imblearn管道中的多个参数

Question

我有一个强烈不平衡的数据集，我想尝试不同的形状模型，支持向量机分类和不同的烟雾“随机”和“打击”我做了这个，但有没有一个更紧凑的方法来做到这一点？

 DecisionShapeModel = ['ovo', 'ovr']
    Smoothing = [SMOTE(k_neighbors=3),RandomOverSampler()]
    ListParam = itertools.product(DecisionShapeModel, Smoothing)

    for DecisionShapeModel, Smoothing in ListParam:

        model = SVC(decision_function_shape=DecisionShapeModel, probability=True)

        PipelineIMB = Pipeline([
            ('smote', Smoothing),
            ('svm', model)
        ])

        """ Define search space """
        param_grid = {
            'svm__C': np.arange(1, 20, 1),
            'svm__kernel': ['linear', 'poly', 'rbf', 'sigmoid']
        }

        kf = KFold(n_splits=10, random_state=42, shuffle=True)

        grid_imba = GridSearchCV(PipelineIMB, param_grid, cv=kf, scoring='f1_macro',
                                 verbose=10, n_jobs=-1, error_score='raise')

        grid_imba.fit(X_train, y_train)
        y_pred = grid_imba.predict(X_test)

Answer 1

如果您的目标是测试这些选项，并且只保留最好的修改模型，那么您可以将所有内容打包到GridSearchCV中，利用参数网格中替换整个步骤的功能。

model = SVC(probability=True)

PipelineIMB = Pipeline([
    ('sample', 'passthrough'),
    ('svm', model),
])

param_grid = {
    'sample': [
        SMOTE(k_neighbors=3),
        RandomOverSampler(),
    ],  # these replace the original definition of "passthrough"
    'svm__decision_function_shape': ['ovo', 'ovr'],
    'svm__C': np.arange(1, 20, 1),
    'svm__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
}

kf = KFold(n_splits=10, random_state=42, shuffle=True)

grid_imba = GridSearchCV(PipelineIMB, param_grid, cv=kf, scoring='f1_macro',
                         verbose=10, n_jobs=-1, error_score='raise')

grid_imba.fit(X_train, y_train)