How to Install the Package for R:

1. First, run the following R script to download dependencies

library(devtools)
# No Remotes ----
# Attachments ----
to_install <- c("catboost", "caTools", "data.table", "doParallel", "foreach", "forecast", "ggplot2", "h2o", "itertools", "lubridate", "monreg", "pROC", "RColorBrewer", "recommenderlab", "ROCR", "scatterplot3d", "stringr", "tm", "tsoutliers", "wordcloud", "xgboost", "zoo")
for (i in to_install) {
  message(paste("looking for ", i))
  if(i == "catboost" & !requireNamespace(i)) {
    devtools::install_github('catboost/catboost', subdir = 'catboost/R-package')
  } else if(i == "h2o" & !requireNamespace(i)) {
    if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) }
    if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") }
    pkgs <- c("RCurl","jsonlite")
    for (pkg in pkgs) {
      if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) }
    }
    install.packages("h2o", type="source", repos="https://h2o-release.s3.amazonaws.com/h2o/rel-yates/3/R")
  } else if (!requireNamespace(i)) {
    message(paste("     installing", i))
    install.packages(i)
  }
}

2. Next, Install R package from GitHub

# Depending on the development state (future versions, etc.) you can install via:
devtools::install_github('AdrianAntico/RemixAutoML', upgrade = FALSE)

or

# ...you can install via:
devtools::install_github('AdrianAntico/RemixAutoML', force = TRUE, dependencies = TRUE, upgrade = FALSE)

RemixAutoML

This is a collection of functions that I have made to speed up machine learning and to ensure high quality modeling output is generated. They are great at establishing solid baselines that are extremely challenging to beat using alternative methods. To see them in action, check out our free tutorials at RemyxCourses.com.

Also, be sure to visit our blog at RemixInstitute.ai for data science, machine learning, and AI content.

You can also contact me via LinkedIn for any questions about the package. You can also go into the vignettes folder to see more detail. If you want to be a contributer, contact me via LinkedIn email.

Hex sticker rendered via the hexSticker package in R: https://github.com/GuangchuangYu/hexSticker

Supervised Learning Functions:

EXPAND

Regression:

AutoCatBoostRegression() GPU + CPU

AutoCatBoostRegression is an automated modeling function that runs a variety of steps. First, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation boxplot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting. You can download the catboost package using devtools, via: devtools::install_github('catboost/catboost', subdir = 'catboost/R-package')

AutoXGBoostRegression() GPU + CPU

AutoXGBoostRegression is an automated XGBoost modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation boxplot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting.

AutoH2oGBMRegression()

AutoH2oGBMRegression is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation boxplot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting.

AutoH2oDRFRegression()

AutoH2oDRFRegression is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation boxplot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting.

Binary Classification:

AutoCatBoostClassifier() GPU + CPU

AutoCatBoostClassifier is an automated modeling function that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, ROC plot, evaluation plot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting. You can download the catboost package using devtools, via: devtools::install_github('catboost/catboost', subdir = 'catboost/R-package')

AutoXGBoostClassifier() GPU + CPU

AutoXGBoostClassifier is an automated XGBoost modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation boxplot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting.

AutoH2oGBMClassifier()

AutoH2oGBMClassifier is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation metrics, variable importance, partial dependence calibration plots, and column names used in model fitting.

AutoH2oDRFClassifier()

AutoH2oDRFClassifier is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation metrics, variable importance, partial dependence calibration plots, and column names used in model fitting.

Multinomial Classification:

AutoCatBoostMultiClass() GPU + CPU

AutoCatBoostMultiClass is an automated modeling function that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation metrics, variable importance, and column names used in model fitting. You can download the catboost package using devtools, via: devtools::install_github('catboost/catboost', subdir = 'catboost/R-package').

AutoXGBoostMultiClass() GPU + CPU

AutoXGBoostMultiClass is an automated XGBoost modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation metrics, variable importance, and column names used in model fitting.

