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("-------------")