[比赛记录] 主流机器学习模型模板代码+经验分享[xgb, lgb, Keras, LR]

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import numpy as np

from scipy import sparse

from sklearn.preprocessing import OneHotEncoder

from sklearn.linear_model import LogisticRegression

from sklearn.preprocessing import StandardScaler

# 1. load data

df_train = pd.DataFrame()

df_test  = pd.DataFrame()

y_train = df_train['label'].values

# 2. process data

ss = StandardScaler()

# 3. feature engineering/encoding

# 3.1 For Labeled Feature

enc = OneHotEncoder()

feats = ["creativeID", "adID", "campaignID"]

for i, feat in enumerate(feats):

x_train = enc.fit_transform(df_train[feat].values.reshape(-1, 1))

x_test = enc.fit_transform(df_test[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))

# 3.2 For Numerical Feature

# It must be a 2-D Data for StandardScalar, otherwise reshape(-1, len(feats)) is required

feats = ["price", "age"]

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