Compboost: Component-wise boosting
In schalkdaniel/compboost: Efficient Component-Wise Boosting Implementation
Compboost R Documentation
Component-wise boosting
Description
Fit a component-wise boosting model (Buehlmann (2003)).
This class wraps the S4 class system with Compboost_internal
as internal model representation exposed by Rcpp.
The two convenient wrapper boostLinear() and boostSplines() are
also creating objects of this class.
Visualizing the internals see plotBaselearnerTraces(), plotBaselearner(), plotFeatureImportance(),
plotPEUni(), plotTensor(), and plotRisk(). Visualizing the contribution for
one new observation see plotIndividualContribution().
Public fields
data(data.frame())
The data used for training the model. Note: If oob_fraction is set, the
input data is split into data and data_oob. Hence, data contains a
subset of the input data to train the model.
data_oob(data.frame())
An out-of-bag data set used for risk logging or early stopping. data_oob
is split from the input data (see the data field).
oob_fraction(numeric(1))
The fraction of nrow(input data) defining the number of observations in
data_oob.
response(ResponseRegr | ResponseBinaryClassif)
A S4 response object. See ?ResponseRegr or ?ResponseBinaryClassif for help.
This object holds the current prediction, pseudo residuals and functions to
transform scores. Note: This response corresponds to the data field and holds
the predictions for that data.frame.
response_oob(ResponseRegr | ResponseBinaryClassif)
A S4 response object. See ?ResponseRegr or ?ResponseBinaryClassif for help.
Same as response but for data_oob.
target(character(1))
Name of the target variable in data.
id(character(1))
Name of the data object defined in $new(data, ...).
optimizer(OptimizerCoordinateDescent | OptimizerCoordinateDescentLineSearch | OptimizerAGBM | OptimizerCosineAnnealing)
An initialized S4 optimizer object (requires to call Optimizer*$new(..).
See the respective help page for further information.
loss(LossQuadratic | LossBinomial | LossHuber | LossAbsolute | LossQuantile)
An initialized S4 loss object (requires to call Loss*$new(...)).
See the respective help page for further information.
learning_rate(numeric(1))
The learning rate of the model. Note: Some optimizer do dynamically vary the learning rate.
model(Compboost_internal)
The internal Compboost object exported from Rcpp. See ?Compboost_internal for details.
bl_factory_list([BlearnerFactoryList)
A container with all base learners. See ?BlearnerFactoryList for details.
positive(character(1))
The positive class in the case of binary classification.
stop_all(logical(1))
Indicator whether all stopper must return TRUE to early stop the algorithm.
Comparable to all() if stop_all = TRUE and any() if stop_all = FALSE.
early_stop(logical(1))
Indicator whether early stopping is used or not.
Active bindings
offset(numeric())
Offset of the estimated model.
baselearner_list(list())
Named list with names $getBaselearnerNames(). Each elements contains
-
"feature" (character(1)): The name of the feature from data.
-
"factory" (Baselearner*): The raw base learner as factoryobject. See ?Baselearner* for details.
boost_intercept(logical(1))
Logical value indicating whether an intercept base learner was added with $addIntercept() or not.
logs(data.frame)
Basic information such as risk, selected base learner etc. about each iteration.
If oob_data is set, further information about the validation/oob risk is also logged.
The same applies for time logging etc. Note: Using the field logs internally is set and updated
after each call to $getLoggerData(). Hence, it cashes the logged data set instead of
recalculating the data set as it is done for $getLoggerData().
idx_oob(integer())
An index vector used to split data into data = data[idx_train, ] and data_oob = data[idx_oob, ].
Note: oob_fraction is ignored if this argument is set.
idx_train(integer())
An index vector used to split data into data = data[idx_train, ] and data_oob = data[idx_oob, ].
Note: oob_fraction is ignored if this argument is set.
Methods
Public methods
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Method new()
Creates a new instance of this R6 class.
Usage
Compboost$new(
data = NULL,
target = NULL,
optimizer = NULL,
loss = NULL,
learning_rate = 0.05,
positive = NULL,
oob_fraction = NULL,
early_stop = FALSE,
idx_oob = NULL,
stop_args = list(eps_for_break = 0, patience = 10L),
file = NULL
)
Arguments
data(data.frame)
The data set to build the object. Note: This data set is completely used for training if is.null(idx_oob).
