From 9e1bd271030a2b651ec5ae46c8d95a8d1b0b81fd Mon Sep 17 00:00:00 2001 From: "Simon P. Couch" Date: Thu, 22 Jul 2021 14:41:43 -0500 Subject: [PATCH 1/4] release prep --- DESCRIPTION | 2 +- NEWS.md | 13 +++---------- cran-comments.md | 2 +- 3 files changed, 5 insertions(+), 12 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f66547be..c31f451e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: stacks Title: Tidy Model Stacking -Version: 0.2.0.9000 +Version: 0.2.1 Authors@R: c( person(given = "Simon", family = "Couch", diff --git a/NEWS.md b/NEWS.md index 1d739fd7..086a5de2 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,19 +1,12 @@ # stacks -## v0.2.0.9000 - -To be released as 0.2.1 - -* Various bug fixes and improvements to documentation. - -### Bug fixes +## v0.2.1 * Updates for importing workflow sets that use the `add_variables()` preprocessor. - * Plot fixes for cases where coefficients are negative. - * Performance and member plots now show the effect of multiple mixture values. +* Package diagrams now have alt text. ## v0.2.0 @@ -55,6 +48,6 @@ be regenerated before predicting, plotting, printing, etc. more informative. * Various improvements to documentation. -### v0.1.0 +## v0.1.0 Initial release! diff --git a/cran-comments.md b/cran-comments.md index a8e1b223..1ad9e821 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,4 +1,4 @@ -# stacks 0.2.0 +# stacks 0.2.1 ## Test environments From 1259c511cbe8181016a0e5f8769741c8c9d6e554 Mon Sep 17 00:00:00 2001 From: "Simon P. Couch" Date: Thu, 22 Jul 2021 15:27:13 -0500 Subject: [PATCH 2/4] site update for v0.2.1 --- NEWS.md | 16 +- docs/404.html | 141 +++---- docs/CODE_OF_CONDUCT.html | 44 ++- docs/LICENSE-text.html | 16 +- docs/LICENSE.html | 16 +- docs/articles/basics.html | 313 +++++++-------- .../figure-html/members-plot-1.png | Bin 44904 -> 41132 bytes .../figure-html/penalty-plot-1.png | Bin 71040 -> 66368 bytes .../figure-html/unnamed-chunk-25-1.png | Bin 48276 -> 48446 bytes .../figure-html/weight-plot-1.png | Bin 37840 -> 35461 bytes .../header-attrs-2.8/header-attrs.js | 12 + .../figure-html/unnamed-chunk-3-1.png | Bin 242433 -> 242485 bytes docs/articles/index.html | 16 +- docs/authors.html | 21 +- docs/index.html | 100 ++--- docs/news/index.html | 25 +- docs/package-logo.png | Bin 0 -> 175007 bytes docs/pkgdown.css | 22 +- docs/pkgdown.js | 2 +- docs/pkgdown.yml | 8 +- docs/reference/add_candidates.html | 170 ++++---- docs/reference/autoplot.linear_stack.html | 20 +- docs/reference/axe_model_stack.html | 92 +++-- docs/reference/blend_predictions.html | 364 ++++++++++-------- docs/reference/build_linear_predictor.html | 20 +- docs/reference/collect_parameters.html | 144 +++---- docs/reference/control_stack.html | 20 +- docs/reference/example_data.html | 103 ++--- docs/reference/fit_members.html | 179 ++++----- docs/reference/get_expressions.html | 20 +- docs/reference/index.html | 18 +- docs/reference/predict.data_stack.html | 20 +- docs/reference/predict.model_stack.html | 273 ++++++------- docs/reference/prediction_eqn.html | 20 +- docs/reference/reexports.html | 16 +- docs/reference/stack_predict.html | 20 +- docs/reference/stacks.html | 18 +- docs/reference/stacks_description.html | 16 +- docs/reference/tree_frogs.html | 21 +- 39 files changed, 1161 insertions(+), 1145 deletions(-) create mode 100644 docs/articles/basics_files/header-attrs-2.8/header-attrs.js create mode 100644 docs/package-logo.png diff --git a/NEWS.md b/NEWS.md index 086a5de2..20837ead 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,4 @@ -# stacks - -## v0.2.1 +# v0.2.1 * Updates for importing workflow sets that use the `add_variables()` preprocessor. @@ -8,15 +6,15 @@ * Performance and member plots now show the effect of multiple mixture values. * Package diagrams now have alt text. -## v0.2.0 +# v0.2.0 -### Breaking changes +## Breaking changes This release of the package changes some elements of the internal structure of model stacks. As such, model stacks stored as saved objects will need to be regenerated before predicting, plotting, printing, etc. -### New features +## New features * The package now supports elastic net models as a meta-learner via the `mixture` argument to `blend_predictions`. @@ -27,7 +25,7 @@ be regenerated before predicting, plotting, printing, etc. * Objects tuned with racing methods from the {finetune} package can now be added as candidate members. -### Bug fixes +## Bug fixes * Fixed bug in determining member hyperparameters during member fitting when using non-RMSE/ROC AUC metrics. @@ -36,7 +34,7 @@ be regenerated before predicting, plotting, printing, etc. are not valid column names and use `make.names` for associated candidate members. -### Miscellaneous improvements +## Miscellaneous improvements * Drop {digest} dependency in favor of {tune}/{rsample} "fingerprinting" to check consistency of resamples. @@ -48,6 +46,6 @@ be regenerated before predicting, plotting, printing, etc. more informative. * Various improvements to documentation. -## v0.1.0 +# v0.1.0 Initial release! diff --git a/docs/404.html b/docs/404.html index 703aa1fc..8629860e 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1,86 +1,43 @@ - - - - + + + + - Page not found (404) • stacks - - - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + - - - - + + + - - - + + + + + - - -
+
+
-
-
+

