In bgreenwell/pdp: Partial Dependence Plots

library(pdp)

# Set global chunk options
knitr::opts_chunk$set(
  cache = TRUE,
  comment = "#>",
  error = FALSE, 
  fig.path = "figure/", 
  cache.path = "cache/", 
  dpi = 300,
  fig.align = "center", 
  fig.asp = 0.618,
  fig.pos = "!htb",
  fig.width = 6, 
  fig.show = "hold",
  message = FALSE,
  out.width = "100%",
  par = TRUE,  # defined below
  size = "small",
  # size = "tiny",
  tidy = FALSE,
  warning = FALSE
)

# Set general hooks
knitr::knit_hooks$set(
  par = function(before, options, envir) {
    if (before && options$fig.show != "none") {
      par(
        mar = c(4, 4, 0.1, 0.1), 
        cex.lab = 0.95, 
        cex.axis = 0.8,  # was 0.9
        mgp = c(2, 0.7, 0), 
        tcl = -0.3, 
        las = 1
      )
      if (is.list(options$par)) {
        do.call(par, options$par)
      }
    }
  }
)

Introduction

Partial dependence (PD) plots are rather straightforward to construct in practice, as discussed in @RJ-2017-016. However, is is difficult to apply the brute force algorithm as is to situations where the data are stored in a Spark data frame; such is the case when fitting models using Spark's MLlib library [@meng-2015-mllib]. Fortunately, the same computations can be done using a couple of simple Spark operations; in particular, a cross-join, followed by a group-by and aggregation step.

To illustrate...

bgreenwell/pdp documentation built on June 2, 2022, 2:55 p.m.