346\216\230.xlsx" |  Bin 118229 -> 162476 bytes
 examples/rule_test.py                         | 1805 +++++++++++++++++
 requirements.txt                              |    5 +-
 scorecardpipeline/feature_engineering.py      |   94 +
 scorecardpipeline/feature_selection.py        |    9 +
 scorecardpipeline/rule_extraction.py          |   98 +-
 scorecardpipeline/scorecard.py                |    3 -
 scorecardpipeline/template.xlsx               |  Bin 8418 -> 8433 bytes
 8 files changed, 1949 insertions(+), 65 deletions(-)
 create mode 100644 examples/rule_test.py
 create mode 100644 scorecardpipeline/feature_engineering.py
 create mode 100644 scorecardpipeline/feature_selection.py

diff --git "a/examples/model_report/\345\206\263\347\255\226\346\240\221\347\273\204\345\220\210\347\255\226\347\225\245\346\214\226\346\216\230.xlsx" "b/examples/model_report/\345\206\263\347\255\226\346\240\221\347\273\204\345\220\210\347\255\226\347\225\245\346\214\226\346\216\230.xlsx"
index 0b51582dfc625b161e91bc56bc3c25ceba3dc038..51f2a642ecf1d15c276aecf50f5872e637b8b1d7 100644
GIT binary patch
delta 160383
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diff --git a/examples/rule_test.py b/examples/rule_test.py
new file mode 100644
index 0000000..6a0ef54
--- /dev/null
+++ b/examples/rule_test.py
@@ -0,0 +1,1805 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2024/4/18 10:15
+@Author  : itlubber
+@Site    : itlubber.art
+"""
+import copy
+import datetime
+import numpy as np
+import pandas as pd
+
+min_num = 40
+hit_num = 30
+sub_div_bin = 0.1
+sample_type_target = {'Total': ['fpd_30_act', 'mob3_dpd_30_act']}
+target_ripe = {'fpd_30_act': ['agr_fpd_30'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30']}
+target_del_col = {'fpd_30_act': ['agr_fpd_30', 'agr_mob3_dpd_30', 'mob3_dpd_30_act'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30', 'agr_fpd_30', 'fpd_30_act']}
+target_min_rate = {'fpd_30_act': [0.015], 'mob3_dpd_30_act': [0.02]}
+sample_type_lift = {'Total': {'fpd_30_act': 1.8, 'mob3_dpd_30_act': 3}}
+
+
+def cv(x):
+    """计算一组数的离散系数"""
+    m = np.mean(x)
+    if m == 0:
+        return None
+    else:
+        return np.std(x) / np.mean(x)
+
+
+def cv_type(x):
+    """基于离散系数判断数据稳定性"""
+    if x < 0.15:
+        return '非常稳定'
+    elif x < 0.4:
+        return '相对稳定'
+    elif x < 0.75:
+        return '不稳定'
+    else:
+        return '极不稳定'
+
+
+def get_trend(data, method=np.mean, key='', flag=['高', '低']):
+    """判断数据的趋势
+
+    :param data: 需要判断的数据,list类型或者dataframe类型
+    :param flag: ['高','低'] 或者 ['快','慢']
+    :param method: 次数对应求和,速度对应平均
+    :param key:默认为None,如果传入数据为dataframe,则需要指定key,但是必须从小到大排好顺序
+
+    :return: 不同趋势
+    """
+    if len(data) == 0: return '无数据'
+
+    if isinstance(data, pd.DataFrame):
+        if key != None:
+            data = list(data[data[key].notnull()][key])
+        else:
+            raise '没有指定需要判断趋势的列名'
+
+    if len(data) == 0:
+        return '无数据'
+    elif len(data) == 1:
+        return '无趋势'
+    elif len(data) > 4:
+        n1 = len(data) // 4
+        n2 = len(data) % 4
+        k1 = n1
+        k2 = 2 * n1 + (1 if n2 >= 3 else 0)
+        k3 = k2 + n1 + (1 if n2 >= 2 else 0)
+        x = [method(data[0:k1]), method(data[k1:k2]), method(data[k2:k3]), method(data[k3:])]
+    else:
+        x = data
+
+    cmax = max(x)
+    cmin = min(x)
+    cv = np.std(x) / np.mean(x) if np.mean(x) != 0 else 0
+
+    if cv < 0.15:
+        return '平稳'
+    elif len(x) == 2:
+        if cmax == x[0]:
+            return '%s > %s' % (flag[0], flag[1])
+        else:
+            return '%s > %s' % (flag[1], flag[0])
+    elif len(x) == 3:
+        if cmax == x[1]:
+            return '%s > %s > %s' % (flag[1], flag[0], flag[1])
+        elif cmin == x[1]:
+            return '%s > %s > %s' % (flag[0], flag[1], flag[0])
+        elif cmax == x[0] and cmin == x[2]:
+            return '%s > %s' % (flag[0], flag[1])
+        else:
+            return '%s > %s' % (flag[1], flag[0])
+    else:
+        if cmax == x[1] or cmax == x[2]:
+            return '%s > %s > %s' % (flag[1], flag[0], flag[1])
+        elif cmin == x[1] or cmin == x[2]:
+            return '%s > %s > %s' % (flag[0], flag[1], flag[0])
+        elif cmax == x[0] and cmin == x[3]:
+            return '%s > %s' % (flag[0], flag[1])
+        else:
+            return '%s > %s' % (flag[1], flag[0])
+
+
+def calculate_var_lift(data, data_sub, hit_flag, target, cut_point, direction):
+    """计算lift
+
+    data: 全量样本
+    data_sub: 满足条件的样本
+    hit_flag: 触碰标签
+    target: string 需要计算lift的列名
+    """
+    if '>' in direction:
+        data_hit = data_sub[data_sub[hit_flag] > cut_point] if len(data_sub) > 0 else data_sub
+        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan
+        hit_bad_rate = bad / len(data_hit) if len(data_hit) > 0 else np.nan
+    else:
+        data_hit = data_sub[data_sub[hit_flag] <= cut_point] if len(data_sub) > 0 else data_sub
+        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan
+        hit_bad_rate = bad / len(data_hit) if len(data_hit) > 0 else np.nan
+    bad = len(data[data[target] == 1])
+    allhit_bad_rate = bad / len(data) if len(data) > 0 else np.nan
+    return hit_bad_rate / allhit_bad_rate if allhit_bad_rate > 0 else np.nan
+
+
+def calculate_var_odds(data, data_sub, hit_flag, target, cut_point, direction):
+    """计算odds ratio
+    
+    data: DataFrame 成熟的样本子集的补集(未触碰所有样本)
+    data_sub : 成熟的样本子集（如某个省份的数据集）
+    hit_flag: 触碰标签
+    target: string 需要计算odds ratio的列名
+    """
+    if '>' in direction:
+        data_hit = data_sub[data_sub[hit_flag] > cut_point] if len(data_sub) > 0 else data_sub
+        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan
+        good = len(data_hit[data_hit[target] != 1]) if len(data_hit) > 0 else np.nan
+        hit_bad_good_rate = bad / good if good > 0 else np.nan
+        # data_nohit = data[data[hit_flag] <= cut_point]
+        bad_nohit = len(data[data[target] == 1])
+        good_nohit = len(data[data[target] != 1])
+        nohit_bad_good_rate = bad_nohit / good_nohit if good_nohit > 0 else np.nan
+    else:
+        data_hit = data_sub[data_sub[hit_flag] <= cut_point] if len(data_sub) > 0 else data_sub
+        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan
+        good = len(data_hit[data_hit[target] != 1]) if len(data_hit) > 0 else np.nan
+        hit_bad_good_rate = bad / good if good > 0 else np.nan
+        # data_nohit = data[data[hit_flag] > cut_point]
+        bad_nohit = len(data[data[target] == 1])
+        good_nohit = len(data[data[target] != 1])
+        nohit_bad_good_rate = bad_nohit / good_nohit if good_nohit > 0 else np.nan
+    return hit_bad_good_rate / nohit_bad_good_rate if nohit_bad_good_rate > 0 else np.nan
+
+
+# 1.7按月对规则进行泛化，分析规则触碰情况和风险表现情况
+def get_month_odds(data, rule_name, rule_type, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_mth, mth, direction, var):
+    """
+    :param data: 数据框
+    :param rule_name: 规则名
+    :param rule_type: 规则类型
+    :param cut_point: 切点，cut-off
+    :param target: 目标字段
+    :param rule_limit: 泛化样本类型
+    :param rule_all: 线上已有规则触碰情况对应的字段列名
+    :param use_credit_flag: 是否用信列
+    :param circle_mth: 申请月对应的列
+    :param mth: 要泛化的具体月
+    :param direction: 变量取值方向
+    :param var: 泛化变量名
+    :return:  按月泛化分析结果
+    """
+    data = copy.deepcopy(data)
+    data = data[data[circle_mth] == mth]
+    dic = {}
+    dic['报告日期'] = datetime.datetime.now().strftime('%Y-%m-%d')
+    if rule_limit == 'Total':
+        len_prod = len(data['product_name'].unique())
+        if len_prod >= 3:
+            dic['产品名称'] = str(data['product_name'].values[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data['product_name'].unique()[0]) + '&' + str(data['product_name'].unique()[1])
+        else:
+            dic['产品名称'] = str(data['product_name'].unique()[0])
+    elif rule_limit == '其他':
+        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]['product_name'].unique()
+        len_prod = len(data_prod)
+        if len_prod >= 3:
+            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])
+        else:
+            dic['产品名称'] = str(data_prod[0])
+    elif rule_limit == sample_type_col[rule_limit][1][0]:
+        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]['product_name'].unique()
+        len_prod = len(data_prod)
+        if len_prod >= 3:
+            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])
+        else:
+            dic['产品名称'] = str(data_prod[0])
+    dic['样本类型'] = rule_limit
+    dic['风险类型'] = '短期风险' if target == 'fpd_30_act' else '中长期风险'
+    dic['目标字段'] = target
+    dic['规则名称'] = rule_name
+    dic['规则类型'] = rule_type
+    dic['申请月'] = mth
+    dic['申请量'] = len(data)
+    dic['通过量'] = len(data[data[rule_all] == 0])
+    dic['通过率'] = dic['通过量'] / dic['申请量'] if dic['申请量'] > 0 else None
+    dic['用信量'] = len(data[data[use_credit_flag] == 1])
+    dic['用信率'] = dic['用信量'] / dic['通过量'] if dic['通过量'] > 0 else None
+    dic['置前触碰量'] = len(data[data[rule_all] == 1])
+    ## 统计逾期成熟样本量
+    tmp15 = data[data['agr_fpd_15'] == 1]
+    tmp = data[data[target_ripe[target][0]] == 1]
+    if '>' in direction:
+        if rule_limit == 'Total':
+            dic['策略触碰量'] = len(data[data[var] > cut_point])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point)])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point)])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] > cut_point)])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['整体成熟量'] = len(tmp)
+            dic['额外触碰成熟量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[var] > cut_point)])  # len(tmp[tmp[var] > cut_point])
+            dic['整体成熟坏样本量'] = len(tmp[tmp[target] == 1])
+            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] > cut_point)])
+            dic['fpd15整体成熟量'] = len(tmp15)
+            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point)])
+            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])
+            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) & (tmp15['fpd_15_act'] == 1)])
+            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None
+            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None
+            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None
+            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None
+            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and
+                                                                                                            dic['整体逾期率'] != 0) else None
+            tmp_sub = tmp
+            tmp_nohit = tmp[(tmp[var] <= cut_point)]
+            tmp_sub15 = tmp15
+            tmp_nohit15 = tmp15[tmp15[var] <= cut_point]
+            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == '其他':
+            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['整体成熟量'] = len(tmp)
+            dic['额外触碰成熟量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])
+            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟量'] = len(tmp15)
+            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])
+            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) &
+                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])
+            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None
+            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None
+            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None
+            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None
+            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and
+                                                                                                            dic['整体逾期率'] != 0) else None
+            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]
+            tmp_nohit = tmp[(((tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |
+                             (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]
+            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]
+            tmp_nohit15 = tmp15[(((tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |
+                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]
+            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['整体成熟量'] = len(tmp)
+            dic['额外触碰成熟量'] = len(tmp[(tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])
+            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟量'] = len(tmp15)
+            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])
+            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) &
+                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])
+            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None
+            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None
+            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None
+            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None
+            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and
+                                                                                                            dic['整体逾期率'] != 0) else None
+            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]
+            tmp_nohit = tmp[(((tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) | (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]
+            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]
+            tmp_nohit15 = tmp15[(((tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) |
+                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]
+            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+    else:
+        if rule_limit == 'Total':
+            dic['策略触碰量'] = len(data[data[var] <= cut_point])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point)])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point)])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] <= cut_point)])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['整体成熟量'] = len(tmp)
+            dic['额外触碰成熟量'] = len(tmp[tmp[var] <= cut_point])
