libuplift.datasets.Criteo#
Criteo online advertising dataset.
See https://ailab.criteo.com/criteo-uplift-prediction-dataset/ for details.
Functions#
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Load the Criteo dataset. |
Module Contents#
- libuplift.datasets.Criteo.fetch_Criteo(data_home=None, download_if_missing=True, random_state=None, shuffle=False, categ_as_strings=False, return_X_y=False, as_frame=False)[source]#
Load the Criteo dataset.
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.target_visitnumpy array
Each value is 1 if website visit occurred 0 otherwise.
- dataset.target_conversionnumpy array
Each value is 1 if purchase occurred 0 otherwise.
- dataset.target_exposurenumpy array
Whether the user has been exposed to the ad
- dataset.DESCRstring
Description of the dataset.
- (data, target_conversion, target_visit, target_exposure)tuple if
return_X_yis True