Type: Package
Title: A Backend for a 'nextflow' Pipeline that Performs Machine-Learning-Based Modeling of Biomedical Data
Version: 0.1.3
Maintainer: Sebastian Malkusch <sebastian.malkusch@boehringer-ingelheim.com>
Description: Provides functionality to perform machine-learning-based modeling in a computation pipeline. Its functions contain the basic steps of machine-learning-based knowledge discovery workflows, including model training and optimization, model evaluation, and model testing. To perform these tasks, the package builds heavily on existing machine-learning packages, such as 'caret' https://github.com/topepo/caret/ and associated packages. The package can train multiple models, optimize model hyperparameters by performing a grid search or a random search, and evaluates model performance by different metrics. Models can be validated either on a test data set, or in case of a small sample size by k-fold cross validation or repeated bootstrapping. It also allows for 0-Hypotheses generation by performing permutation experiments. Additionally, it offers methods of model interpretation and item categorization to identify the most informative features from a high dimensional data space. The functions of this package can easily be integrated into computation pipelines (e.g. 'nextflow' https://www.nextflow.io/) and hereby improve scalability, standardization, and re-producibility in the context of machine-learning.
License: GPL (≥ 3)
Encoding: UTF-8
URL: https://github.com/Boehringer-Ingelheim/flowml
BugReports: https://github.com/Boehringer-Ingelheim/flowml/issues
Imports: ABCanalysis, caret, data.table, dplyr, fastshap, furrr, future, magrittr, optparse, parallel, purrr, R6, readr, rjson, rlang, rsample, stats, stringr, tibble, tidyr, utils, vip
RoxygenNote: 7.2.3
Collate: 'fml_resampler.R' 'fml_resample.R' 'fml_format_response.R' 'fml_parser.R' 'fml_bootstrap.R' 'fml_categorize.R' 'fml_example.R' 'fml_globals.R' 'fml_grids.R' 'fml_interpret.R' 'fml_train.R' 'fml_validate.R'
Suggests: ada, adabag, arm, bartMachine, bst, C50, caTools, class, Cubist, e1071, earth, elasticnet, evtree, fastICA, foreach, frbs, gam, gbm, ggplot2, glmnet, h2o, hda, ipred, keras, kernlab, kknn, klaR, knitr, kohonen, lars, leaps, LiblineaR, LogicReg, MASS, Matrix, mboost, mda, mgcv, monomvn, neuralnet, nnet, nnls, pamr, partDSA, party, partykit, penalized, pls, plyr, proxy, quantregForest, randomForest, ranger, rFerns, rmarkdown, rpart, rrcov, rrcovHD, RSNNS, RWeka, sda, shapviz, spls, superpc, VGAM, xgboost
Depends: R (≥ 3.5.0)
NeedsCompilation: no
Packaged: 2024-02-16 08:55:02 UTC; malkusch
Author: Sebastian Malkusch ORCID iD [aut, cre], Kolja Becker ORCID iD [aut], Alexander Peltzer ORCID iD [ctb], Neslihan Kaya ORCID iD [ctb], Boehringer Ingelheim Ltd. [cph, fnd]
Repository: CRAN
Date/Publication: 2024-02-16 10:40:02 UTC

Resampler

Description

Model validation by repeated bootstrapping

Format

[R6::R6Class] object.

Details

Uses repeated bootstrapping to validate models without a test data set. For each experiment multiple metrics are measured. For classification experiments the confusion matrix is calculated additionally. In order to test hypotheses, either features or the response variable can be permuted.

Active bindings

permute

returns the instance variable 'permute'. (character)

permute_alphabet

returns the instance variable 'permute_alphabet'. (character)

n_resample

returns the instance variable 'n_resample'. (integer)

fml_method

returns the instance variable 'fml_method'. (character)

fml_type

returns the instance variable 'fml_type'. (character)

fml_type_alphabet

returns the instance variable 'fml_type_alphabet'. (character)

pre_process_lst

returns the instance variable 'pre_process_lst'. (character)

hyper_parameters

returns the instance variable 'hyper_parameters'. (list)

response_var

returns the instance variable 'response_var'. (character)

n_features

returns the instance variable 'n_features'. (integer)

strata_var

returns the instance variable 'strata_var'. (character)

metrics_df

returns the instance variable 'metrics_df'. (tibble::tibble)

confusion_df

returns the instance variable 'confusion_df'. (tibble::tibble)

Methods

Public methods

Method new()

checks, if permutation is requested. If true, performs the permutation task.

