"""An A/B testing dataset from Kaggle.
"""
import numpy as np
from .base import _fetch_remote_csv
from .base import RemoteFileMetadata
ARCHIVE = RemoteFileMetadata(
filename="marketing_AB.csv.gz",
url=('https://github.com/jszymon/uplift_sklearn_data/releases/download/marketing_AB/marketing_AB.csv.gz'),
checksum=('a318767a73785b7e54fbf36cbf300411'
'5f61e943844b7ac8d3753a893e093d9d'))
[docs]
def fetch_marketing_AB(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_AB dataset from Kaggle.
Download it if necessary.
See https://www.kaggle.com/datasets/faviovaz/marketing-ab-testing
for details.
The treatment was showing the user an advertisement ('ad'), the
control showing a Public Service Announcement ('psa').
The dataset exhibits very high class and treatment imbalance.
Changes made to the original dataset:
* removed record number column
* changed spaces to _ in column names
* changed target from bool to {0,1}
Parameters
----------
data_home : string, 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_missing : boolean, 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_state : int, RandomState instance or None (default)
Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
shuffle : bool, default=False
Whether to shuffle dataset.
categ_as_strings : bool, default=False
Whether to return categorical variables as strings.
return_X_y : boolean, default=False.
If True, returns ``(data.data, data.target)`` instead of a Bunch
object.
as_frame : boolean, 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
-------
dataset : dict-like object with the following attributes:
dataset.data : numpy array
Each row corresponds to the features in the dataset.
dataset.target_purchase : numpy array
Indicator whether a purchase was made.
dataset.DESCR : string
Description of the dataset.
(data, target_purchase) : tuple if
``return_X_y`` is True
"""
# dictionaries
treatment_values = ['psa', 'ad']
most_ads_day_values = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
# attribute descriptions
treatment_descr = [("treatment", treatment_values, "test_group")]
target_descr = [("target_converted", np.int32, "converted"),]
feature_descr = [("user_id", float),
("total_ads", float),
("most_ads_day", most_ads_day_values),
("most_ads_hour", np.int32),
]
ret = _fetch_remote_csv(ARCHIVE, "marketing_AB",
feature_attrs=feature_descr,
treatment_attrs=treatment_descr,
target_attrs=target_descr,
categ_as_strings=categ_as_strings,
return_X_y=return_X_y, as_frame=as_frame,
download_if_missing=download_if_missing,
random_state=random_state, shuffle=shuffle,
total_attrs=6,
)
if not return_X_y:
ret.descr = __doc__
return ret