5b48f9a9c097d26d395873044ceaa1a0b886682a,solutionbox/code_free_ml/mltoolbox/code_free_ml/analyze.py,,run_local_analysis,#Any#Any#Any#Any#,291

Before Change

                  float(parsed_line[col_name])))
            numerical_results[col_name]["count"] += 1
            numerical_results[col_name]["sum"] += float(parsed_line[col_name])
          elif transform == constant.IMAGE_TRANSFORM:
            pass
          elif transform == constant.KEY_TRANSFORM:
            pass
          else:
            raise ValueError("Unknown transform %s" % transform)

  // Write the vocab files. Each label is on its own line.
  vocab_sizes = {}
  for name, label_count in six.iteritems(vocabs):

After Change

  // Make a copy of inverted_features and update the target transform to be
  // identity or one hot depending on the schema.
  inverted_features_target = copy.deepcopy(inverted_features)
  for name, transform_set in six.iteritems(inverted_features_target):
    if transform_set == set([constant.TARGET_TRANSFORM]):
      target_schema = next(col["type"].lower() for col in schema if col["name"] == name)
      if target_schema in constant.NUMERIC_SCHEMA:
        inverted_features_target[name] = {constant.IDENTITY_TRANSFORM}
      else:
        inverted_features_target[name] = {constant.ONE_HOT_TRANSFORM}

  // initialize the results
  def _init_numerical_results():
    return {"min": float("inf"),
            "max": float("-inf"),
            "count": 0,

Instances