added tests

.github/workflows/test-coverage.yaml

@@ -0,0 +1,61 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+
+name: test-coverage.yaml
+
+permissions: read-all
+
+jobs:
+  test-coverage:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::covr, any::xml2
+          needs: coverage
+
+      - name: Test coverage
+        run: |
+          cov <- covr::package_coverage(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
+          )
+          covr::to_cobertura(cov)
+        shell: Rscript {0}
+
+      - uses: codecov/codecov-action@v4
+        with:
+          # Fail if error if not on PR, or if on PR and token is given
+          fail_ci_if_error: ${{ github.event_name != 'pull_request' || secrets.CODECOV_TOKEN }}
+          file: ./cobertura.xml
+          plugin: noop
+          disable_search: true
+          token: ${{ secrets.CODECOV_TOKEN }}
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package

DESCRIPTION

@@ -19,4 +19,6 @@ Imports:
   stringr,
   tidyr
 Suggests:
-  randomForest
+    randomForest,
+    testthat (>= 3.0.0)
+Config/testthat/edition: 3

NEWS.md

@@ -3,6 +3,7 @@
 - Updated inputs in prep_training_data to match fdasrvf
 - Added examples to documentation
 - Cleaned up wording in documentation a bit
+- Added tests
 
 # veesa 0.1.2
 

README.Rmd

@@ -7,6 +7,8 @@ always_allow_html: yes
 <!-- badges: start -->
 [![CRAN status](https://www.r-pkg.org/badges/version/veesa)](https://CRAN.R-project.org/package=veesa)
 [![R-CMD-check](https://github.com/sandialabs/veesa/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/sandialabs/veesa/actions/workflows/R-CMD-check.yaml)
+[![test-coverage](https://github.com/sandialabs/veesa/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/sandialabs/veesa/actions/workflows/test-coverage.yaml)
+[![Codecov test coverage](https://codecov.io/gh/sandialabs/veesa/branch/master/graph/badge.svg)](https://app.codecov.io/gh/sandialabs/veesa?branch=master)
 <!-- badges: end -->
 
 ```{r setup, include = FALSE}

README.md

@@ -6,6 +6,9 @@ VEESA R Package
 [![CRAN
 status](https://www.r-pkg.org/badges/version/veesa)](https://CRAN.R-project.org/package=veesa)
 [![R-CMD-check](https://github.com/sandialabs/veesa/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/sandialabs/veesa/actions/workflows/R-CMD-check.yaml)
+[![test-coverage](https://github.com/sandialabs/veesa/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/sandialabs/veesa/actions/workflows/test-coverage.yaml)
+[![Codecov test
+coverage](https://codecov.io/gh/sandialabs/veesa/branch/master/graph/badge.svg)](https://app.codecov.io/gh/sandialabs/veesa?branch=master)
 <!-- badges: end -->
 
 # Set Up

tests/testthat.R

@@ -0,0 +1,12 @@
+# This file is part of the standard setup for testthat.
+# It is recommended that you do not modify it.
+#
+# Where should you do additional test configuration?
+# Learn more about the roles of various files in:
+# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
+# * https://testthat.r-lib.org/articles/special-files.html
+
+library(testthat)
+library(veesa)
+
+test_check("veesa")

tests/testthat/test-compute_pfi.R

@@ -0,0 +1,69 @@
+library(testthat)
+library(randomForest)
+library(dplyr)
+
+# Mock data for testing
+set.seed(123)
+mock_x <- data.frame(
+  feature1 = rnorm(100),
+  feature2 = rnorm(100),
+  feature3 = rnorm(100)
+)
+mock_y <- factor(sample(c("A", "B"), 100, replace = TRUE))
+mock_p <- factor(rbeta(n = 100, shape1 = 1, shape2 = 2))
+
+# Train a random forest model
+rf_model <- randomForest(
+  formula = mock_y ~ .,
+  data = mock_x
+)
+
+# Test 1: Check output structure
+test_that("Output structure is correct", {
+  result <- compute_pfi(
+    x = mock_x,
+    y = mock_y,
+    f = rf_model,
+    K = 5,
+    metric = "accuracy"
+  )
+  expect_type(result, "list")
+  expect_true("pfi" %in% names(result))
+  expect_true("pfi_single_reps" %in% names(result))
+})
+
+# Test 2: Check dimensions of output
+test_that("Output dimensions are correct", {
+  result <- compute_pfi(
+    x = mock_x,
+    y = mock_y,
+    f = rf_model,
+    K = 5,
+    metric = "accuracy"
+  )
+
+  expect_equal(length(result$pfi), ncol(mock_x))  # PFI should have length equal to number of features
+  expect_equal(dim(result$pfi_single_reps), c(5, ncol(mock_x)))  # Single reps should be K x p
+})
+
+# Test 3: Check for error with mismatched dimensions
+test_that("Function throws error with mismatched dimensions", {
+  expect_error(compute_pfi(
+    x = mock_x,
+    y = mock_y[1:50],  # Mismatched length
+    f = rf_model,
+    K = 5,
+    metric = "accuracy"
+  ), "Number of observations in x and y disagree.")
+})
+
+# Test 4: Check for invalid metric
+test_that("Function throws error with invalid metric", {
+  expect_error(compute_pfi(
+    x = mock_x,
+    y = mock_y,
+    f = rf_model,
+    K = 5,
+    metric = "invalid_metric"
+  ), "'metric' specified incorrectly. Select either 'accuracy', 'logloss', or 'nmse'.")
+})

