コード例 #1
0
def test_nlu_intents_are_converted(tmpdir: Path):
    converted_data_folder = tmpdir / "converted_data"
    os.mkdir(converted_data_folder)

    training_data_folder = tmpdir / "data/nlu"
    os.makedirs(training_data_folder, exist_ok=True)
    training_data_file = Path(training_data_folder / "nlu.md")

    simple_nlu_md = """
        ## intent:greet
        - hey
        - hello
        """

    with open(training_data_file, "w") as f:
        f.write(simple_nlu_md)

    NLUMarkdownToYamlConverter().convert_and_write(training_data_file,
                                                   converted_data_folder)

    assert len(os.listdir(converted_data_folder)) == 1

    with open(f"{converted_data_folder}/nlu_converted.yml", "r") as f:
        content = f.read()
        assert content == (
            f'version: "{LATEST_TRAINING_DATA_FORMAT_VERSION}"\n'
            "nlu:\n"
            "- intent: greet\n"
            "  examples: |\n"
            "    - hey\n"
            "    - hello\n")
コード例 #2
0
def _convert_nlu_data(args: argparse.Namespace) -> None:
    from rasa.nlu.training_data.converters.nlu_markdown_to_yaml_converter import (
        NLUMarkdownToYamlConverter, )

    if args.format in ["json", "md"]:
        convert_training_data(args.data, args.out, args.format, args.language)
    elif args.format == "yaml":
        _convert_to_yaml(args, NLUMarkdownToYamlConverter())
    else:
        print_error_and_exit(
            "Could not recognize output format. Supported output formats: 'json', "
            "'md', 'yaml'. Specify the desired output format with '--format'.")
コード例 #3
0
ファイル: data.py プロジェクト: alexlana/rasa-1
def _convert_nlu_data(args: argparse.Namespace) -> None:
    from rasa.nlu.training_data.converters.nlu_markdown_to_yaml_converter import (
        NLUMarkdownToYamlConverter, )

    if args.format in ["json", "md"]:
        rasa.nlu.convert.convert_training_data(args.data, args.out,
                                               args.format, args.language)
        telemetry.track_data_convert(args.format, "nlu")
    elif args.format == "yaml":
        rasa.utils.common.run_in_loop(
            _convert_to_yaml(args, NLUMarkdownToYamlConverter()))
        telemetry.track_data_convert(args.format, "nlu")
    else:
        rasa.shared.utils.cli.print_error_and_exit(
            "Could not recognize output format. Supported output formats: 'json', "
            "'md', 'yaml'. Specify the desired output format with '--format'.")
コード例 #4
0
def test_nlu_lookup_tables_are_converted(tmpdir: Path):
    converted_data_folder = tmpdir / "converted_data"
    os.mkdir(converted_data_folder)

    training_data_folder = tmpdir / "data/nlu"
    os.makedirs(training_data_folder, exist_ok=True)
    training_data_file = Path(training_data_folder / "nlu.md")

    simple_nlu_md = f"""
    ## lookup:products.txt
      {tmpdir / "data/nlu/lookups/products.txt"}
    """

    with open(training_data_file, "w") as f:
        f.write(simple_nlu_md)

    lookup_data_folder = training_data_folder / "lookups"
    os.makedirs(lookup_data_folder, exist_ok=True)
    lookup_tables_file = lookup_data_folder / "products.txt"

    simple_lookup_table_txt = "core\n nlu\n x\n"

    with open(lookup_tables_file, "w") as f:
        f.write(simple_lookup_table_txt)

    NLUMarkdownToYamlConverter().convert_and_write(training_data_file,
                                                   converted_data_folder)

    assert len(os.listdir(converted_data_folder)) == 1

    with open(f"{converted_data_folder}/products_converted.yml", "r") as f:
        content = f.read()
        assert content == (
            f'version: "{LATEST_TRAINING_DATA_FORMAT_VERSION}"\n'
            "nlu:\n"
            "- lookup: products\n"
            "  examples: |\n"
            "    - core\n"
            "    - nlu\n"
            "    - x\n")
コード例 #5
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def test_converter_filters_correct_files(training_data_file: Text,
                                         should_filter: bool):
    assert should_filter == NLUMarkdownToYamlConverter.filter(
        Path(training_data_file))