腾讯社交广告高校算法大赛 baseline1

        官方给出的baseline1是基于平均分组转化。接下来根据数据分析，数据清洗，特征工程，模型训练和验证等四个大的模块来进行分析。
1、数据分析

2、数据清洗
        2.1 数据的拼接：将训练数据集(train.csv)和广告特征文件(ad.csv)进行拼接，将训测试数据集(test.csv)和广告特征文件(ad.csv)进行拼接。
3、特征工程

# -*- coding: utf-8 -*-
"""
baseline 1: history pCVR of creativeID/adID/camgaignID/advertiserID/appID/appPlatform
"""

import zipfile
import numpy as np
import pandas as pd

# load data
data_root = "."
dfTrain = pd.read_csv("%s/train.csv"%data_root)
dfTest = pd.read_csv("%s/test.csv"%data_root)
dfAd = pd.read_csv("%s/ad.csv"%data_root)

# process data
dfTrain = pd.merge(dfTrain, dfAd, on="creativeID")
dfTest = pd.merge(dfTest, dfAd, on="creativeID")
y_train = dfTrain["label"].values

# model building
key = "appID"
dfCvr = dfTrain.groupby(key).apply(lambda df: np.mean(df["label"])).reset_index()
dfCvr.columns = [key, "avg_cvr"]
dfTest = pd.merge(dfTest, dfCvr, how="left", on=key)
dfTest["avg_cvr"].fillna(np.mean(dfTrain["label"]), inplace=True)
proba_test = dfTest["avg_cvr"].values

# submission
df = pd.DataFrame({"instanceID": dfTest["instanceID"].values, "proba": proba_test})
df.sort_values("instanceID", inplace=True)
df.to_csv("submission.csv", index=False)
with zipfile.ZipFile("submission.zip", "w") as fout:
    fout.write("submission.csv", compress_type=zipfile.ZIP_DEFLATED)