From fce1469d28f40f871ff9e6810c404b03fe2cec24 Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Sat, 27 Nov 2021 01:10:37 -0500 Subject: [PATCH 01/13] Write calc_pvalue() for permutation tests --- R/feature_importance.R | 15 +++++++++------ R/utils.R | 12 ++++++++++++ 2 files changed, 21 insertions(+), 6 deletions(-) diff --git a/R/feature_importance.R b/R/feature_importance.R index 0692ac08..d737ab7a 100644 --- a/R/feature_importance.R +++ b/R/feature_importance.R @@ -14,11 +14,13 @@ #' grouped together based on `corr_thresh`. #' #' @return Dataframe with performance metrics for when each feature (or group of -#' correlated features; `names`) is permuted (`perf_metric`), and differences +#' correlated features; `names`) is permuted (`perf_metric`), differences #' between test performance metric and permuted performance metric -#' (`perf_metric_diff`; test minus permuted performance). Features with a -#' larger `perf_metric_diff` are more important. The performance metric name -#' (`perf_metric_name`) and seed (`seed`) are also returned. +#' (`perf_metric_diff`; test minus permuted performance), and the p-value +#' (`pvalue`: the probability of obtaining `perf_metric` under the null +#' hypothesis). Features with a larger `perf_metric_diff` are more important. +#' The performance metric name (`perf_metric_name`) and seed (`seed`) are also +#' returned. #' #' @examples #' \dontrun{ @@ -195,9 +197,10 @@ find_permuted_perf_metric <- function(test_data, trained_model, outcome_colname, )[[perf_metric_name]] ) }) - mean_perm_perf <- sum(perm_perfs) / nperms + mean_perm_perf <- mean(perm_perfs) return(c( perf_metric = mean_perm_perf, - perf_metric_diff = test_perf_value - mean_perm_perf + perf_metric_diff = test_perf_value - mean_perm_perf, + pvalue = calc_pvalue(perm_perfs, test_perf_value) )) } diff --git a/R/utils.R b/R/utils.R index c1fb9956..3e803819 100644 --- a/R/utils.R +++ b/R/utils.R @@ -231,3 +231,15 @@ radix_sort <- function(...) { is_whole_number <- function(x, tol = .Machine$double.eps^0.5) { abs(x - round(x)) < tol } + +#' Calculate a p-value given a test statistic and a vector of (permutation) statistics +#' +#' @param vctr vector of statistics +#' @param test_stat the test statistic +#' +#' @return the number of observations in `vctr` that are greater than `test_stat` divided by the number of observations in `vctr` +#' @noRd +#' @author Kelly Sovacool \email{sovacool@@umich.edu} +calc_pvalue <- function(vctr, test_stat) { + return(length(Filter(function(x) {x > test_stat}, vctr)) / length(vctr)) +} From 3737f61bed968fd3786fc790827e08d4db37a624 Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Sat, 27 Nov 2021 01:20:35 -0500 Subject: [PATCH 02/13] Use sum() instead of Filter() --- R/utils.R | 2 +- tests/testthat/test-utils.R | 6 ++++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/R/utils.R b/R/utils.R index 3e803819..1fe4bc76 100644 --- a/R/utils.R +++ b/R/utils.R @@ -241,5 +241,5 @@ is_whole_number <- function(x, tol = .Machine$double.eps^0.5) { #' @noRd #' @author Kelly Sovacool \email{sovacool@@umich.edu} calc_pvalue <- function(vctr, test_stat) { - return(length(Filter(function(x) {x > test_stat}, vctr)) / length(vctr)) + return(sum(vctr > test_stat) / length(vctr)) } diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index c547d401..3f0fef9d 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -104,3 +104,9 @@ test_that("is_whole_number() checks for integer numbers regardless of class", { expect_true(all(is_whole_number(c(1.0, 2.0, 3.0)))) expect_false(is_whole_number(1.2)) }) + +test_that('calc_pvalue() works', { + expect_equal(calc_pvalue(1:20, 10), 0.5) + expect_equal(calc_pvalue(1, 1), 0) + expect_equal(calc_pvalue(1:3, 0), 1) +}) From 5f111bc06064deb96fc8ab51ee53230c4883f73c Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Sat, 27 Nov 2021 01:34:29 -0500 Subject: [PATCH 03/13] Re-generate oracles to get p-values for feat imp --- data-raw/otu_mini_bin.R | 2 +- data-raw/otu_mini_multi.R | 2 +- data/otu_mini_bin.rda | Bin 3385 -> 3386 bytes data/otu_mini_bin_results_rf.rda | Bin 346823 -> 337863 bytes data/otu_mini_cont_results_glmnet.rda | Bin 79623 -> 52858 bytes data/otu_mini_multi.rda | Bin 7375 -> 7376 bytes data/otu_mini_multi_group.rda | Bin 368 -> 371 bytes data/otu_mini_multi_results_glmnet.rda | Bin 336482 -> 266845 bytes tests/testthat/test-feature_importance.R | 93 ++++++++++------------- 9 files changed, 42 insertions(+), 55 deletions(-) diff --git a/data-raw/otu_mini_bin.R b/data-raw/otu_mini_bin.R index e733cfd9..b48fd017 100644 --- a/data-raw/otu_mini_bin.R +++ b/data-raw/otu_mini_bin.R @@ -74,7 +74,7 @@ otu_mini_bin_results_rf <- mikropml::run_ml(otu_mini_bin, find_feature_importance = TRUE, seed = 2019, cv_times = 2, - group = otu_mini_group + groups = otu_mini_group ) usethis::use_data(otu_mini_bin_results_rf, overwrite = TRUE) diff --git a/data-raw/otu_mini_multi.R b/data-raw/otu_mini_multi.R index 37b43bab..1d13f23d 100644 --- a/data-raw/otu_mini_multi.R +++ b/data-raw/otu_mini_multi.R @@ -12,6 +12,6 @@ otu_mini_multi_results_glmnet <- mikropml::run_ml(otu_mini_multi, # use built-in find_feature_importance = TRUE, 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360d9264..6f23ac00 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,8 @@ # development version 1.3.0 -- Allow `kfold >= length(groups)`. (#285, @kelly-sovacool) +- Allow `kfold >= length(groups)` (#285, @kelly-sovacool). - When using the groups parameter, groups are kept together in cross-validation partitions when `kfold` <= the number of groups in the training set. Previously, an error was thrown if this condition was not met. Now, if there are not enough groups in the training set for groups to be kept together during CV, groups are allowed to be split up across CV partitions. +- Report p-values for permutation feature importance (#288, @kelly-sovacool) # mikropml 1.2.0 From 6b95ef001dab04d956ea27663e739fb8f10e2488 Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Sat, 27 Nov 2021 21:47:12 -0500 Subject: [PATCH 05/13] Update PR template; roxygen & vignettes on separate lines --- .github/pull_request_template.