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| import joblib | |
| import numpy as np | |
| import os | |
| import sys | |
| sys.path.append(os.path.dirname(os.path.abspath(__file__))) | |
| from preprocessing import resolve_model_dir | |
| class EnsembleModel: | |
| def __init__(self): | |
| self.model_dir = resolve_model_dir() | |
| self.model_names = ['knn', 'svm', 'logreg', 'rf', 'small_nn'] | |
| self.models = {} | |
| self.load_models() | |
| self.label_encoder = joblib.load(f'{self.model_dir}/label_encoder.pkl') | |
| self.weights = {'knn': 0.15, 'svm': 0.15, 'logreg': 0.20, 'rf': 0.25, 'small_nn': 0.25} | |
| def load_models(self): | |
| for name in self.model_names: | |
| path = f'{self.model_dir}/{name}.pkl' | |
| if os.path.exists(path): | |
| self.models[name] = joblib.load(path) | |
| def predict(self, X): | |
| individual_preds = {} | |
| individual_probs = {} | |
| for name, model in self.models.items(): | |
| probs = model.predict_proba(X) | |
| individual_probs[name] = probs[0] | |
| pred_indices = np.argmax(probs, axis=1) | |
| individual_preds[name] = str(self.label_encoder.inverse_transform(pred_indices)[0]) | |
| num_classes = list(individual_probs.values())[0].shape[0] | |
| weighted_probs = np.zeros(num_classes) | |
| total_weight = 0 | |
| for name in self.models.keys(): | |
| w = self.weights.get(name, 1.0) | |
| weighted_probs += individual_probs[name] * w | |
| total_weight += w | |
| weighted_probs /= total_weight | |
| final_pred_idx = np.argmax(weighted_probs) | |
| final_pred = str(self.label_encoder.inverse_transform([final_pred_idx])[0]) | |
| confidence = float(np.max(weighted_probs)) | |
| return final_pred, confidence, individual_preds, individual_probs, self.weights | |