simpte

  • Description:

Full name: Simulations for Personalized Treatment Effects

Generated with the R's Uplift package: https://rdrr.io/cran/uplift/man/sim_pte.html

The package could be downloaded here: https://cran.r-project.org/src/contrib/Archive/uplift/

Dataset generated in R version 4.1.2 with following code:

  library(uplift)

  set.seed(123)

  train <- sim_pte(n = 1000, p = 20, rho = 0, sigma = sqrt(2), beta.den = 4)
  test <- sim_pte(n = 2000, p = 20, rho = 0, sigma = sqrt(2), beta.den = 4)

  train$treat <- ifelse(train$treat == 1, 2, 1)
  test$treat <- ifelse(test$treat == 1, 2, 1)

  train$y <- ifelse(train$y == 1, 2, 1)
  test$y <- ifelse(test$y == 1, 2, 1)

  train$ts = NULL
  test$ts = NULL

Parameters:

  • n = number of samples
  • p = number of predictors
  • ro = covariance between predictors
  • sigma = mutiplier of the error term
  • beta.den = beta is mutiplied by 1/beta.den

Creator: Leo Guelman leo.guelman@gmail.com

  • Homepage: https://rdrr.io/cran/uplift/man/sim_pte.html

  • Source code: tfds.datasets.simpte.Builder

  • Versions:

    • 1.0.0 (default): Initial release.

  • Download size: Unknown size

  • Dataset size: 1.04 MiB

  • Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
    Please download training data: sim_pte_train.csv and test data: sim_pte_test.csv to ~/tensorflow_datasets/downloads/manual/.

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'test' 2,000
'train' 1,000
  • Feature structure:
FeaturesDict({
    'X1': float32,
    'X10': float32,
    'X11': float32,
    'X12': float32,
    'X13': float32,
    'X14': float32,
    'X15': float32,
    'X16': float32,
    'X17': float32,
    'X18': float32,
    'X19': float32,
    'X2': float32,
    'X20': float32,
    'X3': float32,
    'X4': float32,
    'X5': float32,
    'X6': float32,
    'X7': float32,
    'X8': float32,
    'X9': float32,
    'treat': int32,
    'y': int32,
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
X1 Tensor float32
X10 Tensor float32
X11 Tensor float32
X12 Tensor float32
X13 Tensor float32
X14 Tensor float32
X15 Tensor float32
X16 Tensor float32
X17 Tensor float32
X18 Tensor float32
X19 Tensor float32
X2 Tensor float32
X20 Tensor float32
X3 Tensor float32
X4 Tensor float32
X5 Tensor float32
X6 Tensor float32
X7 Tensor float32
X8 Tensor float32
X9 Tensor float32
treat Tensor int32
y Tensor int32

  • Supervised keys (See as_supervised doc): ({'X1': 'X1', 'X10': 'X10', 'X11': 'X11', 'X12': 'X12', 'X13': 'X13', 'X14': 'X14', 'X15': 'X15', 'X16': 'X16', 'X17': 'X17', 'X18': 'X18', 'X19': 'X19', 'X2': 'X2', 'X20': 'X20', 'X3': 'X3', 'X4': 'X4', 'X5': 'X5', 'X6': 'X6', 'X7': 'X7', 'X8': 'X8', 'X9': 'X9', 'treat': 'treat'}, 'y')

  • Figure (tfds.show_examples): Not supported.

  • Examples (tfds.as_dataframe):

  • Citation:
@misc{https://doi.org/10.48550/arxiv.1212.2995,
  doi = {10.48550/ARXIV.1212.2995},
  url = {https://arxiv.org/abs/1212.2995},
  author = {Tian, Lu and Alizadeh, Ash and Gentles, Andrew and Tibshirani, Robert},
  keywords = {Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates},
  publisher = {arXiv},
  year = {2012},
  copyright = {arXiv.org perpetual, non-exclusive license}
}