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

@@ -176,12 +125,12 @@

Contents

-
+ + + - - diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html index c7fe2bb4..79b4f92f 100644 --- a/docs/CODE_OF_CONDUCT.html +++ b/docs/CODE_OF_CONDUCT.html @@ -75,9 +75,15 @@ gtag('config', 'UA-115082821-1'); + + + + + +
@@ -262,6 +262,8 @@

Contents

+ + diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index a620fa3d..ef512005 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -75,9 +75,15 @@ gtag('config', 'UA-115082821-1'); + + + + + +

Taking a quick look at the data, it seems like the hatch time is pretty closely related to some of our predictors!

-library(ggplot2)
+library(ggplot2)
 
-ggplot(tree_frogs) +
-  aes(x = age, y = latency, color = treatment) +
-  geom_point() +
-  labs(x = "Embryo Age (s)", y = "Time to Hatch (s)", col = "Treatment")
+ggplot(tree_frogs) + + aes(x = age, y = latency, color = treatment) + + geom_point() + + labs(x = "Embryo Age (s)", y = "Time to Hatch (s)", col = "Treatment")

Let’s give this a go!

-Define candidate ensemble members

-

At the highest level, ensembles are formed from model definitions. In this package, model definitions are an instance of a minimal workflow, containing a model specification (as defined in the parsnip package) and, optionally, a preprocessor (as defined in the recipes package). Model definitions specify the form of candidate ensemble members.

-

+Define candidate ensemble members +

At the highest level, ensembles are formed from model definitions. In this package, model definitions are an instance of a minimal workflow, containing a model specification (as defined in the parsnip package) and, optionally, a preprocessor (as defined in the recipes package). Model definitions specify the form of candidate ensemble members.

+

A diagram representing 'model definitions,' which specify the form of candidate ensemble members. Three colored boxes represent three different model types; a K-nearest neighbors model (in salmon), a linear regression model (in yellow), and a support vector machine model (in green).

Defining the constituent model definitions is undoubtedly the longest part of building an ensemble with stacks. If you’re familiar with tidymodels “proper,” you’re probably fine to skip this section, keeping a few things in mind:

    -
  • You’ll need to save the assessment set predictions and workflow utilized in your tune_grid(), tune_bayes(), or fit_resamples() objects by setting the control arguments save_pred = TRUE and save_workflow = TRUE. Note the use of the control_stack_*() convenience functions below!
  • +
  • You’ll need to save the assessment set predictions and workflow utilized in your tune_grid(), tune_bayes(), or fit_resamples() objects by setting the control arguments save_pred = TRUE and save_workflow = TRUE. Note the use of the control_stack_*() convenience functions below!
  • Each model definition must share the same rsample rset object.

We’ll first start out with splitting up the training data, generating resamples, and setting some options that will be used by each model definition.

 # some setup: resampling and a basic recipe
-set.seed(1)
-tree_frogs_split <- initial_split(tree_frogs)
-tree_frogs_train <- training(tree_frogs_split)
-tree_frogs_test  <- testing(tree_frogs_split)
+set.seed(1)
+tree_frogs_split <- initial_split(tree_frogs)
+tree_frogs_train <- training(tree_frogs_split)
+tree_frogs_test  <- testing(tree_frogs_split)
 
-set.seed(1)
-folds <- rsample::vfold_cv(tree_frogs_train, v = 5)
+set.seed(1)
+folds <- rsample::vfold_cv(tree_frogs_train, v = 5)
 
 tree_frogs_rec <- 
-  recipe(latency ~ ., data = tree_frogs_train)
+  recipe(latency ~ ., data = tree_frogs_train)
 
-metric <- metric_set(rmse)
+metric <- metric_set(rmse)

Tuning and fitting results for use in ensembles need to be fitted with the control arguments save_pred = TRUE and save_workflow = TRUE—these settings ensure that the assessment set predictions, as well as the workflow used to fit the resamples, are stored in the resulting object. For convenience, stacks supplies some control_stack_*() functions to generate the appropriate objects for you.

