Changed to safe_sqr and corrected an import bug in test_svd.

Signed-off-by: Timothy Click <tcthepoet@yahoo.com>

src/fluctmatch/decomposition/svd.py

@@ -21,7 +21,7 @@
 from scipy import linalg
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.decomposition.base import _BasePCA
-from sklearn.utils import check_array
+from sklearn.utils import check_array, safe_sqr
 from sklearn.utils.validation import check_is_fitted
 from sklearn.utils.extmath import fast_logdet, svd_flip
 
@@ -135,7 +135,7 @@ def _fit(self, X: np.ndarray):
         U, Vt = svd_flip(U, Vt)
 
         # Get variance explained by singular values
-        explained_variance_: np.ndarray = (S ** 2) / (n_samples - 1)
+        explained_variance_: np.ndarray = safe_sqr(S) / (n_samples - 1)
         total_var: float = explained_variance_.sum()
         explained_variance_ratio_: np.ndarray = explained_variance_ / total_var
         singular_values_: np.ndarray = S.copy()  # Store the singular values.

tests/decomposition/test_svd.py

@@ -31,10 +31,10 @@
 #  These tests were taken from the scikit-learn tests for PCA.
 
 import numpy as np
-from numpy.testing import assert_almost_equal
-from numpy.testing import assert_array_almost_equal
-from numpy.testing import assert_greater
-from numpy.testing import assert_no_warnings
+from sklearn.utils.testing import assert_almost_equal
+from sklearn.utils.testing import assert_array_almost_equal
+from sklearn.utils.testing import assert_greater
+from sklearn.utils.testing import assert_no_warnings
 
 from sklearn import datasets
 from fluctmatch.decomposition.svd import SVD