| |
| """ |
| Test Adaptive Beta with Cricket+Sports Example |
| |
| Tests that the adaptive beta mechanism generates more words for constrained cases |
| like "cricket sentence" + "sports topic". |
| """ |
|
|
| import os |
| import sys |
| import warnings |
| import logging |
|
|
| |
| logging.basicConfig(level=logging.INFO, format='%(message)s') |
|
|
| |
| warnings.filterwarnings("ignore") |
|
|
| def setup_environment(): |
| """Setup environment and add src to path""" |
| |
| cache_dir = os.path.join(os.path.dirname(__file__), '..', 'cache-dir') |
| cache_dir = os.path.abspath(cache_dir) |
| os.environ['HF_HOME'] = cache_dir |
| os.environ['TRANSFORMERS_CACHE'] = cache_dir |
| os.environ['SENTENCE_TRANSFORMERS_HOME'] = cache_dir |
| |
| |
| backend_path = os.path.join(os.path.dirname(__file__), '..', 'crossword-app', 'backend-py', 'src') |
| backend_path = os.path.abspath(backend_path) |
| if backend_path not in sys.path: |
| sys.path.insert(0, backend_path) |
| |
| print(f"Using cache directory: {cache_dir}") |
|
|
| def test_adaptive_beta_cricket_sports(): |
| """Test the cricket+sports case that previously generated only 16 words""" |
| |
| setup_environment() |
| |
| print("🧪 Testing Adaptive Beta with Cricket+Sports Example") |
| print("=" * 60) |
| |
| |
| os.environ['MULTI_TOPIC_METHOD'] = 'soft_minimum' |
| os.environ['SOFT_MIN_BETA'] = '10.0' |
| os.environ['SOFT_MIN_ADAPTIVE'] = 'true' |
| os.environ['SOFT_MIN_MIN_WORDS'] = '15' |
| os.environ['SOFT_MIN_MAX_RETRIES'] = '5' |
| os.environ['SOFT_MIN_BETA_DECAY'] = '0.7' |
| os.environ['THEMATIC_VOCAB_SIZE_LIMIT'] = '5000' |
| |
| try: |
| from services.thematic_word_service import ThematicWordService |
| |
| print("Creating ThematicWordService with adaptive soft minimum...") |
| service = ThematicWordService() |
| |
| print("Initializing service (adaptive beta configuration will be logged)...") |
| service.initialize() |
| |
| |
| test_cases = [ |
| { |
| "name": "Cricket sentence only", |
| "inputs": ["india won test series against england"], |
| "expected": ">30 words (no constraint)", |
| "description": "Single sentence - should generate many words" |
| }, |
| { |
| "name": "Cricket sentence + Sports topic", |
| "inputs": ["india won test series against england", "Sports"], |
| "expected": "~15-25 words (adaptive beta should kick in)", |
| "description": "Sentence + topic - adaptive beta should relax to get more words" |
| }, |
| { |
| "name": "Multiple sports topics", |
| "inputs": ["Cricket", "Tennis", "Football"], |
| "expected": "~15-20 words (adaptive beta for 3 topics)", |
| "description": "Three topics - should auto-adapt for more words" |
| } |
| ] |
| |
| for i, test_case in enumerate(test_cases, 1): |
| print(f"\n📊 Test {i}: {test_case['name']}") |
| print(f" Description: {test_case['description']}") |
| print(f" Expected: {test_case['expected']}") |
| print(f" Inputs: {test_case['inputs']}") |
| print("-" * 50) |
| |
| |
| results = service.generate_thematic_words( |
| test_case['inputs'], |
| num_words=50, |
| min_similarity=0.3, |
| multi_theme=False |
| ) |
| |
| print(f"✅ Generated {len(results)} words") |
| print(f"Top 15 words:") |
| for j, (word, similarity, tier) in enumerate(results[:15], 1): |
| print(f" {j:2d}. {word:15s}: {similarity:.4f} ({tier})") |
| |
| |
| if len(results) >= 15: |
| print(f" ✅ Success: Generated {len(results)} words (≥ 15 minimum)") |
| else: |
| print(f" ⚠️ Warning: Only {len(results)} words generated (< 15 minimum)") |
| print(" This suggests adaptive beta may need tuning") |
| |
| except Exception as e: |
| print(f"❌ Test failed: {e}") |
| import traceback |
| traceback.print_exc() |
|
|
| def test_adaptive_beta_disabled(): |
| """Test with adaptive beta disabled for comparison""" |
| |
| print(f"\n\n🔒 Testing with Adaptive Beta DISABLED") |
| print("=" * 60) |
| |
| |
| os.environ['SOFT_MIN_ADAPTIVE'] = 'false' |
| |
| try: |
| from services.thematic_word_service import ThematicWordService |
| |
| service = ThematicWordService() |
| service.initialize() |
| |
| |
| inputs = ["india won test series against england", "Sports"] |
| print(f"Testing cricket+sports with fixed beta=10.0...") |
| |
| results = service.generate_thematic_words( |
| inputs, |
| num_words=50, |
| min_similarity=0.3, |
| multi_theme=False |
| ) |
| |
| print(f"✅ Generated {len(results)} words (with fixed beta)") |
| print(f"Top 10 words:") |
| for j, (word, similarity, tier) in enumerate(results[:10], 1): |
| print(f" {j:2d}. {word:15s}: {similarity:.4f}") |
| |
| if len(results) < 15: |
| print(f" ⚠️ As expected: Only {len(results)} words with fixed beta (too strict)") |
| else: |
| print(f" ✅ Surprisingly good: {len(results)} words even with fixed beta") |
| |
| except Exception as e: |
| print(f"❌ Test failed: {e}") |
| import traceback |
| traceback.print_exc() |
|
|
| def main(): |
| """Main test runner""" |
| print("🧪 Adaptive Beta Integration Test") |
| print("Testing automatic beta relaxation for constrained word generation") |
| print("=" * 70) |
| |
| try: |
| |
| test_adaptive_beta_cricket_sports() |
| |
| |
| test_adaptive_beta_disabled() |
| |
| print("\n" + "=" * 70) |
| print("🎯 ADAPTIVE BETA TEST RESULTS:") |
| print("1. Adaptive beta should automatically relax when < 15 words found") |
| print("2. Cricket+Sports should now generate 15+ words (was 16)") |
| print("3. Complex multi-topic queries should auto-adapt for sufficient words") |
| print("4. Logging shows beta adjustment process") |
| print("=" * 70) |
| |
| except Exception as e: |
| print(f"❌ Adaptive beta test failed: {e}") |
| import traceback |
| traceback.print_exc() |
|
|
| if __name__ == "__main__": |
| main() |