| import pickle | |
| with open("models/svm_emotion_model.pkl", "rb") as f: | |
| data = pickle.load(f) | |
| # Check the SVM inside the pipeline | |
| pipeline = data['model'] | |
| svm = pipeline.steps[-1][1] # last step is the classifier | |
| print("SVM class_weight:", svm.class_weight) | |
| print("Support vectors per class:", svm.n_support_) | |
| print("Classes (numeric):", svm.classes_) | |
| # Check label encoder to map numbers -> emotion names | |
| le = data['label_encoder'] | |
| print("\nLabel mapping (number -> emotion):") | |
| for i, name in enumerate(le.classes_): | |
| print(f" {i} = {name}") | |
| # Check training history for class distribution | |
| history = data.get('training_history', {}) | |
| print("\nTraining history:", history) |