Python学习系列二十四scikit-learn库逻辑回归实现唯品会用户购买行为预测
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1、背景：http://www.datafountain.cn/#/competitions/260/intro
DataFountain上的唯品会用户购买行为预测比赛题目，笔者用逻辑回归实现，分数是0.48比较弱，代码这里参考。
2、通过比赛提取的特征如下：
| 特征类别 | 特征名 | 特征说明 | 训练说明 |
| 基本特征 | u_id | 用户唯一标识 | |
| spu_id | 商品唯一标识 | ||
| brand_id | 商品所属的品牌标识 | ||
| cat_id | 商品所属的品类标识 | ||
| 人的特征 | u_buy_num | 购买次数 | |
| u_click_num | 点击次数 | ||
| u_buy_date | 购买天数 | ||
| u_click_date | 点击天数 | ||
| u_num_ratio | 购买点击次数比，购买次数/点击次数 | ||
| u_date_ratio | 购买点击天数比，购买天数/点击天数 | ||
| u_buy_freq | 购买频率，购买次数/90天 | ||
| u_click_freq | 点击频率，购买次数/90天 | ||
| 商品的特征 | spu_buy_num | 购买次数 | |
| spu_click_num | 点击次数 | ||
| spu_buy_date | 购买天数 | ||
| spu_click_date | 点击天数 | ||
| spu_num_ratio | 购买点击次数比，购买次数/点击次数 | ||
| spu_date_ratio | 购买点击天数比，购买天数/点击天数 | ||
| spu_buy_freq | 购买频率，购买次数/90天 | ||
| spu_click_freq | 点击频率，购买次数/90天 | ||
| 品牌的特征 | brand_buy_num | 购买次数 | |
| brand_click_num | 点击次数 | ||
| brand_buy_date | 购买天数 | ||
| brand_click_date | 点击天数 | ||
| brand_num_ratio | 购买点击次数比，购买次数/点击次数 | ||
| brand_date_ratio | 购买点击天数比，购买天数/点击天数 | ||
| brand_buy_freq | 购买频率，购买次数/90天 | ||
| brand_click_freq | 点击频率，购买次数/90天 | ||
| 品类的特征 | cat_buy_num | 购买次数 | |
| cat_click_num | 点击次数 | ||
| cat_buy_date | 购买天数 | ||
| cat_click_date | 点击天数 | ||
| cat_num_ratio | 购买点击次数比，购买次数/点击次数 | ||
| cat_date_ratio | 购买点击天数比，购买天数/点击天数 | ||
| cat_buy_freq | 购买频率，购买次数/90天 | ||
| cat_click_freq | 点击频率，购买次数/90天 | ||
| 标记 | action_type | 该用户是否会在当日购买此商品（0否，1是） |
3、逻辑回归参考代码如下：
# -*- coding: utf-8 -*-
import pandas as pd
import time
from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
def main():
#第一步：加载训练集和测试集
#加载带标记数据
label_ds=pd.read_csv(r"train_features_0714.txt",sep='\\t',encoding='utf8',
names=["u_id","u_buy_num","u_click_num","u_buy_date","u_click_date","u_num_ratio","u_date_ratio","u_buy_freq","u_click_freq","u_last_date",\\
"spu_id","spu_buy_num","spu_click_num","spu_buy_date","spu_click_date","spu_num_ratio","spu_date_ratio","spu_buy_freq","spu_click_freq","spu_last_date",\\
"brand_id","brand_buy_num","brand_click_num","brand_buy_date","brand_click_date","brand_num_ratio","brand_date_ratio","brand_buy_freq","brand_click_freq","brand_last_date",\\
"cat_id","cat_buy_num","cat_click_num","cat_buy_date","cat_click_date","cat_num_ratio","cat_date_ratio","cat_buy_freq","cat_click_freq","cat_last_date",\\
"action_type"])
#人特征
label_ds["u_id"] = label_ds["u_id"].astype("int")
label_ds["u_buy_num"] = label_ds["u_buy_num"].astype("int")
label_ds["u_click_num"] = label_ds["u_click_num"].astype("int")
label_ds["u_buy_date"] = label_ds["u_buy_date"].astype("int")
label_ds["u_click_date"] = label_ds["u_click_date"].astype("int")
label_ds["u_num_ratio"] = label_ds["u_num_ratio"].astype("float")
label_ds["u_date_ratio"] = label_ds["u_date_ratio"].astype("float")
label_ds["u_buy_freq"] = label_ds["u_buy_freq"].astype("float")
label_ds["u_click_freq"] = label_ds["u_click_freq"].astype("float")
label_ds["u_last_date"] = label_ds["u_last_date"].astype("int")
#商品特征
label_ds["spu_id"] = label_ds["spu_id"].astype("int")
label_ds["spu_buy_num"] = label_ds["spu_buy_num"].astype("int")
label_ds["spu_click_num"] = label_ds["spu_click_num"].astype("int")
label_ds["spu_buy_date"] = label_ds["spu_buy_date"].astype("int")
label_ds["spu_click_date"] = label_ds["spu_click_date"].astype("int")
label_ds["spu_num_ratio"] = label_ds["spu_num_ratio"].astype("float")
label_ds["spu_date_ratio"] = label_ds["spu_date_ratio"].astype("float")
