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10- 天猫用户复购预测 (机器学习集成算法) (项目十) *

项目难点

  • merchant: 商人

  • 重命名列名: user_log.rename(columns={'seller_id':'merchant_id'}, inplace=True)

  • 数据类型转换: user_log['item_id'] = user_log['item_id'].astype('int32')

  • 主要使用方法: xgboost, lightbm

  • 竞赛地址: 天猫复购预测之挑战Baseline_学习赛_天池大赛-阿里云天池

  • 排名: 448/9361 score: 0.680989

项目简介:

阿里巴巴天池天猫复购预测的机器学习项目, 使用数据分析, 通过机器学习中的线性分类算法, 进行建模, 从而预测消费者行为, 复购情况 .

  • 数据分析
  • 特征工程
  • 算法使用
  • 算法集成

1 数据处理

1.1 模型导入

import gc    # 垃圾回收
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')

# 导入分析库
# 数据拆分
from sklearn.model_selection import train_test_split
# 同分布数据拆分
from sklearn.model_selection import StratifiedGroupKFold
import lightgbm as lgb
import xgboost as xgb

1.2 加载数据

%%time
# 加载数据
# 用户行为日志
user_log = pd.read_csv('./data_format1/user_log_format1.csv', dtype = {'time_stamp':'str'})
# 用户画像
user_info = pd.read_csv('./data_format1/user_info_format1.csv')
# 训练数据和测试数据
train_data = pd.read_csv('./data_format1/train_format1.csv')
test_data = pd.read_csv('./data_format1/test_format1.csv')

1.3 查看数据

print('---data shape---')     
for data in [user_log, user_info, train_data, test_data]:
    print(data.shape)

    ![](https://img-blog.csdnimg.cn/dd21f91d1a9e42f09e3fb25703da03da.png)

print('---data info ---')
for data in [user_log, user_info, train_data, test_data]:
    print(data.info())

     ![](https://img-blog.csdnimg.cn/7f450deb158e46ef8c12a70d28e3d388.png)

display(user_info.head())

    ![](https://img-blog.csdnimg.cn/731dcc52b52342e69d1f2088a2015299.png)

display(train_data.head(),test_data.head())

    ![](https://img-blog.csdnimg.cn/9358067b16dc4dc3ad5ebe0105cddcdd.png)

1.4 数据集成

train_data['origin'] = 'train'
test_data['origin'] = 'test'
# 集成
all_data = pd.concat([train_data, test_data], ignore_index=True, sort=False)
# prob测试数据中特有的一列
all_data.drop(['prob'], axis=1, inplace=True) # 删除概率这一列
display(all_data.head(),all_data.shape)

    ![](https://img-blog.csdnimg.cn/0547f62c65f4478cafef46ef17d00bbc.png)

# 连接user_info表，通过user_id关联
all_data = all_data.merge(user_info, on='user_id', how='left')
display(all_data.shape,all_data.head())

    ![](https://img-blog.csdnimg.cn/23e7ca98d645494c9dd3a2ce3ae25fab.png)

# 使用 merchant_id（原列名seller_id）
user_log.rename(columns={'seller_id':'merchant_id'}, inplace=True)

del train_data,test_data,user_info
gc.collect()

1.5 数据类型转换

%%time
display(user_log.info())

    ![](https://img-blog.csdnimg.cn/0198af1605fe4cf7864ee1491e71b20e.png)

%%time
display(user_log.head())

    ![](https://img-blog.csdnimg.cn/4bde36ad6fe949ca857e235c7225c748.png)

%%time
# 用户行为数据类型转换
user_log['user_id'] = user_log['user_id'].astype('int32')
user_log['merchant_id'] = user_log['merchant_id'].astype('int32')
user_log['item_id'] = user_log['item_id'].astype('int32')
user_log['cat_id'] = user_log['cat_id'].astype('int32')
user_log['brand_id'].fillna(0, inplace=True)
user_log['brand_id'] = user_log['brand_id'].astype('int32')
user_log['time_stamp'] = pd.to_datetime(user_log['time_stamp'], format='%H%M')
user_log['action_type'] = user_log['action_type'].astype('int32')
display(user_log.info(),user_log.head())

