toad使用教程¶
toad是针对工业届建模而开发的工具包，针对风险评分卡的建模有针对性的功能。toad持续更新优化中，本教程针对toad的各类主要功能进行介绍，包括： 1. EDA相关功能 2. 如何使用toad高效分箱并进行特征筛选 3. WOE转化 4. 逐步回归特征筛选 5. 模型检验和评判 6. 标准评分卡转化和输出 7. 其他功能
本教程将使用高维的数据来演示如何使用toad高效建模
安装与升级： 1. pip安装：!pip install toad 2. conda安装：conda install toad –channel conda-forge 3. 升级：!pip install -U toad; conda install -U toad –channel conda-forge
有使用问题欢迎到**`github <https://github.com/amphibian-dev/toad>`__**上提issue
[1]:
import pandas as pd
import numpy as np
import toad
[1]:
'''
请升级到最新版本
'''
[1]:
'\n请升级到最新版本\n'
### 0. 读取数据¶
演示数据为165维，包括一列主键，target列，和month列。其中包含了若干个离散型变量和连续性变量，且有一定的缺失值。
本教程将展示遇到这类很“脏”的数据时，如何使用toad进行有效且高效的建模。
[3]:
data = pd.read_csv('train.csv')
print('Shape:',data.shape)
data.head(10)
Shape: (108940, 167)
[3]:
| APP_ID_C | target | var_d1 | var_d2 | var_d3 | var_d4 | var_d5 | var_d6 | var_d7 | var_d8 | ... | var_l_118 | var_l_119 | var_l_120 | var_l_121 | var_l_122 | var_l_123 | var_l_124 | var_l_125 | var_l_126 | month | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | app_1 | 0 | Hit-6+ Vintage | 816.0 | RESIDENT INDIAN | Post-Graduate | M | RESIDENT INDIAN | SELF-EMPLOYED | Y | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 2019-03 |
| 1 | app_2 | 0 | NaN | 841.0 | RESIDENT INDIAN | Post-Graduate | F | RESIDENT INDIAN | SALARIED | N | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 2019-03 |
| 2 | app_3 | 0 | Hit-6+ Vintage | 791.0 | RESIDENT INDIAN | Post-Graduate | M | RESIDENT INDIAN | PROPRIETOR | Y | ... | 0.0 | 0.088235 | 0.0 | 0.100000 | 0.0 | 0.011494 | 0.5 | 0.000000 | 0.0 | 2019-03 |
| 3 | app_4 | 0 | Hit-6+ Vintage | 821.0 | RESIDENT INDIAN | Graduate | M | RESIDENT INDIAN | SELF-EMPLOYED | N | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 2019-03 |
| 4 | app_5 | 0 | Hit-6+ Vintage | 807.0 | RESIDENT INDIAN | Graduate | M | RESIDENT INDIAN | SALARIED | Y | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.540541 | 0.0 | 0.285714 | 0.0 | 2019-03 |
| 5 | app_6 | 0 | Hit-6+ Vintage | 788.0 | RESIDENT INDIAN | Others | M | RESIDENT INDIAN | SALARIED | N | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 2019-03 |
| 6 | app_7 | 0 | Hit-6+ Vintage | 779.0 | RESIDENT INDIAN | Graduate | M | RESIDENT INDIAN | ATTORNEY AT LAW | Y | ... | 0.0 | 0.722222 | 0.0 | 0.777778 | 0.0 | 0.380952 | 0.0 | 0.571429 | 0.0 | 2019-03 |
| 7 | app_8 | 0 | Hit-6+ Vintage | 801.0 | RESIDENT INDIAN | Post-Graduate | M | RESIDENT INDIAN | SAL(RETIRAL AGE 60) | N | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 2019-03 |
| 8 | app_9 | 0 | Hit-6+ Vintage | 815.0 | RESIDENT INDIAN | Graduate | F | RESIDENT INDIAN | NaN | Y | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 2019-03 |
| 9 | app_10 | 0 | NaN | 804.0 | RESIDENT INDIAN | Graduate | M | RESIDENT INDIAN | PROPRIETOR | N | ... | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 2019-03 |
