`sklearn.datasets`.fetch_openml#

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 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_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=”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.

New in version 1.2.

Changed in version 1.4: The default value of parser changes from "liac-arff" to "auto".

read_csv_kwargsdict, default=None

Keyword arguments passed to pandas.read_csv when loading the data from a ARFF file and using the pandas parser. It can allow to overwrite some default parameters.

New in version 1.3.

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.
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)  
>>> adult.frame.info()  
<class 'pandas.core.frame.DataFrame'>
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

Examples using `sklearn.datasets.fetch_openml`#

Release Highlights for scikit-learn 1.4

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 0.22

Categorical Feature Support in Gradient Boosting

Combine predictors using stacking

Image denoising using kernel PCA

Lagged features for time series forecasting

Time-related feature engineering

Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)

Early stopping of Stochastic Gradient Descent

MNIST classification using multinomial logistic + L1

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Common pitfalls in the interpretation of coefficients of linear models

Partial Dependence and Individual Conditional Expectation Plots

Permutation Importance vs Random Forest Feature Importance (MDI)

Evaluation of outlier detection estimators

Introducing the set_output API

Visualizations with Display Objects

Overview of multiclass training meta-estimators

Multilabel classification using a classifier chain

Approximate nearest neighbors in TSNE

Visualization of MLP weights on MNIST

Column Transformer with Mixed Types

Effect of transforming the targets in regression model

Comparing Target Encoder with Other Encoders

sklearn.datasets.fetch_openml#

Examples using sklearn.datasets.fetch_openml#

`sklearn.datasets`.fetch_openml#

Examples using `sklearn.datasets.fetch_openml`#