ck37r

My R toolkit for organizing analyses, parallelizing code for multiple cores or in a SLURM cluster, and extended functionality for SuperLearner and TMLE. Some of the SuperLearner functions may eventually be migrated into the SuperLearner package.

Installation

Install from github:

install.packages("devtools") # Install devtools if needed.
devtools::install_github("ck37/ck37r")

Functions

• General
• import_csvs - import all CSV files in a given directory.
• impute_missing_values - impute missing values in a dataframe (median for numerics and mode for factors; or k-nearest neighbors), add missingness indicators.
• load_all_code - source() all R files in a given directory.
• load_packages - load a list of packages; for the ones that fail it can attempt to install them automatically from CRAN, then load them again.
• missingness_indicators - return a matrix of missingness indicators for a dataframe, (optionally) omitting any constant or collinear columns.
• standardize - standardize a dataset (center, scale), optionally omitting certain variables.
• Parallelization
• parallelize - starts a multicore or multinode parallel cluster. Automatically detects parallel nodes in a SLURM environment, which makes code work seemlessly on a laptop or a cluster.
• stop_cluster - stops a cluster started by parallelize().
• Random Forest
• rf_count_terminal_nodes - count the number of terminal nodes in each tree in a random forest. That information can then be used to grid-search the maximum number of nodes allowed in a Random Forest (along with mtry).
• SuperLearner
• gen_superlearner - create a SuperLearner and CV.SuperLearner function setup to transparently use a certain parallelization configuration.
• cvsl_weights - table of the meta-weight distribution for each learner in a CV.SuperLearner analysis.
• cvsl_auc - cross-validated AUC for a CV.SuperLearner analysis.
• cvsl_plot_roc - ROC plot with AUC for a CV.SuperLearner analysis.
• plot.SuperLearner - plot risk estimates and CIs for a SuperLearner, similar to CV.Superlearner except without SL or Discrete SL.
• sl_auc_table - table of cross-validated AUCs for each learner in a SuperLearner ensemble, including SE, CI, and p-value.
• sl_stderr - calculate standard error for each learner’s risk in SL.
• sl_plot_roc - ROC curve plot for one learner in an SL ensemble, plus AUC and CI.
• TMLE
• tmle_parallel - allows the SuperLearner estimation in TMLE to be customized, esp. to support parallel estimation via mcSuperLearner and snowSuperLearner.
• setup_parallel_tmle - helper function to start a cluster and setup SuperLearner and tmle_parallel to use the created cluster.

Examples

Impute missing values

# Load a test dataset.
data(PimaIndiansDiabetes2, package = "mlbench")

# Check for missing values.
colSums(is.na(PimaIndiansDiabetes2))

# Impute missing data and add missingness indicators.
# Don't impute the outcome though.
result = impute_missing_values(PimaIndiansDiabetes2, skip_vars = "diabetes")

# Confirm we have no missing data.
colSums(is.na(result$data))

#############
# K-nearest neighbors imputation

result2 = impute_missing_values(PimaIndiansDiabetes2, type = "knn", skip_vars = "diabetes")

# Confirm we have no missing data.
colSums(is.na(result2$data))

Load packages

This loads a vector of packages, automatically installing any packages that aren’t already installed.

# Load these 4 packages and install them if necessary.
load_packages(c("MASS", "SuperLearner", "tmle", "doParallel"), auto_install = TRUE)

Random Forest: count terminal nodes

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
sl = SuperLearner(Boston$medv, subset(Boston, select = -medv), family = gaussian(),
                  cvControl = list(V = 3),
                  SL.library = c("SL.mean", "SL.glmnet", "SL.randomForest"))

sl

summary(rf_count_terminal_nodes(sl$fitLibrary$SL.randomForest_All$object))

max_terminal_nodes = max(rf_count_terminal_nodes(sl$fitLibrary$SL.randomForest_All$object))

max_terminal_nodes

# Now run create.Learner() based on that maximum.

# It is often handy to convert to log scale of a hyperparameter before testing a ~linear grid.
# NOTE: -0.7 ~ 0.69 ~ log(0.5) which is the multiplier that yields sqrt(max)
maxnode_seq = unique(round(exp(log(max_terminal_nodes) * exp(c(-0.97, -0.7, -0.45, -0.15, 0)))))
maxnode_seq