-

In this example, we’ll be working with tune_grid() and fit_resamples() from the tune package, so we will use the following control settings:

+

In this example, we’ll be working with tune_grid() and fit_resamples() from the tune package, so we will use the following control settings:

-ctrl_grid <- control_stack_grid()
-ctrl_res <- control_stack_resamples()
+ctrl_grid <- control_stack_grid() +ctrl_res <- control_stack_resamples()

We’ll define three different model definitions to try to predict time to hatch—a K-nearest neighbors model (with hyperparameters to tune), a linear model, and a support vector machine model (again, with hyperparameters to tune).

Starting out with K-nearest neighbors, we begin by creating a parsnip model specification:

 # create a model definition
 knn_spec <-
-  nearest_neighbor(
+  nearest_neighbor(
     mode = "regression", 
-    neighbors = tune("k")
-  ) %>%
-  set_engine("kknn")
+    neighbors = tune("k")
+  ) %>%
+  set_engine("kknn")
 
 knn_spec
 #> K-Nearest Neighbor Model Specification (regression)
@@ -198,16 +194,18 @@ 

#> neighbors = tune("k") #> #> Computational engine: kknn

-

Note that, since we are tuning over several possible numbers of neighbors, this model specification defines multiple model configurations. The specific form of those configurations will be determined when specifying the grid search in tune_grid().

+

Note that, since we are tuning over several possible numbers of neighbors, this model specification defines multiple model configurations. The specific form of those configurations will be determined when specifying the grid search in tune_grid().

From here, we extend the basic recipe defined earlier to fully specify the form of the design matrix for use in a K-nearest neighbors model:

 # extend the recipe
 knn_rec <-
-  tree_frogs_rec %>%
-  step_dummy(all_nominal()) %>%
-  step_zv(all_predictors(), skip = TRUE) %>%
-  step_meanimpute(all_numeric(), skip = TRUE) %>%
-  step_normalize(all_numeric(), skip = TRUE)
+  tree_frogs_rec %>%
+  step_dummy(all_nominal()) %>%
+  step_zv(all_predictors(), skip = TRUE) %>%
+  step_meanimpute(all_numeric(), skip = TRUE) %>%
+  step_normalize(all_numeric(), skip = TRUE)
+#> Warning: `step_meanimpute()` was deprecated in recipes 0.1.16.
+#> Please use `step_impute_mean()` instead.
 
 knn_rec
 #> Data Recipe
@@ -229,9 +227,9 @@ 

 # add both to a workflow
 knn_wflow <- 
-  workflow() %>% 
-  add_model(knn_spec) %>%
-  add_recipe(knn_rec)
+  workflow() %>% 
+  add_model(knn_spec) %>%
+  add_recipe(knn_rec)
 
 knn_wflow
 #> ══ Workflow ════════════════════════════════════════════════════════════════════
@@ -241,10 +239,10 @@ 

#> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 4 Recipe Steps #> -#> ● step_dummy() -#> ● step_zv() -#> ● step_meanimpute() -#> ● step_normalize() +#> • step_dummy() +#> • step_zv() +#> • step_impute_mean() +#> • step_normalize() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> K-Nearest Neighbor Model Specification (regression) @@ -256,9 +254,9 @@

Finally, we can make use of the workflow, training set resamples, metric set, and control object to tune our hyperparameters. Using the grid argument, we specify that we would like to optimize over four possible values of k using a grid search.

 # tune k and fit to the 5-fold cv
-set.seed(2020)
+set.seed(2020)
 knn_res <- 
-  tune_grid(
+  tune_grid(
     knn_wflow,
     resamples = folds,
     metrics = metric,
@@ -270,37 +268,37 @@ 

#> # Tuning results #> # 5-fold cross-validation #> # A tibble: 5 x 5 -#> splits id .metrics .notes .predictions -#> <list> <chr> <list> <list> <list> -#> 1 <split [343/86… Fold1 <tibble[,5] [4 ×… <tibble[,1] [0 ×… <tibble[,5] [344 × … -#> 2 <split [343/86… Fold2 <tibble[,5] [4 ×… <tibble[,1] [0 ×… <tibble[,5] [344 × … -#> 3 <split [343/86… Fold3 <tibble[,5] [4 ×… <tibble[,1] [0 ×… <tibble[,5] [344 × … -#> 4 <split [343/86… Fold4 <tibble[,5] [4 ×… <tibble[,1] [0 ×… <tibble[,5] [344 × … -#> 5 <split [344/85… Fold5 <tibble[,5] [4 ×… <tibble[,1] [0 ×… <tibble[,5] [340 × …

+#> splits id .metrics .notes .predictions +#> <list> <chr> <list> <list> <list> +#> 1 <split [343/86]> Fold1 <tibble [4 × 5]> <tibble [0 × 1]> <tibble [344 × 5]> +#> 2 <split [343/86]> Fold2 <tibble [4 × 5]> <tibble [0 × 1]> <tibble [344 × 5]> +#> 3 <split [343/86]> Fold3 <tibble [4 × 5]> <tibble [0 × 1]> <tibble [344 × 5]> +#> 4 <split [343/86]> Fold4 <tibble [4 × 5]> <tibble [0 × 1]> <tibble [344 × 5]> +#> 5 <split [344/85]> Fold5 <tibble [4 × 5]> <tibble [0 × 1]> <tibble [340 × 5]>

This knn_res object fully specifies the candidate members, and is ready to be included in a stacks workflow.