+            dic['整体成熟坏样本量'] = len(tmp[tmp[target] == 1])
+            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] <= cut_point)])
+            dic['fpd15整体成熟量'] = len(tmp15)
+            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point)])
+            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])
+            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) & (tmp15['fpd_15_act'] == 1)])
+            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None
+            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None
+            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None
+            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None
+            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and
+                                                                                                            dic['整体逾期率'] != 0) else None
+            tmp_sub = tmp
+            tmp_nohit = tmp[(tmp[var] > cut_point)]
+            tmp_sub15 = tmp15
+            tmp_nohit15 = tmp15[tmp15[var] > cut_point]
+            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == '其他':
+            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['整体成熟量'] = len(tmp)
+            dic['额外触碰成熟量'] = len(tmp[(tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])
+            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[target] == 1) & (tmp[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟量'] = len(tmp15)
+            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])
+            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) &
+                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])
+            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None
+            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None
+            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None
+            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None
+            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and
+                                                                                                            dic['整体逾期率'] != 0) else None
+            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]
+            tmp_nohit = tmp[(((tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |
+                             (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]
+            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]
+            tmp_nohit15 = tmp15[(((tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |
+                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]
+            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['整体成熟量'] = len(tmp)
+            dic['额外触碰成熟量'] = len(tmp[(tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])
+            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[target] == 1) & (tmp[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟量'] = len(tmp15)
+            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])
+            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) &
+                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])
+            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None
+            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None
+            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None
+            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None
+            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and
+                                                                                                            dic['整体逾期率'] != 0) else None
+            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]
+            tmp_nohit = tmp[(((tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) |
+                             (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]
+            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]
+            tmp_nohit15 = tmp15[(((tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) |
+                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]
+            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+    return df_ls
+
+
+# 1.8按周对规则进行泛化，分析规则触碰情况
+def get_weeks_hit(data, rule_name, rule_type, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_week, week, direction, var):
+    """
+    :param data:  数据框
+    :param rule_name: 规则名
+    :param rule_type: 规则类型
+    :param cut_point: 切点，cut-off
+    :param target: 目标字段
+    :param rule_limit: 泛化样本类型
+    :param rule_all: 线上已有规则触碰情况对应的字段列名
+    :param use_credit_flag: 是否用信列
+    :param circle_week: 申请周对应的列
+    :param week: 要泛化的具体周
+    :param direction: 变量取值方向
+    :param var: 泛化变量名
+    :return:  按周泛化分析结果
+    """
+    data = copy.deepcopy(data)
+    data = data[data[circle_week] == week]
+    dic = {}
+    dic['报告日期'] = datetime.datetime.now().strftime('%Y-%m-%d')
+    if rule_limit == 'Total':
+        len_prod = len(data['product_name'].unique())
+        if len_prod >= 3:
+            dic['产品名称'] = str(data['product_name'].values[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data['product_name'].unique()[0]) + '&' + str(data['product_name'].unique()[1])
+        else:
+            dic['产品名称'] = str(data['product_name'].unique()[0])
+    elif rule_limit == '其他':
+        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]['product_name'].unique()
+        len_prod = len(data_prod)
+        if len_prod >= 3:
+            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])
+        else:
+            dic['产品名称'] = str(data_prod[0])
+    elif rule_limit == sample_type_col[rule_limit][1][0]:
+        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]['product_name'].unique()
+        len_prod = len(data_prod)
+        if len_prod >= 3:
+            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])
+        else:
+            dic['产品名称'] = str(data_prod[0])
+    dic['样本类型'] = rule_limit
+    dic['风险类型'] = '短期风险' if target == 'fpd_30_act' else '中长期风险'
+    dic['目标字段'] = target
+    dic['规则名称'] = rule_name;
+    dic['规则类型'] = rule_type;
+    dic['申请周'] = week;
+    dic['申请量'] = len(data);
+    dic['通过量'] = len(data[data[rule_all] == 0])
+    dic['通过率'] = dic['通过量'] / dic['申请量'] if dic['申请量'] > 0 else None
+    dic['用信量'] = len(data[data[use_credit_flag] == 1])
+    dic['用信率'] = dic['用信量'] / dic['通过量'] if dic['通过量'] > 0 else None
+    dic['置前触碰量'] = len(data[data[rule_all] == 1])
+    if '>' in direction:
+        if rule_limit == 'Total':
+            dic['策略触碰量'] = len(data[data[var] > cut_point])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point)])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point)])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] > cut_point)])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == '其他':
+            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+    else:
+        if rule_limit == 'Total':
+            dic['策略触碰量'] = len(data[data[var] <= cut_point])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point)])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point)])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] <= cut_point)])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == '其他':
+            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+    return df_ls
+
+
+# 1.9按日对规则进行泛化，分析规则触碰情况
+def get_days_hit(data, rule_name, rule_type, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_day, day, direction, var):
+    """
+    :param data:  数据框
+    :param rule_name: 规则名
+    :param rule_type: 规则类型
+    :param cut_point: 切点，cut-off
+    :param target: 目标字段
+    :param rule_limit: 泛化样本类型
+    :param rule_all: 线上已有规则触碰情况对应的字段列名
+    :param use_credit_flag: 是否用信列
+    :param circle_day: 申请日对应的列
+    :param day: 要泛化的具体日
+    :param direction: 变量取值方向
+    :param var: 泛化变量名
+    :return:  按日泛化分析结果
+    """
+    data = copy.deepcopy(data)
+    data = data[data[circle_day] == day]
+    dic = {}
+    dic['报告日期'] = datetime.datetime.now().strftime('%Y-%m-%d')
+    if rule_limit == 'Total':
+        len_prod = len(data['product_name'].unique())
+        if len_prod >= 3:
+            dic['产品名称'] = str(data['product_name'].values[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data['product_name'].unique()[0]) + '&' + str(data['product_name'].unique()[1])
+        else:
+            dic['产品名称'] = str(data['product_name'].unique()[0])
+    elif rule_limit == '其他':
+        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]['product_name'].unique()
+        len_prod = len(data_prod)
+        if len_prod >= 3:
+            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])
+        else:
+            dic['产品名称'] = str(data_prod[0])
+    elif rule_limit == sample_type_col[rule_limit][1][0]:
+        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]['product_name'].unique()
+        len_prod = len(data_prod)
+        if len_prod >= 3:
+            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'
+        elif len_prod >= 2:
+            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])
+        else:
+            dic['产品名称'] = str(data_prod[0])
+    dic['样本类型'] = rule_limit
+    dic['风险类型'] = '短期风险' if target == 'fpd_30_act' else '中长期风险'
+    dic['目标字段'] = target
+    dic['规则名称'] = rule_name;
+    dic['规则类型'] = rule_type;
+    dic['申请日'] = day;
+    dic['申请量'] = len(data);
+    dic['通过量'] = len(data[data[rule_all] == 0])
+    dic['通过率'] = dic['通过量'] / dic['申请量'] if dic['申请量'] > 0 else None
+    dic['用信量'] = len(data[data[use_credit_flag] == 1])
+    dic['用信率'] = dic['用信量'] / dic['通过量'] if dic['通过量'] > 0 else None
+    dic['置前触碰量'] = len(data[data[rule_all] == 1])
+    if '>' in direction:
+        if rule_limit == 'Total':
+            dic['策略触碰量'] = len(data[data[var] > cut_point])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point)])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point)])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] > cut_point)])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == '其他':
+            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+    else:
+        if rule_limit == 'Total':
+            dic['策略触碰量'] = len(data[data[var] <= cut_point])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point)])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point)])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] <= cut_point)])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == '其他':
+            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])
+            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])
+            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None
+            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None
+            df_ls = pd.DataFrame.from_dict(dic, orient='index').T
+    return df_ls
+
+
+# 1.10 按月泛化结果汇总
+def get_mths_result(data, rule_name, rule_type, var, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_mth, direction, base_lift, subset_total):
+    """
+    :param data:  数据框
+    :param rule_name: 规则名
+    :param rule_type: 规则类型
+    :param var: 泛化变量名
+    :param cut_point: 切点，cut-off
+    :param target: 目标字段
+    :param rule_limit: 泛化样本类型
+    :param rule_all: 线上已有规则触碰情况对应的字段列名
+    :param use_credit_flag: 是否用信列
+    :param circle_mth: 申请月对应的列
+    :param direction: 变量取值方向
+    :param base_lift: 临界Lift
+    :param subset_total: 是否获取每个子集作为计算整体
+    :return:  所有泛化月泛化结果汇总
+    """
+    if subset_total:  ##获取每个计算整体
+        if rule_limit == 'Total':
+            data = data
+        elif rule_limit == '其他':
+            data = data[
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]
+    mths = sorted(set(data[circle_mth]))
+    df_contra_monthly = get_month_odds(data=data, rule_name=rule_name, rule_type=rule_type,
+                                       cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag, direction=direction,
+                                       circle_mth=circle_mth, mth=mths[0], var=var)
+
+    for mth in mths[1:]:
+        df2_contra_monthly = get_month_odds(data=data, rule_name=rule_name, rule_type=rule_type,
+                                            cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag, direction=direction,
+                                            circle_mth=circle_mth, mth=mth, var=var)
+
+        df_contra_monthly = pd.concat([df_contra_monthly, df2_contra_monthly])
+    df_contra_monthly['额外触碰Lift大于目标值的月份占比'] = sum(df_contra_monthly['额外触碰Lift'] > base_lift) / sum(
+        df_contra_monthly['额外触碰Lift'].notnull()) if sum(df_contra_monthly['额外触碰Lift'].notnull()) > 0 else None
+    df_contra_monthly['置前触碰率离散度'] = cv_type(cv(df_contra_monthly['置前触碰率']) if df_contra_monthly['置前触碰率'].sum() > 0 else 0)
+    df_contra_monthly['策略触碰率离散度'] = cv_type(cv(df_contra_monthly['策略触碰率']) if df_contra_monthly['策略触碰率'].sum() > 0 else 0)
+    df_contra_monthly['重复触碰率离散度'] = cv_type(cv(df_contra_monthly['重复触碰率']) if df_contra_monthly['重复触碰率'].sum() > 0 else 0)
+    df_contra_monthly['额外触碰率离散度'] = cv_type(cv(df_contra_monthly['额外触碰率']) if df_contra_monthly['额外触碰率'].sum() > 0 else 0)
+    df_contra_monthly['置后触碰率离散度'] = cv_type(cv(df_contra_monthly['置后触碰率']) if df_contra_monthly['置后触碰率'].sum() > 0 else 0)
+    try:
+        df_contra_monthly['评估结论'] = '建议上线' if (df_contra_monthly['额外触碰Lift大于目标值的月份占比'].values[0] >= 0.8) \
+                                                      & ((df_contra_monthly['额外触碰率离散度'].values[0] == '非常稳定') | (df_contra_monthly['策略触碰率离散度'].values[0] == '相对稳定')) else '不建议上线'
+    except:
+        df_contra_monthly['评估结论'] = '不建议上线'
+    return df_contra_monthly
+
+
+# 1.11 按周泛化结果汇总
+def get_weeks_result(data, rule_name, rule_type, var, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_week, direction, subset_total):
+    """
+    :param data:  数据框
+    :param rule_name: 规则名
+    :param rule_type: 规则类型
+    :param var: 泛化变量名
+    :param cut_point: 切点，cut-off
+    :param target: 目标字段
+    :param rule_limit: 泛化样本类型
