library(magrittr) knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
modelgrid 1.1.0.0 is now available on CRAN. modelgrid offers a minimalistic but
very flexible framework to create, manage and train a portfolio of caret models.
Note, you should already be fairly familiar with the caret package before giving
modelgrid
a spin.
This is the first official release, so below I describe the key concept
behind modelgrid
as well as the features of modelgrid
divided into three main
categories:
When facing a Machine Learning problem, you typically want to try out a lot of
models in order to find out, what works and what does not. But how can we manage
these experiments in a structured, simple and transparent way? You guessed it -
by using the modelgrid
package (and yes, I am familiar with the caretEnsemble
package, but I wanted something, that was more flexible and easier/more intuitive
to work with).
A tuning grid consists of combinations of hyperparameters for a specific model. A model grid is merely just an extension of that concept in the sense, that it consists of - potentially many - models, each with their own tuning grid. Basically the model grid is built by providing a set of shared settings, that by default will apply to all models within the model grid, and defining the settings for the individual models in the model grid.
You can pre-allocate an empty model grid with the constructor function
model_grid
and take a look at the structure.
library(modelgrid) mg <- model_grid() mg
An object belonging to the model_grid
class has three components:
shared_settings
: these are the settings, that will be shared by all models
in the model grid by default. Generally, it makes sense to keep some settings
fixed for all models, e.g. the choice of target variable, features, resampling scheme
and sometimes also preprocessing options. By providing them as shared settings
the user avoids redundant code.models
: every individual model specification added to the model grid will be
an element in this list. The individual model specification consists of settings
that uniquely identify the indvidual model. If a setting has been set both as part
of the shared settings and the settings of a given individual model specification,
the setting from the individual model specification will apply for that given
model.model_fits
: this element contains the fitted models (one for each individual
model specification), once the model_grid
has been trained.The first natural step of setting up the model grid is to define, which settings
should be shared by all models by default. We will use the GermanCredit data set
from the caret package as example data and do just that with the share_settings
function.
library(magrittr) library(caret) library(dplyr) library(purrr) # Load data on German credit applications. data(GermanCredit) # Construct empty model grid and define shared settings. mg <- model_grid() %>% share_settings( y = GermanCredit[["Class"]], x = GermanCredit %>% select(-Class), preProc = "nzv", metric = "ROC", trControl = trainControl( method = "cv", number = 5, summaryFunction = twoClassSummary, classProbs = TRUE ) ) purrr::map_chr(mg$shared_settings, class)
The shared_settings
component of the model grid is now populated. In order to complete
the model grid we must define a set of individual model specifications, that
we would like to give a shot. A common choice of baseline model could be
a simple parametric model e.g. a Generalized Linear Model. The model specification
is added to the model grid with the add_model
function.
mg <- mg %>% add_model(model_name = "Logistic Regression Baseline", method = "glm", family = binomial(link = "logit")) mg$models
model_grid
requires a (unique) name for each individual model specification, so I
named this one "Logistic Regression Baseline". If the user does not provide a name,
a generic name - 'Model[int]' - is generated automatically.
This is all it takes to create the smallest possible model grid with only one unique
model configuration. The model grid can be trained with the train
function. For
more on this go to 'Training a model grid'.
But a model grid with only one model specification is obviously not a really interesting use case. Let us insert two more model specifications into the model grid: another two logistic regression models, but this time with the features being preprocessed with Principal Component Analysis.
mg <- mg %>% add_model(model_name = "Logistic Regression PCA", method = "glm", family = binomial(link = "logit"), preProc = c("nzv", "center", "scale", "pca")) %>% add_model(model_name = "Logistic Regression PCA 98e-2", method = "glm", family = binomial(link = "logit"), preProc = c("nzv", "center", "scale", "pca"), custom_control = list(preProcOptions = list(thresh = 0.98))) mg$models
You can of course add as many models as you like to the model grid with
the add_model
function.
The models from a model grid can be trained with the train
function
from the caret
package, for which I have implemented a S3 method for the
model_grid
class.
When you call train
with a model_grid
, all of the individual model
specifications are consolidated with the shared settings into complete
caret model specifications, which are then trained one by one with
caret.
For a given model the model settings are consolidated with the
consolidate_model
function. Let us see how this works with the three models.
For the baseline model there is no overlap between the shared settings and
the settings in the individual model spec, and the settings will just
be appended into one configuration.
