diff --git a/.Rbuildignore b/.Rbuildignore index 40e1973c8..fbe868029 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -17,3 +17,4 @@ derby.log ^\.github$ ^CODE_OF_CONDUCT\.md$ ^README\.html$ +^codecov\.yml$ diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 000000000..2d19fc766 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index cf63479cc..c918c583e 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -1,4 +1,7 @@ -on: [push, pull_request] +on: + push: + pull_request: + types: [opened, synchronize, reopened] name: R-CMD-check @@ -17,44 +20,78 @@ jobs: - { os: windows-latest, r: 'devel'} - { os: ubuntu-16.04, r: '3.5', cran: "https://demo.rstudiopm.com/all/__linux__/xenial/latest"} - { os: ubuntu-16.04, r: '3.6', cran: "https://demo.rstudiopm.com/all/__linux__/xenial/latest"} + env: R_REMOTES_NO_ERRORS_FROM_WARNINGS: true - PIP_NO_WARN_SCRIPT_LOCATION: false + RSPM: ${{ matrix.config.rspm }} + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} steps: - - uses: actions/checkout@v1 + - uses: actions/checkout@v2 - uses: r-lib/actions/setup-r@master + with: + r-version: ${{ matrix.config.r }} - uses: r-lib/actions/setup-pandoc@master + - name: Query dependencies + run: | + install.packages('remotes') + saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) + writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") + shell: Rscript {0} + - name: Cache R packages if: runner.os != 'Windows' uses: actions/cache@v1 with: path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-r-${{ matrix.config.r }}-${{ hashFiles('DESCRIPTION') }} + key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} + restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - name: Install system dependencies if: runner.os == 'Linux' env: RHUB_PLATFORM: linux-x86_64-ubuntu-gcc run: | - Rscript -e "install.packages('remotes')" -e "remotes::install_github('r-hub/sysreqs')" + Rscript -e "remotes::install_github('r-hub/sysreqs')" sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))") sudo -s eval "$sysreqs" + - name: Install dependencies - run: Rscript -e "install.packages('remotes')" -e "remotes::install_deps(dependencies = TRUE)" -e "remotes::install_cran('rcmdcheck')" + run: | + remotes::install_deps(dependencies = TRUE) + remotes::install_cran("rcmdcheck") + shell: Rscript {0} - name: Install TensorFlow run: | - Rscript -e "remotes::install_github('rstudio/reticulate') # TODO remove when reticulate 1.14 is on CRAN" Rscript -e "reticulate::install_miniconda()" Rscript -e "reticulate::conda_create('r-reticulate', packages = 'python==3.6.9')" Rscript -e "tensorflow::install_tensorflow(version='1.14.0')" + - name: Check run: Rscript -e "rcmdcheck::rcmdcheck(args = '--no-manual', error_on = 'warning', check_dir = 'check')" + - name: Session info + run: | + options(width = 100) + pkgs <- installed.packages()[, "Package"] + sessioninfo::session_info(pkgs, include_base = TRUE) + shell: Rscript {0} + + - name: Check + env: + _R_CHECK_CRAN_INCOMING_: false + run: rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check") + shell: Rscript {0} + + - name: Show testthat output + if: always() + run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true + shell: bash + - name: Upload check results if: failure() uses: actions/upload-artifact@master diff --git a/.github/workflows/pr-commands.yaml b/.github/workflows/pr-commands.yaml new file mode 100644 index 000000000..0d3cb7162 --- /dev/null +++ b/.github/workflows/pr-commands.yaml @@ -0,0 +1,51 @@ +on: + issue_comment: + types: [created] +name: Commands +jobs: + document: + if: startsWith(github.event.comment.body, '/document') + name: document + runs-on: macOS-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v2 + - uses: r-lib/actions/pr-fetch@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + - uses: r-lib/actions/setup-r@master + - name: Install dependencies + run: Rscript -e 'install.packages(c("remotes", "roxygen2"))' -e 'remotes::install_deps(dependencies = TRUE)' + - name: Document + run: Rscript -e 'roxygen2::roxygenise()' + - name: commit + run: | + git add man/\* NAMESPACE + git commit -m 'Document' + - uses: r-lib/actions/pr-push@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + style: + if: startsWith(github.event.comment.body, '/style') + name: style + runs-on: macOS-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v2 + - uses: r-lib/actions/pr-fetch@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + - uses: r-lib/actions/setup-r@master + - name: Install dependencies + run: Rscript -e 'install.packages("styler")' + - name: Style + run: Rscript -e 'styler::style_pkg()' + - name: commit + run: | + git add \*.R + git commit -m 'Style' + - uses: r-lib/actions/pr-push@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml new file mode 100644 index 000000000..2338abea3 --- /dev/null +++ b/.github/workflows/test-coverage.yaml @@ -0,0 +1,52 @@ +on: + push: + branches: + - master + pull_request: + branches: + - master + +name: test-coverage + +jobs: + test-coverage: + runs-on: macOS-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v2 + + - uses: r-lib/actions/setup-r@master + + - uses: r-lib/actions/setup-pandoc@master + + - name: Query dependencies + run: | + install.packages('remotes') + saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) + writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") + shell: Rscript {0} + + - name: Cache R packages + uses: actions/cache@v1 + with: + path: ${{ env.R_LIBS_USER }} + key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} + restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- + + - name: Install dependencies + run: | + install.packages(c("remotes")) + remotes::install_deps(dependencies = TRUE) + remotes::install_cran("covr") + shell: Rscript {0} + + - name: Install TensorFlow + run: | + Rscript -e "reticulate::install_miniconda()" + Rscript -e "reticulate::conda_create('r-reticulate', packages = 'python==3.6.9')" + Rscript -e "tensorflow::install_tensorflow(version='1.14.0')" + + - name: Test coverage + run: covr::codecov() + shell: Rscript {0} diff --git a/README.Rmd b/README.Rmd index 81178f700..d9f035751 100644 --- a/README.Rmd +++ b/README.Rmd @@ -22,6 +22,8 @@ knitr::opts_chunk$set( [![Downloads](http://cranlogs.r-pkg.org/badges/parsnip)](https://cran.rstudio.com/package=parsnip) [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) +[![Codecov test coverage](https://codecov.io/gh/tidymodels/parsnip/branch/master/graph/badge.svg)](https://codecov.io/gh/tidymodels/parsnip?branch=master) +[![R build status](https://github.com/tidymodels/parsnip/workflows/R-CMD-check/badge.svg)](https://github.com/tidymodels/parsnip/actions) ## Introduction diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 000000000..04c558599 --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +comment: false + +coverage: + status: + project: + default: + target: auto + threshold: 1% + informational: true + patch: + default: + target: auto + threshold: 1% + informational: true diff --git a/docs/404.html b/docs/404.html index 709c736a2..c64cfd877 100644 --- a/docs/404.html +++ b/docs/404.html @@ -9,18 +9,18 @@ Page not found (404) • parsnip - - - - - - + + + + + + - + @@ -35,14 +35,14 @@ - - - + + + - + @@ -75,9 +75,9 @@
- parsnip + parsnip
- part of tidymodels + part of tidymodels 0.1.1
diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html index 69e153953..a4920a6b3 100644 --- a/docs/CODE_OF_CONDUCT.html +++ b/docs/CODE_OF_CONDUCT.html @@ -42,7 +42,7 @@ - + @@ -77,7 +77,7 @@
parsnip
- part of tidymodels + part of tidymodels 0.1.1
diff --git a/docs/articles/articles/Classification.html b/docs/articles/articles/Classification.html index cb8804dcd..2c28b2b51 100644 --- a/docs/articles/articles/Classification.html +++ b/docs/articles/articles/Classification.html @@ -19,7 +19,7 @@ - + @@ -119,7 +120,7 @@

