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")
#for key in keys_:
#final_weights[key] += np.sum(weights_data[dict_.get(key).astype("int64")])
ordered_final_weights = np.argsort(final_weights)[::-1]
if verbose:
print("-------------")