+    :param rule_all: 线上已有规则触碰情况对应的字段列名
+    :param use_credit_flag: 是否用信列
+    :param circle_week: 申请周对应的列
+    :param direction: 变量取值方向
+    :param subset_total: 是否获取每个子集作为计算整体
+    :return:  所有泛化周泛化结果汇总
+    """
+    if subset_total:  ##获取每个计算整体
+        if rule_limit == 'Total':
+            data = data
+        elif rule_limit == '其他':
+            data = data[
+                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]
+    td = datetime.datetime.now().strftime('%Y-%m-%d')
+    max_week = data[circle_week].max()
+    if td <= max_week:
+        weeks = sorted(set(data[circle_week]))[-11:-1]
+        data = data[data[circle_week].map(lambda x: x in weeks)]
+    else:
+        weeks = sorted(set(data[circle_week]))[-10:]
+        data = data[data[circle_week].map(lambda x: x in weeks)]
+    df_monthly = get_weeks_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,
+                               use_credit_flag=use_credit_flag, direction=direction, circle_week=circle_week, week=weeks[0], var=var)
+
+    for week in weeks[1:]:
+        df2_monthly = get_weeks_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,
+                                    use_credit_flag=use_credit_flag, direction=direction, circle_week=circle_week, week=week, var=var)
+
+        df_monthly = pd.concat([df_monthly, df2_monthly])
+    df_monthly['置前触碰率离散度'] = cv_type(cv(df_monthly['置前触碰率']) if df_monthly['置前触碰率'].sum() > 0 else 0)
+    df_monthly['策略触碰率离散度'] = cv_type(cv(df_monthly['策略触碰率']) if df_monthly['策略触碰率'].sum() > 0 else 0)
+    df_monthly['重复触碰率离散度'] = cv_type(cv(df_monthly['重复触碰率']) if df_monthly['重复触碰率'].sum() > 0 else 0)
+    df_monthly['额外触碰率离散度'] = cv_type(cv(df_monthly['额外触碰率']) if df_monthly['额外触碰率'].sum() > 0 else 0)
+    df_monthly['置后触碰率离散度'] = cv_type(cv(df_monthly['置后触碰率']) if df_monthly['置后触碰率'].sum() > 0 else 0)
+    return df_monthly
+
+
+# 1.12 按日泛化结果汇总
+def get_days_result(data, rule_name, rule_type, var, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_day, direction, subset_total):
+    """
+    :param data:  数据框
+    :param rule_name: 规则名
+    :param rule_type: 规则类型
+    :param var: 泛化变量名
+    :param cut_point: 切点，cut-off
+    :param target: 目标字段
+    :param rule_limit: 泛化样本类型
+    :param rule_all: 线上已有规则触碰情况对应的字段列名
+    :param use_credit_flag: 是否用信列
+    :param circle_day: 申请日对应的列
+    :param direction: 变量取值方向
+    :param subset_total: 是否获取每个子集作为计算整体
+    :return:  所有泛化日泛化结果汇总
+    """
+    if subset_total:  ##获取每个计算整体
+        if rule_limit == 'Total':
+            data = data
+        elif rule_limit == '其他':
+            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]
+        elif rule_limit == sample_type_col[rule_limit][1][0]:
+            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]
+    days = sorted(set(data[circle_day]))[-10:]
+    data = data[data[circle_day].map(lambda x: x in days)]
+    df_monthly = get_days_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,
+                              use_credit_flag=use_credit_flag, direction=direction, circle_day=circle_day, day=days[0], var=var)
+
+    for day in days[1:]:
+        df2_monthly = get_days_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,
+                                   use_credit_flag=use_credit_flag, direction=direction, circle_day=circle_day, day=day, var=var)
+
+        df_monthly = pd.concat([df_monthly, df2_monthly])
+    df_monthly['置前触碰率离散度'] = cv_type(cv(df_monthly['置前触碰率']) if df_monthly['置前触碰率'].sum() > 0 else 0)
+    df_monthly['策略触碰率离散度'] = cv_type(cv(df_monthly['策略触碰率']) if df_monthly['策略触碰率'].sum() > 0 else 0)
+    df_monthly['重复触碰率离散度'] = cv_type(cv(df_monthly['重复触碰率']) if df_monthly['重复触碰率'].sum() > 0 else 0)
+    df_monthly['额外触碰率离散度'] = cv_type(cv(df_monthly['额外触碰率']) if df_monthly['额外触碰率'].sum() > 0 else 0)
+    df_monthly['置后触碰率离散度'] = cv_type(cv(df_monthly['置后触碰率']) if df_monthly['置后触碰率'].sum() > 0 else 0)
+    return df_monthly
+
+
+def rule_combine_results(data, rules_dict, rule_all, use_credit_flag, circle_mth, circle_week, circle_day, base_lift, subset_total):
+    """基于数据字典泛化结果汇总并输出
+
+    :param data:  需要分析的数据
+    :param rules_dict:  阈值测算输出的规则字典
+    :param rule_all:  已有所有规则汇总后的字段名称
+    :param use_credit_flag: 是否用信列
+    :param circle_mth: 申请月对应的列
+    :param circle_week: 申请周对应的列
+    :param circle_day: 申请日对应的列
+    :param base_lift: 临界Lift
+    :param subset_total: 是否获取每个子集作为计算整体
+
+    :return:   泛化结果汇总输出
+    """
+    for var in rules_dict.Var.unique():
+        rules_dict01 = rules_dict[rules_dict.Var == var]
+        min_index = rules_dict01.index.min()
+        starttime_init = datetime.datetime.now()
+        for row in rules_dict01.loc[min_index:min_index].iterrows():
+            print('正在泛化的规则为：' + row[1]['Rule_Name'] + '\n' + '规则适用范围为：' + str(row[1]['Rule_Limit']) + '\n' + '目标字段为：' + row[1]['Target'])
+            print('泛化的变量名称为:', var)
+            rule_name = row[1]['Rule_Name']
+            sample_type1 = str(row[1]['Rule_Limit'])
+            rule_type = row[1]['Rule_Type']
+            target = row[1]['Target']
+            cut_point = row[1]['Threshold']
+            var = var
+            direction = row[1]['Direction']
+            df_monthly = get_mths_result(data=data
+                                         , rule_name=rule_name
+                                         , rule_type=rule_type
+                                         , var=var
+                                         , cut_point=cut_point
+                                         , target=target
+                                         , rule_limit=sample_type1
+                                         , rule_all=rule_all
+                                         , use_credit_flag=use_credit_flag
+                                         , circle_mth=circle_mth
+                                         , direction=direction
+                                         , base_lift=base_lift
+                                         , subset_total=subset_total)
+            df_weekly = get_weeks_result(data=data
+                                         , rule_name=rule_name
+                                         , rule_type=rule_type
+                                         , var=var
+                                         , cut_point=cut_point
+                                         , target=target
+                                         , rule_limit=sample_type1
+                                         , rule_all=rule_all
+                                         , use_credit_flag=use_credit_flag
+                                         , circle_week=circle_week
+                                         , direction=direction
+                                         , subset_total=subset_total)
+            df_daily = get_days_result(data=data
+                                       , rule_name=rule_name
+                                       , rule_type=rule_type
+                                       , var=var
+                                       , cut_point=cut_point
+                                       , target=target
+                                       , rule_limit=sample_type1
+                                       , rule_all=rule_all
+                                       , use_credit_flag=use_credit_flag
+                                       , circle_day=circle_day
+                                       , direction=direction
+                                       , subset_total=subset_total)
+        if len(rules_dict01) > 1:
+            for row in rules_dict01.loc[(min_index + 1):].iterrows():
+                print('正在泛化的规则为：' + row[1]['Rule_Name'] + '\n' + '规则适用范围为：' + str(
+                    row[1]['Rule_Limit']) + '\n' + '目标字段为：' + row[1]['Target'])
+                print('泛化的变量名称为:', var)
+                rule_name = row[1]['Rule_Name']
+                sample_type1 = row[1]['Rule_Limit']
+                rule_type = row[1]['Rule_Type']
+                target = row[1]['Target']
+                cut_point = row[1]['Threshold']
+                var = var
+                direction = row[1]['Direction']
+                df_monthly_01 = get_mths_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var, cut_point=cut_point, target=target, rule_limit=sample_type1, rule_all=rule_all,
+                                                use_credit_flag=use_credit_flag, circle_mth=circle_mth, direction=direction, base_lift=base_lift, subset_total=subset_total)
+                df_monthly = pd.concat([df_monthly, df_monthly_01])
+                df_weekly_01 = get_weeks_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var, cut_point=cut_point, target=target, rule_limit=sample_type1, rule_all=rule_all,
+                                                use_credit_flag=use_credit_flag, circle_week=circle_week, direction=direction, subset_total=subset_total)
+                df_weekly = pd.concat([df_weekly, df_weekly_01])
+                df_daily_01 = get_days_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var, cut_point=cut_point, target=target, rule_limit=sample_type1, rule_all=rule_all,
+                                              use_credit_flag=use_credit_flag, circle_day=circle_day, direction=direction, subset_total=subset_total)
+                df_daily = pd.concat([df_daily, df_daily_01])
+
+        df_monthly.insert(loc=0, column="序号", value=df_monthly["规则名称"].map({rule: index for index, rule in enumerate(df_monthly['规则名称'].unique(), start=1)}).values)
+        df_weekly.insert(loc=0, column="序号", value=df_weekly["规则名称"].map({rule: index for index, rule in enumerate(df_weekly['规则名称'].unique(), start=1)}).values)
+        df_daily.insert(loc=0, column="序号", value=df_daily["规则名称"].map({rule: index for index, rule in enumerate(df_daily['规则名称'].unique(), start=1)}).values)
+
+        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 结果输出 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #
+        writer = ExcelWriter()
+
+        # 相关规则说明
+        worksheet = writer.get_sheet_by_name("0.说明")
+
+        end_row, _ = writer.insert_value2sheet(worksheet, "B2", value="1、规则说明", style="header_middle", end_space="E2", align={"horizontal": "left"})
+
+        _, end_col = writer.insert_value2sheet(worksheet, (end_row + 1, 2), value="规则类型", style="header_middle")
+        end_row, _ = writer.insert_value2sheet(worksheet, (end_row + 1, end_col), value="规则上线标准", style="header_middle")
+
+        _, end_col = writer.insert_value2sheet(worksheet, (end_row, 2), value="HC", style="middle")
+        end_row, _ = writer.insert_value2sheet(worksheet, (end_row, end_col), value="准入类规则需结合产品和适用场景进行考虑", style="middle", align={"horizontal": "left"})
+
+        _, end_col = writer.insert_value2sheet(worksheet, (end_row, 2), value="FR/CR", style="last_middle")
+        # end_row, _ = writer.insert_value2sheet(worksheet, (end_row, end_col), value=f"同时满足如下条件：\r\n1.近n个月额外触碰Lift>={base_lift}的月份占比>=80%\r\n2.按月统计的额外触碰率离散度稳定（非常稳定或相对稳定）\r\n注：追踪月份至少 >= 3", style="last_middle", align={"horizontal": "left"})
+        end_row, _ = writer.insert_value2sheet(worksheet, (end_row, end_col), value=f'="同时满足如下条件：" & CHAR(10) & "1.近n个月额外触碰Lift>={base_lift}的月份占比>=80%" & CHAR(10) & "2.按月统计的额外触碰率离散度稳定（非常稳定或相对稳定）" & CHAR(10) & "注：追踪月份至少 >= 3"', style="last_middle", align={"horizontal": "left", "wrap_text": True})
+
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row + 2}", value="2、稳定性划分标准", style="header_middle", end_space=f"E{end_row + 2}", align={"horizontal": "left"})
+        cv_describe = pd.DataFrame({"序号": [1, 2, 3, 4], "波动幅度": ["[0%, 15%)", "[15%, 40%)", "[40%, 75%)", "[75%, 正无穷)"], "类型": ["非常稳定", "相对稳定", "不稳定", "极不稳定"], "备注": ["波动幅度=离散度"] * 4})
+        end_row, _ = dataframe2excel(cv_describe, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=["备注"], merge=True)
+
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row + 2}", value="3、统计指标含义", style="header_middle", end_space=f"E{end_row + 2}", align={"horizontal": "left"})
+        end_row, _ = dataframe2excel(word_desc, writer, sheet_name=worksheet, start_row=end_row + 1)
+
+        # 规则增益效果评估
+        worksheet = writer.get_sheet_by_name("1.规则泛化")
+
+        # 评估规则明细
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B2", value="0、规则逻辑", style="header_middle", end_space=f"AY2")
+        end_row, _ = dataframe2excel(rules_dict01, writer, sheet_name=worksheet, start_row=end_row + 1)
+
+        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 每月规则泛化 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row + 2}", value="1、月度规则泛化", style="header_middle", end_space=f"AY{end_row + 2}", align={"horizontal": "left"})
+
+        # 每月规则命中拒绝及效果评估
+        text1, text2, text3 = std_result_output(df_monthly, "M")
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text1, style="merge_middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text2, style="merge_middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text3, style="middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+
+        end_row, _ = dataframe2excel(df_monthly, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=["序号", "报告日期", "产品名称", "样本类型"], merge=True)
+
+        # # 规则增益效果评估
+        # end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row + 2}", value="2、规则效能", style="header_middle", end_space=f"AY{end_row + 2}")
+
+        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 每周规则泛化 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row + 2}", value="2、每周规则泛化", style="header_middle", end_space=f"AY{end_row + 2}", align={"horizontal": "left"})
+
+        text1, text2, text3 = std_result_output(df_weekly, "W")
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text1, style="merge_middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text2, style="merge_middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text3, style="middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+
+        end_row, _ = dataframe2excel(df_weekly, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=["序号", "报告日期", "产品名称", "样本类型"], merge=True)
+
