baseline_v2.py

# -*- coding: utf-8 -*-
"""
baseline 2: ad.csv (creativeID/adID/camgaignID/advertiserID/appID/appPlatform) + lr
"""

import zipfile
import pandas as pd
from scipy import sparse
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LogisticRegression

# 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

# feature engineering/encoding
enc = OneHotEncoder()
feats = ["creativeID", "adID", "camgaignID", "advertiserID", "appID", "appPlatform"]
for i,feat in enumerate(feats):
    x_train = enc.fit_transform(dfTrain[feat].values.reshape(-1, 1))
    x_test = enc.transform(dfTest[feat].values.reshape(-1, 1))
    if i == 0:
        X_train, X_test = x_train, x_test
    else:
        X_train, X_test = sparse.hstack((X_train, x_train)), sparse.hstack((X_test, x_test))

# model training
lr = LogisticRegression()
lr.fit(X_train, y_train)
proba_test = lr.predict_proba(X_test)[:,1]

# 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)