text: import warnings
  warnings.filterwarnings("ignore", category=UserWarning)
  
  import sys
  import os
  import datetime
  import json
  import subprocess
  
  import click
  
  import gabbar
  
  from sklearn.externals import joblib
  
  def get_prediction(changeset):
      features = gabbar.get_features(changeset)
      filtered = gabbar.filter_features(features)
      normalized = gabbar.normalize_features(filtered)
      prediction = gabbar.get_prediction(normalized)
      return {
          'features': features,
          'prediction': prediction
      };
  
  
  def converter(o):
      if isinstance(o, datetime.datetime):
          return o.__str__()
  
  
  def getFeatureType(types):
      if types[0] == 1:
          return 'node'
      elif types[1] == 1:
          return 'way'
      elif types[2] == 1:
          return 'relation'
  
  def process_changeset(changeset_id):
      # Run node.js script to download real changeset and extract features.
      directory = os.path.dirname(os.path.realpath(__file__))
      # Getting the parent directory.
      directory = os.path.abspath(os.path.join(directory, os.pardir))
      helper = os.path.join(directory, 'helpers/real_changeset.js')
  
      # Arguments must contain only strings.
      data = json.loads(str(subprocess.check_output([helper, '--changesetID', str(changeset_id)], universal_newlines=True)))
  
      results = []
      # If there are no attributes, that means there were no interesting samples to process in this changeset.
      if (not data) or (len(data['attributes']) == 0): return results;
  
      directory = os.path.dirname(os.path.realpath(__file__))
      directory = os.path.abspath(os.path.join(directory, os.pardir))
  
      # Load all pre-trained assets.
      model = joblib.load(os.path.join(directory, 'trained/model.pkl'))
      new_vectorizer = joblib.load(os.path.join(directory, 'trained/new_vectorizer.pkl'))
      old_vectorizer = joblib.load(os.path.join(directory, 'trained/old_vectorizer.pkl'))
  
      directory = os.path.dirname(os.path.realpath(__file__))
      directory = os.path.abspath(os.path.join(directory, os.pardir))
      directory = os.path.abspath(os.path.join(directory, os.pardir))
      version_filepath = os.path.join(directory, 'VERSION')
      with open(version_filepath) as f:
          version = f.read().strip()
  
  
      for i in range(len(data['attributes'])):
          attributes = data['attributes'][i]
  
          mapping = dict(zip(data['header'], attributes))
  
          # Leaving out non training parameters.
          model_attribute_keys = data['header'][3:-2] \
              + ['new_' + item for item in new_vectorizer.get_feature_names()] \
              + ['old_' + item for item in old_vectorizer.get_feature_names()]
          model_attribute_values = list(attributes[3:-2]) \
              + [int(item) for item in new_vectorizer.transform([mapping['new_tags']]).toarray()[0]] \
              + [int(item) for item in old_vectorizer.transform([mapping['old_tags']]).toarray()[0]]
  
          prediction = int(model.predict([model_attribute_values])[0])
  
          result = {
              'changeset_id': attributes[0],
              'feature_id': attributes[2],
              'feature_type': getFeatureType(attributes[7:10]),  # TODO
              'attributes': dict(zip(model_attribute_keys, model_attribute_values)),
              'prediction': prediction,
              'timestamp': datetime.datetime.now(),
              'version': version,
          }
          results.append(result)
      return results
  
  
  def anomaly_detection(changeset_id):
      # Run node.js script to download real changeset and extract features.
      directory = os.path.dirname(os.path.realpath(__file__))
      # Getting the parent directory.
      directory = os.path.abspath(os.path.join(directory, os.pardir))
      helper = os.path.join(directory, 'helpers/real_changeset.js')
  
      # Arguments must contain only strings.
      data = json.loads(str(subprocess.check_output([helper, '--changesetID', str(changeset_id)], universal_newlines=True)))
  
      results = []
      # If there are no attributes, that means there were no interesting samples to process in this changeset.
      if (not data) or (len(data['attributes']) == 0): return results;
  
      directory = os.path.dirname(os.path.realpath(__file__))
      directory = os.path.abspath(os.path.join(directory, os.pardir))
  
      # Load all pre-trained assets.
      model = joblib.load(os.path.join(directory, 'trained/model.pkl'))
  
      directory = os.path.dirname(os.path.realpath(__file__))
      directory = os.path.abspath(os.path.join(directory, os.pardir))
      directory = os.path.abspath(os.path.join(directory, os.pardir))
      version_filepath = os.path.join(directory, 'VERSION')
      with open(version_filepath) as f:
          version = f.read().strip()
  
      for i in range(len(data['attributes'])):
          attributes = data['attributes'][i]
  
          mapping = dict(zip(data['header'], attributes))
  
          # Leaving out non training parameters.
          model_attribute_keys = data['header'][4:]
          model_attribute_values = list(attributes[4:])
  
          prediction = int(model.predict([model_attribute_values])[0])
          score = round(model.decision_function([model_attribute_values])[0], 4)
  
          result = {
              'changeset_id': attributes[0],
              'feature_id': attributes[2],
              'feature_type': attributes[3],
              'attributes': dict(zip(model_attribute_keys, model_attribute_values)),
              'prediction': prediction,
              'score': score,
              'timestamp': datetime.datetime.now(),
              'version': version,
          }
          results.append(result)
      return results
  
  
  @click.command('gabbar')
  @click.argument('changeset', type=str, metavar='changeset')
  def cli(changeset):
  
      results = anomaly_detection(changeset)
      print(json.dumps(results, sort_keys=True, default=converter, indent=4))
  
      # results = get_prediction(changeset);
      #
      # features = results['features'];
      # prediction = results['prediction']
      #
      # if prediction == 1:
      #     prediction = 'good'
      # else:
      #     prediction = 'harmful'
      #
      # directory = os.path.dirname(os.path.realpath(__file__))
      # version_filepath = os.path.join(directory, '../../VERSION')
      # with open(version_filepath) as f:
      #     version = f.read().strip()
      #
      # timestamp = datetime.datetime.now()
      # results = {
      #     'changeset': changeset,
      #     'prediction': prediction,
      #     'features': features,
      #     'version': version,
      #     'timestamp': timestamp
      # }
      # print(json.dumps(results, sort_keys=True, default=converter))
  