-

Now, specifying the linear model, note that we are not optimizing over any hyperparameters. Thus, we use the fit_resamples() function rather than tune_grid() or tune_bayes() when fitting to our resamples.

+

Now, specifying the linear model, note that we are not optimizing over any hyperparameters. Thus, we use the fit_resamples() function rather than tune_grid() or tune_bayes() when fitting to our resamples.

 # create a model definition
 lin_reg_spec <-
-  linear_reg() %>%
-  set_engine("lm")
+  linear_reg() %>%
+  set_engine("lm")
 
 # extend the recipe
 lin_reg_rec <-
-  tree_frogs_rec %>%
-  step_dummy(all_nominal()) %>%
-  step_zv(all_predictors(), skip = TRUE)
+  tree_frogs_rec %>%
+  step_dummy(all_nominal()) %>%
+  step_zv(all_predictors(), skip = TRUE)
 
 # add both to a workflow
 lin_reg_wflow <- 
-  workflow() %>%
-  add_model(lin_reg_spec) %>%
-  add_recipe(lin_reg_rec)
+  workflow() %>%
+  add_model(lin_reg_spec) %>%
+  add_recipe(lin_reg_rec)
 
 # fit to the 5-fold cv
-set.seed(2020)
+set.seed(2020)
 lin_reg_res <- 
-  fit_resamples(
+  fit_resamples(
     lin_reg_wflow,
     resamples = folds,
     metrics = metric,
@@ -311,43 +309,45 @@ 

#> # Resampling results #> # 5-fold cross-validation #> # A tibble: 5 x 5 -#> splits id .metrics .notes .predictions -#> <list> <chr> <list> <list> <list> -#> 1 <split [343/86… Fold1 <tibble[,4] [1 × … <tibble[,1] [0 ×… <tibble[,4] [86 × … -#> 2 <split [343/86… Fold2 <tibble[,4] [1 × … <tibble[,1] [0 ×… <tibble[,4] [86 × … -#> 3 <split [343/86… Fold3 <tibble[,4] [1 × … <tibble[,1] [0 ×… <tibble[,4] [86 × … -#> 4 <split [343/86… Fold4 <tibble[,4] [1 × … <tibble[,1] [0 ×… <tibble[,4] [86 × … -#> 5 <split [344/85… Fold5 <tibble[,4] [1 × … <tibble[,1] [0 ×… <tibble[,4] [85 × …

+#> splits id .metrics .notes .predictions +#> <list> <chr> <list> <list> <list> +#> 1 <split [343/86]> Fold1 <tibble [1 × 4]> <tibble [0 × 1]> <tibble [86 × 4]> +#> 2 <split [343/86]> Fold2 <tibble [1 × 4]> <tibble [0 × 1]> <tibble [86 × 4]> +#> 3 <split [343/86]> Fold3 <tibble [1 × 4]> <tibble [0 × 1]> <tibble [86 × 4]> +#> 4 <split [343/86]> Fold4 <tibble [1 × 4]> <tibble [0 × 1]> <tibble [86 × 4]> +#> 5 <split [344/85]> Fold5 <tibble [1 × 4]> <tibble [0 × 1]> <tibble [85 × 4]>

Finally, putting together the model definition for the support vector machine:

 # create a model definition
 svm_spec <- 
-  svm_rbf(
-    cost = tune("cost"), 
-    rbf_sigma = tune("sigma")
-  ) %>%
-  set_engine("kernlab") %>%
-  set_mode("regression")
+  svm_rbf(
+    cost = tune("cost"), 
+    rbf_sigma = tune("sigma")
+  ) %>%
+  set_engine("kernlab") %>%
+  set_mode("regression")
 
 # extend the recipe
 svm_rec <-
-  tree_frogs_rec %>%
-  step_dummy(all_nominal()) %>%
-  step_zv(all_predictors(), skip = TRUE) %>%
-  step_meanimpute(all_numeric(), skip = TRUE) %>%
-  step_corr(all_predictors(), skip = TRUE) %>%
-  step_normalize(all_numeric(), skip = TRUE)
+  tree_frogs_rec %>%
+  step_dummy(all_nominal()) %>%
+  step_zv(all_predictors(), skip = TRUE) %>%
+  step_meanimpute(all_numeric(), skip = TRUE) %>%
+  step_corr(all_predictors(), skip = TRUE) %>%
+  step_normalize(all_numeric(), skip = TRUE)
+#> Warning: `step_meanimpute()` was deprecated in recipes 0.1.16.
+#> Please use `step_impute_mean()` instead.
 
