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Runtime error
| import os | |
| import joblib | |
| import json | |
| import numpy as np | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.svm import SVC | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.neural_network import MLPClassifier | |
| from sklearn.metrics import f1_score | |
| import pandas as pd | |
| import sys | |
| backend_dir = os.path.dirname(os.path.abspath(__file__)) | |
| if backend_dir not in sys.path: | |
| sys.path.insert(0, backend_dir) | |
| from preprocessing import get_train_test_split, resolve_model_dir, resolve_data_path | |
| def train_models(): | |
| data_path = resolve_data_path() | |
| model_dir = resolve_model_dir() | |
| os.makedirs(model_dir, exist_ok=True) | |
| registry_path = os.path.join(model_dir, 'registry.json') | |
| if os.path.exists(registry_path): | |
| with open(registry_path, 'r') as f: | |
| registry = json.load(f) | |
| else: | |
| registry = {"active_version": "v1", "versions": {"v1": {"score": 0.0}}} | |
| print("Loading data...") | |
| df = pd.read_csv(data_path, encoding='utf-8') | |
| X_train, X_test, y_train, y_test = get_train_test_split(df) | |
| models = { | |
| 'knn': KNeighborsClassifier(n_neighbors=5), | |
| 'svm': SVC(kernel='linear', probability=True, max_iter=2000), | |
| 'logreg': LogisticRegression(max_iter=1000), | |
| 'rf': RandomForestClassifier(n_estimators=30, max_depth=5, random_state=42), | |
| 'small_nn': MLPClassifier(hidden_layer_sizes=(16, 8), max_iter=500, random_state=42) | |
| } | |
| trained_models = {} | |
| scores = [] | |
| for name, model in models.items(): | |
| print(f"Training {name}...") | |
| model.fit(X_train, y_train) | |
| trained_models[name] = model | |
| preds = model.predict(X_test) | |
| f1 = f1_score(y_test, preds, average='weighted') | |
| scores.append(f1) | |
| avg_score = sum(scores) / len(scores) | |
| new_version_num = len(registry["versions"]) + 1 | |
| new_version = f"v{new_version_num}" | |
| print(f"New Version {new_version} Average F1 Score: {avg_score:.4f}") | |
| registry["versions"][new_version] = {"score": avg_score} | |
| active_version = registry["active_version"] | |
| active_score = registry["versions"].get(active_version, {}).get("score", 0.0) | |
| if avg_score >= active_score: | |
| print(f"Score improved ({avg_score:.4f} >= {active_score:.4f}). Overwriting active models.") | |
| for name, model in trained_models.items(): | |
| joblib.dump(model, os.path.join(model_dir, f'{name}.pkl')) | |
| registry["active_version"] = new_version | |
| else: | |
| print(f"Score degraded ({avg_score:.4f} < {active_score:.4f}). Rollback initiated: keeping existing models.") | |
| with open(registry_path, 'w') as f: | |
| json.dump(registry, f, indent=4) | |
| print("Training Complete.") | |
| if __name__ == '__main__': | |
| train_models() | |