# Or the development version from GitHub: # install.packages("devtools") -devtools::install_github("tidymodels/parsnip") +devtools::install_github("tidymodels/parsnip")

@@ -199,7 +200,7 @@

fit(mpg ~ ., data = mtcars) #> parsnip model object #> -#> Fit time: 75ms +#> Fit time: 69ms #> Ranger result #> #> Call: @@ -213,8 +214,8 @@

#> Target node size: 5 #> Variable importance mode: impurity #> Splitrule: variance -#> OOB prediction error (MSE): 5.779248 -#> R squared (OOB): 0.8408977

+#> OOB prediction error (MSE): 5.815633 +#> R squared (OOB): 0.839896

A list of all parsnip models across different CRAN packages can be found at tidymodels.org.

Data sets previously found in parsnip are now find in the modeldata package.

diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index e798725e0..4c4e988db 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -145,15 +145,19 @@

Other Changes

New Features

diff --git a/docs/dev/pkgdown.css b/docs/dev/pkgdown.css index c01e5923b..1273238dd 100644 --- a/docs/dev/pkgdown.css +++ b/docs/dev/pkgdown.css @@ -244,14 +244,14 @@ nav[data-toggle='toc'] .nav .nav > .active:focus > a { .ref-index th {font-weight: normal;} -.ref-index td {vertical-align: top;} +.ref-index td {vertical-align: top; min-width: 100px} .ref-index .icon {width: 40px;} .ref-index .alias {width: 40%;} .ref-index-icons .alias {width: calc(40% - 40px);} .ref-index .title {width: 60%;} .ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} .ref-arguments .name {width: 20%;} .ref-arguments .desc {width: 80%;} diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index f9f24b140..e6716b06c 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -1,6 +1,6 @@ pandoc: 2.9.2.1 -pkgdown: 1.5.1 -pkgdown_sha: ~ +pkgdown: 1.5.1.9000 +pkgdown_sha: ac78596154e403df5f4e683f2185d88225a0fea6 articles: Classification: articles/Classification.html Models: articles/Models.html @@ -8,7 +8,7 @@ articles: Scratch: articles/Scratch.html Submodels: articles/Submodels.html parsnip_Intro: parsnip_Intro.html -last_built: 2020-05-11T17:19Z +last_built: 2020-05-27T00:06Z urls: reference: https://parsnip.tidymodels.org/reference article: https://parsnip.tidymodels.org/articles diff --git a/docs/dev/reference/boost_tree.html b/docs/dev/reference/boost_tree.html index ebb4484ab..8dd6013bf 100644 --- a/docs/dev/reference/boost_tree.html +++ b/docs/dev/reference/boost_tree.html @@ -322,66 +322,61 @@ 

boost_tree() %&gt;%
-  set_engine("xgboost") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Boosted Tree Model Specification (regression)
-## 
-## Computational engine: xgboost 
-## 
-## Model fit template:
-## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1, 
-##     verbose = 0)
-

boost_tree() %&gt;%
-  set_engine("xgboost") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Boosted Tree Model Specification (classification)
-## 
-## Computational engine: xgboost 
-## 
-## Model fit template:
-## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1, 
-##     verbose = 0)
-
+

boost_tree() %>%
+  set_engine("xgboost") %>%
+  set_mode("regression") %>%
+  translate()

## Boosted Tree Model Specification (regression)
+## 
+## Computational engine: xgboost 
+## 
+## Model fit template:
+## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1, 
+##     verbose = 0)

boost_tree() %>%
+  set_engine("xgboost") %>%
+  set_mode("classification") %>%
+  translate()

## Boosted Tree Model Specification (classification)
+## 
+## Computational engine: xgboost 
+## 
+## Model fit template:
+## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1, 
+##     verbose = 0)

C5.0

-

boost_tree() %&gt;%
-  set_engine("C5.0") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Boosted Tree Model Specification (classification)
-## 
-## Computational engine: C5.0 
-## 
-## Model fit template:
-## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg())
-
+

boost_tree() %>%
+  set_engine("C5.0") %>%
+  set_mode("classification") %>%
+  translate()

## Boosted Tree Model Specification (classification)
+## 
+## Computational engine: C5.0 
+## 
+## Model fit template:
+## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg())

Note that C50::C5.0() does not require factor predictors to be converted to indicator variables.

spark

-

boost_tree() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Boosted Tree Model Specification (regression)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(), 
-##     type = "regression", seed = sample.int(10^5, 1))
-

boost_tree() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Boosted Tree Model Specification (classification)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(), 
-##     type = "classification", seed = sample.int(10^5, 1))
-
+

boost_tree() %>%
+  set_engine("spark") %>%
+  set_mode("regression") %>%
+  translate()