+        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 每日规则泛化 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row + 2}", value="3、每日规则泛化", style="header_middle", end_space=f"AY{end_row + 2}", align={"horizontal": "left"})
+
+        text1, text2, text3 = std_result_output(df_daily, "D")
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text1, style="merge_middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text2, style="merge_middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+        end_row, _ = writer.insert_value2sheet(worksheet, f"B{end_row}", value=text3, style="middle", end_space=f"AY{end_row}", align={"horizontal": "left"})
+
+        end_row, _ = dataframe2excel(df_daily, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=["序号", "报告日期", "产品名称", "样本类型"], merge=True)
+
+        writer.save(path_rule + var + '_single_rule_' + str(datetime.datetime.now().strftime('%Y%m%d%H%M%S')) + '.xlsx')
+
+
+# 2.9 规则合并泛化时  按月、周、日输出分析结果函数
+def std_result_output_01(wb, sheetname, data, offset):
+    """
+    泛化结果输出函数
+    """
+    nrows, ncols = data.shape
+    first_title = wb.add_format(title_dic)
+    body_text_title = wb.add_format(subtitle_format)
+    body_text_center = wb.add_format(body_text_format_01)
+    body_text_left = wb.add_format(body_text_left_format_01)
+    body_text_percent = wb.add_format(body_text_per_format_01)
+    body_text_center2 = wb.add_format(body_text_format_02)
+    body_text_left2 = wb.add_format(body_text_left_format_02)
+    body_text_percent2 = wb.add_format(body_text_per_format_02)
+    ws = wb.add_worksheet(sheetname)
+    ws.hide_gridlines({'option': 1})
+    ws.autofilter(offset + 5, offset, offset + 5 + nrows, ncols)
+    ws.set_column(0, 0, 2)
+    ws.freeze_panes(7, 10)  ## 冻结单元格
+    ws.merge_range(offset, offset, offset, ncols, sheetname, first_title)
+    if '规则泛化' in sheetname:
+        data_01 = data[-3:]
+        data_02 = data[data['额外触碰成熟量'] >= 10][-3:]
+        data_03 = data[data['整体逾期率'] > 0][-3:]
+        hit_rate = sum(data_01['额外触碰量']) / sum(data_01['申请量'])
+        risk_double = (data_02['额外触碰成熟坏样本量'].sum() / data_02['额外触碰成熟量'].sum()) / (sum(data_02['整体成熟坏样本量']) / sum(data_02['整体成熟量'])) if sum(data_02['整体成熟坏样本量']) > 0 else np.nan
+        overdue_minus = sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量']) - sum(data_03['整体成熟坏样本量'] - data_03['额外触碰成熟坏样本量']) / sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) if sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) > 0 else np.nan
+        overdue_range = overdue_minus / (sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量'])) if sum(data_03['整体成熟坏样本量']) > 0 else np.nan
+        text1 = "1.策略上线后通过率下降预估值 = 近3个月额外触碰量之和/近3个月申请量之和；策略上线后逾期率变化预估值 = （近n个有表现月逾期样本之和/近n个有表现月成熟样本之和）-（近n个有表现月置后触碰坏样本之和/近n个有表现月置后触碰成熟样本之和） ；备注：取整体逾期率大于0的近n个月计算策略上线后逾期率变化值，n默认取3，若不足3个月，有几个月计算几个月;"
+        text2 = "2.策略上线后额外触碰Lift预估值 = (近n个有表现月额外触碰成熟坏样本量之和/近n个有表现月额外触碰成熟量之和)/(近n个有表现月整体成熟坏样本量之和/近n个有表现月整体成熟量之和)；备注：取额外触碰成熟量大于等于10的近n个月计算策略上线后额外触碰Lift预估值，n默认取3，若不足3个月，有几个月计算几个月;"
+        if overdue_minus > 0:
+            text3 = "3.策略上线后通过率预计下降：{:.2f}%，逾期率下降：{:.2f}%，逾期率下降幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。".format(hit_rate * 100, overdue_minus * 100, overdue_range * 100, risk_double)
+        else:
+            text3 = "3.策略上线后通过率预计下降：{:.2f}%，逾期率上升：{:.2f}%，逾期率上升幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。".format(hit_rate * 100, abs(overdue_minus) * 100, abs(overdue_range) * 100, risk_double)
+    if 'Weekly' in sheetname:
+        week_num = str(len(data['申请周'].unique()))
+        mean_obs = data['置前触碰率'].mean() * 100
+        cv_rate = cv(data['置前触碰率'])
+        if data['置前触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='置前触碰率')
+        text1 = "1.近" + week_num + "个申请周平均置前触碰率%.2f" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + "；"
+        mean_obs = data['策略触碰率'].mean() * 100
+        cv_rate = cv(data['策略触碰率'])
+        if data['策略触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='策略触碰率')
+        text2 = "2.近" + week_num + "个申请周策略触碰率%.2f" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + "；"
+        mean_obs = data['额外触碰率'].mean() * 100
+        cv_rate = cv(data['额外触碰率'])
+        if data['额外触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='额外触碰率')
+        text3 = "3.近" + week_num + "个申请周额外触碰率%.2f" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + "；"
+    if 'Dayly' in sheetname:
+        day_num = str(len(data['申请日'].unique()))
+        mean_obs = data['置前触碰率'].mean() * 100
+        cv_rate = cv(data['置前触碰率'])
+        if data['置前触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='置前触碰率')
+        text1 = "1.近" + day_num + "个申请日平均置前触碰率%.2f" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + "；"
+        mean_obs = data['策略触碰率'].mean() * 100
+        cv_rate = cv(data['策略触碰率'])
+        if data['策略触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='策略触碰率')
+        text2 = "2.近" + day_num + "个申请日策略触碰率%.2f" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + "；"
+        mean_obs = data['额外触碰率'].mean() * 100
+        cv_rate = cv(data['额外触碰率'])
+        if data['额外触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='额外触碰率')
+        text3 = "3.近" + day_num + "个申请日额外触碰率%.2f" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + "；"
+    ws.merge_range(offset + 1, offset, offset + 1, ncols, text1, body_text_left2)
+    ws.merge_range(offset + 2, offset, offset + 2, ncols, text2, body_text_left2)
+    ws.merge_range(offset + 3, offset, offset + 3, ncols, text3, body_text_left2)
+    ws.set_row(offset + 4, 7)
+    column = data.columns
+    need_len = get_max_len(data=data, nrows=nrows, ncols=ncols, max_len=40)
+    for i in range(len(need_len)):
+        ws.set_column(i + 1, i + 1, need_len[i])
+    data = data.replace(np.nan, '')
+    data = data.replace(np.inf, 'Inf')
+    for j in range(ncols):
+        ws.write(offset + 5, j + 1, column[j], body_text_title)
+    for i in range(nrows):
+        for j in range(ncols):
+            if '量' in column[j]:
+                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_green_no)
+            elif (('率' in column[j]) or ('占比' in column[j])):
+                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_pink_no)
+            elif 'Odds' in column[j] or 'Lift' in column[j] or '风险' in column[j]:
+                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_red_no)
+            elif '%' in column[j] or '比例' in column[j]:
+                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_blue_no)
+            value = data.iloc[i][j]
+            if int(data.iloc[i, 0] % 2) == 1:
+                if '%' in column[j] or 'rate' in column[j] or '占比' in column[j] or '率' in column[j] or '比例' in column[j]:
+                    ws.write(i + offset + 6, j + 1, value, body_text_percent)
+                else:
+                    ws.write(i + offset + 6, j + 1, value, body_text_center)
+            else:
+                if '%' in column[j] or 'rate' in column[j] or '占比' in column[j] or '率' in column[j] or '比例' in column[j]:
+                    ws.write(i + offset + 6, j + 1, value, body_text_percent2)
+                else:
+                    ws.write(i + offset + 6, j + 1, value, body_text_center2)
+    ws.set_row(offset + 5 + nrows + 1, 7)
+    ws.merge_range(offset + 5 + nrows + 2, offset, offset + 5 + nrows + 2, ncols, '2.规则效能', first_title)
+    ws.set_row(offset + 5 + nrows + 3, 7)
+    if '规则泛化' in sheetname:
+        add_combine_plot(wb=wb, ws=ws, column=column, label_x_start=offset + 5, x_start=offset + 5 + 1, x_end=offset + 5 + 1 + nrows,
+                         title='规则效能分析', x_label='申请月', y1_list=['额外触碰Lift', '额外触碰fpd15_Lift'],
+                         y2_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],
+                         x_title='申请月', y1_title='额外触碰Lift', y2_title='规则触碰率', width=750, height=500, if_combine=True,
+                         chart1_type='column', chart2_type='line', chart_row=offset + 5 + nrows + 4, chart_col=1)
+    if 'Weekly' in sheetname:
+        add_plot(wb=wb, ws=ws, column=column, label_x_start=offset + 5, x_start=offset + 5 + 1,
+                 x_end=offset + 6 + nrows, title='规则效能分析', x_label='申请周',
+                 y1_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],
+                 x_title='申请周', y1_title='规则触碰率', width=720, height=500, chart1_type='line', chart_row=offset + 5 + nrows + 4, chart_col=1)
+    if 'Dayly' in sheetname:
+        add_plot(wb=wb, ws=ws, column=column, label_x_start=offset + 5, x_start=offset + 5 + 1,
+                 x_end=offset + 5 + nrows, title='规则效能分析', x_label='申请日',
+                 y1_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],
+                 x_title='申请日', y1_title='规则触碰率', width=720, height=500, chart1_type='line',
+                 chart_row=offset + 5 + nrows + 4, chart_col=1)
+
+
+# 1.14 规则合并泛化结果汇总并输出
+def rule_combine_results_01(data, rule_all, use_credit_flag, cut_point, rule_name, rule_name_chinese,
+                            rule_type, rule_limit, circle_mth, circle_week, circle_day, target, direction, var, base_lift, subset_total):
+    """
+    :param data:  需要分析的数据
+    :param rule_all:  已有所有规则汇总后的字段名称
+    :param use_credit_flag: 是否用信列
+    :param cut_point: 切点，cut-off
+    :param rule_name: 泛化规则英文名
+    :param rule_name_chinese: 泛化规则中文名
+    :param rule_type: 泛化规则类型
+    :param rule_limit: 泛化样本类型
+    :param circle_mth: 申请月对应的列
+    :param circle_week: 申请周对应的列
+    :param circle_day: 申请日对应的列
+    :param target: 目标字段
+    :param direction: 变量取值方向
+    :param var: 泛化变量名
+    :param base_lift: 临界Lift
+    :param subset_total: 是否获取每个子集作为计算整体
+    :return:   泛化结果汇总输出
+    """
+    ana_date = datetime.datetime.now().strftime('%Y-%m-%d')
+    rules_dict01 = pd.DataFrame(
+        {'Ana_Date': [ana_date], 'Rule_Name': [rule_name], 'Description': [rule_name_chinese], 'Rule_Type': [rule_type],
+         'Rule_Limit': [rule_limit], 'Target': [target], 'Threshold': [cut_point], 'Direction': [direction], 'var': [var]})
+    starttime_init = datetime.datetime.now()
+    print('程序开始执行时间为: ' + str(starttime_init))
+    print('正在泛化的规则为：' + rule_name + '\n' + '规则适用范围为：' + rule_limit + '\n' + '目标字段为：' + target)
+    df_monthly = get_mths_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var,
+                                 cut_point=cut_point, direction=direction, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag,
+                                 circle_mth=circle_mth, base_lift=base_lift, subset_total=subset_total)
+    df_weekly = get_weeks_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var,
+                                 cut_point=cut_point, direction=direction, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag,
+                                 circle_week=circle_week, subset_total=subset_total)
+    df_daily = get_days_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var,
+                               cut_point=cut_point, direction=direction, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag,
+                               circle_day=circle_day, subset_total=subset_total)
+    cols = df_monthly.columns.tolist()
+    df_monthly['序号'] = 1
+    for i, j in enumerate(list(df_monthly['规则名称'].unique())):
+        df_monthly['序号'][df_monthly['规则名称'] == j] = (i + 1)
+    col1 = ['序号']
+    col1.extend(cols)
+    df_monthly = df_monthly[col1]
+
+    cols = df_weekly.columns.tolist()
+    df_weekly['序号'] = 1
+    for i, j in enumerate(list(df_weekly['规则名称'].unique())):
+        df_weekly['序号'][df_weekly['规则名称'] == j] = (i + 1)
+    col1 = ['序号']
+    col1.extend(cols)
+    df_weekly = df_weekly[col1]
+
+    cols = df_daily.columns.tolist()
+    df_daily['序号'] = 1
+    for i, j in enumerate(list(df_daily['规则名称'].unique())):
+        df_daily['序号'][df_daily['规则名称'] == j] = (i + 1)
+    col1 = ['序号']
+    col1.extend(cols)
+    df_daily = df_daily[col1]
+    endtime_init = datetime.datetime.now()
+    print('程序结束执行时间为: ' + str(endtime_init))
+    print('程序执行时间为: ' + str(endtime_init - starttime_init))
+
+    # 结果输出
+    wb = xlsxwriter.Workbook(path_rule + rule_name + '_' + str(datetime.datetime.now().strftime('%Y%m%d%H%M%S')) + '.xlsx')
+    description_output(wb=wb, sheetname='0.说明', df1=rules_dict01, df2=word_desc, offset=1, base_lift=base_lift)
+    std_result_output_01(wb=wb, sheetname='1.规则泛化', data=df_monthly, offset=1)
+    std_result_output_01(wb=wb, sheetname='2A.Weekly触碰模拟', data=df_weekly, offset=1)
+    std_result_output_01(wb=wb, sheetname='2B.Dayly触碰模拟', data=df_daily, offset=1)
+    wb.close()
+
+
+"""
+2.规则泛化分析结果输出相关函数
+"""
+
+# 2.1 excel格式设置
+biaotou = '#44546A'
+title = '#44546A'
+title_sub1 = '#00868B'
+title_sub2 = '#00E5EE'
+text = '#F2F2F2'
+xunhuan1 = '#D1D1D1'
+xunhuan2 = '#E3E3E3'
+title_size = 12
+text_title_size = 9
+text_size = 8
+split_color = 'white'
+
+# 条件格式: 蓝色数据条，不指定最大、最小值，绿色
+condition_format_green_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,
+                             'bar_color': '#65d97d'}
+# 条件格式: 黄色数据条，不指定最大、最小值，橙色
+condition_format_red_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,
+                           'bar_color': '#f2572d'}
+# 条件格式: 黄色数据条，不指定最大、最小值，橙色
+condition_format_pink_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,
+                            'bar_color': '#FF69B4'}
+condition_format_blue_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,
+                            'bar_color': '#1E90FF'}
+condition_format_3_color = {'type': '3_color_scale',
+                            'max_color': '#F8696B', 'mid_color': '#FFEB84', 'min_color': '#63BE7B'}
+
+#  总标题
+title_dic = {'bold': True, 'font_name': 'Arial', 'font_size': title_size, 'font_color': 'white',
+             'top_color': biaotou, 'bottom_color': biaotou, 'left_color': biaotou,
+             'right_color': biaotou, 'bg_color': biaotou}
+
+# 表格正文: 边框白色，字体12，背景深灰色、居中
+subtitle_format = {'border': True, 'font_size': text_title_size, 'font_name': 'Arial', 'font_color': 'white',
+                   'left_color': 'white', 'right_color': 'white', 'bg_color': biaotou,
+                   'align': 'center', 'valign': 'vcenter'}
+
+# 表格正文: 边框白色，字体12，背景灰色1、居中
