def test_threading(self):
np.random.seed(0)
m = 20
n = 10
As, bs, cs, cone_dicts = [], [], [], []
results = []
for _ in range(50):
A, b, c, cone_dims = utils.least_squares_eq_scs_data(m, n)
As += [A]
bs += [b]
cs += [c]
cone_dicts += [cone_dims]
results.append(cone_prog.solve_and_derivative(A, b, c, cone_dims))
for n_jobs in [1, -1]:
xs, ys, ss, _, DT_batch = cone_prog.solve_and_derivative_batch(
As, bs, cs, cone_dicts, n_jobs_forward=n_jobs, n_jobs_backward=n_jobs)
for i in range(50):
np.testing.assert_allclose(results[i][0], xs[i])
np.testing.assert_allclose(results[i][1], ys[i])
np.testing.assert_allclose(results[i][2], ss[i])
dAs, dbs, dcs = DT_batch(xs, ys, ss)
for i in range(50):
dA, db, dc = results[i][-1](results[i][0], results[i][1], results[i][2])
np.testing.assert_allclose(dA.todense(), dAs[i].todense())
np.testing.assert_allclose(dbs[i], db)
np.testing.assert_allclose(dcs[i], dc)
def test_threading(self):
m = 20
n = 10
As, bs, cs, cone_dicts = [], [], [], []
results = []
serial_time = 0.0
for _ in range(50):
A, b, c, cone_dims = utils.least_squares_eq_scs_data(m, n)
As += [A]
bs += [b]
cs += [c]
cone_dicts += [cone_dims]
tic = time.time()
results.append(cone_prog.solve_and_derivative(A, b, c, cone_dims))
toc = time.time()
serial_time += toc - tic
tic = time.time()
results_thread = cone_prog.solve_and_derivative_batch(
As, bs, cs, cone_dicts)
toc = time.time()
parallel_time = toc - tic
self.assertTrue(parallel_time < serial_time)
for i in range(50):
np.testing.assert_allclose(results[i][0], results_thread[i][0])
np.testing.assert_allclose(results[i][1], results_thread[i][1])
np.testing.assert_allclose(results[i][2], results_thread[i][2])
def conic_transform_solve_batch(Qs, qs, Gs, hs, As, bs, cp_sol=cp.SCS, n_jobs=4):
results = np.array([__single_cvxprob_formulate(Q, q, G, h, A, b, sol_opt=cp_sol) \
for Q, q, G, h, A, b in zip(Qs, qs, Gs, hs, As, bs)])
As_ = results[:, 0]
bs_ = results[:, 1]
cs_ = results[:, 2]
cons_dims_list = results[:, 3]
res = diffcp_cprog.solve_and_derivative_batch(As_, bs_, cs_, cons_dims_list, n_jobs=n_jobs, eps=1e-5)
res = np.array(res)
x_sols_batch =res[:, 0]
y_sols_batch =res[:, 1]
s_sols_batch =res[:, 2]
derivative_batch =res[:, 3]
adjoint_derivative = res[:, 4]
return x_sols_batch, y_sols_batch, s_sols_batch, derivative_batch, adjoint_derivative, As_, bs_, cs_