# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import gzip import hashlib import json import os import shutil import time from contextlib import closing from functools import wraps from os.path import join from tempfile import TemporaryDirectory from typing import Any, Callable, Dict, List, Optional, Tuple, Union from urllib.error import HTTPError, URLError from urllib.request import Request, urlopen from warnings import warn import numpy as np from ..utils import Bunch from ..utils._optional_dependencies import check_pandas_support # noqa from ..utils._param_validation import ( Integral, Interval, Real, StrOptions, validate_params, ) from . import get_data_home from ._arff_parser import load_arff_from_gzip_file __all__ = ["fetch_openml"] _OPENML_PREFIX = "https://api.openml.org/" _SEARCH_NAME = "api/v1/json/data/list/data_name/{}/limit/2" _DATA_INFO = "api/v1/json/data/{}" _DATA_FEATURES = "api/v1/json/data/features/{}" _DATA_QUALITIES = "api/v1/json/data/qualities/{}" _DATA_FILE = "data/v1/download/{}" OpenmlQualitiesType = List[Dict[str, str]] OpenmlFeaturesType = List[Dict[str, str]] def _get_local_path(openml_path: str, data_home: str) -> str: return os.path.join(data_home, "openml.org", openml_path + ".gz") def _retry_with_clean_cache( openml_path: str, data_home: Optional[str], no_retry_exception: Optional[Exception] = None, ) -> Callable: """If the first call to the decorated function fails, the local cached file is removed, and the function is called again. If ``data_home`` is ``None``, then the function is called once. We can provide a specific exception to not retry on using `no_retry_exception` parameter. """ def decorator(f): @wraps(f) def wrapper(*args, **kw): if data_home is None: return f(*args, **kw) try: return f(*args, **kw) except URLError: raise except Exception as exc: if no_retry_exception is not None and isinstance( exc, no_retry_exception ): raise warn("Invalid cache, redownloading file", RuntimeWarning) local_path = _get_local_path(openml_path, data_home) if os.path.exists(local_path): os.unlink(local_path) return f(*args, **kw) return wrapper return decorator def _retry_on_network_error( n_retries: int = 3, delay: float = 1.0, url: str = "" ) -> Callable: """If the function call results in a network error, call the function again up to ``n_retries`` times with a ``delay`` between each call. If the error has a 412 status code, don't call the function again as this is a specific OpenML error. The url parameter is used to give more information to the user about the error. """ def decorator(f): @wraps(f) def wrapper(*args, **kwargs): retry_counter = n_retries while True: try: return f(*args, **kwargs) except (URLError, TimeoutError) as e: # 412 is a specific OpenML error code. if isinstance(e, HTTPError) and e.code == 412: raise if retry_counter == 0: raise warn( f"A network error occurred while downloading {url}. Retrying..." ) retry_counter -= 1 time.sleep(delay) return wrapper return decorator def _open_openml_url( openml_path: str, data_home: Optional[str], n_retries: int = 3, delay: float = 1.0 ): """ Returns a resource from OpenML.org. Caches it to data_home if required. Parameters ---------- openml_path : str OpenML URL that will be accessed. This will be prefixes with _OPENML_PREFIX. data_home : str Directory to which the files will be cached. If None, no caching will be applied. n_retries : int, default=3 Number of retries when HTTP errors are encountered. Error with status code 412 won't be retried as they represent OpenML generic errors. delay : float, default=1.0 Number of seconds between retries. Returns ------- result : stream A stream to the OpenML resource. """ def is_gzip_encoded(_fsrc): return _fsrc.info().get("Content-Encoding", "") == "gzip" req = Request(_OPENML_PREFIX + openml_path) req.add_header("Accept-encoding", "gzip") if data_home is None: fsrc = _retry_on_network_error(n_retries, delay, req.full_url)(urlopen)(req) if is_gzip_encoded(fsrc): return gzip.GzipFile(fileobj=fsrc, mode="rb") return fsrc local_path = _get_local_path(openml_path, data_home) dir_name, file_name = os.path.split(local_path) if not os.path.exists(local_path): os.makedirs(dir_name, exist_ok=True) try: # Create a tmpdir as a subfolder of dir_name where the final file will # be moved to if the download is successful. This guarantees that the # renaming operation to the final location is atomic to ensure the # concurrence safety of the dataset caching mechanism. with TemporaryDirectory(dir=dir_name) as tmpdir: with closing( _retry_on_network_error(n_retries, delay, req.full_url)(urlopen)( req ) ) as fsrc: opener: Callable if is_gzip_encoded(fsrc): opener = open else: opener = gzip.GzipFile with opener(os.path.join(tmpdir, file_name), "wb") as fdst: shutil.copyfileobj(fsrc, fdst) shutil.move(fdst.name, local_path) except Exception: if os.path.exists(local_path): os.unlink(local_path) raise # XXX: First time, decompression will not be necessary (by using fsrc), but # it will happen nonetheless return gzip.GzipFile(local_path, "rb") class OpenMLError(ValueError): """HTTP 412 is a specific OpenML error code, indicating a generic error""" pass def _get_json_content_from_openml_api( url: str, error_message: Optional[str], data_home: Optional[str], n_retries: int = 3, delay: float = 1.0, ) -> Dict: """ Loads json data from the openml api. Parameters ---------- url : str The URL to load from. Should be an official OpenML endpoint. error_message : str or None The error message to raise if an acceptable OpenML error is thrown (acceptable error is, e.g., data id not found. Other errors, like 404's will throw the native error message). data_home : str or None Location to cache the response. None if no cache is required. n_retries : int, default=3 Number of retries when HTTP errors are encountered. Error with status code 412 won't be retried as they represent OpenML generic errors. delay : float, default=1.0 Number of seconds between retries. Returns ------- json_data : json the json result from the OpenML server if the call was successful. An exception otherwise. """ @_retry_with_clean_cache(url, data_home=data_home) def _load_json(): with closing( _open_openml_url(url, data_home, n_retries=n_retries, delay=delay) ) as response: return json.loads(response.read().decode("utf-8")) try: return _load_json() except HTTPError as error: # 412 is an OpenML specific error code, indicating a generic error # (e.g., data not found) if error.code != 412: raise error # 412 error, not in except for nicer traceback raise OpenMLError(error_message) def _get_data_info_by_name( name: str, version: Union[int, str], data_home: Optional[str], n_retries: int = 3, delay: float = 1.0, ): """ Utilizes the openml dataset listing api to find a dataset by name/version OpenML api function: https://www.openml.org/api_docs#!/data/get_data_list_data_name_data_name Parameters ---------- name : str name of the dataset version : int or str If version is an integer, the exact name/version will be obtained from OpenML. If version is a string (value: "active") it will take the first version from OpenML that is annotated as active. Any other string values except "active" are treated as integer. data_home : str or None Location to cache the response. None if no cache is required. n_retries : int, default=3 Number of retries when HTTP errors are encountered. Error with status code 412 won't be retried as they represent OpenML generic errors. delay : float, default=1.0 Number of seconds between retries. Returns ------- first_dataset : json json representation of the first dataset object that adhired to the search criteria """ if version == "active": # situation in which we return the oldest active version url = _SEARCH_NAME.format(name) + "/status/active/" error_msg = "No active dataset {} found.".format(name) json_data = _get_json_content_from_openml_api( url, error_msg, data_home=data_home, n_retries=n_retries, delay=delay, ) res = json_data["data"]["dataset"] if len(res) > 1: first_version = version = res[0]["version"] warning_msg = ( "Multiple active versions of the dataset matching the name" f" {name} exist. Versions may be fundamentally different, " f"returning version {first_version}. " "Available versions:\n" ) for r in res: warning_msg += f"- version {r['version']}, status: {r['status']}\n" warning_msg += ( f" url: https://www.openml.org/search?type=data&id={r['did']}\n" ) warn(warning_msg) return