k = sys.argv[1]
XTrain, YTrain, XTest, YTest = results.extract_train_test()
##transformation of data
transf = EnelWindSpeedTransformation()
XTrain_transf, dict_ = transf.transform(XTrain)
XTest_transf, dict_ = transf.transform(XTest)
Coord = np.load("ENEL_2014/Coord.npz")["Coord"]
neight_= find_nearest(Coord,k)
XTrain_transf = transf.nearest_products_levels(neight_,dict_,XTrain)
XTest_transf = transf.nearest_products_levels(neight_,dict_,XTest)
##center data
XTrain_noCenter, XVal_noCenter, YTrain_noCenter, YVal_noCenter = train_test_split(XTrain_transf, YTrain, test_size=0.33,random_state=0)
XTrain_, YTrain_, X_mean, y_mean, X_std = center_data(XTrain_noCenter, YTrain_noCenter, fit_intercept=True, normalize = True)
XVal_, YVal_ = center_test(XVal_noCenter,YVal_noCenter,X_mean,y_mean,X_std)
#new_loss, beta = compute_lasso(XTrain_, YTrain_, XVal_, YVal_,score = "mean_squared_error")
#print("loss", new_loss)
n_features_transf = XTrain_.shape[1]
####generation blocks
num_blocks = 1000
r = np.random.RandomState(11)
from ExtractResult import Result
from Transformation import EnelWindSpeedTransformation
import numpy as np
import sys
from utility import find_nearest
folder_train = "ENEL_2014/PSC/0-23_0001-0049/"
folder_test = "ENEL_2014/PSC/24-47_0001-0049/"
label_file = "ENEL_2014/PSC/Metering_2011-2014_UTC.txt"
file = "ENEL_2014/Enel_dataset.npz"
results = Result(file, "lasso")
XTrain, YTrain, XTest, YTest = results.extract_train_test()
Coord = np.load("ENEL_2014/Coord.npz")["Coord"]
sys.argv[1:] = [int(x) for x in sys.argv[1:]]
k = sys.argv[1]
neight_= find_nearest(Coord,k)
enel_transf = EnelWindSpeedTransformation()
XTrain, dict_ = enel_transf.transform(XTrain)
XTrain, output_dict = enel_transf.nearest_products_levels(neight_,dict_,XTrain)
np.savez("ENEL_2014/Product_level_"+str(k)+"_dict", dict_ = output_dict)
