Remove tocsc method where possible

laoyuer · May 25, 2019 · b566609 · b566609
1 parent 6dacf67
commit b566609
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Showing 15 changed files with 41 additions and 40 deletions.
diff --git a/docs/examples/huber.rst b/docs/examples/huber.rst
@@ -49,14 +49,14 @@ Python
         + np.multiply(10.*np.random.rand(m), 1. - ind95)
 
     # OSQP data
-    Im = sparse.eye(m).tocsc()
+    Im = sparse.eye(m, format='csc')
     P = sparse.block_diag((sparse.csc_matrix((n, n)), 2*Im,
-                           sparse.csc_matrix((m, m)))).tocsc()
+                           sparse.csc_matrix((m, m))),
+                           format='csc')
     q = np.append(np.zeros(m+n), 2*np.ones(m))
     A = sparse.vstack([
             sparse.hstack([Ad, -Im, Im]),
-            sparse.hstack([Ad, -Im, -Im])
-        ]).tocsc()
+            sparse.hstack([Ad, -Im, -Im])], format='csc')
     l = np.hstack([b, -np.inf*np.ones(m)])
     u = np.hstack([np.inf*np.ones(m), b])
 

diff --git a/docs/examples/lasso.rst b/docs/examples/lasso.rst
@@ -58,7 +58,7 @@ Python
     q = np.zeros(2*n + m)
     A = sparse.vstack([sparse.hstack([Ad, -Im, Onm.T]),
                        sparse.hstack([In, Onm, -In]),
-                       sparse.hstack([In, Onm, In])]).tocsc()
+                       sparse.hstack([In, Onm, In])], format='csc')
     l = np.hstack([b, -np.inf * np.ones(n), np.zeros(n)])
     u = np.hstack([b, np.zeros(n), np.inf * np.ones(n)])
 

diff --git a/docs/examples/least-squares.rst b/docs/examples/least-squares.rst
@@ -42,8 +42,7 @@ Python
     q = np.zeros(n+m)
     A = sparse.vstack([
             sparse.hstack([Ad, -sparse.eye(m)]),
-            sparse.hstack((sparse.eye(n), sparse.csc_matrix((n, m))))
-        ]).tocsc()
+            sparse.hstack([sparse.eye(n), sparse.csc_matrix((n, m))])], format='csc')
     l = np.hstack([b, np.zeros(n)])
     u = np.hstack([b, np.ones(n)])
 

diff --git a/docs/examples/mpc.rst b/docs/examples/mpc.rst
@@ -81,7 +81,7 @@ Python
     # Cast MPC problem to a QP: x = (x(0),x(1),...,x(N),u(0),...,u(N-1))
     # - quadratic objective
     P = sparse.block_diag([sparse.kron(sparse.eye(N), Q), QN,
-                           sparse.kron(sparse.eye(N), R)]).tocsc()
+                           sparse.kron(sparse.eye(N), R)], format='csc')
     # - linear objective
     q = np.hstack([np.kron(np.ones(N), -Q.dot(xr)), -QN.dot(xr),
                    np.zeros(N*nu)])
@@ -96,7 +96,7 @@ Python
     lineq = np.hstack([np.kron(np.ones(N+1), xmin), np.kron(np.ones(N), umin)])
     uineq = np.hstack([np.kron(np.ones(N+1), xmax), np.kron(np.ones(N), umax)])
     # - OSQP constraints
-    A = sparse.vstack([Aeq, Aineq]).tocsc()
+    A = sparse.vstack([Aeq, Aineq], format='csc')
     l = np.hstack([leq, lineq])
     u = np.hstack([ueq, uineq])
 

diff --git a/docs/examples/portfolio.rst b/docs/examples/portfolio.rst
@@ -60,7 +60,7 @@ Python
             sparse.hstack([F.T, -sparse.eye(k)]),
             sparse.hstack([sparse.csc_matrix(np.ones((1, n))), sparse.csc_matrix((1, k))]),
             sparse.hstack((sparse.eye(n), sparse.csc_matrix((n, k))))
-        ]).tocsc()
+        ], format='csc')
     l = np.hstack([np.zeros(k), 1., np.zeros(n)])
     u = np.hstack([np.zeros(k), 1., np.ones(n)])
 

diff --git a/docs/examples/svm.rst b/docs/examples/svm.rst
@@ -45,7 +45,7 @@ Python
     Ad = sparse.vstack([
             A_upp / np.sqrt(n) + (A_upp != 0.).astype(float) / n,
             A_low / np.sqrt(n) - (A_low != 0.).astype(float) / n
-         ]).tocsc()
+         ], format='csc')
 
