model_spec: Model Specification Information

Description Details Argument Details

Description

An object with class "model_spec" is a container for information about a model that will be fit.

Details

The main elements of the object are:

Argument Details

An important detail to understand when creating model specifications is that they are intended to be functionally independent of the data. While it is true that some tuning parameters are data dependent, the model specification does not interact with the data at all.

For example, most R functions immediately evaluate their arguments. For example, when calling mean(dat_vec), the object dat_vec is immediately evaluated inside of the function.

parsnip model functions do not do this. For example, using

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 rand_forest(mtry = ncol(mtcars) - 1)

does not execute ncol(mtcars) - 1 when creating the specification. This can be seen in the output:

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 > rand_forest(mtry = ncol(mtcars) - 1)
 Random Forest Model Specification (unknown)

 Main Arguments:
   mtry = ncol(mtcars) - 1

The model functions save the argument expressions and their associated environments (a.k.a. a quosure) to be evaluated later when either fit() or fit_xy() are called with the actual data.

The consequence of this strategy is that any data required to get the parameter values must be available when the model is fit. The two main ways that this can fail is if:

  1. The data have been modified between the creation of the model specification and when the model fit function is invoked.

  2. If the model specification is saved and loaded into a new session where those same data objects do not exist.

The best way to avoid these issues is to not reference any data objects in the global environment but to use data descriptors such as .cols(). Another way of writing the previous specification is

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 rand_forest(mtry = .cols() - 1)

This is not dependent on any specific data object and is evaluated immediately before the model fitting process begins.

One less advantageous approach to solving this issue is to use quasiquotation. This would insert the actual R object into the model specification and might be the best idea when the data object is small. For example, using

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 rand_forest(mtry = ncol(!!mtcars) - 1)

would work (and be reproducible between sessions) but embeds the entire mtcars data set into the mtry expression:

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 > rand_forest(mtry = ncol(!!mtcars) - 1)
 Random Forest Model Specification (unknown)

 Main Arguments:
   mtry = ncol(structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, <snip>

However, if there were an object with the number of columns in it, this wouldn't be too bad:

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 > mtry_val <- ncol(mtcars) - 1
 > mtry_val
 [1] 10
 > rand_forest(mtry = !!mtry_val)
 Random Forest Model Specification (unknown)

 Main Arguments:
   mtry = 10

More information on quosures and quasiquotation can be found at https://tidyeval.tidyverse.org.


parsnip documentation built on July 8, 2020, 7:22 p.m.