text: import sys
  import time
  
  import operator
  import pandas as pd
  import numpy as np
  import itertools
  from sklearn.preprocessing import LabelEncoder, StandardScaler
  from sklearn.model_selection import GridSearchCV
  from sklearn.metrics import *
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  import itertools
  
  from sklearn import linear_model
  
  # read arguments (1: dataset name, 2: output file name, 3: trial #, 4: # cores)
  dataset = sys.argv[1]
  #output_file = sys.argv[2]
  trial = sys.argv[3]
  
  # Read the data set into memory
  input_data = pd.read_csv(dataset, compression='gzip', sep='\t')
  
  
  TARGET_NAME = 'target'
  INPUT_SEPARATOR = '\t'
  n_jobs = 1
  
  
  
  
  hyper_params = [
      {
          'alpha': (1e-04,0.001,0.01,0.1,1,),
      },
  ]
  
  
  
  sc_y = StandardScaler()
  X = StandardScaler().fit_transform(input_data.drop(TARGET_NAME, axis=1).values.astype(float))
  y = sc_y.fit_transform(input_data[TARGET_NAME].values.reshape(-1,1))
  
  
  
  
  
  X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                      train_size=0.75,
                                                      test_size=0.25,
                                                      random_state=None)
  
  
  est=linear_model.LassoLars()
  
  
  
  
  grid_clf = GridSearchCV(est,cv=5,param_grid=hyper_params,
                     verbose=0,n_jobs=n_jobs,scoring='r2')
  
  t0 = time.time()
  #fit model
  grid_clf.fit(X_train,y_train.ravel())
  #get fit time
  runtime = time.time()-t0
  
  
  
  
  
  train_score_mse = mean_squared_error(sc_y.inverse_transform(grid_clf.predict(X_train)),
                                    sc_y.inverse_transform(y_train))
  
  train_score_mae = mean_absolute_error(sc_y.inverse_transform(grid_clf.predict(X_train)),
                                    sc_y.inverse_transform(y_train))
  train_score_r2 = r2_score(sc_y.inverse_transform(grid_clf.predict(X_train)),
                                  sc_y.inverse_transform(y_train))
  
  test_score_mse = mean_squared_error(sc_y.inverse_transform(grid_clf.predict(X_test)),
                                    sc_y.inverse_transform(y_test))
  test_score_mae = mean_absolute_error(sc_y.inverse_transform(grid_clf.predict(X_test)),
                                    sc_y.inverse_transform(y_test))
  test_score_r2 = r2_score(sc_y.inverse_transform(grid_clf.predict(X_test)),
                                  sc_y.inverse_transform(y_test))
  
  
  
  sorted_grid_params = sorted(grid_clf.best_params_.items(), key=operator.itemgetter(0))
  
  
  # print results
  out_text = '\t'.join([dataset.split('/')[-1][:-7],
                        'lasso-lars',
                        str(trial),
                        str(sorted_grid_params).replace('\n',','),
                        str(train_score_mse),
                        str(train_score_mae),
                        str(train_score_r2),
                        str(test_score_mse),
                        str(test_score_mae),
                        str(test_score_r2),
                        str(runtime)])
  
  
  print(out_text)
  sys.stdout.flush()
  
  