     # OSQP data
     Im = sparse.eye(m)
@@ -54,7 +54,7 @@ Python
     A = sparse.vstack([
             sparse.hstack([sparse.diags(b).dot(Ad), -Im]),
             sparse.hstack([sparse.csc_matrix((m, n)), Im])
-        ]).tocsc()
+        ], format='csc')
     l = np.hstack([-np.inf*np.ones(m), np.zeros(m)])
     u = np.hstack([-np.ones(m), np.inf*np.ones(m)])
 
@@ -126,7 +126,7 @@ CVXPY
     A = sparse.vstack([
             A_upp / np.sqrt(n) + (A_upp != 0.).astype(float) / n,
             A_low / np.sqrt(n) - (A_low != 0.).astype(float) / n
-        ]).tocsc()
+        ], format='csc')
 
     # Define problem
     x = Variable(n)

diff --git a/tests/basic_qp/generate_problem.py b/tests/basic_qp/generate_problem.py
@@ -2,7 +2,7 @@
 from scipy import sparse
 import utils.codegen_utils as cu
 
-P = sparse.triu([[4., 1.], [1., 2.]]).tocsc()
+P = sparse.triu([[4., 1.], [1., 2.]], format='csc')
 q = np.ones(2)
 
 A = sparse.csc_matrix(np.array([[1., 1.], [1., 0.], [0., 1.], [0., 1.]]))

diff --git a/tests/basic_qp2/generate_problem.py b/tests/basic_qp2/generate_problem.py
@@ -2,7 +2,7 @@
 from scipy import sparse
 import utils.codegen_utils as cu
 
-P = sparse.triu([[11., 0.], [0., 0.]]).tocsc()
+P = sparse.triu([[11., 0.], [0., 0.]], format='csc')
 q = np.array([3., 4.])
 
 A = sparse.csc_matrix(np.array([[-1., 0.], [0., -1.], [-1., 3.],

diff --git a/tests/lin_alg/generate_problem.py b/tests/lin_alg/generate_problem.py
@@ -28,9 +28,9 @@
 
 # Test matrix operations
 test_mat_ops_n = 2
-test_mat_ops_A = sparse.random(test_mat_ops_n, test_mat_ops_n, density=0.8).tocsc()
+test_mat_ops_A = sparse.random(test_mat_ops_n, test_mat_ops_n, density=0.8, format='csc')
 test_mat_ops_d = np.random.randn(test_mat_ops_n)
-D = sparse.diags(test_mat_ops_d).tocsc()
+D = sparse.diags(test_mat_ops_d, format='csc')
 test_mat_ops_prem_diag = D.dot(test_mat_ops_A).tocoo().tocsc()  # Force matrix reordering
 test_mat_ops_postm_diag = test_mat_ops_A.dot(D).tocoo().tocsc()  # Force matrix reordering
 test_mat_ops_inf_norm_cols = np.amax(np.abs(
@@ -45,10 +45,10 @@
 
 test_mat_vec_n = n
 test_mat_vec_m = m
-test_mat_vec_A = sparse.random(m, n, density=1.0).tocsc()
-test_mat_vec_P = sparse.random(n, n, density=0.8).tocsc()
+test_mat_vec_A = sparse.random(m, n, density=1.0, format='csc')
+test_mat_vec_P = sparse.random(n, n, density=0.8, format='csc')
 test_mat_vec_P = test_mat_vec_P + test_mat_vec_P.T
-test_mat_vec_Pu = sparse.triu(test_mat_vec_P).tocsc()
+test_mat_vec_Pu = sparse.triu(test_mat_vec_P, format='csc')
 test_mat_vec_x = np.random.randn(n)
 test_mat_vec_y = np.random.randn(m)
 test_mat_vec_Ax = test_mat_vec_A.dot(test_mat_vec_x)
@@ -61,18 +61,18 @@
 