## Boosted Tree Model Specification (regression)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(), 
+##     type = "regression", seed = sample.int(10^5, 1))

boost_tree() %>%
+  set_engine("spark") %>%
+  set_mode("classification") %>%
+  translate()

## Boosted Tree Model Specification (classification)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(), 
+##     type = "classification", seed = sample.int(10^5, 1))

Parameter translations

diff --git a/docs/dev/reference/decision_tree.html b/docs/dev/reference/decision_tree.html index ede787ad6..fe0e95745 100644 --- a/docs/dev/reference/decision_tree.html +++ b/docs/dev/reference/decision_tree.html @@ -267,67 +267,62 @@ 

decision_tree() %&gt;%
-  set_engine("rpart") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Decision Tree Model Specification (regression)
-## 
-## Computational engine: rpart 
-## 
-## Model fit template:
-## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-

decision_tree() %&gt;%
-  set_engine("rpart") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Decision Tree Model Specification (classification)
-## 
-## Computational engine: rpart 
-## 
-## Model fit template:
-## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
+

decision_tree() %>%
+  set_engine("rpart") %>%
+  set_mode("regression") %>%
+  translate()

## Decision Tree Model Specification (regression)
+## 
+## Computational engine: rpart 
+## 
+## Model fit template:
+## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

decision_tree() %>%
+  set_engine("rpart") %>%
+  set_mode("classification") %>%
+  translate()

## Decision Tree Model Specification (classification)
+## 
+## Computational engine: rpart 
+## 
+## Model fit template:
+## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

Note that rpart::rpart() does not require factor predictors to be converted to indicator variables.

C5.0

-

decision_tree() %&gt;%
-  set_engine("C5.0") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Decision Tree Model Specification (classification)
-## 
-## Computational engine: C5.0 
-## 
-## Model fit template:
-## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     trials = 1)
-
+

decision_tree() %>%
+  set_engine("C5.0") %>%
+  set_mode("classification") %>%
+  translate()

## Decision Tree Model Specification (classification)
+## 
+## Computational engine: C5.0 
+## 
+## Model fit template:
+## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     trials = 1)

Note that C50::C5.0() does not require factor predictors to be converted to indicator variables.

spark

-

decision_tree() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Decision Tree Model Specification (regression)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(), 
-##     seed = sample.int(10^5, 1))
-

decision_tree() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Decision Tree Model Specification (classification)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(), 
-##     seed = sample.int(10^5, 1))
-
+

decision_tree() %>%
+  set_engine("spark") %>%
+  set_mode("regression") %>%
+  translate()

## Decision Tree Model Specification (regression)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(), 
+##     seed = sample.int(10^5, 1))

decision_tree() %>%
+  set_engine("spark") %>%
+  set_mode("classification") %>%
+  translate()

## Decision Tree Model Specification (classification)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(), 
+##     seed = sample.int(10^5, 1))

Parameter translations

diff --git a/docs/dev/reference/descriptors.html b/docs/dev/reference/descriptors.html index 6db6fa88f..faa7abdaf 100644 --- a/docs/dev/reference/descriptors.html +++ b/docs/dev/reference/descriptors.html @@ -195,9 +195,9 @@

Details .obs() = 150 .lvls() = NA (no factor outcome) .facts() = 1 (the Species predictor) - .y() = &lt;vector&gt; (Sepal.Width as a vector) - .x() = &lt;data.frame&gt; (The other 4 columns as a data frame) - .dat() = &lt;data.frame&gt; (The full data set) + .y() = <vector> (Sepal.Width as a vector) + .x() = <data.frame> (The other 4 columns as a data frame) + .dat() = <data.frame> (The full data set)

If the formula Species ~ . where used:

@@ -206,9 +206,9 @@ 
Details
  .obs()   = 150
  .lvls()  =  c(setosa = 50, versicolor = 50, virginica = 50)
  .facts() =   0
- .y()     = &lt;vector&gt;     (Species as a vector)
- .x()     = &lt;data.frame&gt; (The other 4 columns as a data frame)
- .dat()   = &lt;data.frame&gt; (The full data set)
+ .y()     = <vector>     (Species as a vector)
+ .x()     = <data.frame> (The other 4 columns as a data frame)
+ .dat()   = <data.frame> (The full data set)

To use these in a model fit, pass them to a model specification. diff --git a/docs/dev/reference/fit.html b/docs/dev/reference/fit.html index 37b87cc8f..52522745e 100644 --- a/docs/dev/reference/fit.html +++ b/docs/dev/reference/fit.html @@ -263,7 +263,8 @@

Examp #> #> Fit time: 26ms #> -#> Call: stats::glm(formula = formula, family = stats::binomial, data = data) +#> Call: stats::glm(formula = Class ~ funded_amnt + int_rate, family = stats::binomial, +#> data = data) #> #> Coefficients: #> (Intercept) funded_amnt int_rate @@ -273,9 +274,9 @@

Examp #> Null Deviance: 4055 #> Residual Deviance: 3698 AIC: 3704
using_xy
#> parsnip model object #> -#> Fit time: 15ms +#> Fit time: 23ms #> -#> Call: stats::glm(formula = formula, family = stats::binomial, data = data) +#> Call: stats::glm(formula = ..y ~ ., family = stats::binomial, data = data) #> #> Coefficients: #> (Intercept) funded_amnt int_rate diff --git a/docs/dev/reference/has_multi_predict.html b/docs/dev/reference/has_multi_predict.html index d04cccaef..d8729056c 100644 --- a/docs/dev/reference/has_multi_predict.html +++ b/docs/dev/reference/has_multi_predict.html @@ -204,13 +204,13 @@

Examp multi_predict_args(knn_fit)

#> [1] "neighbors"
multi_predict(knn_fit, mtcars[1, -1], neighbors = 1:4)$.pred
#> [[1]] -#> # A tibble: 4 x 2 +#> # A tibble: 4 x 2 #> neighbors .pred -#> <int> <dbl> -#> 1 1 21 -#> 2 2 21 -#> 3 3 20.9 -#> 4 4 21.0 +#> <int> <dbl> +#> 1 1 21 +#> 2 2 21 +#> 3 3 20.9 +#> 4 4 21.0 #> 