Otherwise, the data set is split into data = data[idx_train, ] and data_oob = data[idx_oob, ].
target(character(1))
Character indicating the name of the target variable.
optimizer(OptimizerCoordinateDescent | OptimizerCoordinateDescentLineSearch | OptimizerAGBM | OptimizerCosineAnnealing)
An initialized S4 optimizer object (requires to call Optimizer*.new(..).
See the respective help page for further information.
loss(LossQuadratic | LossBinomial | LossHuber | LossAbsolute | LossQuantile)
An initialized S4 loss object (requires to call Loss*$new(...)).
See the respective help page for further information.
learning_rate(numeric(1))
Learning rate of the model (default is 0.05).
positive(character(1))
The name of the positive class (in the case of binary classification).
oob_fraction(numeric(1))
The fraction of nrow(input data) defining the number of observations in
data_oob. This argument is ignored if idx_oob is set.
early_stop(logical(1))
Indicator whether early stopping should be used or not.
idx_oob(integer())
An index vector used to split data into data = data[idx_train, ] and data_oob = data[idx_oob, ].
Note: oob_fraction is ignored if this argument is set.
stop_args(list(integer(1), integer(1)))
list containing two elements patience and eps_for_break which are used for early stopping.
file(character(1)
File from which a model should be loaded. If NULL, data and target must be defined.
Method addLogger()
Add a logger to the model.
Usage
Compboost$addLogger(logger, use_as_stopper = FALSE, logger_id, ...)
Arguments
logger(LoggerIteration | LoggerTime | LoggerInbagRisk | LoggerOobRisk)
The uninitialized logger.
use_as_stopper(logical(1))
Indicator defining the logger as stopper considering it for early stopping.
logger_id(character(1))
The id of the logger. This allows to define two logger of the same type (e.g. risk logging) but with different arguments.
...Additional arguments passed to loger$new(logger_id, use_as_stopper, ...).
Method getCurrentIteration()
Get the number of the current iteration.
Usage
Compboost$getCurrentIteration()
Returns
integer(1) value.
Method addIntercept()
This functions adds a base learner that adjusts the intercept (if selected).
Adding an intercept base learner may be necessary, e.g., when adding linear effects
without intercept.
Usage
Compboost$addIntercept(id = "intercept", data_source = InMemoryData)
Arguments
id(character(1))
The id of the base learner (default is "intercept").
data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory
storing is supported, see ?InMemorydata for details.
data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory
storing is supported, see ?InMemorydata for details.
Method addBaselearner()
Add a base learner of one feature to the model that is considered in each iteration.
Using $addBaselearner() just allows including univariate features. See $addTensor() for
bivariate effect modelling and $addComponents() for an effect decomposition.
Usage
Compboost$addBaselearner(
feature,
id,
bl_factory,
data_source = InMemoryData,
...
)
Arguments
feature(character(1))
Name of the feature, must be a column in data.
feature(character(1))
Name of the feature, must be a column in data.
id(character(1))
The name of the base learner.
bl_factory(BaselearnerPolynomial | BaselearnerPSpline | BaselearnerCategoricalBinary | BaselearnerCategoricalRidge)
Uninitialized base learner class. See the respective help page for details.
data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory
storing is supported, see ?InMemorydata for details.
data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory
storing is supported, see ?InMemorydata for details.
...Further argument spassed to the $new(...) constructor of bl_factory.
Method rmBaselearner()
Remove a base learner from the model.
Usage
Compboost$rmBaselearner(blname)
Arguments
blname(character(1))
Name of the base learner that should be removed. Must be an element of $getBaselearnerNames().
Method addTensor()
Add a row-wise tensor product of features. Note: The base learner are pre-defined
by the type of the feature. Numerical features uses a BaselearnerPSpline while categorical
features are included using a BaselearnerCategoricalRidge base learner.
To include an arbitrary tensor product requires to use the S4 API with using
BaselearnerTensor on two base learners of any type.
Usage
Compboost$addTensor(
feature1,
feature2,
df = NULL,
df1 = NULL,
df2 = NULL,
isotrop = FALSE,
...
)
Arguments
feature1(character(1))
Name of the first feature. Must be an element of names(data).
feature2(character(1))
Name of the second feature. Must be an element of names(data).
df(numeric(1))
The degrees of freedom used for both base learner (this parameter overwrites df1 and df2).
df1(numeric(1))
The degrees of freedom used for the first base learner.
df2(numeric(1))
The degrees of freedom used for the first base learner.
isotrop(logical(1))
Indicator how the two penalties should be combined, if isotrop == TRUE,
the total degrees of freedom are uniformly distributed over the dimensions while
isotrop == FALSE allows to define how strong each of the two dimensions is penalized.
