libuplift.datasets.uis#

The UIS dataset from R quantreg package.

Functions#

fetch_uis([data_home, download_if_missing, ...])

Load the UIS (Unemployment Insurance Study) drug treatment dataset.

Module Contents#

libuplift.datasets.uis.fetch_uis(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 UIS (Unemployment Insurance Study) drug treatment dataset.

Use a local copy of the data.

This dataset comes from a randomized clinical trial comparing short (3 months) vs long (6 months) drug treatment programs for substance abuse. The main outcome is time to drug relapse.

Original source of the data is [1]. The version used here is from the R quantreg package [Rd50379bff5a4-2].

Treatment Variables

  • treatment: Treatment assignment (0 = Short, 1 = Long)

Target Variables

  • target_time: Time to drug relapse (days)

  • target_censor: Censoring status (1 = Returned to drugs or lost to follow-up, 0 = Otherwise)

  • target_log_time: Log of time to drug relapse (Y variable)

  • target_FRAC: Compliance fraction (LEN.T/90 for short treatment, LEN.T/180 for long treatment). This is included as another target variable since it is a post-randomization variable and sould not be used as predictor

Variables

  • AGE: Age at Enrollment (Years)

  • BECK: Beck Depression Score (0.000 - 54.000)

  • HC: Heroin/Cocaine Use During 3 Months Prior to Admission

    • 1 = Heroin & Cocaine

    • 2 = Heroin Only

    • 3 = Cocaine Only

    • 4 = Neither Heroin nor Cocaine

  • IV: History of IV Drug Use

    • 1 = Never

    • 2 = Previous

    • 3 = Recent

  • NDT: Number of Prior Drug Treatments (0 - 40)

  • RACE: Subject’s Race (0 = White, 1 = Non-White)

  • SITE: Treatment Site (0 = A, 1 = B)

  • LEN.T: Length of Stay in Treatment (Days)

  • ND1: Component of NDT

  • ND2: Component of NDT

  • LNDT: (Description not provided in original documentation)

  • IV3: Recent IV use (1 = Yes, 0 = No)

Changes to the original dataset

  • remove the ID attribute

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. Note that this returns a tuple with multiple target variables.

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.DESCRstring

Description of the dataset.

(data, target_time, target_censor, target_log_time, treatment)tuple if

return_X_y is True

References

[1]

S.M. Hammer, et al., “A Controlled Trial of Two Nucleoside Analogues plus Indinavir in Persons with Human Immunodeficiency Virus Infection and CD4 Cell Counts of 200 per Cubic Millimeter or Less”, New England Journal of Medicine, 337(11), 725–733, 1997 (https://www.nejm.org/doi/10.1056/NEJMoa040595).