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index f1c204fb..745f3805 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -10,6 +10,8 @@ (~Strikethrough~ any points that are not applicable.) - [ ] Write unit tests for any new functionality or bug fixes. -- [ ] Update roxygen comments & vignettes if there are any API changes. +- [ ] Update docs if there are any API changes: + - [ ] roxygen comments + - [ ] vignettes - [ ] Update `NEWS.md` if this includes any user-facing changes. - [ ] The check workflow succeeds on your most recent commit. **This is always required before the PR can be merged.** From 5fc2aab34c9ecd17394a8e11e4af20b35856042d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Sun, 28 Nov 2021 02:59:46 +0000 Subject: [PATCH 06/13] =?UTF-8?q?=F0=9F=8E=A8=20Style=20R=20code?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tests/testthat/test-feature_importance.R | 95 ++++++++++++++++-------- tests/testthat/test-utils.R | 2 +- 2 files changed, 63 insertions(+), 34 deletions(-) diff --git a/tests/testthat/test-feature_importance.R b/tests/testthat/test-feature_importance.R index 8591b5a3..7db5805f 100644 --- a/tests/testthat/test-feature_importance.R +++ b/tests/testthat/test-feature_importance.R @@ -63,27 +63,43 @@ test_that("find_permuted_perf_metric works", { ) }) -feat_imp <- structure(list(perf_metric = c(0.629157894736842, 0.605473684210526, -0.63878947368421, 0.636763157894737, 0.629447368421053, 0.637868421052632, -0.642552631578947, 0.592157894736842, 0.639684210526316, 0.637526315789474 +feat_imp <- structure(list( + perf_metric = c( + 0.629157894736842, 0.605473684210526, + 0.63878947368421, 0.636763157894737, 0.629447368421053, 0.637868421052632, + 0.642552631578947, 0.592157894736842, 0.639684210526316, 0.637526315789474 + ), + perf_metric_diff = c( + 0.0182105263157895, 0.0418947368421053, + 0.00857894736842113, 0.0106052631578948, 0.0179210526315789, + 0.00950000000000006, 0.00481578947368422, 0.0552105263157895, + 0.00768421052631585, 0.00984210526315787 + ), + pvalue = c( + 0.17, 0.06, + 0.09, 0.28, 0.37, 0.28, 0.24, 0.09, 0.12, 0.41 + ), + names = structure(1:10, .Label = c( + "Otu00001", + "Otu00002", "Otu00003", "Otu00004", "Otu00005", "Otu00006", "Otu00007", + "Otu00008", "Otu00009", "Otu00010" + ), class = "factor"), + method = c( + "glmnet", + "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", + "glmnet", "glmnet" + ), + perf_metric_name = c( + "AUC", "AUC", "AUC", + "AUC", "AUC", "AUC", "AUC", "AUC", "AUC", "AUC" + ), + seed = c( + 2019, + 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019 + ) ), -perf_metric_diff = c(0.0182105263157895, 0.0418947368421053, -0.00857894736842113, 0.0106052631578948, 0.0179210526315789, -0.00950000000000006, 0.00481578947368422, 0.0552105263157895, -0.00768421052631585, 0.00984210526315787), -pvalue = c(0.17, 0.06, -0.09, 0.28, 0.37, 0.28, 0.24, 0.09, 0.12, 0.41), -names = structure(1:10, .Label = c("Otu00001", -"Otu00002", "Otu00003", "Otu00004", "Otu00005", "Otu00006", "Otu00007", -"Otu00008", "Otu00009", "Otu00010"), class = "factor"), -method = c("glmnet", -"glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", -"glmnet", "glmnet"), -perf_metric_name = c("AUC", "AUC", "AUC", -"AUC", "AUC", "AUC", "AUC", "AUC", "AUC", "AUC"), -seed = c(2019, -2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019)), -row.names = c(NA, -10L), class = "data.frame") +row.names = c(NA, -10L), class = "data.frame" +) test_that("feature importances are correct", { set.seed(2019) @@ -150,18 +166,31 @@ test_that("custom grouped features works", { seed = 2019, groups = groups ), - structure(list(perf_metric = c(0.629157894736842, 0.596921052631579, -0.633605263157895, 0.639105263157895, 0.642421052631579, 0.596842105263158, -0.640289473684211, 0.629868421052632), perf_metric_diff = c(0.0182105263157895, -0.0504473684210527, 0.0137631578947369, 0.00826315789473686, -0.0049473684210527, 0.0505263157894738, 0.00707894736842107, -0.0175000000000001), pvalue = c(0.17, 0.24, 0.27, 0.3, 0.26, -0.08, 0.11, 0.3), names = structure(1:8, .Label = c("Otu00001", -"Otu00002|Otu00003|Otu00005", "Otu00004", "Otu00006", "Otu00007", -"Otu00008", "Otu00009", "Otu00010"), class = "factor"), method = c("glmnet", -"glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet" -), perf_metric_name = c("AUC", "AUC", "AUC", "AUC", "AUC", "AUC", -"AUC", "AUC"), seed = c(2019, 2019, 2019, 2019, 2019, 2019, 2019, -2019)), row.names = c(NA, -8L), class = "data.frame") + structure(list(perf_metric = c( + 0.629157894736842, 0.596921052631579, + 0.633605263157895, 0.639105263157895, 0.642421052631579, 0.596842105263158, + 0.640289473684211, 0.629868421052632 + ), perf_metric_diff = c( + 0.0182105263157895, + 0.0504473684210527, 0.0137631578947369, 0.00826315789473686, + 0.0049473684210527, 0.0505263157894738, 0.00707894736842107, + 0.0175000000000001 + ), pvalue = c( + 0.17, 0.24, 0.27, 0.3, 0.26, + 0.08, 0.11, 0.3 + ), names = structure(1:8, .Label = c( + "Otu00001", + "Otu00002|Otu00003|Otu00005", "Otu00004", "Otu00006", "Otu00007", + "Otu00008", "Otu00009", "Otu00010" + ), class = "factor"), method = c( + "glmnet", + "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet", "glmnet" + ), perf_metric_name = c( + "AUC", "AUC", "AUC", "AUC", "AUC", "AUC", + "AUC", "AUC" + ), seed = c( + 2019, 2019, 2019, 2019, 2019, 2019, 2019, + 2019 + )), row.names = c(NA, -8L), class = "data.frame") ) }) diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index 3f0fef9d..338b792e 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -105,7 +105,7 @@ test_that("is_whole_number() checks for integer numbers regardless of class", { expect_false(is_whole_number(1.2)) }) -test_that('calc_pvalue() works', { +test_that("calc_pvalue() works", { expect_equal(calc_pvalue(1:20, 10), 0.5) expect_equal(calc_pvalue(1, 1), 0) expect_equal(calc_pvalue(1:3, 0), 1) From 154578fd45f5970469f1f681645a5c68003f02a4 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Sun, 28 Nov 2021 03:24:17 +0000 Subject: [PATCH 07/13] =?UTF-8?q?=F0=9F=93=91=20Build=20docs=20site?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/articles/introduction.html | 30 +++++++++++++------------- docs/pkgdown.yml | 2 +- docs/pull_request_template.html | 10 ++++++++- docs/reference/get_perf_metric_fn.html | 6 +++--- 4 files changed, 28 insertions(+), 20 deletions(-) diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index ab988cf6..dc0270a0 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -438,17 +438,17 @@

