dataset

OpenMLDataset

Bases: OpenMLBase

Dataset object.

Allows fetching and uploading datasets to OpenML.

Parameters:

Name	Type	Description	Default
name	str	Name of the dataset.	required
description	str	Description of the dataset.	required
data_format	str	Format of the dataset which can be either 'arff' or 'sparse_arff'.	'arff'
cache_format	str	Format for caching the dataset which can be either 'feather' or 'pickle'.	'pickle'
dataset_id	int	Id autogenerated by the server.	None
version	int	Version of this dataset. '1' for original version. Auto-incremented by server.	None
creator	str	The person who created the dataset.	None
contributor	str	People who contributed to the current version of the dataset.	None
collection_date	str	The date the data was originally collected, given by the uploader.	None
upload_date	str	The date-time when the dataset was uploaded, generated by server.	None
language	str	Language in which the data is represented. Starts with 1 upper case letter, rest lower case, e.g. 'English'.	None
licence	str	License of the data.	None
url	str	Valid URL, points to actual data file. The file can be on the OpenML server or another dataset repository.	None
default_target_attribute	str	The default target attribute, if it exists. Can have multiple values, comma separated.	None
row_id_attribute	str	The attribute that represents the row-id column, if present in the dataset.	None
ignore_attribute	str | list	Attributes that should be excluded in modelling, such as identifiers and indexes.	None
version_label	str	Version label provided by user. Can be a date, hash, or some other type of id.	None
citation	str	Reference(s) that should be cited when building on this data.	None
tag	str	Tags, describing the algorithms.	None
visibility	str	Who can see the dataset. Typical values: 'Everyone','All my friends','Only me'. Can also be any of the user's circles.	None
original_data_url	str	For derived data, the url to the original dataset.	None
paper_url	str	Link to a paper describing the dataset.	None
update_comment	str	An explanation for when the dataset is uploaded.	None
md5_checksum	str	MD5 checksum to check if the dataset is downloaded without corruption.	None
data_file	str	Path to where the dataset is located.	None
features_file	dict	A dictionary of dataset features, which maps a feature index to a OpenMLDataFeature.	None
qualities_file	dict	A dictionary of dataset qualities, which maps a quality name to a quality value.	None
dataset	str | None	Serialized arff dataset string.	None
parquet_url	str | None	This is the URL to the storage location where the dataset files are hosted. This can be a MinIO bucket URL. If specified, the data will be accessed from this URL when reading the files.	None
parquet_file	str | None	Path to the local file.	None

Source code in openml/datasets/dataset.py

class OpenMLDataset(OpenMLBase):
    """Dataset object.

    Allows fetching and uploading datasets to OpenML.

    Parameters
    ----------
    name : str
        Name of the dataset.
    description : str
        Description of the dataset.
    data_format : str
        Format of the dataset which can be either 'arff' or 'sparse_arff'.
    cache_format : str
        Format for caching the dataset which can be either 'feather' or 'pickle'.
    dataset_id : int, optional
        Id autogenerated by the server.
    version : int, optional
        Version of this dataset. '1' for original version.
        Auto-incremented by server.
    creator : str, optional
        The person who created the dataset.
    contributor : str, optional
        People who contributed to the current version of the dataset.
    collection_date : str, optional
        The date the data was originally collected, given by the uploader.
    upload_date : str, optional
        The date-time when the dataset was uploaded, generated by server.
    language : str, optional
        Language in which the data is represented.
        Starts with 1 upper case letter, rest lower case, e.g. 'English'.
    licence : str, optional
        License of the data.
    url : str, optional
        Valid URL, points to actual data file.
        The file can be on the OpenML server or another dataset repository.
    default_target_attribute : str, optional
        The default target attribute, if it exists.
        Can have multiple values, comma separated.
    row_id_attribute : str, optional
        The attribute that represents the row-id column,
        if present in the dataset.
    ignore_attribute : str | list, optional
        Attributes that should be excluded in modelling,
        such as identifiers and indexes.
    version_label : str, optional
        Version label provided by user.
        Can be a date, hash, or some other type of id.
    citation : str, optional
        Reference(s) that should be cited when building on this data.
    tag : str, optional
        Tags, describing the algorithms.
    visibility : str, optional
        Who can see the dataset.
        Typical values: 'Everyone','All my friends','Only me'.
        Can also be any of the user's circles.
    original_data_url : str, optional
        For derived data, the url to the original dataset.
    paper_url : str, optional
        Link to a paper describing the dataset.
    update_comment : str, optional
        An explanation for when the dataset is uploaded.
    md5_checksum : str, optional
        MD5 checksum to check if the dataset is downloaded without corruption.
    data_file : str, optional
        Path to where the dataset is located.
    features_file : dict, optional
        A dictionary of dataset features,
        which maps a feature index to a OpenMLDataFeature.
    qualities_file : dict, optional
        A dictionary of dataset qualities,
        which maps a quality name to a quality value.
    dataset: string, optional
        Serialized arff dataset string.
    parquet_url: string, optional
        This is the URL to the storage location where the dataset files are hosted.
        This can be a MinIO bucket URL. If specified, the data will be accessed
        from this URL when reading the files.
    parquet_file: string, optional
        Path to the local file.
    """

