R/ngbreg.R
In Akai01/ngboost: R Interface to Ngboost
#' @title Constructor for NGBoost regression models.
#' @description
#' NGBRegressor is a wrapper for the generic NGBoost class that facilitates
#' regression.Use this class if you want to predict an outcome that could take
#' an infinite number of (ordered) values.
#'
#' @examples
#' \dontrun{
#'
#' data(Boston, package = "MASS")
#'
#' dta <- rsample::initial_split(Boston)
#'
#' train <- rsample::training(dta)
#'
#' test <- rsample::testing(dta)
#'
#'
#' x_train = train[,1:13]
#' y_train = train[,14]
#'
#' x_test = test[,1:13]
#' y_test = test[,14]
#'
#'
#' model <- NGBRegression$new(Dist = Dist("Exponential"),
#' Base=DecisionTreeRegressor(
#' criterion="mae",
#' min_samples_split=2,
#' min_samples_leaf=1,
#' min_weight_fraction_leaf=0.0,
#' max_depth=5,
#' splitter="best",
#' random_state=NULL),
#' Score = Scores("MLE"),
#' natural_gradient=TRUE,
#' n_estimators= 600,
#' learning_rate= 0.002,
#' minibatch_frac= 0.8,
#' col_sample= 0.9,
#' verbose=TRUE,
#' verbose_eval=100,
#' tol=1e-5)
#'
#' model$fit(X = x_train, Y = y_train, X_val = x_test, Y_val = y_test)
#'
#' model$feature_importances()
#'
#' model$plot_feature_importance()
#'
#' model$predict(x_test)
#'
#' }
#'
#'
#' @author Resul Akay
#' @importFrom R6 R6Class
#' @export
NGBRegression <- R6::R6Class(
classname = "NGBRegression",
public = list(
#' @description Initialize NGBoost regression model.
#' @param Dist Assumed distributional form of Y|X=x.
#' @param A Distribution from ngboost.distns, e.g. Normal
#' @param Score Rule to compare probabilistic predictions to the observed
#' data. A score from ngboost.scores, e.g. LogScore
#' @param Base Base learner to use in the boosting algorithm.
#' Any instantiated sklearn regressor, e.g. DecisionTreeRegressor()
#' @param natural_gradient Logical flag indicating whether the natural
#' gradient should be used
#' @param n_estimators The number of boosting iterations to fit
#' @param learning_rate The learning rate
#' @param minibatch_frac The percent subsample of rows to use in each
#' boosting iteration
#' @param col_sample The percent subsample of columns to use in each
#' boosting iteration
#' @param verbose Flag indicating whether output should be printed during
#' fitting
#' @param verbose_eval Increment (in boosting iterations) at which output
#' should be printed
#' @param tol Numerical tolerance to be used in optimization
#' @param random_state Seed for reproducibility.
#' @return An NGBRegressor object that can be fit.
initialize = function(Dist = NULL,
Score = NULL,
Base = NULL,
natural_gradient = TRUE,
n_estimators = as.integer(500),
learning_rate = 0.01,
minibatch_frac = 1.0,
col_sample = 1.0,
verbose = TRUE,
verbose_eval = as.integer(100),
tol = 0.0001,
random_state = NULL){
private$Dist <- Dist
private$Base <- Base
private$Score <- Score
private$natural_gradient <- natural_gradient
private$n_estimators <- as.integer(n_estimators)
private$learning_rate <- learning_rate
private$minibatch_frac <- minibatch_frac
private$col_sample <- col_sample
private$verbose <- verbose
private$verbose_eval <- as.integer(verbose_eval)
private$tol <- tol
private$random_state <- random_state
#ngboost <- reticulate::import("ngboost")
private$model <- ngboost$NGBRegressor(
Dist = private$Dist,
Score = private$Score,
Base = private$Base,
natural_gradient = private$natural_gradient,
n_estimators = private$n_estimators,
learning_rate = private$learning_rate,
minibatch_frac = private$minibatch_frac,
col_sample = private$col_sample,
verbose = private$verbose,
verbose_eval = private$verbose_eval,
tol = private$tol,
random_state = private$random_state
)
return(self)
},
#' @description An NGBRegressor object that can be fit.
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in Numeric format
#' @param Y DataFrame object or List or numpy array of outcomes (n)
#' in numeric format. Should be floats for regression and integers from 0
#' to K-1 for K-class classification
#' @param X_val DataFrame object or List or numpy array of validation-set
#' predictors in numeric format
#' @param Y_val DataFrame object or List or numpy array of validation-set
#' outcomes in numeric format
#' @param sample_weight how much to weigh each example in the training set.