AutoH2oGBMMultiClass()

AutoH2oGBMMultiClass is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation metrics, confusion matrix, and variable importance.

AutoH2oDRFMultiClass()

AutoH2oDRFMultiClass is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation metrics, confusion matrix, and variable importance.

AutoCatBoostScoring()

AutoCatBoostScoring is an automated scoring function that compliments the AutoCatBoost model training functions. This function requires you to supply features for scoring. It will run ModelDataPrep() to prepare your features for catboost data conversion and scoring.

AutoXGBoostScoring()

AutoXGBoostScoring is an automated scoring function that compliments the AutoCatBoost model training functions. This function requires you to supply features for scoring. It will run ModelDataPrep() and the DummifyDT() function to prepare your features for xgboost data conversion and scoring.

AutoH2oScoring()

General Purpose H2O Automated Modeling:

AutoH2OModeler()

Automated machine learning. Automatically build any number of models along with generating partial dependence calibration plots, model evaluation calibration plots, grid tuning, and file storage for easy production implementation. Handles regression, quantile regression, time until event, and classification models (binary and multinomial) using numeric and factor variables without the need for monotonic transformations nor one-hot-encoding.

• Models include:
  • RandomForest (DRF)
  • GBM
  • Deeplearning
  • XGBoost (for Linux)
  • LightGBM (for Linux)
  • AutoML - medium debth grid tuning for Deeplearning, XGBoost (if available), DRF, GBM, GLM, and StackedEnsembles

Model Scoring:

AutoH2OScoring()

Scoring models that were built with the AutoH2OModeler, AutoKMeans, and AutoWord2VecModeler functions. Scores models either via mojo or the standard method by loading models into the H2O environment and scoring them. You can choose which output you wish to keep as well.

Time Series Modeling:

AutoTS()

Automated time series modeling function. Automatically finds the most accurate time series model from the list of models below (using optimized Box-Cox transformations and tests both user-supplied time series frequency and model-based time series frequency). The best model is chosen by looking at the lowest out-of-sample error, and the output from AutoTS() includes forecasts, model evaluation metrics, and metadata on the competing models.

• Automated Time Series Models include:
  • DSHW: Double Seasonal Holt-Winters
  • ARFIMA: Auto Regressive Fractional Integrated Moving Average
  • ARIMA: Stepwise Auto Regressive Integrated Moving Average with specified max lags, seasonal lags, moving averages, and seasonal moving averages
  • ETS: Additive and Multiplicative Exponential Smoothing and Holt-Winters
  • NNetar: Auto Regressive Neural Network models automatically compares models with 1 lag or 1 seasonal lag compared to models with up to N lags and N seasonal lags
  • TBATS: Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components
  • TSLM: Time Series Linear Model - builds a linear model with trend and season components extracted from the data

Nonlinear Regression Modeling:

AutoNLS()

Automated nonlinear regression modeling. Automatically finds the best model fit from the suite of models below and merges predictions to source data file. Great for forecasting growth over time or estimating single variable nonlinear functions.

• Models included:
  • Asymptotic
  • Asymptotic through origin
  • Asymptotic with offset
  • Bi-exponential
  • Four parameter logistic
  • Three parameter logistic
  • Gompertz
  • Michal Menton
  • Weibull
  • Polynomial regression or monotonic regression

Recommenders:

AutoRecommender()

Automated collaborative filtering modeling where each model competes against each other

• RandomItems
• PopularItems
• UserBasedCF
• ItemBasedCF
• AssociationRules

AutoRecommenderScoring()

Automatically score a recommender model from AutoRecommender

Unsupervised Learning Functions:

EXPAND

GenTSAnomVars()

Generate time series anomaly variables. (Cross with Feature Engineering) Create indicator variables (high, low) along with cumulative anomaly rates (high, low) based on control limits methodology over a max of two grouping variables and a date variable (effectively a rolling GLM).