10 rows × 167 columns
数据包含2019年5月 - 7月的数据。其中我们将用3月和4月数据用于训练样本，5月、6月、7月数据作为时间外样本（OOT）。¶
[4]:
print('month:',data.month.unique())
month: ['2019-03' '2019-04' '2019-05' '2019-06' '2019-07']
[5]:
train = data.loc[data.month.isin(['2019-03','2019-04'])==True,:]
OOT = data.loc[data.month.isin(['2019-03','2019-04'])==False,:]
#train = data.loc[data.month.isin(['Mar-19','Apr-19'])==True,:]
#OOT = data.loc[data.month.isin(['Mar-19','Apr-19'])==False,:]
print('train size:',train.shape,'\nOOT size:',OOT.shape)
train size: (43576, 167)
OOT size: (65364, 167)
### I. EDA相关功能¶
1. toad.detect(dataframe):¶
用于检测数据情况（EDA）。输出每列特征的统计性特征和其他信息，主要的信息包括：缺失值、unique values、数值变量的平均值、离散值变量的众数等。如下面的cell，可以得到以下信息：
正样本占比2.2%：target的mean是0.0219479
部分特征有缺失值，且缺失值不等：注意missing列。
数值型变量和离散型变量有若干个，部分离散型变量的unique values较多，有10多个甚至84个：离散型变量的unique列。
[6]:
toad.detect(train)[:10]
[6]:
| type | size | missing | unique | mean_or_top1 | std_or_top2 | min_or_top3 | 1%_or_top4 | 10%_or_top5 | 50%_or_bottom5 | 75%_or_bottom4 | 90%_or_bottom3 | 99%_or_bottom2 | max_or_bottom1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| APP_ID_C | object | 43576 | 0.00% | 43576 | app_36227:0.00% | app_29819:0.00% | app_35476:0.00% | app_10104:0.00% | app_35794:0.00% | app_25789:0.00% | app_36858:0.00% | app_12750:0.00% | app_24:0.00% | app_13004:0.00% |
| target | int64 | 43576 | 0.00% | 2 | 0.0213191 | 0.144447 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| var_d1 | object | 43576 | 37.57% | 2 | Hit-6+ Vintage:60.32% | Hit-lt 6 Vinta:2.10% | None | None | None | None | None | None | Hit-6+ Vintage:60.32% | Hit-lt 6 Vinta:2.10% |
| var_d2 | float64 | 43576 | 5.44% | 389 | 570.492 | 355.565 | -1 | -1 | -1 | 778 | 810 | 832 | 864 | 900 |
| var_d3 | object | 43576 | 5.31% | 6 | RESIDENT INDIAN:94.00% | NON-RESIDENT INDIAN:0.64% | PARTNERSHIP FIRM:0.02% | PRIVATE LTD COMPANIES:0.02% | PUBLIC LTD COMPANIES:0.00% | NON-RESIDENT INDIAN:0.64% | PARTNERSHIP FIRM:0.02% | PRIVATE LTD COMPANIES:0.02% | PUBLIC LTD COMPANIES:0.00% | OVERSEAS CITIZEN OF INDIA:0.00% |
| var_d4 | object | 43576 | 1.08% | 5 | Graduate:55.30% | Post-Graduate:21.57% | Others:10.71% | Under Graduate:10.67% | Professional:0.67% | Graduate:55.30% | Post-Graduate:21.57% | Others:10.71% | Under Graduate:10.67% | Professional:0.67% |
| var_d5 | object | 43576 | 1.08% | 3 | M:79.70% | F:14.33% | O:4.89% | None | None | None | None | M:79.70% | F:14.33% | O:4.89% |
| var_d6 | object | 43576 | 1.08% | 13 | RESIDENT INDIAN:93.34% | PRIVATE LTD COMPANIES:2.57% | PARTNERSHIP FIRM:1.45% | PUBLIC LTD COMPANIES:0.73% | NON-RESIDENT INDIAN:0.64% | CO-OPERATIVE SOCIETIES:0.01% | LIMITED LIABILITY PARTNERSHIP:0.00% | ASSOCIATION:0.00% | TRUST-NGO:0.00% | OVERSEAS CITIZEN OF INDIA:0.00% |