## Linear Regression Model Specification (regression)
-## 
-## Computational engine: lm 
-## 
-## Model fit template:
-## stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
+

linear_reg() %>%
+  set_engine("lm") %>%
+  set_mode("regression") %>%
+  translate()

## Linear Regression Model Specification (regression)
+## 
+## Computational engine: lm 
+## 
+## Model fit template:
+## stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

glmnet

-

linear_reg() %&gt;%
-  set_engine("glmnet") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Linear Regression Model Specification (regression)
-## 
-## Computational engine: glmnet 
-## 
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     family = "gaussian")
-
+

linear_reg() %>%
+  set_engine("glmnet") %>%
+  set_mode("regression") %>%
+  translate()

## Linear Regression Model Specification (regression)
+## 
+## Computational engine: glmnet 
+## 
+## Model fit template:
+## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     family = "gaussian")

For glmnet models, the full regularization path is always fit regardless of the value given to penalty. Also, there is the option to @@ -293,17 +291,16 @@

glmnet

results.

stan

-

linear_reg() %&gt;%
-  set_engine("stan") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Linear Regression Model Specification (regression)
-## 
-## Computational engine: stan 
-## 
-## Model fit template:
-## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), family = stats::gaussian, refresh = 0)
-
+

linear_reg() %>%
+  set_engine("stan") %>%
+  set_mode("regression") %>%
+  translate()

## Linear Regression Model Specification (regression)
+## 
+## Computational engine: stan 
+## 
+## Model fit template:
+## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(), 
+##     weights = missing_arg(), family = stats::gaussian, refresh = 0)

Note that the refresh default prevents logging of the estimation process. Change this value in set_engine() will show the logs.

@@ -314,31 +311,29 @@

stan

predictive distribution as appropriate) is returned.

spark

-

linear_reg() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Linear Regression Model Specification (regression)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_linear_regression(x = missing_arg(), formula = missing_arg(), 
-##     weight_col = missing_arg())
-
+

linear_reg() %>%
+  set_engine("spark") %>%
+  set_mode("regression") %>%
+  translate()

## Linear Regression Model Specification (regression)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_linear_regression(x = missing_arg(), formula = missing_arg(), 
+##     weight_col = missing_arg())

keras

-

linear_reg() %&gt;%
-  set_engine("keras") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Linear Regression Model Specification (regression)
-## 
-## Computational engine: keras 
-## 
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1, 
-##     act = "linear")
-
+

linear_reg() %>%
+  set_engine("keras") %>%
+  set_mode("regression") %>%
+  translate()

## Linear Regression Model Specification (regression)
+## 
+## Computational engine: keras 
+## 
+## Model fit template:
+## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1, 
+##     act = "linear")

Parameter translations

diff --git a/docs/dev/reference/logistic_reg.html b/docs/dev/reference/logistic_reg.html index a1e10c327..4bb0a12d7 100644 --- a/docs/dev/reference/logistic_reg.html +++ b/docs/dev/reference/logistic_reg.html @@ -255,31 +255,29 @@ 

logistic_reg() %&gt;%
-  set_engine("glm") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Logistic Regression Model Specification (classification)
-## 
-## Computational engine: glm 
-## 
-## Model fit template:
-## stats::glm(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
-##     family = stats::binomial)
-
+

logistic_reg() %>%
+  set_engine("glm") %>%
+  set_mode("classification") %>%
+  translate()

## Logistic Regression Model Specification (classification)
+## 
+## Computational engine: glm 
+## 
+## Model fit template:
+## stats::glm(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     family = stats::binomial)

glmnet

-

logistic_reg() %&gt;%
-  set_engine("glmnet") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Logistic Regression Model Specification (classification)
-## 
-## Computational engine: glmnet 
-## 
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     family = "binomial")
-
+

logistic_reg() %>%
+  set_engine("glmnet") %>%
+  set_mode("classification") %>%
+  translate()

## Logistic Regression Model Specification (classification)
+## 
+## Computational engine: glmnet 
+## 
+## Model fit template:
+## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     family = "binomial")

For glmnet models, the full regularization path is always fit regardless of the value given to penalty. Also, there is the option to @@ -292,17 +290,16 @@

glmnet

results.

stan

-

logistic_reg() %&gt;%
-  set_engine("stan") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Logistic Regression Model Specification (classification)
-## 
-## Computational engine: stan 
-## 
-## Model fit template:
-## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), family = stats::binomial, refresh = 0)
-
+

logistic_reg() %>%
+  set_engine("stan") %>%
+  set_mode("classification") %>%
+  translate()

## Logistic Regression Model Specification (classification)
+## 
+## Computational engine: stan 
+## 
+## Model fit template:
+## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(), 
+##     weights = missing_arg(), family = stats::binomial, refresh = 0)

Note that the refresh default prevents logging of the estimation process. Change this value in set_engine() will show the logs.

@@ -313,31 +310,29 @@

stan

predictive distribution as appropriate) is returned.

spark

-

logistic_reg() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Logistic Regression Model Specification (classification)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(), 
-##     weight_col = missing_arg(), family = "binomial")
-
+

logistic_reg() %>%
+  set_engine("spark") %>%
+  set_mode("classification") %>%
+  translate()

## Logistic Regression Model Specification (classification)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(), 
+##     weight_col = missing_arg(), family = "binomial")

keras

-

logistic_reg() %&gt;%
-  set_engine("keras") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Logistic Regression Model Specification (classification)
-## 
-## Computational engine: keras 
-## 
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1, 
-##     act = "linear")
-
+

logistic_reg() %>%
+  set_engine("keras") %>%
+  set_mode("classification") %>%
+  translate()

## Logistic Regression Model Specification (classification)
+## 
+## Computational engine: keras 
+## 
+## Model fit template:
+## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1, 
+##     act = "linear")

