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PCA: remove a sklearn 1.4.0 workaround #6940

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Nov 28, 2024
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PCA: remove a sklearn 1.4.0 workaround #6940

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7 changes: 0 additions & 7 deletions Orange/projection/pca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,13 +48,6 @@ def fit(self, X, Y=None):
if sp.issparse(X) and params["n_components"] == min(X.shape):
X = X.toarray()

# In scikit-learn==1.4.0, only the arpack solver is supported for sparse
# data and `svd_solver="auto"` doesn't auto-resolve to this. This is
# fixed in scikit-learn 1.5.0, but for the time being, override these
# settings here
if sp.issparse(X) and params["svd_solver"] == "auto":
params["svd_solver"] = "arpack"

proj = self.__wraps__(**params)
proj = proj.fit(X, Y)
return PCAModel(proj, self.domain, len(proj.components_))
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9 changes: 2 additions & 7 deletions Orange/widgets/unsupervised/tests/test_owpca.py
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Expand Up @@ -225,13 +225,8 @@ def test_normalized_gives_correct_result(self, prepare_table):
U, S, Va = U[:, :2], S[:2], Va[:2]
x_pca = U * S

# In scikit-learn==1.4.0, the svd_flip function requires a `V` matrix,
# therefore, we provide a dummy matrix of the correct size, so we can
# call the function. In scikit-learn==1.5.0, we can remove this since
# V can be None if we are passing `u_based_decision=True`.
dummy_v = np.zeros_like(x_pca.T)
x_pca, _ = svd_flip(x_pca, dummy_v, u_based_decision=True)
x_widget, _ = svd_flip(widget_result.X.copy(), dummy_v, u_based_decision=True)
x_pca, _ = svd_flip(x_pca, None, u_based_decision=True)
x_widget, _ = svd_flip(widget_result.X.copy(), None, u_based_decision=True)

np.testing.assert_almost_equal(x_widget, x_pca)

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