102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
import torch
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import pytest
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from pina.model.block.pod_block import PODBlock
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x = torch.linspace(-1, 1, 100)
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toy_snapshots = torch.vstack(
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[torch.exp(-(x**2)) * c for c in torch.linspace(0, 1, 10)]
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)
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def test_constructor():
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pod = PODBlock(2)
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pod = PODBlock(2, True)
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pod = PODBlock(2, False)
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with pytest.raises(TypeError):
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pod = PODBlock()
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@pytest.mark.parametrize("rank", [1, 2, 10])
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def test_fit(rank, scale):
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pod = PODBlock(rank, scale)
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assert pod._basis == None
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assert pod.basis == None
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assert pod._scaler == None
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assert pod._singular_values == None
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assert pod.singular_values == None
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assert pod.rank == rank
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assert pod.scale_coefficients == scale
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@pytest.mark.parametrize("scale", [True, False])
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@pytest.mark.parametrize("rank", [1, 2, 10])
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@pytest.mark.parametrize("randomized", [True, False])
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def test_fit(rank, scale, randomized):
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pod = PODBlock(rank, scale)
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pod.fit(toy_snapshots, randomized)
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n_snap = toy_snapshots.shape[0]
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dof = toy_snapshots.shape[1]
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assert pod.basis.shape == (rank, dof)
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assert pod._basis.shape == (n_snap, dof)
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assert pod.singular_values.shape == (rank,)
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assert pod._singular_values.shape == (n_snap,)
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if scale is True:
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assert pod._mean.shape == (n_snap,)
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assert pod._std.shape == (n_snap,)
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assert pod.scaler["mean"].shape == (rank,)
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assert pod.scaler["std"].shape == (rank,)
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assert pod.scaler["mean"].shape[0] == pod.basis.shape[0]
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else:
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assert pod._std == None
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assert pod._mean == None
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assert pod.scaler == None
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def test_forward():
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pod = PODBlock(1)
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pod.fit(toy_snapshots)
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c = pod(toy_snapshots)
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assert c.shape[0] == toy_snapshots.shape[0]
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assert c.shape[1] == pod.rank
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torch.testing.assert_close(c.mean(dim=0), torch.zeros(pod.rank))
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torch.testing.assert_close(c.std(dim=0), torch.ones(pod.rank))
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c = pod(toy_snapshots[0])
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assert c.shape[1] == pod.rank
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assert c.shape[0] == 1
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pod = PODBlock(2, False)
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pod.fit(toy_snapshots)
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c = pod(toy_snapshots)
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torch.testing.assert_close(c, (pod.basis @ toy_snapshots.T).T)
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with pytest.raises(AssertionError):
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torch.testing.assert_close(c.mean(dim=0), torch.zeros(pod.rank))
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torch.testing.assert_close(c.std(dim=0), torch.ones(pod.rank))
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@pytest.mark.parametrize("scale", [True, False])
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@pytest.mark.parametrize("rank", [1, 2, 10])
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@pytest.mark.parametrize("randomized", [True, False])
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def test_expand(rank, scale, randomized):
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pod = PODBlock(rank, scale)
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pod.fit(toy_snapshots, randomized)
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c = pod(toy_snapshots)
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torch.testing.assert_close(pod.expand(c), toy_snapshots)
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torch.testing.assert_close(pod.expand(c[0]), toy_snapshots[0].unsqueeze(0))
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@pytest.mark.parametrize("scale", [True, False])
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@pytest.mark.parametrize("rank", [1, 2, 10])
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@pytest.mark.parametrize("randomized", [True, False])
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def test_reduce_expand(rank, scale, randomized):
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pod = PODBlock(rank, scale)
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pod.fit(toy_snapshots, randomized)
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torch.testing.assert_close(
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pod.expand(pod.reduce(toy_snapshots)), toy_snapshots
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)
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torch.testing.assert_close(
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pod.expand(pod.reduce(toy_snapshots[0])), toy_snapshots[0].unsqueeze(0)
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)
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# torch.testing.assert_close(pod.expand(pod.reduce(c[0])), c[0])
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