Parameter translations

diff --git a/docs/dev/reference/mars.html b/docs/dev/reference/mars.html index df178c155..daa4827c9 100644 --- a/docs/dev/reference/mars.html +++ b/docs/dev/reference/mars.html @@ -248,30 +248,28 @@ 

mars() %&gt;%
-  set_engine("earth") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## MARS Model Specification (regression)
-## 
-## Computational engine: earth 
-## 
-## Model fit template:
-## earth::earth(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     keepxy = TRUE)
-

mars() %&gt;%
-  set_engine("earth") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## MARS Model Specification (classification)
-## 
-## Engine-Specific Arguments:
-##   glm = list(family = stats::binomial)
-## 
-## Computational engine: earth 
-## 
-## Model fit template:
-## earth::earth(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     glm = list(family = stats::binomial), keepxy = TRUE)
-
+

mars() %>%
+  set_engine("earth") %>%
+  set_mode("regression") %>%
+  translate()

## MARS Model Specification (regression)
+## 
+## Computational engine: earth 
+## 
+## Model fit template:
+## earth::earth(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     keepxy = TRUE)

mars() %>%
+  set_engine("earth") %>%
+  set_mode("classification") %>%
+  translate()

## MARS Model Specification (classification)
+## 
+## Engine-Specific Arguments:
+##   glm = list(family = stats::binomial)
+## 
+## Computational engine: earth 
+## 
+## Model fit template:
+## earth::earth(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     glm = list(family = stats::binomial), keepxy = TRUE)

Note that, when the model is fit, the earth package only has its namespace loaded. However, if multi_predict is used, the package is diff --git a/docs/dev/reference/mlp.html b/docs/dev/reference/mlp.html index 8d4bdcee1..a84fd2ae5 100644 --- a/docs/dev/reference/mlp.html +++ b/docs/dev/reference/mlp.html @@ -276,57 +276,53 @@ 

mlp() %&gt;%
-  set_engine("keras") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Single Layer Neural Network Specification (regression)
-## 
-## Computational engine: keras 
-## 
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())
-

mlp() %&gt;%
-  set_engine("keras") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Single Layer Neural Network Specification (classification)
-## 
-## Computational engine: keras 
-## 
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())
-
+

mlp() %>%
+  set_engine("keras") %>%
+  set_mode("regression") %>%
+  translate()

## Single Layer Neural Network Specification (regression)
+## 
+## Computational engine: keras 
+## 
+## Model fit template:
+## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())

mlp() %>%
+  set_engine("keras") %>%
+  set_mode("classification") %>%
+  translate()

## Single Layer Neural Network Specification (classification)
+## 
+## Computational engine: keras 
+## 
+## Model fit template:
+## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())

An error is thrown if both penalty and dropout are specified for keras models.

nnet

-

mlp() %&gt;%
-  set_engine("nnet") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Single Layer Neural Network Specification (regression)
-## 
-## Main Arguments:
-##   hidden_units = 5
-## 
-## Computational engine: nnet 
-## 
-## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
-##     size = 5, trace = FALSE, linout = TRUE)
-

mlp() %&gt;%
-  set_engine("nnet") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Single Layer Neural Network Specification (classification)
-## 
-## Main Arguments:
-##   hidden_units = 5
-## 
-## Computational engine: nnet 
-## 
-## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
-##     size = 5, trace = FALSE, linout = FALSE)
-
+

mlp() %>%
+  set_engine("nnet") %>%
+  set_mode("regression") %>%
+  translate()

## Single Layer Neural Network Specification (regression)
+## 
+## Main Arguments:
+##   hidden_units = 5
+## 
+## Computational engine: nnet 
+## 
+## Model fit template:
+## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     size = 5, trace = FALSE, linout = TRUE)

mlp() %>%
+  set_engine("nnet") %>%
+  set_mode("classification") %>%
+  translate()

## Single Layer Neural Network Specification (classification)
+## 
+## Main Arguments:
+##   hidden_units = 5
+## 
+## Computational engine: nnet 
+## 
+## Model fit template:
+## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     size = 5, trace = FALSE, linout = FALSE)

Parameter translations

diff --git a/docs/dev/reference/model_fit.html b/docs/dev/reference/model_fit.html index d438aa8c2..fc4198ce9 100644 --- a/docs/dev/reference/model_fit.html +++ b/docs/dev/reference/model_fit.html @@ -189,10 +189,10 @@

Examp fit_obj <- fit(spec_obj, mpg ~ ., data = mtcars) fit_obj
#> parsnip model object #> -#> Fit time: 1ms +#> Fit time: 2ms #> #> Call: -#> stats::lm(formula = formula, data = data, x = ~ifelse(.obs() < +#> stats::lm(formula = mpg ~ ., data = data, x = ~ifelse(.obs() < #> 500, TRUE, FALSE)) #> #> Coefficients: diff --git a/docs/dev/reference/model_spec.html b/docs/dev/reference/model_spec.html index 310fa17a2..53794323f 100644 --- a/docs/dev/reference/model_spec.html +++ b/docs/dev/reference/model_spec.html @@ -200,7 +200,7 @@ 

multinom_reg() %&gt;%
-  set_engine("glmnet") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Multinomial Regression Model Specification (classification)
-## 
-## Computational engine: glmnet 
-## 
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     family = "multinomial")
-
+

multinom_reg() %>%
+  set_engine("glmnet") %>%
+  set_mode("classification") %>%
+  translate()

## Multinomial Regression Model Specification (classification)
+## 
+## Computational engine: glmnet 
+## 
+## Model fit template:
+## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+##     family = "multinomial")

For glmnet models, the full regularization path is always fit regardless of the value given to penalty. Also, there is the option to @@ -277,45 +276,42 @@ 

multinom_reg() %&gt;%
-  set_engine("nnet") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Multinomial Regression Model Specification (classification)
-## 
-## Computational engine: nnet 
-## 
-## Model fit template:
-## nnet::multinom(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), trace = FALSE)
-
+

multinom_reg() %>%
+  set_engine("nnet") %>%
+  set_mode("classification") %>%
+  translate()

## Multinomial Regression Model Specification (classification)
+## 
+## Computational engine: nnet 
+## 
+## Model fit template:
+## nnet::multinom(formula = missing_arg(), data = missing_arg(), 
+##     weights = missing_arg(), trace = FALSE)

spark

-

multinom_reg() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Multinomial Regression Model Specification (classification)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(), 
-##     weight_col = missing_arg(), family = "multinomial")
-
+

multinom_reg() %>%
+  set_engine("spark") %>%
+  set_mode("classification") %>%
+  translate()

