validate_fn function

Validate a custom model function on a test set

lifecycle::badge("experimental")

Fit your model function on a training set and validate it by predicting a test/validation set. Validate different hyperparameter combinations and formulas at once. Preprocess the train/test split. Returns results and fitted models in a tibble for easy reporting and further analysis.

Compared to validate(), this function allows you supply a custom model function, a predict function, a preprocess function and the hyperparameter values to validate.

Supports regression and classification (binary and multiclass). See type.

Note that some metrics may not be computable for some types of model objects.

validate_fn(
  train_data,
  formulas,
  type,
  model_fn,
  predict_fn,
  test_data = NULL,
  preprocess_fn = NULL,
  preprocess_once = FALSE,
  hyperparameters = NULL,
  partitions_col = ".partitions",
  cutoff = 0.5,
  positive = 2,
  metrics = list(),
  rm_nc = FALSE,
  parallel = FALSE,
  verbose = TRUE
)

Arguments

• train_data: data.frame.

  Can contain a grouping factor for identifying partitions - as made with groupdata2::partition(). See partitions_col.

• formulas: Model formulas as strings. (Character)

  Will be converted to formula objects before being passed to model_fn.

  E.g. c("y~x", "y~z").

  Can contain random effects.

  E.g. c("y~x+(1|r)", "y~z+(1|r)").

• type: Type of evaluation to perform:

  "gaussian" for regression (like linear regression).

  "binomial" for binary classification.

  "multinomial" for multiclass classification.

• model_fn: Model function that returns a fitted model object. Will usually wrap an existing model function like e1071::svm

  or nnet::multinom.

  Must have the following function arguments:

  function(train_data, formula,

  ``hyperparameters)

• predict_fn: Function for predicting the targets in the test folds/sets using the fitted model object. Will usually wrap stats::predict(), but doesn't have to.

  Must have the following function arguments:

  function(test_data, model, formula,

  ``hyperparameters, train_data)

  Must return predictions in the following formats, depending on type:

  Binomial

  vector or one-column matrix / data.frame with probabilities (0-1) of the second class, alphabetically . E.g.:

  c(0.3, 0.5, 0.1, 0.5)

  N.B. When unsure whether a model type produces probabilities based off the alphabetic order of your classes, using 0 and 1 as classes in the dependent variable instead of the class names should increase the chance of getting probabilities of the right class.

  Gaussian

  vector or one-column matrix / data.frame with the predicted value. E.g.:

  c(3.7, 0.9, 1.2, 7.3)

  Multinomial

  data.frame with one column per class containing probabilities of the class. Column names should be identical to how the class names are written in the target column. E.g.:

  class_1	class_2	class_3
  0.269	0.528	0.203
  0.368	0.322	0.310
  0.375	0.371	0.254
  ...	...	...

• test_data: data.frame. If specifying partitions_col, this can be NULL.

• preprocess_fn: Function for preprocessing the training and test sets.

  Can, for instance, be used to standardize both the training and test sets with the scaling and centering parameters from the training set.

  Must have the following function arguments:

  function(train_data, test_data,

  ``formula, hyperparameters)

  Must return a list with the preprocessed train_data and test_data. It may also contain a tibble with the parameters used in preprocessing:

  list("train" = train_data,

  ``"test" = test_data,

  ``"parameters" = preprocess_parameters)

  Additional elements in the returned list will be ignored.

  The optional parameters tibble will be included in the output. It could have the following format:

  Measure	var_1	var_2
  Mean	37.921	88.231
  SD	12.4	5.986
  ...	...	...

  N.B. When preprocess_once is FALSE, the current formula and hyperparameters will be provided. Otherwise, these arguments will be NULL.

• preprocess_once: Whether to apply the preprocessing once (ignoring the formula and hyperparameters arguments in preprocess_fn) or for every model separately. (Logical)

  When preprocessing does not depend on the current formula or hyperparameters, we can do the preprocessing of each train/test split once, to save time. This may require holding a lot more data in memory though, why it is not the default setting.

• hyperparameters: Either a named list with hyperparameter values to combine in a grid or a data.frame with one row per hyperparameter combination.

  Named list for grid search

  Add ".n" to sample the combinations. Can be the number of combinations to use, or a percentage between 0 and 1.

  E.g.

  list(".n" = 10, # sample 10 combinations

  ``"lrn_rate" = c(0.1, 0.01, 0.001),

  ``"h_layers" = c(10, 100, 1000),

  ``"drop_out" = runif(5, 0.3, 0.7))

  data.frame with specific hyperparameter combinations

  One row per combination to test.

  E.g.

  lrn_rate	h_layers	drop_out
  0.1	10	0.65
  0.1	1000	0.65
  0.01	1000	0.63
  ...	...	...

• partitions_col: Name of grouping factor for identifying partitions. (Character)

  Rows with the value 1 in partitions_col are used as training set and rows with the value 2 are used as test set.

  N.B. Only used if ‘test_data’ is ‘NULL’ .

• cutoff: Threshold for predicted classes. (Numeric)

  N.B. Binomial models only

• positive: Level from dependent variable to predict. Either as character (preferable) or level index (1 or 2 - alphabetically).