 # add both to a workflow
 svm_wflow <- 
-  workflow() %>% 
-  add_model(svm_spec) %>%
-  add_recipe(svm_rec)
+  workflow() %>% 
+  add_model(svm_spec) %>%
+  add_recipe(svm_rec)
 
 # tune cost and sigma and fit to the 5-fold cv
-set.seed(2020)
+set.seed(2020)
 svm_res <- 
-  tune_grid(
+  tune_grid(
     svm_wflow, 
     resamples = folds, 
     grid = 6,
@@ -359,33 +359,33 @@ 

#> # Tuning results #> # 5-fold cross-validation #> # A tibble: 5 x 5 -#> splits id .metrics .notes .predictions -#> <list> <chr> <list> <list> <list> -#> 1 <split [343/86… Fold1 <tibble[,6] [6 ×… <tibble[,1] [0 ×… <tibble[,6] [516 × … -#> 2 <split [343/86… Fold2 <tibble[,6] [6 ×… <tibble[,1] [0 ×… <tibble[,6] [516 × … -#> 3 <split [343/86… Fold3 <tibble[,6] [6 ×… <tibble[,1] [0 ×… <tibble[,6] [516 × … -#> 4 <split [343/86… Fold4 <tibble[,6] [6 ×… <tibble[,1] [0 ×… <tibble[,6] [516 × … -#> 5 <split [344/85… Fold5 <tibble[,6] [6 ×… <tibble[,1] [0 ×… <tibble[,6] [510 × …

+#> splits id .metrics .notes .predictions +#> <list> <chr> <list> <list> <list> +#> 1 <split [343/86]> Fold1 <tibble [6 × 6]> <tibble [0 × 1]> <tibble [516 × 6]> +#> 2 <split [343/86]> Fold2 <tibble [6 × 6]> <tibble [0 × 1]> <tibble [516 × 6]> +#> 3 <split [343/86]> Fold3 <tibble [6 × 6]> <tibble [0 × 1]> <tibble [516 × 6]> +#> 4 <split [343/86]> Fold4 <tibble [6 × 6]> <tibble [0 × 1]> <tibble [516 × 6]> +#> 5 <split [344/85]> Fold5 <tibble [6 × 6]> <tibble [0 × 1]> <tibble [510 × 6]>

Altogether, we’ve created three model definitions, where the K-nearest neighbors model definition specifies 4 model configurations, the linear regression specifies 1, and the support vector machine specifies 6.

-

+

A diagram representing 'candidate members' generated from each model definition. Four salmon-colored boxes labeled 'KNN' represent K-nearest neighbors models trained on the resamples with differing hyperparameters. Similarly, the linear regression (LM) model generates one candidate member, and the support vector machine (SVM) model generates six.

With these three model definitions fully specified, we are ready to begin stacking these model configurations. (Note that, in most applied settings, one would likely specify many more than 11 candidate members.)

-Putting together a stack

+Putting together a stack

The first step to building an ensemble with stacks is to create a data_stack object—in this package, data stacks are tibbles (with some extra attributes) that contain the assessment set predictions for each candidate ensemble member.

-

+

A diagram representing a 'data stack,' a specific kind of data frame. Colored 'columns' depict, in white, the true value of the outcome variable in the validation set, followed by four columns (in salmon) representing the predictions from the K-nearest neighbors model, one column (in tan) representing the linear regression model, and six (in green) representing the support vector machine model.

We can initialize a data stack using the stacks() function.

 stacks()
 #> # A data stack with 0 model definitions and 0 candidate members.
-

The stacks() function works sort of like the ggplot() constructor from ggplot2—the function creates a basic structure that the object will be built on top of—except you’ll pipe the outputs rather than adding them with +.

+

The stacks() function works sort of like the ggplot() constructor from ggplot2—the function creates a basic structure that the object will be built on top of—except you’ll pipe the outputs rather than adding them with +.

The add_candidates() function adds ensemble members to the stack.

 tree_frogs_data_st <- 
-  stacks() %>%
-  add_candidates(knn_res) %>%
-  add_candidates(lin_reg_res) %>%
+  stacks() %>%
+  add_candidates(knn_res) %>%
+  add_candidates(lin_reg_res) %>%
   add_candidates(svm_res)
 
 tree_frogs_data_st
@@ -396,7 +396,7 @@ 

#> # Outcome: latency (numeric)

As mentioned before, under the hood, a data_stack object is really just a tibble with some extra attributes. Checking out the actual data:

