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])
for key in np.arange(0, 49):
current_values = np.arange(key * 12, key * 12 + 12).reshape([12, 1])
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()
index_mse = len(weights_data)-1
weights_data = weights_data[index_mse]