Checks if ml.type is classification. If true, calculates confusion matrix.

Creates and returns instance of Resampler class.

Usage
Resampler$new(
  n_resample = 500,
  fml_method = "pcr",
  fml_type = "classification",
  hyper_parameters = "list",
  pre_process_lst = c("center", "scale"),
  permute = NULL,
  n_features = 0,
  response_var = "character",
  strata_var = NULL
)
Arguments
n_resample

number of bootstrap resamples. The default is 500 (integer)

fml_method

ML model that is being used. The default is 'pcr' (character).

fml_type

ML model type. Needs to be 'classification', 'regression' or 'censored'. Default is 'classification' (character).

hyper_parameters

List of model hyper parameters. (list)

pre_process_lst

Vector of pre-processing steps. Default is 'c("center", "scale")' (character).

permute

Permutation method. Needs to be 'none', 'features' or 'response'. (character)

n_features

Number of features to be chosen in the permutation experiment. Default is 0 (integer).

response_var

Response variable of the model (character).

strata_var

Stratification variable (character).

Returns

Resampler

Method print()

Print instance variables of Resampler class.

Usage
Resampler$print()
Returns

character

Method fit()

Runs the bootstrap analysis based on the instance variables chosen under initialize.

Usage
Resampler$fit(data_df = "tbl_df")
Arguments
data_df

data set to be analyzed (tibble::tibble).

Returns

None

Method clone()

The objects of this class are cloneable with this method.

Usage
Resampler$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Author(s)

Sebastian Malkusch

create_parser

Description

Creates an object that defines and handles command line arguments.

Usage

create_parser()

Details

A parser that organizes the communication between the user and th function. It also provides a help message.

Value

An instance of type 'optparse::OptionParser'.

Author(s)

Sebastian Malkusch

create_resample_experiment

Description

Creates an object of a resampling experiment.

Usage

create_resample_experiment(
  seed,
  data_df,
  parser_inst,
  model_inst,
  config_inst,
  n_features
)

Arguments

seed

sets the seed for the random number generator to guarantee reproducibility. (int)

data_df

data frame to be learned from. (tibble::tibble)

parser_inst

instance of parser object. (optparse::parse_args).

model_inst

instance of caret_train object (caret::train).

config_inst

list of config options (list).

n_features

number of features (int).

Details

Creates a resampling experiment. It uses user defined parameters to set up the experiment. It creates an instance of the Resampler object and runs the experiment according to the user-defined parameters.

Value

An instance of type 'Resampler'.

Author(s)

Sebastian Malkusch

fml_bootstrap

Description

Pipeline function that sets up and runs a resampling experiment.

Usage

fml_bootstrap(parser_inst)

Arguments

parser_inst

Instance of fml_parser class that comprises command line arguments.

Details

The experiment is run in parallel. All results are written to files.

Value

none

Author(s)

Sebastian Malkusch

Examples

## Not run: 
parser_inst <-  flowml::create_parser()

parser_inst$pipeline_segment <- "bootstrap"
parser_inst$config <- flowml::fml_example(file = "reg_config.json")
parser_inst$data <- flowml::fml_example(file = "reg_data.csv")
parser_inst$samples_train <- flowml::fml_example(file = "reg_samples_train.txt")
parser_inst$samples_test <- flowml::fml_example(file = "reg_samples_test.txt")
parser_inst$features <- flowml::fml_example(file = "reg_features.txt")
parser_inst$extended_features <- flowml::fml_example(file = "reg_features_extended.txt")
parser_inst$trained <- flowml::fml_example(file = "reg_fit.rds")
parser_inst$permutation <- "none"
parser_inst$result_dir <- tempdir()

flowml::fml_bootstrap(parser_inst = parser_inst)

## End(Not run)

fml_example

Description

path to flowml examples data

Usage

fml_example(file = NULL)

Arguments

file

Name of file. If 'NULL', the example files will be listed.

Details

flowml comes bundled with a number of sample files in its 'inst/extdata' directory. This function allows to access them.

Value

The path of to an example file, if file is defined. Else, a list of example files.