Now, we can check out the feature importances:

 results_imp$feature_importance
-#>    perf_metric perf_metric_diff    names method perf_metric_name seed
-#> 1    0.5542375        0.0082625 Otu00001     rf              AUC 2019
-#> 2    0.5731750       -0.0106750 Otu00002     rf              AUC 2019
-#> 3    0.5548750        0.0076250 Otu00003     rf              AUC 2019
-#> 4    0.6414750       -0.0789750 Otu00004     rf              AUC 2019
-#> 5    0.5049625        0.0575375 Otu00005     rf              AUC 2019
-#> 6    0.5444500        0.0180500 Otu00006     rf              AUC 2019
-#> 7    0.5417125        0.0207875 Otu00007     rf              AUC 2019
-#> 8    0.5257750        0.0367250 Otu00008     rf              AUC 2019
-#> 9    0.5395750        0.0229250 Otu00009     rf              AUC 2019
-#> 10   0.4977625        0.0647375 Otu00010     rf              AUC 2019
+#> perf_metric perf_metric_diff pvalue names method perf_metric_name seed +#> 1 0.5542375 0.0082625 0.37 Otu00001 rf AUC 2019 +#> 2 0.5731750 -0.0106750 0.57 Otu00002 rf AUC 2019 +#> 3 0.5548750 0.0076250 0.38 Otu00003 rf AUC 2019 +#> 4 0.6414750 -0.0789750 0.99 Otu00004 rf AUC 2019 +#> 5 0.5049625 0.0575375 0.05 Otu00005 rf AUC 2019 +#> 6 0.5444500 0.0180500 0.18 Otu00006 rf AUC 2019 +#> 7 0.5417125 0.0207875 0.21 Otu00007 rf AUC 2019 +#> 8 0.5257750 0.0367250 0.05 Otu00008 rf AUC 2019 +#> 9 0.5395750 0.0229250 0.02 Otu00009 rf AUC 2019 +#> 10 0.4977625 0.0647375 0.05 Otu00010 rf AUC 2019