| var_d7 | object | 43576 | 1.60% | 84 | SALARIED:31.43% | PROPRIETOR:31.31% | SELF-EMPLOYED:10.74% | OTHERS:6.40% | FIRST TIME USERS:2.72% | NURSE:0.00% | PHARMACIST:0.00% | RETAIL BUS OPERATOR:0.00% | PRIVATE TAILOR:0.00% | TAXI DRIVER:0.00% |
| var_d8 | object | 43576 | 1.08% | 2 | Y:59.90% | N:39.03% | None | None | None | None | None | None | Y:59.90% | N:39.03% |
2. toad.quality(dataframe, target=’target’, iv_only=False):¶
输出每个变量的iv值，gini，entropy，和unique values，结果以iv值排序。”target”为目标列，”iv_only”决定是否只输出iv值。
注意：1. 对于数据量大或高维度数据，建议使用iv_only=True 2. 要去掉主键，日期等高unique values且不用于建模的特征
[7]:
to_drop = ['APP_ID_C','month'] # 去掉ID列和month列
toad.quality(data.drop(to_drop,axis=1),'target',iv_only=True)[:15]
[7]:
| iv | gini | entropy | unique | |
|---|---|---|---|---|
| var_b19 | 0.353043 | NaN | NaN | 88.0 |
| var_b18 | 0.317603 | NaN | NaN | 46.0 |
| var_d2 | 0.313443 | NaN | NaN | 411.0 |
| var_d7 | 0.309985 | NaN | NaN | 95.0 |
| var_b10 | 0.301111 | NaN | NaN | 15726.0 |
| var_b17 | 0.240104 | NaN | NaN | 235.0 |
| var_b16 | 0.231403 | NaN | NaN | 104.0 |
| var_b24 | 0.226939 | NaN | NaN | 30928.0 |
| var_b20 | 0.198655 | NaN | NaN | 34.0 |
| var_b11 | 0.187306 | NaN | NaN | 239.0 |
| var_l_19 | 0.160020 | NaN | NaN | 32240.0 |
| var_b9 | 0.157585 | NaN | NaN | 197.0 |
| var_l_68 | 0.150068 | NaN | NaN | 757.0 |
| var_l_123 | 0.146634 | NaN | NaN | 10602.0 |
| var_l_125 | 0.146274 | NaN | NaN | 6338.0 |
### II. 如何使用toad高效分箱并进行特征筛选¶
3. toad.selection.select(dataframe, target=’target’, empty=0.9, iv=0.02, corr=0.7, return_drop=False, exclude=None):¶
根据缺失值占比，iv值，和高相关性进行变量筛选，赋值为：
（1）empty=0.9: 若变量的缺失值大于0.9被删除
（2）iv=0.02: 若变量的iv值小于0.02被删除
（3）corr=0.7: 若两个相关性高于0.7时，iv值低的变量被删除
（4）return_drop=False: 若为True，function将返回被删去的变量列
（5）exclude=None: 明确不被删去的列名，输入为list格式
如下面的cell，没有变量由于缺失值高被删除，大量变量因为低iv值被删除，部分相关性高的变量被删除。从165个特征中选出了32个变量。
[8]:
train_selected, dropped = toad.selection.select(train,target = 'target', empty = 0.5, iv = 0.05, corr = 0.7, return_drop=True, exclude=['APP_ID_C','month'])
print(dropped)
print(train_selected.shape)
{'empty': array([], dtype=float64), 'iv': array(['var_d1', 'var_d4', 'var_d8', 'var_d9', 'var_b5', 'var_b6',
'var_b7', 'var_l_1', 'var_l_2', 'var_l_3', 'var_l_4', 'var_l_5',
'var_l_6', 'var_l_7', 'var_l_8', 'var_l_10', 'var_l_12',
'var_l_14', 'var_l_15', 'var_l_16', 'var_l_17', 'var_l_18',
'var_l_21', 'var_l_23', 'var_l_24', 'var_l_25', 'var_l_26',
'var_l_27', 'var_l_28', 'var_l_29', 'var_l_30', 'var_l_31',
'var_l_32', 'var_l_33', 'var_l_34', 'var_l_35', 'var_l_37',
'var_l_38', 'var_l_39', 'var_l_40', 'var_l_41', 'var_l_42',
'var_l_43', 'var_l_44', 'var_l_45', 'var_l_47', 'var_l_49',
'var_l_51', 'var_l_53', 'var_l_55', 'var_l_56', 'var_l_57',
'var_l_59', 'var_l_61', 'var_l_62', 'var_l_63', 'var_l_65',