    def __init__(  # noqa: C901, PLR0912, PLR0913, PLR0915
        self,
        name: str,
        description: str | None,
        data_format: Literal["arff", "sparse_arff"] = "arff",
        cache_format: Literal["feather", "pickle"] = "pickle",
        dataset_id: int | None = None,
        version: int | None = None,
        creator: str | None = None,
        contributor: str | None = None,
        collection_date: str | None = None,
        upload_date: str | None = None,
        language: str | None = None,
        licence: str | None = None,
        url: str | None = None,
        default_target_attribute: str | None = None,
        row_id_attribute: str | None = None,
        ignore_attribute: str | list[str] | None = None,
        version_label: str | None = None,
        citation: str | None = None,
        tag: str | None = None,
        visibility: str | None = None,
        original_data_url: str | None = None,
        paper_url: str | None = None,
        update_comment: str | None = None,
        md5_checksum: str | None = None,
        data_file: str | None = None,
        features_file: str | None = None,
        qualities_file: str | None = None,
        dataset: str | None = None,
        parquet_url: str | None = None,
        parquet_file: str | None = None,
    ):
        if cache_format not in ["feather", "pickle"]:
            raise ValueError(
                "cache_format must be one of 'feather' or 'pickle. "
                f"Invalid format specified: {cache_format}",
            )

        def find_invalid_characters(string: str, pattern: str) -> str:
            invalid_chars = set()
            regex = re.compile(pattern)
            for char in string:
                if not regex.match(char):
                    invalid_chars.add(char)
            return ",".join(
                [f"'{char}'" if char != "'" else f'"{char}"' for char in invalid_chars],
            )

        if dataset_id is None:
            pattern = "^[\x00-\x7f]*$"
            if description and not re.match(pattern, description):
                # not basiclatin (XSD complains)
                invalid_characters = find_invalid_characters(description, pattern)
                raise ValueError(
                    f"Invalid symbols {invalid_characters} in description: {description}",
                )
            pattern = "^[\x00-\x7f]*$"
            if citation and not re.match(pattern, citation):
                # not basiclatin (XSD complains)
                invalid_characters = find_invalid_characters(citation, pattern)
                raise ValueError(
                    f"Invalid symbols {invalid_characters} in citation: {citation}",
                )
            pattern = "^[a-zA-Z0-9_\\-\\.\\(\\),]+$"
            if not re.match(pattern, name):
                # regex given by server in error message
                invalid_characters = find_invalid_characters(name, pattern)
                raise ValueError(f"Invalid symbols {invalid_characters} in name: {name}")

        self.ignore_attribute: list[str] | None = None
        if isinstance(ignore_attribute, str):
            self.ignore_attribute = [ignore_attribute]
        elif isinstance(ignore_attribute, list) or ignore_attribute is None:
            self.ignore_attribute = ignore_attribute
        else:
            raise ValueError("Wrong data type for ignore_attribute. Should be list.")

        # TODO add function to check if the name is casual_string128
        # Attributes received by querying the RESTful API
        self.dataset_id = int(dataset_id) if dataset_id is not None else None
        self.name = name
        self.version = int(version) if version is not None else None
        self.description = description
        self.cache_format = cache_format
        # Has to be called format, otherwise there will be an XML upload error
        self.format = data_format
        self.creator = creator
        self.contributor = contributor
        self.collection_date = collection_date
        self.upload_date = upload_date
        self.language = language
        self.licence = licence
        self.url = url
        self.default_target_attribute = default_target_attribute
        self.row_id_attribute = row_id_attribute

        self.version_label = version_label
        self.citation = citation
        self.tag = tag
        self.visibility = visibility
        self.original_data_url = original_data_url
        self.paper_url = paper_url
        self.update_comment = update_comment
        self.md5_checksum = md5_checksum
        self.data_file = data_file
        self.parquet_file = parquet_file
        self._dataset = dataset
        self._parquet_url = parquet_url

        self._features: dict[int, OpenMLDataFeature] | None = None
        self._qualities: dict[str, float] | None = None
        self._no_qualities_found = False

        if features_file is not None:
            self._features = _read_features(Path(features_file))

        # "" was the old default value by `get_dataset` and maybe still used by some
        if qualities_file == "":
            # TODO(0.15): to switch to "qualities_file is not None" below and remove warning
            warnings.warn(
                "Starting from Version 0.15 `qualities_file` must be None and not an empty string "
                "to avoid reading the qualities from file. Set `qualities_file` to None to avoid "
                "this warning.",
                FutureWarning,
                stacklevel=2,
            )
            qualities_file = None

        if qualities_file is not None:
            self._qualities = _read_qualities(Path(qualities_file))

        if data_file is not None:
            data_pickle, data_feather, feather_attribute = self._compressed_cache_file_paths(
                Path(data_file)
            )
            self.data_pickle_file = data_pickle if Path(data_pickle).exists() else None
            self.data_feather_file = data_feather if Path(data_feather).exists() else None
            self.feather_attribute_file = feather_attribute if Path(feather_attribute) else None
        else:
            self.data_pickle_file = None
            self.data_feather_file = None
            self.feather_attribute_file = None

    @property
    def features(self) -> dict[int, OpenMLDataFeature]:
        """Get the features of this dataset."""
        if self._features is None:
            # TODO(eddiebergman): These should return a value so we can set it to be not None
            self._load_features()

        assert self._features is not None
        return self._features

    @property
    def qualities(self) -> dict[str, float] | None:
        """Get the qualities of this dataset."""
        # TODO(eddiebergman): Better docstring, I don't know what qualities means

