67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
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import pytest
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import torch
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from pytorch_lightning.metrics.regression import (
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MAE, MSE, RMSE, RMSLE
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)
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@pytest.mark.parametrize(['pred', 'target', 'exp'], [
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pytest.param([0., 1., 2., 3.], [0., 1., 2., 2.], .25),
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pytest.param([4., 3., 2., 1.], [1., 4., 3., 2.], 3.)
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])
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def test_mse(pred, target, exp):
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mse = MSE()
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assert mse.name == 'mse'
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score = mse(pred=torch.tensor(pred),
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target=torch.tensor(target))
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assert isinstance(score, torch.Tensor)
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assert score.item() == exp
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@pytest.mark.parametrize(['pred', 'target', 'exp'], [
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pytest.param([0., 1., 2., 3.], [0., 1., 2., 2.], .5),
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pytest.param([4., 3., 2., 1.], [1., 4., 3., 2.], 1.7321)
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])
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def test_rmse(pred, target, exp):
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rmse = RMSE()
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assert rmse.name == 'rmse'
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score = rmse(pred=torch.tensor(pred),
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target=torch.tensor(target))
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assert isinstance(score, torch.Tensor)
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assert pytest.approx(score.item(), rel=1e-3) == exp
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@pytest.mark.parametrize(['pred', 'target', 'exp'], [
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pytest.param([0., 1., 2., 3.], [0., 1., 2., 2.], .25),
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pytest.param([4., 3., 2., 1.], [1., 4., 3., 2.], 1.5)
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])
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def test_mae(pred, target, exp):
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mae = MAE()
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assert mae.name == 'mae'
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score = mae(pred=torch.tensor(pred),
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target=torch.tensor(target))
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assert isinstance(score, torch.Tensor)
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assert score.item() == exp
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@pytest.mark.parametrize(['pred', 'target', 'exp'], [
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pytest.param([0., 1., 2., 3.], [0., 1., 2., 2.], .0207),
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pytest.param([4., 3., 2., 1.], [1., 4., 3., 2.], .2841)
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])
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def test_rmsle(pred, target, exp):
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rmsle = RMSLE()
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assert rmsle.name == 'rmsle'
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score = rmsle(pred=torch.tensor(pred),
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target=torch.tensor(target))
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assert isinstance(score, torch.Tensor)
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assert pytest.approx(score.item(), rel=1e-3) == exp
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