100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
from functools import partial
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import numpy as np
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import pytest
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import torch
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from sklearn.metrics import roc_curve as sk_roc_curve
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from pytorch_lightning.metrics.classification.roc import ROC
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from pytorch_lightning.metrics.functional.roc import roc
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from tests.metrics.classification.inputs import _input_binary_prob
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from tests.metrics.classification.inputs import _input_multiclass_prob as _input_mcls_prob
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from tests.metrics.classification.inputs import _input_multidim_multiclass_prob as _input_mdmc_prob
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from tests.metrics.utils import MetricTester, NUM_CLASSES
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torch.manual_seed(42)
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def _sk_roc_curve(y_true, probas_pred, num_classes=1):
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""" Adjusted comparison function that can also handles multiclass """
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if num_classes == 1:
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return sk_roc_curve(y_true, probas_pred, drop_intermediate=False)
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fpr, tpr, thresholds = [], [], []
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for i in range(num_classes):
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y_true_temp = np.zeros_like(y_true)
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y_true_temp[y_true == i] = 1
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res = sk_roc_curve(y_true_temp, probas_pred[:, i], drop_intermediate=False)
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fpr.append(res[0])
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tpr.append(res[1])
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thresholds.append(res[2])
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return fpr, tpr, thresholds
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def _sk_roc_binary_prob(preds, target, num_classes=1):
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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_roc_curve(y_true=sk_target, probas_pred=sk_preds, num_classes=num_classes)
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def _sk_roc_multiclass_prob(preds, target, num_classes=1):
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sk_preds = preds.reshape(-1, num_classes).numpy()
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sk_target = target.view(-1).numpy()
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return _sk_roc_curve(y_true=sk_target, probas_pred=sk_preds, num_classes=num_classes)
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def _sk_roc_multidim_multiclass_prob(preds, target, num_classes=1):
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sk_preds = preds.transpose(0, 1).reshape(num_classes, -1).transpose(0, 1).numpy()
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sk_target = target.view(-1).numpy()
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return _sk_roc_curve(y_true=sk_target, probas_pred=sk_preds, num_classes=num_classes)
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@pytest.mark.parametrize(
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"preds, target, sk_metric, num_classes", [
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(_input_binary_prob.preds, _input_binary_prob.target, _sk_roc_binary_prob, 1),
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(_input_mcls_prob.preds, _input_mcls_prob.target, _sk_roc_multiclass_prob, NUM_CLASSES),
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(_input_mdmc_prob.preds, _input_mdmc_prob.target, _sk_roc_multidim_multiclass_prob, NUM_CLASSES),
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]
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)
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class TestROC(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_roc(self, preds, target, sk_metric, num_classes, ddp, dist_sync_on_step):
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self.run_class_metric_test(
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ddp=ddp,
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preds=preds,
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target=target,
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metric_class=ROC,
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sk_metric=partial(sk_metric, num_classes=num_classes),
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dist_sync_on_step=dist_sync_on_step,
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metric_args={"num_classes": num_classes}
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)
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def test_roc_functional(self, preds, target, sk_metric, num_classes):
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self.run_functional_metric_test(
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preds,
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target,
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metric_functional=roc,
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sk_metric=partial(sk_metric, num_classes=num_classes),
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metric_args={"num_classes": num_classes},
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)
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@pytest.mark.parametrize(['pred', 'target', 'expected_tpr', 'expected_fpr'], [
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pytest.param([0, 1], [0, 1], [0, 1, 1], [0, 0, 1]),
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pytest.param([1, 0], [0, 1], [0, 0, 1], [0, 1, 1]),
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pytest.param([1, 1], [1, 0], [0, 1], [0, 1]),
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pytest.param([1, 0], [1, 0], [0, 1, 1], [0, 0, 1]),
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pytest.param([0.5, 0.5], [0, 1], [0, 1], [0, 1]),
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])
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def test_roc_curve(pred, target, expected_tpr, expected_fpr):
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fpr, tpr, thresh = roc(torch.tensor(pred), torch.tensor(target))
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assert fpr.shape == tpr.shape
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assert fpr.size(0) == thresh.size(0)
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assert torch.allclose(fpr, torch.tensor(expected_fpr).to(fpr))
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assert torch.allclose(tpr, torch.tensor(expected_tpr).to(tpr))
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