        # We have to check `_no_qualities_found` as there might not be qualities for a dataset
        if self._qualities is None and (not self._no_qualities_found):
            self._load_qualities()

        return self._qualities

    @property
    def id(self) -> int | None:
        """Get the dataset numeric id."""
        return self.dataset_id

    def _get_repr_body_fields(self) -> Sequence[tuple[str, str | int | None]]:
        """Collect all information to display in the __repr__ body."""
        # Obtain number of features in accordance with lazy loading.
        n_features: int | None = None
        if self._qualities is not None and self._qualities["NumberOfFeatures"] is not None:
            n_features = int(self._qualities["NumberOfFeatures"])
        elif self._features is not None:
            n_features = len(self._features)

        fields: dict[str, int | str | None] = {
            "Name": self.name,
            "Version": self.version,
            "Format": self.format,
            "Licence": self.licence,
            "Download URL": self.url,
            "Data file": str(self.data_file) if self.data_file is not None else None,
            "Pickle file": (
                str(self.data_pickle_file) if self.data_pickle_file is not None else None
            ),
            "# of features": n_features,
        }
        if self.upload_date is not None:
            fields["Upload Date"] = self.upload_date.replace("T", " ")
        if self.dataset_id is not None:
            fields["OpenML URL"] = self.openml_url
        if self._qualities is not None and self._qualities["NumberOfInstances"] is not None:
            fields["# of instances"] = int(self._qualities["NumberOfInstances"])

        # determines the order in which the information will be printed
        order = [
            "Name",
            "Version",
            "Format",
            "Upload Date",
            "Licence",
            "Download URL",
            "OpenML URL",
            "Data File",
            "Pickle File",
            "# of features",
            "# of instances",
        ]
        return [(key, fields[key]) for key in order if key in fields]

    def __eq__(self, other: Any) -> bool:
        if not isinstance(other, OpenMLDataset):
            return False

        server_fields = {
            "dataset_id",
            "version",
            "upload_date",
            "url",
            "_parquet_url",
            "dataset",
            "data_file",
            "format",
            "cache_format",
        }

        cache_fields = {
            "_dataset",
            "data_file",
            "data_pickle_file",
            "data_feather_file",
            "feather_attribute_file",
            "parquet_file",
        }

        # check that common keys and values are identical
        ignore_fields = server_fields | cache_fields
        self_keys = set(self.__dict__.keys()) - ignore_fields
        other_keys = set(other.__dict__.keys()) - ignore_fields
        return self_keys == other_keys and all(
            self.__dict__[key] == other.__dict__[key] for key in self_keys
        )

    def _download_data(self) -> None:
        """Download ARFF data file to standard cache directory. Set `self.data_file`."""
        # import required here to avoid circular import.
        from .functions import _get_dataset_arff, _get_dataset_parquet

        skip_parquet = os.environ.get(OPENML_SKIP_PARQUET_ENV_VAR, "false").casefold() == "true"
        if self._parquet_url is not None and not skip_parquet:
            parquet_file = _get_dataset_parquet(self)
            self.parquet_file = None if parquet_file is None else str(parquet_file)
        if self.parquet_file is None:
            self.data_file = str(_get_dataset_arff(self))

    def _get_arff(self, format: str) -> dict:  # noqa: A002
        """Read ARFF file and return decoded arff.

        Reads the file referenced in self.data_file.

        Parameters
        ----------
        format : str
            Format of the ARFF file.
            Must be one of 'arff' or 'sparse_arff' or a string that will be either of those
            when converted to lower case.



        Returns
        -------
        dict
            Decoded arff.

        """
        # TODO: add a partial read method which only returns the attribute
        # headers of the corresponding .arff file!
        import struct

        filename = self.data_file
        assert filename is not None
        filepath = Path(filename)

        bits = 8 * struct.calcsize("P")

        # Files can be considered too large on a 32-bit system,
        # if it exceeds 120mb (slightly more than covtype dataset size)
        # This number is somewhat arbitrary.
        if bits != 64:
            MB_120 = 120_000_000
            file_size = filepath.stat().st_size
            if file_size > MB_120:
                raise NotImplementedError(
                    f"File {filename} too big for {file_size}-bit system ({bits} bytes).",
                )

        if format.lower() == "arff":
            return_type = arff.DENSE
        elif format.lower() == "sparse_arff":
            return_type = arff.COO
        else:
            raise ValueError(f"Unknown data format {format}")

        def decode_arff(fh: Any) -> dict:
            decoder = arff.ArffDecoder()
            return decoder.decode(fh, encode_nominal=True, return_type=return_type)  # type: ignore

        if filepath.suffix.endswith(".gz"):
            with gzip.open(filename) as zipfile:
                return decode_arff(zipfile)
        else:
            with filepath.open(encoding="utf8") as fh:
                return decode_arff(fh)

    def _parse_data_from_arff(  # noqa: C901, PLR0912, PLR0915
        self,
        arff_file_path: Path,
    ) -> tuple[pd.DataFrame | scipy.sparse.csr_matrix, list[bool], list[str]]:
        """Parse all required data from arff file.

        Parameters
        ----------
        arff_file_path : str
            Path to the file on disk.