+body_text_format_01 = {'border': True, 'font_size': text_size, 'font_name': 'Arial',
+                       'bg_color': text, 'top_color': split_color,
+                       'bottom_color': split_color,
+                       'left_color': split_color,
+                       'right_color': split_color,
+                       'align': 'center', 'valign': 'vcenter'}
+
+# 正文左对齐
+body_text_left_format_01 = copy.deepcopy(body_text_format_01)
+body_text_left_format_01['align'] = 'left'
+# 正文为比率
+body_text_per_format_01 = copy.deepcopy(body_text_format_01)
+body_text_per_format_01['num_format'] = '0.00%'
+
+# 表格正文: 边框白色，字体12，背景灰色2、居中
+body_text_format_02 = {'border': True, 'font_size': text_size, 'font_name': 'Arial',
+                       'bg_color': xunhuan2, 'top_color': split_color,
+                       'bottom_color': split_color,
+                       'left_color': split_color,
+                       'right_color': split_color,
+                       'align': 'center', 'valign': 'vcenter'}
+
+
+# 正文左对齐
+body_text_left_format_02 = copy.deepcopy(body_text_format_02)
+body_text_left_format_02['align'] = 'left'
+# 正文为比率
+body_text_per_format_02 = copy.deepcopy(body_text_format_02)
+body_text_per_format_02['num_format'] = '0.00%'
+
+
+# 2.2 规则稳定性说明
+type_dic = pd.DataFrame([[1, '0%~15%', '非常稳定'], [2, '15%~40%', '相对稳定'], [3, '40%~75%', '不稳定'], [4, '>75%', '极不稳定']])
+
+
+# 2.3自动获取单元格内容的length，如果包含汉字，则一个汉字计数为2，如果是英文，则计数为1
+def get_same_len(x):
+    """
+    :param x: 输入内容
+    :return: 字符串长度
+    """
+    import re
+    if type(x) != str:
+        x = str(x)
+    num = 0
+    for i in list(x):
+        if re.match("[\u4e00-\u9fa5]+", i):
+            num = num + 2
+        else:
+            num = num + 1
+    return num
+
+
+# 2.4 自动获取每一列的最大长度
+def get_max_len(data, nrows, ncols, max_len=60):
+    """
+    自动计算每一列的长度
+    （1）先获得每一列的最长的一个单元格内容的长度
+    （2）如果超过最大长度限制，则只取最大程度，其余内容自动换行，否则无需限制
+    字符串length对应到excel的单元格列宽需要缩小到0.73684，具体缩放比例可以修改
+    """
+    data = data.fillna('')
+    res = []
+    res1 = []
+    column = data.columns
+    for i in range(len(column)):
+        x = column[[i]][0]
+        res1.append(get_same_len(x))
+    res2 = []
+    for i in range(ncols):
+        tmp = []
+        for j in range(nrows):
+            tmp.append(get_same_len(data.iloc[j, i]))
+        tmp = max(tmp)
+        if tmp > max_len:
+            tmp = max_len
+        res2.append(tmp * 0.73684)
+    for i, j in zip(res1, res2):
+        res.append(max(i, j))
+    return res
+
+
+# 2.5 规则泛化说明函数
+def description_output(wb, sheetname, df1, df2, offset, base_lift):
+    """
+    :param wb:              excel 文件
+    :param sheetname:       sheetname
+    :param df1:            待输出数据框1
+    :param df2:            待输出数据框2
+    :param offset:          输出位置
+    :param base_lift:       最小lift
+    :return:        策略泛化结果说明
+    """
+    nrows, ncols = df1.shape
+    first_title = wb.add_format(title_dic)
+    body_text_title = wb.add_format(subtitle_format)
+    body_text_center = wb.add_format(body_text_format_01)
+    body_text_left = wb.add_format(body_text_left_format_01)
+    body_text_percent = wb.add_format(body_text_per_format_01)
+    body_text_format_01_01 = copy.deepcopy(body_text_format_01)
+    body_text_format_01_01['text_wrap'] = 1
+    body_text_center_01 = wb.add_format(body_text_format_01_01)
+    column = df1.columns
+    ws = wb.add_worksheet(sheetname)
+    ws.hide_gridlines({'option': 1})
+    ws.set_column(0, 0, 2)
+    for i in range(len(column)):
+        x = column[[i]][0]
+        ll = get_same_len(x)
+        lll = max(8, ll)
+        ws.set_column(i + offset, i + offset, lll)
+    ws.merge_range(offset, offset, offset, ncols, '1.规则说明', first_title)
+    ws.set_row(2, 7)
+    title_01 = ['规则类型', '规则上线标准']
+    ws.write(3, 1, title_01[0], body_text_title)
+    ws.merge_range(3, 2, 3, 5, title_01[1], body_text_title)
+    rule_type01 = 'HC';
+    rule_type02 = 'FR/CR';
+    text_01 = '准入类规则需结合产品和适用场景进行考虑'
+    text_021 = "同时满足如下条件："
+    text_022 = "1.近n个月额外触碰Lift>=" + str(base_lift) + "的月份占比>=80%"
+    text_023 = "2.按月统计的额外触碰率离散度稳定（非常稳定或相对稳定）"
+    text_024 = "注：追踪月份至少 >= 3"
+    ws.write(4, 1, rule_type01, body_text_center)
+    ws.merge_range(4, 2, 4, 5, text_01, body_text_left)
+    ws.merge_range(5, 1, 8, 1, rule_type02, body_text_center)
+    ws.merge_range(5, 2, 5, 5, text_021, body_text_left)
+    ws.merge_range(6, 2, 6, 5, text_022, body_text_left)
+    ws.merge_range(7, 2, 7, 5, text_023, body_text_left)
+    ws.merge_range(8, 2, 8, 5, text_024, body_text_left)
+    ws.set_row(9, 7)
+    # 增加备注
+    title = ['序号', '波动幅度', '类型', '备注']
+    for i in range(len(title) - 1):
+        ws.write(10, i + 1, title[i], body_text_title)
+    ws.merge_range(10, len(title), 10, len(title) + 1, '备注', body_text_title)
+    rshape, cshape = type_dic.shape
+    for i in range(rshape):
+        for j in range(cshape):
+            ws.write(10 + 1 + i, j + 1, type_dic.iloc[i][j], body_text_center)
+    ws.merge_range(10 + 1, cshape + 1, 10 + rshape, cshape + 2, '波动幅度=离散度', body_text_center)
+    ws.set_row(15, 7)
+    ws.merge_range(16, 1, 16, ncols, '2.规则逻辑', first_title)
+    ws.set_row(17, 7)
+    df1 = df1.replace(np.nan, '')
+    df1 = df1.replace(np.inf, 'Inf')
+    for i in range(len(column)):
+        x = column[[i]][0]
+        ll = get_same_len(x)
+        lll = max(8, ll)
+        ws.set_column(i + 1, i + 1, lll)
+    for j in range(ncols):
+        ws.write(18, j + 1, column[j], body_text_title)
+    for i in range(nrows):
+        for j in range(ncols):
+            if 'odds' in column[j] or 'Odds' in column[j]:
+                ws.conditional_format(19, j + 1, 19 + nrows - 1, j + 1, condition_format_pink_no)
+            value = df1.iloc[i][j]
+            if '%' in column[j] or 'Rate' in column[j] or '占比' in column[j] or '率' in column[j]:
+                ws.write(19 + i, j + 1, value, body_text_percent)
+            else:
+                ws.write(19 + i, j + 1, value, body_text_center)
+    ws.set_row(19 + nrows, 7)
+    ws.merge_range(19 + nrows + 1, 1, 19 + nrows + 1, ncols, '3.规则字段', first_title)
+    ws.set_row(19 + nrows + 2, 7)
+    nrows2, ncols2 = df2.shape
+    df2 = df2.replace(np.nan, '')
+    df2 = df2.replace(np.inf, 'Inf')
+    column = df2.columns
+    need_len = get_max_len(data=df2.iloc[:, 1:], nrows=nrows2, ncols=ncols2 - 1, max_len=45)
+    for i in range(len(need_len)):
+        ws.set_column(i + 2, i + 2, need_len[i])
+    for j in range(ncols2):
+        ws.write(19 + nrows + 3, j + 1, column[j], body_text_title)
+    for i in range(nrows2):
+        for j in range(ncols2):
+            value = df2.iloc[i][j]
+            ws.write(19 + nrows + 4 + i, j + 1, value, body_text_center_01)
+
+
+# 2.6 规则泛化指标说明
+word_desc = [{'评估内容': '样本类型', '内容详细含义': '测算和泛化样本细分类型', '备注': '取值可以是地域、渠道等'},
+             {'评估内容': '风险类型', '内容详细含义': '测算和泛化变量为欺诈类变量则属于短期风险；测算和泛化变量为信用变量则属于中长期风险', '备注': None},
+             {'评估内容': '目标字段', '内容详细含义': '测算和泛化的目标字段', '备注': None},
+             {'评估内容': '规则类型', '内容详细含义': 'FR/CR/HC', '备注': 'FR是欺诈类规则；CR为信用类规则；HC为强规则'},
+             {'评估内容': '申请量', '内容详细含义': '申请量', '备注': None},
+             {'评估内容': '通过量', '内容详细含义': '审批通过量', '备注': None},
+             {'评估内容': '通过率', '内容详细含义': '通过量/申请量', '备注': None},
+             {'评估内容': '用信量', '内容详细含义': '审批通过且支用量', '备注': None},
+             {'评估内容': '用信率', '内容详细含义': '用信量/通过量', '备注': None},
+             {'评估内容': '置前触碰量', '内容详细含义': '已有所有规则触碰量', '备注': None},
+             {'评估内容': '策略触碰量', '内容详细含义': '待上线规则触碰量（包含申请拒绝触碰和申请通过触碰）', '备注': None},
+             {'评估内容': '重复触碰量', '内容详细含义': '待上线规则和已有规则重复触碰量', '备注': None},
+             {'评估内容': '额外触碰量', '内容详细含义': '待上线规则在申请通过样本上触碰量', '备注': None},
+             {'评估内容': '置后触碰量', '内容详细含义': '已有所有规则和待上线规则一共触碰量', '备注': None},
+             {'评估内容': '置前触碰率', '内容详细含义': '置前触碰量/申请量', '备注': None},
+             {'评估内容': '策略触碰率', '内容详细含义': '策略触碰量/申请量', '备注': None},
+             {'评估内容': '重复触碰率', '内容详细含义': '重复触碰量/申请量', '备注': None},
+             {'评估内容': '额外触碰率', '内容详细含义': '额外触碰量/申请量', '备注': None},
+             {'评估内容': '置后触碰率', '内容详细含义': '置后触碰量/申请量', '备注': None},
+             {'评估内容': '重复触碰占策略触碰比例', '内容详细含义': '重复触碰量）/策略触碰量', '备注': None},
+             {'评估内容': '额外触碰占策略触碰比例', '内容详细含义': '额外触碰量）/策略触碰量', '备注': None},
+             {'评估内容': '整体成熟量', '内容详细含义': '观测成熟量', '备注': None},
+             {'评估内容': '额外触碰成熟量', '内容详细含义': '观测成熟且额外触碰量', '备注': None},
+             {'评估内容': '整体成熟坏样本量', '内容详细含义': '观测成熟坏样本量', '备注': None},
+             {'评估内容': '额外触碰成熟坏样本量', '内容详细含义': '观测成熟且额外触碰坏样本量', '备注': None},
+             {'评估内容': 'fpd15整体成熟量', '内容详细含义': 'fpd15观测成熟量', '备注': None},
+             {'评估内容': 'fpd15额外触碰成熟量', '内容详细含义': 'fpd15观测成熟且额外触碰量', '备注': None},
+             {'评估内容': 'fpd15整体成熟坏样本量', '内容详细含义': 'fpd15观测成熟坏样本量', '备注': None},
+             {'评估内容': 'fpd15额外触碰成熟坏样本量', '内容详细含义': 'fpd15观测成熟且额外触碰坏样本量', '备注': None},
+             {'评估内容': '整体逾期率', '内容详细含义': '观测成熟样本中逾期样本占比', '备注': None},
+             {'评估内容': '额外触碰逾期率', '内容详细含义': '观测成熟且额外触碰样本中逾期样本占比', '备注': None},
+             {'评估内容': '额外触碰后整体逾期率', '内容详细含义': '观测成熟样本中剔除额外触碰样本后逾期样本占比', '备注': None},
+             {'评估内容': '逾期率下降值', '内容详细含义': '策略上线后逾期率下降值', '备注': None},
+             {'评估内容': '逾期率下降幅度', '内容详细含义': '策略上线后逾期率下降幅度', '备注': None},
+             {'评估内容': '额外触碰Odds', '内容详细含义': '（额外触碰成熟申请单中坏好比）/（非额外触碰成熟申请单坏/好）', '备注': None},
+             {'评估内容': '额外触碰Lift', '内容详细含义': '（额外触碰成熟申请单中坏样本占比）/（全量成熟申请单坏样本占比）', '备注': None},
+             {'评估内容': '额外触碰fpd15_Odds', '内容详细含义': '（额外触碰fpd15成熟申请单中坏好比）/（非额外触碰fpd15成熟申请单中坏/好）', '备注': None},
+             {'评估内容': '额外触碰fpd15_Lift', '内容详细含义': '（额外触碰fpd15成熟申请单中坏样本占比）/（全量fpd15成熟申请单坏样本占比）', '备注': None},
+             {'评估内容': '额外触碰Lift大于目标值的月份占比', '内容详细含义': '额外触碰非空Lift大于阈值月份数/额外触碰非空Lift月份数', '备注': '阈值可根据实际情况自己设定，如取值为3'},
+             {'评估内容': '置前触碰率离散度', '内容详细含义': '置前触碰率是否稳定', '备注': '0%~15%：非常稳定\n15%~40%：相对稳定\n40%~75%：不稳定\n>75%：极不稳定'},
+             {'评估内容': '策略触碰率离散度', '内容详细含义': '策略触碰率是否稳定', '备注': '0%~15%：非常稳定\n15%~40%：相对稳定\n40%~75%：不稳定\n>75%：极不稳定'},
+             {'评估内容': '重复触碰率离散度', '内容详细含义': '重复触碰率是否稳定', '备注': '0%~15%：非常稳定\n15%~40%：相对稳定\n40%~75%：不稳定\n>75%：极不稳定'},
+             {'评估内容': '额外触碰率离散度', '内容详细含义': '额外触碰率是否稳定', '备注': '0%~15%：非常稳定\n15%~40%：相对稳定\n40%~75%：不稳定\n>75%：极不稳定'},
+             {'评估内容': '置后触碰率离散度', '内容详细含义': '置后触碰率是否稳定', '备注': '0%~15%：非常稳定\n15%~40%：相对稳定\n40%~75%：不稳定\n>75%：极不稳定'},
+             {'评估内容': '评估结论', '内容详细含义': '基于策略筛选规则，评估策略是否进行上线', '备注': None}]
+
+word_desc = pd.DataFrame(word_desc)[['评估内容', '内容详细含义', '备注']]
+
+
+# 2.8 按月泛化时规则风险表现情况画图函数
+def add_combine_plot(wb, ws, column, label_x_start=1 + 6, x_start=1 + 6 + 1, x_end=int(1 + 6 + 1 + 12 * 1 / 2), title='客户级规则效能分析', x_label='申请月',
+                     y1_list=['额外触碰Lift', '额外触碰fpd15_Lift'], y2_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],
+                     x_title='申请月', y1_title='额外触碰Lift', y2_title='规则触碰率', width=760, height=520, if_combine=True, chart1_type='column', chart2_type='line',
+                     chart_row=1 + 6 + 12 + 4, chart_col=1):
+    """
+    :param column: 数据框列名
+    :param label_x_start:  数据框列名开始行
+    :param x_start:   数据框取值开始行
+    :param x_end:    数据框取值结束行
+    :param title:    图表名称
+    :param x_label:  X轴数据列名
+    :param y1_list:  Y1轴数据列名
+    :param y2_list:   Y2轴数据列名
+    :param x_title:  X轴标题
+    :param y1_title:   Y1轴标题
+    :param y2_title:    Y2轴标题
+    :param width:   图表宽
+    :param height:  图表高
+    :param if_combine: 是否画组合图，默认是
+    :param chart1_type: 组合图1类型，默认是柱状图
+    :param chart2_type: 组合图2类型，默认是折线图
+    :param chart_row:  图表插入行
+    :param chart_col:  图表插入列
+    :return:
+    """
+    x1 = list(column).index(x_label) + 1
+    y1_index_list = [list(column).index(i) + 1 for i in y1_list]
+    y2_index_list = [list(column).index(i) + 1 for i in y2_list]
+    column_chart1 = wb.add_chart({'type': chart1_type})
+    column_chart1.set_size({'width': width, 'height': height})
+    for k in y1_index_list:
+        column_chart1.add_series(
+            {'name': [ws.name, label_x_start, k],
+             'num_font': {'name': '微软雅黑', 'size': 9},
+             'categories': [ws.name, x_start, x1, x_end, x1],
+             'values': [ws.name, x_start, k, x_end, k],
+             'data_labels': {'value': False}
+             })
+    column_chart1.set_title({'name': title, 'name_font': {'name': '微软雅黑', 'size': 10, 'bold': False}})
+    column_chart1.set_x_axis({'name': x_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})
+    column_chart1.set_y_axis({'name': y1_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})
+    column_chart1.set_chartarea({'border': {'none': True}, 'fill': {'color': text}})
+    column_chart1.set_plotarea({'border': {'none': True}, 'fill': {'color': text}})
+    if if_combine:
+        line_chart2 = wb.add_chart({'type': chart2_type})
+        for k in y2_index_list:
+            line_chart2.add_series(
+                {'name': [ws.name, label_x_start, k],
+                 'num_font': {'name': '微软雅黑', 'size': 9},
+                 'categories': [ws.name, x_start, x1, x_end, x1],
+                 'values': [ws.name, x_start, k, x_end, k],
+                 'data_labels': {'value': False},
+                 'y2_axis': True
+                 })
+        column_chart1.combine(line_chart2)
+        line_chart2.set_y2_axis({'name': y2_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})
+    ws.insert_chart(chart_row, chart_col, column_chart1)
+
+
+def add_plot(wb, ws, column, label_x_start=1 + 6, x_start=1 + 6 + 1, x_end=int(1 + 6 + 1 + 12 * 1 / 2), title='客户级规则效能分析', x_label='申请周',
+             y1_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'], x_title='申请周', y1_title='规则触碰率', width=760, height=520, chart1_type='line',
+             chart_row=1 + 6 + 12 + 4, chart_col=1):
+    """
+    插入图形函数
+    """
+    x1 = list(column).index(x_label) + 1
+    y1_index_list = [list(column).index(i) + 1 for i in y1_list]
+    column_chart1 = wb.add_chart({'type': chart1_type})
+    column_chart1.set_size({'width': width, 'height': height})
+    for k in y1_index_list:
+        column_chart1.add_series(
+            {'name': [ws.name, label_x_start, k],
