为什么管道中的过采样会爆炸模型系数的数量？

Question

我有这样一个管道模型：

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer, make_column_transformer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# define preprocessor
preprocess = make_column_transformer(
    (StandardScaler(), ['attr1', 'attr2', 'attr3', 'attr4', 'attr5', 
                        'attr6', 'attr7', 'attr8', 'attr9']),
    (OneHotEncoder(categories='auto'), ['attrcat1', 'attrcat2'])
)

# define train and test datasets
X_train, X_test, y_train, y_test = 
    train_test_split(features, target, test_size=0.3, random_state=0)

当我在没有过度采样的情况下执行管道时，我得到：

# don't do over-sampling in this case
os_X_train = X_train
os_y_train = y_train

print('Training data is type %s and shape %s' % (type(os_X_train), os_X_train.shape))
logreg = LogisticRegression(penalty='l2',solver='lbfgs',max_iter=1000)
model = make_pipeline(preprocess, logreg)
model.fit(os_X_train, np.ravel(os_y_train))
print("The coefficients shape is: %s" % logreg.coef_.shape)
print("Model coefficients: ", logreg.intercept_, logreg.coef_)
print("Logistic Regression score: %f" % model.score(X_test, y_test))

输出为：

Training data is type <class 'pandas.core.frame.DataFrame'> and shape (87145, 11)
The coefficients shape is: (1, 47)
Model coefficients:  [-7.51822124] [[ 0.10011794  0.10313989 ... -0.14138371  0.01612046  0.12064405]]
Logistic Regression score: 0.999116

这意味着我得到了一个训练集的47个模型系数87145个样本，这是有意义的考虑到定义的预处理。OneHotEncoder在attrcat1和attrcat2上工作，它们总共有31+7个类别，加上38列，加上我已经拥有的9列，总共有47个特性。你知道吗

现在如果我也这么做，但是这次用SMOTE进行过度采样，像这样：

from imblearn.over_sampling import SMOTE
# balance the classes by oversampling the training data
os = SMOTE(random_state=0)
os_X_train,os_y_train=os.fit_sample(X_train, y_train.ravel())
os_X_train = pd.DataFrame(data=os_X_train, columns=X_train.columns)
os_y_train = pd.DataFrame(data=os_y_train, columns=['response'])

输出变为：

Training data is type <class 'pandas.core.frame.DataFrame'> and shape (174146, 11)
The coefficients shape is: (1, 153024)
Model coefficients:  [12.02830778] [[ 0.42926969  0.14192505 -1.89354062 ...  0.008847    0.00884372 -8.15123962]]
Logistic Regression score: 0.997938

在这种情况下，我得到了大约两倍的训练样本大小来平衡我想要的反应类，但我的逻辑回归模型爆炸到153024系数。这没有任何意义。。。知道为什么吗？你知道吗

Answer 1

好吧，我找到了这个问题的罪魁祸首。问题是SMOTE将所有特性列转换为float（包括这两个分类特性）。因此，当对column types float应用columns transformer OneHotEncoder时，会将列数分解为样本数，即它将相同float值的每次出现视为不同的类别。你知道吗

解决方案只是在运行管道之前将这些分类列类型转换回int：

# balance the classes by over-sampling the training data
os = SMOTE(random_state=0)
os_X_train, os_y_train = os.fit_sample(X_train, y_train.ravel())
os_X_train = pd.DataFrame(data=os_X_train, columns=X_train.columns)
# critically important to have the categorical variables from float back to int
os_X_train['attrcat1'] = os_X_train['attrcat1'].astype(int)
os_X_train['attrcat2'] = os_X_train['attrcat2'].astype(int)