Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fixes earth GLM models and prediction test routine #102

Merged
merged 4 commits into from
May 25, 2022
Merged

Fixes earth GLM models and prediction test routine #102

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
3be0ba2
Forces test predictions to vector. It surfaces multiple test failures
edgararuiz Sep 28, 2021
86bd2a7
Switches between GLM and non-GLM coefs for earth models
edgararuiz Sep 28, 2021
5242e5a
Merge branch 'tidymodels:master' into fix-test
edgararuiz Sep 28, 2021
eeeddd6
Updates NEWS
edgararuiz Sep 28, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions NEWS.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
# tidypredict (development version)

- Fixes issue handling GLM Binomial earth models (#97)

- Adds capability to handle single simple Cubist models (#57)

- Fixed parenthesis issue in the creation of the interval formula (#76)
Expand Down
18 changes: 11 additions & 7 deletions R/model-earth.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,10 +14,7 @@ parse_model.earth <- function(model) {
}

is_glm <- !is.null(model$glm.list)

all_coefs <- model$coefficients
if (is_glm) all_coefs <- model$glm.coefficients


pm <- list()
pm$general$model <- "earth"
pm$general$type <- "tree"
Expand All @@ -29,12 +26,12 @@ parse_model.earth <- function(model) {
pm$general$family <- fam$family
pm$general$link <- fam$link
}
pm$terms <- mars_terms(model)
pm$terms <- mars_terms(model, is_glm)
as_parsed_model(pm)
}


mars_terms <- function(mod) {
mars_terms <- function(mod, is_glm) {
feature_types <-
tibble::as_tibble(mod$dirs, rownames = "feature") %>%
dplyr::mutate(feature_num = dplyr::row_number()) %>%
Expand All @@ -48,9 +45,16 @@ mars_terms <- function(mod) {
tidyr::pivot_longer(cols = c(-feature, -feature_num),
values_to = "value",
names_to = "term")

if (is_glm) {
all_coefs <- mod$glm.coefficients
} else {
all_coefs <- mod$coefficients
}

feature_coefs <-
# Note coef(mod) formats data differently for logistic regression
tibble::as_tibble(mod$coefficients, rownames = "feature") %>%
tibble::as_tibble(all_coefs, rownames = "feature") %>%
setNames(c("feature", "coefficient"))

term_to_column <-
Expand Down
6 changes: 3 additions & 3 deletions R/test-predictions.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -68,12 +68,12 @@ tidypredict_test_default <- function(model, df = model$model, threshold = 0.0000

if (is.numeric(max_rows)) df <- head(df, max_rows)

base <- predict(model, df, interval = interval, type = "response")
preds <- predict(model, df, interval = interval, type = "response")

if (!include_intervals) {
base <- data.frame(fit = base, row.names = NULL)
base <- data.frame(fit = as.vector(preds), row.names = NULL)
} else {
base <- as.data.frame(base)
base <- as.data.frame(preds)
}

te <- tidypredict_to_column(
Expand Down