NGBRegression: Constructor for NGBoost regression models.
In Akai01/ngboost: R Interface to Ngboost
NGBRegression R Documentation
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.
Methods
Public methods
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Method new()
Initialize NGBoost regression model.
Usage
NGBRegression$new(
Dist = NULL,
Score = NULL,
Base = NULL,
natural_gradient = TRUE,
n_estimators = as.integer(500),
learning_rate = 0.01,
minibatch_frac = 1,
col_sample = 1,
verbose = TRUE,
verbose_eval = as.integer(100),
tol = 1e-04,
random_state = NULL
)
Arguments
DistAssumed distributional form of Y|X=x.
ScoreRule to compare probabilistic predictions to the observed
data. A score from ngboost.scores, e.g. LogScore
BaseBase learner to use in the boosting algorithm.
Any instantiated sklearn regressor, e.g. DecisionTreeRegressor()
natural_gradientLogical flag indicating whether the natural
gradient should be used
n_estimatorsThe number of boosting iterations to fit
learning_rateThe learning rate
minibatch_fracThe percent subsample of rows to use in each
boosting iteration
col_sampleThe percent subsample of columns to use in each
boosting iteration
verboseFlag indicating whether output should be printed during
fitting
verbose_evalIncrement (in boosting iterations) at which output
should be printed
tolNumerical tolerance to be used in optimization
random_stateSeed for reproducibility.
ADistribution from ngboost.distns, e.g. Normal
Returns
An NGBRegressor object that can be fit.
Method fit()
An NGBRegressor object that can be fit.
Usage
NGBRegression$fit(
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
)
Arguments
XDataFrame object or List or numpy array of predictors (n x p)
in Numeric format
YDataFrame 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
X_valDataFrame object or List or numpy array of validation-set
predictors in numeric format
Y_valDataFrame object or List or numpy array of validation-set
outcomes in numeric format
sample_weighthow much to weigh each example in the training set.
numpy array of size (n) (defaults to 1)
val_sample_weightHow much to weigh each example in the validation
set. (defaults to 1)
train_loss_monitorA custom score or set of scores to track on the
training set during training. Defaults to the score defined in the
NGBoost constructor.
val_loss_monitorA custom score or set of scores to track on the
validation set during training. Defaults to the score defined in the
NGBoost constructor
early_stopping_roundsThe number of consecutive boosting
iterations during which the loss has to increase before the algorithm
stops early.
Returns
NULL
Method feature_importances()
Return the feature importances for all parameters in the
distribution (the higher, the more important the feature).
Usage
NGBRegression$feature_importances()
Returns
A data frame
Method plot_feature_importance()
Plot feature importance
Usage
NGBRegression$plot_feature_importance()
Method predict()
Point prediction of Y at the points X=x
Usage
NGBRegression$predict(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p)
in numeric Format
max_iterGet the prediction at the specified number of boosting
iterations
Returns
Numpy array of the estimates of Y
Method staged_pred_dist()
Predict the conditional distribution of Y at the points X=x
at multiple boosting iterations
Usage
NGBRegression$staged_pred_dist(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p)
in numeric Format
max_iterGet the prediction at the specified number of boosting
iterations
Returns
A list of NGBoost distribution objects, one per boosting stage
up to max_iter.
Method staged_pred()
Point prediction of Y at the points X=x at multiple boosting
iterations.
Usage
NGBRegression$staged_pred(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p)
in numeric Format
max_iterGet the prediction at the specified number of boosting
iterations
Returns
A list of NGBoost distribution objects, one per boosting stage
up to max_iter.
Method set_params()
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
Usage
NGBRegression$set_params(...)
Arguments
...dict (a named R list). Estimator parameters.
Returns
self : estimator instance. Estimator instance.
Method get_params()
Get parameters for this estimator.
Usage
NGBRegression$get_params(deep = TRUE)
Arguments
deepbool, default = TRUE
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
params. A dict (R list). Parameter names mapped to their values.
Method pred_dist()
Predict the conditional distribution of Y at the points X=x
Usage
NGBRegression$pred_dist(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p) in
numeric format.
max_iterget the prediction at the specified number of boosting
iterations.
Details
See for available methods NGBDistReg
Returns
A NGBDistReg Class
Method clone()
The objects of this class are cloneable with this method.
Usage
NGBRegression$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Resul Akay
Examples
## Not run:
data(Boston, package = "MASS")
dta <- rsample::initial_split(Boston)
train <- rsample::training(dta)
test <- rsample::testing(dta)
x_train = train[,1:13]
y_train = train[,14]
x_test = test[,1:13]
y_test = test[,14]
model <- NGBRegression$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)
## End(Not run)
Akai01/ngboost documentation built on Nov. 21, 2022, 10:05 a.m.
