Replace non-existent top level scipy functions.

Author: desilinguist

factor_analyzer/factor_analyzer.py

@@ -366,7 +366,7 @@ def _fit_uls_objective(psi, corr_mtx, n_factors):  # noqa: D401
         # implemented here using `np.tril()` when this change is
         # merged into the stable version of `psych`.
         residual = (corr_mtx - model) ** 2
-        error = sp.sum(residual)
+        error = np.sum(residual)
         return error
 
     @staticmethod
@@ -995,8 +995,8 @@ def sufficiency(self, num_observations: int) -> Tuple[float, int, float]:
 
         References
         ----------
-        [1] Lawley, D. N. and Maxwell, A. E. (1971). Factor Analysis as a Statistical Method. Second edition.
-        Butterworths. P. 36.
+        [1] Lawley, D. N. and Maxwell, A. E. (1971). Factor Analysis as a
+             Statistical Method. Second edition. Butterworths. P. 36.
 
         Examples
         --------

factor_analyzer/rotator.py

@@ -494,7 +494,6 @@ def _varimax(self, loadings):
 
         d = 0
         for _ in range(self.max_iter):
-
             old_d = d
 
             # take inner product of loading matrix
@@ -560,9 +559,9 @@ def _promax(self, loadings):
             # pre-normalization is done in R's
             # `kaiser()` function when rotate='Promax'.
             array = X.copy()
-            h2 = sp.diag(np.dot(array, array.T))
+            h2 = np.diag(np.dot(array, array.T))
             h2 = np.reshape(h2, (h2.shape[0], 1))
-            weights = array / sp.sqrt(h2)
+            weights = array / np.sqrt(h2)
 
         else:
             weights = X.copy()
@@ -576,20 +575,20 @@ def _promax(self, loadings):
 
         # calculate diagonal of inverse square
         try:
-            diag_inv = sp.diag(sp.linalg.inv(sp.dot(coef.T, coef)))
+            diag_inv = np.diag(sp.linalg.inv(np.dot(coef.T, coef)))
         except np.linalg.LinAlgError:
-            diag_inv = sp.diag(sp.linalg.pinv(sp.dot(coef.T, coef)))
+            diag_inv = np.diag(sp.linalg.pinv(np.dot(coef.T, coef)))
 
         # transform and calculate inner products
-        coef = sp.dot(coef, sp.diag(sp.sqrt(diag_inv)))
-        z = sp.dot(X, coef)
+        coef = np.dot(coef, np.diag(np.sqrt(diag_inv)))
+        z = np.dot(X, coef)
 
         if self.normalize:
             # post-normalization is done in R's
             # `kaiser()` function when rotate='Promax'
-            z = z * sp.sqrt(h2)
+            z = z * np.sqrt(h2)
 
-        rotation_mtx = sp.dot(rotation_mtx, coef)
+        rotation_mtx = np.dot(rotation_mtx, coef)
 
         coef_inv = np.linalg.inv(coef)
         phi = np.dot(coef_inv, coef_inv.T)