There are several columns:

  1. @@ -484,10 +484,10 @@

    #> Finding feature importance... #> Feature importance complete. results_imp_corr$feature_importance -#> perf_metric perf_metric_diff -#> 1 0.5502105 0.09715789 -#> 2 0.6369474 0.01042105 -#> 3 0.5951316 0.05223684 +#> perf_metric perf_metric_diff pvalue +#> 1 0.5502105 0.09715789 0.08 +#> 2 0.6369474 0.01042105 0.40 +#> 3 0.5951316 0.05223684 0.08 #> names #> 1 Otu00001|Otu00002|Otu00003|Otu00005|Otu00006|Otu00007|Otu00009|Otu00010 #> 2 Otu00004 diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index ad2e9f24..7a3cb23d 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -7,7 +7,7 @@ articles: parallel: parallel.html preprocess: preprocess.html tuning: tuning.html -last_built: 2021-11-22T15:57Z +last_built: 2021-11-28T03:01Z urls: reference: http://www.schlosslab.org/mikropml/reference article: http://www.schlosslab.org/mikropml/articles diff --git a/docs/pull_request_template.html b/docs/pull_request_template.html index fc7a8cb4..c3d0a27c 100644 --- a/docs/pull_request_template.html +++ b/docs/pull_request_template.html @@ -174,7 +174,15 @@

    Write unit tests for any new functionality or bug fixes.

  2. -Update roxygen comments & vignettes if there are any API changes.
  3. +Update docs if there are any API changes:
      +
    • + +roxygen comments
    • +
    • + +vignettes
    • +
    +
  4. Update NEWS.md if this includes any user-facing changes.
  5. diff --git a/docs/reference/get_perf_metric_fn.html b/docs/reference/get_perf_metric_fn.html index e285da89..f5d033fd 100644 --- a/docs/reference/get_perf_metric_fn.html +++ b/docs/reference/get_perf_metric_fn.html @@ -182,7 +182,7 @@

    Examp #> data$obs <- factor(data$obs, levels = lev) #> postResample(data[, "pred"], data[, "obs"]) #> } -#> <bytecode: 0x7f7fa0eaf3c0> +#> <bytecode: 0x7f9380617c08> #> <environment: namespace:caret> get_perf_metric_fn("binary") #> function (data, lev = NULL, model = NULL) @@ -240,7 +240,7 @@

    Examp #> stats <- stats[c(stat_list)] #> return(stats) #> } -#> <bytecode: 0x7f7f9f130158> +#> <bytecode: 0x7f939d1362b0> #> <environment: namespace:caret> get_perf_metric_fn("multiclass") #> function (data, lev = NULL, model = NULL) @@ -298,7 +298,7 @@

