YTest = dataset.YTest XTrainTransf = dataset.XTrainTransf XTestTransf = dataset.XTestTransf lambda_opt = {"alpha": 67.1590893061} #model_list = {ISTA(), FISTA(), Shooting(), ADMM()} model_list = {modifiedShooting(DistanceCorrelation())} ext_data = ".npz" ext_model = ".pkl" folder = "AlgorithmResults/" for model in model_list: lasso = LASSOEstimator(model) lasso.set_params(**lambda_opt) lasso.fit(XTrainTransf,YTrain) y_pred_test = lasso.predict(XTestTransf) mse_test = mean_squared_error(YTest, y_pred_test) print ("mse_test "+model.__class__.__name__,mse_test) y_pred_train = lasso.predict(XTrainTransf) mse_train = mean_squared_error(YTrain, y_pred_train) print("mse_train "+model.__class__.__name__,mse_train) np.savez(folder+model.__class__.__name__+ext_data, XTrain=XTrain, YTrain = YTrain, mse_test=mse_test, XTest=dataset.XTest, YTest = YTest, y_pred_test=y_pred_test, XTrainTransf=XTrainTransf, XTestTransf=XTestTransf, mse_train = mse_train)
current_informative = np.intersect1d(ordered_indexes, informative_indexes) current_not_informative = np.array(list(set(ordered_indexes)-set(current_informative))) if verbose: print("informative", len(current_informative), "su", len(ordered_indexes)) print("non informative",len(current_not_informative),"su", len(ordered_indexes)) current_train, current_test = get_current_data(XTrain, XTest, ordered_indexes) indexes_to_extract.append(ordered_indexes) model_list = {Shooting(weights)} ext_data = ".npz" ext_model = ".pkl" for model in model_list: lasso = LASSOEstimator(model) clf = GridSearchCV(lasso, parameters, fit_params = {"verbose" : False}, cv=3, scoring="mean_squared_error") clf.fit(current_train, YTrain) lambda_opt = clf.best_params_ if verbose: print("best lambda", lambda_opt) lasso.set_params(**lambda_opt) lasso.fit(current_train,YTrain) y_pred_train = lasso.predict(current_train) mse_train = mean_squared_error(YTrain, y_pred_train) if verbose: print("mse_train "+model.__class__.__name__,mse_train)