Код: Выделить всё
X = data[['Year', 'Month', 'Day', 'Raw Conc.', 'NowCast Conc.']]
y = data['AQI']
# Split data into training and test sets using time series splitting
tscv = TimeSeriesSplit(n_splits=2)
for train_index, test_index in tscv.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
# Standardize the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Define parameter grids for each model
param_grids = {
"Decision Tree": {'max_depth': [3, 5, 7, 10]},
"Random Forest": {'n_estimators': [50, 100, 200], 'max_depth': [3, 5, 7, 10]},
"Gradient Boosting": {'n_estimators': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.2], 'max_depth': [3, 5, 7]},
"AdaBoost": {'n_estimators': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.5]},
"XGBoost": {'n_estimators': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.2], 'max_depth': [3, 5, 7]},
"CatBoost": {'iterations': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.2], 'depth': [3, 5, 7]}, 0.7]},
}
# List of models to evaluate
models = [
("Decision Tree", DecisionTreeRegressor(random_state=42)),
("Random Forest", RandomForestRegressor(random_state=42)),
("Gradient Boosting", GradientBoostingRegressor(random_state=42)),
("AdaBoost", AdaBoostRegressor(random_state=42)),
("XGBoost", XGBRegressor(random_state=42)),
("CatBoost", CatBoostRegressor(verbose=0)),
]
#Dictionaries to store model performance and feature importances
model_performance = {}
feature_importance_dict = {}
predictions = {}
for name, model in models:
param_grid = param_grids[name]
if param_grid:
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, scoring='neg_mean_squared_error')
grid_search.fit(X_train_scaled, y_train)
best_model = grid_search.best_estimator_
else:
best_model = model
best_model.fit(X_train_scaled, y_train)
# Calculate predictions
y_train_pred = best_model.predict(X_train_scaled)
y_test_pred = best_model.predict(X_test_scaled)
# Store predictions
predictions[name] = {'model_name': name, 'y_test_pred': y_test_pred}
# Calculate evaluation metrics for train set
train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))
train_r2 = r2_score(y_train, y_train_pred)
train_mae = mean_absolute_error(y_train, y_train_pred)
# Calculate evaluation metrics for test set
test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))
test_r2 = r2_score(y_test, y_test_pred)
test_mae = mean_absolute_error(y_test, y_test_pred)
# Store model performance metrics
model_performance[name] = {
"Train_RMSE": train_rmse,
"Train_R2": train_r2,
"Train_MAE": train_mae,
"Test_RMSE": test_rmse,
"Test_R2": test_r2,
"Test_MAE": test_mae
}
# For all models, try to extract feature importances
if hasattr(best_model, 'feature_importances_') or hasattr(best_model, 'coef_'):
feature_importances = best_model.feature_importances_ if hasattr(best_model, 'feature_importances_') else best_model.coef_
# Get feature names
if isinstance(best_model, (LinearRegression, Ridge, Lasso)): # For linear models
feature_names = ['Raw Conc.', 'NowCast Conc.']
else: # For other models, get feature names from original DataFrame
feature_names = ['Raw Conc.', 'NowCast Conc.'] # Replace this with the actual feature names
# Store feature importances with feature names
feature_importance_dict[name] = {feature_names[i]: feature_importances[i] for i in range(min(len(feature_importances), len(feature_names)))}
# Convert model performance dictionary to DataFrame
model_performance_df = pd.DataFrame.from_dict(model_performance, orient='index')
# Print model performance
print(model_performance_df)
Подробнее здесь: https://stackoverflow.com/questions/786 ... learning-m
