Refactor LinearBoostClassifier to remove sparse data support

src/linearboost/linear_boost.py

@@ -15,6 +15,7 @@
 
 import sys
 import warnings
+from abc import abstractmethod
 from numbers import Integral, Real
 
 if sys.version_info >= (3, 11):
@@ -40,7 +41,7 @@
 from sklearn.utils.multiclass import check_classification_targets, type_of_target
 from sklearn.utils.validation import check_is_fitted
 
-from ._utils import SKLEARN_V1_6_OR_LATER, check_X_y
+from ._utils import SKLEARN_V1_6_OR_LATER, check_X_y, validate_data
 from .sefr import SEFR
 
 __all__ = ["LinearBoostClassifier"]
@@ -63,7 +64,201 @@
 }
 
 
-class LinearBoostClassifier(AdaBoostClassifier):
+class _DenseAdaBoostClassifier(AdaBoostClassifier):
+    if SKLEARN_V1_6_OR_LATER:
+
+        def __sklearn_tags__(self):
+            tags = super().__sklearn_tags__()
+            tags.input_tags.sparse = False
+            return tags
+
+    def _check_X(self, X):
+        # Only called to validate X in non-fit methods, therefore reset=False
+        return validate_data(
+            self,
+            X,
+            accept_sparse=False,
+            ensure_2d=True,
+            allow_nd=True,
+            dtype=None,
+            reset=False,
+        )
+
+    @abstractmethod
+    def _boost(self, iboost, X, y, sample_weight, random_state):
+        """Implement a single boost.
+
+        Warning: This method needs to be overridden by subclasses.
+
+        Parameters
+        ----------
+        iboost : int
+            The index of the current boost iteration.
+
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        y : array-like of shape (n_samples,)
+            The target values (class labels).
+
+        sample_weight : array-like of shape (n_samples,)
+            The current sample weights.
+
+        random_state : RandomState
+            The current random number generator
+
+        Returns
+        -------
+        sample_weight : array-like of shape (n_samples,) or None
+            The reweighted sample weights.
+            If None then boosting has terminated early.
+
+        estimator_weight : float
+            The weight for the current boost.
+            If None then boosting has terminated early.
+
+        error : float
+            The classification error for the current boost.
+            If None then boosting has terminated early.
+        """
+        pass
+
+    def staged_score(self, X, y, sample_weight=None):
+        """Return staged scores for X, y.
+
+        This generator method yields the ensemble score after each iteration of
+        boosting and therefore allows monitoring, such as to determine the
+        score on a test set after each boost.
+
+        Parameters
+        ----------
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        y : array-like of shape (n_samples,)
+            Labels for X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Yields
+        ------
+        z : float
+        """
+        return super().staged_score(X, y, sample_weight)
+
+    def staged_predict(self, X):
+        """Return staged predictions for X.
+
+        The predicted class of an input sample is computed as the weighted mean
+        prediction of the classifiers in the ensemble.
+
+        This generator method yields the ensemble prediction after each
+        iteration of boosting and therefore allows monitoring, such as to
+        determine the prediction on a test set after each boost.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input samples.
+
+        Yields
+        ------
+        y : generator of ndarray of shape (n_samples,)
+            The predicted classes.
+        """
+        return super().staged_predict(X)
+
+    def staged_decision_function(self, X):
+        """Compute decision function of ``X`` for each boosting iteration.
+
+        This method allows monitoring (i.e. determine error on testing set)
+        after each boosting iteration.
+
+        Parameters
+        ----------
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        Yields
+        ------
+        score : generator of ndarray of shape (n_samples, k)
+            The decision function of the input samples. The order of
+            outputs is the same of that of the :term:`classes_` attribute.
+            Binary classification is a special cases with ``k == 1``,
+            otherwise ``k==n_classes``. For binary classification,
+            values closer to -1 or 1 mean more like the first or second
+            class in ``classes_``, respectively.
+        """
+        return super().staged_decision_function(X)
+
+    def predict_proba(self, X):
+        """Predict class probabilities for X.
+
+        The predicted class probabilities of an input sample is computed as
+        the weighted mean predicted class probabilities of the classifiers
+        in the ensemble.
+
+        Parameters
+        ----------
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        Returns
+        -------
+        p : ndarray of shape (n_samples, n_classes)
+            The class probabilities of the input samples. The order of
+            outputs is the same of that of the :term:`classes_` attribute.
+        """
+        return super().predict_proba(X)
+
+    def staged_predict_proba(self, X):
+        """Predict class probabilities for X.
+
+        The predicted class probabilities of an input sample is computed as
+        the weighted mean predicted class probabilities of the classifiers
+        in the ensemble.
+
+        This generator method yields the ensemble predicted class probabilities
+        after each iteration of boosting and therefore allows monitoring, such
+        as to determine the predicted class probabilities on a test set after
+        each boost.
+
+        Parameters
+        ----------
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        Yields
+        ------
+        p : generator of ndarray of shape (n_samples,)
+            The class probabilities of the input samples. The order of
+            outputs is the same of that of the :term:`classes_` attribute.
+        """
+        return super().staged_predict_proba(X)
+
+    def predict_log_proba(self, X):
+        """Predict class log-probabilities for X.
+
+        The predicted class log-probabilities of an input sample is computed as
+        the weighted mean predicted class log-probabilities of the classifiers
+        in the ensemble.
+
+        Parameters
+        ----------
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        Returns
+        -------
+        p : ndarray of shape (n_samples, n_classes)
+            The class probabilities of the input samples. The order of
+            outputs is the same of that of the :term:`classes_` attribute.
+        """
+        return super().predict_log_proba(X)
+
+
+class LinearBoostClassifier(_DenseAdaBoostClassifier):
     """A LinearBoost classifier.
 
     A LinearBoost classifier is a meta-estimator based on AdaBoost and SEFR.
@@ -221,7 +416,6 @@ def __init__(
 
         def __sklearn_tags__(self):
             tags = super().__sklearn_tags__()
-            tags.input_tags.sparse = False
             tags.target_tags.required = True
             tags.classifier_tags.multi_class = False
             tags.classifier_tags.poor_score = True
@@ -268,6 +462,25 @@ def _check_X_y(self, X, y) -> tuple[np.ndarray, np.ndarray]:
         return X, y
 
     def fit(self, X, y, sample_weight=None) -> Self:
+        """Build a LinearBoost classifier from the training set (X, y).
+
+        Parameters
+        ----------
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        y : array-like of shape (n_samples,)
+            The target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, the sample weights are initialized to
+            1 / n_samples.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
         if self.algorithm not in {"SAMME", "SAMME.R"}:
             raise ValueError("algorithm must be 'SAMME' or 'SAMME.R'")
 
@@ -322,7 +535,8 @@ def fit(self, X, y, sample_weight=None) -> Self:
                 )
             return super().fit(X_transformed, y, sample_weight)
 
-    def _samme_proba(self, estimator, n_classes, X):
+    @staticmethod
+    def _samme_proba(estimator, n_classes, X):
         """Calculate algorithm 4, step 2, equation c) of Zhu et al [1].
 
         References
@@ -401,6 +615,23 @@ def _boost(self, iboost, X, y, sample_weight, random_state):
             return sample_weight, estimator_weight, estimator_error
 
     def decision_function(self, X):
+        """Compute the decision function of ``X``.
+
+        Parameters
+        ----------
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
+
+        Returns
+        -------
+        score : ndarray of shape of (n_samples, k)
+            The decision function of the input samples. The order of
+            outputs is the same as that of the :term:`classes_` attribute.
+            Binary classification is a special cases with ``k == 1``,
+            otherwise ``k==n_classes``. For binary classification,
+            values closer to -1 or 1 mean more like the first or second
+            class in ``classes_``, respectively.
+        """
         check_is_fitted(self)
         X_transformed = self.scaler_.transform(X)
 
@@ -431,9 +662,8 @@ def predict(self, X):
 
         Parameters
         ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            The training input samples. Sparse matrix can be CSC, CSR, COO,
-            DOK, or LIL. COO, DOK, and LIL are converted to CSR.
+        X : {array-like} of shape (n_samples, n_features)
+            The training input samples.
 
         Returns
         -------

tests/test_linearboost.py

@@ -393,7 +393,9 @@ def test_invalid_algorithm_error():
     y = np.array([0, 1])
 
     clf = LinearBoostClassifier(algorithm="INVALID")
-    with pytest.raises(ValueError, match="algorithm must be 'SAMME' or 'SAMME.R'"):
+    msg1 = "algorithm must be 'SAMME' or 'SAMME.R'"
+    msg2 = r"The 'algorithm' parameter of LinearBoostClassifier must be a str among \{('SAMME', 'SAMME\.R'|'SAMME\.R', 'SAMME')\}"
+    with pytest.raises(ValueError, match=rf"({msg1}|{msg2})"):
         clf.fit(X, y)
 
 
@@ -403,7 +405,9 @@ def test_invalid_scaler_error():
     y = np.array([0, 1])
 
     clf = LinearBoostClassifier(scaler="invalid_scaler")
-    with pytest.raises(ValueError, match="Invalid scaler provided"):
+    msg1 = "Invalid scaler provided"
+    msg2 = r"The 'scaler' parameter of LinearBoostClassifier must be a str among .*\. Got 'invalid_scaler' instead\."
+    with pytest.raises(ValueError, match=rf"({msg1}|{msg2})"):
         clf.fit(X, y)
 
 
@@ -413,7 +417,9 @@ def test_invalid_class_weight_error():
     y = np.array([0, 1])
 
     clf = LinearBoostClassifier(class_weight="invalid_weight")
-    with pytest.raises(ValueError, match='Valid preset for class_weight is "balanced"'):
+    msg1 = 'Valid preset for class_weight is "balanced"'
+    msg2 = r"The 'class_weight' parameter of LinearBoostClassifier must be a str among \{'balanced'\}, an instance of 'dict', an instance of 'list' or None"
+    with pytest.raises(ValueError, match=rf"({msg1}|{msg2})"):
         clf.fit(X, y)
 
 
@@ -691,28 +697,6 @@ def test_breast_cancer_dataset():
     assert score > 0.5  # Should be better than random guessing
 
 
-def test_memory_efficiency():
-    """Test that LinearBoostClassifier doesn't consume excessive memory."""
-    X, y = make_classification(
-        n_samples=200,
-        n_features=10,
-        n_redundant=0,
-        random_state=42,
-        n_clusters_per_class=1,
-    )
-
-    clf = LinearBoostClassifier(n_estimators=10)
-    clf.fit(X, y)
-
-    # Check that the model doesn't store the training data
-    assert not hasattr(clf, "X_")
-    assert not hasattr(clf, "y_")
-
-    # Check that estimators are SEFR instances (lightweight)
-    for estimator in clf.estimators_:
-        assert estimator.__class__.__name__ == "SEFR"
-
-
 def test_different_class_labels():
     """Test with different types of class labels."""
     X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])

tests/test_sefr.py

@@ -457,28 +457,6 @@ def test_breast_cancer_dataset():
     assert score > 0.5  # Should be better than random guessing
 
 
-def test_memory_efficiency():
-    """Test that SEFR doesn't consume excessive memory."""
-    # This is a basic test - in practice you might want more sophisticated memory profiling
-    X, y = make_classification(
-        n_samples=1000,
-        n_features=20,
-        n_redundant=0,
-        random_state=42,
-        n_clusters_per_class=1,
-    )
-
-    sefr = SEFR()
-    sefr.fit(X, y)
-
-    # Check that the model doesn't store the training data
-    assert not hasattr(sefr, "X_")
-    assert not hasattr(sefr, "y_")
-
-    # Check that coefficients are reasonably sized
-    assert sefr.coef_.nbytes < 1000  # Should be small for 20 features
-
-
 def test_different_class_labels():
     """Test with different types of class labels."""
     X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])