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#!/usr/bin/env python3
"""
Test script for Context-Aware Classifier implementation.
This script validates the context-aware classification functionality including:
- Context-aware classification with conversation history
- Defensive response pattern detection
- Contextual indicator weighting
- Contextual follow-up question generation
- Medical context integration
"""
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
from datetime import datetime, timedelta
from config.prompt_management.context_aware_classifier import ContextAwareClassifier
from config.prompt_management.data_models import ConversationHistory, Message, Classification
def test_context_aware_classifier():
"""Test the ContextAwareClassifier implementation."""
print("Testing Context-Aware Classifier...")
classifier = ContextAwareClassifier()
# Test 1: Basic classification without context
print("\n1. Testing basic classification...")
message = "I'm feeling stressed about work"
empty_history = ConversationHistory(
messages=[],
distress_indicators_found=[],
context_flags=[]
)
result = classifier.classify_with_context(message, empty_history)
print(f" Message: '{message}'")
print(f" Classification: {result.category} (confidence: {result.confidence:.2f})")
print(f" Reasoning: {result.reasoning}")
assert result.category in ['GREEN', 'YELLOW', 'RED'], "Invalid category"
assert 0.0 <= result.confidence <= 1.0, "Invalid confidence"
print(" ✓ Basic classification works")
# Test 2: Historical distress with dismissive response
print("\n2. Testing historical distress with dismissive response...")
history_with_distress = ConversationHistory(
messages=[
Message("I'm really struggling with anxiety", "YELLOW", datetime.now() - timedelta(hours=1)),
Message("I feel overwhelmed and sad", "YELLOW", datetime.now() - timedelta(minutes=30))
],
distress_indicators_found=['anxiety', 'overwhelmed', 'sad'],
context_flags=['distress_expressed']
)
dismissive_message = "I'm fine now, everything is okay"
result = classifier.classify_with_context(dismissive_message, history_with_distress)
print(f" Message: '{dismissive_message}'")
print(f" Classification: {result.category} (confidence: {result.confidence:.2f})")
print(f" Context factors: {result.context_factors}")
print(f" Reasoning: {result.reasoning}")
# Should be YELLOW due to historical context
assert result.category in ['YELLOW', 'RED'], f"Expected YELLOW/RED with historical distress, got {result.category}"
assert 'historical' in result.reasoning.lower() or 'previous' in result.reasoning.lower(), "Should mention historical context"
print(" ✓ Historical context influences classification")
# Test 3: Defensive response detection
print("\n3. Testing defensive response detection...")
defensive_responses = [
"I'm fine",
"Everything is okay",
"No problems here",
"I don't need help"
]
for response in defensive_responses:
is_defensive = classifier.detect_defensive_responses(response, history_with_distress)
print(f" '{response}' -> Defensive: {is_defensive}")
assert is_defensive == True, f"Should detect '{response}' as defensive with distress history"
print(" ✓ Defensive response detection works")
# Test 4: Contextual indicator weighting
print("\n4. Testing contextual indicator weighting...")
context_scenarios = [
{'historical_mentions': 0, 'recent_mention': False, 'conversation_length': 1},
{'historical_mentions': 3, 'recent_mention': True, 'conversation_length': 5},
{'historical_mentions': 1, 'recent_mention': False, 'conversation_length': 2}
]
for i, context in enumerate(context_scenarios):
weight = classifier.evaluate_contextual_indicators(['stress'], context)
print(f" Scenario {i+1}: {context} -> Weight: {weight:.2f}")
assert 0.0 <= weight <= 1.0, "Weight should be between 0 and 1"
# Higher historical mentions should generally increase weight
if context['historical_mentions'] >= 2:
assert weight >= 0.5, "High historical mentions should increase weight"
print(" ✓ Contextual indicator weighting works")
# Test 5: Contextual follow-up generation
print("\n5. Testing contextual follow-up generation...")
follow_up = classifier.generate_contextual_follow_up(
"I'm not sure how I feel",
history_with_distress,
"YELLOW"
)
print(f" Follow-up question: '{follow_up}'")
assert len(follow_up.strip()) > 0, "Follow-up should not be empty"
assert '?' in follow_up, "Follow-up should be a question"
print(" ✓ Contextual follow-up generation works")
# Test 6: Medical context integration
print("\n6. Testing medical context integration...")
medical_history = ConversationHistory(
messages=[],
distress_indicators_found=[],
context_flags=[],
medical_context={'conditions': ['anxiety disorder'], 'medications': ['SSRI']}
)
medical_message = "I'm managing my anxiety with medication but still feel stressed"
result = classifier.classify_with_context(medical_message, medical_history)
print(f" Message: '{medical_message}'")
print(f" Classification: {result.category} (confidence: {result.confidence:.2f})")
print(f" Reasoning: {result.reasoning}")
# Should consider medical context
assert result.category in ['YELLOW', 'RED'], "Medical context with stress should be YELLOW/RED"
print(" ✓ Medical context integration works")
# Test 7: Classification consistency
print("\n7. Testing classification consistency...")
test_messages = [
("I feel great today", "GREEN"),
("I'm worried about my job", "YELLOW"),
("I want to end it all", "RED")
]
for message, expected_category in test_messages:
result = classifier.classify_with_context(message, empty_history)
print(f" '{message}' -> {result.category} (expected: {expected_category})")
# Allow some flexibility in classification
if expected_category == "RED":
assert result.category == "RED", f"RED messages should be classified as RED"
# Other categories can have some variation based on context
print(" ✓ Classification consistency maintained")
return True
def test_data_model_integration():
"""Test integration with data models."""
print("\nTesting data model integration...")
# Test Message serialization
message = Message(
content="Test message",
classification="YELLOW",
timestamp=datetime.now(),
confidence=0.8
)
message_dict = message.to_dict()
restored_message = Message.from_dict(message_dict)
assert restored_message.content == message.content, "Message content should match"
assert restored_message.classification == message.classification, "Classification should match"
print(" ✓ Message serialization works")
# Test Classification serialization
classification = Classification(
category="YELLOW",
confidence=0.7,
reasoning="Test reasoning",
indicators_found=['stress'],
context_factors=['historical_distress']
)
class_dict = classification.to_dict()
restored_class = Classification.from_dict(class_dict)
assert restored_class.category == classification.category, "Category should match"
assert restored_class.confidence == classification.confidence, "Confidence should match"
print(" ✓ Classification serialization works")
# Test ConversationHistory serialization
history = ConversationHistory(
messages=[message],
distress_indicators_found=['stress', 'anxiety'],
context_flags=['distress_expressed'],
medical_context={'conditions': ['anxiety'], 'medications': []}
)
history_dict = history.to_dict()
restored_history = ConversationHistory.from_dict(history_dict)
assert len(restored_history.messages) == 1, "Should have one message"
assert restored_history.distress_indicators_found == history.distress_indicators_found, "Indicators should match"
print(" ✓ ConversationHistory serialization works")
return True
def main():
"""Run all tests."""
print("=" * 60)
print("CONTEXT-AWARE CLASSIFIER TEST SUITE")
print("=" * 60)
try:
# Run tests
test_context_aware_classifier()
test_data_model_integration()
print("\n" + "=" * 60)
print("✅ ALL TESTS PASSED!")
print("Context-Aware Classifier implementation is working correctly.")
print("=" * 60)
return True
except Exception as e:
print(f"\n❌ TEST FAILED: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1) |