libuplift.datasets.Information

The marketing campaign dataset from the CRAN Information package by Kim Larsen.

See: https://cran.r-project.org/web/packages/Information/index.html for details.

Functions

fetch_Information([version, data_home, ...])

Load the marketing campaign dataset from the CRAN Information

Module Contents

libuplift.datasets.Information.fetch_Information(version='train', data_home=None, download_if_missing=True, random_state=None, shuffle=False, categ_as_strings=False, return_X_y=False, as_frame=False)

Load the marketing campaign dataset from the CRAN Information package by Kim Larsen.

See: https://cran.r-project.org/web/packages/Information/index.html

Two datasets are available: “train” and “validation”. Use version argument to select.

Download it if necessary.

Parameters:

data_homestring, optional

Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.

download_if_missingboolean, default=True

If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

random_stateint, RandomState instance or None (default)

Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls.

shufflebool, default=False

Whether to shuffle dataset.

categ_as_stringsbool, default=False

Whether to return categorical variables as strings.

return_X_yboolean, default=False.

If True, returns (data.data, data.target) instead of a Bunch object.

as_frameboolean, default=False

If True features are returned as pandas DataFrame. If False features are returned as object or float array. Float array is returned if all features are floats.

Returns:

datasetdict-like object with the following attributes:

dataset.datanumpy array

Each row corresponds to the features in the dataset.

dataset.targetnumpy array

Each value is 1 if a purchase was made 0 otherwise.

dataset.DESCRstring

Description of the dataset.

(data, target_conversion, target_visit, target_exposure)tuple if

return_X_y is True