        Returns
        -------
        Tuple[Union[pd.DataFrame, scipy.sparse.csr_matrix], List[bool], List[str]]
            DataFrame or csr_matrix: dataset
            List[bool]: List indicating which columns contain categorical variables.
            List[str]: List of column names.
        """
        try:
            data = self._get_arff(self.format)
        except OSError as e:
            logger.critical(
                f"Please check that the data file {arff_file_path} is there and can be read.",
            )
            raise e

        ARFF_DTYPES_TO_PD_DTYPE = {
            "INTEGER": "integer",
            "REAL": "floating",
            "NUMERIC": "floating",
            "STRING": "string",
        }
        attribute_dtype = {}
        attribute_names = []
        categories_names = {}
        categorical = []
        for name, type_ in data["attributes"]:
            # if the feature is nominal and a sparse matrix is
            # requested, the categories need to be numeric
            if isinstance(type_, list) and self.format.lower() == "sparse_arff":
                try:
                    # checks if the strings which should be the class labels
                    # can be encoded into integers
                    pd.factorize(type_)[0]
                except ValueError as e:
                    raise ValueError(
                        "Categorical data needs to be numeric when using sparse ARFF."
                    ) from e

            # string can only be supported with pandas DataFrame
            elif type_ == "STRING" and self.format.lower() == "sparse_arff":
                raise ValueError("Dataset containing strings is not supported with sparse ARFF.")

            # infer the dtype from the ARFF header
            if isinstance(type_, list):
                categorical.append(True)
                categories_names[name] = type_
                if len(type_) == 2:
                    type_norm = [cat.lower().capitalize() for cat in type_]
                    if {"True", "False"} == set(type_norm):
                        categories_names[name] = [cat == "True" for cat in type_norm]
                        attribute_dtype[name] = "boolean"
                    else:
                        attribute_dtype[name] = "categorical"
                else:
                    attribute_dtype[name] = "categorical"
            else:
                categorical.append(False)
                attribute_dtype[name] = ARFF_DTYPES_TO_PD_DTYPE[type_]
            attribute_names.append(name)

        if self.format.lower() == "sparse_arff":
            X = data["data"]
            X_shape = (max(X[1]) + 1, max(X[2]) + 1)
            X = scipy.sparse.coo_matrix((X[0], (X[1], X[2])), shape=X_shape, dtype=np.float32)
            X = X.tocsr()
        elif self.format.lower() == "arff":
            X = pd.DataFrame(data["data"], columns=attribute_names)

            col = []
            for column_name in X.columns:
                if attribute_dtype[column_name] in ("categorical", "boolean"):
                    categories = self._unpack_categories(
                        X[column_name],  # type: ignore
                        categories_names[column_name],
                    )
                    col.append(categories)
                elif attribute_dtype[column_name] in ("floating", "integer"):
                    X_col = X[column_name]
                    if X_col.min() >= 0 and X_col.max() <= 255:
                        try:
                            X_col_uint = X_col.astype("uint8")
                            if (X_col == X_col_uint).all():
                                col.append(X_col_uint)
                                continue
                        except ValueError:
                            pass
                    col.append(X[column_name])
                else:
                    col.append(X[column_name])
            X = pd.concat(col, axis=1)
        else:
            raise ValueError(f"Dataset format '{self.format}' is not a valid format.")

        return X, categorical, attribute_names  # type: ignore

    def _compressed_cache_file_paths(self, data_file: Path) -> tuple[Path, Path, Path]:
        data_pickle_file = data_file.with_suffix(".pkl.py3")
        data_feather_file = data_file.with_suffix(".feather")
        feather_attribute_file = data_file.with_suffix(".feather.attributes.pkl.py3")
        return data_pickle_file, data_feather_file, feather_attribute_file

    def _cache_compressed_file_from_file(
        self,
        data_file: Path,
    ) -> tuple[pd.DataFrame | scipy.sparse.csr_matrix, list[bool], list[str]]:
        """Store data from the local file in compressed format.

        If a local parquet file is present it will be used instead of the arff file.
        Sets cache_format to 'pickle' if data is sparse.
        """
        (
            data_pickle_file,
            data_feather_file,
            feather_attribute_file,
        ) = self._compressed_cache_file_paths(data_file)

        attribute_names, categorical, data = self._parse_data_from_file(data_file)

        # Feather format does not work for sparse datasets, so we use pickle for sparse datasets
        if scipy.sparse.issparse(data):
            self.cache_format = "pickle"

        logger.info(f"{self.cache_format} write {self.name}")
        if self.cache_format == "feather":
            assert isinstance(data, pd.DataFrame)

            data.to_feather(data_feather_file)
            with open(feather_attribute_file, "wb") as fh:  # noqa: PTH123
                pickle.dump((categorical, attribute_names), fh, pickle.HIGHEST_PROTOCOL)
            self.data_feather_file = data_feather_file
            self.feather_attribute_file = feather_attribute_file

        else:
            with open(data_pickle_file, "wb") as fh:  # noqa: PTH123
                pickle.dump((data, categorical, attribute_names), fh, pickle.HIGHEST_PROTOCOL)
            self.data_pickle_file = data_pickle_file

        data_file = data_pickle_file if self.cache_format == "pickle" else data_feather_file
        logger.debug(f"Saved dataset {int(self.dataset_id or -1)}: {self.name} to file {data_file}")

        return data, categorical, attribute_names

    def _parse_data_from_file(
        self,
        data_file: Path,
    ) -> tuple[list[str], list[bool], pd.DataFrame | scipy.sparse.csr_matrix]:
        if data_file.suffix == ".arff":
            data, categorical, attribute_names = self._parse_data_from_arff(data_file)
        elif data_file.suffix == ".pq":
            attribute_names, categorical, data = self._parse_data_from_pq(data_file)
        else:
            raise ValueError(f"Unknown file type for file '{data_file}'.")

