old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  """ 
  Pipeline Classification with scikit-learn wrapped Classifier
  ============================================================
  
  In this example, we show how to use scikit-learn wrapper's 
  `fit(X, y)` and `predict(X)` functions. 
  
  """
  
  from jubakit.wrapper import LinearClassifier, NearestNeighborsClassifier
  
  from sklearn.datasets import load_digits
  from sklearn.decomposition import PCA
  from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import classification_report
  from sklearn.pipeline import Pipeline
  from sklearn.utils import shuffle
  
  # load hand-writtern number recognition dataset
  digits = load_digits()
  # shuffle and separate the dataset
  X, y = shuffle(digits.data, digits.target, random_state=42)
  n_train = int(X.shape[0] / 2) 
  X_train, y_train = X[:n_train], y[:n_train]
  X_test, y_test = X[n_train:], y[n_train:]
  
  # launch linear classifier (AROW)
  clf = LinearClassifier(method='AROW', embedded=False, seed=42)
  
  # scale dataset 
  scaled_pipeline = Pipeline([
      ('scaler', StandardScaler()),
      ('classifier', clf)
  ])
  
  # decompose dataset
  pca_pipeline = Pipeline([
      ('pca', PCA()),
      ('classifier', clf)
  ])
  
  # evaluate each pipelines
  pipelines = [clf, scaled_pipeline, pca_pipeline]
  for pipeline in pipelines:
      print(pipeline)
      pipeline.fit(X_train, y_train)
      y_pred = pipeline.predict(X_test)
      print(classification_report(y_test, y_pred))
  
  
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  """ 
  Pipeline Classification with scikit-learn wrapped Classifier
  ============================================================
  
  In this example, we show how to use scikit-learn wrapper's 
  `fit(X, y)` and `predict(X)` functions. 
  
  """
  
  from jubakit.classifier.wrapper import LinearClassifier, NearestNeighborsClassifier
  
  from sklearn.datasets import load_digits
  from sklearn.decomposition import PCA
  from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import classification_report
  from sklearn.pipeline import Pipeline
  from sklearn.utils import shuffle
  
  # load hand-writtern number recognition dataset
  digits = load_digits()
  # shuffle and separate the dataset
  X, y = shuffle(digits.data, digits.target, random_state=42)
  n_train = int(X.shape[0] / 2) 
  X_train, y_train = X[:n_train], y[:n_train]
  X_test, y_test = X[n_train:], y[n_train:]
  
  # launch linear classifier (AROW)
  clf = LinearClassifier(method='AROW', embedded=False, seed=42)
  
  # scale dataset 
  scaled_pipeline = Pipeline([
      ('scaler', StandardScaler()),
      ('classifier', clf)
  ])
  
  # decompose dataset
  pca_pipeline = Pipeline([
      ('pca', PCA()),
      ('classifier', clf)
  ])
  
  # evaluate each pipelines
  pipelines = [clf, scaled_pipeline, pca_pipeline]
  for pipeline in pipelines:
      print(pipeline)
      pipeline.fit(X_train, y_train)
      y_pred = pipeline.predict(X_test)
      print(classification_report(y_test, y_pred))
  
  
  
subject: fix sklearn wrapper example import error