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| NGBRegression | R Documentation |
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.
Methods
Public methods
Method new()
Initialize NGBoost regression model.
Usage
NGBRegression$new( Dist = NULL, Score = NULL, Base = NULL, natural_gradient = TRUE, n_estimators = as.integer(500), learning_rate = 0.01, minibatch_frac = 1, col_sample = 1, verbose = TRUE, verbose_eval = as.integer(100), tol = 1e-04, random_state = NULL )
Arguments
DistAssumed distributional form of Y|X=x.
ScoreRule to compare probabilistic predictions to the observed data. A score from ngboost.scores, e.g. LogScore
BaseBase learner to use in the boosting algorithm. Any instantiated sklearn regressor, e.g. DecisionTreeRegressor()
natural_gradientLogical flag indicating whether the natural gradient should be used
n_estimatorsThe number of boosting iterations to fit
learning_rateThe learning rate
minibatch_fracThe percent subsample of rows to use in each boosting iteration
col_sampleThe percent subsample of columns to use in each boosting iteration
verboseFlag indicating whether output should be printed during fitting
verbose_evalIncrement (in boosting iterations) at which output should be printed
tolNumerical tolerance to be used in optimization
random_stateSeed for reproducibility.
ADistribution from ngboost.distns, e.g. Normal
Returns
An NGBRegressor object that can be fit.
Method fit()
An NGBRegressor object that can be fit.
Usage
NGBRegression$fit( 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 )
Arguments
XDataFrame object or List or numpy array of predictors (n x p) in Numeric format
YDataFrame 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
X_valDataFrame object or List or numpy array of validation-set predictors in numeric format
Y_valDataFrame object or List or numpy array of validation-set outcomes in numeric format
sample_weighthow much to weigh each example in the training set. numpy array of size (n) (defaults to 1)
val_sample_weightHow much to weigh each example in the validation set. (defaults to 1)
train_loss_monitorA custom score or set of scores to track on the training set during training. Defaults to the score defined in the NGBoost constructor.
val_loss_monitorA custom score or set of scores to track on the validation set during training. Defaults to the score defined in the NGBoost constructor
early_stopping_roundsThe number of consecutive boosting iterations during which the loss has to increase before the algorithm stops early.
Returns
NULL
Method feature_importances()
Return the feature importances for all parameters in the distribution (the higher, the more important the feature).
Usage
NGBRegression$feature_importances()
Returns
A data frame
Method plot_feature_importance()
Plot feature importance
Usage
NGBRegression$plot_feature_importance()
Method predict()
Point prediction of Y at the points X=x
Usage
NGBRegression$predict(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p) in numeric Format
max_iterGet the prediction at the specified number of boosting iterations
Returns
Numpy array of the estimates of Y
Method staged_pred_dist()
Predict the conditional distribution of Y at the points X=x at multiple boosting iterations
Usage
NGBRegression$staged_pred_dist(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p) in numeric Format
max_iterGet the prediction at the specified number of boosting iterations
Returns
A list of NGBoost distribution objects, one per boosting stage up to max_iter.
Method staged_pred()
Point prediction of Y at the points X=x at multiple boosting iterations.
Usage
NGBRegression$staged_pred(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p) in numeric Format
max_iterGet the prediction at the specified number of boosting iterations
Returns
A list of NGBoost distribution objects, one per boosting stage up to max_iter.
Method set_params()
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:~sklearn.pipeline.Pipeline). The latter have
parameters of the form <component>__<parameter> so that it's
possible to update each component of a nested object.
Usage
NGBRegression$set_params(...)
Arguments
...dict (a named R list). Estimator parameters.
Returns
self : estimator instance. Estimator instance.
Method get_params()
Get parameters for this estimator.
Usage
NGBRegression$get_params(deep = TRUE)
Arguments
deepbool, default = TRUE If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
params. A dict (R list). Parameter names mapped to their values.
Method pred_dist()
Predict the conditional distribution of Y at the points X=x
Usage
NGBRegression$pred_dist(X, max_iter = NULL)
Arguments
XDataFrame object or List or numpy array of predictors (n x p) in numeric format.
max_iterget the prediction at the specified number of boosting iterations.
Details
See for available methods NGBDistReg
Returns
A NGBDistReg Class
Method clone()
The objects of this class are cloneable with this method.
Usage
NGBRegression$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Resul Akay
Examples
## Not run:
data(Boston, package = "MASS")
dta <- rsample::initial_split(Boston)
train <- rsample::training(dta)
test <- rsample::testing(dta)
x_train = train[,1:13]
y_train = train[,14]
x_test = test[,1:13]
y_test = test[,14]
model <- NGBRegression$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)
## End(Not run)
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