## Multinomial Regression Model Specification (classification)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(), 
+##     weight_col = missing_arg(), family = "multinomial")

keras

-

multinom_reg() %&gt;%
-  set_engine("keras") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Multinomial Regression Model Specification (classification)
-## 
-## Computational engine: keras 
-## 
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1, 
-##     act = "linear")
-
+

multinom_reg() %>%
+  set_engine("keras") %>%
+  set_mode("classification") %>%
+  translate()

## Multinomial Regression Model Specification (classification)
+## 
+## Computational engine: keras 
+## 
+## Model fit template:
+## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1, 
+##     act = "linear")

Parameter translations

diff --git a/docs/dev/reference/nearest_neighbor.html b/docs/dev/reference/nearest_neighbor.html index 4b5c5a7fa..73e82ad99 100644 --- a/docs/dev/reference/nearest_neighbor.html +++ b/docs/dev/reference/nearest_neighbor.html @@ -222,27 +222,25 @@ 

nearest_neighbor() %&gt;%
-  set_engine("kknn") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## K-Nearest Neighbor Model Specification (regression)
-## 
-## Computational engine: kknn 
-## 
-## Model fit template:
-## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), 
-##     ks = 5)
-

nearest_neighbor() %&gt;%
-  set_engine("kknn") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## K-Nearest Neighbor Model Specification (classification)
-## 
-## Computational engine: kknn 
-## 
-## Model fit template:
-## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), 
-##     ks = 5)
-
+

nearest_neighbor() %>%
+  set_engine("kknn") %>%
+  set_mode("regression") %>%
+  translate()

## K-Nearest Neighbor Model Specification (regression)
+## 
+## Computational engine: kknn 
+## 
+## Model fit template:
+## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), 
+##     ks = 5)

nearest_neighbor() %>%
+  set_engine("kknn") %>%
+  set_mode("classification") %>%
+  translate()

## K-Nearest Neighbor Model Specification (classification)
+## 
+## Computational engine: kknn 
+## 
+## Model fit template:
+## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), 
+##     ks = 5)

For kknn, the underlying modeling function used is a restricted version of train.kknn() and not kknn(). It is set up in this way so diff --git a/docs/dev/reference/null_model.html b/docs/dev/reference/null_model.html index c299fd299..f5890685b 100644 --- a/docs/dev/reference/null_model.html +++ b/docs/dev/reference/null_model.html @@ -171,25 +171,23 @@ 

null_model() %&gt;%
-  set_engine("parsnip") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Model Specification (regression)
-## 
-## Computational engine: parsnip 
-## 
-## Model fit template:
-## nullmodel(x = missing_arg(), y = missing_arg())
-

null_model() %&gt;%
-  set_engine("parsnip") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Model Specification (classification)
-## 
-## Computational engine: parsnip 
-## 
-## Model fit template:
-## nullmodel(x = missing_arg(), y = missing_arg())
-
+

null_model() %>%
+  set_engine("parsnip") %>%
+  set_mode("regression") %>%
+  translate()

## Model Specification (regression)
+## 
+## Computational engine: parsnip 
+## 
+## Model fit template:
+## nullmodel(x = missing_arg(), y = missing_arg())

null_model() %>%
+  set_engine("parsnip") %>%
+  set_mode("classification") %>%
+  translate()

## Model Specification (classification)
+## 
+## Computational engine: parsnip 
+## 
+## Model fit template:
+## nullmodel(x = missing_arg(), y = missing_arg())

See also

diff --git a/docs/dev/reference/predict.model_fit.html b/docs/dev/reference/predict.model_fit.html index 8250b21d9..21f71b4f6 100644 --- a/docs/dev/reference/predict.model_fit.html +++ b/docs/dev/reference/predict.model_fit.html @@ -253,37 +253,37 @@

Examp slice(1:10) %>% select(-mpg) -predict(lm_model, pred_cars)
#> # A tibble: 10 x 1 +predict(lm_model, pred_cars)
#> # A tibble: 10 x 1 #> .pred -#> <dbl> -#> 1 23.4 -#> 2 23.3 -#> 3 27.6 -#> 4 21.5 -#> 5 17.6 -#> 6 21.6 -#> 7 13.9 -#> 8 21.7 -#> 9 25.6 -#> 10 17.1
+#> <dbl> +#> 1 23.4 +#> 2 23.3 +#> 3 27.6 +#> 4 21.5 +#> 5 17.6 +#> 6 21.6 +#> 7 13.9 +#> 8 21.7 +#> 9 25.6 +#> 10 17.1
predict( lm_model, pred_cars, type = "conf_int", level = 0.90 -)
#> # A tibble: 10 x 2 +)
#> # A tibble: 10 x 2 #> .pred_lower .pred_upper -#> <dbl> <dbl> -#> 1 17.9 29.0 -#> 2 18.1 28.5 -#> 3 24.0 31.3 -#> 4 17.5 25.6 -#> 5 14.3 20.8 -#> 6 17.0 26.2 -#> 7 9.65 18.2 -#> 8 16.2 27.2 -#> 9 14.2 37.0 -#> 10 11.5 22.7
+#> <dbl> <dbl> +#> 1 17.9 29.0 +#> 2 18.1 28.5 +#> 3 24.0 31.3 +#> 4 17.5 25.6 +#> 5 14.3 20.8 +#> 6 17.0 26.2 +#> 7 9.65 18.2 +#> 8 16.2 27.2 +#> 9 14.2 37.0 +#> 10 11.5 22.7
predict( lm_model, pred_cars, diff --git a/docs/dev/reference/rand_forest.html b/docs/dev/reference/rand_forest.html index c045444a6..e9fbe86c2 100644 --- a/docs/dev/reference/rand_forest.html +++ b/docs/dev/reference/rand_forest.html @@ -256,29 +256,27 @@ 

rand_forest() %&gt;%
-  set_engine("ranger") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Random Forest Model Specification (regression)
-## 
-## Computational engine: ranger 
-## 
-## Model fit template:
-## ranger::ranger(formula = missing_arg(), data = missing_arg(), 
-##     case.weights = missing_arg(), num.threads = 1, verbose = FALSE, 
-##     seed = sample.int(10^5, 1))
-

rand_forest() %&gt;%
-  set_engine("ranger") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Random Forest Model Specification (classification)
-## 
-## Computational engine: ranger 
-## 
-## Model fit template:
-## ranger::ranger(formula = missing_arg(), data = missing_arg(), 
-##     case.weights = missing_arg(), num.threads = 1, verbose = FALSE, 
-##     seed = sample.int(10^5, 1), probability = TRUE)
-
+

rand_forest() %>%
+  set_engine("ranger") %>%
+  set_mode("regression") %>%
+  translate()

