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Tests fails #1165
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medaminezghal
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The example that you included is one where the optimization was expected to fail but actually ended up succeeding (so its a good thing). If you change 'xfail' to None in the first line of of the parametrize list, you can see if that fixes the problem.
Please provide information on which version of control, numpy, and scipy you are using as well as your operating system.
@murrayrm I'am using Arch Linux with
numpy 2.3.1andscipy 1.16.0. I'm using latest version ofcontrol 0.10.2.@murrayrm If I replace
xfailwithNone, I go this:____________________ test_optimal_doc[shooting-3-None-None] ____________________ method = 'shooting', npts = 3, initial_guess = None, fail = None @pytest.mark.slow @pytest.mark.parametrize( "method, npts, initial_guess, fail", [ ('shooting', 3, None, None), # doesn't converge ('shooting', 3, 'zero', None), # doesn't converge ('shooting', 3, 'u0', None), # github issue #782 ('shooting', 3, 'input', 'endpoint'), # doesn't converge to optimal ('shooting', 5, 'input', 'endpoint'), # doesn't converge to optimal ('collocation', 3, 'u0', 'endpoint'), # doesn't converge to optimal ('collocation', 5, 'u0', 'endpoint'), ('collocation', 5, 'input', 'openloop'),# open loop sim fails ('collocation', 10, 'input', None), ('collocation', 10, 'u0', None), # from documentation ('collocation', 10, 'state', None), ('collocation', 20, 'state', None), ]) def test_optimal_doc(method, npts, initial_guess, fail): """Test optimal control problem from documentation""" def vehicle_update(t, x, u, params): # Get the parameters for the model l = params.get('wheelbase', 3.) # vehicle wheelbase phimax = params.get('maxsteer', 0.5) # max steering angle (rad) # Saturate the steering input phi = np.clip(u[1], -phimax, phimax) # Return the derivative of the state return np.array([ np.cos(x[2]) * u[0], # xdot = cos(theta) v np.sin(x[2]) * u[0], # ydot = sin(theta) v (u[0] / l) * np.tan(phi) # thdot = v/l tan(phi) ]) def vehicle_output(t, x, u, params): return x # return x, y, theta (full state) # Define the vehicle steering dynamics as an input/output system vehicle = ct.NonlinearIOSystem( vehicle_update, vehicle_output, states=3, name='vehicle', inputs=('v', 'phi'), outputs=('x', 'y', 'theta')) # Define the initial and final points and time interval x0 = np.array([0., -2., 0.]); u0 = np.array([10., 0.]) xf = np.array([100., 2., 0.]); uf = np.array([10., 0.]) Tf = 10 # Define the cost functions Q = np.diag([0, 0, 0.1]) # don't turn too sharply R = np.diag([1, 1]) # keep inputs small P = np.diag([1000, 1000, 1000]) # get close to final point traj_cost = opt.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf) term_cost = opt.quadratic_cost(vehicle, P, 0, x0=xf) # Define the constraints constraints = [ opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] # Define an initial guess at the trajectory timepts = np.linspace(0, Tf, npts, endpoint=True) if initial_guess == 'zero': initial_guess = 0 elif initial_guess == 'u0': initial_guess = u0 elif initial_guess == 'input': # Velocity = constant that gets us from start to end initial_guess = np.zeros((vehicle.ninputs, timepts.size)) initial_guess[0, :] = (xf[0] - x0[0]) / Tf # Steering = rate required to turn to proper slope in first segment approximate_angle = math.atan2(xf[1] - x0[1], xf[0] - x0[0]) initial_guess[1, 0] = approximate_angle / (timepts[1] - timepts[0]) initial_guess[1, -1] = -approximate_angle / (timepts[-1] - timepts[-2]) elif initial_guess == 'state': input_guess = np.outer(u0, np.ones((1, npts))) state_guess = np.array([ x0 + (xf - x0) * time/Tf for time in timepts]).transpose() initial_guess = (state_guess, input_guess) # Solve the optimal control problem with warnings.catch_warnings(): warnings.filterwarnings( 'ignore', message="unable to solve", category=UserWarning) result = opt.solve_optimal_trajectory( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=initial_guess, trajectory_method=method, # minimize_method='COBYLA', # SLSQP', ) if fail == 'xfail': assert not result.success pytest.xfail("optimization fails to converge") elif fail == 'precision': assert result.status == 2 pytest.xfail("optimization precision not achieved") else: # Make sure the optimization was successful assert result.success # Make sure we started and stopped at the right spot if fail == 'endpoint': assert not np.allclose(result.states[:, -1], xf, rtol=1e-4) pytest.xfail("optimization does not converge to endpoint") else: np.testing.assert_almost_equal(result.states[:, 0], x0, decimal=4) > np.testing.assert_almost_equal(result.states[:, -1], xf, decimal=2) E AssertionError: E Arrays are not almost equal to 2 decimals E E Mismatched elements: 1 / 3 (33.3%) E Max absolute difference among violations: 0.03711455 E Max relative difference among violations: inf E ACTUAL: NamedSignal([1.00e+02, 2.00e+00, 3.71e-02]) E DESIRED: array([100., 2., 0.]) control/tests/optimal_test.py:771: AssertionError ----------------------------- Captured stdout call ----------------------------- Summary statistics: * Cost function calls: 678 * Constraint calls: 760 * System simulations: 1204 * Final cost: 2.4136235872518714 ____________________ test_optimal_doc[shooting-3-zero-None] ____________________ method = 'shooting', npts = 3, initial_guess = 0, fail = None @pytest.mark.slow @pytest.mark.parametrize( "method, npts, initial_guess, fail", [ ('shooting', 3, None, None), # doesn't converge ('shooting', 3, 'zero', None), # doesn't converge ('shooting', 3, 'u0', None), # github issue #782 ('shooting', 3, 'input', 'endpoint'), # doesn't converge to optimal ('shooting', 5, 'input', 'endpoint'), # doesn't converge to optimal ('collocation', 3, 'u0', 'endpoint'), # doesn't converge to optimal ('collocation', 5, 'u0', 'endpoint'), ('collocation', 5, 'input', 'openloop'),# open loop sim fails ('collocation', 10, 'input', None), ('collocation', 10, 'u0', None), # from documentation ('collocation', 10, 'state', None), ('collocation', 20, 'state', None), ]) def test_optimal_doc(method, npts, initial_guess, fail): """Test optimal control problem from documentation""" def vehicle_update(t, x, u, params): # Get the parameters for the model l = params.get('wheelbase', 3.) # vehicle wheelbase phimax = params.get('maxsteer', 0.5) # max steering angle (rad) # Saturate the steering input phi = np.clip(u[1], -phimax, phimax) # Return the derivative of the state return np.array([ np.cos(x[2]) * u[0], # xdot = cos(theta) v np.sin(x[2]) * u[0], # ydot = sin(theta) v (u[0] / l) * np.tan(phi) # thdot = v/l tan(phi) ]) def vehicle_output(t, x, u, params): return x # return x, y, theta (full state) # Define the vehicle steering dynamics as an input/output system vehicle = ct.NonlinearIOSystem( vehicle_update, vehicle_output, states=3, name='vehicle', inputs=('v', 'phi'), outputs=('x', 'y', 'theta')) # Define the initial and final points and time interval x0 = np.array([0., -2., 0.]); u0 = np.array([10., 0.]) xf = np.array([100., 2., 0.]); uf = np.array([10., 0.]) Tf = 10 # Define the cost functions Q = np.diag([0, 0, 0.1]) # don't turn too sharply R = np.diag([1, 1]) # keep inputs small P = np.diag([1000, 1000, 1000]) # get close to final point traj_cost = opt.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf) term_cost = opt.quadratic_cost(vehicle, P, 0, x0=xf) # Define the constraints constraints = [ opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] # Define an initial guess at the trajectory timepts = np.linspace(0, Tf, npts, endpoint=True) if initial_guess == 'zero': initial_guess = 0 elif initial_guess == 'u0': initial_guess = u0 elif initial_guess == 'input': # Velocity = constant that gets us from start to end initial_guess = np.zeros((vehicle.ninputs, timepts.size)) initial_guess[0, :] = (xf[0] - x0[0]) / Tf # Steering = rate required to turn to proper slope in first segment approximate_angle = math.atan2(xf[1] - x0[1], xf[0] - x0[0]) initial_guess[1, 0] = approximate_angle / (timepts[1] - timepts[0]) initial_guess[1, -1] = -approximate_angle / (timepts[-1] - timepts[-2]) elif initial_guess == 'state': input_guess = np.outer(u0, np.ones((1, npts))) state_guess = np.array([ x0 + (xf - x0) * time/Tf for time in timepts]).transpose() initial_guess = (state_guess, input_guess) # Solve the optimal control problem with warnings.catch_warnings(): warnings.filterwarnings( 'ignore', message="unable to solve", category=UserWarning) result = opt.solve_optimal_trajectory( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=initial_guess, trajectory_method=method, # minimize_method='COBYLA', # SLSQP', ) if fail == 'xfail': assert not result.success pytest.xfail("optimization fails to converge") elif fail == 'precision': assert result.status == 2 pytest.xfail("optimization precision not achieved") else: # Make sure the optimization was successful assert result.success # Make sure we started and stopped at the right spot if fail == 'endpoint': assert not np.allclose(result.states[:, -1], xf, rtol=1e-4) pytest.xfail("optimization does not converge to endpoint") else: np.testing.assert_almost_equal(result.states[:, 0], x0, decimal=4) > np.testing.assert_almost_equal(result.states[:, -1], xf, decimal=2) E AssertionError: E Arrays are not almost equal to 2 decimals E E Mismatched elements: 1 / 3 (33.3%) E Max absolute difference among violations: 0.03711455 E Max relative difference among violations: inf E ACTUAL: NamedSignal([1.00e+02, 2.00e+00, 3.71e-02]) E DESIRED: array([100., 2., 0.]) control/tests/optimal_test.py:771: AssertionError ----------------------------- Captured stdout call ----------------------------- Summary statistics: * Cost function calls: 678 * Constraint calls: 760 * System simulations: 1204 * Final cost: 2.4136235872518714Note that when I remove the comment tag from this line, the test succeed.
@murrayrm change it to
endpointfix the test problem.Thanks for the info. I'll try to recreate it and then update the unit test. My guess is that the newer version of SciPy is converging on some cases when the old one wasn't.
@murrayrm If I use
BLASwithNumPy, The tests can go to endpoint and succeeds, if I useOpenBLAS, the tests will fails.