        return attribute_names, categorical, data

    def _parse_data_from_pq(self, data_file: Path) -> tuple[list[str], list[bool], pd.DataFrame]:
        try:
            data = pd.read_parquet(data_file)
        except Exception as e:
            raise Exception(f"File: {data_file}") from e
        categorical = [data[c].dtype.name == "category" for c in data.columns]
        attribute_names = list(data.columns)
        return attribute_names, categorical, data

    def _load_data(self) -> tuple[pd.DataFrame, list[bool], list[str]]:  # noqa: PLR0912, C901, PLR0915
        """Load data from compressed format or arff. Download data if not present on disk."""
        need_to_create_pickle = self.cache_format == "pickle" and self.data_pickle_file is None
        need_to_create_feather = self.cache_format == "feather" and self.data_feather_file is None

        if need_to_create_pickle or need_to_create_feather:
            if self.data_file is None:
                self._download_data()

            file_to_load = self.data_file if self.parquet_file is None else self.parquet_file
            assert file_to_load is not None
            data, cats, attrs = self._cache_compressed_file_from_file(Path(file_to_load))
            return _ensure_dataframe(data, attrs), cats, attrs

        # helper variable to help identify where errors occur
        fpath = self.data_feather_file if self.cache_format == "feather" else self.data_pickle_file
        logger.info(f"{self.cache_format} load data {self.name}")
        try:
            if self.cache_format == "feather":
                assert self.data_feather_file is not None
                assert self.feather_attribute_file is not None

                data = pd.read_feather(self.data_feather_file)
                fpath = self.feather_attribute_file
                with self.feather_attribute_file.open("rb") as fh:
                    categorical, attribute_names = pickle.load(fh)  # noqa: S301
            else:
                assert self.data_pickle_file is not None
                with self.data_pickle_file.open("rb") as fh:
                    data, categorical, attribute_names = pickle.load(fh)  # noqa: S301

        except FileNotFoundError as e:
            raise ValueError(
                f"Cannot find file for dataset {self.name} at location '{fpath}'."
            ) from e
        except (EOFError, ModuleNotFoundError, ValueError, AttributeError) as e:
            error_message = getattr(e, "message", e.args[0])
            hint = ""

            if isinstance(e, EOFError):
                readable_error = "Detected a corrupt cache file"
            elif isinstance(e, (ModuleNotFoundError, AttributeError)):
                readable_error = "Detected likely dependency issues"
                hint = (
                    "This can happen if the cache was constructed with a different pandas version "
                    "than the one that is used to load the data. See also "
                )
                if isinstance(e, ModuleNotFoundError):
                    hint += "https://github.com/openml/openml-python/issues/918. "
                elif isinstance(e, AttributeError):
                    hint += "https://github.com/openml/openml-python/pull/1121. "

            elif isinstance(e, ValueError) and "unsupported pickle protocol" in e.args[0]:
                readable_error = "Encountered unsupported pickle protocol"
            else:
                raise e

            logger.warning(
                f"{readable_error} when loading dataset {self.id} from '{fpath}'. "
                f"{hint}"
                f"Error message was: {error_message}. "
                "We will continue loading data from the arff-file, "
                "but this will be much slower for big datasets. "
                "Please manually delete the cache file if you want OpenML-Python "
                "to attempt to reconstruct it.",
            )
            file_to_load = self.data_file if self.parquet_file is None else self.parquet_file
            assert file_to_load is not None
            attr, cat, df = self._parse_data_from_file(Path(file_to_load))
            return _ensure_dataframe(df), cat, attr

        data_up_to_date = isinstance(data, pd.DataFrame) or scipy.sparse.issparse(data)
        if self.cache_format == "pickle" and not data_up_to_date:
            logger.info("Updating outdated pickle file.")
            file_to_load = self.data_file if self.parquet_file is None else self.parquet_file
            assert file_to_load is not None

            data, cats, attrs = self._cache_compressed_file_from_file(Path(file_to_load))

        return _ensure_dataframe(data, attribute_names), categorical, attribute_names

    @staticmethod
    def _unpack_categories(series: pd.Series, categories: list) -> pd.Series:
        # nan-likes can not be explicitly specified as a category
        def valid_category(cat: Any) -> bool:
            return isinstance(cat, str) or (cat is not None and not np.isnan(cat))

        filtered_categories = [c for c in categories if valid_category(c)]
        col = []
        for x in series:
            try:
                col.append(categories[int(x)])
            except (TypeError, ValueError):
                col.append(np.nan)

        # We require two lines to create a series of categories as detailed here:
        # https://pandas.pydata.org/pandas-docs/version/0.24/user_guide/categorical.html#series-creation
        raw_cat = pd.Categorical(col, ordered=True, categories=filtered_categories)
        return pd.Series(raw_cat, index=series.index, name=series.name)

    def get_data(  # noqa: C901
        self,
        target: list[str] | str | None = None,
        include_row_id: bool = False,  # noqa: FBT001, FBT002
        include_ignore_attribute: bool = False,  # noqa: FBT001, FBT002
    ) -> tuple[pd.DataFrame, pd.Series | None, list[bool], list[str]]:
        """Returns dataset content as dataframes.

        Parameters
        ----------
        target : string, List[str] or None (default=None)
            Name of target column to separate from the data.
            Splitting multiple columns is currently not supported.
        include_row_id : boolean (default=False)
            Whether to include row ids in the returned dataset.
        include_ignore_attribute : boolean (default=False)
            Whether to include columns that are marked as "ignore"
            on the server in the dataset.