rf = SuperLearner::create.Learner("SL.randomForest", detailed_names = T, name_prefix = "rf",
                             params = list(ntree = 100), # fewer trees for testing speed only.
                             tune = list(maxnodes = maxnode_seq))

sl = SuperLearner(Boston$medv, subset(Boston, select = -medv), family = gaussian(),
                  cvControl = list(V = 3),
                  SL.library = c("SL.mean", "SL.glmnet", rf$names))

sl

Parallel TMLE

library(ck37r)
library(tmle)

# Use multiple cores as available.
ck37r::setup_parallel_tmle()

# Basic SL library.
sl_lib = c("SL.mean", "SL.rpart", "SL.glmnet")

# Set a parallel-compatible seed so cross-validation folds are deterministic.
set.seed(1, "L'Ecuyer-CMRG")
result = run_tmle(Y = Y, A = A, W = W, family = "binomial",
                  g.SL.library = sl_lib, Q.SL.library = sl_lib)

SuperLearner AUC

This will return an AUC table for all learners. It does not include Discrete SL or SuperLearner as those require CV.SuperLearner.

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
sl = SuperLearner(Boston$chas, subset(Boston, select = -chas), family = binomial(),
                       SL.library = c("SL.mean", "SL.glmnet"))

sl_auc(sl, Y = Boston$chas)

SuperLearner plot of risk estimates

This is similar to CV.SuperLearner’s plot except SuperLearner cannot estimate risk for the Discrete SL and SuperLearner, so those must be omitted here.

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
sl = SuperLearner(Boston$medv, subset(Boston, select = -medv), family = gaussian(),
                  SL.library = c("SL.mean", "SL.glmnet"))

sl
plot(sl, Y = Boston$chas)

SuperLearner ROC plot

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
sl = SuperLearner(Boston$chas, subset(Boston, select = -chas), family = binomial(),
                  SL.library = c("SL.mean", "SL.glmnet"))

sl

sl_plot_roc(sl, Y = Boston$chas)

CV.SuperLearner AUC

This will return the AUC inference for the CV.SuperLearner.

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
cvsl = CV.SuperLearner(Boston$chas, subset(Boston, select = -chas), family = binomial(),
                       cvControl = list(V = 2, stratifyCV = T),
                       SL.library = c("SL.mean", "SL.glmnet"))
cvsl_auc(cvsl)

CV.SuperLearner plot ROC

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
cvsl = CV.SuperLearner(Boston$chas, subset(Boston, select = -chas), family = binomial(),
                       cvControl = list(V = 2, stratifyCV = T),
                       SL.library = c("SL.mean", "SL.glmnet"))
cvsl_plot_roc(cvsl)

CV.SuperLearner weight table

Returns summary statistics (mean, sd, min, max) on the distribution of the weights assigned to each learner across SuperLearner ensembles. This makes it easier to understand the stochastic nature of the SL learner weights and to see how often certain learners are used.

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
cvsl = CV.SuperLearner(Boston$chas, subset(Boston, select = -chas), family = binomial(),
                       cvControl = list(V = 2, stratifyCV = T),
                       SL.library = c("SL.mean", "SL.glmnet"))
cvsl_weights(cvsl)

More examples to be added.

References

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

Dudoit, S., & van der Laan, M. J. (2005). Asymptotics of cross-validated risk estimation in estimator selection and performance assessment. Statistical Methodology, 2(2), 131-154.

LeDell, E., Petersen, M., & van der Laan, M. (2015). Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electronic journal of statistics, 9(1), 1583.

Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/

Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005). ROCR: visualizing classifier performance in R. Bioinformatics, 21(20), 3940-3941.

van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml

van der Laan, M. J., & Rose, S. (2011). Targeted learning: causal inference for observational and experimental data. Springer Science & Business Media.

van der Laan, M. J., & Rubin, D. (2006). Targeted Maximum Likelihood Learning. The International Journal of Biostatistics, 2(1), 1-38.