fixed dswah#357 where methode is depricated

pygam/pygam.py

@@ -703,7 +703,7 @@ def _initial_estimate(self, y, modelmat):
 
         # solve the linear problem
         return np.linalg.solve(
-            load_diagonal(modelmat.T.dot(modelmat).A), modelmat.T.dot(y_)
+            load_diagonal(modelmat.T.dot(modelmat).toarray()), modelmat.T.dot(y_)
         )
 
         # not sure if this is faster...
@@ -780,7 +780,7 @@ def _pirls(self, X, Y, weights):
             self._on_loop_start(vars())
 
             WB = W.dot(modelmat[mask, :])  # common matrix product
-            Q, R = np.linalg.qr(WB.A)
+            Q, R = np.linalg.qr(WB.toarray())
 
             if not np.isfinite(Q).all() or not np.isfinite(R).all():
                 raise ValueError(
@@ -1401,7 +1401,7 @@ def _get_quantiles(
         idxs = self.terms.get_coef_indices(term)
         cov = self.statistics_['cov'][idxs][:, idxs]
 
-        var = (modelmat.dot(cov) * modelmat.A).sum(axis=1)
+        var = (modelmat.dot(cov) * modelmat.toarray()).sum(axis=1)
         if prediction:
             var += self.distribution.scale
 

pygam/terms.py

@@ -1500,7 +1500,7 @@ def _build_marginal_constraints(self, i, coef, constraint_lam, constraint_l2):
             )
 
             # now enter it into the composite
-            composite_C[tuple(np.meshgrid(slice_, slice_))] = slice_C.A
+            composite_C[tuple(np.meshgrid(slice_, slice_))] = slice_C.toarray()
 
         return sp.sparse.csc_matrix(composite_C)
 

pygam/utils.py

@@ -64,7 +64,7 @@ def cholesky(A, sparse=True, verbose=True):  # noqa: F811
 
         if sparse:
             return L.T  # upper triangular factorization
-        return L.T.A  # upper triangular factorization
+        return L.T.toarray()  # upper triangular factorization
 
     else:
         msg = (
@@ -78,7 +78,7 @@ def cholesky(A, sparse=True, verbose=True):  # noqa: F811
             warnings.warn(msg)
 
         if sp.sparse.issparse(A):
-            A = A.A
+            A = A.toarray()
 
         try:
             L = sp.linalg.cholesky(A, lower=False)
@@ -951,10 +951,10 @@ def tensor_product(a, b, reshape=True):
         raise ValueError('both arguments must have the same number of samples')
 
     if sp.sparse.issparse(a):
-        a = a.A
+        a = a.toarray()
 
     if sp.sparse.issparse(b):
-        b = b.A
+        b = b.toarray()
 
     tensor = a[..., :, None] * b[..., None, :]