#' numpy array of size (n) (defaults to 1)
#' @param val_sample_weight How much to weigh each example in the validation
#' set. (defaults to 1)
#' @param train_loss_monitor A custom score or set of scores to track on the
#' training set during training. Defaults to the score defined in the
#' NGBoost constructor.
#' @param val_loss_monitor A custom score or set of scores to track on the
#' validation set during training. Defaults to the score defined in the
#' NGBoost constructor
#' @param early_stopping_rounds The number of consecutive boosting
#' iterations during which the loss has to increase before the algorithm
#' stops early.
#' @return NULL
#'
fit = function(X,
Y,
X_val=NULL,
Y_val=NULL,
sample_weight=NULL,
val_sample_weight=NULL,
train_loss_monitor=NULL,
val_loss_monitor=NULL,
early_stopping_rounds=NULL){
private$feature_names <- colnames(X)
model = private$model
model$fit(X = X,
Y = Y,
X_val = X_val,
Y_val = Y_val,
sample_weight = sample_weight,
val_sample_weight = val_sample_weight,
train_loss_monitor = train_loss_monitor,
val_loss_monitor = val_loss_monitor,
early_stopping_rounds = early_stopping_rounds)
return(invisible(NULL))
},
#' @description Return the feature importances for all parameters in the
#' distribution (the higher, the more important the feature).
#' @return A data frame
#'
feature_importances = function(){
model = private$model
out <- model$feature_importances_
feature_names <- private$feature_names
out <- data.frame("features" = c(feature_names), "importance" = c(out))
private$feature_importance_data <- out
return(out)
},
#' @description Plot feature importance
plot_feature_importance = function(){
feature_importance_data <- private$feature_importance_data
if(is.null(feature_importance_data)){
stop("Please use feature_importances method fist.")
}
ggplot2::ggplot(data = feature_importance_data,
ggplot2::aes(y = .data$features, x = .data$importance)) +
ggplot2::geom_col()
},
#' @description Point prediction of Y at the points X=x
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in numeric Format
#' @param max_iter Get the prediction at the specified number of boosting
#' iterations
#' @return Numpy array of the estimates of Y
#'
predict = function(X, max_iter = NULL){
model = private$model
model$predict(X = X, max_iter = max_iter)
},
#' @description Predict the conditional distribution of Y at the points X=x
#' at multiple boosting iterations
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in numeric Format
#' @param max_iter Get the prediction at the specified number of boosting
#' iterations
#' @return A list of NGBoost distribution objects, one per boosting stage
#' up to max_iter.
staged_pred_dist = function(X, max_iter = NULL){
model = private$model
model$staged_pred_dist(X = as.matrix(X), max_iter = max_iter)
},
#' @description Point prediction of Y at the points X=x at multiple boosting
#' iterations.
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in numeric Format
#' @param max_iter Get the prediction at the specified number of boosting
#' iterations
#' @return A list of NGBoost distribution objects, one per boosting stage
#' up to max_iter.
staged_pred = function(X, max_iter = NULL){
model = private$model
model$staged_pred(X = as.matrix(X), max_iter = max_iter)
},
#' @description Set the parameters of this estimator.
#' The method works on simple estimators as well as on nested objects
#' (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
#' parameters of the form ``<component>__<parameter>`` so that it's
#' possible to update each component of a nested object.
#' @param ... dict (a named R list). Estimator parameters.
#' @return self : estimator instance. Estimator instance.
#'
set_params = function(...){
model = private$model
model$set_params(...)
return(self)
},
#' @description Get parameters for this estimator.
#' @param deep bool, default = TRUE
#' If True, will return the parameters for this estimator and
#' contained subobjects that are estimators.
#' @return params. A dict (R list). Parameter names mapped to their values.
get_params = function(deep = TRUE){
model = private$model
model$get_params(deep = deep)
},
#' @description Predict the conditional distribution of Y at the points X=x
#' @param X DataFrame object or List or numpy array of predictors (n x p) in
#' numeric format.
#' @param max_iter get the prediction at the specified number of boosting
#' iterations.
#' @return A NGBDistReg Class
#'
#' @details See for available methods \code{\link{NGBDistReg}}
#'
pred_dist = function(X, max_iter=NULL){
model = private$model
NGBDistReg$new(model$pred_dist(X = X, max_iter=max_iter))
}
),
private = list(Dist = NULL,
Score = NULL,
Base = NULL,
natural_gradient = NULL,
n_estimators = NULL,
learning_rate = NULL,
minibatch_frac = NULL,
col_sample = NULL,
verbose = NULL,
verbose_eval = NULL,
tol = NULL,
random_state = NULL,
model = NULL,
feature_names = NULL,
feature_importance_data = NULL)
)
Akai01/ngboost documentation built on Nov. 21, 2022, 10:05 a.m.