'var_l_67', 'var_l_70', 'var_l_72', 'var_l_75', 'var_l_76',
'var_l_77', 'var_l_78', 'var_l_79', 'var_l_80', 'var_l_81',
'var_l_82', 'var_l_83', 'var_l_84', 'var_l_85', 'var_l_86',
'var_l_87', 'var_l_88', 'var_l_90', 'var_l_92', 'var_l_93',
'var_l_94', 'var_l_95', 'var_l_96', 'var_l_97', 'var_l_98',
'var_l_100', 'var_l_102', 'var_l_104', 'var_l_106', 'var_l_108',
'var_l_109', 'var_l_110', 'var_l_112', 'var_l_114', 'var_l_116',
'var_l_117', 'var_l_118', 'var_l_120', 'var_l_122', 'var_l_124',
'var_l_126'], dtype=object), 'corr': array(['var_b27', 'var_b28', 'var_b4', 'var_l_105', 'var_b25', 'var_b2',
'var_l_113', 'var_l_46', 'var_b26', 'var_b8', 'var_b1',
'var_l_103', 'var_l_99', 'var_l_74', 'var_b14', 'var_l_13',
'var_l_22', 'var_b22', 'var_l_101', 'var_l_111', 'var_b12',
'var_l_69', 'var_b11', 'var_l_115', 'var_l_11', 'var_l_36',
'var_l_50', 'var_l_54', 'var_l_121', 'var_b15', 'var_l_73',
'var_l_66', 'var_l_125', 'var_b16', 'var_b24'], dtype=object)}
(43576, 34)
4. 分箱¶
toad的分箱功能支持数值型数据和离散型分箱，默认分箱方法使用 卡方分箱。
toad.transform.Combiner 是用来分箱的class，分箱步骤如下：
*** initalise: ***c = toad.transform.Combiner()
训练分箱: c.fit(dataframe, y = ‘target’, method = ‘chi’, min_samples = None, n_bins = None, empty_separate = False)
y: 目标列
method: 分箱方法，支持’chi’ (卡方分箱), ‘dt’ (决策树分箱), ‘kmean’ , ‘quantile’ (等频分箱), ‘step’ (等步长分箱)
min_samples: 每箱至少包含样本量，可以是数字或者占比
n_bins: 箱数，若无法分出这么多箱数，则会分出最多的箱数
empty_separate: 是否将空箱单独分开
查看分箱节点：c.export()
手动调整分箱: c.load(dict)
apply分箱结果: c.transform(dataframe, labels=False):
labels: 是否将分箱结果转化成箱标签。False时输出0,1,2…（离散变量根据占比高低排序），True输出(-inf, 0], (0,10], (10, inf)。
注意：1. 注意删去不需要分箱的列，特别是ID列和时间列
[9]:
# initialise
c = toad.transform.Combiner()
# 使用特征筛选后的数据进行训练：使用稳定的卡方分箱，规定每箱至少有5%数据, 空值将自动被归到最佳箱。
c.fit(train_selected.drop(to_drop, axis=1), y = 'target', method = 'chi', min_samples = 0.05) #empty_separate = False
# 为了演示，仅展示部分分箱
print('var_d2:',c.export()['var_d2'])
print('var_d5:',c.export()['var_d5'])
print('var_d6:',c.export()['var_d6'])
var_d2: [747.0, 782.0, 820.0]
var_d5: [['O', 'nan', 'F'], ['M']]
var_d6: [['PUBLIC LTD COMPANIES', 'NON-RESIDENT INDIAN', 'PRIVATE LTD COMPANIES', 'PARTNERSHIP FIRM', 'nan'], ['RESIDENT INDIAN', 'TRUST', 'TRUST-CLUBS/ASSN/SOC/SEC-25 CO.', 'HINDU UNDIVIDED FAMILY', 'CO-OPERATIVE SOCIETIES', 'LIMITED LIABILITY PARTNERSHIP', 'ASSOCIATION', 'OVERSEAS CITIZEN OF INDIA', 'TRUST-NGO']]
5. 观察分箱并调整¶
toad.plot的module提供了一部分的可视化功能，帮助调整分箱节点。
时间内观察： toad.plot.bin_plot(dataframe, x = None, target = ‘target)
bar代表了样本量占比，红线代表了正样本占比（e.g. 坏账率）
- x: 需要观察的特征
- target: 目标列
[10]:
from toad.plot import bin_plot
# 看'var_d2'在时间内的分箱
col = 'var_d2'
bin_plot(c.transform(train_selected[[col,'target']], labels=True), x=col, target='target')
No handles with labels found to put in legend.
No handles with labels found to put in legend.