+             'num_font': {'name': '微软雅黑', 'size': 9},
+             'categories': [ws.name, x_start, x1, x_end, x1],
+             'values': [ws.name, x_start, k, x_end, k],
+             'data_labels': {'value': False}
+             })
+    column_chart1.set_title({'name': title, 'name_font': {'name': '微软雅黑', 'size': 10, 'bold': False}})
+    column_chart1.set_x_axis({'name': x_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})
+    column_chart1.set_y_axis({'name': y1_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})
+    column_chart1.set_chartarea({'border': {'none': True}, 'fill': {'color': text}})
+    column_chart1.set_plotarea({'border': {'none': True}, 'fill': {'color': text}})
+    ws.insert_chart(chart_row, chart_col, column_chart1)
+
+
+def std_result_output(data, freq):
+    """泛化结果输出函数,按月、周、日输出分析结果函数
+    """
+    if freq == "M":
+        data_01 = data[-3:]
+        data_02 = data[data['额外触碰成熟量'] >= 10][-3:]
+        data_03 = data[data['整体逾期率'] > 0][-3:]
+        hit_rate = sum(data_01['额外触碰量']) / sum(data_01['申请量'])
+        risk_double = (data_02['额外触碰成熟坏样本量'].sum() / data_02['额外触碰成熟量'].sum()) / (sum(data_02['整体成熟坏样本量']) / sum(data_02['整体成熟量'])) if sum(data_02['整体成熟坏样本量']) > 0 else np.nan
+        overdue_minus = sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量']) - sum(data_03['整体成熟坏样本量'] - data_03['额外触碰成熟坏样本量']) / sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) if sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) > 0 else np.nan
+        overdue_range = overdue_minus / (sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量'])) if sum(data_03['整体成熟坏样本量']) > 0 else np.nan
+        text1 = "1.策略上线后通过率下降预估值 = 近3个月额外触碰量之和/近3个月申请量之和；策略上线后逾期率变化预估值 = （近n个有表现月逾期样本之和/近n个有表现月成熟样本之和）-（近n个有表现月置后触碰坏样本之和/近n个有表现月置后触碰成熟样本之和） ；备注：取整体逾期率大于0的近n个月计算策略上线后逾期率变化值，n默认取3，若不足3个月，有几个月计算几个月;"
+        text2 = "2.策略上线后额外触碰Lift预估值 = (近n个有表现月额外触碰成熟坏样本量之和/近n个有表现月额外触碰成熟量之和)/(近n个有表现月整体成熟坏样本量之和/近n个有表现月整体成熟量之和)；备注：取额外触碰成熟量大于等于10的近n个月计算策略上线后额外触碰Lift预估值，n默认取3，若不足3个月，有几个月计算几个月;"
+        if overdue_minus > 0:
+            text3 = "3.策略上线后通过率预计下降：{:.2f}%，逾期率下降：{:.2f}%，逾期率下降幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。".format(hit_rate * 100, overdue_minus * 100, overdue_range * 100, risk_double)
+        else:
+            text3 = "3.策略上线后通过率预计下降：{:.2f}%，逾期率上升：{:.2f}%，逾期率上升幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。".format(hit_rate * 100, abs(overdue_minus) * 100, abs(overdue_range) * 100, risk_double)
+    elif freq == "W":
+        week_num = str(len(data['申请周'].unique()))
+        mean_obs = data['置前触碰率'].mean() * 100
+        cv_rate = cv(data['置前触碰率'])
+        if data['置前触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='置前触碰率')
+        text1 = "1.近" + week_num + "个申请周平均置前触碰率%.2f" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + "；"
+        mean_obs = data['策略触碰率'].mean() * 100
+        cv_rate = cv(data['策略触碰率'])
+        if data['策略触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='策略触碰率')
+        text2 = "2.近" + week_num + "个申请周策略触碰率%.2f" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + "；"
+        mean_obs = data['额外触碰率'].mean() * 100
+        cv_rate = cv(data['额外触碰率'])
+        if data['额外触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='额外触碰率')
+        text3 = "3.近" + week_num + "个申请周额外触碰率%.2f" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + "；"
+    elif freq == "D":
+        day_num = str(len(data['申请日'].unique()))
+        mean_obs = data['置前触碰率'].mean() * 100
+        cv_rate = cv(data['置前触碰率'])
+        if data['置前触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='置前触碰率')
+        text1 = "1.近" + day_num + "个申请日平均置前触碰率%.2f" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + "；"
+        mean_obs = data['策略触碰率'].mean() * 100
+        cv_rate = cv(data['策略触碰率'])
+        if data['策略触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='策略触碰率')
+        text2 = "2.近" + day_num + "个申请日策略触碰率%.2f" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + "；"
+        mean_obs = data['额外触碰率'].mean() * 100
+        cv_rate = cv(data['额外触碰率'])
+        if data['额外触碰率'].sum() == 0:
+            cv_rate = 0
+        info = cv_type(cv_rate)
+        trend = get_trend(data, key='额外触碰率')
+        text3 = "3.近" + day_num + "个申请日额外触碰率%.2f" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + "；"
+    else:
+        raise "frqp must in [M\W\D]"
+
+    return text1, text2, text3
+
+
+if __name__ == '__main__':
+    import os
+    import xlsxwriter
+    import sys
+
+    sys.path.append("../")
+    from scorecardpipeline import *
+
+    init_setting()
+
+    # 参数值设置
+    sample_range = '202108-202110'
+    seq = 1
+    # sample_type取值只有Total，表示测算全量样本，不需要配置参数sample_type_col；若还有其他样本类型，也支持同时测算
+    sample_type = ['Total']
+
+    # 若sample_type需测算类型1和类型2的样本，类型1和类型2所在的字段为ordertype，sample_type_col参数示例如下
+    # sample_type_col = {
+    #                    '类型1': ['ordertype', ['类型1']],
+    #                    '类型2': ['ordertype', ['类型2']]
+    #                   }
+
+    sample_type_target = {'Total': ['fpd_30_act', 'mob3_dpd_30_act']}
+
+    target_ripe = {'fpd_30_act': ['agr_fpd_30'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30']}
+
+    target_del_col = {'fpd_30_act': ['agr_fpd_30', 'agr_mob3_dpd_30', 'mob3_dpd_30_act'],
+                      'mob3_dpd_30_act': ['agr_mob3_dpd_30', 'agr_fpd_30', 'fpd_30_act']}
+
+    sub_div_bin = 0.1
+    target_min_rate = {'fpd_30_act': [0.015], 'mob3_dpd_30_act': [0.02]}
+    min_num = 40
+    hit_num = 30
+    sample_type_lift = {'Total': {'fpd_30_act': 1.8, 'mob3_dpd_30_act': 3}}
+
+    # 泛化结果输出路径
+    path_rule = "rule_data/rule_result/"
+
+    if not os.path.exists(path_rule):
+        os.makedirs(path_rule)
+
+    # 4.读入数据并进行数据预处理
+    #  读入测算环节筛选的待泛化规则
+    rules_dict = pd.read_excel("rule_data/规则字典.xlsx")
+
+    # 加载数据
+    mydata = pd.read_csv("rule_data/rule_data.csv", encoding="gbk")
+    mydata.columns = mydata.columns.map(lambda x: x.lower())
+
+    for i in mydata.columns[mydata.dtypes != 'object']:
+        mydata[i][mydata[i].map(lambda x: x in [-999, -9999, -999999])] = np.nan
+
+    for i in mydata.columns[mydata.dtypes == 'object']:
+        mydata[i] = mydata[i].map(lambda x: str(x).strip())
+        mydata[i][mydata[i].map(lambda x: x in ['-999', '-9999', '-999999'])] = np.nan
+        try:
+            mydata[i] = mydata[i].astype('float64')
+        except:
+            pass
+
+    # 处理灰样本
+    mydata['mob3_dpd_30_act'] = mydata['mob3_dpd_30_act'].map(lambda x: 1 if x == 1 else 0)
+
+    # 5.基于加载的函数进行策略自动泛化；（需要基于阈值测算的数据字典）
+
+    """ # 泛化参数说明
+    target_ripe      获取目标字段对应的是否成熟标签，与测算环节参数一样
+    sample_type_col  若测算样本是整个样本的子集，需配置sample_type_col参数，指名样本类型从哪个字段获取，与测算环节参数一样
+    rules_dict       测算环节筛选的待泛化规则集
+    rule_all         当前已经在线上运行的所有规则对应的决策结果标签字段，1代表拒绝，0代表未拒绝
+    use_credit_flag  授信申请通过后是否用信标签字段，1代表用信，0代表未用信
+    circle_mth       申请月对应的字段，在进行策略泛化的时候的会按月进行泛化，分析策略触碰情况和风险表现情况
+    circle_week      申请周对应的字段，在进行策略泛化的时候会按申请周进行泛化，分析近10周策略触碰情况
+    circle_day       申请日对应的字段，在进行策略泛化的时候会按申请日进行泛化，分析近10日策略触碰情况
+    base_lift        策略泛化时，取额外触碰样本的Lift值与base_lift进行比较，若额外触碰Lift值大于base_lift则说明策略效果较好
+    subset_total     是否获取全量样本作为计算整体，若取值为False则分母为全样本，计算各种指标时是从全量样本维度考虑的，若取值为True则分母为样本类型对应的样本
+    """
+
+    target_ripe = {'fpd_30_act': ['agr_fpd_30'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30']}
+
+    # 在进行策略测算时，若除测算全量样本（Total）外还测算了类型1和类型2的样本，假设类型1和类型2所在的字段为ordertype，则需设置sample_type_col参数，否则不需要进行设置。
+    # sample_type_col = {
+    #                    '类型1': ['ordertype', ['类型1']],
+    #                    '类型2': ['ordertype', ['类型2']]
+    #                   }
+
+    # 对测算环节筛选的样本进行自动化泛化
+    starttime = datetime.datetime.now()
+    print('程序开始执行时间为: ' + str(starttime))
+
+    rule_combine_results(data=mydata, rules_dict=rules_dict, rule_all='apply_refuse_flag', use_credit_flag='if_loan_flag', circle_mth='apply_mth', circle_week='apply_week', circle_day='apply_day', base_lift=3, subset_total=True)
+
+    endtime = datetime.datetime.now()
+    print('程序开始执行时间为: ' + str(endtime - starttime))
+
+    # 6.基于步骤3策略泛化结果筛选泛化效果好的规则集进行合并泛化（不需要测算结果数据字典）
+
+    # 此次未筛选到足够多的泛化效果好的规则，只为跑通代码流程，代码执行结果供参考。基于泛化效果好的规则集，重构一条规则进行泛化，变量取值为1表示规则触碰，取值为0表示规则未触碰。
+
+    mydata['all_need_online_rule'] = mydata.apply(lambda x: 1 if (x['var3'] > 3 or x['var6'] > 5) else 0, axis=1)
+
+    starttime = datetime.datetime.now()
+    print('程序开始执行时间为: ' + str(starttime))
+    rule_combine_results_01(data=mydata, rule_all='apply_refuse_flag', use_credit_flag='if_loan_flag', cut_point=0, direction='>', rule_name='待上线规则合并泛化', rule_name_chinese='待上线规则合并泛化', var='all_need_online_rule', rule_type='CR', rule_limit='Total', circle_mth='apply_mth', circle_week='apply_week', circle_day='apply_day', target='mob3_dpd_30_act', base_lift=3, subset_total=True)
+    endtime = datetime.datetime.now()
+    print('程序执行时间为: ' + str(endtime - starttime))
diff --git a/requirements.txt b/requirements.txt
index 67ae1ab..0c1b3d8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 Cython
 wget
-numpy<=1.23.5,>1.23.1
+numpy>1.23.1
 pandas
 matplotlib
 seaborn>=0.10.0
@@ -9,7 +9,7 @@ statsmodels<0.14,>=0.13.2
 scikit-learn>=1.3.1
 toad
 scorecardpy
-ortools
+ortools<9.8.0
 ropwr>=0.4.0
 optbinning
 CairoSVG>=2.7.0
@@ -26,3 +26,4 @@ six>=1.15.0
 openpyxl==3.0.7
 sweetviz
 numexpr
+minepy
diff --git a/scorecardpipeline/feature_engineering.py b/scorecardpipeline/feature_engineering.py
new file mode 100644
index 0000000..9b9b2d8
--- /dev/null
+++ b/scorecardpipeline/feature_engineering.py
@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2024/5/10 10:28
+@Author  : itlubber
+@Site    : itlubber.art
+"""
+import numpy as np
+import numexpr as ne
+from pandas import DataFrame
+from sklearn.base import BaseEstimator, TransformerMixin
+
+
+class NumExprDerive(BaseEstimator, TransformerMixin):
+    """Derive features by expressions.
+
+    Parameters
+    -----------
+    derivings : list, default=None
+        Each entry is a (name, expr) pair representing a deriving rule.
+
+    Examples
+    -----------
+    >>> import pandas as pd
+    >>> from scorecardpipeline.feature_engineering import NumExprDerive
+    >>> X = pd.DataFrame({"f0": [2, 1.0, 3], "f1": [np.inf, 2, 3], "f2": [2, 3, 4], "f3": [2.1, 1.4, -6.2]})
+    >>> fd = NumExprDerive(derivings=[("f4", "where(f1>1, 0, 1)"), ("f5"、"f1+f2"), ("f6", "sin(f1)"), ("f7", "abs(f3)"))
+    >>> fd.fit_transform(X)
+    """
+    def __init__(self, derivings=None):
+        self.derivings = derivings
+
+    def fit(self, X, y=None):
+        self._check_keywords()
+        self._validate_data(X, dtype=None, ensure_2d=True, force_all_finite=False)
+        return self
+
+    def _check_keywords(self):
+        derivings = self.derivings
+        if derivings is None:
+            raise ValueError("Deriving rules should not be empty!")
+        if not isinstance(derivings, list):
+            raise ValueError("Deriving rules should be a list!")
+        for i, entry in enumerate(derivings):
+            if not isinstance(entry, tuple):
+                raise ValueError("The {}-th deriving rule should be a tuple!".format(i))
+            if len(entry) != 2:
+                raise ValueError("The f}-th deriving rule is not a two-element (drived_name, expression) tuple!".format(i))
+            name, expr = entry
+            if not isinstance(name, str) or not isinstance(expr, str):
+                raise ValueError("The {}-th deriving rule is not a two-string tuple!".format(i))
+
+    @staticmethod
+    def _get_context(X, feature_names=None):
+        if feature_names is not None:
+            return {name: X[:, i] for i, name in enumerate(feature_names)}
+        return {"f%d" % i: X[:, i] for i in range(X.shape[1])}
+
+    def _transform_frame(self, X):
+        feature_names = X.columns.tolist()
+        self.features_names = feature_names
+        index = X.index
+        X = self._validate_data(X, dtype="numeric", ensure_2d=True, force_all_finite=False)
+        context = self._get_context(X, feature_names=feature_names)
+        n_derived = len(self.derivings)
+        X_derived = np.empty((X.shape[0], n_derived), dtype=np.float64)
+        derived_names = []
+        for i, (name, expr) in enumerate(self.derivings):
+            derived_names.append(name)
+            X_derived[:, i] = ne.evaluate(expr, local_dict=context)
+        data = np.hstack((X, X_derived))
+        columns = feature_names + derived_names
+        return DataFrame(data=data, columns=columns, index=index)
+
+    def _transform_ndarray(self, X):
+        X = self._validate_data(X, dtype="numeric", ensure_2d=True, force_all_finite=False)
+        context = self._get_context(X, feature_names=None)
+        n_derived = len(self.derivings)
+        X_derived = np.empty((X.shape[0], n_derived), dtype=np.float64)
+        derived_names = []
+        for i, (name, expr) in enumerate(self.derivings):
+            derived_names.append(name)
+            X_derived[:, i] = ne.evaluate(expr, local_dict=context)
+        return np.hstack((X, X_derived))
+
+    def transform(self, X):
+        if isinstance(X, DataFrame):
+            return self._transform_frame(X)
+        return self._transform_ndarray(X)
+
+    def _more_tags(self):
+        return {
+            "X_types": ["2darray"],
+            "allow_nan": True,
+        }
diff --git a/scorecardpipeline/feature_selection.py b/scorecardpipeline/feature_selection.py
new file mode 100644
index 0000000..c8abc74
--- /dev/null
+++ b/scorecardpipeline/feature_selection.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2024/5/8 14:06
+@Author  : itlubber
+@Site    : itlubber.art
+"""
+
+from sklearn.feature_selection import RFECV, RFE, SelectFromModel, SelectKBest
+from sklearn.model_selection import StratifiedKFold, GroupKFold
diff --git a/scorecardpipeline/rule_extraction.py b/scorecardpipeline/rule_extraction.py
index 1b6fe73..be625c8 100644
--- a/scorecardpipeline/rule_extraction.py
+++ b/scorecardpipeline/rule_extraction.py
@@ -19,12 +19,13 @@
 from optbinning import OptimalBinning
 from sklearn.tree import DecisionTreeClassifier
 
