old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  """
  Bulk Train-Test Classifier
  ========================================
  
  This example uses bulk train-test method of Classifier.
  """
  
  import sklearn.metrics
  
  from jubakit.classifier import Classifier, Schema, Dataset, Config
  from jubakit.loader.csv import CSVLoader
  
  # Load a CSV file.
  loader = CSVLoader('iris.csv')
  
  # Define a Schema that defines types for each columns of the CSV file.
  schema = Schema({
    'Species': Schema.LABEL,
  }, Schema.NUMBER)
  
  # Create a Dataset.
  dataset = Dataset(loader, schema).shuffle()
  n_samples = len(dataset)
  n_train_samples = int(n_samples / 2)
  
  # Create a Classifier configuration.
  cfg = Config()
  
  # Bulk train-test the classifier.
  result = Classifier.train_and_classify(
    Config(),
    dataset[:n_train_samples],
    dataset[n_train_samples:],
    sklearn.metrics.classification_report
  )
  
  print(result)
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  """
  Bulk Train-Test Classifier
  ========================================
  
  This example uses bulk train-test method of Classifier.
  """
  
  import sklearn.metrics
  
  from jubakit.classifier import Classifier, Schema, Dataset, Config
  from jubakit.loader.csv import CSVLoader
  
  # Load a CSV file.
  loader = CSVLoader('iris.csv')
  
  # Define a Schema that defines types for each columns of the CSV file.
  schema = Schema({
    'Species': Schema.LABEL,
  }, Schema.NUMBER)
  
  # Create a Dataset.
  dataset = Dataset(loader, schema).shuffle()
  n_samples = len(dataset)
  n_train_samples = int(n_samples / 2)
  
  # Create a Classifier configuration.
  cfg = Config()
  
  # Bulk train-test the classifier.
  result = Classifier.train_and_classify(
    cfg,
    dataset[:n_train_samples],
    dataset[n_train_samples:],
    sklearn.metrics.classification_report
  )
  
  print(result)
  
subject: fix config unused in classifier_bulk example