-as_tibble(tree_frogs_data_st)
+as_tibble(tree_frogs_data_st)
 #> # A tibble: 429 x 12
 #>    latency knn_res_1_1 knn_res_1_2 knn_res_1_3 knn_res_1_4 lin_reg_res_1_1
 #>      <dbl>       <dbl>       <dbl>       <dbl>       <dbl>           <dbl>
@@ -410,90 +410,89 @@ 

#> 8 46 -0.523 -0.549 -0.581 -0.587 37.1 #> 9 137 -0.287 -0.352 -0.447 -0.456 78.8 #> 10 73 -0.523 -0.549 -0.581 -0.587 38.8 -#> # … with 419 more rows, and 6 more variables: svm_res_1_5 <dbl>, -#> # svm_res_1_6 <dbl>, svm_res_1_1 <dbl>, svm_res_1_4 <dbl>, svm_res_1_3 <dbl>, -#> # svm_res_1_2 <dbl>

+#> # … with 419 more rows, and 6 more variables: svm_res_1_1 <dbl>, +#> # svm_res_1_4 <dbl>, svm_res_1_3 <dbl>, svm_res_1_5 <dbl>, svm_res_1_2 <dbl>, +#> # svm_res_1_6 <dbl>

The first column gives the first response value, and the remaining columns give the assessment set predictions for each ensemble member. Since we’re in the regression case, there’s only one column per ensemble member. In classification settings, there are as many columns as there are levels of the outcome variable per candidate ensemble member.

That’s it! We’re now ready to evaluate how it is that we need to combine predictions from each candidate ensemble member.

-Fit the stack

+Fit the stack

The outputs from each of these candidate ensemble members are highly correlated, so the blend_predictions() function performs regularization to figure out how we can combine the outputs from the stack members to come up with a final prediction.

 tree_frogs_model_st <-
-  tree_frogs_data_st %>%
+  tree_frogs_data_st %>%
   blend_predictions()

The blend_predictions function determines how member model output will ultimately be combined in the final prediction by fitting a LASSO model on the data stack, predicting the true assessment set outcome using the predictions from each of the candidate members. Candidates with nonzero stacking coefficients become members.

-

-

To make sure that we have the right trade-off between minimizing the number of members and optimizing performance, we can use the autoplot() method:

+

A diagram representing 'stacking coefficients,' the coefficients of the linear model combining each of the candidate member predictions to generate the ensemble's ultimate prediction. Boxes for each of the candidate members are placed besides each other, filled in with color if the coefficient for the associated candidate member is nonzero.

+

To make sure that we have the right trade-off between minimizing the number of members and optimizing performance, we can use the autoplot() method:

-theme_set(theme_bw())
-autoplot(tree_frogs_model_st)
+theme_set(theme_bw()) +autoplot(tree_frogs_model_st)

To show the relationship more directly:

-autoplot(tree_frogs_model_st, type = "members")
+autoplot(tree_frogs_model_st, type = "members")

If these results were not good enough, blend_predictions() could be called again with different values of penalty. As it is, blend_predictions() picks the penalty parameter with the numerically optimal results. To see the top results:

-autoplot(tree_frogs_model_st, type = "weights")
+autoplot(tree_frogs_model_st, type = "weights")

Now that we know how to combine our model output, we can fit the candidates with non-zero stacking coefficients on the full training set.

 tree_frogs_model_st <-
-  tree_frogs_model_st %>%
+  tree_frogs_model_st %>%
   fit_members()
-

+

A diagram representing the ensemble members, where each are pentagons labeled and colored-in according to the candidate members they arose from.

Model stacks can be thought of as a group of fitted member models and a set of instructions on how to combine their predictions.

-

+

A diagram representing the 'model stack' class, which collates the stacking coefficients and members (candidate members with nonzero stacking coefficients that are trained on the full training set). The representation of the stacking coefficients and members is as before. Model stacks are a list subclass.

To identify which model configurations were assigned what stacking coefficients, we can make use of the collect_parameters() function:

 collect_parameters(tree_frogs_model_st, "svm_res")
 #> # A tibble: 6 x 4
-#>   member         cost         sigma  coef
-#>   <chr>         <dbl>         <dbl> <dbl>
-#> 1 svm_res_1_1 0.153   0.0196         13.9
-#> 2 svm_res_1_2 5.76    0.00000856    516. 
-#> 3 svm_res_1_3 1.72    0.0000239       0  
-#> 4 svm_res_1_4 0.192   0.0000000552    0  
-#> 5 svm_res_1_5 0.00315 0.00000000359   0  
-#> 6 svm_res_1_6 0.00733 0.0326         12.5
+#> member cost sigma coef +#> <chr> <dbl> <dbl> <dbl> +#> 1 svm_res_1_1 0.00143 6.64e- 9 0 +#> 2 svm_res_1_2 3.59 3.95e- 4 27.1 +#> 3 svm_res_1_3 0.0978 1.81e- 2 0 +#> 4 svm_res_1_4 0.00849 2.16e-10 0 +#> 5 svm_res_1_5 0.256 4.54e- 1 0 +#> 6 svm_res_1_6 7.64 3.16e- 7 352.

