def GridSearch(makePolynomialFeatures): for i in range(1): print("*" * 20, i) X_train, X_test, y_train, y_test = getPlantsPropulsionData( splitData=True, makePolynomialFeatures=makePolynomialFeatures) AdaBoostGS(X_train, X_test, y_train, y_test) DecisionTreeGS(X_train, X_test, y_train, y_test) ElasticNetRegressorGS(X_train, X_test, y_train, y_test) ExtraTreeGS(X_train, X_test, y_train, y_test) GradientBoostingGS(X_train, X_test, y_train, y_test) LarsRegressorGS(X_train, X_test, y_train, y_test) LassoRegressorGS(X_train, X_test, y_train, y_test) LinearSVRRegressorGS(X_train, X_test, y_train, y_test) NeuralNetGS(X_train, X_test, y_train, y_test) NuSVRRegressorGS(X_train, X_test, y_train, y_test) PoissonRegGS(X_train, X_test, y_train, y_test) RidgeRegressorGS(X_train, X_test, y_train, y_test) SGD_GS(X_train, X_test, y_train, y_test) SVRRegressorGS(X_train, X_test, y_train, y_test) XgBoostGS(X_train, X_test, y_train, y_test)
grid_reg = GridSearchCV( reg, param_grid=grid_values, scoring=['neg_mean_squared_error', 'neg_mean_absolute_error', 'r2'], refit='r2', n_jobs=-1, cv=2, verbose=100) grid_reg.fit(X_train, y_train) reg = grid_reg.best_estimator_ reg.fit(X_train, y_train) y_pred = reg.predict(X_test) printMetrics(y_true=y_test, y_pred=y_pred) val_metrics = getMetrics(y_true=y_test, y_pred=y_pred) y_pred = reg.predict(X=X_train) metrics = getMetrics(y_true=y_train, y_pred=y_pred) printMetrics(y_true=y_train, y_pred=y_pred) best_params: dict = grid_reg.best_params_ saveBestParams(nameOfModel="LarsRegressorGS", best_params=best_params) logSave(nameOfModel="LarsRegressorGS", reg=reg, metrics=metrics, val_metrics=val_metrics) X_train, X_test, y_train, y_test = getPlantsPropulsionData( splitData=True, makePolynomialFeatures=True) LarsRegressor(X_train, X_test, y_train, y_test)