code: def lr_untuned(x_train, y_train, x_test, y_test):
      predict, w, b = train_lr(x_train, y_train)
      yhat = predict(x_test)
      loss = optunity.metrics.logloss(y_test, yhat)
      brier = optunity.metrics.brier(y_test, yhat)
      print(
          "+ model: "
          + str(b.get_value())[:5]
          + " + "
          + str(w.get_value()[0])[:5]
          + " * x1 + "
          + str(w.get_value()[1])[:5]
          + " * x2"
      )
      print("++ log loss in test fold: " + str(loss))
      print("++ Brier loss in test fold: " + str(brier))
      print("")
      return loss, brier
  
file_path: optunity/bin/examples/python/theano/logistic_regression.py
imports: import numpy
  import theano
  import theano.tensor
  from numpy.random import multivariate_normal
  import optunity
  import optunity.metrics
label_desc: Use image node_id 2 for calling a global function with example usage: lr_untuned(x_train, y_train, x_test, y_test) and returns: loss,  brier