Enel_directionPowerCurveTransformation from utility import generate_samples_dynamic_set, get_current_data, get_common_indexes, \ extract_chosen_indexes_from_start, center_test import sys sys.argv[1:2] = [str(x) for x in sys.argv[1:2]] output_folder = sys.argv[1] threshold_dir = (int)(sys.argv[2]) num_blocks = (int)(sys.argv[3]) compute_mse_current = (int)(sys.argv[4]) cycles = (int)(sys.argv[5]) max_active_set = (int)(sys.argv[6]) ####load data file = "ENEL_2014/Enel_dataset.npz" results = Result(file, "lasso") XTrain, YTrain, XTest, YTest = results.extract_train_test() enel_dict = results.extract_dict() Coord, Coord_turb, power_curve = results.extract_coords() angles_coord_turb, _ = compute_angle(Coord, Coord_turb) ##transformation of data X = np.concatenate((XTrain, XTest), axis=0) enel_transf = Enel_powerCurveTransformation() X_angle, _, _ = enel_transf.compute_angle_matrix(X) output_dict = dict.fromkeys(np.arange(0, 49), np.array([[]], dtype="int64")) k_levels = np.arange(0, 12).reshape([12, 1])
from Enel_utils import find_nearest_turbine from ExtractResult import Result from Fit import Linear_fit from Transformation import EnelWindSpeedTransformation import numpy as np file_coord = np.load("ENEL_2014/Coord.npz") Coord = file_coord["Coord"] Coord_turb = file_coord["Coord_turb"] power_curve = file_coord["power_curve"] file = "ENEL_2014/Enel_dataset.npz" results = Result(file, "lasso") XTrain, YTrain, XTest, YTest = results.extract_train_test() enel_transf = EnelWindSpeedTransformation() XTrain, dict_ = enel_transf.transform(XTrain) XTest, dict_ = EnelWindSpeedTransformation().transform(XTest) for k in range(5,10): print(k, "vicini") turbine_dict = find_nearest_turbine(Coord,Coord_turb,k) XTrain_, output_dict = enel_transf.nearest_mean_turbine(turbine_dict,dict_,XTrain, power_curve) print(XTrain_.shape) XTest_, _ = enel_transf.nearest_mean_turbine(turbine_dict,dict_,XTest,power_curve) print(XTest_.shape) Linear_fit().fitting(XTrain_, YTrain, XTest_,YTest)
from sklearn.metrics import r2_score, mean_squared_error from ExtractResult import Result import numpy as np from LASSOModel import Shooting, LASSOEstimator from Lasso_utils import compute_weightedLASSO, compute_lasso from utility import get_current_data, assign_weights, assign_weights_ordered, \ get_beta_div_zeros, print_features_active import sys sys.argv[1:] = [str(x) for x in sys.argv[1:]] file_name = sys.argv[1] ext = ".npz" file = "ENEL_2014/"+file_name+ext results = Result(file, "lasso") dict_ = results.extract_dict() XTrain, YTrain, XVal, YVal = results.extract_train_val() score = "mean_squared_error" if score=="r2_score": score_f = r2_score scoring = "r2" else: score_f = mean_squared_error scoring = "mean_squared_error" verbose = True
file_name = sys.argv[1] compute_lasso_current = 1 score = "mean_squared_error" if score=="r2_score": score_f = r2_score scoring = "r2" else: score_f = mean_squared_error scoring = "mean_squared_error" ext = ".npz" file_cross_val = file_name+ext fine_name_weights = file_name+"ranking"+ext results_cross_val = Result(file_cross_val, "lasso") results_weighted_lasso = Result(fine_name_weights, "lasso") mses = results_weighted_lasso.extract_mses() mses_int = list(map(int, mses)) iter = np.argmin(mses_int) print ("iter chosen:",iter, "with mse:",mses_int[iter]) print("--------------") indexes_beta = results_weighted_lasso.extract_beta_div_zeros()[iter] ##get transformed data XTrain, XTest = results_cross_val.extract_data_transf() _,YTrain,_, YTest = results_cross_val.extract_train_test() ### centratura dei dati
compute_lasso_current = False score = "mean_squared_error" if score=="r2_score": score_f = r2_score scoring = "r2" else: score_f = mean_squared_error scoring = "mean_squared_error" folder = "ENEL_2014/" ext = ".npz" file_cross_val = folder+file_name+ext results_cross_val = Result(file_cross_val, "lasso") ##get transformed data XTrain, XTest = results_cross_val.extract_data_transf() _,YTrain,_, YTest = results_cross_val.extract_train_test() ### centratura dei dati XTrain, YTrain, X_mean, y_mean, X_std = center_data(XTrain, YTrain, fit_intercept=True, normalize = True) XTest, YTest = center_test(XTest,YTest,X_mean,y_mean,X_std) ##ranking verbose = True dict_ = results_cross_val.extract_dict() weights_data = results_cross_val.extract_weights()
