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