    Examp #> stats <- stats[c(stat_list)] #> return(stats) #> } -#> <bytecode: 0x7f7f9f130158> +#> <bytecode: 0x7f939d1362b0> #> <environment: namespace:caret> From 78f8816e30d5fb40d81fbeeda442d87b7e8ac12b Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Tue, 30 Nov 2021 13:11:41 -0500 Subject: [PATCH 08/13] Add details about the p-value to the docs --- R/feature_importance.R | 29 +++++++++++++++++++++-------- R/utils.R | 6 ++++-- 2 files changed, 25 insertions(+), 10 deletions(-) diff --git a/R/feature_importance.R b/R/feature_importance.R index d737ab7a..8e18caa9 100644 --- a/R/feature_importance.R +++ b/R/feature_importance.R @@ -13,14 +13,26 @@ #' character (`|`). If this is `NULL` (default), correlated features will be #' grouped together based on `corr_thresh`. #' -#' @return Dataframe with performance metrics for when each feature (or group of -#' correlated features; `names`) is permuted (`perf_metric`), differences -#' between test performance metric and permuted performance metric -#' (`perf_metric_diff`; test minus permuted performance), and the p-value -#' (`pvalue`: the probability of obtaining `perf_metric` under the null -#' hypothesis). Features with a larger `perf_metric_diff` are more important. -#' The performance metric name (`perf_metric_name`) and seed (`seed`) are also -#' returned. +#' @return Data frame with performance metrics for when each feature (or group +#' of correlated features; `names`) is permuted (`perf_metric`), differences +#' between the actual test performance metric on and the permuted performance +#' metric (`perf_metric_diff`; test minus permuted performance), and the +#' p-value (`pvalue`: the probability of obtaining the actual performance +#' value under the null hypothesis). Features with a larger `perf_metric_diff` +#' are more important. The performance metric name (`perf_metric_name`) and +#' seed (`seed`) are also returned. +#' +#' @details +#' For permutation tests, the p-value is the number of permutation statistics +#' that are greater than the test statistic, divided by the number of +#' permutations. In our case, the permutation statistic is the model performance +#' (e.g. AUROC) after randomizing the order of observations for one feature, and +#' the test statistic is the actual performance on the test data. By default we +#' perform 100 permutations per feature; increasing this will increase the +#' precision of estimating the null distribution, but also increases runtime. +#' The p-value represents the probability of obtaining the actual performance in +#' the event that the null hypothesis is true, where the null hypothesis is that +#' the feature is not important for model performance. #' #' @examples #' \dontrun{ @@ -85,6 +97,7 @@ #' @export #' @author Begüm Topçuoğlu, \email{topcuoglu.begum@@gmail.com} #' @author Zena Lapp, \email{zenalapp@@umich.edu} +#' @author Kelly Sovacool, \email{sovacool@@umich.edu} get_feature_importance <- function(trained_model, train_data, test_data, outcome_colname, perf_metric_function, perf_metric_name, class_probs, method, diff --git a/R/utils.R b/R/utils.R index 1fe4bc76..1b6bcb98 100644 --- a/R/utils.R +++ b/R/utils.R @@ -232,12 +232,14 @@ is_whole_number <- function(x, tol = .Machine$double.eps^0.5) { abs(x - round(x)) < tol } -#' Calculate a p-value given a test statistic and a vector of (permutation) statistics +#' Calculate the p-value for a permutation test #' #' @param vctr vector of statistics #' @param test_stat the test statistic #' -#' @return the number of observations in `vctr` that are greater than `test_stat` divided by the number of observations in `vctr` +#' @return the number of observations in `vctr` that are greater than +#' `test_stat` divided by the number of observations in `vctr` +#' #' @noRd #' @author Kelly Sovacool \email{sovacool@@umich.edu} calc_pvalue <- function(vctr, test_stat) { From 3cb6f42344bbe0c22987a7316d65cd3b30453bfd Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Tue, 30 Nov 2021 13:12:27 -0500 Subject: [PATCH 09/13] Document the feature importance p-value in intro vignette --- vignettes/introduction.Rmd | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd index 17e0c5a1..9cb1d8a9 100644 --- a/vignettes/introduction.Rmd +++ b/vignettes/introduction.Rmd @@ -422,7 +422,8 @@ results_imp$feature_importance There are several columns: 1. `perf_metric`: The performance value of the permuted feature. -1. `perf_metric_diff`: The difference between the performance for the true and permuted data (i.e. test performance minus permuted performance). Features with a larger `perf_metric_diff` are more important. +1. `perf_metric_diff`: The difference between the performance for the actual and permuted data (i.e. test performance minus permuted performance). Features with a larger `perf_metric_diff` are more important. +1. `pvalue`: the probability of obtaining the actual performance value under the null hypothesis. 1. `names`: The feature that was permuted. 1. `method`: The ML method used. 1. `perf_metric_name`: The peformance metric used. From 0352bcee1fb79f1392c9e819b5cd7c4f2cc3e595 Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Tue, 30 Nov 2021 13:17:00 -0500 Subject: [PATCH 10/13] Reformat /doc/ /Meta/ paths --- .gitignore | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index c030667b..c58a0f37 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,3 @@ -Meta/ -doc/ .DS_Store *.Rproj *.Rproj.user @@ -7,5 +5,5 @@ doc/ .Rhistory inst/doc .snakemake -doc -Meta +/doc/ +/Meta/ From 910a51b93df7f14d4d4d2f37e9ce45c0dd3b8742 Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Tue, 30 Nov 2021 13:17:08 -0500 Subject: [PATCH 11/13] document() --- man/get_feature_importance.Rd | 28 ++++++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/man/get_feature_importance.Rd b/man/get_feature_importance.Rd index fda505bc..e6b48868 100644 --- a/man/get_feature_importance.Rd +++ b/man/get_feature_importance.Rd @@ -72,17 +72,31 @@ grouped together based on \code{corr_thresh}.} by \code{stats::cor}: spearman, pearson, kendall. (default: spearman)} } \value{ -Dataframe with performance metrics for when each feature (or group of -correlated features; \code{names}) is permuted (\code{perf_metric}), and differences -between test performance metric and permuted performance metric -(\code{perf_metric_diff}; test minus permuted performance). Features with a -larger \code{perf_metric_diff} are more important. The performance metric name -(\code{perf_metric_name}) and seed (\code{seed}) are also returned. +Data frame with performance metrics for when each feature (or group +of correlated features; \code{names}) is permuted (\code{perf_metric}), differences +between the actual test performance metric on and the permuted performance +metric (\code{perf_metric_diff}; test minus permuted performance), and the +p-value (\code{pvalue}: the probability of obtaining the actual performance +value under the null hypothesis). Features with a larger \code{perf_metric_diff} +are more important. The performance metric name (\code{perf_metric_name}) and +seed (\code{seed}) are also returned. } \description{ Calculates feature importance using a trained model and test data. Requires the \code{future.apply} package. } +\details{ +For permutation tests, the p-value is the number of permutation statistics +that are greater than the test statistic, divided by the number of +permutations. In our case, the permutation statistic is the model performance +(e.g. AUROC) after randomizing the order of observations for one feature, and +the test statistic is the actual performance on the test data. By default we +perform 100 permutations per feature; increasing this will increase the +precision of estimating the null distribution, but also increases runtime. +The p-value represents the probability of obtaining the actual performance in +the event that the null hypothesis is true, where the null hypothesis is that +the feature is not important for model performance. +} \examples{ \dontrun{ results <- run_ml(otu_small, "glmnet", kfold = 2, cv_times = 2) @@ -148,4 +162,6 @@ feat_imp <- get_feature_importance(results$trained_model, Begüm Topçuoğlu, \email{topcuoglu.begum@gmail.com} Zena Lapp, \email{zenalapp@umich.edu} + +Kelly Sovacool, \email{sovacool@umich.edu} } From 7a298d1073fa1c5d90c360ec933232d69c559451 Mon Sep 17 00:00:00 2001 From: Kelly Sovacool Date: Tue, 30 Nov 2021 13:19:04 -0500 Subject: [PATCH 12/13] pkgdown::build_article('introduction') --- docs/articles/introduction.html | 55 +++++++++---------- .../header-attrs-2.11/header-attrs.js | 12 ++++ 2 files changed, 39 insertions(+), 28 deletions(-) create mode 100644 docs/articles/introduction_files/header-attrs-2.11/header-attrs.js diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index dc0270a0..ceb61f90 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -102,7 +102,7 @@ -
    +