  E.g. if we have the levels "cat" and "dog" and we want "dog" to be the positive class, we can either provide "dog" or 2, as alphabetically, "dog" comes after "cat".

  Note: For reproducibility, it's preferable to specify the name directly , as different locales may sort the levels differently.

  Used when calculating confusion matrix metrics and creating ROC curves.

  The Process column in the output can be used to verify this setting.

  N.B. Only affects evaluation metrics, not the model training or returned predictions.

  N.B. Binomial models only .

• metrics: list for enabling/disabling metrics.

  E.g. list("RMSE" = FALSE) would remove RMSE from the regression results, and list("Accuracy" = TRUE) would add the regular Accuracy metric to the classification results. Default values (TRUE/FALSE) will be used for the remaining available metrics.

  You can enable/disable all metrics at once by including "all" = TRUE/FALSE in the list. This is done prior to enabling/disabling individual metrics, why f.i. list("all" = FALSE, "RMSE" = TRUE) would return only the RMSE metric.

  The list can be created with gaussian_metrics(), binomial_metrics(), or multinomial_metrics().

  Also accepts the string "all".

• rm_nc: Remove non-converged models from output. (Logical)

• parallel: Whether to cross-validate the list of models in parallel. (Logical)

  Remember to register a parallel backend first. E.g. with doParallel::registerDoParallel.

• verbose: Whether to message process information like the number of model instances to fit. (Logical)

Returns

tibble with the results and model objects.

Shared across families

A nested tibble with coefficients of the models. The coefficients are extracted from the model object with parameters::model_parameters() or coef() (with some restrictions on the output). If these attempts fail, a default coefficients tibble filled with NAs is returned.

Nested tibble with the used preprocessing parameters , if a passed preprocess_fn returns the parameters in a tibble.

Count of convergence warnings , using a limited set of keywords (e.g. "convergence"). If a convergence warning does not contain one of these keywords, it will be counted with other warnings . Consider discarding models that did not converge on all iterations. Note: you might still see results, but these should be taken with a grain of salt!

Nested tibble with the warnings and messages caught for each model.

Specified family .

Nested model objects.

Name of dependent variable.

Names of fixed effects.

Names of random effects, if any.

---

Gaussian Results

---

‘RMSE’ , ‘MAE’ , ‘NRMSE(IQR)’ , ‘RRSE’ , ‘RAE’ , and ‘RMSLE’ .

See the additional metrics (disabled by default) at ?gaussian_metrics.

A nested tibble with the predictions and targets.

---

Binomial Results

---

Based on predictions of the test set, a confusion matrix and a ROC curve are created to get the following:

ROC:

‘AUC’ , ‘Lower CI’ , and ‘Upper CI’

Confusion Matrix:

‘Balanced Accuracy’ , ‘F1’ , ‘Sensitivity’ , ‘Specificity’ , ‘Positive Predictive Value’ , ‘Negative Predictive Value’ , ‘Kappa’ , ‘Detection Rate’ , ‘Detection Prevalence’ , ‘Prevalence’ , and ‘MCC’ (Matthews correlation coefficient).

See the additional metrics (disabled by default) at ?binomial_metrics.

Also includes:

A nested tibble with predictions , predicted classes (depends on cutoff), and the targets. Note, that the predictions are not necessarily of the specified positive class, but of the model's positive class (second level of dependent variable, alphabetically).

The pROC::roc ‘ROC’ curve object(s).

A nested tibble with the confusion matrix /matrices. The Pos_ columns tells you whether a row is a True Positive (TP), True Negative (TN), False Positive (FP), or False Negative (FN), depending on which level is the "positive" class. I.e. the level you wish to predict.

The name of the Positive Class .

---

Multinomial Results

---

For each class, a one-vs-all binomial evaluation is performed. This creates a Class Level Results tibble containing the same metrics as the binomial results described above (excluding MCC, AUC, Lower CI and Upper CI), along with a count of the class in the target column (‘Support’ ). These metrics are used to calculate the macro-averaged metrics. The nested class level results tibble is also included in the output tibble, and could be reported along with the macro and overall metrics.

The output tibble contains the macro and overall metrics. The metrics that share their name with the metrics in the nested class level results tibble are averages of those metrics (note: does not remove NAs before averaging). In addition to these, it also includes the ‘Overall Accuracy’ and the multiclass ‘MCC’ .

Note: ‘Balanced Accuracy’ is the macro-averaged metric, not the macro sensitivity as sometimes used!

Other available metrics (disabled by default, see metrics): ‘Accuracy’ , multiclass ‘AUC’ , ‘Weighted Balanced Accuracy’ , ‘Weighted Accuracy’ , ‘Weighted F1’ , ‘Weighted Sensitivity’ , ‘Weighted Sensitivity’ , ‘Weighted Specificity’ , ‘Weighted Pos Pred Value’ , ‘Weighted Neg Pred Value’ , ‘Weighted Kappa’ , ‘Weighted Detection Rate’ , ‘Weighted Detection Prevalence’ , and ‘Weighted Prevalence’ .

Note that the "Weighted" average metrics are weighted by the Support.

