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Commit 3ee2ed54 authored by Alex Spies's avatar Alex Spies
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add public test files for students on colab

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tests/1. Function 1 Minima Check.py 0 → 100644
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OK_FORMAT = True
test = { 'name': '1. Function 1 Minima Check',
'points': 2,
'suites': [ { 'cases': [ { 'code': '>>> # Check with minimum\n>>> f1_check_minimum(B, a, b)\nTrue',
'failure_message': 'F1 Minimum Check (with minimum) Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'F1 Minimum Check (with minimum) Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/2.a Method of Finite Differences.py 0 → 100644
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OK_FORMAT = True
test = { 'name': '2.a Method of Finite Differences',
'points': 4,
'suites': [ { 'cases': [ { 'code': '>>> B = np.array([[4, -2], [-2, 4]])\n'
'>>> a = np.array([[0], [1]])\n'
'>>> b = np.array([[-2], [1]])\n'
'>>> def f1(x):\n'
'... return float(x.T @ B @ x - x.T @ x + a.T @ x - b.T @ x)\n'
'>>> dummy_input = np.array([[2.5], [3.5]])\n'
'>>> output = grad_fd(f1, dummy_input)\n'
'>>> \n'
'>>> target_output = np.array([[3.00003, 11.00003]])\n'
'>>> np.isclose(output, target_output, atol=1e-3).all()\n'
'True',
'failure_message': 'Finite Differences on f1 Test Failed',
'hidden': False,
'locked': False,
'points': 1,
'success_message': 'Finite Differences on f1 Test Passed'},
{ 'code': '>>> B = np.array([[4, -2], [-2, 4]])\n'
'>>> a = np.array([[0], [1]])\n'
'>>> b = np.array([[-2], [1]])\n'
'>>> def f2(x):\n'
'... return float(np.cos((x - b).T @ (x - b)) + (x - a).T @ B @ (x - a))\n'
'>>> dummy_input = np.array([[2.5], [3.5]])\n'
'>>> output = grad_fd(f2, dummy_input)\n'
'>>> \n'
'>>> target_output = np.array([[1.18572957, 5.10321673]])\n'
'>>> np.isclose(output, target_output, atol=1e-3).all()\n'
'True',
'failure_message': 'Finite Differences on f2 Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Finite Differences on f2 Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/Gradient Descent Initialization.py 0 → 100644
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OK_FORMAT = True
test = {'name': 'Gradient Descent Initialization', 'points': 2, 'suites': [{'cases': [], 'scored': True, 'setup': '', 'teardown': '', 'type': 'doctest'}]}
tests/Gradient Descent.py 0 → 100644
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View file @ 3ee2ed54
OK_FORMAT = True
test = { 'name': 'Gradient Descent',
'points': 4,
'suites': [ { 'cases': [ { 'code': '>>> c = np.array([[4], [5]])\n'
'>>> dummy_fn = lambda x: float(c.T @ x)\n'
'>>> dummy_fn_grad = lambda x: c.T\n'
'>>> dummy_start_x, dummy_start_y = 1.0, 2.0\n'
'>>> \n'
'>>> trajectory, minimum_loc, minimum_value = gradient_descent(dummy_fn, dummy_fn_grad, start_x=dummy_start_x, start_y=dummy_start_y, lr=0.01, '
'n_steps=5, silent=True)\n'
'>>> \n'
'>>> target_trajectory = [np.array([[1], [2]]), np.array([[0.96], [1.95]]), np.array([[0.92], [1.9]]),\n'
'... np.array([[0.88], [1.85]]), np.array([[0.84], [1.8]]), np.array([[0.8], [1.75]])]\n'
'>>> \n'
'>>> np.array([np.isclose(target.flatten(), output.flatten(), atol=1e-3) for target, output in zip(target_trajectory, trajectory)]).all()\n'
'True',
'failure_message': 'Gradient Descent Trajectory Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Gradient Descent Trajectory Test Passed'},
{ 'code': '>>> c = np.array([[4], [5]])\n'
'>>> dummy_fn = lambda x: float(c.T @ x)\n'
'>>> dummy_fn_grad = lambda x: c.T\n'
'>>> dummy_start_x, dummy_start_y = 1.0, 2.0\n'
'>>> \n'
'>>> trajectory, minimum_loc, minimum_value = gradient_descent(dummy_fn, dummy_fn_grad, start_x=dummy_start_x, start_y=dummy_start_y, lr=0.01, '
'n_steps=5, silent=True)\n'
'>>> target_minimum_loc = np.array([[0.8], [1.75]])\n'
'>>> np.isclose(target_minimum_loc, minimum_loc, atol=1e-3).all()\n'
'True',
'failure_message': 'Gradient Descent Minimum Location Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Gradient Descent Minimum Location Test Passed'},
{ 'code': '>>> c = np.array([[4], [5]])\n'
'>>> dummy_fn = lambda x: float(c.T @ x)\n'
'>>> dummy_fn_grad = lambda x: c.T\n'
'>>> dummy_start_x, dummy_start_y = 1.0, 2.0\n'
'>>> \n'
'>>> trajectory, minimum_loc, minimum_value = gradient_descent(dummy_fn, dummy_fn_grad, start_x=dummy_start_x, start_y=dummy_start_y, lr=0.01, '
'n_steps=5, silent=True)\n'
'>>> target_minimum_value = 11.949999999999998\n'
'>>> np.isclose(target_minimum_value, minimum_value, atol=1e-3)\n'
'True',
'failure_message': 'Gradient Descent Minimum Value Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Gradient Descent Minimum Value Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/Q2.b.i Gradients of the Functions - f1.py 0 → 100644
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OK_FORMAT = True