    In the above case, all observations from A & B will be used for training, all from C & D will be used for testing, and the remaining groups will be randomly assigned to one or the other to satisfy the training_frac as closely as possible.

    @@ -416,7 +414,6 @@

    #> Fraction of data in the training set: 0.5 #> Groups in the training set: A B C D E F #> Groups in the testing set: A B C D E F G H -#> Groups will be kept together in CV partitions #> Training the model... #> Training complete.

    If you need even more control than this, take a look at setting custom training indices. You might also prefer to provide your own train control scheme with the cross_val parameter in run_ml().

    @@ -438,23 +435,25 @@

    Now, we can check out the feature importances:

     results_imp$feature_importance
    -#>    perf_metric perf_metric_diff pvalue    names method perf_metric_name seed
    -#> 1    0.5542375        0.0082625   0.37 Otu00001     rf              AUC 2019
    -#> 2    0.5731750       -0.0106750   0.57 Otu00002     rf              AUC 2019
    -#> 3    0.5548750        0.0076250   0.38 Otu00003     rf              AUC 2019
    -#> 4    0.6414750       -0.0789750   0.99 Otu00004     rf              AUC 2019
    -#> 5    0.5049625        0.0575375   0.05 Otu00005     rf              AUC 2019
    -#> 6    0.5444500        0.0180500   0.18 Otu00006     rf              AUC 2019
    -#> 7    0.5417125        0.0207875   0.21 Otu00007     rf              AUC 2019
    -#> 8    0.5257750        0.0367250   0.05 Otu00008     rf              AUC 2019
    -#> 9    0.5395750        0.0229250   0.02 Otu00009     rf              AUC 2019
    -#> 10   0.4977625        0.0647375   0.05 Otu00010     rf              AUC 2019
    +#> perf_metric perf_metric_diff names method perf_metric_name seed +#> 1 0.5542375 0.0082625 Otu00001 rf AUC 2019 +#> 2 0.5731750 -0.0106750 Otu00002 rf AUC 2019 +#> 3 0.5548750 0.0076250 Otu00003 rf AUC 2019 +#> 4 0.6414750 -0.0789750 Otu00004 rf AUC 2019 +#> 5 0.5049625 0.0575375 Otu00005 rf AUC 2019 +#> 6 0.5444500 0.0180500 Otu00006 rf AUC 2019 +#> 7 0.5417125 0.0207875 Otu00007 rf AUC 2019 +#> 8 0.5257750 0.0367250 Otu00008 rf AUC 2019 +#> 9 0.5395750 0.0229250 Otu00009 rf AUC 2019 +#> 10 0.4977625 0.0647375 Otu00010 rf AUC 2019

    There are several columns:

    1. perf_metric: The performance value of the permuted feature.
    2. -perf_metric_diff: The difference between the performance for the true and permuted data (i.e. test performance minus permuted performance). Features with a larger perf_metric_diff are more important.
    3. +perf_metric_diff: The difference between the performance for the actual and permuted data (i.e. test performance minus permuted performance). Features with a larger perf_metric_diff are more important. +
    4. +pvalue: the probability of obtaining the actual performance value under the null hypothesis.
    5. names: The feature that was permuted.
    6. @@ -484,10 +483,10 @@

      #> Finding feature importance... #> Feature importance complete. results_imp_corr$feature_importance -#> perf_metric perf_metric_diff pvalue -#> 1 0.5502105 0.09715789 0.08 -#> 2 0.6369474 0.01042105 0.40 -#> 3 0.5951316 0.05223684 0.08 +#> perf_metric perf_metric_diff +#> 1 0.5502105 0.09715789 +#> 2 0.6369474 0.01042105 +#> 3 0.5951316 0.05223684 #> names #> 1 Otu00001|Otu00002|Otu00003|Otu00005|Otu00006|Otu00007|Otu00009|Otu00010 #> 2 Otu00004 @@ -542,7 +541,7 @@

      'svmRadial', cv_times = 5, seed = 2019)

    -

    If you get a message “maximum number of iterations reached”, see this issue in caret.

    +

    If you get a message “maximum number of iterations reached,” see this issue in caret.

    @@ -553,7 +552,7 @@

    Multiclass data

    We provide otu_mini_multi with a multiclass outcome (three or more outcomes):

    -otu_mini_multi %>% dplyr::pull('dx') %>% unique()
    +otu_mini_multi %>% dplyr::pull('dx') %>% unique()
     #> [1] "adenoma"   "carcinoma" "normal"

    Here’s an example of running multiclass data:

    @@ -594,12 +593,12 @@ 

    References

    -
    -
    -

    Tang, Shengpu, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, and Jenna Wiens. 2020. “Democratizing EHR Analyses with FIDDLE: A Flexible Data-Driven Preprocessing Pipeline for Structured Clinical Data.” J Am Med Inform Assoc, October. https://doi.org/10.1093/jamia/ocaa139.