R Package Documentation
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#' @title Constructor for NGBoost regression models.
#' @description
#' NGBRegressor is a wrapper for the generic NGBoost class that facilitates
#' regression.Use this class if you want to predict an outcome that could take
#' an infinite number of (ordered) values.
#'
#' @examples
#' \dontrun{
#'
#' data(Boston, package = "MASS")
#'
#' dta <- rsample::initial_split(Boston)
#'
#' train <- rsample::training(dta)
#'
#' test <- rsample::testing(dta)
#'
#'
#' x_train = train[,1:13]
#' y_train = train[,14]
#'
#' x_test = test[,1:13]
#' y_test = test[,14]
#'
#'
#' model <- NGBRegression$new(Dist = Dist("Exponential"),
#' Base=DecisionTreeRegressor(
#' criterion="mae",
#' min_samples_split=2,
#' min_samples_leaf=1,
#' min_weight_fraction_leaf=0.0,
#' max_depth=5,
#' splitter="best",
#' random_state=NULL),
#' Score = Scores("MLE"),
#' natural_gradient=TRUE,
#' n_estimators= 600,
#' learning_rate= 0.002,
#' minibatch_frac= 0.8,
#' col_sample= 0.9,
#' verbose=TRUE,
#' verbose_eval=100,
#' tol=1e-5)
#'
#' model$fit(X = x_train, Y = y_train, X_val = x_test, Y_val = y_test)
#'
#' model$feature_importances()
#'
#' model$plot_feature_importance()
#'
#' model$predict(x_test)
#'
#' }
#'
#'
#' @author Resul Akay
#' @importFrom R6 R6Class
#' @export
NGBRegression <- R6::R6Class(
classname = "NGBRegression",
public = list(
#' @description Initialize NGBoost regression model.
#' @param Dist Assumed distributional form of Y|X=x.
#' @param A Distribution from ngboost.distns, e.g. Normal
#' @param Score Rule to compare probabilistic predictions to the observed
#' data. A score from ngboost.scores, e.g. LogScore
#' @param Base Base learner to use in the boosting algorithm.
#' Any instantiated sklearn regressor, e.g. DecisionTreeRegressor()
#' @param natural_gradient Logical flag indicating whether the natural
#' gradient should be used
#' @param n_estimators The number of boosting iterations to fit
#' @param learning_rate The learning rate
#' @param minibatch_frac The percent subsample of rows to use in each
#' boosting iteration
#' @param col_sample The percent subsample of columns to use in each
#' boosting iteration
#' @param verbose Flag indicating whether output should be printed during
#' fitting
#' @param verbose_eval Increment (in boosting iterations) at which output
#' should be printed
#' @param tol Numerical tolerance to be used in optimization
#' @param random_state Seed for reproducibility.
#' @return An NGBRegressor object that can be fit.
initialize = function(Dist = NULL,
Score = NULL,
Base = NULL,
natural_gradient = TRUE,
n_estimators = as.integer(500),
learning_rate = 0.01,
minibatch_frac = 1.0,
col_sample = 1.0,
verbose = TRUE,
verbose_eval = as.integer(100),
tol = 0.0001,
random_state = NULL){
private$Dist <- Dist
private$Base <- Base
private$Score <- Score
private$natural_gradient <- natural_gradient
private$n_estimators <- as.integer(n_estimators)
private$learning_rate <- learning_rate
private$minibatch_frac <- minibatch_frac
private$col_sample <- col_sample
private$verbose <- verbose
private$verbose_eval <- as.integer(verbose_eval)
private$tol <- tol
private$random_state <- random_state
#ngboost <- reticulate::import("ngboost")
private$model <- ngboost$NGBRegressor(
Dist = private$Dist,
Score = private$Score,
Base = private$Base,
natural_gradient = private$natural_gradient,
n_estimators = private$n_estimators,
learning_rate = private$learning_rate,
minibatch_frac = private$minibatch_frac,
col_sample = private$col_sample,
verbose = private$verbose,
verbose_eval = private$verbose_eval,
tol = private$tol,
random_state = private$random_state
)
return(self)
},
#' @description An NGBRegressor object that can be fit.
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in Numeric format
#' @param Y DataFrame object or List or numpy array of outcomes (n)
#' in numeric format. Should be floats for regression and integers from 0
#' to K-1 for K-class classification
#' @param X_val DataFrame object or List or numpy array of validation-set
#' predictors in numeric format
#' @param Y_val DataFrame object or List or numpy array of validation-set
#' outcomes in numeric format
#' @param sample_weight how much to weigh each example in the training set.