+from .rule import Rule
 from .utils import init_setting
 from .excel_writer import ExcelWriter, dataframe2excel
 
 
 class DecisionTreeRuleExtractor:
-    def __init__(self, target="target", labels=["positive", "negative"], feature_map={}, nan=-1., max_iter=128, writer=None, combiner=None, seed=None, theme_color="2639E9"):
+    def __init__(self, target="target", labels=["positive", "negative"], feature_map={}, nan=-1., max_iter=128, writer=None, seed=None, theme_color="2639E9", decimal=4):
         """决策树自动规则挖掘工具包
 
         :param target: 数据集中好坏样本标签列名称，默认 target
@@ -33,7 +34,11 @@ def __init__(self, target="target", labels=["positive", "negative"], feature_map
         :param nan: 在决策树策略挖掘时，默认空值填充的值，默认 -1
         :param max_iter: 最多支持在数据集上训练多少颗树模型，每次生成一棵树后，会剔除特征重要性最高的特征后，再生成树，默认 128
         :param writer: 在之前程序运行时生成的 ExcelWriter，可以支持传入一个已有的writer，后续所有内容将保存至该workbook中，默认 None
+        :param seed: 随机种子，保证结果可复现使用，默认为 None
+        :param theme_color: 主题色，默认 2639E9 克莱因蓝，可设置位其他颜色
+        :param decimal: 精度，决策树分裂节点阈值的精度范围，默认 4，即保留4位小数
         """
+        self.decimal = decimal
         self.seed = seed
         self.nan = nan
         self.target = target
@@ -77,57 +82,42 @@ def encode_cat_features(self, X, y):
         else:
             return X
 
