Sklearn SequentialFeatureSelector "Pipeline should either be a classifier" when using a classifier

I get this error when using a classifier and SFS as a part of sklearn pipeline:

Traceback (most recent call last):
  File "main.py", line 45, in <module>
    rs.fit(X_train, y_train)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/model_selection/_search.py", line 898, in fit
    self._run_search(evaluate_candidates)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/model_selection/_search.py", line 1419, in _run_search
    evaluate_candidates(ParameterGrid(self.param_grid))
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/model_selection/_search.py", line 845, in evaluate_candidates
    out = parallel(
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/utils/parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/joblib/parallel.py", line 1855, in __call__
    return output if self.return_generator else list(output)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/joblib/parallel.py", line 1784, in _get_sequential_output
    res = func(*args, **kwargs)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/utils/parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 754, in _fit_and_score
    test_scores = _score(estimator, X_test, y_test, scorer, error_score)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 813, in _score
    scores = scorer(estimator, X_test, y_test)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 459, in _score
    y_pred = method_caller(clf, "decision_function", X, pos_label=pos_label)
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
  File "/home/runner/SFSpredictproba/venv/lib/python3.10/site-packages/sklearn/utils/_response.py", line 103, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=decision_function or the response_method should be 'predict'. Got a regressor with response_method=decision_function instead.

Code to reproduce (replit):

clf = LogisticRegression()
cv = StratifiedKFold(n_splits=2)
sfs = SFS(clf, n_features_to_select=1, scoring='accuracy', cv=cv, n_jobs=-1)
imputer = SimpleImputer(missing_values=np.nan, strategy='median')
lr_param_grid = {
  'sequentialfeatureselector__estimator__class_weight': ['balanced', None]
}
pipe = make_pipeline(imputer, sfs)
rs = GridSearchCV(estimator=pipe,
                  param_grid=lr_param_grid,
                  cv=cv,
                  scoring="roc_auc",
                  error_score="raise")

# Generate random data for binary classification
X, y = make_classification(
  n_samples=10,  # Number of samples
  n_features=3,  # Number of features
  n_informative=2,  # Number of informative features
  n_redundant=1,  # Number of redundant features
  n_clusters_per_class=1,  # Number of clusters per class
  random_state=42)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rs.fit(X_train, y_train)

I get the same error when using other classifiers, other performance metrics, and when using mlxtend version of SFS.

Versions of packages:

• python = 3.10.8
• scikit-learn = 1.3.0

The issue you're encountering stems from an interaction between the Sequential Feature Selector, GridSearchCV, and the scoring method being used.

GridSearchCV validates your model using cross-validation internally. For some scoring methods, such as 'roc_auc', it requires class probabilities provided by the model. These probabilities are typically obtained via the predict_proba() or decision_function() methods from the classifier.

However, the SFS does not expose these methods from the classifier it encapsulates. As a result, when GridSearchCV attempts to apply the scoring function 'roc_auc', it encounters an error because it cannot access the required probability estimates.

Similarly, if you change the scoring function to 'accuracy' or others that rely on the predict() method, you may face another issue since SFS does not expose the predict() method from the encapsulated classifier.

This is the root cause of the error message you're seeing - a lack of access to required methods due to the encapsulation of the classifier within the SFS.

As for mlxtend, it seems probable that you're encountering the same issue. If mlxtend's Sequential Feature Selector also does not expose the predict_proba(), decision_function(), or predict() methods from the encapsulated classifier, you would face a similar problem.