res[0] # an integer version has been provided url = (_SEARCH_NAME + "/data_version/{}").format(name, version) try: json_data = _get_json_content_from_openml_api( url, error_message=None, data_home=data_home, n_retries=n_retries, delay=delay, ) except OpenMLError: # we can do this in 1 function call if OpenML does not require the # specification of the dataset status (i.e., return datasets with a # given name / version regardless of active, deactivated, etc. ) # TODO: feature request OpenML. url += "/status/deactivated" error_msg = "Dataset {} with version {} not found.".format(name, version) json_data = _get_json_content_from_openml_api( url, error_msg, data_home=data_home, n_retries=n_retries, delay=delay, ) return json_data["data"]["dataset"][0] def _get_data_description_by_id( data_id: int, data_home: Optional[str], n_retries: int = 3, delay: float = 1.0, ) -> Dict[str, Any]: # OpenML API function: https://www.openml.org/api_docs#!/data/get_data_id url = _DATA_INFO.format(data_id) error_message = "Dataset with data_id {} not found.".format(data_id) json_data = _get_json_content_from_openml_api( url, error_message, data_home=data_home, n_retries=n_retries, delay=delay, ) return json_data["data_set_description"] def _get_data_features( data_id: int, data_home: Optional[str], n_retries: int = 3, delay: float = 1.0, ) -> OpenmlFeaturesType: # OpenML function: # https://www.openml.org/api_docs#!/data/get_data_features_id url = _DATA_FEATURES.format(data_id) error_message = "Dataset with data_id {} not found.".format(data_id) json_data = _get_json_content_from_openml_api( url, error_message, data_home=data_home, n_retries=n_retries, delay=delay, ) return json_data["data_features"]["feature"] def _get_data_qualities( data_id: int, data_home: Optional[str], n_retries: int = 3, delay: float = 1.0, ) -> OpenmlQualitiesType: # OpenML API function: # https://www.openml.org/api_docs#!/data/get_data_qualities_id url = _DATA_QUALITIES.format(data_id) error_message = "Dataset with data_id {} not found.".format(data_id) json_data = _get_json_content_from_openml_api( url, error_message, data_home=data_home, n_retries=n_retries, delay=delay, ) # the qualities might not be available, but we still try to process # the data return json_data.get("data_qualities", {}).get("quality", []) def _get_num_samples(data_qualities: OpenmlQualitiesType) -> int: """Get the number of samples from data qualities. Parameters ---------- data_qualities : list of dict Used to retrieve the number of instances (samples) in the dataset. Returns ------- n_samples : int The number of samples in the dataset or -1 if data qualities are unavailable. """ # If the data qualities are unavailable, we return -1 default_n_samples = -1 qualities = {d["name"]: d["value"] for d in data_qualities} return int(float(qualities.get("NumberOfInstances", default_n_samples))) def _load_arff_response( url: str, data_home: Optional[str], parser: str, output_type: str, openml_columns_info: dict, feature_names_to_select: List[str], target_names_to_select: List[str], shape: Optional[Tuple[int, int]], md5_checksum: str, n_retries: int = 3, delay: float = 1.0, read_csv_kwargs: Optional[Dict] = None, ): """Load the ARFF data associated with the OpenML URL. In addition of loading the data, this function will also check the integrity of the downloaded file from OpenML using MD5 checksum. Parameters ---------- url : str The URL of the ARFF file on OpenML. data_home : str The location where to cache the data. parser : {"liac-arff", "pandas"} The parser used to parse the ARFF file. output_type : {"numpy", "pandas", "sparse"} The type of the arrays that will be returned. The possibilities are: - `"numpy"`: both `X` and `y` will be NumPy arrays; - `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array; - `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a pandas Series or DataFrame. openml_columns_info : dict The information provided by OpenML regarding the columns of the ARFF file. feature_names_to_select : list of str The list of the features to be selected. target_names_to_select : list of str The list of the target variables to be selected. shape : tuple or