        Returns
        -------
        X : dataframe, shape (n_samples, n_columns)
            Dataset, may have sparse dtypes in the columns if required.
        y : pd.Series, shape (n_samples, ) or None
            Target column
        categorical_indicator : list[bool]
            Mask that indicate categorical features.
        attribute_names : list[str]
            List of attribute names.
        """
        data, categorical_mask, attribute_names = self._load_data()

        to_exclude = []
        if not include_row_id and self.row_id_attribute is not None:
            if isinstance(self.row_id_attribute, str):
                to_exclude.append(self.row_id_attribute)
            elif isinstance(self.row_id_attribute, Iterable):
                to_exclude.extend(self.row_id_attribute)

        if not include_ignore_attribute and self.ignore_attribute is not None:
            if isinstance(self.ignore_attribute, str):
                to_exclude.append(self.ignore_attribute)
            elif isinstance(self.ignore_attribute, Iterable):
                to_exclude.extend(self.ignore_attribute)

        if len(to_exclude) > 0:
            logger.info(f"Going to remove the following attributes: {to_exclude}")
            keep = np.array([column not in to_exclude for column in attribute_names])
            data = data.drop(columns=to_exclude)
            categorical_mask = [cat for cat, k in zip(categorical_mask, keep) if k]
            attribute_names = [att for att, k in zip(attribute_names, keep) if k]

        if target is None:
            return data, None, categorical_mask, attribute_names

        if isinstance(target, str):
            target_names = target.split(",") if "," in target else [target]
        else:
            target_names = target

        # All the assumptions below for the target are dependant on the number of targets being 1
        n_targets = len(target_names)
        if n_targets > 1:
            raise NotImplementedError(f"Number of targets {n_targets} not implemented.")

        target_name = target_names[0]
        x = data.drop(columns=[target_name])
        y = data[target_name].squeeze()

        # Finally, remove the target from the list of attributes and categorical mask
        target_index = attribute_names.index(target_name)
        categorical_mask.pop(target_index)
        attribute_names.remove(target_name)

        assert isinstance(y, pd.Series)
        return x, y, categorical_mask, attribute_names

    def _load_features(self) -> None:
        """Load the features metadata from the server and store it in the dataset object."""
        # Delayed Import to avoid circular imports or having to import all of dataset.functions to
        # import OpenMLDataset.
        from openml.datasets.functions import _get_dataset_features_file

        if self.dataset_id is None:
            raise ValueError(
                "No dataset id specified. Please set the dataset id. Otherwise we cannot load "
                "metadata.",
            )

        features_file = _get_dataset_features_file(None, self.dataset_id)
        self._features = _read_features(features_file)

    def _load_qualities(self) -> None:
        """Load qualities information from the server and store it in the dataset object."""
        # same reason as above for _load_features
        from openml.datasets.functions import _get_dataset_qualities_file

        if self.dataset_id is None:
            raise ValueError(
                "No dataset id specified. Please set the dataset id. Otherwise we cannot load "
                "metadata.",
            )

        qualities_file = _get_dataset_qualities_file(None, self.dataset_id)

        if qualities_file is None:
            self._no_qualities_found = True
        else:
            self._qualities = _read_qualities(qualities_file)

    def retrieve_class_labels(self, target_name: str = "class") -> None | list[str]:
        """Reads the datasets arff to determine the class-labels.

        If the task has no class labels (for example a regression problem)
        it returns None. Necessary because the data returned by get_data
        only contains the indices of the classes, while OpenML needs the real
        classname when uploading the results of a run.

        Parameters
        ----------
        target_name : str
            Name of the target attribute

        Returns
        -------
        list
        """
        for feature in self.features.values():
            if feature.name == target_name:
                if feature.data_type == "nominal":
                    return feature.nominal_values

                if feature.data_type == "string":
                    # Rel.: #1311
                    # The target is invalid for a classification task if the feature type is string
                    # and not nominal. For such miss-configured tasks, we silently fix it here as
                    # we can safely interpreter string as nominal.
                    df, *_ = self.get_data()
                    return list(df[feature.name].unique())

        return None

    def get_features_by_type(  # noqa: C901
        self,
        data_type: str,
        exclude: list[str] | None = None,
        exclude_ignore_attribute: bool = True,  # noqa: FBT002, FBT001
        exclude_row_id_attribute: bool = True,  # noqa: FBT002, FBT001
    ) -> list[int]:
        """
        Return indices of features of a given type, e.g. all nominal features.
        Optional parameters to exclude various features by index or ontology.