tests/testthat/test-plot_pc_directions.R

@@ -0,0 +1,64 @@
+# library(testthat)
+# library(ggplot2)
+# library(dplyr)
+# library(tidyr)
+# library(purrr)
+# 
+# # Mock data for testing
+# set.seed(123)
+# mock_f <- matrix(rnorm(1000), nrow = 100, ncol = 10)  # 10 time points, 10 functions
+# mock_time <- seq(0, 1, length.out = 100)
+# 
+# # Mock training preparation data
+# mock_fd <- prep_training_data(mock_f, mock_time, "jfpca")
+# 
+# # Test 1: Check output structure
+# test_that("Output is a ggplot object", {
+#   result <- plot_pc_directions(
+#     fpcs = 1,
+#     fdasrvf = mock_fd$fpca_res,
+#     fpca_method = "jfpca"
+#   )
+#   expect_s3_class(result, "ggplot")
+# })
+# 
+# # Test 2: Check error for invalid fpca_method
+# test_that("Function throws error with invalid fpca_method", {
+#   expect_error(plot_pc_directions(
+#     fpcs = 1,
+#     fdasrvf = mock_fd,
+#     fpca_method = "invalid_method"
+#   ), "'fpca_method' entered incorrectly. Must be 'jfpca', 'vfpca', or 'hfpca'.")
+# })
+# 
+# # Test 3: Check handling of multiple PCs
+# test_that("Function handles multiple PCs correctly", {
+#   result <- plot_pc_directions(
+#     fpcs = 1:3,
+#     fdasrvf = mock_fd$fpca_res,
+#     fpca_method = "jfpca"
+#   )
+#   expect_s3_class(result, "ggplot")
+# })
+# 
+# # Test 4: Check for correct number of lines in the plot
+# test_that("Plot contains correct number of lines", {
+#   result <- plot_pc_directions(
+#     fpcs = 1,
+#     fdasrvf = mock_fd$fpca_res,
+#     fpca_method = "jfpca"
+#   )
+#   # Check the number of lines in the plot
+#   expect_equal(length(result$layers), 1)  # Should have one layer for the line
+# })
+# 
+# # Test 5: Check for correct alpha values
+# test_that("Function applies correct alpha values", {
+#   result <- plot_pc_directions(
+#     fpcs = 1,
+#     fdasrvf = mock_fd$fpca_res,
+#     fpca_method = "jfpca",
+#     alpha = 0.5
+#   )
+#   expect_equal(result$layers[[1]]$aes_params$alpha, 0.5)  # Check if alpha is set correctly
+# })

tests/testthat/test-prep_testing_data.R

@@ -0,0 +1,101 @@
+library(testthat)
+library(fdasrvf)
+
+# Mock data for testing (100 time points, 10 functions)
+set.seed(123)
+mock_f <- matrix(rnorm(1000), nrow = 100, ncol = 10)
+mock_time <- seq(0, 1, length.out = 100)
+
+# Mock training preparation data
+mock_train_prep <- prep_training_data(mock_f, mock_time, "jfpca")
+
+# Test 1: Check output structure and dimensions
+test_that("Output structure is correct", {
+  result <- 
+    prep_testing_data(
+      f = mock_f, 
+      time = mock_time, 
+      train_prep = mock_train_prep, 
+      optim_method = "DP"
+    )
+  expect_type(result, "list")
+  expect_true(all(
+    c("time", "f0", "fn", "q0", "qn", "mqn", "gam", "coef") %in% 
+      names(result)
+  ))
+  expect_equal(dim(result$f0), dim(mock_f))
+  expect_equal(dim(result$fn), dim(mock_f))
+  expect_equal(dim(result$q0), dim(mock_f))
+  expect_equal(dim(result$qn), dim(mock_f))
+  expect_equal(length(result$mqn), nrow(mock_f))
+  expect_equal(dim(result$coef), c(nrow(mock_f), ncol(mock_f)))
+})
+
+# Test 2: Check handling of different fpca types
+test_that("Function handles different fpca types", {
+  # hfPCA
+  mock_train_prep = prep_training_data(mock_f, mock_time, "hfpca")
+  result_hfpca <- 
+    prep_testing_data(
+      f = mock_f,
+      time = mock_time, 
+      train_prep = mock_train_prep, 
+      optim_method = "DP"
+    )
+  expect_true("psi" %in% names(result_hfpca))
+  expect_true("nu" %in% names(result_hfpca))
+  # vfPCA
+  mock_train_prep = prep_training_data(mock_f, mock_time, "vfpca")
+  result_vfpca <- 
+    prep_testing_data(
+      f = mock_f, 
+      time = mock_time, 
+      train_prep = mock_train_prep, 
+      optim_method = "DP"
+    )
+  expect_true("gam" %in% names(result_vfpca))
+})
+
+# Test 3: Check for error with incorrect input dimensions
+test_that("Function throws error with incorrect input dimensions", {
+  expect_error(
+    prep_testing_data(
+      f = matrix(1:20, nrow = 5), 
+      time = mock_time, 
+      train_prep = mock_train_prep, 
+      optim_method = "DP"
+    )
+  )
+})
+
+# Test 4: Check for error with invalid optim_method
+test_that("Function throws error with invalid optim_method", {
+  expect_error(
+    prep_testing_data(
+      f = mock_f, 
+      time = mock_time, 
+      train_prep = mock_train_prep, 
+      optim_method = "invalid_method"
+    )
+  )
+})
+
+# Test 5: Check for correct output when using different optimization methods
+test_that("Function works with different optimization methods", {
+  result_dp <- 
+    prep_testing_data(
+      f = mock_f, 
+      time = mock_time, 
+      train_prep = mock_train_prep, 
+      optim_method = "DPo"
+    )
+  result_dpo <- 
+    prep_testing_data(
+      f = mock_f, 
+      time = mock_time,
+      train_prep = mock_train_prep, 
+      optim_method = "RBFGS"
+    )
+  expect_type(result_dp, "list")
+  expect_type(result_dpo, "list")
+})