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为什么管道中的过度采样会导致模型系数的数量激增?

logistic-regression scikit-learn machine-learning python

SkyWalker · 技术社区 · 6 年前

我有一个这样的模型管道:

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)

当我在不进行过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))

输出是:

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

也就是说,我得到了87145个样本的训练集的47个模型系数,考虑到定义的预处理,这是有意义的。这个 OneHotEncoder 作品 attrcat1 和 attrcat2 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。

现在,如果我做同样的事情,但这次使用类似于下面的smote进行抽样:

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,我得到两倍的训练样本大小来平衡我想要的响应类,但是我的逻辑回归模型爆炸到153024个系数。这没有任何意义…有什么想法吗?

1 回复 | 直到 6 年前

SkyWalker 6 年前

好吧,我找到了这个问题的罪魁祸首。问题是smote将所有特征列转换为float(包括这两个分类特征)。因此,当应用柱变压器时 OneHotEncoder 在列类型上,float将列数分解为样本数,即它将同一个float值的每次出现视为不同的类别。

解决方案是在运行管道之前,简单地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)