test = { 'name': 'Q2.b.i Gradients of the Functions - f1',
'points': 1,
'suites': [ { 'cases': [ { 'code': '>>> B = np.array([[4, -2], [-2, 4]])\n'
'>>> a = np.array([[0], [1]])\n'
'>>> b = np.array([[-2], [1]])\n'
'>>> dummy_input = np.array([[2.5], [3.5]])\n'
'>>> output = f1_grad_exact(dummy_input)\n'
'>>> \n'
'>>> target_output = np.array([[3.0, 11.0]])\n'
'>>> np.isclose(output, target_output, atol=1e-3).all()\n'
'True',
'failure_message': 'Exact Gradients of f1 Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Exact Gradients of f1 Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/Q2.b.ii Gradients of the Functions - f2.py 0 → 100644
+ 22
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OK_FORMAT = True
test = { 'name': 'Q2.b.ii Gradients of the Functions - f2',
'points': 1,
'suites': [ { 'cases': [ { 'code': '>>> B = np.array([[4, -2], [-2, 4]])\n'
'>>> a = np.array([[0], [1]])\n'
'>>> b = np.array([[-2], [1]])\n'
'>>> dummy_input = np.array([[2.5], [3.5]])\n'
'>>> output = f2_grad_exact(dummy_input)\n'
'>>> \n'
'>>> target_output = np.array([[1.1858, 5.1032]])\n'
'>>> np.isclose(output, target_output, atol=1e-3).all()\n'
'True',
'failure_message': 'Exact Gradients of f2 Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Exact Gradients of f2 Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/Q2.b.iii Gradients of the Functions - f3.py 0 → 100644
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OK_FORMAT = True
test = { 'name': 'Q2.b.iii Gradients of the Functions - f3',
'points': 2,
'suites': [ { 'cases': [ { 'code': '>>> B = np.array([[4, -2], [-2, 4]])\n'
'>>> a = np.array([[0], [1]])\n'
'>>> b = np.array([[-2], [1]])\n'
'>>> dummy_input = np.array([[2.5], [3.5]])\n'
'>>> output = f3_grad_exact(dummy_input)\n'
'>>> \n'
'>>> target_output = np.array([[0.02699379, 0.03779876]])\n'
'>>> np.isclose(output, target_output, atol=1e-3).all()\n'
'True',
'failure_message': 'Exact Gradients of f2 Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Exact Gradients of f2 Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/Q4.a Squared Distance Function.py 0 → 100644
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OK_FORMAT = True
test = { 'name': 'Q4.a Squared Distance Function',
'points': 1,
'suites': [ { 'cases': [ { 'code': '>>> p = np.random.rand(4)\n>>> q = np.random.rand(4)\n>>> x = np.random.rand(4)\n>>> () == sq_dist_fwd(p, q, x).shape\nTrue',
'failure_message': 'Squared Distance Function Output Shape Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Squared Distance Function Output Shape Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/Q4.c Squared Distance Function Gradients.py 0 → 100644
+ 33
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View file @ 3ee2ed54
OK_FORMAT = True
test = { 'name': 'Q4.c Squared Distance Function Gradients',
'points': 2,
'suites': [ { 'cases': [ { 'code': '>>> p = np.random.rand(4)\n>>> q = np.random.rand(4)\n>>> x = np.random.rand(4)\n>>> (4,) == sq_dist_grad(p,q,x).shape \nTrue',
'failure_message': 'Squared Distance Gradient Function Shape Test Failed',
'hidden': False,
'locked': False,
'points': 0,
'success_message': 'Squared Distance Gradient Function Shape Test Passed'},
{ 'code': '>>> #! think I prefer to test for us of "grad" from autograd, rather than do memory test\n'
'>>> # 1. this is more robust ; 2. we can them provide demo of the memory use as proof of utility \n'
'>>> # """ # BEGIN TEST CONFIG\n'
'>>> # name: test_sq_dist_grad_memory\n'
'>>> # points: 2\n'
'>>> # hidden: true \n'
">>> # success_message: 'Squared Distance Gradient Memory Test Passed'\n"
">>> # failure_message: 'Squared Distance Gradient Memory Test Failed'\n"
'>>> # """ # END TEST CONFIG\n'
'>>> # %%capture result\n'
'>>> # %load_ext memory_profiler\n'
'>>> # a,b,x = np.random.randn(100), np.random.randn(100), np.random.randn(100)\n'
'>>> # %memit manual_grad_vec(a,b,x)\n'
'>>> # %%capture result2\n'
'>>> # %memit grad(sq_dist_fwd, 2)(a,b,x) \n'
">>> # get_mem_usage = lambda x: float(str(x)[12:str(x).find('MiB')])\n"
'>>> # 3*get_mem_usage(result) < get_mem_usage(result2)\n',
'hidden': False,
'locked': False}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
tests/Q4.d Squared Distance Autodiff.py 0 → 100644
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OK_FORMAT = True
test = { 'name': 'Q4.d Squared Distance Autodiff',
'points': 2,
'suites': [ { 'cases': [ { 'code': '>>> our_grad = grad(sq_dist, 0)(z) # This is calling our custom gradient function\n'
'>>> their_grad = grad(sq_dist_fwd, 2)(p,q,z) # This is uses slow gradient primitives\n'
'>>> np.allclose(our_grad, their_grad, atol=1e-3)\n'
'True',
'failure_message': 'Squared Distance Autodiff Value Test Failed',
'hidden': False,
'locked': False,
'points': 2,
'success_message': 'Squared Distance Autodiff Value Test Passed'}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
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