## Random Forest Model Specification (regression)
+## 
+## Computational engine: ranger 
+## 
+## Model fit template:
+## ranger::ranger(formula = missing_arg(), data = missing_arg(), 
+##     case.weights = missing_arg(), num.threads = 1, verbose = FALSE, 
+##     seed = sample.int(10^5, 1))

rand_forest() %>%
+  set_engine("ranger") %>%
+  set_mode("classification") %>%
+  translate()

## Random Forest Model Specification (classification)
+## 
+## Computational engine: ranger 
+## 
+## Model fit template:
+## ranger::ranger(formula = missing_arg(), data = missing_arg(), 
+##     case.weights = missing_arg(), num.threads = 1, verbose = FALSE, 
+##     seed = sample.int(10^5, 1), probability = TRUE)

Note that ranger::ranger() does not require factor predictors to be converted to indicator variables.

@@ -288,52 +286,48 @@ 

rand_forest() %&gt;%
-  set_engine("randomForest") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Random Forest Model Specification (regression)
-## 
-## Computational engine: randomForest 
-## 
-## Model fit template:
-## randomForest::randomForest(x = missing_arg(), y = missing_arg())
-

rand_forest() %&gt;%
-  set_engine("randomForest") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Random Forest Model Specification (classification)
-## 
-## Computational engine: randomForest 
-## 
-## Model fit template:
-## randomForest::randomForest(x = missing_arg(), y = missing_arg())
-
+

rand_forest() %>%
+  set_engine("randomForest") %>%
+  set_mode("regression") %>%
+  translate()

## Random Forest Model Specification (regression)
+## 
+## Computational engine: randomForest 
+## 
+## Model fit template:
+## randomForest::randomForest(x = missing_arg(), y = missing_arg())

rand_forest() %>%
+  set_engine("randomForest") %>%
+  set_mode("classification") %>%
+  translate()

## Random Forest Model Specification (classification)
+## 
+## Computational engine: randomForest 
+## 
+## Model fit template:
+## randomForest::randomForest(x = missing_arg(), y = missing_arg())

Note that randomForest::randomForest() does not require factor predictors to be converted to indicator variables.

spark

-

rand_forest() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Random Forest Model Specification (regression)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(), 
-##     type = "regression", seed = sample.int(10^5, 1))
-

rand_forest() %&gt;%
-  set_engine("spark") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Random Forest Model Specification (classification)
-## 
-## Computational engine: spark 
-## 
-## Model fit template:
-## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(), 
-##     type = "classification", seed = sample.int(10^5, 1))
-
+

rand_forest() %>%
+  set_engine("spark") %>%
+  set_mode("regression") %>%
+  translate()

## Random Forest Model Specification (regression)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(), 
+##     type = "regression", seed = sample.int(10^5, 1))

rand_forest() %>%
+  set_engine("spark") %>%
+  set_mode("classification") %>%
+  translate()

## Random Forest Model Specification (classification)
+## 
+## Computational engine: spark 
+## 
+## Model fit template:
+## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(), 
+##     type = "classification", seed = sample.int(10^5, 1))

Parameter translations

diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index 1d4231dc8..823e3eba5 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -149,7 +149,7 @@

Objects exported from other packages

These objects are imported from other packages. Follow the links below to see their documentation.

- +
generics

fit, fit_xy, tidy, varying_args

magrittr

%>%

diff --git a/docs/dev/reference/repair_call.html b/docs/dev/reference/repair_call.html new file mode 100644 index 000000000..bb6f503dc --- /dev/null +++ b/docs/dev/reference/repair_call.html @@ -0,0 +1,220 @@ + + + + + + + + +Repair a model call object — repair_call • parsnip + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + +
+ +
+
+
+

Repair a model call object

+ Source: R/repair_call.R + +
+ +
+

When the user passes a formula to fit() and the underyling model function +uses a formula, the call object produced by fit() may not be usable by +other functions. For example, some arguments may still be quosures and the +data portion of the call will not correspond to the original data.

+
+ + 
repair_call(x, data)
+ +

Arguments

+ + + + + + + + + + +
x

A fitted parsnip model. An error will occur if the underlying model +does not have a call element.

data

A data object that is relavant to the call. In most cases, this +is the data frame that was given to parsnip for the model fit (i.e., the +training set data). The name of this data object is inserted into the call.

+ +

Value

+ +

A modified parsnip fitted model.

+

Details

+ +

repair_call() call can adjust the model objects call to be usable by other +functions and methods.

+ +

Examples

+ 

+fitted_model <-
+  linear_reg() %>%
+  set_engine("lm", model = TRUE) %>%
+  fit(mpg ~ ., data = mtcars)
+
+# In this call, note that `data` is not `mtcars` and the `model = ~TRUE`
+# indicates that the `model` argument is an `rlang` quosure.
+fitted_model$fit$call
#> stats::lm(formula = mpg ~ ., data = data, model = ~TRUE)

+# All better:
+repair_call(fitted_model, mtcars)$fit$call
#> stats::lm(formula = mpg ~ ., data = mtcars, model = TRUE)
+
+ +
+ + +
+
+

parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

+
+ +
+

+ Developed by Max Kuhn, Davis Vaughan. + Site built by pkgdown. +

+
+ +
+
+ + + + + + + + diff --git a/docs/dev/reference/surv_reg.html b/docs/dev/reference/surv_reg.html index 3612ec471..332f4aedc 100644 --- a/docs/dev/reference/surv_reg.html +++ b/docs/dev/reference/surv_reg.html @@ -226,31 +226,29 @@ 

surv_reg() %&gt;%
-  set_engine("flexsurv") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Parametric Survival Regression Model Specification (regression)
-## 
-## Computational engine: flexsurv 
-## 
-## Model fit template:
-## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg())
-
+

surv_reg() %>%
+  set_engine("flexsurv") %>%
+  set_mode("regression") %>%
+  translate()