None With `parser="liac-arff"`, when using a generator to load the data, one needs to provide the shape of the data beforehand. md5_checksum : str The MD5 checksum provided by OpenML to check the data integrity. n_retries : int, default=3 The number of times to retry downloading the data if it fails. delay : float, default=1.0 The delay between two consecutive downloads in seconds. read_csv_kwargs : dict, default=None Keyword arguments to pass to `pandas.read_csv` when using the pandas parser. It allows to overwrite the default options. .. versionadded:: 1.3 Returns ------- X : {ndarray, sparse matrix, dataframe} The data matrix. y : {ndarray, dataframe, series} The target. frame : dataframe or None A dataframe containing both `X` and `y`. `None` if `output_array_type != "pandas"`. categories : list of str or None The names of the features that are categorical. `None` if `output_array_type == "pandas"`. """ gzip_file = _open_openml_url(url, data_home, n_retries=n_retries, delay=delay) with closing(gzip_file): md5 = hashlib.md5() for chunk in iter(lambda: gzip_file.read(4096), b""): md5.update(chunk) actual_md5_checksum = md5.hexdigest() if actual_md5_checksum != md5_checksum: raise ValueError( f"md5 checksum of local file for {url} does not match description: " f"expected: {md5_checksum} but got {actual_md5_checksum}. " "Downloaded file could have been modified / corrupted, clean cache " "and retry..." ) def _open_url_and_load_gzip_file(url, data_home, n_retries, delay, arff_params): gzip_file = _open_openml_url(url, data_home, n_retries=n_retries, delay=delay) with closing(gzip_file): return load_arff_from_gzip_file(gzip_file, **arff_params) arff_params: Dict = dict( parser=parser, output_type=output_type, openml_columns_info=openml_columns_info, feature_names_to_select=feature_names_to_select, target_names_to_select=target_names_to_select, shape=shape, read_csv_kwargs=read_csv_kwargs or {}, ) try: X, y, frame, categories = _open_url_and_load_gzip_file( url, data_home, n_retries, delay, arff_params ) except Exception as exc: if parser != "pandas": raise from pandas.errors import ParserError if not isinstance(exc, ParserError): raise # A parsing error could come from providing the wrong quotechar # to pandas. By default, we use a double quote. Thus, we retry # with a single quote before to raise the error. arff_params["read_csv_kwargs"].update(quotechar="'") X, y, frame, categories = _open_url_and_load_gzip_file( url, data_home, n_retries, delay, arff_params ) return X, y, frame, categories def _download_data_to_bunch( url: str, sparse: bool, data_home: Optional[str], *, as_frame: bool, openml_columns_info: List[dict], data_columns: List[str], target_columns: List[str], shape: Optional[Tuple[int, int]], md5_checksum: str, n_retries: int = 3, delay: float = 1.0, parser: str, read_csv_kwargs: Optional[Dict] = None, ): """Download ARFF data, load it to a specific container and create to Bunch. This function has a mechanism to retry/cache/clean the data. Parameters ---------- url : str The URL of the ARFF file on OpenML. sparse : bool Whether the dataset is expected to use the sparse ARFF format. data_home : str The location where to cache the data. as_frame : bool Whether or not to return the data into a pandas DataFrame. openml_columns_info : list of dict The information regarding the columns provided by OpenML for the ARFF dataset. The information is stored as a list of dictionaries. data_columns : list of str The list of the features to be selected. target_columns : list of str The list of the target variables to be selected. shape : tuple or None With `parser="liac-arff"`, when using a generator to load the data, one needs to provide the shape of the data beforehand. md5_checksum : str The MD5 checksum provided by OpenML to check the data integrity. n_retries : int, default=3 Number of retries when HTTP errors are encountered. Error with status code 412 won't be retried as they represent OpenML generic errors. delay : float, default=1.0 Number of seconds between retries. parser : {"liac-arff", "pandas"} The parser used to parse the ARFF file. read_csv_kwargs : dict, default=None Keyword arguments to pass to `pandas.read_csv` when