DOC: Add efficiency condition for CSC sparse matrix and remove CSC wa…

…rning. (#15846)

Closes gh-11145

scipy/sparse/linalg/_dsolve/linsolve.py

@@ -277,7 +277,8 @@ def splu(A, permc_spec=None, diag_pivot_thresh=None,
     Parameters
     ----------
     A : sparse matrix
-        Sparse matrix to factorize. Should be in CSC format.
+        Sparse matrix to factorize. Most efficient when provided in CSC
+        format. Other formats will be converted to CSC before factorization.
     permc_spec : str, optional
         How to permute the columns of the matrix for sparsity preservation.
         (default: 'COLAMD')
@@ -343,7 +344,7 @@ def splu(A, permc_spec=None, diag_pivot_thresh=None,
 
     if not isspmatrix_csc(A):
         A = csc_matrix(A)
-        warn('splu requires CSC matrix format', SparseEfficiencyWarning)
+        warn('splu converted its input to CSC format', SparseEfficiencyWarning)
 
     # sum duplicates for non-canonical format
     A.sum_duplicates()
@@ -377,7 +378,8 @@ def spilu(A, drop_tol=None, fill_factor=None, drop_rule=None, permc_spec=None,
     Parameters
     ----------
     A : (N, N) array_like
-        Sparse matrix to factorize. Should be in CSC format.
+        Sparse matrix to factorize. Most efficient when provided in CSC format.
+        Other formats will be converted to CSC before factorization.
     drop_tol : float, optional
         Drop tolerance (0 <= tol <= 1) for an incomplete LU decomposition.
         (default: 1e-4)
@@ -432,7 +434,8 @@ def spilu(A, drop_tol=None, fill_factor=None, drop_rule=None, permc_spec=None,
 
     if not isspmatrix_csc(A):
         A = csc_matrix(A)
-        warn('spilu requires CSC matrix format', SparseEfficiencyWarning)
+        warn('spilu converted its input to CSC format',
+             SparseEfficiencyWarning)
 
     # sum duplicates for non-canonical format
     A.sum_duplicates()
@@ -465,7 +468,8 @@ def factorized(A):
     Parameters
     ----------
     A : (N, N) array_like
-        Input.
+        Input. A in CSC format is most efficient. A CSR format matrix will
+        be converted to CSC before factorization.
 
     Returns
     -------
@@ -494,7 +498,8 @@ def factorized(A):
 
         if not isspmatrix_csc(A):
             A = csc_matrix(A)
-            warn('splu requires CSC matrix format', SparseEfficiencyWarning)
+            warn('splu converted its input to CSC format',
+                 SparseEfficiencyWarning)
 
         A = A.asfptype()  # upcast to a floating point format
 

scipy/sparse/linalg/_isolve/tests/test_iterative.py

@@ -570,7 +570,8 @@ def test_leftright_precond(self):
         U = spdiags([4*dat, -dat], [0,1], n, n)
 
         with suppress_warnings() as sup:
-            sup.filter(SparseEfficiencyWarning, "splu requires CSC matrix format")
+            sup.filter(SparseEfficiencyWarning,
+                       "splu converted its input to CSC format")
             L_solver = splu(L)
             U_solver = splu(U)
 

scipy/sparse/linalg/tests/test_expm_multiply.py

@@ -114,7 +114,7 @@ def test_sparse_expm_multiply(self):
             observed = expm_multiply(A, B)
             with suppress_warnings() as sup:
                 sup.filter(SparseEfficiencyWarning,
-                           "splu requires CSC matrix format")
+                           "splu converted its input to CSC format")
                 sup.filter(SparseEfficiencyWarning,
                            "spsolve is more efficient when sparse b is in the"
                            " CSC matrix format")
@@ -153,7 +153,7 @@ def test_sparse_expm_multiply_interval(self):
                         num=num, endpoint=endpoint)
                 with suppress_warnings() as sup:
                     sup.filter(SparseEfficiencyWarning,
-                               "splu requires CSC matrix format")
+                               "splu converted its input to CSC format")
                     sup.filter(SparseEfficiencyWarning,
                                "spsolve is more efficient when sparse b is in the CSC matrix format")
                     for solution, t in zip(X, samples):
@@ -249,4 +249,3 @@ def _help_test_specific_expm_interval_status(self, target_status):
         if not nsuccesses:
             msg = 'failed to find a status-' + str(target_status) + ' interval'
             raise Exception(msg)
-

scipy/sparse/tests/test_base.py

@@ -1113,7 +1113,8 @@ def test_expm(self):
         Nexp = scipy.linalg.expm(N)
 
         with suppress_warnings() as sup:
-            sup.filter(SparseEfficiencyWarning, "splu requires CSC matrix format")
+            sup.filter(SparseEfficiencyWarning,
+                       "splu converted its input to CSC format")
             sup.filter(SparseEfficiencyWarning,
                        "spsolve is more efficient when sparse b is in the CSC matrix format")
             sup.filter(SparseEfficiencyWarning,
@@ -1133,7 +1134,7 @@ def check(dtype):
                 sup.filter(SparseEfficiencyWarning,
                            "spsolve is more efficient when sparse b is in the CSC matrix format")
                 sup.filter(SparseEfficiencyWarning,
-                           "splu requires CSC matrix format")
+                           "splu converted its input to CSC format")
                 sM = self.spmatrix(M, shape=(3,3), dtype=dtype)
                 sMinv = inv(sM)
             assert_array_almost_equal(sMinv.dot(sM).toarray(), np.eye(3))
@@ -2337,7 +2338,8 @@ def test_solve(self):
             A[i+1,i] = conjugate(y[i])
         A = self.spmatrix(A)
         with suppress_warnings() as sup:
-            sup.filter(SparseEfficiencyWarning, "splu requires CSC matrix format")
+            sup.filter(SparseEfficiencyWarning,
+                       "splu converted its input to CSC format")
             x = splu(A).solve(r)
         assert_almost_equal(A*x,r)