This object is now ready to predict with new data!

 tree_frogs_test <- 
-  tree_frogs_test %>%
-  bind_cols(predict(tree_frogs_model_st, .))
+ tree_frogs_test %>% + bind_cols(predict(tree_frogs_model_st, .))

Juxtaposing the predictions with the true data:

-ggplot(tree_frogs_test) +
-  aes(x = latency, 
+ggplot(tree_frogs_test) +
+  aes(x = latency, 
       y = .pred) +
-  geom_point() + 
-  coord_obs_pred()
+ geom_point() + + coord_obs_pred()

Looks like our predictions were pretty strong! How do the stacks predictions perform, though, as compared to the members’ predictions? We can use the type = "members" argument to generate predictions from each of the ensemble members.

 member_preds <- 
-  tree_frogs_test %>%
-  select(latency) %>%
-  bind_cols(predict(tree_frogs_model_st, tree_frogs_test, members = TRUE))
+ tree_frogs_test %>% + select(latency) %>% + bind_cols(predict(tree_frogs_model_st, tree_frogs_test, members = TRUE))

Now, evaluating the root mean squared error from each model:

-map_dfr(member_preds, rmse, truth = latency, data = member_preds) %>%
-  mutate(member = colnames(member_preds))
-#> # A tibble: 7 x 4
+map_dfr(member_preds, rmse, truth = latency, data = member_preds) %>%
+  mutate(member = colnames(member_preds))
+#> # A tibble: 6 x 4
 #>   .metric .estimator .estimate member         
 #>   <chr>   <chr>          <dbl> <chr>          
 #> 1 rmse    standard         0   latency        
-#> 2 rmse    standard        55.5 .pred          
+#> 2 rmse    standard        55.3 .pred          
 #> 3 rmse    standard       114.  knn_res_1_4    
 #> 4 rmse    standard        55.5 lin_reg_res_1_1
-#> 5 rmse    standard       114.  svm_res_1_6    
-#> 6 rmse    standard       114.  svm_res_1_1    
-#> 7 rmse    standard       114.  svm_res_1_2
+#> 5 rmse standard 114. svm_res_1_2 +#> 6 rmse standard 114. svm_res_1_6

As we can see, the stacked ensemble outperforms each of the member models, though is closely followed by one of its members.

-

Voila! You’ve now made use of the stacks package to predict red-eyed tree frog embryo hatching using a stacked ensemble! The full visual outline for these steps can be found here.

+

Voila! You’ve now made use of the stacks package to predict red-eyed tree frog embryo hatching using a stacked ensemble! The full visual outline for these steps can be found here.

@@ -514,7 +513,7 @@

Developed by Simon Couch, Max Kuhn. - Site built by pkgdown. + Site built by pkgdown.

@@ -524,5 +523,7 @@

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a/docs/articles/index.html b/docs/articles/index.html index 2ac22925..60dc5f51 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -75,9 +75,15 @@ gtag('config', 'UA-115082821-1'); + + + + + + @@ -188,6 +189,8 @@

Authors

+ + diff --git a/docs/index.html b/docs/index.html index bbe15203..9a295618 100644 --- a/docs/index.html +++ b/docs/index.html @@ -39,6 +39,8 @@ + +

stacks is generalized with respect to:

    -
  • Model type: Any model type implemented in parsnip or adjacent packages is fair game to add to a stacks model stack. Here’s a table of many of the implemented model types in the tidymodels core, with a link there to an article about implementing your own model classes as well.
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  • Cross-validation scheme: Any resampling algorithm implemented in rsample or adjacent packages is fair game for resampling data for use in training a model stack.
  • -
  • Error metric: Any metric function implemented in yardstick or adjacent packages is fair game for evaluating model stacks and their members. That package provides some infrastructure for creating your own metric functions as well!
  • +
  • Model type: Any model type implemented in parsnip or adjacent packages is fair game to add to a stacks model stack. Here’s a table of many of the implemented model types in the tidymodels core, with a link there to an article about implementing your own model classes as well.
  • +
  • Cross-validation scheme: Any resampling algorithm implemented in rsample or adjacent packages is fair game for resampling data for use in training a model stack.
  • +
  • Error metric: Any metric function implemented in yardstick or adjacent packages is fair game for evaluating model stacks and their members. That package provides some infrastructure for creating your own metric functions as well!
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stacks uses a regularized linear model to combine predictions from ensemble members, though this model type is only one of many possible learning algorithms that could be used to fit a stacked ensemble model. For implementations of additional ensemble learning algorithms, check out h2o and SuperLearner.

+

stacks uses a regularized linear model to combine predictions from ensemble members, though this model type is only one of many possible learning algorithms that could be used to fit a stacked ensemble model. For implementations of additional ensemble learning algorithms, check out h2o and SuperLearner.

