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Fix Codacy Warnings (#477)

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Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-10 15:38:45 +01:00
committed by Nicola Demo
parent e3790e049a
commit 4177bfbb50
157 changed files with 3473 additions and 3839 deletions
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29
tests/test_blocks/test_pod.py
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@@ -4,7 +4,10 @@ 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)])
toy_snapshots = torch.vstack(
[torch.exp(-(x**2)) * c for c in torch.linspace(0, 1, 10)]
)
def test_constructor():
pod = PODBlock(2)
@@ -23,6 +26,7 @@ def test_fit(rank, scale):
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])
@@ -34,15 +38,16 @@ def test_fit(rank, scale, randomized):
assert pod.basis.shape == (rank, dof)
assert pod._basis.shape == (n_snap, dof)
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]
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)
@@ -64,6 +69,7 @@ def test_forward():
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])
@@ -74,6 +80,7 @@ def test_expand(rank, scale, randomized):
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])
@@ -81,9 +88,9 @@ 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)
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])
pod.expand(pod.reduce(toy_snapshots[0])), toy_snapshots[0].unsqueeze(0)
)
# torch.testing.assert_close(pod.expand(pod.reduce(c[0])), c[0])