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| from unittest.mock import patch | |
| import pytest | |
| from app.models import ( | |
| AnomalyDetector, | |
| ClickbaitDetector, | |
| GenreClassifier, | |
| TagRecommender, | |
| VelocityPredictor, | |
| ViralTrendPredictor, | |
| ) | |
| from app.schemas import ( | |
| AnomalyInput, | |
| ClickbaitInput, | |
| GenreInput, | |
| TagInput, | |
| VelocityInput, | |
| ViralInput, | |
| ) | |
| # --- Fixtures for reusable input data --- | |
| def velocity_input(): | |
| return VelocityInput( | |
| log_start_views=6.9, | |
| log_duration=5.7, | |
| initial_virality_slope=1.2, | |
| interaction_density=0.1, | |
| like_view_ratio=0.05, | |
| comment_view_ratio=0.01, | |
| video_age_hours=2.0, | |
| hour_sin=0.5, | |
| hour_cos=-0.8, | |
| publish_day=1, | |
| is_weekend=0, | |
| title_len=50, | |
| caps_ratio=0.1, | |
| exclamation_count=1, | |
| question_count=0, | |
| has_digits=0, | |
| category_id=10, | |
| ) | |
| def clickbait_input(): | |
| return ClickbaitInput( | |
| title="YOU WON'T BELIEVE THIS", | |
| view_count=50000, | |
| like_count=100, | |
| comment_count=10, | |
| ) | |
| def viral_input(): | |
| return ViralInput( | |
| view_velocity=100.0, | |
| like_velocity=10.0, | |
| comment_velocity=2.0, | |
| like_ratio=0.1, | |
| comment_ratio=0.02, | |
| log_start_views=5.0, | |
| video_age_hours=2.0, | |
| duration_seconds=300, | |
| hour_sin=0.5, | |
| hour_cos=-0.8, | |
| initial_virality_slope=1.5, | |
| interaction_density=0.2, | |
| title_len=40, | |
| caps_ratio=0.1, | |
| has_digits=0, | |
| ) | |
| def enable_mock_inference(): | |
| """Force mock inference for all tests in this module.""" | |
| # Patch the settings object where it is used in base.py | |
| with patch("app.models.base.settings.ENABLE_MOCK_INFERENCE", True): | |
| yield | |
| # --- Tests --- | |
| def test_velocity_model_initialization(velocity_input): | |
| model = VelocityPredictor("test_velocity", repo_path="/tmp/mock") | |
| model.load() # Should trigger mock init | |
| assert model.is_loaded is True | |
| prediction = model.predict(velocity_input) | |
| assert isinstance(prediction, int) | |
| assert prediction >= 0 | |
| def test_viral_trend_prediction(viral_input): | |
| model = ViralTrendPredictor("test_viral", repo_path="/tmp/mock") | |
| model.load() | |
| label, prob = model.predict(viral_input) | |
| assert isinstance(label, int) | |
| assert isinstance(prob, float) | |
| assert 0 <= prob <= 1 | |
| def test_clickbait_logic(clickbait_input): | |
| model = ClickbaitDetector("test_clickbait", repo_path="/tmp/mock") | |
| model.load() | |
| # Test the heuristic logic defined in business rules | |
| # High views + Low engagement = Clickbait | |
| label, prob = model.predict(clickbait_input) | |
| assert isinstance(label, int) | |
| assert isinstance(prob, float) | |
| assert 0 <= prob <= 1 | |
| def test_genre_pca_pipeline(): | |
| model = GenreClassifier("test_genre", repo_path="/tmp/mock") | |
| model.load() | |
| input_data = GenreInput(title="Minecraft Speedrun", tags=["gaming", "glitch"]) | |
| genre, confidence = model.predict(input_data) | |
| assert isinstance(genre, str) # Should return label like "Gaming" | |
| assert isinstance(confidence, float) | |
| def test_tag_recommender(): | |
| model = TagRecommender("test_tags", repo_path="/tmp/mock") | |
| model.load() | |
| # Test specific association rule defined in mock | |
| input_data = TagInput(current_tags=["minecraft", "speedrun"]) | |
| recs = model.predict(input_data) | |
| assert isinstance(recs, list) | |
| assert "dream" in recs or "manhunt" in recs | |
| def test_anomaly_detection(): | |
| model = AnomalyDetector("test_anomaly", repo_path="/tmp/mock") | |
| model.load() | |
| input_data = AnomalyInput( | |
| view_count=1000000, | |
| like_count=0, # suspicious | |
| comment_count=0, | |
| duration_seconds=60, | |
| ) | |
| is_anomaly, score = model.predict(input_data) | |
| assert isinstance(is_anomaly, bool) | |
| assert isinstance(score, float) | |