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
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)
values_TM = np.array([[24,281], [24,214]])
##ranking
verbose = True
dict_ = results_cross_val.extract_dict()
weights_data = results_cross_val.extract_weights()
