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| from sklearn.linear_model import LogisticRegression | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.svm import SVC | |
| from sklearn.naive_bayes import GaussianNB | |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score | |
| from time import perf_counter as timer # ✅ Use perf_counter for better precision | |
| import joblib | |
| import os | |
| def get_model_instance(model_name, params): | |
| if model_name == "Logistic Regression": | |
| return LogisticRegression(**params) | |
| elif model_name == "Decision Tree": | |
| return DecisionTreeClassifier(**params) | |
| elif model_name == "Random Forest": | |
| return RandomForestClassifier(**params) | |
| elif model_name == "SVM": | |
| return SVC(**params) | |
| elif model_name == "Naive Bayes": | |
| return GaussianNB(**params) | |
| else: | |
| raise ValueError(f"Unsupported model: {model_name}") | |
| def train_models(X_train, X_test, y_train, y_test, selected_models, model_params, preprocessing_steps, experiment_name="DefaultExperiment", dataset_name="Uploaded CSV"): | |
| results = {} | |
| for model_name in selected_models: | |
| params = model_params.get(model_name, {}) | |
| model = get_model_instance(model_name, params) | |
| # ✅ Measure training time with high precision | |
| start_time = timer() | |
| model.fit(X_train, y_train) | |
| training_time = round(timer() - start_time, 4) | |
| # ✅ Measure model size | |
| temp_model_path = f"models/{model_name.replace(' ', '_')}_temp.joblib" | |
| os.makedirs("models", exist_ok=True) | |
| joblib.dump(model, temp_model_path) | |
| model_size = os.path.getsize(temp_model_path) | |
| os.remove(temp_model_path) | |
| # ✅ Predictions | |
| y_train_pred = model.predict(X_train) | |
| y_test_pred = model.predict(X_test) | |
| # ✅ Inference time on a single sample | |
| single_sample = X_test.iloc[[0]] if hasattr(X_test, "iloc") else X_test[0].reshape(1, -1) | |
| start_inf = timer() | |
| _ = model.predict(single_sample) | |
| inference_time = round(timer() - start_inf, 6) | |
| # ✅ Evaluation metrics | |
| metrics = { | |
| "accuracy_train": accuracy_score(y_train, y_train_pred), | |
| "accuracy_test": accuracy_score(y_test, y_test_pred), | |
| "precision_train": precision_score(y_train, y_train_pred, average='weighted', zero_division=0), | |
| "precision_test": precision_score(y_test, y_test_pred, average='weighted', zero_division=0), | |
| "recall_train": recall_score(y_train, y_train_pred, average='weighted', zero_division=0), | |
| "recall_test": recall_score(y_test, y_test_pred, average='weighted', zero_division=0), | |
| "f1_score_train": f1_score(y_train, y_train_pred, average='weighted', zero_division=0), | |
| "f1_score_test": f1_score(y_test, y_test_pred, average='weighted', zero_division=0), | |
| "inference_time": inference_time | |
| } | |
| results[model_name] = { | |
| "model": model, | |
| "metrics": metrics, | |
| "training_time": training_time, | |
| "inference_time": inference_time, | |
| "model_size": model_size | |
| } | |
| return results | |