| import numpy as np |
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|
| from scripts.eval_graph_feature_classifier import metrics as graph_metrics |
| from scripts.run_graph_text_stack import score as stack_metrics |
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|
| def test_graph_metrics_compute_both_class_accuracies(): |
| y = np.array([0, 1, 0, 1], dtype=np.int8) |
| pred = np.array([0, 1, 1, 0], dtype=np.int8) |
| result = graph_metrics(y, pred, pair_count=2) |
| assert result["accuracy"] == 0.5 |
| assert result["safe_accuracy"] == 0.5 |
| assert result["vulnerable_accuracy"] == 0.5 |
| assert result["pair_pass_at_1"] == 0.5 |
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|
| def test_stack_metric_flags_safe_collapse(): |
| y = np.array([0, 1, 0, 1], dtype=np.int8) |
| pred = np.zeros(4, dtype=np.int8) |
| result = stack_metrics(y, pred, n_pairs=2) |
| assert result["safe_accuracy"] == 1.0 |
| assert result["vulnerable_accuracy"] == 0.0 |
| assert result["predicted_vulnerable_fraction"] == 0.0 |
| assert result["pair_pass_at_1"] == 0.0 |
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|