using the pandas parser. It allows to overwrite the default options. .. versionadded:: 1.3 Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. X : {ndarray, sparse matrix, dataframe} The data matrix. y : {ndarray, dataframe, series} The target. frame : dataframe or None A dataframe containing both `X` and `y`. `None` if `output_array_type != "pandas"`. categories : list of str or None The names of the features that are categorical. `None` if `output_array_type == "pandas"`. """ # Prepare which columns and data types should be returned for the X and y features_dict = {feature["name"]: feature for feature in openml_columns_info} if sparse: output_type = "sparse" elif as_frame: output_type = "pandas" else: output_type = "numpy" # XXX: target columns should all be categorical or all numeric _verify_target_data_type(features_dict, target_columns) for name in target_columns: column_info = features_dict[name] n_missing_values = int(column_info["number_of_missing_values"]) if n_missing_values > 0: raise ValueError( f"Target column '{column_info['name']}' has {n_missing_values} missing " "values. Missing values are not supported for target columns." ) no_retry_exception = None if parser == "pandas": # If we get a ParserError with pandas, then we don't want to retry and we raise # early. from pandas.errors import ParserError no_retry_exception = ParserError X, y, frame, categories = _retry_with_clean_cache( url, data_home, no_retry_exception )(_load_arff_response)( url, data_home, parser=parser, output_type=output_type, openml_columns_info=features_dict, feature_names_to_select=data_columns, target_names_to_select=target_columns, shape=shape, md5_checksum=md5_checksum, n_retries=n_retries, delay=delay, read_csv_kwargs=read_csv_kwargs, ) return Bunch( data=X, target=y, frame=frame, categories=categories, feature_names=data_columns, target_names=target_columns, ) def _verify_target_data_type(features_dict, target_columns): # verifies the data type of the y array in case there are multiple targets # (throws an error if these targets do not comply with sklearn support) if not isinstance(target_columns, list): raise ValueError("target_column should be list, got: %s" % type(target_columns)) found_types = set() for target_column in target_columns: if target_column not in features_dict: raise KeyError(f"Could not find target_column='{target_column}'") if features_dict[target_column]["data_type"] == "numeric": found_types.add(np.float64) else: found_types.add(object) # note: we compare to a string, not boolean if features_dict[target_column]["is_ignore"] == "true": warn(f"target_column='{target_column}' has flag is_ignore.") if features_dict[target_column]["is_row_identifier"] == "true": warn(f"target_column='{target_column}' has flag is_row_identifier.") if len(found_types) > 1: raise ValueError( "Can only handle homogeneous multi-target datasets, " "i.e., all targets are either numeric or " "categorical." ) def _valid_data_column_names(features_list, target_columns): # logic for determining on which columns can be learned. Note that from the # OpenML guide follows that columns that have the `is_row_identifier` or # `is_ignore` flag, these can not be learned on. Also target columns are # excluded. valid_data_column_names = [] for feature in features_list: if ( feature["name"] not in target_columns and feature["is_ignore"] != "true" and feature["is_row_identifier"] != "true" ): valid_data_column_names.append(feature["name"]) return valid_data_column_names @validate_params( { "name": [str, None], "version": [Interval(Integral, 1, None, closed="left"), StrOptions({"active"})], "data_id": [Interval(Integral, 1, None, closed="left"), None], "data_home": [str, os.PathLike, None], "target_column": [str, list, None], "cache": [bool], "return_X_y": [bool], "as_frame": [bool, StrOptions({"auto"})], "n_retries": [Interval(Integral, 1, None, closed="left")], "delay": [Interval(Real, 0.0, None, closed="neither")], "parser": [ StrOptions({"auto", "pandas", "liac-arff"}), ], "read_csv_kwargs": [dict, None], }, prefer_skip_nested_validation=True, ) def fetch_openml( name: Optional[str] = None, *, version: Union[str, int] = "active", data_id: