73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
from collections import namedtuple
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from functools import partial
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import pytest
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import torch
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from sklearn.metrics import explained_variance_score
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from pytorch_lightning.metrics.regression import ExplainedVariance
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from pytorch_lightning.metrics.functional import explained_variance
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from tests.metrics.utils import BATCH_SIZE, NUM_BATCHES, MetricTester
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torch.manual_seed(42)
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num_targets = 5
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Input = namedtuple('Input', ["preds", "target"])
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_single_target_inputs = Input(preds=torch.rand(NUM_BATCHES, BATCH_SIZE), target=torch.rand(NUM_BATCHES, BATCH_SIZE),)
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_multi_target_inputs = Input(
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preds=torch.rand(NUM_BATCHES, BATCH_SIZE, num_targets), target=torch.rand(NUM_BATCHES, BATCH_SIZE, num_targets),
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)
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def _single_target_sk_metric(preds, target, sk_fn=explained_variance_score):
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sk_preds = preds.view(-1).numpy()
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sk_target = target.view(-1).numpy()
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return sk_fn(sk_target, sk_preds)
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def _multi_target_sk_metric(preds, target, sk_fn=explained_variance_score):
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sk_preds = preds.view(-1, num_targets).numpy()
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sk_target = target.view(-1, num_targets).numpy()
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return sk_fn(sk_target, sk_preds)
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@pytest.mark.parametrize("multioutput", ['raw_values', 'uniform_average', 'variance_weighted'])
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@pytest.mark.parametrize(
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"preds, target, sk_metric",
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[
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(_single_target_inputs.preds, _single_target_inputs.target, _single_target_sk_metric),
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(_multi_target_inputs.preds, _multi_target_inputs.target, _multi_target_sk_metric),
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],
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)
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class TestExplainedVariance(MetricTester):
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@pytest.mark.parametrize("ddp", [True, False])
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@pytest.mark.parametrize("dist_sync_on_step", [True, False])
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def test_explained_variance(self, multioutput, preds, target, sk_metric, ddp, dist_sync_on_step):
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self.run_class_metric_test(
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ddp,
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preds,
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target,
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ExplainedVariance,
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partial(sk_metric, sk_fn=partial(explained_variance_score, multioutput=multioutput)),
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dist_sync_on_step,
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metric_args=dict(multioutput=multioutput),
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)
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def test_explained_variance_functional(self, multioutput, preds, target, sk_metric):
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self.run_functional_metric_test(
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preds,
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target,
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explained_variance,
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partial(sk_metric, sk_fn=partial(explained_variance_score, multioutput=multioutput)),
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metric_args=dict(multioutput=multioutput),
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)
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def test_error_on_different_shape(metric_class=ExplainedVariance):
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metric = metric_class()
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with pytest.raises(RuntimeError, match='Predictions and targets are expected to have the same shape'):
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metric(torch.randn(100,), torch.randn(50,))
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