在cross_validate()函数中使用Pipeline来测试不同的ML算法

Question

我有一个数据集，包含17个特征(x(和二进制分类结果(y(。我已经准备好了数据集并对其执行了train_test_split()。我正在使用以下脚本在数据集上运行不同的ML算法来进行比较：

def run_exps(X_train: pd.DataFrame , y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame) -> pd.DataFrame:

# Lightweight script to test many models and find winners
# :param X_train: training split
# :param y_train: training target vector
# :param X_test: test split
# :param y_test: test target vector
# :return: DataFrame of predictions
models = [
('LogReg', LogisticRegression()),
('RF', RandomForestClassifier()),
('KNN - Euclidean', KNeighborsClassifier(metric='euclidean')),
('SVM', SVC()),
('XGB', XGBClassifier(use_label_encoder =False, eval_metric='error'))
]
names = []
scoring = ['accuracy', 'precision_weighted', 'recall_weighted', 'f1_weighted', 'roc_auc']
# For Loop that takes each model and perform training, cross validation, prediction and evaluation
for name, model in models:
# Making pipleline that normalize, oversmaple the dataset
pipe = Pipeline([
('normalization', MinMaxScaler()),
('oversampling', SMOTE())
])
kfold = StratifiedKFold(n_splits=5)
# How can I call the pipeline inside the cross_validate() Function ?
cv_results = cross_validate(model, X_train, y_train, cv=kfold, scoring=scoring, verbose=3)
clf = model.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('''
{}
{}
{}
''' .format(name, classification_report(y_test, y_pred), confusion_matrix(y_test, y_pred)))
names.append(name)

我注意到，在运行脚本之前，我使用的数据需要进行规范化和过采样。

但是，由于我在脚本中使用cross_validate()函数，因此需要对每个折叠执行规范化和过采样。

为了做到这一点，我在for循环中创建了一个管道(对数据集进行归一化和过采样((采用每个模型并执行训练、交叉验证、预测和评估(，但我不确定如何调用管道，因为cross_validate()中的estimator参数已经采用model变量来基于它执行预测

这种情况下我该怎么办？

Answer 1

您可以将模型集成到管道中，然后在管道上调用cross_validate，如下所示：

pipe = Pipeline([
('normalization', MinMaxScaler()),
('oversampling', SMOTE()),
('name', model)
])
cv_results = cross_validate(pipe, X_train, y_train, cv=kfold, scoring=scoring, verbose=3)