[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2d8a14e0>
跨时间观察： toad.plot.badrate_plot(dataframe, target = ‘target’, x = None, by = None)
输出不同时间段中每箱的正样本占比
- target: 目标列
- x: 时间列, string格式
- by: 需要观察的特征
注意：时间列需要预先分好并设成string，不支持timestampe
[11]:
from toad.plot import badrate_plot
col = 'var_d2'
# 观察 'var_d2' 分别在时间内和OOT中的稳定性
badrate_plot(c.transform(train[[col,'target','month']], labels=True), target='target', x='month', by=col)
badrate_plot(c.transform(OOT[[col,'target','month']], labels=True), target='target', x='month', by=col)
badrate_plot(c.transform(data[[col,'target','month']], labels=True), target='target', x='month', by=col)
'''
敞口随时间变化而增大为优，代表了变量在更新的时间区分度更强。线之前没有交叉为优，代表分箱稳定。
'''
[11]:
'\n敞口随时间变化而增大为优，代表了变量在更新的时间区分度更强。线之前没有交叉为优，代表分箱稳定。\n'
[12]:
# 看'var_d5'在时间内的分箱
col = 'var_d5'
#观察单个变量分箱结果时，建议设置'labels = True'
bin_plot(c.transform(train_selected[[col,'target']], labels=True), x=col, target='target')
No handles with labels found to put in legend.
No handles with labels found to put in legend.
[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2d1b5f60>
调整分箱：c.update(dict)
update 后会更新被修改的箱
[13]:
# iv值较低，假设我们要 'F' 淡出分出一组来提高iv
#设置分组
rule = {'var_d5':[['O', 'nan'],['F'], ['M']]}
#调整分箱
c.update(rule)
#查看手动分箱稳定性
bin_plot(c.transform(train_selected[['var_d5','target']], labels=True), x='var_d5', target='target')
badrate_plot(c.transform(OOT[['var_d5','target','month']], labels=True), target='target', x='month', by='var_d5')
No handles with labels found to put in legend.
No handles with labels found to put in legend.
[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a24375208>
### III. WOE转化¶
WOE转化在分箱调整好之后进行，步骤如下：
用调整好的Combiner转化数据: c.transform(dataframe, labels=False)
只会转化被分箱的变量
初始化woe transer: transer = toad.transform.WOETransformer()
fit_transform: transer.fit_transform(dataframe, target, exclude = None)
训练并输出woe转化的数据，用于转化train/时间内数据
target：目标列数据（非列名）
exclude: 不需要被WOE转化的列 注意：会转化所有列，包括未被分箱transform的列，通过 ‘exclude’ 删去不要WOE转化的列，特别是target列
根据训练好的transer，转化test/OOT数据：transer.transform(dataframe)
根据训练好的transer输出woe转化的数据，用于转化test/OOT数据。
[14]:
# 初始化
transer = toad.transform.WOETransformer()
# combiner.transform() & transer.fit_transform() 转化训练数据，并去掉target列
train_woe = transer.fit_transform(c.transform(train_selected), train_selected['target'], exclude=to_drop+['target'])
OOT_woe = transer.transform(c.transform(OOT))
print(train_woe.head(3))
APP_ID_C target var_d2 var_d3 var_d5 var_d6 var_d7 \
0 app_1 0 -0.178286 0.046126 0.090613 0.047145 0.365305
1 app_2 0 -1.410248 0.046126 -0.271655 0.047145 -0.734699
2 app_3 0 -0.178286 0.046126 0.090613 0.047145 0.365305
var_d11 var_b3 var_b9 ... var_l_60 var_l_64 var_l_68 var_l_71 \
0 -0.152228 -0.141182 -0.237656 ... 0.132170 0.080656 0.091919 0.150975
1 -0.152228 0.199186 0.199186 ... 0.132170 0.080656 0.091919 0.150975
2 -0.152228 -0.141182 0.388957 ... -0.926987 -0.235316 -0.883896 -0.385976
var_l_89 var_l_91 var_l_107 var_l_119 var_l_123 month
0 0.091901 0.086402 -0.034434 0.027322 0.087378 2019-03
