sklearn.datasets.fetch_openml

sklearn.datasets.fetch_openml(name: Optional[str] = None, *, version: Union[str, int] = 'active', data_id: Optional[int] = None, data_home: Optional[str] = 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: Optional[str] = 'warn')[source]

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 User Guide.

New in version 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
namestr, default=None

String identifier of the dataset. Note that OpenML can have multiple datasets with the same name.

versionint 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_idint, 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_homestr, 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_columnstr, 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).

cachebool, default=True

Whether to cache the downloaded datasets into data_home.

return_X_ybool, default=False

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target objects.

as_framebool 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.

Changed in version 0.24: The default value of as_frame changed from False to 'auto' in 0.24.

n_retriesint, 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.

delayfloat, default=1.0

Number of seconds between retries.

parser{“auto”, “pandas”, “liac-arff”}, default=”liac-arff”

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 dataset.

If "auto" (future default), the parser is chosen automatically such that "liac-arff" is selected for sparse ARFF datasets, otherwise "pandas" is selected.

New in version 1.2.

Changed in version 1.4: The default value of parser will change from "liac-arff" to "auto" in 1.4. You can set parser="auto" to silence this warning. Therefore, an ImportError will be raised from 1.4 if the dataset is dense and pandas is not installed.

Returns
dataBunch

Dictionary-like object, with the following attributes.

datanp.array, scipy.sparse.csr_matrix of floats, or pandas DataFrame

The feature matrix. Categorical features are encoded as ordinals.

targetnp.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.

DESCRstr

The full description of the dataset.

feature_nameslist

The names of the dataset columns.

target_names: list

The names of the target columns.

New in version 0.22.

categoriesdict 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.

detailsdict

More metadata from OpenML.

framepandas 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.

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 using sklearn.datasets.fetch_openml