def transformation()

in machine-learning/container/scripts/predictor.py [0:0]

• 17 lines of code
• 2 McCabe index (conditional complexity)

def transformation():
    """
    Do an inference on a single batch of data. In this sample server, we take
    data as CSV, convert it to a pandas data frame for internal use and then
    convert the predictions back to CSV (which really just means one prediction
    per line, since there's a single column.
    """
    data = None

    # Convert from CSV to pandas
    if flask.request.content_type == 'text/csv':
        data = flask.request.data.decode('utf-8')
        s = StringIO(data)
        data = pd.read_csv(s, header=None)
        print('data.head()',data.head())
        print('data.columns',data.columns)
        data = transform_data(data)
    else:
        return flask.Response(response='This predictor only supports CSV data',status=415, mimetype='text/plain')

    print('Invoked with {} records'.format(data.shape[0]))

    # Do the prediction
    predictions = ScoringService.predict(data)

    # Convert from numpy back to CSV
    out = StringIO()
    pd.DataFrame(predictions).to_csv(out, header=False, index=False)
    result = out.getvalue()

    return flask.Response(response=result, status=200, mimetype='text/csv')