1 0.091901 0.086402 -0.034434 0.027322 0.087378 2019-03
2 0.091901 -0.620829 -0.034434 -0.806599 -0.731941 2019-03
[3 rows x 34 columns]
toad.selection.stepwise(dataframe, target=’target’, estimator=’ols’, direction=’both’, criterion=’aic’, max_iter=None, return_drop=False, exclude=None):
逐步回归特征筛选，支持向前，向后和双向（推荐）。
- estimator: 用于拟合的模型，支持'ols', 'lr', 'lasso', 'ridge'
- direction: 逐步回归的方向，支持'forward', 'backward', 'both' （推荐）
- criterion: 评判标准，支持'aic', 'bic', 'ks', 'auc'
- max_iter: 最大循环次数
- return_drop: 是否返回被剔除的列名
- exclude: 不需要被训练的列名，比如ID列和时间列
tip: 经验证，direction = ‘both’效果最好。estimator = ‘ols’以及criterion = ‘aic’运行速度快且结果对逻辑回归建模有较好的代表性
[15]:
# 将woe转化后的数据做逐步回归
final_data = toad.selection.stepwise(train_woe,target = 'target', estimator='ols', direction = 'both', criterion = 'aic', exclude = to_drop)
# 将选出的变量应用于test/OOT数据
final_OOT = OOT_woe[final_data.columns]
print(final_data.shape) # 逐步回归从31个变量中选出了10个
(43576, 13)
[16]:
# 确定建模要用的变量
col = list(final_data.drop(to_drop+['target'],axis=1).columns)
toad.metrics.PSI(df_train, df_test):
输出每列特征的PSI，可以用于检验WOE转化后的特征稳定性
[17]:
toad.metrics.PSI(final_data[col], final_OOT[col])
[17]:
var_d2 0.000254
var_d5 0.000012
var_d7 0.000079
var_d11 0.000191
var_b10 0.000209
var_b18 0.000026
var_b19 0.000049
var_b23 0.000037
var_l_20 0.000115
var_l_68 0.000213
dtype: float64
常用模型评分: toad. metrics. KS, F1, AUC
[18]:
# 用逻辑回归建模
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(final_data[col], final_data['target'])
# 预测训练和隔月的OOT
pred_train = lr.predict_proba(final_data[col])[:,1]
pred_OOT_may =lr.predict_proba(final_OOT.loc[final_OOT.month == '2019-05',col])[:,1]
pred_OOT_june =lr.predict_proba(final_OOT.loc[final_OOT.month == '2019-06',col])[:,1]
pred_OOT_july =lr.predict_proba(final_OOT.loc[final_OOT.month == '2019-07',col])[:,1]
/Users/zhouxiyu/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
[19]:
from toad.metrics import KS, AUC
print('train KS',KS(pred_train, final_data['target']))
print('train AUC',AUC(pred_train, final_data['target']))
print('OOT结果')
print('5月 KS',KS(pred_OOT_may, final_OOT.loc[final_OOT.month == '2019-05','target']))
print('6月 KS',KS(pred_OOT_june, final_OOT.loc[final_OOT.month == '2019-06','target']))
print('7月 KS',KS(pred_OOT_july, final_OOT.loc[final_OOT.month == '2019-07','target']))
train KS 0.3707986228750539
train AUC 0.75060723924743
OOT结果
5月 KS 0.3686687175756087
6月 KS 0.3495273403486497
7月 KS 0.3796914199845523
PSI 同样可以用于验证分数的稳定性
[20]:
print(toad.metrics.PSI(pred_train,pred_OOT_may))
print(toad.metrics.PSI(pred_train,pred_OOT_june))
print(toad.metrics.PSI(pred_train,pred_OOT_june))
0.12760761722158315
0.1268648506657109
0.1268648506657109
toad.metrics.KS_bucket(predicted_proba, y_true, bucket=10, method = ‘quantile’):
KS bucket输出模型预测分箱后评判信息，包括每组的分数区间，样本量，坏账率，KS等
- bucket：分箱的数量
- method：分箱方法，建议用'quantile'（等人数），或'step' (等分数步长)
bad_rate为每组坏账率：（1）组之间的坏账率差距越大越好（2）可以用于观察是否有跳点（3）可以用与找最佳切点（4）可以对比
[21]:
# 将预测等频分箱，观测每组的区别
toad.metrics.KS_bucket(pred_train, final_data['target'], bucket=10, method = 'quantile')