Author(s)

Sebastian Malkusch

Examples

## Not run: 
fml_example()
fml_example(file = "reg_config.json")

## End(Not run)

fml_interpret

Description

Pipeline function that sets up and runs a post-hoc interpretation of an ml experiment. All results are written to rds files.

Usage

fml_interpret(parser_inst)

Arguments

parser_inst

instance of fml_parser class that comprises command line arguments.

Value

none

Author(s)

Sebastian Malkusch

Examples

## Not run: 
parser_inst <-  flowml::create_parser()

parser_inst$pipeline_segment <- "interpret"
parser_inst$config <- flowml::fml_example(file = "reg_config.json")
parser_inst$data <- flowml::fml_example(file = "reg_data.csv")
parser_inst$samples_train <- flowml::fml_example(file = "reg_samples_train.txt")
parser_inst$samples_test <- flowml::fml_example(file = "reg_samples_test.txt")
parser_inst$features <- flowml::fml_example(file = "reg_features.txt")
parser_inst$extended_features <- flowml::fml_example(file = "reg_features_extended.txt")
parser_inst$trained <- flowml::fml_example(file = "reg_fit.rds")
parser_inst$interpretation <- "shap"
parser_inst$result_dir <- tempdir()

flowml::fml_interpret(parser_inst = parser_inst)

## End(Not run)

fml_train

Description

Pipeline function that performs a hyper-parameter screeing experiment.

Usage

fml_train(parser_inst)

Arguments

parser_inst

instance of fml_parser class that comprises command line arguments.

Value

none

Author(s)

Kolja Becker

Examples

## Not run: 
parser_inst <-  flowml::create_parser()

parser_inst$pipeline_segment <- "train"
parser_inst$config <- flowml::fml_example(file = "reg_config.json")
parser_inst$data <- flowml::fml_example(file = "reg_data.csv")
parser_inst$samples_train <- flowml::fml_example(file = "reg_samples_train.txt")
parser_inst$samples_test <- flowml::fml_example(file = "reg_samples_test.txt")
parser_inst$features <- flowml::fml_example(file = "reg_features.txt")
parser_inst$extended_features <- flowml::fml_example(file = "reg_features_extended.txt")
parser_inst$result_dir <- tempdir()

flowml::fml_train(parser_inst = parser_inst)

## End(Not run)

fml_validate

Description

Pipeline function that performs a validation experiment on a a caret train object based on test samples.

Usage

fml_validate(parser_inst)

Arguments

parser_inst

instance of fml_parser class that comprises command line arguments.

Value

none

Author(s)

Kolja Becker

Examples

## Not run: 
parser_inst <-  flowml::create_parser()

parser_inst$pipeline_segment <- "validate"
parser_inst$config <- flowml::fml_example(file = "reg_config.json")
parser_inst$data <- flowml::fml_example(file = "reg_data.csv")
parser_inst$samples_train <- flowml::fml_example(file = "reg_samples_train.txt")
parser_inst$samples_test <- flowml::fml_example(file = "reg_samples_test.txt")
parser_inst$features <- flowml::fml_example(file = "reg_features.txt")
parser_inst$extended_features <- flowml::fml_example(file = "reg_features_extended.txt")
parser_inst$trained <- flowml::fml_example(file = "reg_fit.rds")
parser_inst$permutation <- "none"
parser_inst$result_dir <- tempdir()

flowml::fml_validate(parser_inst = parser_inst)

## End(Not run)

format_y

Description

Formats response variable based on the ml-type variable passed by the config file. For regression analyses the response variable will be explicitly transformed to type numeric. For Classification experiments the response variable will be explicitly transformed to a factor. Time-to-event models are to be implemented in the near future.

Usage

format_y(y, ml.type)

Arguments

y

vector of response varibale.

ml.type

type of experiment (chracater).

Value

a transformed version of the response variable y.

Author(s)

Kolja Becker

Performs item categorization

Description

Performs item categorization on permutation or shap analysis object

Usage

run_abc_analysis(data_obj, method)

Arguments

data_obj

Resuls of model interpretation experiment

method

Method used for model interpretation (permutatopn or shap)

Details

Interpretation results are passed to the function. Based on the type of interpretation experiment the data is transformed into a uniformly structured data frame. Item categorization is performed by computed ABC analysis. The result is returned in form of a tibble.

Value

A tibble with item categories

a tibble

Author(s)

Sebastian Malkusch