Rather than diving right into the implementation, we’ll focus here on how the pieces fit together, conceptually, in building an ensemble with stacks. See the basics vignette for an example of the API in action!

-a grammar

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At the highest level, ensembles are formed from model definitions. In this package, model definitions are an instance of a minimal workflow, containing a model specification (as defined in the parsnip package) and, optionally, a preprocessor (as defined in the recipes package). Model definitions specify the form of candidate ensemble members.

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To be used in the same ensemble, each of these model definitions must share the same resample. This rsample rset object, when paired with the model definitions, can be used to generate the tuning/fitting results objects for the candidate ensemble members with tune.

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Candidate members first come together in a data_stack object through the add_candidates() function. Principally, these objects are just tibbles, where the first column gives the true outcome in the assessment set (the portion of the training set used for model validation), and the remaining columns give the predictions from each candidate ensemble member. (When the outcome is numeric, there’s only one column per candidate ensemble member. Classification requires as many columns per candidate as there are levels in the outcome variable.) They also bring along a few extra attributes to keep track of model definitions.

-

+a grammar

+

At the highest level, ensembles are formed from model definitions. In this package, model definitions are an instance of a minimal workflow, containing a model specification (as defined in the parsnip package) and, optionally, a preprocessor (as defined in the recipes package). Model definitions specify the form of candidate ensemble members.

+
+

A diagram representing “model definitions,” which specify the form of candidate ensemble members. Three colored boxes represent three different model types; a K-nearest neighbors model (in salmon), a linear regression model (in yellow), and a support vector machine model (in green).

+
+

To be used in the same ensemble, each of these model definitions must share the same resample. This rsample rset object, when paired with the model definitions, can be used to generate the tuning/fitting results objects for the candidate ensemble members with tune.

+
+

A diagram representing “candidate members” generated from each model definition. Four salmon-colored boxes labeled “KNN” represent K-nearest neighbors models trained on the resamples with differing hyperparameters. Similarly, the linear regression model generates one candidate member, and the support vector machine model generates six.

+
+

Candidate members first come together in a data_stack object through the add_candidates() function. Principally, these objects are just tibbles, where the first column gives the true outcome in the assessment set (the portion of the training set used for model validation), and the remaining columns give the predictions from each candidate ensemble member. (When the outcome is numeric, there’s only one column per candidate ensemble member. Classification requires as many columns per candidate as there are levels in the outcome variable.) They also bring along a few extra attributes to keep track of model definitions.

+
+

A diagram representing a “data stack,” a specific kind of data frame. Colored “columns” depict, in white, the true value of the outcome variable in the validation set, followed by four columns (in salmon) representing the predictions from the K-nearest neighbors model, one column (in tan) representing the linear regression model, and six (in green) representing the support vector machine model.

+

Then, the data stack can be evaluated using blend_predictions() to determine to how best to combine the outputs from each of the candidate members. In the stacking literature, this process is commonly called metalearning.

The outputs of each member are likely highly correlated. Thus, depending on the degree of regularization you choose, the coefficients for the inputs of (possibly) many of the members will zero out—their predictions will have no influence on the final output, and those terms will thus be thrown out.

-

+
+

A diagram representing “stacking coefficients,” the coefficients of the linear model combining each of the candidate member predictions to generate the ensemble’s ultimate prediction. Boxes for each of the candidate members are placed besides each other, filled in with color if the coefficient for the associated candidate member is nonzero.

+

These stacking coefficients determine which candidate ensemble members will become ensemble members. Candidates with non-zero stacking coefficients are then fitted on the whole training set, altogether making up a model_stack object.

-

+
+

A diagram representing the “model stack” class, which collates the stacking coefficients and members (candidate members with nonzero stacking coefficients that are trained on the full training set). The representation of the stacking coefficients is as before, where the members (shown next to their associated stacking coefficients) are colored-in pentagons. Model stacks are a list subclass.

+

This model stack object, outputted from fit_members(), is ready to predict on new data! The trained ensemble members are often referred to as base models in the stacking literature.

-

The full visual outline for these steps can be found here. The API for the package closely mirrors these ideas. See the basics vignette for an example of how this grammar is implemented!

+

The full visual outline for these steps can be found here. The API for the package closely mirrors these ideas. See the basics vignette for an example of how this grammar is implemented!

-contributing

-

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

+contributing

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+

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

In the stacks package, some test objects take too long to build with every commit. If your contribution changes the structure of data_stack or model_stacks objects, please regenerate these test objects by running the scripts in man-roxygen/example_models.Rmd, including those with chunk options eval = FALSE.

@@ -176,7 +182,7 @@

@@ -237,5 +247,7 @@

Dev status

+ + diff --git a/docs/news/index.html b/docs/news/index.html index c592cb77..b64a6ad6 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -75,9 +75,15 @@ gtag('config', 'UA-115082821-1'); + + + + + +
@@ -142,7 +147,7 @@
@@ -174,6 +179,8 @@

Contents

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