def test_save(self):
#Arrange
input_data = get_input_data()
input_data_file = InputDataFile()
#Mocks
input_data.to_csv = MagicMock()
#Act
input_data_file.save(input_data, batch_id, epoch_id)
#Assert
input_data.to_csv.assert_called_with(input_data_file.file_name(batch_id, epoch_id))
update_params(image_generation_params, **image_generation_params_update)
logger.info('Updated image generation parameters: %s', image_generation_params)
#Compute predictions
num_prediction_steps = num_prediction_steps or ceil(len(input_data) / image_generation_params.batch_size)
predictor = Prediction(model, input_params, image_generation_params)
predicted_data = predictor.predict(input_data, num_prediction_steps)
#Compute accuracy
num_matches = (predicted_data[constants.PANDAS_MATCH_COLUMN].to_numpy().nonzero())[0].shape[0]
num_mismatches = len(predicted_data[constants.PANDAS_MATCH_COLUMN]) - num_matches
accuracy = (num_matches/len(predicted_data[constants.PANDAS_MATCH_COLUMN])) * 100.
#Write-out predicted output
prediction_result_file = InputDataFile(constants.PREDICTION_RESULT_FILE_NAME_GUIDANCE)
prediction_result_file.save(predicted_data, 0, 0)
input_files_client.put_all([prediction_result_file.file_name(0, 0)])
print_summary = """
Result Dataframe: {}
Total predictions: {}
Correct predictions: {}
Wrong predictions: {}
Accuracy: {}
""".format(
predicted_data,
len(predicted_data),
num_matches,
num_mismatches,
accuracy)