        Parameters
        ----------
        data_type : str
            The data type to return (e.g., nominal, numeric, date, string)
        exclude : list(int)
            List of columns to exclude from the return value
        exclude_ignore_attribute : bool
            Whether to exclude the defined ignore attributes (and adapt the
            return values as if these indices are not present)
        exclude_row_id_attribute : bool
            Whether to exclude the defined row id attributes (and adapt the
            return values as if these indices are not present)

        Returns
        -------
        result : list
            a list of indices that have the specified data type
        """
        if data_type not in OpenMLDataFeature.LEGAL_DATA_TYPES:
            raise TypeError("Illegal feature type requested")
        if self.ignore_attribute is not None and not isinstance(self.ignore_attribute, list):
            raise TypeError("ignore_attribute should be a list")
        if self.row_id_attribute is not None and not isinstance(self.row_id_attribute, str):
            raise TypeError("row id attribute should be a str")
        if exclude is not None and not isinstance(exclude, list):
            raise TypeError("Exclude should be a list")
            # assert all(isinstance(elem, str) for elem in exclude),
            #            "Exclude should be a list of strings"
        to_exclude = []
        if exclude is not None:
            to_exclude.extend(exclude)
        if exclude_ignore_attribute and self.ignore_attribute is not None:
            to_exclude.extend(self.ignore_attribute)
        if exclude_row_id_attribute and self.row_id_attribute is not None:
            to_exclude.append(self.row_id_attribute)

        result = []
        offset = 0
        # this function assumes that everything in to_exclude will
        # be 'excluded' from the dataset (hence the offset)
        for idx in self.features:
            name = self.features[idx].name
            if name in to_exclude:
                offset += 1
            elif self.features[idx].data_type == data_type:
                result.append(idx - offset)
        return result

    def _get_file_elements(self) -> dict:
        """Adds the 'dataset' to file elements."""
        file_elements: dict = {}
        path = None if self.data_file is None else Path(self.data_file).absolute()

        if self._dataset is not None:
            file_elements["dataset"] = self._dataset
        elif path is not None and path.exists():
            with path.open("rb") as fp:
                file_elements["dataset"] = fp.read()

            try:
                dataset_utf8 = str(file_elements["dataset"], encoding="utf8")
                arff.ArffDecoder().decode(dataset_utf8, encode_nominal=True)
            except arff.ArffException as e:
                raise ValueError("The file you have provided is not a valid arff file.") from e

        elif self.url is None:
            raise ValueError("No valid url/path to the data file was given.")
        return file_elements

    def _parse_publish_response(self, xml_response: dict) -> None:
        """Parse the id from the xml_response and assign it to self."""
        self.dataset_id = int(xml_response["oml:upload_data_set"]["oml:id"])

    def _to_dict(self) -> dict[str, dict]:
        """Creates a dictionary representation of self."""
        props = [
            "id",
            "name",
            "version",
            "description",
            "format",
            "creator",
            "contributor",
            "collection_date",
            "upload_date",
            "language",
            "licence",
            "url",
            "default_target_attribute",
            "row_id_attribute",
            "ignore_attribute",
            "version_label",
            "citation",
            "tag",
            "visibility",
            "original_data_url",
            "paper_url",
            "update_comment",
            "md5_checksum",
        ]

        prop_values = {}
        for prop in props:
            content = getattr(self, prop, None)
            if content is not None:
                prop_values["oml:" + prop] = content

        return {
            "oml:data_set_description": {
                "@xmlns:oml": "http://openml.org/openml",
                **prop_values,
            }
        }

features: dict[int, OpenMLDataFeature] property

Get the features of this dataset.

id: int | None property

Get the dataset numeric id.

qualities: dict[str, float] | None property

Get the qualities of this dataset.

get_data(target=None, include_row_id=False, include_ignore_attribute=False)

Returns dataset content as dataframes.

Parameters:

Name	Type	Description	Default
target	(string, List[str] or None(default=None))	Name of target column to separate from the data. Splitting multiple columns is currently not supported.	None
include_row_id	boolean(default=False)	Whether to include row ids in the returned dataset.	False
include_ignore_attribute	boolean(default=False)	Whether to include columns that are marked as "ignore" on the server in the dataset.	False

Returns:

Name	Type	Description
X	(dataframe, shape(n_samples, n_columns))	Dataset, may have sparse dtypes in the columns if required.
y	(Series, shape(n_samples) or None)	Target column
categorical_indicator	list[bool]	Mask that indicate categorical features.
attribute_names	list[str]	List of attribute names.

Source code in openml/datasets/dataset.py

def get_data(  # noqa: C901
    self,
    target: list[str] | str | None = None,
    include_row_id: bool = False,  # noqa: FBT001, FBT002
    include_ignore_attribute: bool = False,  # noqa: FBT001, FBT002
) -> tuple[pd.DataFrame, pd.Series | None, list[bool], list[str]]:
    """Returns dataset content as dataframes.

    Parameters
    ----------
    target : string, List[str] or None (default=None)
        Name of target column to separate from the data.
        Splitting multiple columns is currently not supported.
    include_row_id : boolean (default=False)
        Whether to include row ids in the returned dataset.
    include_ignore_attribute : boolean (default=False)
        Whether to include columns that are marked as "ignore"
        on the server in the dataset.


    Returns
    -------
    X : dataframe, shape (n_samples, n_columns)
        Dataset, may have sparse dtypes in the columns if required.
    y : pd.Series, shape (n_samples, ) or None
        Target column
    categorical_indicator : list[bool]
        Mask that indicate categorical features.
    attribute_names : list[str]
        List of attribute names.
    """
    data, categorical_mask, attribute_names = self._load_data()

    to_exclude = []
    if not include_row_id and self.row_id_attribute is not None:
        if isinstance(self.row_id_attribute, str):
            to_exclude.append(self.row_id_attribute)
        elif isinstance(self.row_id_attribute, Iterable):
            to_exclude.extend(self.row_id_attribute)

    if not include_ignore_attribute and self.ignore_attribute is not None:
        if isinstance(self.ignore_attribute, str):
            to_exclude.append(self.ignore_attribute)
        elif isinstance(self.ignore_attribute, Iterable):
            to_exclude.extend(self.ignore_attribute)

    if len(to_exclude) > 0:
        logger.info(f"Going to remove the following attributes: {to_exclude}")
        keep = np.array([column not in to_exclude for column in attribute_names])
        data = data.drop(columns=to_exclude)
        categorical_mask = [cat for cat, k in zip(categorical_mask, keep) if k]
        attribute_names = [att for att, k in zip(attribute_names, keep) if k]

    if target is None:
        return data, None, categorical_mask, attribute_names

    if isinstance(target, str):
        target_names = target.split(",") if "," in target else [target]
    else:
        target_names = target

