Data-Informed Thinking + Doing

AutoML: Scaled & Efficient Unsupervised Learning

Automating the time-consuming task of modeling and hyperparameter tuning—using DataRobot in R, Python, and Julia.

Appendix A: Environment, Language & Package Versions, and Coding Style

If you are interested in reproducing this work, here are the versions of R, Python, and Julia that I used (as well as the respective packages for each). Additionally, my coding style here is verbose, in order to trace back where functions/methods and variables are originating from, and make this a learning experience for everyone—including me. Finally, the data visualizations are mostly (if not entirely) implemented using the Grammar of Graphics framework.

cat(
    R.version$version.string, "-", R.version$nickname,
    "\nOS:", Sys.info()["sysname"], R.version$platform,
    "\nCPU:", benchmarkme::get_cpu()$no_of_cores, "x", benchmarkme::get_cpu()$model_name
)
R version 4.2.3 (2023-03-15) - Shortstop Beagle 
OS: Darwin x86_64-apple-darwin17.0 
CPU: 8 x Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
require(devtools)
devtools::install_version("dplyr", version="1.1.4", repos="http://cran.us.r-project.org")
devtools::install_version("ggplot2", version="3.5.0", repos="http://cran.us.r-project.org")
devtools::install_version("dataroboto", version="2.18.6", repos="http://cran.us.r-project.org")

library(package=dplyr)
library(package=ggplot2)
library(package=datarobot)
import sys
import platform
import os
import cpuinfo
print(
    "Python", sys.version,
    "\nOS:", platform.system(), platform.platform(),
    "\nCPU:", os.cpu_count(), "x", cpuinfo.get_cpu_info()["brand_raw"]
)
Python 3.11.4 (v3.11.4:d2340ef257, Jun  6 2023, 19:15:51) [Clang 13.0.0 (clang-1300.0.29.30)] 
OS: Darwin macOS-10.16-x86_64-i386-64bit 
CPU: 8 x Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
!pip install numpy==1.25.1
!pip install pandas==2.0.3
!pip install scipy==1.11.1

import numpy
import pandas
from scipy import stats
using InteractiveUtils
InteractiveUtils.versioninfo()
Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin22.4.0)
  CPU: 8 × Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, skylake)
  Threads: 1 on 8 virtual cores
Environment:
  DYLD_FALLBACK_LIBRARY_PATH = /Library/Frameworks/R.framework/Resources/lib:/Library/Java/JavaVirtualMachines/jdk-21.jdk/Contents/Home/lib/server
using Pkg
Pkg.add(name="HTTP", version="1.10.2")
Pkg.add(name="CSV", version="0.10.13")
Pkg.add(name="DataFrames", version="1.6.1")
Pkg.add(name="CategoricalArrays", version="0.10.8")
Pkg.add(name="StatsBase", version="0.34.2")

using HTTP
using CSV
using DataFrames
using CategoricalArrays
using StatsBase

Appendix B: A Case for DataRobot for AutoML

Advantages

  • DataRobot accelerates the end-to-end journey from data to value, allowing users to deploy AI applications at scale.
  • It offers a suite of built-in automation capabilities that tries out various machine learning algorithms and feature engineering, performs feature selection, and quickly surfaces what works the best.
  • DataRobot users can build accurate, transparent predictive models within minutes.
  • It provides features like Automated Feature Engineering and Rapid Experimentation.

Disadvantages

  • Specific weaknesses of DataRobot are not explicitly mentioned in the search results. However, it’s important to note that the effectiveness of any AutoML tool can depend on factors such as the specific use case, the quality and nature of the input data, and the expertise of the users.

Further Readings

Applied Advanced Analytics & AI in Sports