## Parametric Survival Regression Model Specification (regression)
+## 
+## Computational engine: flexsurv 
+## 
+## Model fit template:
+## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(), 
+##     weights = missing_arg())

survival

-

surv_reg() %&gt;%
-  set_engine("survival") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Parametric Survival Regression Model Specification (regression)
-## 
-## Computational engine: survival 
-## 
-## Model fit template:
-## survival::survreg(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), model = TRUE)
-
+

surv_reg() %>%
+  set_engine("survival") %>%
+  set_mode("regression") %>%
+  translate()

## Parametric Survival Regression Model Specification (regression)
+## 
+## Computational engine: survival 
+## 
+## Model fit template:
+## survival::survreg(formula = missing_arg(), data = missing_arg(), 
+##     weights = missing_arg(), model = TRUE)

Note that model = TRUE is needed to produce quantile predictions when there is a stratification variable and can be overridden in other cases.

diff --git a/docs/dev/reference/svm_poly.html b/docs/dev/reference/svm_poly.html index 161acd1a3..6d8d510eb 100644 --- a/docs/dev/reference/svm_poly.html +++ b/docs/dev/reference/svm_poly.html @@ -253,26 +253,24 @@ 

svm_poly() %&gt;%
-  set_engine("kernlab") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Polynomial Support Vector Machine Specification (regression)
-## 
-## Computational engine: kernlab 
-## 
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "polydot")
-

svm_poly() %&gt;%
-  set_engine("kernlab") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Polynomial Support Vector Machine Specification (classification)
-## 
-## Computational engine: kernlab 
-## 
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "polydot", 
-##     prob.model = TRUE)
-
+

svm_poly() %>%
+  set_engine("kernlab") %>%
+  set_mode("regression") %>%
+  translate()

## Polynomial Support Vector Machine Specification (regression)
+## 
+## Computational engine: kernlab 
+## 
+## Model fit template:
+## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "polydot")

svm_poly() %>%
+  set_engine("kernlab") %>%
+  set_mode("classification") %>%
+  translate()

## Polynomial Support Vector Machine Specification (classification)
+## 
+## Computational engine: kernlab 
+## 
+## Model fit template:
+## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "polydot", 
+##     prob.model = TRUE)

Parameter translations

diff --git a/docs/dev/reference/svm_rbf.html b/docs/dev/reference/svm_rbf.html index a2834a923..f0690ecb4 100644 --- a/docs/dev/reference/svm_rbf.html +++ b/docs/dev/reference/svm_rbf.html @@ -243,50 +243,46 @@ 

svm_rbf() %&gt;%
-  set_engine("kernlab") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Radial Basis Function Support Vector Machine Specification (regression)
-## 
-## Computational engine: kernlab 
-## 
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "rbfdot")
-

svm_rbf() %&gt;%
-  set_engine("kernlab") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Radial Basis Function Support Vector Machine Specification (classification)
-## 
-## Computational engine: kernlab 
-## 
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "rbfdot", 
-##     prob.model = TRUE)
-
+

svm_rbf() %>%
+  set_engine("kernlab") %>%
+  set_mode("regression") %>%
+  translate()

## Radial Basis Function Support Vector Machine Specification (regression)
+## 
+## Computational engine: kernlab 
+## 
+## Model fit template:
+## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "rbfdot")

svm_rbf() %>%
+  set_engine("kernlab") %>%
+  set_mode("classification") %>%
+  translate()

## Radial Basis Function Support Vector Machine Specification (classification)
+## 
+## Computational engine: kernlab 
+## 
+## Model fit template:
+## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "rbfdot", 
+##     prob.model = TRUE)

liquidSVM

-

svm_rbf() %&gt;%
-  set_engine("liquidSVM") %&gt;%
-  set_mode("regression") %&gt;%
-  translate()

## Radial Basis Function Support Vector Machine Specification (regression)
-## 
-## Computational engine: liquidSVM 
-## 
-## Model fit template:
-## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1, 
-##     threads = 0)
-

svm_rbf() %&gt;%
-  set_engine("liquidSVM") %&gt;%
-  set_mode("classification") %&gt;%
-  translate()

## Radial Basis Function Support Vector Machine Specification (classification)
-## 
-## Computational engine: liquidSVM 
-## 
-## Model fit template:
-## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1, 
-##     threads = 0)
-
+

svm_rbf() %>%
+  set_engine("liquidSVM") %>%
+  set_mode("regression") %>%
+  translate()

## Radial Basis Function Support Vector Machine Specification (regression)
+## 
+## Computational engine: liquidSVM 
+## 
+## Model fit template:
+## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1, 
+##     threads = 0)

svm_rbf() %>%
+  set_engine("liquidSVM") %>%
+  set_mode("classification") %>%
+  translate()

## Radial Basis Function Support Vector Machine Specification (classification)
+## 
+## Computational engine: liquidSVM 
+## 
+## Model fit template:
+## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1, 
+##     threads = 0)

Note that models created using the liquidSVM engine cannot be saved like conventional R objects. The fit slot of the model_fit object diff --git a/docs/dev/reference/tidy.nullmodel.html b/docs/dev/reference/tidy.nullmodel.html index ddc40e72a..80b406d17 100644 --- a/docs/dev/reference/tidy.nullmodel.html +++ b/docs/dev/reference/tidy.nullmodel.html @@ -164,14 +164,14 @@

Value

A tibble with column value.

Examples

- 
nullmodel(iris[,-5], iris$Species) %>% tidy()
#> # A tibble: 1 x 1
+    
nullmodel(iris[,-5], iris$Species) %>% tidy()
#> # A tibble: 1 x 1
 #>   value 
-#>   <chr> 
-#> 1 setosa

-nullmodel(mtcars[,-1], mtcars$mpg) %>% tidy()
#> # A tibble: 1 x 1
+#>   <chr> 
+#> 1 setosa

+nullmodel(mtcars[,-1], mtcars$mpg) %>% tidy()
#> # A tibble: 1 x 1
 #>   value
-#>   <dbl>
-#> 1  20.1

+#>   <dbl>
+#> 1  20.1