A more pipeable fit() interface

[DavisVaughan]

I gave a little thought to the fit() interface problem and this is what I came up with. I don't really like the interface arg name but thats just a naming thing.

# helper if required
xy <- function(x, y) {
  list(x = x, y = y)
}

# notice how engine would come before the _optional_ data param for pipeability
# all required params are now moved to the front
# engine could come before interface if you want to keep interface+data together
fit <- function(model_spec, interface, engine, data, control, ...) {
  #switch based on interface being a formula VS list
}

linear_reg() %>%
  fit(y ~ x1 + x2, "lm", fit_data)

linear_reg() %>%
  fit(xy(fit_data[,c("x1", "x2")], fit_data[,c("y")]), "lm")

# slightly simpler
xy_defn <- xy(
  x = fit_data[,c("x1", "x2")], 
  y = fit_data[,c("y")]
)

linear_reg() %>%
  fit(xy_defn, "lm")

[DavisVaughan]

I think I would call interface the definition or representation of the model instead

[topepo]

That's funny, the two alternatives that Hadley and I discussed were:

fit(model, data, formula)
fit_xy(model, x, y)

# and 

fit(model, data, recipe/formula)
fit(model, data_xy(x, y))

I think that I'm leaning towards adding fit_xy.

One issue is that parsnip builds an expression for the model fit function. Because of that, we avoid evaluating the model arguments (i.e. x, data, etc) so that, when embedded into an expression, it doesn't embed the data set in the expression (or resulting call object in the model). It would be doable with xy() or data_xy but much cleaner to use an alternate fit function.

In any case, I'll probably make two branches with each and see what is best (or least worse).

[kevinykuo]

In the case of fit(model, data, formula) or fit(model, formula, data) it seems like the information encoded in formula is lost when the model is fit. Someone else looking at model wouldn't be able to know what needs to be done to the input data in order to reproduce the fitted model. Similarly, inspecting the fitted model doesn't tell you what's needed for scoring. Also, it may be harder to tune formula as a hyperparameter with this API, since here it's neither an "algorithm hyperparameter" nor a "data transformation hyperparameter."

An alternative design would be to require specification of input and output columns in the model object, and require the transformations needed to be encoded in the data processing, so the user would call fit(model, data) to fit the model, where data is a data.frameesque thing. Or perhaps we always require x and y, so the interface becomes fit(model, list(x, y)), and the data preprocessing would return a list by convention.

This is related to #19 -- I think this design decision needs to be clearly justified and articulated.

[topepo]

it seems like the information encoded in formula is lost when the model is fit.

fit retains this information. If the underlying model interface is also a formula, the information is in that model object, otherwise, fit saves it so that the predict method will work:

> library(tidymodels)
> library(parsnip)
> 
> rf_fit <- rand_forest(mode = "regression") %>%
+   fit(engine = "randomForest", mpg ~ ., data = mtcars)
> names(rf_fit)
[1] "lvl"     "spec"    "fit"     "preproc"
> names(rf_fit$preproc)
[1] "terms"       "xlevels"     "offset_expr" "options"  

inspecting the fitted model doesn't tell you what's needed for scoring. Also, it may be harder to tune formula as a hyperparameter with this API, since here it's neither an "algorithm hyperparameter" nor a "data transformation hyperparameter."

I agree that it is neither of these things. We generally don't tune the formula per se.

An alternative design would be to require specification of input and output columns in the model object, and require the transformations needed to be encoded in the data processing

maybe we could call that a recipe :-)

parsnip is one part of a bigger whole that is intended to accomplish a simple goal: give a uniform interface to the models and standardize parameter/argument names. The parts that you are talking about are higher level apis that will absorb/utlize parsnip to just get the model part.

[DavisVaughan]

Re) An alternative design would be to require specification of input and output columns in the model object, and require the transformations needed to be encoded in the data processing

I had a thought while looking at this. Are recipes going to be support as an input to fit() rather than the data + formula case? For example should I be able to do both of these? Do they mean the same thing conceptually?

fit(model_spec, log(Sales_Price) ~ Latitude + Longitude, data = ames)

recip <- recipe(Sales_Price ~ Latitude + Longitude, data = ames) %>%
   step_log(Sales_Price, base = 10)

fit(model_spec, recip)

[topepo]

Originally, the three interfaces for fit also included on for recipes.

I decoupled them because, if you were doing model tuning, you would want to train the recipe once and then apply the results to many models (or submodels when tuning) without remaking the recipe. So, once you have a trained recipe, the x/y interface is fine:

recip <- recipe(Sales_Price ~ Latitude + Longitude, data = ames) %>%
   step_log(Sales_Price, base = 10) %>%
   prep(training = ames, retain = TRUE)

rf_fit <- 
   rf_model_spec %>% 
   fit(x = juice(recip, all_predictors()), y = juice(recip, all_outcomes())

glm_fit <- 
   glm_model_spec %>% 
   fit(x = juice(recip, all_predictors()), y = juice(recip, all_outcomes())

# etc. 

That was the reason for:

parsnip is one part of a bigger whole that is intended to accomplish a simple goal: give a uniform interface to the models and standardize parameter/argument names. The parts that you are talking about are higher level apis that will absorb/utilize parsnip to just get the model part.

You can still get to a fit object from a recipe (vas above), but that will be made really easy using a more general data structure (=pipeline or workflow or protocol or whatever we call it). It is in my nature to try to have a single call to do many things (a la caret) but I want these tools to be more modular and to have another structure that provides the higher level api.

# something like

my_analysis <- workflow() %>% 
   add_recipe(recip) %>%
   add_model(rf_model_spec) %>% 
   # pipe in other elements
   fit(training = training_set)

[DavisVaughan]

😲 very cool! Definitely a balance of wanting to make that fit(juice(), juice()) line simpler, and keeping things general enough to be used with/without recipes.

I'm like 80% convinced that retain = TRUE should be the default, especially if it get's used often for this situation.

[topepo]

I'm like 80% convinced that retain = TRUE should be the default, especially if it get's used often for this situation.

Yeah. That'll change for the next version. I'm tried of typing it.

[topepo]

There is a fit_xy branch that is working if anyone wants to comment.

[lorenzwalthert]

Yeah. That'll change for the next version. I'm tried of typing it.

+1 for this one.

[alexpghayes]

I am a fan of fit_xy(). It's informative and clear, but also irritating enough that people hopefully won't actually use it that much. My hope would be that this minor irritation would work to normalize a high-level data specification over matrix/vector interfaces.

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