#%%

#载入数据 、查看相关信息

import pandas as pd

import numpy as np

from  sklearn.preprocessing import LabelEncoder

print('第一步：加载、查看数据')

file_path = r'D:\train\201905data\liwang.csv'

band_data = pd.read_csv(file_path,encoding='UTF-8')

band_data.info()

band_data.shape

#%%

#

print('第二步：清洗、处理数据，某些数据可以使用数据库处理数据代替')

#数据清洗:缺失值处理：丢去、

#查看缺失值

band_data.isnull().sum

band_data = band_data.dropna()

#band_data = band_data.drop(['state'],axis=1)

# 去除空格

band_data['voice_mail_plan'] = band_data['voice_mail_plan'].map(lambda x: x.strip())

band_data['intl_plan'] = band_data['intl_plan'].map(lambda x: x.strip())

band_data['churned'] = band_data['churned'].map(lambda x: x.strip())

band_data['voice_mail_plan'] = band_data['voice_mail_plan'].map({'no':0, 'yes':1})

band_data.intl_plan = band_data.intl_plan.map({'no':0, 'yes':1})

for column in band_data.columns:

    if band_data[column].dtype == type(object):

        le = LabelEncoder()

        band_data[column] = le.fit_transform(band_data[column])

#band_data = band_data.drop(['phone_number'],axis=1)

#band_data['churned'] = band_data['churned'].replace([' True.',' False.'],[1,0])

#band_data['intl_plan'] = band_data['intl_plan'].replace([' yes',' no'],[1,0])

#band_data['voice_mail_plan'] = band_data['voice_mail_plan'].replace([' yes',' no'],[1,0])

#%%

# 模型  [重复、调优]

print('第三步：选择、训练模型')

x = band_data.drop(['churned'],axis=1)

y = band_data['churned']

from sklearn import model_selection

train,test,t_train,t_test = model_selection.train_test_split(x,y,test_size=0.3,random_state=1)

from sklearn import tree

model = tree.DecisionTreeClassifier(max_depth=2)

model.fit(train,t_train)

fea_res = pd.DataFrame(x.columns,columns=['features'])

fea_res['importance'] = model.feature_importances_

t_name= band_data['churned'].value_counts()

t_name.index

import graphviz

import os

os.environ["PATH"] += os.pathsep + r'D:\software\developmentEnvironment\graphviz-2.38\release\bin'

dot_data= tree.export_graphviz(model,out_file=None,feature_names=x.columns,max_depth=2,

                         class_names=t_name.index.astype(str),

                         filled=True, rounded=True,

                         special_characters=False)

graph = graphviz.Source(dot_data)

#graph

graph.render("dtr")

#%%

print('第四步：查看、分析模型')

#结果预测

res = model.predict(test)

#混淆矩阵

from sklearn.metrics import confusion_matrix

confmat = confusion_matrix(t_test,res)

print(confmat)

#分类指标 https://blog.csdn.net/akadiao/article/details/78788864

from sklearn.metrics import classification_report

print(classification_report(t_test,res))

#%%

print('第五步：保存模型')

from sklearn.externals import joblib

joblib.dump(model,r'D:\train\201905data\mymodel.model')

#%%

print('第六步：加载新数据、使用模型')

file_path_do = r'D:\train\201905data\do_liwang.csv'

deal_data = pd.read_csv(file_path_do,encoding='UTF-8')

#数据清洗:缺失值处理

deal_data = deal_data.dropna()

deal_data['voice_mail_plan'] = deal_data['voice_mail_plan'].map(lambda x: x.strip())

deal_data['intl_plan'] = deal_data['intl_plan'].map(lambda x: x.strip())

deal_data['churned'] = deal_data['churned'].map(lambda x: x.strip())

deal_data['voice_mail_plan'] = deal_data['voice_mail_plan'].map({'no':0, 'yes':1})

deal_data.intl_plan = deal_data.intl_plan.map({'no':0, 'yes':1})

for column in deal_data.columns:

    if deal_data[column].dtype == type(object):

        le = LabelEncoder()

        deal_data[column] = le.fit_transform(deal_data[column])

#数据清洗

#加载模型

model_file_path = r'D:\train\201905data\mymodel.model'

deal_model = joblib.load(model_file_path)

#预测

res = deal_model.predict(deal_data.drop(['churned'],axis=1))

#%%

print('第七步：执行模型，提供数据')

result_file_path = r'D:\train\201905data\result_liwang.csv'

deal_data.insert(1,'pre_result',res)

deal_data[['state','pre_result']].to_csv(result_file_path,sep=',',index=True,encoding='UTF-8')