    ![](https://img-blog.csdnimg.cn/68845d02c49941acae590e420b5bc1df.png)

display(all_data.isnull().sum())

    ![](https://img-blog.csdnimg.cn/566ef67f237d4ef19b09bcd0bce0b37e.png)

# 缺失值填充
all_data['age_range'].fillna(0, inplace=True)
all_data['gender'].fillna(2, inplace=True)
all_data.isnull().sum()

    ![](https://img-blog.csdnimg.cn/d66aa54fc3744e56b3699711da96767e.png)

all_data.info()

    ![](https://img-blog.csdnimg.cn/f6e40857c9014d07b08d67ed39ecafcf.png)

all_data['age_range'] = all_data['age_range'].astype('int8')
all_data['gender'] = all_data['gender'].astype('int8')
all_data['label'] = all_data['label'].astype('str')
all_data['user_id'] = all_data['user_id'].astype('int32')
all_data['merchant_id'] = all_data['merchant_id'].astype('int32')
all_data.info()

    ![](https://img-blog.csdnimg.cn/316ce13fd68942ecb352270a5819cbda.png)

1.6 用户特征工程(5min)

%%time
##### 特征处理
##### User特征处理
groups = user_log.groupby(['user_id'])

# 用户交互行为数量 u1
temp = groups.size().reset_index().rename(columns={0:'u1'})
all_data = all_data.merge(temp, on='user_id', how='left')

# 细分
# 使用 agg 基于列的聚合操作，统计唯一值个数 item_id, cat_id, merchant_id, brand_id
# 用户，交互行为：点了多少商品呢？
temp = groups['item_id'].agg([('u2', 'nunique')]).reset_index()
all_data = all_data.merge(temp, on='user_id', how='left')

# 用户，交互行为，具体统计：类目多少
temp = groups['cat_id'].agg([('u3', 'nunique')]).reset_index()
all_data = all_data.merge(temp, on='user_id', how='left')

temp = groups['merchant_id'].agg([('u4', 'nunique')]).reset_index()
all_data = all_data.merge(temp, on='user_id', how='left')

temp = groups['brand_id'].agg([('u5', 'nunique')]).reset_index()
all_data = all_data.merge(temp, on='user_id', how='left')

# 购物时间间隔特征 u6 按照小时
temp = groups['time_stamp'].agg([('F_time', 'min'), ('B_time', 'max')]).reset_index()
temp['u6'] = (temp['B_time'] - temp['F_time']).dt.seconds/3600
all_data = all_data.merge(temp[['user_id', 'u6']], on='user_id', how='left')

# 统计操作类型为0，1，2，3的个数
temp = groups['action_type'].value_counts().unstack().reset_index().rename(
    columns={0:'u7', 1:'u8', 2:'u9', 3:'u10'})
all_data = all_data.merge(temp, on='user_id', how='left')

del temp,groups
gc.collect()

all_data.head()

    ![](https://img-blog.csdnimg.cn/6e9af4b8a6ac4a3fb59e302df88dede1.png)

1.7 店铺特征工程(5min)

%%time
##### 商家特征处理
groups = user_log.groupby(['merchant_id'])

# 商家被交互行为数量 m1
temp = groups.size().reset_index().rename(columns={0:'m1'})
all_data = all_data.merge(temp, on='merchant_id', how='left')

# 统计商家被交互的 user_id, item_id, cat_id, brand_id 唯一值
temp = groups['user_id', 'item_id', 'cat_id', 'brand_id'].nunique().reset_index().rename(
    columns={
    'user_id':'m2',
    'item_id':'m3', 
    'cat_id':'m4', 
    'brand_id':'m5'})
all_data = all_data.merge(temp, on='merchant_id', how='left')