    # All the assumptions below for the target are dependant on the number of targets being 1
    n_targets = len(target_names)
    if n_targets > 1:
        raise NotImplementedError(f"Number of targets {n_targets} not implemented.")

    target_name = target_names[0]
    x = data.drop(columns=[target_name])
    y = data[target_name].squeeze()

    # Finally, remove the target from the list of attributes and categorical mask
    target_index = attribute_names.index(target_name)
    categorical_mask.pop(target_index)
    attribute_names.remove(target_name)

    assert isinstance(y, pd.Series)
    return x, y, categorical_mask, attribute_names

get_features_by_type(data_type, exclude=None, exclude_ignore_attribute=True, exclude_row_id_attribute=True)

Return indices of features of a given type, e.g. all nominal features. Optional parameters to exclude various features by index or ontology.

Parameters:

Name	Type	Description	Default
data_type	str	The data type to return (e.g., nominal, numeric, date, string)	required
exclude	list(int)	List of columns to exclude from the return value	None
exclude_ignore_attribute	bool	Whether to exclude the defined ignore attributes (and adapt the return values as if these indices are not present)	True
exclude_row_id_attribute	bool	Whether to exclude the defined row id attributes (and adapt the return values as if these indices are not present)	True

Returns:

Name	Type	Description
result	list	a list of indices that have the specified data type

Source code in openml/datasets/dataset.py

def get_features_by_type(  # noqa: C901
    self,
    data_type: str,
    exclude: list[str] | None = None,
    exclude_ignore_attribute: bool = True,  # noqa: FBT002, FBT001
    exclude_row_id_attribute: bool = True,  # noqa: FBT002, FBT001
) -> list[int]:
    """
    Return indices of features of a given type, e.g. all nominal features.
    Optional parameters to exclude various features by index or ontology.

    Parameters
    ----------
    data_type : str
        The data type to return (e.g., nominal, numeric, date, string)
    exclude : list(int)
        List of columns to exclude from the return value
    exclude_ignore_attribute : bool
        Whether to exclude the defined ignore attributes (and adapt the
        return values as if these indices are not present)
    exclude_row_id_attribute : bool
        Whether to exclude the defined row id attributes (and adapt the
        return values as if these indices are not present)

    Returns
    -------
    result : list
        a list of indices that have the specified data type
    """
    if data_type not in OpenMLDataFeature.LEGAL_DATA_TYPES:
        raise TypeError("Illegal feature type requested")
    if self.ignore_attribute is not None and not isinstance(self.ignore_attribute, list):
        raise TypeError("ignore_attribute should be a list")
    if self.row_id_attribute is not None and not isinstance(self.row_id_attribute, str):
        raise TypeError("row id attribute should be a str")
    if exclude is not None and not isinstance(exclude, list):
        raise TypeError("Exclude should be a list")
        # assert all(isinstance(elem, str) for elem in exclude),
        #            "Exclude should be a list of strings"
    to_exclude = []
    if exclude is not None:
        to_exclude.extend(exclude)
    if exclude_ignore_attribute and self.ignore_attribute is not None:
        to_exclude.extend(self.ignore_attribute)
    if exclude_row_id_attribute and self.row_id_attribute is not None:
        to_exclude.append(self.row_id_attribute)

    result = []
    offset = 0
    # this function assumes that everything in to_exclude will
    # be 'excluded' from the dataset (hence the offset)
    for idx in self.features:
        name = self.features[idx].name
        if name in to_exclude:
            offset += 1
        elif self.features[idx].data_type == data_type:
            result.append(idx - offset)
    return result

retrieve_class_labels(target_name='class')

Reads the datasets arff to determine the class-labels.

If the task has no class labels (for example a regression problem) it returns None. Necessary because the data returned by get_data only contains the indices of the classes, while OpenML needs the real classname when uploading the results of a run.

Parameters:

Name	Type	Description	Default
target_name	str	Name of the target attribute	'class'

Returns:

Type	Description
list

Source code in openml/datasets/dataset.py

def retrieve_class_labels(self, target_name: str = "class") -> None | list[str]:
    """Reads the datasets arff to determine the class-labels.

    If the task has no class labels (for example a regression problem)
    it returns None. Necessary because the data returned by get_data
    only contains the indices of the classes, while OpenML needs the real
    classname when uploading the results of a run.

    Parameters
    ----------
    target_name : str
        Name of the target attribute

    Returns
    -------
    list
    """
    for feature in self.features.values():
        if feature.name == target_name:
            if feature.data_type == "nominal":
                return feature.nominal_values

            if feature.data_type == "string":
                # Rel.: #1311
                # The target is invalid for a classification task if the feature type is string
                # and not nominal. For such miss-configured tasks, we silently fix it here as
                # we can safely interpreter string as nominal.
                df, *_ = self.get_data()
                return list(df[feature.name].unique())

    return None