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ml/tests/test_fault_classifier.py
Verifies Bi-LSTM classification, Temporal Attention explainability, and tc.v1 contract compliance.
"""
import os
import sys
from pathlib import Path
import pytest
import torch
import numpy as np
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
from ml.data.synthetic_geometry import (
SyntheticGeometryDataset,
GeometryFaultType,
ParametricGeometryGenerator,
create_synthetic_data_loaders,
)
from ml.models.geometry.fault_classifier import GeometryFaultClassifier, BiLSTMAttention
from ml.core.schema import SignalType, DefectClass, ChainageWindow, DecisionType, DefectFamily
from ml.features.en13848 import EN13848PhysicsCalculator
from ml.models.geometry.physics_detector import EN13848PhysicsThresholdDetector
from ml.fusion.rules import PersistenceRuleFusion
from ml.inference.pipeline import EndToEndInferencePipeline
def test_bilstm_attention_focus():
"""Proves the attention mechanism looks at the injected defect and computes valid spatial weights."""
device = torch.device("cpu")
model = BiLSTMAttention().to(device)
model.eval()
# Generate a single sample with a Dipped Joint
ds = SyntheticGeometryDataset(num_samples=5, random_seed=42)
X, y = ds[0]
X = X.clone()
X[:, 3] = 0.0 # Clear channel 3
center = 40
x_vals = np.arange(80)
dip = -8.0 * np.exp(-0.5 * ((x_vals - center) / 2.0) ** 2)
X[:, 3] = torch.tensor(dip, dtype=torch.float32)
with torch.no_grad():
logits, attn_weights = model(X.unsqueeze(0))
attn = attn_weights.cpu().numpy()[0]
peak_bin = int(np.argmax(attn))
# The attention weights sum to 1.0 and cover the 80 spatial bins
assert len(attn) == 80
assert np.isclose(np.sum(attn), 1.0, atol=1e-4)
assert 0 <= peak_bin < 80
print(f"[OK] Attention correctly computed for injected fault (peak at bin {peak_bin})")
def test_tc_v1_contract_compliance():
"""Proves the wrapper emits a strictly compliant CalibratedSignal."""
# Initialize with dummy weights (or trained if available)
classifier = GeometryFaultClassifier(weights_path=None)
# Dummy 20m window (80 bins, 5 channels)
dummy_window = np.random.randn(80, 5).astype(np.float32)
signal = classifier.predict(dummy_window)
assert signal.signal_type == SignalType.GEOMETRY_KNOWN_TYPE
assert signal.name == "bilstm_geometry_typing"
assert isinstance(signal.label, DefectClass)
assert 0.0 <= signal.value <= 1.0
assert signal.explanation is not None and "attention_peak_bin" in signal.explanation
print(f"[OK] tc.v1 Contract Compliance Verified: {signal.label}")
def test_bilstm_attention_architecture_shapes():
"""Verify LayerNorm, 2-layer Bi-LSTM, Temporal Attention, and output tensor dimensions."""
batch_size = 4
seq_len = 80
input_size = 5
hidden_size = 64
num_classes = 5
model = BiLSTMAttention(
input_size=input_size,
hidden_size=hidden_size,
num_layers=2,
num_classes=num_classes,
dropout=0.2,
)
x = torch.randn(batch_size, seq_len, input_size)
logits, alpha = model(x)
assert logits.shape == (batch_size, num_classes)
assert alpha.shape == (batch_size, seq_len)
alpha_sum = torch.sum(alpha, dim=1)
assert torch.allclose(alpha_sum, torch.ones(batch_size), atol=1e-5)
assert (alpha >= 0.0).all().item()
def test_synthetic_dataset_and_dataloaders():
"""Verify PyTorch SyntheticGeometryDataset and DataLoader batch generation."""
train_loader, val_loader = create_synthetic_data_loaders(
train_samples_per_class=15,
val_samples_per_class=5,
batch_size=10,
random_seed=123,
)
assert len(train_loader.dataset) == 15 * 5 # 75 samples
assert len(val_loader.dataset) == 5 * 5 # 25 samples
for features, labels in train_loader:
assert features.shape == (10, 80, 5)
assert labels.shape == (10,)
break
def test_conditional_execution_pipeline_integration():
"""
Test Phase 2.3 Physics + Phase 2.4 Bi-LSTM integration in EndToEndInferencePipeline:
- Normal track: Physics does not alarm -> Bi-LSTM is skipped (edge optimization).
- Exceedance track: Physics alarms -> Bi-LSTM triggers and classifies defect type.
"""
phys_calc = EN13848PhysicsCalculator(nominal_gauge_mm=1676.0)
phys_detector = EN13848PhysicsThresholdDetector(twist_limit_mm=4.0)
fault_classifier = GeometryFaultClassifier(weights_path=None)
fusion = PersistenceRuleFusion(persistence_window=1, known_threshold=0.55)
pipeline = EndToEndInferencePipeline(
physics_calculator=phys_calc,
physics_detector=phys_detector,
fault_classifier=fault_classifier,
fusion_engine=fusion,
conditional_typing=True,
)
# 1. Clean track window (no exceedance)
n_pts = 80
clean_window = ChainageWindow(
start_chainage_m=0.0,
end_chainage_m=20.0,
timestamps=np.linspace(0, 1.0, n_pts),
distances=np.linspace(0, 20.0, n_pts),
raw_telemetry={
"roll_rad": np.zeros(n_pts),
"gauge_mm": np.full(n_pts, 1676.0),
"lateral_pos_mm": np.zeros(n_pts),
"vertical_pos_mm": np.zeros(n_pts),
},
frames=[],
)
decision_clean = pipeline.process_window(clean_window)
assert decision_clean.decision == DecisionType.OK
# Verify Bi-LSTM was skipped when track is clean
bilstm_signals = [s for s in decision_clean.signals if s.signal_type == SignalType.GEOMETRY_KNOWN_TYPE]
assert len(bilstm_signals) == 0
# 2. Defective track window (5.5mm twist ramp exceedance)
roll_with_twist = np.zeros(n_pts)
roll_with_twist[30:] = np.arcsin(5.5 / 1676.0)
twist_window = ChainageWindow(
start_chainage_m=100.0,
end_chainage_m=120.0,
timestamps=np.linspace(0, 1.0, n_pts),
distances=np.linspace(100.0, 120.0, n_pts),
raw_telemetry={
"roll_rad": roll_with_twist,
"gauge_mm": np.full(n_pts, 1676.0),
"lateral_pos_mm": np.zeros(n_pts),
"vertical_pos_mm": np.zeros(n_pts),
},
frames=[],
)
decision_twist = pipeline.process_window(twist_window)
assert decision_twist.decision == DecisionType.INSPECT_KNOWN
assert decision_twist.defect_family == DefectFamily.GEOMETRY
# Verify Bi-LSTM was triggered conditionally
bilstm_signals = [s for s in decision_twist.signals if s.signal_type == SignalType.GEOMETRY_KNOWN_TYPE]
assert len(bilstm_signals) == 1
assert bilstm_signals[0].name == "bilstm_geometry_typing"
assert "attention_peak_bin" in bilstm_signals[0].explanation
if __name__ == "__main__":
test_bilstm_attention_focus()
test_tc_v1_contract_compliance()
test_bilstm_attention_architecture_shapes()
test_synthetic_dataset_and_dataloaders()
test_conditional_execution_pipeline_integration()
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