...Additional arguments passed to the $new() constructor of the BaselearnerPSpline class.
Method addComponents()
Add an effect with individual components. A linear term is added as well as
a non-linear term without the linear effect. This ensures that the linear
component is selected prior to the non-linear effect. The non-linear effect
is only included if a deviation from a linear effect is required.
Note: Internally, a BaselearnerPolynomial with degree one and a BaselearnerCentered is added.
Centering a base learner makes the design matrix dense and hence memory is filled very fast.
Considering binning may be an option to reduce the memory consumption.
Usage
Compboost$addComponents(feature, ...)
Arguments
feature(character(1))
Name of the feature, must be a column in data.
feature(character(1))
Name of the feature, must be a column in data.
...Additional arguments passed to the $new() constructor of the BaselearnerPSpline class.
Method train()
Start fitting a model.
Usage
Compboost$train(iteration = 100, trace = -1)
Arguments
iteration(integer(1))
The maximal number of iteration. The algorithm can be stopped earlier
if early stopping is active.
trace(integer(1))
The number of integers after which the status of the fitting is printed to the screen.
The default trace = -1 internally uses trace = round(iteration / 40).
To silently fit the model use trace = 0.
Method prepareData()
Internally, each base learner is build on a InMemoryData object. Some
methods (e.g. adding a LoggerOobRisk) requires to pass the data as
list(InMemoryData | CategoricalDataRaw) with data objects as elements.
This function converts the given data.frame into that format.
Usage
Compboost$prepareData(newdata)
Arguments
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
Returns
list(InMemoryData | CategoricalDataRaw) with data container as elements.
Numeric features are wrapped by InMemoryData while categorical features
are included with CategoricalDataRaw.
Method prepareResponse()
Same as for $prepareData() but for the response. Internally, vectorToResponse() is
used to generate a ResponseRegr or ResponseBinaryClassif object.
Usage
Compboost$prepareResponse(response)
Arguments
response(vector())
A vector of type numberic or categorical that is transformed to an
response object.
Returns
ResponseRegr | ResponseBinaryClassif object.
Method predict()
Calculate predictions.
Usage
Compboost$predict(newdata = NULL, as_response = FALSE)
Arguments
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
as_response(logical(1))
In the case of binary classification, as_response = TRUE returns predictions as
response, i.e. classes.
Returns
Vector of predictions.
Method predictIndividual()
While $predict() returns the sum of all base learner predictions, this function
returns a list with the predictions for each base learner.
Usage
Compboost$predictIndividual(newdata)
Arguments
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
Returns
Named list() with the included base learner names as names and the base learner
predictions as elements.
Method transformData()
Get design matrices of all (or a subset) base learners for a new data.frame.
Usage
Compboost$transformData(newdata, blnames = NULL)
Arguments
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
newdata(data.frame)
New data set of the same structure as data.
blnames(character())
Names of the base learners for which the design matrices are returned. If
is.null(blnames), compboost tries to guess all base learners that were
constructed based on the feature names of newdata.
Returns
list(matrix | Matrix::Matrix) matrices as elements.
Method getInbagRisk()
Return the training risk of each iteration.
Usage
Compboost$getInbagRisk()
Returns
numeric() vector of risk values or NULL if $train() was not called previously.
Method getSelectedBaselearner()
Get a vector with the name of the selected base learner of each iteration.
Usage
Compboost$getSelectedBaselearner()
Returns
character() vector of base learner names.
Method print()
Printer of the object.
Usage
Compboost$print()
Returns
Invisibly returns the object.
Method getCoef()
Get the estimated coefficients.
Usage
Compboost$getCoef()
Returns
list(pars, offset) with estimated coefficients/parameters and intercept/offset.
Method getEstimatedCoef()
DEPRICATED use $getCoef() instead.
Get the estimated coefficients.
Usage
Compboost$getEstimatedCoef()
Returns
list(pars, offset) with estimated coefficients/parameters and intercept/offset.
Method getBaselearnerNames()
Get the names of the registered base learners.
Usage
Compboost$getBaselearnerNames()
Returns
charcter() of base learner names.
Method getLoggerData()
Get the logged information.
Usage
Compboost$getLoggerData()
Returns
data.frame of logging information.
Method calculateFeatureImportance()
Calculate feature important based on the training risk. Note that early
stopping should be used to get adequate importance measures.
