Method fit()
fit tunes user-specified model hyper-parameters via Grid Search and
Cross-Validation.
Usage
GridSearchCV$fit(
formula = NULL,
data = NULL,
x = NULL,
y = NULL,
response = NULL,
convert_response = NULL,
progress = FALSE
)
Arguments
formulaAn object of class formula: a symbolic description of
the model to be fitted.
data
An optional data frame, or other object containing the
variables in the model. If data is not provided, how formula is
handled depends on $learner.
x
Predictor data (independent variables), alternative interface to
data with formula.
y
Response vector (dependent variable), alternative interface to
data with formula.
response
String; In the absence of formula or y, this specifies
which element of learner_args is the response vector.
convert_response
Function; This should be a single function that
transforms the response vector. E.g. a function converting a numeric binary
variable to a factor variable.
progress
Logical; indicating whether to print progress across
the hyper-parameter grid.
Details
fit follows standard R modeling convention by surfacing a formula
modeling interface as well as an alternate matrix option. The user should
use whichever interface is supported by the specified $learner
function.
Returns
An object of class FittedGridSearchCV.
Examples
\donttest{
if (require(e1071) && require(rpart) && require(yardstick)) {
iris_new <- iris[sample(1:nrow(iris), nrow(iris)), ]
iris_new$Species <- factor(iris_new$Species == "virginica")
iris_train <- iris_new[1:100, ]
iris_validate <- iris_new[101:150, ]
### Decision Tree example
iris_grid_cv <- GridSearchCV$new(
learner = rpart::rpart,
learner_args = list(method = "class"),
tune_params = list(
minsplit = seq(10, 30, by = 5),
maxdepth = seq(20, 30, by = 2)
),
splitter = cv_split,
splitter_args = list(v = 3),
scorer = list(accuracy = yardstick::accuracy_vec),
optimize_score = "max",
prediction_args = list(accuracy = list(type = "class"))
)
iris_grid_cv_fitted <- iris_grid_cv$fit(
formula = Species ~ .,
data = iris_train
)
### Example with multiple metric functions
iris_grid_cv <- GridSearchCV$new(
learner = rpart::rpart,
learner_args = list(method = "class"),
tune_params = list(
minsplit = seq(10, 30, by = 5),
maxdepth = seq(20, 30, by = 2)
),
splitter = cv_split,
splitter_args = list(v = 3),
scorer = list(
accuracy = yardstick::accuracy_vec,
auc = yardstick::roc_auc_vec
),
optimize_score = "max",
prediction_args = list(
accuracy = list(type = "class"),
auc = list(type = "prob")
),
convert_predictions = list(
accuracy = NULL,
auc = function(i) i[, "FALSE"]
)
)
iris_grid_cv_fitted <- iris_grid_cv$fit(
formula = Species ~ .,
data = iris_train,
)
# Grab the best model
iris_grid_cv_fitted$best_model
# Grab the best hyper-parameters
iris_grid_cv_fitted$best_params
# Grab the best model performance metrics
iris_grid_cv_fitted$best_metric
### Matrix interface example - SVM
mtcars_train <- mtcars[1:25, ]
mtcars_eval <- mtcars[26:nrow(mtcars), ]
mtcars_grid_cv <- GridSearchCV$new(
learner = e1071::svm,
tune_params = list(
degree = 2:4,
kernel = c("linear", "polynomial")
),
splitter = cv_split,
splitter_args = list(v = 2),
learner_args = list(scale = TRUE),
scorer = list(
rmse = yardstick::rmse_vec,
mae = yardstick::mae_vec
),
optimize_score = "min"
)
mtcars_grid_cv_fitted <- mtcars_grid_cv$fit(
x = mtcars_train[, -1],
y = mtcars_train$mpg
)
}
}
Method new()
Create a new GridSearchCV object.
Usage
GridSearchCV$new(
learner = NULL,
tune_params = NULL,
splitter = NULL,
scorer = NULL,
optimize_score = c("min", "max"),
learner_args = NULL,
splitter_args = NULL,
scorer_args = NULL,
prediction_args = NULL,
convert_predictions = NULL
)
Arguments
learnerFunction that estimates a predictive model. It is
essential that this function support either a formula interface with
formula and data arguments, or an alternate matrix interface with
x and y arguments.
tune_params
A named list specifying the arguments of $learner to
tune.
splitter
A function that computes cross validation folds from an
input data set or a pre-computed list of cross validation fold indices.
If splitter is a function, it must have a data argument for the
input data, and it must return a list of cross validation fold indices.
If splitter is a list of integers, the number of cross validation
folds is length(splitter) and each element contains the indices of
the data observations that are included in that fold.
scorer
A named list of metric functions to evaluate model
performance on evaluation_data. Any provided metric function
must have truth and estimate arguments, for true outcome values and
predicted outcome values respectively, and must return a single numeric
metric value. The last metric function will be the one used to identify
the optimal model from the Grid Search.
optimize_score
One of "max" or "min"; Whether to maximize or
minimize the metric defined in scorer to find the optimal Grid Search
parameters.
learner_args
A named list of additional arguments to pass to
learner.
splitter_args
A named list of additional arguments to pass to
splitter.
scorer_args
A named list of additional arguments to pass to
scorer. scorer_args must either be length 1 or length(scorer) in
the case where different arguments are being passed to each scoring
function.
prediction_args
A named list of additional arguments to pass to
predict. prediction_args must either be length 1 or
length(scorer) in the case where different arguments are being passed
to each scoring function.
convert_predictions
A list of functions to convert predicted
values prior to being evaluated by the metric functions supplied in
scorer. This list should either be length 1, in which case the same
function will be applied to all predicted values, or length(scorer)
in which case each function in convert_predictions will correspond
with each function in scorer.
Returns
An object of class GridSearch.
Method clone()
The objects of this class are cloneable with this method.
Usage
GridSearchCV$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.