    +
    +
    +Tang, Shengpu, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, and Jenna Wiens. 2020. “Democratizing EHR Analyses with FIDDLE: A Flexible Data-Driven Preprocessing Pipeline for Structured Clinical Data.” J Am Med Inform Assoc, October. https://doi.org/10.1093/jamia/ocaa139.
    -
    -

    Topçuoğlu, Begüm D., Nicholas A. Lesniak, Mack T. Ruffin, Jenna Wiens, and Patrick D. Schloss. 2020. “A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.” mBio 11 (3). https://doi.org/10.1128/mBio.00434-20.

    +
    +Topçuoğlu, Begüm D., Nicholas A. Lesniak, Mack T. Ruffin, Jenna Wiens, and Patrick D. Schloss. 2020. “A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.” mBio 11 (3). https://doi.org/10.1128/mBio.00434-20.
    diff --git a/docs/articles/introduction_files/header-attrs-2.11/header-attrs.js b/docs/articles/introduction_files/header-attrs-2.11/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/introduction_files/header-attrs-2.11/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); From a8173d8aeb26c976f477172707640efb9a82b665 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 30 Nov 2021 19:10:04 +0000 Subject: [PATCH 13/13] =?UTF-8?q?=F0=9F=93=91=20Build=20docs=20site?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/articles/introduction.html | 51 ++++++++++++---------- docs/pkgdown.yml | 2 +- docs/reference/get_feature_importance.html | 27 +++++++++--- docs/reference/get_perf_metric_fn.html | 6 +-- 4 files changed, 52 insertions(+), 34 deletions(-) diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index ceb61f90..769488ae 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -102,7 +102,7 @@ -
    +

    In the above case, all observations from A & B will be used for training, all from C & D will be used for testing, and the remaining groups will be randomly assigned to one or the other to satisfy the training_frac as closely as possible.

    @@ -414,6 +416,7 @@

    #> Fraction of data in the training set: 0.5 #> Groups in the training set: A B C D E F #> Groups in the testing set: A B C D E F G H +#> Groups will be kept together in CV partitions #> Training the model... #> Training complete.

    If you need even more control than this, take a look at setting custom training indices. You might also prefer to provide your own train control scheme with the cross_val parameter in run_ml().

    @@ -435,17 +438,17 @@

    Now, we can check out the feature importances:

     results_imp$feature_importance
    -#>    perf_metric perf_metric_diff    names method perf_metric_name seed
    -#> 1    0.5542375        0.0082625 Otu00001     rf              AUC 2019
    -#> 2    0.5731750       -0.0106750 Otu00002     rf              AUC 2019
    -#> 3    0.5548750        0.0076250 Otu00003     rf              AUC 2019
    -#> 4    0.6414750       -0.0789750 Otu00004     rf              AUC 2019
    -#> 5    0.5049625        0.0575375 Otu00005     rf              AUC 2019
    -#> 6    0.5444500        0.0180500 Otu00006     rf              AUC 2019
    -#> 7    0.5417125        0.0207875 Otu00007     rf              AUC 2019
    -#> 8    0.5257750        0.0367250 Otu00008     rf              AUC 2019
    -#> 9    0.5395750        0.0229250 Otu00009     rf              AUC 2019
    -#> 10   0.4977625        0.0647375 Otu00010     rf              AUC 2019
    +#> perf_metric perf_metric_diff pvalue names method perf_metric_name seed +#> 1 0.5542375 0.0082625 0.37 Otu00001 rf AUC 2019 +#> 2 0.5731750 -0.0106750 0.57 Otu00002 rf AUC 2019 +#> 3 0.5548750 0.0076250 0.38 Otu00003 rf AUC 2019 +#> 4 0.6414750 -0.0789750 0.99 Otu00004 rf AUC 2019 +#> 5 0.5049625 0.0575375 0.05 Otu00005 rf AUC 2019 +#> 6 0.5444500 0.0180500 0.18 Otu00006 rf AUC 2019 +#> 7 0.5417125 0.0207875 0.21 Otu00007 rf AUC 2019 +#> 8 0.5257750 0.0367250 0.05 Otu00008 rf AUC 2019 +#> 9 0.5395750 0.0229250 0.02 Otu00009 rf AUC 2019 +#> 10 0.4977625 0.0647375 0.05 Otu00010 rf AUC 2019

    There are several columns:

    1. @@ -483,10 +486,10 @@

      #> Finding feature importance... #> Feature importance complete. results_imp_corr$feature_importance -#> perf_metric perf_metric_diff -#> 1 0.5502105 0.09715789 -#> 2 0.6369474 0.01042105 -#> 3 0.5951316 0.05223684 +#> perf_metric perf_metric_diff pvalue +#> 1 0.5502105 0.09715789 0.08 +#> 2 0.6369474 0.01042105 0.40 +#> 3 0.5951316 0.05223684 0.08 #> names #> 1 Otu00001|Otu00002|Otu00003|Otu00005|Otu00006|Otu00007|Otu00009|Otu00010 #> 2 Otu00004 @@ -541,7 +544,7 @@

      'svmRadial', cv_times = 5, seed = 2019) -

      If you get a message “maximum number of iterations reached,” see this issue in caret.