[21]:
| min | max | bads | goods | total | bad_rate | good_rate | odds | bad_prop | good_prop | total_prop | cum_bads | cum_goods | cum_total | cum_bads_prop | cum_goods_prop | cum_total_prop | ks | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.000275 | 0.003380 | 9 | 4332 | 4341 | 0.002073 | 0.997927 | 0.002078 | 0.009688 | 0.101578 | 0.099619 | 9 | 4332 | 4341 | 0.009688 | 0.101578 | 0.099619 | -0.091890 |
| 1 | 0.003398 | 0.005207 | 12 | 3585 | 3597 | 0.003336 | 0.996664 | 0.003347 | 0.012917 | 0.084062 | 0.082545 | 21 | 7917 | 7938 | 0.022605 | 0.185640 | 0.182164 | -0.163035 |
| 2 | 0.005207 | 0.008116 | 37 | 5071 | 5108 | 0.007244 | 0.992756 | 0.007296 | 0.039828 | 0.118906 | 0.117220 | 58 | 12988 | 13046 | 0.062433 | 0.304547 | 0.299385 | -0.242114 |
| 3 | 0.008125 | 0.010862 | 26 | 3854 | 3880 | 0.006701 | 0.993299 | 0.006746 | 0.027987 | 0.090370 | 0.089040 | 84 | 16842 | 16926 | 0.090420 | 0.394916 | 0.388425 | -0.304497 |
| 4 | 0.010868 | 0.014651 | 59 | 4759 | 4818 | 0.012246 | 0.987754 | 0.012398 | 0.063509 | 0.111590 | 0.110565 | 143 | 21601 | 21744 | 0.153929 | 0.506507 | 0.498990 | -0.352578 |
| 5 | 0.014661 | 0.019846 | 76 | 3901 | 3977 | 0.019110 | 0.980890 | 0.019482 | 0.081808 | 0.091472 | 0.091266 | 219 | 25502 | 25721 | 0.235737 | 0.597979 | 0.590256 | -0.362241 |
| 6 | 0.019858 | 0.025968 | 116 | 4665 | 4781 | 0.024263 | 0.975737 | 0.024866 | 0.124865 | 0.109386 | 0.109716 | 335 | 30167 | 30502 | 0.360603 | 0.707365 | 0.699972 | -0.346762 |
| 7 | 0.025986 | 0.032467 | 108 | 4188 | 4296 | 0.025140 | 0.974860 | 0.025788 | 0.116254 | 0.098202 | 0.098586 | 443 | 34355 | 34798 | 0.476857 | 0.805567 | 0.798559 | -0.328710 |
| 8 | 0.032484 | 0.044998 | 173 | 4187 | 4360 | 0.039679 | 0.960321 | 0.041318 | 0.186222 | 0.098178 | 0.100055 | 616 | 38542 | 39158 | 0.663079 | 0.903745 | 0.898614 | -0.240666 |
| 9 | 0.045115 | 0.370055 | 313 | 4105 | 4418 | 0.070847 | 0.929153 | 0.076248 | 0.336921 | 0.096255 | 0.101386 | 929 | 42647 | 43576 | 1.000000 | 1.000000 | 1.000000 | 0.000000 |
toad.ScoreCard( combiner = {}, transer = None, pdo = 60, rate = 2, base_odds = 20, base_score = 750, card = None, C=0.1,kwargs):
逻辑回归模型转标准评分卡，支持传入逻辑回归参数，进行调参。
- combiner: 传入训练好的 toad.Combiner 对象
- transer: 传入先前训练的 toad.WOETransformer 对象
- pdo、rate、base_odds、base_score:
e.g. pdo=60, rate=2, base_odds=20,base_score=750
实际意义为当比率为1/20，输出基准评分750，当比率为基准比率2倍时，基准分下降60分
- card: 支持传入专家评分卡
- **kwargs: 支持传入逻辑回归参数（参数详见 sklearn.linear_model.LogisticRegression）
[22]:
card = toad.ScoreCard(
combiner = c,
transer = transer,
#class_weight = 'balanced',
#C=0.1,
#base_score = 600,
#base_odds = 35 ,
#pdo = 60,
#rate = 2
)
card.fit(final_data[col], final_data['target'])
/Users/zhouxiyu/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
[22]:
ScoreCard(base_odds=35, base_score=750, card=None,
combiner=<toad.transform.Combiner object at 0x1a2434fdd8>, pdo=60,
rate=2,
transer=<toad.transform.WOETransformer object at 0x1a235a5358>)
注意!
