05_机器学习赛事_优惠券使用预测

1. 函数库导入

# import libraries necessary for this project
import os, sys, pickle

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import matplotlib.dates as mdates

import seaborn as sns
import datetime as dt

from datetime import date

from sklearn.linear_model import SGDClassifier, LogisticRegression
from sklearn.metrics import log_loss, roc_auc_score, auc, roc_curve

# display for this notebook
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

2. 读取文件数据

dfoff = pd.read_csv('./data/ccf_offline_stage1_train.csv')
dftest = pd.read_csv('./data/ccf_offline_stage1_test_revised.csv')
dfon = pd.read_csv('./data/ccf_online_stage1_train.csv')

dfoff.head()

3. 数据处理

# 1. 将满xx减yy类型(`xx:yy`)的券变成折扣率 : `1 - yy/xx`，同时建立折扣券相关的特征 `discount_rate, discount_man, discount_jian, discount_type`
# 2. 将距离 `str` 转为 `int`
# convert Discount_rate and Distance
def getDiscountType(row):
    if pd.isnull(row):
        return np.nan
    elif ':' in row:
        return 1
    else:
        return 0

    
def convertRate(row):
    """Convert discount to rate"""
    if pd.isnull(row):
        return 1.0
    elif ':' in str(row):
        rows = row.split(':')
        return 1.0 - float(rows[1])/float(rows[0])
    else:
        return float(row)

def getDiscountMan(row):
    if ':' in str(row):
        rows = row.split(':')
        return int(rows[0])
    else:
        return 0

def getDiscountJian(row):
    if ':' in str(row):
        rows = row.split(':')
        return int(rows[1])
    else:
        return 0
    
def processData(df):
    # convert discunt_rate
    df['discount_rate'] = df['Discount_rate'].apply(convertRate)
    df['discount_man'] = df['Discount_rate'].apply(getDiscountMan)
    df['discount_jian'] = df['Discount_rate'].apply(getDiscountJian)
    df['discount_type'] = df['Discount_rate'].apply(getDiscountType)
    #print(df['discount_rate'].unique())
    # convert distance
    df['distance'] = df['Distance'].fillna(-1).astype(int)
    return df

dfoff = processData(dfoff)
dftest = processData(dftest)

dfoff.head()
dftest.head()

date_received = dfoff['Date_received'].unique()
date_received = sorted(date_received[pd.notnull(date_received)])

date_buy = dfoff['Date'].unique()
date_buy = sorted(date_buy[pd.notnull(date_buy)])
date_buy