Can't create an entity set: AttributeError: 'EntitySet' object has no attribute 'entity_from_dataframe'

[jcalebsmith]

When following the instructions in the README, under the 'Creating and EntitySet' heading. The following code results in an error:

library(featuretoolsR)
library(magrittr)

set_1 <- data.frame(key = 1:100, value = sample(letters, 100, T), a = rep(Sys.Date(), 100))
set_2 <- data.frame(key = 1:100, value = sample(LETTERS, 100, T), b = rep(Sys.time(), 100))

es <- as_entityset(
  set_1, 
  index = "key", 
  entity_id = "set_1", 
  id = "demo", 
  time_index = "a"
)

The error states:

Error in py_get_attr_impl(x, name, silent) : 
  AttributeError: 'EntitySet' object has no attribute 'entity_from_dataframe'

Session Info:

> sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] magrittr_2.0.2      dplyr_1.0.8         foreign_0.8-81      featuretoolsR_0.4.4

loaded via a namespace (and not attached):
 [1] reticulate_1.24      tidyselect_1.1.2     purrr_0.3.4          reshape2_1.4.4       listenv_0.8.0       
 [6] splines_4.1.2        lattice_0.20-45      colorspace_2.0-3     vctrs_0.3.8          generics_0.1.2      
[11] stats4_4.1.2         utf8_1.2.2           survival_3.2-13      prodlim_2019.11.13   rlang_1.0.1         
[16] ModelMetrics_1.2.2.2 pillar_1.7.0         glue_1.6.2           withr_2.4.3          rappdirs_0.3.3      
[21] foreach_1.5.2        lifecycle_1.0.1      plyr_1.8.6           lava_1.6.10          stringr_1.4.0       
[26] timeDate_3043.102    munsell_0.5.0        gtable_0.3.0         future_1.24.0        recipes_0.2.0       
[31] codetools_0.2-18     caret_6.0-90         parallel_4.1.2       class_7.3-19         fansi_1.0.2         
[36] Rcpp_1.0.8           scales_1.1.1         ipred_0.9-12         jsonlite_1.8.0       parallelly_1.30.0   
[41] png_0.1-7            ggplot2_3.3.5        digest_0.6.29        stringi_1.7.6        rprojroot_2.0.2     
[46] grid_4.1.2           here_1.0.1           hardhat_0.2.0        cli_3.2.0            tools_4.1.2         
[51] tibble_3.1.6         crayon_1.5.0         future.apply_1.8.1   pkgconfig_2.0.3      ellipsis_0.3.2      
[56] MASS_7.3-54          Matrix_1.3-4         data.table_1.14.2    pROC_1.18.0          lubridate_1.8.0     
[61] gower_1.0.0          rstudioapi_0.13      iterators_1.0.14     R6_2.5.1             globals_0.14.0      
[66] rpart_4.1-15         nnet_7.3-16          nlme_3.1-153         compiler_4.1.2 

[jcalebsmith]

Replacing the current version of add_entity.R corrects the issue:

#' add_entity
#' @description Add an entity to an entityset.
#' @export
#'
#' @param entityset The entity set to modify.
#' @param entity_id The name of the entity to add.
#' @param df The data frame to add as an entity.
#' @param index The index parameter specifies the column that uniquely identifies rows in the dataframe
#' @param time_index Name of the time column in the dataframe.
#' @param ... Additional parameters passed to `featuretools.entity_from_dataframe`.
#' @return A modified entityset.
#'
#' @examples
#' \donttest{
#' library(magrittr)
#' create_entityset("set") %>%
#'   add_entity(df = cars,
#'              entity_id = "cars",
#'              index = "row_number")
#' }
add_entity <- function(
  entityset,
  entity_id,
  df,
  index = NULL,
  time_index = NULL,
  ...
) {
  # Construct logical_types to handle factors as categorical variables.
  classes <- purrr::map_dfr(sapply(df, FUN = function(col) {
    c <- class(col)
    # prettify difficult data types
    if(length(c > 1))
      c <- paste0(c, collapse = ", ")
    return(c)
  }), c)

  logical_types = list() #initialize
  if (any(classes == "factor")) {
    for (i in 1:length(classes)) {
      suppressWarnings({
        if (class(df[, i]) == "factor") {
          logical_types[[names(df)[i]]] <- .Categorical
        }
      })
    }
  }

  logical_types <- reticulate::r_to_py(logical_types)

  # Add df as entity to entityset.
  es <- entityset$add_dataframe(
    dataframe_name = entity_id,
    dataframe = reticulate::r_to_py(x = df),
    index = index,
    time_index = time_index,
    logical_types = logical_types,
    ...
  )

  return(es)

}

[praktiskt]

Hi there, great that you found a solution. I'm not really working with R currently, but if you'd like to submit this as a PR (and update tests if required), I can create a new release for it (and send to CRAN).