# there are no conflicts. dplyr::intersect(names(mg$shared_settings), names(mg$models$`Logistic Regression Baseline`)) # consolidate model settings into one model. consolidate_model( mg$shared_settings, mg$models$`Logistic Regression Baseline` ) %>% purrr::map_chr(class)
In case the same setting has been specified both in the shared settings of the model grid and in the individual settings for a specific model, the individual setting will apply. This is the case for the model 'Logistic Regression PCA', where the 'preProc' argument has also been defined in the model specific configuration.
# the 'preProc' setting is defined both in the shared and model specific settings. dplyr::intersect(names(mg$shared_settings), names(mg$models$`Logistic Regression PCA`)) mg$shared_settings$preProc mg$models$`Logistic Regression PCA`$preProc # consolidate model settings into one model. consolidate_model( mg$shared_settings, mg$models$`Logistic Regression PCA` ) %>% magrittr::extract2("preProc")
Also, if the 'trControl' argument is defined as part of the shared settings, the
subsettings of 'trControl' can be modified for a specific model with the special
setting 'custom_control' (which itself is given as an explicit argument to the
add_model
function) in the model specific settings.
For the model 'Logistic Regression PCA 98e-2', the preprocessing options for PCA were adjusted with 'custom_control'. When the model is consolidated, the model specific customizations of subsettings of the shared 'trControl' argument will apply.
# the 'trControl$preProcOptions$thresh' setting is defined in the shared # settings but customized in the model specific settings. mg$shared_settings$trControl$preProcOptions$thresh mg$models$`Logistic Regression PCA 98e-2`$custom_control$preProcOptions$thresh # consolidate model settings into one model. consolidate_model( mg$shared_settings, mg$models$`Logistic Regression PCA 98e-2` ) %>% magrittr::extract2(c("trControl", "preProcOptions", "thresh"))
When calling the train
function, the consolidate_model
function is called
under the hood with all of the individual models and the shared settings, and
a set of complete caret model specifications is generated - one for each
individual model specification.
Afterwards the models are trained one by one with caret
, and the fitted
models are saved in the model_fits
component of the model grid.
# train models from model grid. mg <- train(mg) # the fitted models now appear in the 'model_fits' component. names(mg$model_fits) # extract performance. mg$model_fits %>% caret::resamples(.) %>% summary(.)
If we now add an additional models to the model grid, and call train on the model grid again, only the new models (those that do not yet have a fit) will be trained by default.
# train models from model grid. mg <- mg %>% add_model(model_name = "Funky Forest", method = "rf") %>% train(.) names(mg$model_fits)
If you call train
with the train_all
argument set to TRUE, all models will
be trained regardless.
The training of a model_grid
supports both the explicit 'x', 'y' interface to train,
the formula interface and last but not least the new powerful 'recipe' interface.
Let us try out the latter. First we will create a basic recipe.
# create base recipe. library(recipes) rec <- recipe(GermanCredit, formula = Class ~ .) %>% step_nzv(all_predictors())
With that as a starting point I will create and train a minimal model grid as an example. I will tweak the recipe for one of the models.
mg_rec <- model_grid() %>% share_settings( metric = "ROC", data = GermanCredit, trControl = trainControl( method = "cv", number = 5, summaryFunction = twoClassSummary, classProbs = TRUE ) ) %>% add_model( model_name = "Log Reg", x = rec, method = "glm", family = binomial(link = "logit") ) %>% add_model( model_name = "Log Reg PCA", x = rec %>% step_center(all_predictors()) %>% step_scale(all_predictors()) %>% step_pca(all_predictors()), method = "glm", family = binomial(link = "logit") ) %>% train(.) mg_rec$model_fits %>% caret::resamples(.) %>% summary(.)
modelgrid
has a couple of functions, that makes it easy to work iteratively
with the model specifications in a model grid. If you want to modify an
existing model configuration, please use the edit_model
function. Below
I use it to modify one of the GLM models.
# existing model configuration. mg$models$`Logistic Regression PCA` # edit model configuration. mg <- mg %>% edit_model(model_name = "Logistic Regression PCA", preProc = c("nzv", "center", "scale", "ICA")) mg$models$`Logistic Regression PCA`
As you see, when you modify an existing model specification, any corresponding fitted model is deleted, so that nothing is out of sync.
You can also remove a model specification (including any fitted model) from
the model grid with the remove_model
function.
names(mg$models) # remove model configuration. mg <- mg %>% remove_model("Funky Forest") names(mg$models)
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