评分卡在 fit 时使用 WOE 转换后的数据来计算最终的分数，分数一旦计算完成，便无需 WOE 值，可以直接使用 原始数据 进行评分。
[ ]:
# 直接使用原始数据进行评分
card.predict(train)
[23]:
#输出标准评分卡
card.export()
[23]:
{'var_d2': {'[-inf ~ 747.0)': 65.54,
'[747.0 ~ 782.0)': 45.72,
'[782.0 ~ 820.0)': 88.88,
'[820.0 ~ inf)': 168.3},
'var_d5': {'O,nan': 185.9, 'F': 103.26, 'M': 68.76},
'var_d7': {'LARGE FLEET OPERATOR,COMPANY,STRATEGIC TRANSPRTER,SALARIED,HOUSEWIFE': 120.82,
'DOCTOR-SELF EMPLOYED,nan,SAL(RETIRAL AGE 60),SERVICES,SAL(RETIRAL AGE 58),OTHERS,DOCTOR-SALARIED,AGENT,CONSULTANT,DIRECTOR,MEDIUM FLEETOPERATOR,TRADER,RETAIL TRANSPORTER,MANUFACTURING,FIRST TIME USERS,STUDENT,PENSIONER': 81.32,
'PROPRIETOR,TRADING,STRATEGIC CAPTIVE,SELF-EMPLOYED,SERV-PRIVATE SECTOR,SMALL RD TRANS.OPR,BUSINESSMAN,CARETAKER,RETAIL,AGRICULTURIST,RETIRED PERSONNEL,MANAGER,CONTRACTOR,ACCOUNTANT,BANKS SERVICE,GOVERNMENT SERVICE,ADVISOR,STRATEGIC S1,SCHOOLS,TEACHER,GENARAL RETAILER,RESTAURANT KEEPER,OFFICER,POLICEMAN,SERV-PUBLIC SECTOR,BARRISTER,Salaried,SALESMAN,RETAIL CAPTIVE,Defence (NCO),STRATEGIC S2,OTHERS NOT DEFINED,JEWELLER,SECRETARY,SUP STRAT TRANSPORT,LECTURER,ATTORNEY AT LAW,TAILOR,TECHNICIAN,CLERK,PLANTER,DRIVER,PRIEST,PROGRAMMER,EXECUTIVE ASSISTANT,PROOF READER,STOCKBROKER(S)-COMMD,TYPIST,ADMINSTRATOR,INDUSTRY,PHARMACIST,Trading,TAXI DRIVER,STRATEGIC BUS OP,CHAIRMAN,CARPENTER,DISPENSER,HELPER,STRATEGIC S3,RETAIL BUS OPERATOR,GARAGIST,PRIVATE TAILOR,NURSE': 55.79},
'var_d11': {'N': 88.69, 'U': 23.72},
'var_b10': {'[-inf ~ -8888.0)': 67.76,
'[-8888.0 ~ 0.548229531)': 97.51,
'[0.548229531 ~ inf)': 36.22},
'var_b18': {'[-inf ~ 2)': 83.72, '[2 ~ inf)': 39.23},
'var_b19': {'[-inf ~ -9999)': 70.78, '[-9999 ~ 4)': 97.51, '[4 ~ inf)': 42.2},
'var_b23': {'[-inf ~ -8888)': 64.51, '[-8888 ~ inf)': 102.69},
'var_l_20': {'[-inf ~ 0.000404297)': 78.55,
'[0.000404297 ~ 0.003092244)': 103.85,
'[0.003092244 ~ inf)': 36.21},
'var_l_68': {'[-inf ~ 0.000255689)': 70.63,
'[0.000255689 ~ 0.002045513)': 24.56,
'[0.002045513 ~ 0.007414983000000002)': 66.63,
'[0.007414983000000002 ~ 0.019943748)': 99.55,
'[0.019943748 ~ inf)': 142.36}}
### VII. 其他功能¶
toad.transform.GBDTTransformer
用gbdt编码，用于gbdt + lr建模的前置
[28]:
gbdt_transer = toad.transform.GBDTTransformer()
gbdt_transer.fit(final_data[col+['target']], 'target', n_estimators = 10, max_depth = 2)
/Users/zhouxiyu/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.
If you want the future behaviour and silence this warning, you can specify "categories='auto'".
In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
warnings.warn(msg, FutureWarning)
[28]:
<toad.transform.GBDTTransformer at 0x1a2daf60f0>
[29]:
gbdt_vars = gbdt_transer.transform(final_data[col])
[31]:
gbdt_vars.shape
[31]:
(43576, 40)
[ ]: