MNT fix minibatch X_transformed_ changes per loop

fastcan/_fastcan.py

@@ -117,6 +117,7 @@ class FastCan(SelectorMixin, BaseEstimator):
     def __init__(
         self,
         n_features_to_select=1,
+        *,
         indices_include=None,
         indices_exclude=None,
         eta=False,

fastcan/_minibatch.py

@@ -11,7 +11,7 @@
 from sklearn.utils.validation import check_X_y
 
 from ._cancorr_fast import _forward_search  # type: ignore
-from ._fastcan import FastCan, _prepare_search
+from ._fastcan import _prepare_search
 
 
 @validate_params(
@@ -26,17 +26,21 @@
         ],
         "verbose": ["verbose"],
     },
-    prefer_skip_nested_validation=False,
+    prefer_skip_nested_validation=True,
 )
 def minibatch(X, y, n_features_to_select=1, batch_size=1, verbose=1):
     """Feature selection using :class:`fastcan.FastCan` with mini batches.
 
     It is suitable for selecting a very large number of features
     even larger than the number of samples.
 
-    Similar to the correlation filter which selects each feature without considering
-    the redundancy, the function selects features in mini-batch and the
-    redundancy between the two mini-batches will be ignored.
+    The function splits `n_features_to_select` into `n_outputs` parts and selects
+    features for each part separately, ignoring the redundancy among outputs.
+    In each part, the function selects features batch-by-batch. The batch size is less
+    than or equal to `batch_size`.
+    Like correlation filters, which select features one-by-one without considering
+    the redundancy between two features, the function ignores the redundancy between
+    two mini-batches.
 
     Parameters
     ----------
@@ -70,7 +74,7 @@ def minibatch(X, y, n_features_to_select=1, batch_size=1, verbose=1):
     >>> print(f"Indices: {indices}")
     Indices: [0 1 2]
     """
-    X, y = check_X_y(X, y, ensure_2d=True, multi_output=True)
+    X, y = check_X_y(X, y, ensure_2d=True, multi_output=True, order="F")
     if y.ndim == 1:
         y = y.reshape(-1, 1)
 
@@ -90,41 +94,35 @@ def minibatch(X, y, n_features_to_select=1, batch_size=1, verbose=1):
             0, n_features_to_select, num=n_outputs + 1, endpoint=True, dtype=int
         )
     )
+    X_transformed_ = X - X.mean(0)
+    y_transformed_ = y - y.mean(0)
+    indices_include = np.zeros(0, dtype=int)  # just an empty array
     indices_select = np.zeros(0, dtype=int)
+
     for i in range(n_outputs):
-        y_i = y[:, i]
-        batch_split_i = np.diff(
-            np.r_[
-                np.arange(n_to_select_split[i], step=batch_size, dtype=int),
-                n_to_select_split[i],
-            ]
-        )
-        for j, batch_size_j in enumerate(batch_split_i):
-            if j == 0:
-                selector_j = FastCan(
-                    batch_size_j, indices_exclude=indices_select, verbose=0
-                ).fit(X, y_i)
-                X_transformed_ = deepcopy(selector_j.X_transformed_)
-                indices = selector_j.indices_
-            else:
-                indices, scores, mask = _prepare_search(
-                    n_features,
-                    batch_size_j,
-                    selector_j.indices_include_,
-                    np.r_[selector_j.indices_exclude_, indices_select],
-                )
-                _forward_search(
-                    X=X_transformed_,
-                    V=selector_j.y_transformed_,
-                    t=batch_size_j,
-                    tol=selector_j.tol,
-                    num_threads=n_threads,
-                    verbose=0,
-                    mask=mask,
-                    indices=indices,
-                    scores=scores,
-                )
+        y_i = y_transformed_[:, [i]]
+        n_selected_i = 0
+        while n_to_select_split[i] > n_selected_i:
+            batch_size_temp = min(batch_size, n_to_select_split[i] - n_selected_i)
+            indices, scores, mask = _prepare_search(
+                n_features,
+                batch_size_temp,
+                indices_include,
+                indices_select,
+            )
+            _forward_search(
+                X=deepcopy(X_transformed_),
+                V=y_i,
+                t=batch_size_temp,
+                tol=0.01,
+                num_threads=n_threads,
+                verbose=0,
+                mask=mask,
+                indices=indices,
+                scores=scores,
+            )
             indices_select = np.r_[indices_select, indices]
+            n_selected_i += batch_size_temp
             if verbose == 1:
                 print(
                     f"Progress: {indices_select.size}/{n_features_to_select}", end="\r"

meson.build

@@ -1,7 +1,7 @@
 project(
   'fastcan',
   'c', 'cython',
-  version: '0.3.1',
+  version: '0.3.2',
   license: 'MIT',
   meson_version: '>= 1.1.0',
   default_options: [