-    def get_dt_rules(self, tree, feature_names, total_bad_rate, total_count):
+    def get_dt_rules(self, tree, feature_names):
         tree_ = tree.tree_
         left = tree.tree_.children_left
         right = tree.tree_.children_right
         feature_name = [feature_names[i] if i != -2 else "undefined!" for i in tree_.feature]
         rules = dict()
 
-        result_dataframe = pd.DataFrame()
-
         def recurse(node, depth, parent):  # 搜每个节点的规则
-            nonlocal result_dataframe
+            nonlocal rules
 
             if tree_.feature[node] != -2:  # 非叶子节点,搜索每个节点的规则
                 name = feature_name[node]
-                thd = np.round(tree_.threshold[node], 3)
-                s = "{} <= {} ".format(name, thd, node)
+                thd = np.round(tree_.threshold[node], self.decimal)
+                s = Rule("{} <= {}".format(name, thd))
                 # 左子
                 if node == 0:
                     rules[node] = s
                 else:
-                    rules[node] = rules[parent] + ' & ' + s
+                    rules[node] = rules[parent] & s
+
                 recurse(left[node], depth + 1, node)
-                s = "{} > {}".format(name, thd)
+
+                s = Rule("{} > {}".format(name, thd))
                 # 右子
                 if node == 0:
                     rules[node] = s
                 else:
-                    rules[node] = rules[parent] + ' & ' + s
+                    rules[node] = rules[parent] & s
                 recurse(right[node], depth + 1, node)
-            else:
-                result = pd.DataFrame()
-                result['组合策略'] = rules[parent],
-                result['好样本数'] = tree_.value[node][0][0].astype(int)
-                result['好样本占比'] = result['好样本数'] / (total_count * (1 - total_bad_rate))
-                result['坏样本数'] = tree_.value[node][0][1].astype(int)
-                result['坏样本占比'] = result['坏样本数'] / (total_count * total_bad_rate)
-                result['命中数'] = result['好样本数'] + result['坏样本数']
-                result['命中率'] = result['命中数'] / total_count
-                result['坏率'] = result['坏样本数'] / result['命中数']
-                result['样本整体坏率'] = total_bad_rate
-                result['LIFT值'] = result['坏率'] / result['样本整体坏率']
-
-                result_dataframe = pd.concat([result_dataframe, result], axis=0)
 
         recurse(0, 1, 0)
 
-        return result_dataframe.sort_values("LIFT值", ascending=True)[self.describe_columns].reset_index(drop=True)
+        return list(rules.values())
 
     def select_dt_rules(self, decision_tree, x, y, lift=0., max_samples=1., save=None, verbose=False, drop=False):
-        rules = self.get_dt_rules(decision_tree, x.columns, sum(y) / len(y), len(y))
-        total_rules = len(rules)
+        rules = self.get_dt_rules(decision_tree, x.columns)
 
         try:
             viz_model = dtreeviz.model(decision_tree,
@@ -140,9 +130,15 @@ def select_dt_rules(self, decision_tree, x, y, lift=0., max_samples=1., save=Non
         except AttributeError:
             raise "请检查 dtreeviz 版本"
 
-        rules = rules.query(f"LIFT值 >= {lift} & 命中率 <= {max_samples}").reset_index(drop=True)
+        rules_reports = pd.DataFrame()
+        for rule in rules:
+            rules_reports = pd.concat([rules_reports, rule.report(x.join(y), target=y.name).query("分箱 == '命中'")])
 
-        if len(rules) > 0:
+        rules_reports = rules_reports.rename(columns={"指标名称": "组合策略", "样本总数": "命中数", "样本占比": "命中率"}).drop(columns=["分箱"])
+        rules_reports["样本整体坏率"] = round(y.mean(), self.decimal)
+        rules_reports = rules_reports.query(f"LIFT值 >= {lift} & 命中率 <= {max_samples}").reset_index(drop=True)
+
+        if len(rules_reports) > 0:
             # font_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'matplot_chinese.ttf')
             # font_manager.fontManager.addfont(font_path)
             # plt.rcParams['font.family'] = font_manager.FontProperties(fname=font_path).get_name()
@@ -166,9 +162,9 @@ def select_dt_rules(self, decision_tree, x, y, lift=0., max_samples=1., save=Non
             if verbose:
                 from IPython.core.display_functions import display
                 if self.feature_map is not None and len(self.feature_map) > 0:
-                    display(rules.replace(self.feature_map, regex=True))
+                    display(rules_reports.replace(self.feature_map, regex=True))
                 else:
-                    display(rules)
+                    display(rules_reports)
                 display(decision_tree_viz)
             if save:
                 if os.path.dirname(save) and not os.path.exists(os.path.dirname(save)):
@@ -195,43 +191,25 @@ def select_dt_rules(self, decision_tree, x, y, lift=0., max_samples=1., save=Non
             os.remove("combine_rules_cache")
 
         if drop:
-            if len(rules) > 0:
-                return rules, decision_tree.feature_names_in_[list(decision_tree.feature_importances_).index(max(decision_tree.feature_importances_))], total_rules
+            if len(rules_reports) > 0:
+                return rules_reports, decision_tree.feature_names_in_[list(decision_tree.feature_importances_).index(max(decision_tree.feature_importances_))], len(rules_reports)
             else:
-                return rules, decision_tree.feature_names_in_[list(decision_tree.feature_importances_).index(min(decision_tree.feature_importances_))], total_rules
+                return rules_reports, decision_tree.feature_names_in_[list(decision_tree.feature_importances_).index(min(decision_tree.feature_importances_))], len(rules_reports)
         else:
-            return rules, total_rules
+            return rules_reports, len(rules_reports)
 
     def query_dt_rules(self, x, y, parsed_rules=None):
-        total_count = len(y)
-        total_bad_rate = y.sum() / len(y)
-
-        rules = pd.DataFrame()
-
         if isinstance(parsed_rules, pd.DataFrame):
-            parsed_rules = parsed_rules["组合策略"].unique()
+            parsed_rules = [Rule(r) for r in parsed_rules["组合策略"].unique()]
 
+        rules_reports = pd.DataFrame()
         for rule in parsed_rules:
-            select_index = x.query(rule).index
-            if len(select_index) > 0:
-                y_select = y[select_index]
-                df = pd.Series()
-                df['组合策略'] = rule
-                df['好样本数'] = len(y_select) - y_select.sum()
-                df['好样本占比'] = df['好样本数'] / (total_count * (1 - total_bad_rate))
-                df['坏样本数'] = y_select.sum()
-                df['坏样本占比'] = df['坏样本数'] / (total_count * total_bad_rate)
-                df['命中数'] = df['好样本数'] + df['坏样本数']
-                df['命中率'] = df['命中数'] / total_count
-                df['坏率'] = df['坏样本数'] / df['命中数']
-                df['样本整体坏率'] = total_bad_rate
-                df['LIFT值'] = df['坏率'] / df['样本整体坏率']
-            else:
-                df = pd.Series({'组合策略': rule, '好样本数': 0, '好样本占比': 0., '坏样本数': 0, '坏样本占比': 0., '命中数': 0, '命中率': 0., '坏率': 0., '样本整体坏率': total_bad_rate, 'LIFT值': 0., })
+            rules_reports = pd.concat([rules_reports, rule.report(x.join(y), target=y.name).query("分箱 == '命中'")])
 
-            rules = pd.concat([rules, pd.DataFrame(df).T]).reset_index(drop=True)
+        rules_reports = rules_reports.rename(columns={"指标名称": "组合策略", "样本总数": "命中数", "样本占比": "命中率"}).drop(columns=["分箱"])
+        rules_reports["样本整体坏率"] = round(y.mean(), self.decimal)
 
-        return rules[self.describe_columns]
+        return rules_reports
 
     def insert_dt_rules(self, parsed_rules, end_row, start_col, save=None, sheet=None, figsize=(500, 350)):
         if isinstance(sheet, Worksheet):
@@ -239,7 +217,7 @@ def insert_dt_rules(self, parsed_rules, end_row, start_col, save=None, sheet=Non
         else:
             worksheet = self.writer.get_sheet_by_name(sheet or "决策树组合策略挖掘")
 
-        end_row, end_col = dataframe2excel(parsed_rules, self.writer, sheet_name=worksheet, start_row=end_row + 1, start_col=start_col, percent_cols=['好样本占比', '坏样本占比', '命中率', '坏率', '样本整体坏率', 'LIFT值'], condition_cols=["坏率", "LIFT值"])
+        end_row, end_col = dataframe2excel(parsed_rules, self.writer, sheet_name=worksheet, start_row=end_row + 1, start_col=start_col, percent_cols=['好样本占比', '坏样本占比', '命中率', '坏样本率', '样本整体坏率', 'LIFT值', '坏账改善', '准确率', '精确率', '召回率', 'F1分数'], condition_cols=["坏样本率", "LIFT值"])
 
         if save is not None:
             end_row, end_col = self.writer.insert_pic2sheet(worksheet, save, (end_row + 1, start_col), figsize=figsize)
diff --git a/scorecardpipeline/scorecard.py b/scorecardpipeline/scorecard.py
index 57603a6..e9278ec 100644
--- a/scorecardpipeline/scorecard.py
+++ b/scorecardpipeline/scorecard.py
@@ -146,13 +146,11 @@ def inverse_transform(self, X):
 
     def _more_tags(self):
         return {
-            "X_types": ["2darray"],
             "allow_nan": False,
         }
 
 
 class NPRoundStandardScoreTransformer(StandardScoreTransformer):
-    """Stretch the predict probability to a normal distributed score."""
 
     def __init__(self, base_score=660, pdo=75, bad_rate=0.15, down_lmt=300, up_lmt=1000, round_decimals=0, greater_is_better=True, cutoff=None):
         self.round_decimals = round_decimals
@@ -205,7 +203,6 @@ def fit(self, X, y=None, **fit_params):
             self.lambdas_ = np.array([self._box_cox_optimize(col) for col in X.T])
         for i, lmbda in enumerate(self.lambdas_):
             X[:, i] = stats.boxcox(X[:, i], lmbda)
-        print(X.max(), X.min(), X.mean())
         self.scaler_ = MinMaxScaler(feature_range=(self.down_lmt, self.up_lmt)).fit(X)
         return self
 
diff --git a/scorecardpipeline/template.xlsx b/scorecardpipeline/template.xlsx
index aff95ef67fc2bfb93e20349d118533c7b0536905..47b6a0b0a751f7371f13e0db9f8c1762aaec7155 100644
GIT binary patch
delta 2719
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