# 统计商家被交互的 action_type 唯一值
temp = groups['action_type'].value_counts().unstack().reset_index().rename(  
    columns={0:'m6', 1:'m7', 2:'m8', 3:'m9'})
all_data = all_data.merge(temp, on='merchant_id', how='left')

del temp
gc.collect()

display(all_data.tail())

    ![](https://img-blog.csdnimg.cn/3c843b597164411aa66079c5ae39460c.png)

1.8 用户和店铺联合特征工程(4min)

%%time
##### 用户+商户特征
groups = user_log.groupby(['user_id', 'merchant_id'])

# 用户在不同商家交互统计
temp = groups.size().reset_index().rename(columns={0:'um1'})
all_data = all_data.merge(temp, on=['user_id', 'merchant_id'], how='left')

# 统计用户在不同商家交互的 item_id, cat_id, brand_id 唯一值
temp = groups['item_id', 'cat_id', 'brand_id'].nunique().reset_index().rename(
    columns={
    'item_id':'um2',
    'cat_id':'um3',
    'brand_id':'um4'})
all_data = all_data.merge(temp, on=['user_id', 'merchant_id'], how='left')

# 统计用户在不同商家交互的 action_type 唯一值
temp = groups['action_type'].value_counts().unstack().reset_index().rename(
    columns={
    0:'um5',
    1:'um6',
    2:'um7',
    3:'um8'})
all_data = all_data.merge(temp, on=['user_id', 'merchant_id'], how='left')

# 统计用户在不同商家购物时间间隔特征 um9 按照小时
temp = groups['time_stamp'].agg([('F_time', 'min'), ('B_time', 'max')]).reset_index()
temp['um9'] = (temp['B_time'] - temp['F_time']).dt.seconds/3600
all_data = all_data.merge(temp[['user_id','merchant_id','um9']], on=['user_id', 'merchant_id'], how='left')

del temp,groups
gc.collect()

display(all_data.head())

    ![](https://img-blog.csdnimg.cn/ed40b5b105b5454c9d88200e9506b880.png)

1.9 购买点击比

all_data['r1'] = all_data['u9']/all_data['u7']    # 用户购买点击比
all_data['r2'] = all_data['m8']/all_data['m6']    # 商家购买点击比
all_data['r3'] = all_data['um7']/all_data['um5']  # 不同用户不同商家购买点击比
display(all_data.head())

    ![](https://img-blog.csdnimg.cn/02661887aa834c69a935121d9c38cac6.png)

1.10 空数据填充

display(all_data.isnull().sum())

    ![](https://img-blog.csdnimg.cn/15e4889ef843407cae7bd4100ad92055.png)

all_data.fillna(0, inplace=True)
all_data.isnull().sum()

1.11 年龄性别类别型转换

all_data['age_range']

    ![](https://img-blog.csdnimg.cn/b2913a2090fc439db46f0dbc20e62335.png)

%%time
# 修改age_range字段名称为 age_0, age_1, age_2... age_8
# 独立编码
temp = pd.get_dummies(all_data['age_range'], prefix='age')
display(temp.head(10))
all_data = pd.concat([all_data, temp], axis=1)

    ![](https://img-blog.csdnimg.cn/87113a5710ec4071bed6ee2e85ec7a82.png)

# 性别转换
temp = pd.get_dummies(all_data['gender'], prefix='g')
all_data = pd.concat([all_data, temp], axis=1) # 列进行合并

# 删除原数据
all_data.drop(['age_range', 'gender'], axis=1, inplace=True)

del temp
gc.collect()

all_data.head()

    ![](https://img-blog.csdnimg.cn/186edc1b2c1d4711bb49692bb06c326d.png)

1.12 数据存储

%%time
# train_data、test-data
train_data = all_data[all_data['origin'] == 'train'].drop(['origin'], axis=1)
test_data = all_data[all_data['origin'] == 'test'].drop(['label', 'origin'], axis=1)

train_data.to_csv('train_data.csv')
test_data.to_csv('test_data.csv')

