tests/testthat/test-featureImportance.R

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

# Copyright 2021 Observational Health Data Sciences and Informatics
#
# This file is part of PatientLevelPrediction
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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context("FeatureImportance")

# Test unit for the creation of the study externalValidatePlp


test_that("pfi feature importance returns data.frame", {
  
  # limit to a sample of 2 covariates for faster test
  covariates <- plpResult$model$covariateImportance %>% 
    dplyr::filter("covariateValue" != 0) %>% 
    dplyr::select("covariateId") %>% 
    dplyr::arrange(desc("covariateValue")) %>%
    dplyr::pull()
  
  # if the model had non-zero covariates
  if(length(covariates) > 0){
    if (length(covariates) > 2) {
      covariates <- covariates[1:2]
    }
    pfiTest <- pfi(plpResult, population, plpData, repeats = 1,
                   covariates = covariates, cores = 1, log = NULL,
                   logthreshold = "INFO")
    
    expect_equal(class(pfiTest), 'data.frame')
    expect_equal(sum(names(pfiTest) %in% c("covariateId", "pfi")), 2)
    expect_true(all(!is.nan(pfiTest$pfi)))
    
  }
  
})

test_that('pfi feature importance works with logger or without covariates', {

  pfiTest <- pfi(tinyResults, population, nanoData, cores = 1, 
                 covariates = NULL, log = file.path(tempdir(), 'pfiLog'))
  
  expect_equal(class(pfiTest), 'data.frame')
  expect_equal(sum(names(pfiTest) %in% c("covariateId", "pfi")), 2)
  expect_true(all(!is.nan(pfiTest$pfi)))

})