      +

      If you get a message “maximum number of iterations reached”, see this issue in caret.

      @@ -552,7 +555,7 @@

      Multiclass data

      We provide otu_mini_multi with a multiclass outcome (three or more outcomes):

      -otu_mini_multi %>% dplyr::pull('dx') %>% unique()
      +otu_mini_multi %>% dplyr::pull('dx') %>% unique()
       #> [1] "adenoma"   "carcinoma" "normal"

      Here’s an example of running multiclass data:

      @@ -593,12 +596,12 @@ 

      References

      -
      -
      -Tang, Shengpu, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, and Jenna Wiens. 2020. “Democratizing EHR Analyses with FIDDLE: A Flexible Data-Driven Preprocessing Pipeline for Structured Clinical Data.” J Am Med Inform Assoc, October. https://doi.org/10.1093/jamia/ocaa139. +
      +
      +

      Tang, Shengpu, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, and Jenna Wiens. 2020. “Democratizing EHR Analyses with FIDDLE: A Flexible Data-Driven Preprocessing Pipeline for Structured Clinical Data.” J Am Med Inform Assoc, October. https://doi.org/10.1093/jamia/ocaa139.

      -
      -Topçuoğlu, Begüm D., Nicholas A. Lesniak, Mack T. Ruffin, Jenna Wiens, and Patrick D. Schloss. 2020. “A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.” mBio 11 (3). https://doi.org/10.1128/mBio.00434-20. +
      +

      Topçuoğlu, Begüm D., Nicholas A. Lesniak, Mack T. Ruffin, Jenna Wiens, and Patrick D. Schloss. 2020. “A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.” mBio 11 (3). https://doi.org/10.1128/mBio.00434-20.

      diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 7a3cb23d..18bf8145 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -7,7 +7,7 @@ articles: parallel: parallel.html preprocess: preprocess.html tuning: tuning.html -last_built: 2021-11-28T03:01Z +last_built: 2021-11-30T18:45Z urls: reference: http://www.schlosslab.org/mikropml/reference article: http://www.schlosslab.org/mikropml/articles diff --git a/docs/reference/get_feature_importance.html b/docs/reference/get_feature_importance.html index 2f93efb4..759ea311 100644 --- a/docs/reference/get_feature_importance.html +++ b/docs/reference/get_feature_importance.html @@ -256,16 +256,31 @@

      Arg

      Value

      -

      Dataframe with performance metrics for when each feature (or group of -correlated features; names) is permuted (perf_metric), and differences -between test performance metric and permuted performance metric -(perf_metric_diff; test minus permuted performance). Features with a -larger perf_metric_diff are more important. The performance metric name -(perf_metric_name) and seed (seed) are also returned.

      +

      Data frame with performance metrics for when each feature (or group +of correlated features; names) is permuted (perf_metric), differences +between the actual test performance metric on and the permuted performance +metric (perf_metric_diff; test minus permuted performance), and the +p-value (pvalue: the probability of obtaining the actual performance +value under the null hypothesis). Features with a larger perf_metric_diff +are more important. The performance metric name (perf_metric_name) and +seed (seed) are also returned.

      +

      Details

      + +

      For permutation tests, the p-value is the number of permutation statistics +that are greater than the test statistic, divided by the number of +permutations. In our case, the permutation statistic is the model performance +(e.g. AUROC) after randomizing the order of observations for one feature, and +the test statistic is the actual performance on the test data. By default we +perform 100 permutations per feature; increasing this will increase the +precision of estimating the null distribution, but also increases runtime. +The p-value represents the probability of obtaining the actual performance in +the event that the null hypothesis is true, where the null hypothesis is that +the feature is not important for model performance.

      Author

      Begüm Topçuoğlu, topcuoglu.begum@gmail.com

      Zena Lapp, zenalapp@umich.edu

      +

      Kelly Sovacool, sovacool@umich.edu

      Examples

      if (FALSE) {
      diff --git a/docs/reference/get_perf_metric_fn.html b/docs/reference/get_perf_metric_fn.html
      index f5d033fd..82c5d2d0 100644
      --- a/docs/reference/get_perf_metric_fn.html
      +++ b/docs/reference/get_perf_metric_fn.html
      @@ -182,7 +182,7 @@ 

      Examp #> data$obs <- factor(data$obs, levels = lev) #> postResample(data[, "pred"], data[, "obs"]) #> } -#> <bytecode: 0x7f9380617c08> +#> <bytecode: 0x7fc428237898> #> <environment: namespace:caret> get_perf_metric_fn("binary") #> function (data, lev = NULL, model = NULL) @@ -240,7 +240,7 @@

      Examp #> stats <- stats[c(stat_list)] #> return(stats) #> } -#> <bytecode: 0x7f939d1362b0> +#> <bytecode: 0x7fc445fd1c50> #> <environment: namespace:caret> get_perf_metric_fn("multiclass") #> function (data, lev = NULL, model = NULL) @@ -298,7 +298,7 @@

      Examp #> stats <- stats[c(stat_list)] #> return(stats) #> } -#> <bytecode: 0x7f939d1362b0> +#> <bytecode: 0x7fc445fd1c50> #> <environment: namespace:caret>