python - imblearn 管道是否会关闭采样以进行测试？

Question

让我们假设以下代码(来自 imblearn example on pipelines)

...    
# Instanciate a PCA object for the sake of easy visualisation
pca = PCA(n_components=2)

# Create the samplers
enn = EditedNearestNeighbours()
renn = RepeatedEditedNearestNeighbours()

# Create the classifier
knn = KNN(1)

# Make the splits
X_train, X_test, y_train, y_test = tts(X, y, random_state=42)

# Add one transformers and two samplers in the pipeline object
pipeline = make_pipeline(pca, enn, renn, knn)

pipeline.fit(X_train, y_train)
y_hat = pipeline.predict(X_test)

我想确保在执行 pipeline.predict(X_test) 时采样程序 enn和 renn不会被执行(当然 pca 必须被执行)。

1. First, it is clear to me that over-, under-, and mixed-sampling areprocedures to be applied to the training set, not to thetest/validation set. Please correct me here if I am wrong.

2. I browsed though the imblearn Pipeline code but I could not findthe predict method there.

3. I also would like to be sure that this correct behavior works whenthe pipeline is inside a GridSearchCV

我只需要确保 imblearn.Pipeline 会发生这种情况。 .
编辑:2020-08-28
@wundermahn 答案就是我所需要的。
此编辑只是补充说这是应该使用 imblearn.Pipeline 的原因。用于不平衡的预处理而不是 sklearn.Pipeline imblearn 无处可去文档我找到了解释为什么需要 imblearn.Pipeline当有 sklearn.Pipeline

Answer 1

很好的问题。要按照您发布的顺序浏览它们:

1. First, it is clear to me that over-, under-, and mixed-sampling are procedures to be applied to the training set, not to thetest/validation set. Please correct me here if I am wrong.

那是正确的。您当然不想在 的数据上测试(无论是在您的 test 还是 validation 数据上)不是 代表实际的、实时的、“生产”数据集。您真的应该只将其应用于培训。请注意，如果您使用交叉折叠验证等技术，则应将采样单独应用于每个折叠，如 this IEEE paper 所示。 .

2. I browsed though the imblearn Pipeline code but I could not find the predict method there.

我假设您找到了 imblearn.pipeline source code ，所以如果你这样做了，你想做的是看看 fit_predict方法:

 @if_delegate_has_method(delegate="_final_estimator")
    def fit_predict(self, X, y=None, **fit_params):
        """Apply `fit_predict` of last step in pipeline after transforms.
        Applies fit_transforms of a pipeline to the data, followed by the
        fit_predict method of the final estimator in the pipeline. Valid
        only if the final estimator implements fit_predict.
        Parameters
        ----------
        X : iterable
            Training data. Must fulfill input requirements of first step of
            the pipeline.
        y : iterable, default=None
            Training targets. Must fulfill label requirements for all steps
            of the pipeline.
        **fit_params : dict of string -> object
            Parameters passed to the ``fit`` method of each step, where
            each parameter name is prefixed such that parameter ``p`` for step
            ``s`` has key ``s__p``.
        Returns
        -------
        y_pred : ndarray of shape (n_samples,)
            The predicted target.
        """
        Xt, yt, fit_params = self._fit(X, y, **fit_params)
        with _print_elapsed_time('Pipeline',
                                 self._log_message(len(self.steps) - 1)):
            y_pred = self.steps[-1][-1].fit_predict(Xt, yt, **fit_params)
        return y_pred

在这里，我们可以看到 pipeline使用 .predict管道中最终估算器的方法，在您发布的示例中， scikit-learn's knn :

 def predict(self, X):
        """Predict the class labels for the provided data.
        Parameters
        ----------
        X : array-like of shape (n_queries, n_features), \
                or (n_queries, n_indexed) if metric == 'precomputed'
            Test samples.
        Returns
        -------
        y : ndarray of shape (n_queries,) or (n_queries, n_outputs)
            Class labels for each data sample.
        """
        X = check_array(X, accept_sparse='csr')

        neigh_dist, neigh_ind = self.kneighbors(X)
        classes_ = self.classes_
        _y = self._y
        if not self.outputs_2d_:
            _y = self._y.reshape((-1, 1))
            classes_ = [self.classes_]

        n_outputs = len(classes_)
        n_queries = _num_samples(X)
        weights = _get_weights(neigh_dist, self.weights)

        y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)
        for k, classes_k in enumerate(classes_):
            if weights is None:
                mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
            else:
                mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)

            mode = np.asarray(mode.ravel(), dtype=np.intp)
            y_pred[:, k] = classes_k.take(mode)

        if not self.outputs_2d_:
            y_pred = y_pred.ravel()

        return y_pred

3. I also would like to be sure that this correct behaviour works when the pipeline is inside a GridSearchCV

这种假设上述两个假设是正确的，我认为这意味着您想要一个 complete, minimal, reproducible example这在 GridSearchCV 中工作。来自 scikit-learn on this 的大量文档，但是我使用 knn 创建的示例在下面:

import pandas as pd, numpy as np

from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import GridSearchCV, train_test_split

param_grid = [
    {
        'classification__n_neighbors': [1,3,5,7,10],
    }
]

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.20)

pipe = Pipeline([
    ('sampling', SMOTE()),
    ('classification', KNeighborsClassifier())
])

grid = GridSearchCV(pipe, param_grid=param_grid)
grid.fit(X_train, y_train)
mean_scores = np.array(grid.cv_results_['mean_test_score'])
print(mean_scores)

# [0.98051926 0.98121129 0.97981998 0.98050474 0.97494193]

你的直觉很准，干得好:)