2 算法建模预测

# 训练数据和目标值
train_X, train_y = train_data.drop(['label'], axis=1), train_data['label']

# 数据拆分保留20%作为测试数据
X_train, X_valid, y_train, y_valid = train_test_split(train_X, train_y, test_size=.2)

2.1 LGB 模型

def lgb_train(X_train, y_train, X_valid, y_valid, verbose=True):
    model_lgb = lgb.LGBMClassifier(
        max_depth=10, # 8 # 树最大的深度
        n_estimators=5000, # 集成算法，树数量
        min_child_weight=100, 
        colsample_bytree=0.7, # 特征筛选
        subsample=0.9,  # 样本采样比例
        learning_rate=0.1) # 学习率

    model_lgb.fit(
        X_train, 
        y_train,
        eval_metric='auc',
        eval_set=[(X_train, y_train), (X_valid, y_valid)],
        verbose=verbose, # 是否打印输出训练过程
        early_stopping_rounds=10) # 早停，等10轮决策，评价指标不在变化，停止

    print(model_lgb.best_score_['valid_1']['auc'])
    return model_lgb

X_train

    ![](https://img-blog.csdnimg.cn/07ba736a61fe44b5aeea5b08d19ed64b.png)

model_lgb = lgb_train(X_train.values, y_train, X_valid.values, y_valid, verbose=True)

    ![](https://img-blog.csdnimg.cn/59962d59b146453e943bffb543ee3ba7.png)

%%time
prob = model_lgb.predict_proba(test_data.values) # 预测
submission = pd.read_csv('./data_format1/test_format1.csv')

# 复购的概率
submission['prob'] = pd.Series(prob[:,1]) # 预测数据赋值给提交数据
display(submission.head())
submission.to_csv('submission_lgb.csv', index=False)

del submission
gc.collect()

    ![](https://img-blog.csdnimg.cn/16ee522720d84a2cad2b88e9b6887216.png)

2.2 XGB 模型

def xgb_train(X_train, y_train, X_valid, y_valid, verbose=True):
    model_xgb = xgb.XGBClassifier(
        max_depth=10, # raw8
        n_estimators=5000,
        min_child_weight=300, 
        colsample_bytree=0.7, 
        subsample=0.9, 
        learing_rate=0.1)
    
    model_xgb.fit(
        X_train, 
        y_train,
        eval_metric='auc',
        eval_set=[(X_train, y_train), (X_valid, y_valid)],
        verbose=verbose,
        early_stopping_rounds=10)  # 早停法，如果auc在10epoch没有进步就stop
    print(model_xgb.best_score)
    return model_xgb

模型训练

model_xgb = xgb_train(X_train, y_train, X_valid, y_valid, verbose=False)

模型预测

%%time
prob = model_xgb.predict_proba(test_data)
submission = pd.read_csv('./data_format1/test_format1.csv')
submission['prob'] = pd.Series(prob[:,1])
submission.to_csv('submission_xgb.csv', index=False)
display(submission.head())
del submission
gc.collect()

3 交叉验证多轮建模

# 构造训练集和测试集
def get_train_test_datas(train_df,label_df):
    skv = StratifiedKFold(n_splits=10, shuffle=True)
    trainX = []
    trainY = []
    testX = []
    testY = []
    # 索引：训练数据索引train_index,目标值的索引test_index
    for train_index, test_index in skv.split(X=train_df, y=label_df):  # 10轮for循环
        
        train_x, train_y, test_x, test_y = train_df.iloc[train_index, :], label_df.iloc[train_index], \
                                            train_df.iloc[test_index, :], label_df.iloc[test_index]

        trainX.append(train_x)
        trainY.append(train_y)
        testX.append(test_x)
        testY.append(test_y)
    return trainX, testX, trainY, testY

3.1 LGB 模型（1min）

%%time
train_X, train_y = train_data.drop(['label'], axis=1), train_data['label']