ResidualOutliers()

Residual outliers from time series modeling. (Cross with Feature Engineering) Utilize tsoutliers to indicate outliers within a time series data set

AutoKMeans()

Generalized low rank model followed by KMeans. (Possible cross with Feature Engineering) Generate a column with a cluster identifier based on a grid tuned (optional) generalized low rank model and a grid tuned (optimal) K-Optimal searching K-Means algorithm

Feature Engineering Functions:

EXPAND

FAST_GDL_Feature_Engineering()

For models with target variables within the realm of the current time frame but not too far back in time, this function creates autoregressive and rolling stats from target columns and distributed lags and distributed rolling stats for independent features distributed across time. On top of that, you can also create time between instances along with their associated lags and rolling stats. This function works for data with groups and without groups.

GDL_Feature_Engineering()

Builds autoregressive and rolling stats from target columns and distributed lags and distributed rolling stats for independent features distributed across time. On top of that, you can also create time between instances along with their associated lags and rolling stats. This function works for data with groups and without groups.

Scoring_GDL_Feature_Engineering()

For scoring purposes (brings back a single row by group), this function creates autoregressive and rolling stats from target columns and distributed lags and distributed rolling stats for independent features distributed across time. On top of that, you can also create time between instances along with their associated lags and rolling stats. This function works for data with groups and without groups.

DT_GDL_Feature_Engineering()

100% data.table built. Builds autoregressive and moving average from target columns and distributed lags and distributed moving average for independent features distributed across time. On top of that, you can also create time between instances along with their associated lags and moving averages. This function works for data with groups and without groups.

AutoWord2VecModeler()

Generate a specified number of vectors for each column of text data in your data set and save the models for re-creating them later in the scoring process.

ModelDataPrep()

Rapidly convert "inf" values to NA, convert character columns to factor columns, and impute with specified values for factor and numeric columns.

DummifyDT()

Rapidly dichotomize a list of columns in a data table (N+1 columns for N levels using one hot encoding or N columns for N levels otherwise)

AutoDataPartition()

This function is designed to achieve a few things that standard data partitioning processes or functions don't handle. First, you can choose to build any number of partitioned data sets beyond the standard train, validate, and test data sets. Second, you can choose between random sampling to split your data or you can choose a time-based partitioning. Third, for the random partitioning, you can specify stratification columns in your data to stratify by in order to ensure a proper split amongst your categorical features (E.g. think MultiClass targets). Lastly, it's 100% data.table so it will run fast and with low memory overhead.

Model Evaluation, Interpretation, and Cost-Sensitive Functions:

EXPAND

ParDepCalPlots()

Great for features effects estimation and reliability of model in predicting those effects. Build a partial dependence calibration plot on train, test, or all data

EvalPlot()

Great for assessing accuracy across range of predicted values. Build a calibration plot on test data

threshOptim()

Great for situations with asymmetric costs across the confusion matrix. Generate a cost-sensitive optimized threshold for classification models

RedYellowGreen()

Computes optimal thresholds for binary classification models when "don't classify" is an option

Utilities and Misc. Functions:

EXPAND

AutoWordFreq()

creates a word frequency data.table and a word cloud

AutoH2OTextPrepScoring()

Prepares your data for scoring based on models built with Word2VecModel

AutoRecomDataCreate()

Turns your transactional data into a binary ratings matrix

ProblematicFeatures()

Identify columns that have either little to no variance, extremely high cardinality, too many NA's, too many zeros, or too high of a skew

ProblematicRecords()

Identify records identified as outliers via Isolation Forests from H2O

RemixTheme()

Fonts, colors, style for plots.

ChartTheme()

Fonts, colors, style for plots.

multiplot()

Useful for displaying multiple plots in a single pane.

tokenizeH2O()

Tokenize and H2O string column.

percRank()

Inner function for calibration plots and partial dependence plots. Computes PercentRank.

SimpleCap()

Apply proper case to text.

PrintObjectsSize()

print out objects and their sizes that are in the envrionment

tempDatesFun()

Special case for character conversion to date when importing from Excel.