Optional[int] = None, data_home: Optional[Union[str, os.PathLike]] = None, target_column: Optional[Union[str, List]] = "default-target", cache: bool = True, return_X_y: bool = False, as_frame: Union[str, bool] = "auto", n_retries: int = 3, delay: float = 1.0, parser: str = "auto", read_csv_kwargs: Optional[Dict] = None, ): """Fetch dataset from openml by name or dataset id. Datasets are uniquely identified by either an integer ID or by a combination of name and version (i.e. there might be multiple versions of the 'iris' dataset). Please give either name or data_id (not both). In case a name is given, a version can also be provided. Read more in the :ref:`User Guide `. .. versionadded:: 0.20 .. note:: EXPERIMENTAL The API is experimental (particularly the return value structure), and might have small backward-incompatible changes without notice or warning in future releases. Parameters ---------- name : str, default=None String identifier of the dataset. Note that OpenML can have multiple datasets with the same name. version : int or 'active', default='active' Version of the dataset. Can only be provided if also ``name`` is given. If 'active' the oldest version that's still active is used. Since there may be more than one active version of a dataset, and those versions may fundamentally be different from one another, setting an exact version is highly recommended. data_id : int, default=None OpenML ID of the dataset. The most specific way of retrieving a dataset. If data_id is not given, name (and potential version) are used to obtain a dataset. data_home : str or path-like, default=None Specify another download and cache folder for the data sets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. target_column : str, list or None, default='default-target' Specify the column name in the data to use as target. If 'default-target', the standard target column a stored on the server is used. If ``None``, all columns are returned as data and the target is ``None``. If list (of strings), all columns with these names are returned as multi-target (Note: not all scikit-learn classifiers can handle all types of multi-output combinations). cache : bool, default=True Whether to cache the downloaded datasets into `data_home`. return_X_y : bool, default=False If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` objects. as_frame : bool or 'auto', default='auto' If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. The Bunch will contain a ``frame`` attribute with the target and the data. If ``return_X_y`` is True, then ``(data, target)`` will be pandas DataFrames or Series as describe above. If `as_frame` is 'auto', the data and target will be converted to DataFrame or Series as if `as_frame` is set to True, unless the dataset is stored in sparse format. If `as_frame` is False, the data and target will be NumPy arrays and the `data` will only contain numerical values when `parser="liac-arff"` where the categories are provided in the attribute `categories` of the `Bunch` instance. When `parser="pandas"`, no ordinal encoding is made. .. versionchanged:: 0.24 The default value of `as_frame` changed from `False` to `'auto'` in 0.24. n_retries : int, default=3 Number of retries when HTTP errors or network timeouts are encountered. Error with status code 412 won't be retried as they represent OpenML generic errors. delay : float, default=1.0 Number of seconds between retries. parser : {"auto", "pandas", "liac-arff"}, default="auto" Parser used to load the ARFF file. Two parsers are implemented: - `"pandas"`: this is the most efficient parser. However, it requires pandas to be installed and can only open dense datasets. - `"liac-arff"`: this is a pure Python ARFF parser that is much less memory- and CPU-efficient. It deals with sparse ARFF datasets. If `"auto"`, the parser is chosen automatically such that `"liac-arff"` is selected for sparse ARFF datasets, otherwise `"pandas"` is selected. .. versionadded:: 1.2 .. versionchanged:: 1.4 The default value of `parser` changes from `"liac-arff"` to `"auto"`. read_csv_kwargs : dict, default=None Keyword arguments passed to :func:`pandas.read_csv` when loading the data from a ARFF file and using the