print(sys.argv[1]) sys.argv[1:] = [int(x) for x in sys.argv[1:]] n_samples = sys.argv[1] print("n_samples", n_samples) original_features = sys.argv[2] print("original_features", original_features) transformation = F2() print("function", transformation) end = sys.argv[3] print("lambda_max", end) verbose = True file = "nonLinearDataset/"+transformation.__class__.__name__+"/test"+transformation.__class__.__name__+"num_blocks_modified1000num_samples"+str(n_samples)+"n_features"+str(original_features)+"dynamic_set.npz" results = Result(file, "lasso") XTrain, YTrain, XTest, YTest,mses = results.extract_data() #dict_ = results.extract_dict() weights_data = results.extract_weights() informative_indexes = results.extract_informative() print (informative_indexes) n_features = XTrain.shape[1] index_mse = len(weights_data)-1 weights_data = weights_data[index_mse] final_weights = np.zeros(original_features) #keys_ = np.array(dict_.keys()).astype("int64")
Enel_directionPowerCurveTransformation, Enel_turbineTransformation from utility import generate_samples_dynamic_set, get_current_data,get_common_indexes, \ extract_chosen_indexes_from_start, center_test import sys sys.argv[1:2] = [str(x) for x in sys.argv[1:2]] output_folder = sys.argv[1] threshold_dir = (int)(sys.argv[2]) num_blocks = (int)(sys.argv[3]) compute_mse_current = (int)(sys.argv[4]) cycles = (int)(sys.argv[5]) max_active_set = (int)(sys.argv[6]) ####load data file = "ENEL_2014/Enel_dataset.npz" results = Result(file, "lasso") XTrain, YTrain, XTest, YTest = results.extract_train_test() _, _, power_curve = results.extract_coords() ##transformation of data X = np.concatenate((XTrain, XTest), axis = 0) enel_transf = Enel_powerCurveTransformation() X_speed,_, wind_direction = EnelWindSpeedTransformation().transform(X) print("wind speed computed") enel_transf = Enel_turbineTransformation() X_transf, output_dict = enel_transf.transform(X_speed, power_curve) print("transformation done")
file_name = sys.argv[1] weights_all = 1 score = "mean_squared_error" if score=="r2_score": score_f = r2_score scoring = "r2" else: score_f = mean_squared_error scoring = "mean_squared_error" ext = ".npz" file_cross_val = file_name+ext fine_name_weights = file_name+"_ranking_not_levels"+ext results_weighted_lasso = Result(fine_name_weights, "lasso") mses = results_weighted_lasso.extract_mses() results_cross_val = Result(file_cross_val, "lasso") ##get transformed data XTrain, YTrain, XTest, YTest = results_cross_val.extract_train_test() ### centratura dei dati XTrain, YTrain, X_mean, y_mean, X_std = center_data(XTrain, YTrain, fit_intercept=True, normalize = True) XTest, YTest = center_test(XTest,YTest,X_mean,y_mean,X_std) ##ranking verbose = True weights_data = results_cross_val.extract_weights()
import numpy as np import sys from Enel_utils import find_nearest_turbine, compute_angle from ExtractResult import Result from Transformation import Enel_powerCurveTransformation, EnelWindSpeedTransformation, \ Enel_directionVersoPowerCurveTransformation, Enel_directionPowerCurveTransformation sys.argv[1:3] = [str(x) for x in sys.argv[1:]] folder_name = sys.argv[1] filename = sys.argv[2] compute_dict = (int)(sys.argv[3]) tot_filename = folder_name+"/"+filename results = Result(tot_filename, "lasso") weights_list = results.extract_weights() mses = results.extract_mses() XTrain_transf, _ = results.extract_data_transf() XTrain_, YTrain_, XVal_, YVal_ = results.extract_train_val() saved_indexes_list = results.get_saved_indexes() XTrain_ValNoCenter, YTrainVal_noCenter, XVal_noCenter,YVal_noCenter = results.extract_train_val_no_centered() output_dict = results.extract_dict() file = "ENEL_2014/Enel_dataset.npz" results = Result(file, "lasso") XTrain, YTrain, XTest, YTest = results.extract_train_test() X_speed,_ = EnelWindSpeedTransformation().transform(XTest) Coord, Coord_turb, power_curve = results.extract_coords() angles_coord_turb,verso_turb_point = compute_angle(Coord, Coord_turb) enel_transf = Enel_powerCurveTransformation()
import numpy as np import sys from Enel_utils import find_nearest_turbine, compute_angle from ExtractResult import Result from Transformation import Enel_powerCurveTransformation, EnelWindSpeedTransformation sys.argv[1:] = [str(x) for x in sys.argv[1:]] filename = sys.argv[1] results = Result(filename, "lasso") XTrain, YTrain, XTest, YTest = results.extract_train_test() weights_list = results.extract_weights() mses = results.extract_mses() XTrain_transf, XTest_transf = results.extract_data_transf() XTrain_, YTrain_, XVal_, YVal_ = results.extract_train_val() saved_indexes_list = results.get_saved_indexes() XTrain_ValNoCenter, YTrainVal_noCenter, XVal_noCenter,YVal_noCenter = results.extract_train_val_no_centered() enel_dict = results.extract_dict() file = "ENEL_2014/Enel_dataset.npz" results = Result(file, "lasso") ##transformation of data X = np.concatenate((XTrain, XTest), axis = 0) enel_transf = Enel_powerCurveTransformation() output_dict = dict.fromkeys(np.arange(0,49),np.array([[]], dtype = "int64")) X_speed,_ = EnelWindSpeedTransformation().transform(X)