# -*- coding:utf-8 -*-

__author__ = 'xujia'

import pandas as pd

import numpy as np

import binning

import evaluate

import modeling

import woe

import feature_index

import feature_selection

import math

from pandas import Interval

from numpy import inf

from pprint import pprint

def file_info(file_path):

"""

获取文件信息

:param file_path: 文件路径

:return: {字段名称：[字段类型，数据量，空值个数]}

"""

info_dict = {}

raw_data = pd.read_csv(file_path)

for c in raw_data.columns:

c_type = raw_data[c].dtype

nc = raw_data[c].size - raw_data[c].notnull().sum()

info_dict[c] = [c_type, raw_data[c].size, nc] # 字段类型，数据量，空值个数

return info_dict, raw_data

def change_type(df, fea_type_dict):

"""

改变数据类型

:param df:

:param fea_type_dict:

:return:

"""

type_dict = {1: 'float64', 2: 'int64', 3: 'str'}

feature_dict = dict(zip(list(range(df.shape[1])), df.columns.values))

print('当前数据类型为：')

for (k, v) in fea_type_dict.items():

print(k.rjust(15), v[0])

print('字段名称对应数字为：')

for (n, m) in feature_dict.items():

print(n, m)

if_change = input('是否需要修改字段类型？(y/n)')

if if_change == 'y':

fea_name = int(input('请输入需要更改数据类型的字段对应的数字：'))

if fea_name not in feature_dict.keys():

fea_name = int(input('输入字段名称错误，请重新输入：'))

if fea_name not in fea_dict.keys():

pass

fea_name = feature_dict[fea_name]

target_type = int(input('请输入目标类型对应的数字(1: 浮点型(float64)，2: 整型(int64)，3: 字符型(str)：'))

if target_type not in type_dict.keys():

target_type = int(input('请输入目标类型对应的数字(1: 浮点型(float64)，2: 整型(int64)，3: 字符型(str)：'))

if target_type not in type_dict.keys():

pass

target_type = type_dict[target_type]

df[fea_name] = df[fea_name].astype(target_type)

elif if_change == 'n':

pass

else:

pass

def split_data(data_to_split, ratio):

"""

数据分割

:param data_to_split:带分割数据

:param ratio:数据分割比例

:return: （数据集1，数据集2）

"""

data_count = data_to_split.shape[0]

selected_count = int(data_count * ratio)

if selected_count > 0:

splited_data = np.split(data.sample(frac=1), [selected_count], axis=0)

else:

return 'Data is too less'

return splited_data

if __name__ == '__main__':

# path=input('Please input the file path: ')

path = 'iris.csv'

fea_dict, data = file_info(path)

print('字段名', '数据类型', '数据总量', '缺失值个数')

pprint(fea_dict)

data = data.fillna(0.0)

change_type(data, fea_dict)

print(data.dtypes)

bin = binning.Bin(data, 'Label', 5)

for n in data.columns.values[:-1]:

bins = bin.chi_merge(n)

woe.add_woe_col(data, bins)

# 单变量ar值计算

# ar = ARUtil.cal_ar(data['SepalWidth_woe'], data['Label'])

train_data, test_data = split_data(data, 0.7)

model = modeling.model(train_data, ['SepalLength_woe', 'PetalLength_woe', 'PetalWidth_woe'], 'Label')

predict_score = modeling.score_trans(test_data[['SepalLength_woe', 'PetalLength_woe', 'PetalWidth_woe']], model, 300, 25)

pprint(list(zip(test_data['Label'].values, predict_score)))

auc = evaluate.auc(model, test_data[['SepalLength_woe', 'PetalLength_woe', 'PetalWidth_woe', 'Label']])

print("auc值: " + str(auc))

evaluate.roc(model, test_data[['SepalLength_woe', 'PetalLength_woe', 'PetalWidth_woe', 'Label']])

# select_func = feature_selection.fea_select(data[['SepalLength', 'SepalWidth']], data['Label'], 1)

# print(select_func.transform(data[['SepalLength', 'SepalWidth']]))

# feature_selection.fea_select(data[['SepalLength_woe', 'SepalWidth_woe']], data['Label'])

# feature_selection.mi(data['SepalWidth_woe'], data['Label'])