# 拆分为10份训练数据和验证数据
X_train, X_valid, y_train, y_valid = get_train_test_datas(train_X, train_y)

print('----训练数据，长度',len(X_train))
print('----验证数据，长度',len(X_valid))

pred_lgbms = [] # 列表，接受目标值，10轮，平均值

for i in range(10):
    print('\n=========LGB training use Data {}/10===========\n'.format(i+1))
    model_lgb = lgb.LGBMClassifier(
        max_depth=10, # 8
        n_estimators=1000,
        min_child_weight=100,
        colsample_bytree=0.7,
        subsample=0.9,
        learning_rate=0.05)

    model_lgb.fit(
        X_train[i].values, 
        y_train[i],
        eval_metric='auc',
        eval_set=[(X_train[i].values, y_train[i]), (X_valid[i].values, y_valid[i])],
        verbose=False,
        early_stopping_rounds=10)

    print(model_lgb.best_score_['valid_1']['auc'])
    pred = model_lgb.predict_proba(test_data.values)
    pred = pd.DataFrame(pred[:,1]) # 将预测概率（复购）去处理，转换成DataFrame
    pred_lgbms.append(pred)

# 求10轮平均值生成预测结果，保存
# 每一轮的结果，作为一列，进行了添加
pred_lgbms = pd.concat(pred_lgbms, axis=1) # 级联，列进行级联

# 加载提交数据
submission = pd.read_csv('./data_format1/test_format1.csv')
submission['prob'] = pred_lgbms.mean(axis=1) # 10轮训练的平均值
submission.to_csv('submission_KFold_lgb.csv', index=False)

3.2 XGB 模型（4min）

# 构造训练集和测试集
def get_train_test_datas(train_df,label_df):
    skv = StratifiedKFold(n_splits=20, shuffle=True)
    trainX = []
    trainY = []
    testX = []
    testY = []
    # 索引：训练数据索引train_index,目标值的索引test_index
    for train_index, test_index in skv.split(X=train_df, y=label_df):# 10轮for循环
        
        train_x, train_y, test_x, test_y = train_df.iloc[train_index, :], label_df.iloc[train_index], \
                                           train_df.iloc[test_index, :], label_df.iloc[test_index]

        trainX.append(train_x)
        trainY.append(train_y)
        testX.append(test_x)
        testY.append(test_y)
    return trainX, testX, trainY, testY

%%time
train_X, train_y = train_data.drop(['label'], axis=1), train_data['label']

# 拆分为20份训练数据和验证数据
X_train, X_valid, y_train, y_valid = get_train_test_datas(train_X, train_y)

print('------数据长度',len(X_train),len(y_train))

pred_xgbs = []
for i in range(20):
    print('\n============XGB training use Data {}/20========\n'.format(i+1))
    model_xgb = xgb.XGBClassifier(
        max_depth=10, # raw8
        n_estimators=5000,
        min_child_weight=200, 
        colsample_bytree=0.7, 
        subsample=0.9,
        learning_rate = 0.1)

    model_xgb.fit(
        X_train[i], 
        y_train[i],
        eval_metric='auc',
        eval_set=[(X_train[i], y_train[i]), (X_valid[i], y_valid[i])],
        verbose=False,
        early_stopping_rounds=10 # 早停法，如果auc在10epoch没有进步就stop
    )    

    print(model_xgb.best_score)

    pred = model_xgb.predict_proba(test_data)
    pred = pd.DataFrame(pred[:,1])
    pred_xgbs.append(pred)

# 求20轮平均值生成预测结果，保存
pred_xgbs = pd.concat(pred_xgbs, axis=1)
submission = pd.read_csv('./data_format1/test_format1.csv')
submission['prob'] = pred_xgbs.mean(axis=1)
submission.to_csv('submission_KFold_xgb.csv', index=False)
标签： 机器学习 算法 人工智能
本文转载自: https://blog.csdn.net/March_A/article/details/129112581
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