1.3.py CART回归树做组合规则特征

本次章节的2个疑点：

1.决策树是不需要处理缺失值得，梅老师也乜有处理缺失值，tree.DecisionTreeRegressor在梅老师那里是运行成功的，但是我的报错ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

可能是版本问题，现版本确实不能接受缺失值，于是我用均值填补了缺失值，最后得到的结果也和梅老师不一样，仔细看了一下，数据量和梅老师的也不一样

2.GraphViz的安装问题，InvocationException: GraphViz's executables not found，我们可以参看这个https://blog.csdn.net/lizzy05/article/details/88529483，直接下载msi文件，后面就迎刃而解了。

3.该章节最主要的是使用CART回归树做组合规则：

 

 

 下面布上代码

# -*- coding: utf-8 -*-
"""
Created on Tue Dec 24 14:31:54 2019

@author: zixing.mei
"""

import pandas as pd  
import numpy as np  
import os  
#为画图指定路径  
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'  
#读取数据  
data = pd.read_excel( 'xxx/data_for_tree.xlsx')  
data.head()  

org_lst = ['uid','create_dt','oil_actv_dt','class_new','bad_ind']
agg_lst = ['oil_amount','discount_amount','sale_amount','amount','pay_amount','coupon_amount','payment_coupon_amount']
dstc_lst = ['channel_code','oil_code','scene','source_app','call_source']

df = data[org_lst].copy()
df[agg_lst] = data[agg_lst].copy()
df[dstc_lst] = data[dstc_lst].copy()

base = df[org_lst].copy()
base = base.drop_duplicates(['uid'],keep = 'first')

#%%
gn = pd.DataFrame()  
for i in agg_lst:  
    #计算个数  
    tp = pd.DataFrame(df.groupby('uid').apply(
                                       lambda df:len(df[i])).reset_index())  
    tp.columns = ['uid',i + '_cnt']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #求历史特征值大于零的个数  
    tp = pd.DataFrame(df.groupby('uid').apply(
                          lambda df:np.where(df[i]>0,1,0).sum()).reset_index())  
    tp.columns = ['uid',i + '_num']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #对历史数据求和  
    tp = pd.DataFrame(df.groupby('uid').apply(
                                  lambda df:np.nansum(df[i])).reset_index())  
    tp.columns = ['uid',i + '_tot']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #对历史数据求均值  
    tp = pd.DataFrame(df.groupby('uid').apply(
                                    lambda df:np.nanmean(df[i])).reset_index())  
    tp.columns = ['uid',i + '_avg']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #对历史数据求最大值  
    tp = pd.DataFrame(df.groupby('uid').apply(
                                     lambda df:np.nanmax(df[i])).reset_index())  
    tp.columns = ['uid',i + '_max']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #对历史数据求最小值  
    tp = pd.DataFrame(df.groupby('uid').apply(
                                    lambda df:np.nanmin(df[i])).reset_index())  
    tp.columns = ['uid',i + '_min']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #对历史数据求方差  
    tp = pd.DataFrame(df.groupby('uid').apply(
                                     lambda df:np.nanvar(df[i])).reset_index())  
    tp.columns = ['uid',i + '_var']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #对历史数据求极差  
    tp = pd.DataFrame(df.groupby('uid').apply(
                lambda df:np.nanmax(df[i])-np.nanmin(df[i]) ).reset_index())  
    tp.columns = ['uid',i + '_ran']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left')  
    #对历史数据求变异系数,为防止除数为0，利用0.01进行平滑  
    tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmean(df[i])/(np.nanvar(df[i])+0.01))).reset_index()  
    tp.columns = ['uid',i + '_cva']  
    if gn.empty == True:  
        gn = tp  
    else:  
        gn = pd.merge(gn,tp,on = 'uid',how = 'left') 

gc = pd.DataFrame()  
for i in dstc_lst:  
    tp = pd.DataFrame(df.groupby('uid').apply(
                                   lambda df: len(set(df[i]))).reset_index())  
    tp.columns = ['uid',i + '_dstc']  
    if gc.empty == True:  
        gc = tp  
    else:  
        gc = pd.merge(gc,tp,on = 'uid',how = 'left')

fn =  base.merge(gn,on='uid').merge(gc,on='uid')  
fn = pd.merge(fn,gc,on= 'uid')   
fn.shape 

#%%

x = fn.drop(['uid','oil_actv_dt','create_dt','bad_ind','class_new'],axis = 1)
for i in x.columns:
    x[i].fillna(x[i].mean(),inplace=True)
y = fn.bad_ind.copy()

from sklearn import tree  
dtree = tree.DecisionTreeRegressor(max_depth = 2,min_samples_leaf = 500,min_samples_split = 5000)  
dtree = dtree.fit(x,y) 

import pydotplus   
from IPython.display import Image  
from six import StringIO 
import os  
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'

dot_data = StringIO()  
tree.export_graphviz(dtree, out_file=dot_data,  
                         feature_names=x.columns,  
                         class_names=['bad_ind'],  
                         filled=True, rounded=True,  
                         special_characters=True)  
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())   
Image(graph.create_png())  

'''
dff1 = fn.loc[(fn.amount_tot>9614.5)&(fn.coupon_amount_cnt>6)].copy()  
dff1['level'] = 'past_A'  
dff2 = fn.loc[(fn.amount_tot>9614.5)&(fn.coupon_amount_cnt<=6)].copy()  
dff2['level'] = 'past_B'  
dff3 = fn.loc[fn.amount_tot<=9614.5].copy()  
dff3['level'] = 'past_C'
'''