pandas parser. It can allow to overwrite some default parameters. .. versionadded:: 1.3 Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. data : np.array, scipy.sparse.csr_matrix of floats, or pandas DataFrame The feature matrix. Categorical features are encoded as ordinals. target : np.array, pandas Series or DataFrame The regression target or classification labels, if applicable. Dtype is float if numeric, and object if categorical. If ``as_frame`` is True, ``target`` is a pandas object. DESCR : str The full description of the dataset. feature_names : list The names of the dataset columns. target_names: list The names of the target columns. .. versionadded:: 0.22 categories : dict or None Maps each categorical feature name to a list of values, such that the value encoded as i is ith in the list. If ``as_frame`` is True, this is None. details : dict More metadata from OpenML. frame : pandas DataFrame Only present when `as_frame=True`. DataFrame with ``data`` and ``target``. (data, target) : tuple if ``return_X_y`` is True .. note:: EXPERIMENTAL This interface is **experimental** and subsequent releases may change attributes without notice (although there should only be minor changes to ``data`` and ``target``). Missing values in the 'data' are represented as NaN's. Missing values in 'target' are represented as NaN's (numerical target) or None (categorical target). Notes ----- The `"pandas"` and `"liac-arff"` parsers can lead to different data types in the output. The notable differences are the following: - The `"liac-arff"` parser always encodes categorical features as `str` objects. To the contrary, the `"pandas"` parser instead infers the type while reading and numerical categories will be casted into integers whenever possible. - The `"liac-arff"` parser uses float64 to encode numerical features tagged as 'REAL' and 'NUMERICAL' in the metadata. The `"pandas"` parser instead infers if these numerical features corresponds to integers and uses panda's Integer extension dtype. - In particular, classification datasets with integer categories are typically loaded as such `(0, 1, ...)` with the `"pandas"` parser while `"liac-arff"` will force the use of string encoded class labels such as `"0"`, `"1"` and so on. - The `"pandas"` parser will not strip single quotes - i.e. `'` - from string columns. For instance, a string `'my string'` will be kept as is while the `"liac-arff"` parser will strip the single quotes. For categorical columns, the single quotes are stripped from the values. In addition, when `as_frame=False` is used, the `"liac-arff"` parser returns ordinally encoded data where the categories are provided in the attribute `categories` of the `Bunch` instance. Instead, `"pandas"` returns a NumPy array were the categories are not encoded. Examples -------- >>> from sklearn.datasets import fetch_openml >>> adult = fetch_openml("adult", version=2) # doctest: +SKIP >>> adult.frame.info() # doctest: +SKIP RangeIndex: 48842 entries, 0 to 48841 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 age 48842 non-null int64 1 workclass 46043 non-null category 2 fnlwgt 48842 non-null int64 3 education 48842 non-null category 4 education-num 48842 non-null int64 5 marital-status 48842 non-null category 6 occupation 46033 non-null category 7 relationship 48842 non-null category 8 race 48842 non-null category 9 sex 48842 non-null category 10 capital-gain 48842 non-null int64 11 capital-loss 48842 non-null int64 12 hours-per-week 48842 non-null int64 13 native-country 47985 non-null category 14 class 48842 non-null category dtypes: category(9), int64(6) memory usage: 2.7 MB """ if cache is False: # no caching will be applied data_home = None else: data_home = get_data_home(data_home=data_home) data_home = join(str(data_home), "openml") # check valid function arguments. data_id XOR (name, version) should be # provided if name is not None: # OpenML is case-insensitive, but the caching mechanism is not # convert all data names (str) to lower case name = name.lower() if data_id is not None: raise ValueError( "Dataset data_id={} and name={} passed, but you can only " "specify a numeric data_id or a name, not " "both.".format(data_id, name) ) data_info = _get_data_info_by_name( name, version, data_home, n_retries=n_retries, delay=delay ) data_id = data_info["did"] elif data_id is not None: # from the previous if statement, it is given that name is None if version != "active": raise ValueError( "Dataset data_id={} and version={} passed, but you can only " "specify a numeric data_id or a version, not " "both.".format(data_id, version) ) else: raise ValueError( "Neither name nor data_id are provided. Please provide name or data_id." ) data_description = _get_data_description_by_id(data_id, data_home) if data_description["status"] != "active": warn( "Version {} of dataset {} is inactive, meaning that issues have " "been found in the dataset. Try using a newer version from " "this URL: {}".format( data_description["version"], data_description["name"], data_description["url"], ) ) if "error" in data_description: warn( "OpenML registered a problem with the dataset. It might be " "unusable. Error: {}".format(data_description["error"]) ) if "warning" in data_description: warn( "OpenML raised a warning on the dataset. It might be " "unusable. Warning: {}".format(data_description["warning"]) ) return_sparse = data_description["format"].lower() == "sparse_arff" as_frame = not return_sparse if as_frame == "auto" else as_frame if parser == "auto": parser_ = "liac-arff" if return_sparse else "pandas" else: parser_ = parser if parser_ == "pandas": try: check_pandas_support("`fetch_openml`") except ImportError as exc: if as_frame: err_msg = ( "Returning pandas objects requires pandas to be installed. " "Alternatively, explicitly set `as_frame=False` and " "`parser='liac-arff'`." ) else: err_msg = ( f"Using `parser={parser!r}` with dense data requires pandas to be " "installed. Alternatively, explicitly set `parser='liac-arff'`." ) raise ImportError(err_msg) from exc if return_sparse: if as_frame: raise ValueError( "Sparse ARFF datasets cannot be loaded with as_frame=True. " "Use as_frame=False or as_frame='auto' instead." ) if parser_ == "pandas": raise ValueError( f"Sparse ARFF datasets cannot be loaded with parser={parser!r}. " "Use parser='liac-arff' or parser='auto' instead." ) # download data features, meta-info about column types features_list = _get_data_features(data_id, data_home) if not as_frame: for feature in features_list: if "true" in (feature["is_ignore"], feature["is_row_identifier"]): continue if feature["data_type"] == "string": raise ValueError( "STRING attributes are not supported for " "array representation. Try as_frame=True" ) if target_column == "default-target": # determines the default target based on the data feature results # (which is currently more reliable than the data description; # see issue: https://github.com/openml/OpenML/issues/768) target_columns = [ feature["name"] for feature in features_list if feature["is_target"] == "true" ] elif isinstance(target_column, str): # for code-simplicity, make target_column by default a list target_columns = [target_column] elif target_column is None: target_columns = [] else: # target_column already is of type list target_columns = target_column data_columns = _valid_data_column_names(features_list, target_columns) shape: Optional[Tuple[int, int]] # determine arff encoding to return if not return_sparse: # The shape must include the ignored features to keep the right indexes # during the arff data conversion. data_qualities = _get_data_qualities(data_id, data_home) shape = _get_num_samples(data_qualities), len(features_list) else: shape = None # obtain the data url = _DATA_FILE.format(data_description["file_id"]) bunch = _download_data_to_bunch( url, return_sparse, data_home, as_frame=bool(as_frame), openml_columns_info=features_list, shape=shape, target_columns=target_columns, data_columns=data_columns, md5_checksum=data_description["md5_checksum"], n_retries=n_retries, delay=delay, parser=parser_, read_csv_kwargs=read_csv_kwargs, ) if return_X_y: return bunch.data, bunch.target description = "{}\n\nDownloaded from openml.org.".format( data_description.pop("description") ) bunch.update( DESCR=description, details=data_description, url="https://www.openml.org/d/{}".format(data_id), ) return bunch