#' numpy array of size (n) (defaults to 1)
#' @param val_sample_weight How much to weigh each example in the validation
#' set. (defaults to 1)
#' @param train_loss_monitor A custom score or set of scores to track on the
#' training set during training. Defaults to the score defined in the
#' NGBoost constructor.
#' @param val_loss_monitor A custom score or set of scores to track on the
#' validation set during training. Defaults to the score defined in the
#' NGBoost constructor
#' @param early_stopping_rounds The number of consecutive boosting
#' iterations during which the loss has to increase before the algorithm
#' stops early.
#' @return NULL
#'
fit = function(X,
Y,
X_val=NULL,
Y_val=NULL,
sample_weight=NULL,
val_sample_weight=NULL,
train_loss_monitor=NULL,
val_loss_monitor=NULL,
early_stopping_rounds=NULL){
private$feature_names <- colnames(X)
model = private$model
model$fit(X = X,
Y = Y,
X_val = X_val,
Y_val = Y_val,
sample_weight = sample_weight,
val_sample_weight = val_sample_weight,
train_loss_monitor = train_loss_monitor,
val_loss_monitor = val_loss_monitor,
early_stopping_rounds = early_stopping_rounds)
return(invisible(NULL))
},
#' @description Return the feature importances for all parameters in the
#' distribution (the higher, the more important the feature).
#' @return A data frame
#'
feature_importances = function(){
model = private$model
out <- model$feature_importances_
feature_names <- private$feature_names
out <- data.frame("features" = c(feature_names), "importance" = c(out))
private$feature_importance_data <- out
return(out)
},
#' @description Plot feature importance
plot_feature_importance = function(){
feature_importance_data <- private$feature_importance_data
if(is.null(feature_importance_data)){
stop("Please use feature_importances method fist.")
}
ggplot2::ggplot(data = feature_importance_data,
ggplot2::aes(y = .data$features, x = .data$importance)) +
ggplot2::geom_col()
},
#' @description Point prediction of Y at the points X=x
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in numeric Format
#' @param max_iter Get the prediction at the specified number of boosting
#' iterations
#' @return Numpy array of the estimates of Y
#'
predict = function(X, max_iter = NULL){
model = private$model
model$predict(X = X, max_iter = max_iter)
},
#' @description Predict the conditional distribution of Y at the points X=x
#' at multiple boosting iterations
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in numeric Format
#' @param max_iter Get the prediction at the specified number of boosting
#' iterations
#' @return A list of NGBoost distribution objects, one per boosting stage
#' up to max_iter.
staged_pred_dist = function(X, max_iter = NULL){
model = private$model
model$staged_pred_dist(X = as.matrix(X), max_iter = max_iter)
},
#' @description Point prediction of Y at the points X=x at multiple boosting
#' iterations.
#' @param X DataFrame object or List or numpy array of predictors (n x p)
#' in numeric Format
#' @param max_iter Get the prediction at the specified number of boosting
#' iterations
#' @return A list of NGBoost distribution objects, one per boosting stage
#' up to max_iter.
staged_pred = function(X, max_iter = NULL){
model = private$model
model$staged_pred(X = as.matrix(X), max_iter = max_iter)
},
#' @description Set the parameters of this estimator.
#' The method works on simple estimators as well as on nested objects
#' (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
#' parameters of the form ``<component>__<parameter>`` so that it's
#' possible to update each component of a nested object.
#' @param ... dict (a named R list). Estimator parameters.
#' @return self : estimator instance. Estimator instance.
#'
set_params = function(...){
model = private$model
model$set_params(...)
return(self)
},
#' @description Get parameters for this estimator.
#' @param deep bool, default = TRUE
#' If True, will return the parameters for this estimator and
#' contained subobjects that are estimators.
#' @return params. A dict (R list). Parameter names mapped to their values.
get_params = function(deep = TRUE){
model = private$model
model$get_params(deep = deep)
},
#' @description Predict the conditional distribution of Y at the points X=x
#' @param X DataFrame object or List or numpy array of predictors (n x p) in
#' numeric format.
#' @param max_iter get the prediction at the specified number of boosting
#' iterations.
#' @return A NGBDistReg Class
#'
#' @details See for available methods \code{\link{NGBDistReg}}
#'
pred_dist = function(X, max_iter=NULL){
model = private$model
NGBDistReg$new(model$pred_dist(X = X, max_iter=max_iter))
}
),
private = list(Dist = NULL,
Score = NULL,
Base = NULL,
natural_gradient = NULL,
n_estimators = NULL,
learning_rate = NULL,
minibatch_frac = NULL,
col_sample = NULL,
verbose = NULL,
verbose_eval = NULL,
tol = NULL,
random_state = NULL,
model = NULL,
feature_names = NULL,
feature_importance_data = NULL)
)
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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