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"""
Test suite for ML Polymer Aging enhanced components
"""
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import numpy as np
import torch
from modules.enhanced_data import (
EnhancedDataManager,
ContextualSpectrum,
SpectralMetadata,
)
from modules.transparent_ai import TransparentAIEngine, UncertaintyEstimator
from modules.educational_framework import EducationalFramework
def test_enhanced_data_manager():
"""Test enhanced data management functionality"""
print("Testing Enhanced Data Manager...")
# Create data manager
data_manager = EnhancedDataManager()
# Create sample spectrum
x_data = np.linspace(400, 4000, 500)
y_data = np.exp(-(((x_data - 2900) / 100) ** 2)) + np.random.normal(0, 0.01, 500)
metadata = SpectralMetadata(
filename="test_spectrum.txt", instrument_type="Raman", laser_wavelength=785.0
)
spectrum = ContextualSpectrum(x_data, y_data, metadata)
# Test quality assessment
quality_score = data_manager._assess_data_quality(y_data)
print(f"Quality score: {quality_score:.3f}")
# Test preprocessing recommendations
recommendations = data_manager.get_preprocessing_recommendations(spectrum)
print(f"Preprocessing recommendations: {recommendations}")
# Test preprocessing with tracking
processed_spectrum = data_manager.preprocess_with_tracking(
spectrum, **recommendations
)
print(f"Provenance records: {len(processed_spectrum.provenance)}")
print("✅ Enhanced Data Manager tests passed!")
return True
def test_transparent_ai():
"""Test transparent AI functionality"""
print("Testing Transparent AI Engine...")
# Create dummy model
class DummyModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(500, 2)
def forward(self, x):
return self.linear(x)
model = DummyModel()
# Test uncertainty estimator
uncertainty_estimator = UncertaintyEstimator(model, n_samples=10)
# Create test input
x = torch.randn(1, 500)
# Test uncertainty estimation
uncertainties = uncertainty_estimator.estimate_uncertainty(x)
print(f"Uncertainty metrics: {uncertainties}")
# Test confidence intervals
intervals = uncertainty_estimator.confidence_intervals(x)
print(f"Confidence intervals: {intervals}")
# Test transparent AI engine
ai_engine = TransparentAIEngine(model)
explanation = ai_engine.predict_with_explanation(x)
print(f"Prediction: {explanation.prediction}")
print(f"Confidence: {explanation.confidence:.3f}")
print(f"Reasoning chain: {len(explanation.reasoning_chain)} steps")
print("✅ Transparent AI tests passed!")
return True
def test_educational_framework():
"""Test educational framework functionality"""
print("Testing Educational Framework...")
# Create educational framework
framework = EducationalFramework()
# Initialize user
user_progress = framework.initialize_user("test_user")
print(f"User initialized: {user_progress.user_id}")
# Test competency assessment
domain = "spectroscopy_basics"
responses = [2, 1, 0] # Sample responses
results = framework.assess_user_competency(domain, responses)
print(f"Assessment results: {results['score']:.2f}")
# Test learning path generation
target_competencies = ["spectroscopy", "polymer_science"]
learning_path = framework.get_personalized_learning_path(target_competencies)
print(f"Learning path objectives: {len(learning_path)}")
# Test virtual experiment
experiment_result = framework.run_virtual_experiment(
"polymer_identification", {"polymer_type": "PE"}
)
print(f"Virtual experiment success: {experiment_result.get('success', False)}")
# Test analytics
analytics = framework.get_learning_analytics()
print(f"Analytics available: {bool(analytics)}")
print("✅ Educational Framework tests passed!")
return True
def run_all_tests():
"""Run all component tests"""
print("Starting ML Polymer Aging Component Tests...\n")
tests = [
test_enhanced_data_manager,
test_transparent_ai,
test_educational_framework,
]
passed = 0
for test in tests:
try:
if test():
passed += 1
print()
except Exception as e:
print(f"❌ Test failed: {e}\n")
print(f"Tests completed: {passed}/{len(tests)} passed")
if passed == len(tests):
print("🎉 All ML Polymer Aging components working correctly!")
else:
print("⚠️ Some components need attention")
if __name__ == "__main__":
run_all_tests()
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