mikropml (version 1.6.1)

get_feature_importance: Get feature importance using the permutation method

Description

Calculates feature importance using a trained model and test data. Requires the future.apply package.

Usage

get_feature_importance(
  trained_model,
  test_data,
  outcome_colname,
  perf_metric_function,
  perf_metric_name,
  class_probs,
  method,
  seed = NA,
  corr_thresh = 1,
  groups = NULL,
  nperms = 100,
  corr_method = "spearman"
)

Value

Data frame with performance metrics for when each feature (or group of correlated features; feat) 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.

Arguments

trained_model

Trained model from caret::train().

test_data

Held out test data: dataframe of outcome and features.

outcome_colname

Column name as a string of the outcome variable (default NULL; the first column will be chosen automatically).

perf_metric_function

Function to calculate the performance metric to be used for cross-validation and test performance. Some functions are provided by caret (see caret::defaultSummary()). Defaults: binary classification = twoClassSummary, multi-class classification = multiClassSummary, regression = defaultSummary.

perf_metric_name

The column name from the output of the function provided to perf_metric_function that is to be used as the performance metric. Defaults: binary classification = "ROC", multi-class classification = "logLoss", regression = "RMSE".

class_probs

Whether to use class probabilities (TRUE for categorical outcomes, FALSE for numeric outcomes).

method

ML method. Options: c("glmnet", "rf", "rpart2", "svmRadial", "xgbTree").

• glmnet: linear, logistic, or multiclass regression

• rf: random forest

• rpart2: decision tree

• svmRadial: support vector machine

• xgbTree: xgboost

seed

Random seed (default: NA). Your results will only be reproducible if you set a seed.

corr_thresh

For feature importance, group correlations above or equal to corr_thresh (range 0 to 1; default: 1).

groups

Vector of feature names to group together during permutation. Each element should be a string with feature names separated by a pipe character (|). If this is NULL (default), correlated features will be grouped together based on corr_thresh.

nperms

number of permutations to perform (default: 100).

corr_method

correlation method. options or the same as those supported by stats::cor: spearman, pearson, kendall. (default: spearman)

Author

Begüm Topçuoğlu,

Zena Lapp,

Kelly Sovacool,

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.

We strongly recommend providing multiple cores to speed up computation time. See our vignette on parallel processing for more details.

Examples

if (FALSE) {
# If you called `run_ml()` with `feature_importance = FALSE` (the default),
# you can use `get_feature_importance()` later as long as you have the
# trained model and test data.
results <- run_ml(otu_small, "glmnet", kfold = 2, cv_times = 2)
names(results$trained_model$trainingData)[1] <- "dx"
feat_imp <- get_feature_importance(results$trained_model,
  results$trained_model$trainingData,
  results$test_data,
  "dx",
  multiClassSummary,
  "AUC",
  class_probs = TRUE,
  method = "glmnet"
)

# We strongly recommend providing multiple cores to speed up computation time.
# Do this before calling `get_feature_importance()`.
doFuture::registerDoFuture()
future::plan(future::multicore, workers = 2)

# Optionally, you can group features together with a custom grouping
feat_imp <- get_feature_importance(results$trained_model,
  results$trained_model$trainingData,
  results$test_data,
  "dx",
  multiClassSummary,
  "AUC",
  class_probs = TRUE,
  method = "glmnet",
  groups = c(
    "Otu00007", "Otu00008", "Otu00009", "Otu00011", "Otu00012",
    "Otu00015", "Otu00016", "Otu00018", "Otu00019", "Otu00020", "Otu00022",
    "Otu00023", "Otu00025", "Otu00028", "Otu00029", "Otu00030", "Otu00035",
    "Otu00036", "Otu00037", "Otu00038", "Otu00039", "Otu00040", "Otu00047",
    "Otu00050", "Otu00052", "Otu00054", "Otu00055", "Otu00056", "Otu00060",
    "Otu00003|Otu00002|Otu00005|Otu00024|Otu00032|Otu00041|Otu00053",
    "Otu00014|Otu00021|Otu00017|Otu00031|Otu00057",
    "Otu00013|Otu00006", "Otu00026|Otu00001|Otu00034|Otu00048",
    "Otu00033|Otu00010",
    "Otu00042|Otu00004", "Otu00043|Otu00027|Otu00049", "Otu00051|Otu00045",
    "Otu00058|Otu00044", "Otu00059|Otu00046"
  )
)

# the function can show a progress bar if you have the `progressr` package installed.
## optionally, specify the progress bar format:
progressr::handlers(progressr::handler_progress(
  format = ":message :bar :percent | elapsed: :elapsed | eta: :eta",
  clear = FALSE,
  show_after = 0
))
## tell progressr to always report progress
progressr::handlers(global = TRUE)
## run the function and watch the live progress udpates
feat_imp <- get_feature_importance(results$trained_model,
  results$trained_model$trainingData,
  results$test_data,
  "dx",
  multiClassSummary,
  "AUC",
  class_probs = TRUE,
  method = "glmnet"
)

# You can specify any correlation method supported by `stats::cor`:
feat_imp <- get_feature_importance(results$trained_model,
  results$trained_model$trainingData,
  results$test_data,
  "dx",
  multiClassSummary,
  "AUC",
  class_probs = TRUE,
  method = "glmnet",
  corr_method = "pearson"
)
}