label_ds["spu_buy_freq"] = label_ds["spu_buy_freq"].astype("float")
label_ds["spu_click_freq"] = label_ds["spu_click_freq"].astype("float")
label_ds["spu_last_date"] = label_ds["spu_last_date"].astype("int")
#品牌特征
label_ds["brand_id"] = label_ds["brand_id"].astype("int")
label_ds["brand_buy_num"] = label_ds["brand_buy_num"].astype("int")
label_ds["brand_click_num"] = label_ds["brand_click_num"].astype("int")
label_ds["brand_buy_date"] = label_ds["brand_buy_date"].astype("int")
label_ds["brand_click_date"] = label_ds["brand_click_date"].astype("int")
label_ds["brand_num_ratio"] = label_ds["brand_num_ratio"].astype("float")
label_ds["brand_date_ratio"] = label_ds["brand_date_ratio"].astype("float")
label_ds["brand_buy_freq"] = label_ds["brand_buy_freq"].astype("float")
label_ds["brand_click_freq"] = label_ds["brand_click_freq"].astype("float")
label_ds["brand_last_date"] = label_ds["brand_last_date"].astype("int")
#品类特征
label_ds["cat_id"] = label_ds["cat_id"].astype("int")
label_ds["cat_buy_num"] = label_ds["cat_buy_num"].astype("int")
label_ds["cat_click_num"] = label_ds["cat_click_num"].astype("int")
label_ds["cat_buy_date"] = label_ds["cat_buy_date"].astype("int")
label_ds["cat_click_date"] = label_ds["cat_click_date"].astype("int")
label_ds["cat_num_ratio"] = label_ds["cat_num_ratio"].astype("float")
label_ds["cat_date_ratio"] = label_ds["cat_date_ratio"].astype("float")
label_ds["cat_buy_freq"] = label_ds["cat_buy_freq"].astype("float")
label_ds["cat_click_freq"] = label_ds["cat_click_freq"].astype("float")
label_ds["cat_last_date"] = label_ds["cat_last_date"].astype("int")
#标记
label_ds["action_type"] = label_ds["action_type"].astype("int")
print "训练集，有", label_ds.shape[0], "行", label_ds.shape[1], "列"
#加载未标记数据
unlabel_ds=pd.read_csv(r"test_features_0714.txt",sep='\\t',encoding='utf8',
names=["id","uid","spu_id","brand_id","cat_id",\\
"u_buy_num","u_click_num","u_buy_date","u_click_date","u_num_ratio","u_date_ratio","u_buy_freq","u_click_freq","u_last_date",\\
"spu_buy_num","spu_click_num","spu_buy_date","spu_click_date","spu_num_ratio","spu_date_ratio","spu_buy_freq","spu_click_freq","spu_last_date",\\
"brand_buy_num","brand_click_num","brand_buy_date","brand_click_date","brand_num_ratio","brand_date_ratio","brand_buy_freq","brand_click_freq","brand_last_date",\\
"cat_buy_num","cat_click_num","cat_buy_date","cat_click_date","cat_num_ratio","cat_date_ratio","cat_buy_freq","cat_click_freq","cat_last_date",])
#人特征
unlabel_ds["id"] = unlabel_ds["id"].astype("int")
unlabel_ds["u_id"] = unlabel_ds["u_id"].astype("int")
unlabel_ds["u_buy_num"] = unlabel_ds["u_buy_num"].astype("int")#391万
unlabel_ds["u_click_num"] = unlabel_ds["u_click_num"].astype("int")
unlabel_ds["u_buy_date"] = unlabel_ds["u_buy_date"].astype("int")
unlabel_ds["u_click_date"] = unlabel_ds["u_click_date"].astype("int")
unlabel_ds["u_num_ratio"] = unlabel_ds["u_num_ratio"].astype("float")
unlabel_ds["u_date_ratio"] = unlabel_ds["u_date_ratio"].astype("float")
unlabel_ds["u_buy_freq"] = unlabel_ds["u_buy_freq"].astype("float")
unlabel_ds["u_click_freq"] = unlabel_ds["u_click_freq"].astype("float")
unlabel_ds["u_last_date"] = unlabel_ds["u_last_date"].astype("int")
#商品特征
unlabel_ds["spu_id"] = unlabel_ds["spu_id"].astype("int")
unlabel_ds["spu_buy_num"] = unlabel_ds["spu_buy_num"].astype("int")
unlabel_ds["spu_click_num"] = unlabel_ds["spu_click_num"].astype("int")
unlabel_ds["spu_buy_date"] = unlabel_ds["spu_buy_date"].astype("int")