 # Test extract upper triangular
 test_mat_extr_triu_n = 5
-test_mat_extr_triu_P = sparse.random(test_mat_extr_triu_n, test_mat_extr_triu_n, density=0.8).tocsc()
+test_mat_extr_triu_P = sparse.random(test_mat_extr_triu_n, test_mat_extr_triu_n, density=0.8, format='csc')
 test_mat_extr_triu_P = test_mat_extr_triu_P + test_mat_extr_triu_P.T
-test_mat_extr_triu_Pu = sparse.triu(test_mat_extr_triu_P).tocsc()
+test_mat_extr_triu_Pu = sparse.triu(test_mat_extr_triu_P, format='csc')
 test_mat_extr_triu_P_inf_norm_cols = np.amax(np.abs(
     np.asarray(test_mat_extr_triu_P.todense())), axis=0)
 
 
 # Test compute quad form
 test_qpform_n = 4
-test_qpform_P = sparse.random(test_qpform_n, test_qpform_n, density=0.8).tocsc()
+test_qpform_P = sparse.random(test_qpform_n, test_qpform_n, density=0.8, format='csc')
 test_qpform_P = test_qpform_P + test_qpform_P.T
-test_qpform_Pu = sparse.triu(test_qpform_P).tocsc()
+test_qpform_Pu = sparse.triu(test_qpform_P, format='csc')
 test_qpform_x = np.random.randn(test_qpform_n)
 test_qpform_value = .5 * test_qpform_x.T.dot(test_qpform_P.dot(test_qpform_x))
 

diff --git a/tests/non_cvx/generate_problem.py b/tests/non_cvx/generate_problem.py
@@ -2,11 +2,10 @@
 from scipy import sparse
 import utils.codegen_utils as cu
 
-P = sparse.triu([[2., 5.], [5., 1.]]).tocsc()
+P = sparse.triu([[2., 5.], [5., 1.]], format='csc')
 q = np.array([3., 4.])
 
-A = sparse.csc_matrix(np.array([[-1.0, 0.], [0., -1.], [-1., 3.],
-                                [2., 5.], [3., 4]]))
+A = sparse.csc_matrix([[-1., 0.], [0., -1.], [-1., 3.], [2., 5.], [3., 4]])
 l = -np.inf * np.ones(A.shape[0])
 u = np.array([0., 0., -15., 100., 80.])
 

diff --git a/tests/primal_dual_infeasibility/generate_problem.py b/tests/primal_dual_infeasibility/generate_problem.py
@@ -2,7 +2,7 @@
 from scipy import sparse
 import utils.codegen_utils as cu
 
-P = sparse.diags([1., 0.]).tocsc()
+P = sparse.diags([1., 0.], format='csc')
 q = np.array([1., -1.])
 
 A12 = sparse.csc_matrix([[1., 1.], [1., 0.], [0., 1.]])

diff --git a/tests/primal_infeasibility/generate_problem.py b/tests/primal_infeasibility/generate_problem.py
@@ -12,7 +12,7 @@
 # Generate random Matrices
 Pt = sparse.random(n, n)
 P = Pt.T.dot(Pt) + sparse.eye(n)
-P = sparse.triu(P).tocsc()
+P = sparse.triu(P, format='csc')
 q = sp.randn(n)
 A = sparse.random(m, n).tolil()  # Lil for efficiency
 u = 3 + sp.randn(m)

diff --git a/tests/solve_linsys/generate_problem.py b/tests/solve_linsys/generate_problem.py
@@ -15,17 +15,18 @@
 test_solve_KKT_P = test_solve_KKT_P.dot(test_solve_KKT_P.T).tocsc()
 test_solve_KKT_A = sparse.random(test_solve_KKT_m, test_solve_KKT_n,
                                  density=0.4, format='csc')
-test_solve_KKT_Pu = sparse.triu(test_solve_KKT_P).tocsc()
+test_solve_KKT_Pu = sparse.triu(test_solve_KKT_P, format='csc')
 