Also includes:

A nested tibble with the predictions , predicted classes, and targets.

A list of ROC curve objects when AUC is enabled.

A nested tibble with the multiclass Confusion Matrix .

Class Level Results

Besides the binomial evaluation metrics and the Support, the nested class level results tibble also contains a nested tibble with the Confusion Matrix from the one-vs-all evaluation. The Pos_ columns tells you whether a row is a True Positive (TP), True Negative (TN), False Positive (FP), or False Negative (FN), depending on which level is the "positive" class. In our case, 1 is the current class and 0 represents all the other classes together.

Details

Packages used:

Results

Shared

AIC : stats::AIC

AICc : MuMIn::AICc

BIC : stats::BIC

Gaussian

r2m : MuMIn::r.squaredGLMM

r2c : MuMIn::r.squaredGLMM

Binomial and Multinomial

ROC and related metrics:

Binomial: pROC::roc

Multinomial: pROC::multiclass.roc

Examples

# Attach packages
library(cvms)
library(groupdata2) # fold()
library(dplyr) # %>% arrange() mutate()

# Note: More examples of custom functions can be found at:
# model_fn: model_functions()
# predict_fn: predict_functions()
# preprocess_fn: preprocess_functions()

# Data is part of cvms
data <- participant.scores

# Set seed for reproducibility
set.seed(7)

# Fold data
data <- partition(
  data,
  p = 0.8,
  cat_col = "diagnosis",
  id_col = "participant",
  list_out = FALSE
) %>%
  mutate(diagnosis = as.factor(diagnosis)) %>%
  arrange(.partitions)

# Formulas to validate

formula_gaussian <- "score ~ diagnosis"
formula_binomial <- "diagnosis ~ score"

#
# Gaussian
#

# Create model function that returns a fitted model object
lm_model_fn <- function(train_data, formula, hyperparameters) {
  lm(formula = formula, data = train_data)
}

# Create predict function that returns the predictions
lm_predict_fn <- function(test_data, model, formula,
                          hyperparameters, train_data) {
  stats::predict(
    object = model,
    newdata = test_data,
    type = "response",
    allow.new.levels = TRUE
  )
}

# Validate the model function
v <- validate_fn(
  data,
  formulas = formula_gaussian,
  type = "gaussian",
  model_fn = lm_model_fn,
  predict_fn = lm_predict_fn,
  partitions_col = ".partitions"
)

v

# Extract model object
v$Model[[1]]

#
# Binomial
#

# Create model function that returns a fitted model object
glm_model_fn <- function(train_data, formula, hyperparameters) {
  glm(formula = formula, data = train_data, family = "binomial")
}

# Create predict function that returns the predictions
glm_predict_fn <- function(test_data, model, formula,
                           hyperparameters, train_data) {
  stats::predict(
    object = model,
    newdata = test_data,
    type = "response",
    allow.new.levels = TRUE
  )
}

# Validate the model function
validate_fn(
  data,
  formulas = formula_binomial,
  type = "binomial",
  model_fn = glm_model_fn,
  predict_fn = glm_predict_fn,
  partitions_col = ".partitions"
)

#
# Support Vector Machine (svm)
# with known hyperparameters
#

# Only run if the `e1071` package is installed
if (requireNamespace("e1071", quietly = TRUE)){

# Create model function that returns a fitted model object
# We use the hyperparameters arg to pass in the kernel and cost values
# These will usually have been found with cross_validate_fn()
svm_model_fn <- function(train_data, formula, hyperparameters) {

  # Expected hyperparameters:
  #  - kernel
  #  - cost
  if (!"kernel" %in% names(hyperparameters))
    stop("'hyperparameters' must include 'kernel'")
  if (!"cost" %in% names(hyperparameters))
    stop("'hyperparameters' must include 'cost'")

  e1071::svm(
    formula = formula,
    data = train_data,
    kernel = hyperparameters[["kernel"]],
    cost = hyperparameters[["cost"]],
    scale = FALSE,
    type = "C-classification",
    probability = TRUE
  )
}

# Create predict function that returns the predictions
svm_predict_fn <- function(test_data, model, formula,
                           hyperparameters, train_data) {
  predictions <- stats::predict(
    object = model,
    newdata = test_data,
    allow.new.levels = TRUE,
    probability = TRUE
  )

  # Extract probabilities
  probabilities <- dplyr::as_tibble(
    attr(predictions, "probabilities")
  )

  # Return second column
  probabilities[[2]]
}

# Specify hyperparameters to use
# We found these in the examples in ?cross_validate_fn()
svm_hparams <- list(
  "kernel" = "linear",
  "cost" = 10
)

# Validate the model function
validate_fn(
  data,
  formulas = formula_binomial,
  type = "binomial",
  model_fn = svm_model_fn,
  predict_fn = svm_predict_fn,
  hyperparameters = svm_hparams,
  partitions_col = ".partitions"
)
}  # closes `e1071` package check

See Also

Other validation functions: cross_validate(), cross_validate_fn(), validate()

Author(s)

Ludvig Renbo Olsen, r-pkgs@ludvigolsen.dk