def save_prediction_outputs(
postprocessed_output,
output_directory,
backend,
):
postprocessed_output, column_shapes = flatten_df(postprocessed_output, backend)
postprocessed_output.to_parquet(os.path.join(output_directory, "predictions.parquet"))
save_json(os.path.join(output_directory, "predictions.shapes.json"), column_shapes)
def save_prediction_outputs(
postprocessed_output,
output_features,
output_directory,
backend,
):
postprocessed_output, column_shapes = flatten_df(postprocessed_output, backend)
postprocessed_output.to_parquet(os.path.join(output_directory, PREDICTIONS_PARQUET_FILE_NAME))
save_json(os.path.join(output_directory, PREDICTIONS_SHAPES_FILE_NAME), column_shapes)
if not backend.df_engine.partitioned:
# csv can only be written out for unpartitioned df format (i.e., pandas)
postprocessed_dict = convert_to_dict(postprocessed_output, output_features)
csv_filename = os.path.join(output_directory, "{}_{}.csv")
for output_field, outputs in postprocessed_dict.items():
for output_type, values in outputs.items():
save_csv(csv_filename.format(output_field, output_type), values)