 test_solve_KKT_rho = 4.0
 test_solve_KKT_sigma = 1.0
 test_solve_KKT_KKT = sparse.vstack([
                         sparse.hstack([test_solve_KKT_P + test_solve_KKT_sigma *
                         sparse.eye(test_solve_KKT_n), test_solve_KKT_A.T]),
                         sparse.hstack([test_solve_KKT_A,
-                        -1./test_solve_KKT_rho * sparse.eye(test_solve_KKT_m)])]).tocsc()
+                        -1./test_solve_KKT_rho * sparse.eye(test_solve_KKT_m)])
+                        ], format='csc')
 test_solve_KKT_rhs = np.random.randn(test_solve_KKT_m + test_solve_KKT_n)
-test_solve_KKT_x = spla.splu(test_solve_KKT_KKT.tocsc()).solve(test_solve_KKT_rhs)
+test_solve_KKT_x = spla.splu(test_solve_KKT_KKT).solve(test_solve_KKT_rhs)
 
 test_solve_KKT_x[test_solve_KKT_n:] = test_solve_KKT_rhs[test_solve_KKT_n:] + \
                                       test_solve_KKT_x[test_solve_KKT_n:] / test_solve_KKT_rho

diff --git a/tests/unconstrained/generate_problem.py b/tests/unconstrained/generate_problem.py
@@ -2,7 +2,7 @@
 from scipy import sparse
 import utils.codegen_utils as cu
 
-P = sparse.diags([0.617022, 0.92032449, 0.20011437, 0.50233257, 0.34675589]).tocsc()
+P = sparse.diags([0.617022, 0.92032449, 0.20011437, 0.50233257, 0.34675589], format='csc')
 q = np.array([-1.10593508, -1.65451545, -2.3634686, 1.13534535, -1.01701414])
 A = sparse.csc_matrix((0,5))
 l = np.array([])

diff --git a/tests/update_matrices/generate_problem.py b/tests/update_matrices/generate_problem.py
@@ -13,18 +13,19 @@
 test_form_KKT_m = m
 p = 0.7
 
-test_form_KKT_A = sparse.random(test_form_KKT_m, test_form_KKT_n, density=p).tocsc()
+test_form_KKT_A = sparse.random(test_form_KKT_m, test_form_KKT_n, density=p, format='csc')
 test_form_KKT_P = sparse.random(n, n, density=p)
-test_form_KKT_P = test_form_KKT_P.dot(test_form_KKT_P.T).tocsc() + sparse.eye(n).tocsc()
-test_form_KKT_Pu = sparse.triu(test_form_KKT_P).tocsc()
+test_form_KKT_P = test_form_KKT_P.dot(test_form_KKT_P.T).tocsc() + sparse.eye(n, format='csc')
+test_form_KKT_Pu = sparse.triu(test_form_KKT_P, format='csc')
 test_form_KKT_rho = 1.6
 test_form_KKT_sigma = 0.1
 test_form_KKT_KKT = sparse.vstack([
                         sparse.hstack([test_form_KKT_P + test_form_KKT_sigma *
                         sparse.eye(test_form_KKT_n), test_form_KKT_A.T]),
                         sparse.hstack([test_form_KKT_A,
-                        -1./test_form_KKT_rho * sparse.eye(test_form_KKT_m)])]).tocsc()
-test_form_KKT_KKTu = sparse.triu(test_form_KKT_KKT).tocsc()
+                        -1./test_form_KKT_rho * sparse.eye(test_form_KKT_m)])
+                        ], format='csc')
+test_form_KKT_KKTu = sparse.triu(test_form_KKT_KKT, format='csc')
 
 
 # Create new P, A and KKT
@@ -38,8 +39,9 @@
                         sparse.hstack([test_form_KKT_P_new + test_form_KKT_sigma *
                         sparse.eye(test_form_KKT_n), test_form_KKT_A_new.T]),
                         sparse.hstack([test_form_KKT_A_new,
-                        -1./test_form_KKT_rho * sparse.eye(test_form_KKT_m)])]).tocsc()
-test_form_KKT_KKTu_new = sparse.triu(test_form_KKT_KKT_new).tocsc()
+                        -1./test_form_KKT_rho * sparse.eye(test_form_KKT_m)])
+                        ], format='csc')
+test_form_KKT_KKTu_new = sparse.triu(test_form_KKT_KKT_new, format='csc')
 
 
 # Test solve problem with initial P and A