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aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | """Test training curve generators — now using real mini-training."""
from __future__ import annotations
from ml_training_debugger.scenarios import sample_scenario
from ml_training_debugger.simulation import (
gen_data_batch_stats,
gen_loss_history,
gen_val_accuracy_history,
gen_val_loss_history,
)
class TestGenLossHistory:
def test_returns_20_floats(self):
s = sample_scenario("task_001", seed=42)
hist = gen_loss_history(s)
assert len(hist) == 20
assert all(isinstance(v, (float, int)) for v in hist)
def test_task_001_has_instability(self):
s = sample_scenario("task_001", seed=42)
hist = gen_loss_history(s)
# With high LR, loss should show instability (high max or spikes)
max_loss = max(v for v in hist if v != float("inf"))
assert max_loss > 5.0 # Real training with high LR produces spikes
def test_task_003_reasonable(self):
s = sample_scenario("task_003", seed=42)
hist = gen_loss_history(s)
# Data leakage — training looks normal
assert all(v != float("inf") for v in hist)
def test_task_005_no_crash(self):
s = sample_scenario("task_005", seed=42)
hist = gen_loss_history(s)
assert len(hist) == 20
class TestGenValAccuracy:
def test_returns_20_floats(self):
s = sample_scenario("task_001", seed=42)
hist = gen_val_accuracy_history(s)
assert len(hist) == 20
assert all(isinstance(v, float) for v in hist)
def test_task_003_leakage_shows_higher_acc(self):
s = sample_scenario("task_003", seed=42)
hist = gen_val_accuracy_history(s)
# With data leakage, val accuracy should be somewhat elevated
avg_acc = sum(hist) / len(hist)
assert avg_acc > 0.0 # At minimum non-zero
def test_task_005_low_accuracy(self):
s = sample_scenario("task_005", seed=42)
hist = gen_val_accuracy_history(s)
# BatchNorm eval mode — model can't learn properly
assert len(hist) == 20
class TestGenValLoss:
def test_returns_20_floats(self):
s = sample_scenario("task_001", seed=42)
hist = gen_val_loss_history(s)
assert len(hist) == 20
class TestGenDataBatchStats:
def test_leakage_high_overlap(self):
s = sample_scenario("task_003", seed=42)
stats = gen_data_batch_stats(s)
assert stats["class_overlap_score"] > 0.5
assert stats["duplicate_ratio"] > 0.0
def test_normal_low_overlap(self):
s = sample_scenario("task_001", seed=42)
stats = gen_data_batch_stats(s)
assert stats["class_overlap_score"] < 0.3
def test_confusion_matrix_present(self):
s = sample_scenario("task_003", seed=42)
stats = gen_data_batch_stats(s)
assert "confusion_matrix" in stats
cm = stats["confusion_matrix"]
assert len(cm) == 10
assert len(cm[0]) == 10
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