unlabel_ds["spu_click_date"] = unlabel_ds["spu_click_date"].astype("int")
unlabel_ds["spu_num_ratio"] = unlabel_ds["spu_num_ratio"].astype("float")#241万
unlabel_ds["spu_date_ratio"] = unlabel_ds["spu_date_ratio"].astype("float")
unlabel_ds["spu_buy_freq"] = unlabel_ds["spu_buy_freq"].astype("float")
unlabel_ds["spu_click_freq"] = unlabel_ds["spu_click_freq"].astype("float")
unlabel_ds["spu_last_date"] = unlabel_ds["spu_last_date"].astype("int")
#品牌特征
unlabel_ds["brand_id"] = unlabel_ds["brand_id"].astype("int")
unlabel_ds["brand_buy_num"] = unlabel_ds["brand_buy_num"].astype("int")
unlabel_ds["brand_click_num"] = unlabel_ds["brand_click_num"].astype("int")
unlabel_ds["brand_buy_date"] = unlabel_ds["brand_buy_date"].astype("int")
unlabel_ds["brand_click_date"] = unlabel_ds["brand_click_date"].astype("int")
unlabel_ds["brand_num_ratio"] = unlabel_ds["brand_num_ratio"].astype("float")
unlabel_ds["brand_date_ratio"] = unlabel_ds["brand_date_ratio"].astype("float")
unlabel_ds["brand_buy_freq"] = unlabel_ds["brand_buy_freq"].astype("float")
unlabel_ds["brand_click_freq"] = unlabel_ds["brand_click_freq"].astype("float")
unlabel_ds["brand_last_date"] = unlabel_ds["brand_last_date"].astype("int")
#品类特征
unlabel_ds["cat_id"] = unlabel_ds["cat_id"].astype("int")
unlabel_ds["cat_buy_num"] = unlabel_ds["cat_buy_num"].astype("int")
unlabel_ds["cat_click_num"] = unlabel_ds["cat_click_num"].astype("int")
unlabel_ds["cat_buy_date"] = unlabel_ds["cat_buy_date"].astype("int")
unlabel_ds["cat_click_date"] = unlabel_ds["cat_click_date"].astype("int")
unlabel_ds["cat_num_ratio"] = unlabel_ds["cat_num_ratio"].astype("float")
unlabel_ds["cat_date_ratio"] = unlabel_ds["cat_date_ratio"].astype("float")
unlabel_ds["cat_buy_freq"] = unlabel_ds["cat_buy_freq"].astype("float")
unlabel_ds["cat_click_freq"] = unlabel_ds["cat_click_freq"].astype("float")
unlabel_ds["cat_last_date"] = unlabel_ds["cat_last_date"].astype("int")
print "测试集，有", unlabel_ds.shape[0], "行", unlabel_ds.shape[1], "列"
#模型训练
ds_0=label_ds[label_ds['action_type']==0]#标记为0的样本
ds_0_train=ds_0.sample(frac=0.01)#抽0.01出来训练
ds_1=label_ds[label_ds['action_type']==1]#标记为1的样本
ds_train=ds_1.append(ds_0_train)
label_X=ds_train[['u_num_ratio','spu_num_ratio','brand_num_ratio','cat_num_ratio']]
label_X_scale=preprocessing.scale(label_X)#归一化
label_y = ds_train['action_type']#类别 ds=label_ds[label_ds['action_type']==0]
model =LogisticRegression()#ensemble.GradientBoostingClassifier()
model.fit(label_X_scale, label_y)
#第五步：模型验证和选择
test_df=ds_train.sample(frac=0.2)#抽0.2验证
test_X=test_df[['u_num_ratio','spu_num_ratio','brand_num_ratio','cat_num_ratio']]
test_X_scale=preprocessing.scale(test_X)#归一化
test_y=test_df['action_type']#类别
predicted = model.predict(test_X_scale)
f1_score = metrics.f1_score(test_y, predicted) #模型评估
print f1_score
#第六步：模型预测
unlabe_X = unlabel_ds[['u_num_ratio','spu_num_ratio','brand_num_ratio','cat_num_ratio']]
unlabe_X_scale=preprocessing.scale(unlabe_X)#归一化
unlabel_y=model.predict_proba(unlabe_X_scale)[:,1]#预测返回概率值，通过概率值阈值选择正例样本
out_y=pd.DataFrame(unlabel_y,columns=['prob']) #返回判定正例的比例
out_y["prob"]=out_y["prob"].apply(lambda x: '0:.3f'.format(x))
out_1=out_y[out_y["prob"]>'0.5'] #看大于0.5的个数
print out_1.shape
out_y['prob'].value_counts() #看值分布
out_y.to_csv('fangjs/outvip.txt',index=False,header=None)#输出预测数据
#执行
if __name__ == '__main__':
start = time.clock()
main()
end = time.clock()
print('finish all in %s' % str(end - start))
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