Usage
Compboost$calculateFeatureImportance(
num_feats = NULL,
aggregate_bl_by_feat = FALSE
)
Arguments
num_feats(integer(1))
The number considered features, the num_feats most important feature names and
the respective value is returned. If num_feats = NULL, all features are considered.
aggregate_bl_by_feat(logical(1))
Indicator whether the importance is aggregated based on feature level. For example,
adding components included two different base learners for the same feature. If
aggregate_bl_by_feat == TRUE, the importance of these two base learners is aggregated
instead of considering them individually.
Returns
Named numeric() vector of length num_feats (if at least num_feats were selected)
with importance values as elements.
Method saveToJson()
Save a Compboost object to a JSON file. Because of the underlying C++ objects,
it is not possible to use R's native load and save methods.
Usage
Compboost$saveToJson(file, rm_data = FALSE)
Arguments
file(character(1))
Name/path to the file.
rm_data(logical(1))
Remove all data from the model. This applies to the training data, response, as well as
the test data and response used for the test risk logging. Note: If data is removed, no
continuation of the training is possible after reloading. Also, everything related to
predictions based on the training data throws an error.
Method clone()
The objects of this class are cloneable with this method.
Usage
Compboost$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
References
Buehlmann, Peter, Yu, Bin (2003).
“Boosting with the L2 loss: regression and classification.”
Journal of the American Statistical Association, 98(462), 324–339.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/016214503000125")}.
Examples
cboost = Compboost$new(mtcars, "mpg", loss = LossQuadratic$new(), oob_fraction = 0.3)
cboost$addBaselearner("hp", "spline", BaselearnerPSpline, degree = 3,
n_knots = 10, df = 3, differences = 2)
cboost$addBaselearner("wt", "spline", BaselearnerPSpline)
cboost$train(1000, 0)
table(cboost$getSelectedBaselearner())
head(cboost$logs)
names(cboost$baselearner_list)
# Access information about the a base learner in the list:
cboost$baselearner_list$hp_spline$factory$getDF()
cboost$baselearner_list$hp_spline$factory$getPenalty()
plotBaselearner(cboost, "hp_spline")
schalkdaniel/compboost documentation built on April 15, 2023, 9:03 p.m.
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| Compboost | R Documentation |
Component-wise boosting
Description
Fit a component-wise boosting model (Buehlmann (2003)).
This class wraps the S4 class system with Compboost_internal
as internal model representation exposed by Rcpp.
The two convenient wrapper boostLinear() and boostSplines() are
also creating objects of this class.
Visualizing the internals see plotBaselearnerTraces(), plotBaselearner(), plotFeatureImportance(),
plotPEUni(), plotTensor(), and plotRisk(). Visualizing the contribution for
one new observation see plotIndividualContribution().
Public fields
data(
data.frame())
The data used for training the model. Note: Ifoob_fractionis set, the input data is split intodataanddata_oob. Hence,datacontains a subset of the input data to train the model.data_oob(
data.frame())
An out-of-bag data set used for risk logging or early stopping.data_oobis split from the input data (see thedatafield).oob_fraction(
numeric(1))
The fraction ofnrow(input data)defining the number of observations indata_oob.response(ResponseRegr | ResponseBinaryClassif)
AS4response object. See?ResponseRegror?ResponseBinaryClassiffor help. This object holds the current prediction, pseudo residuals and functions to transform scores. Note: This response corresponds to thedatafield and holds the predictions for thatdata.frame.response_oob(ResponseRegr | ResponseBinaryClassif)
AS4response object. See?ResponseRegror?ResponseBinaryClassiffor help. Same asresponsebut fordata_oob.target(
character(1))
Name of the target variable indata.id(
character(1))
Name of the data object defined in$new(data, ...).optimizer(OptimizerCoordinateDescent | OptimizerCoordinateDescentLineSearch | OptimizerAGBM | OptimizerCosineAnnealing)
An initializedS4optimizer object (requires to callOptimizer*$new(..). See the respective help page for further information.loss(LossQuadratic | LossBinomial | LossHuber | LossAbsolute | LossQuantile)
An initializedS4loss object (requires to callLoss*$new(...)). See the respective help page for further information.learning_rate(
numeric(1))
The learning rate of the model. Note: Some optimizer do dynamically vary the learning rate.model(Compboost_internal)
The internal Compboost object exported fromRcpp. See?Compboost_internalfor details.bl_factory_list([BlearnerFactoryList)
A container with all base learners. See?BlearnerFactoryListfor details.positive(
character(1))
The positive class in the case of binary classification.stop_all(
logical(1))
Indicator whether all stopper must returnTRUEto early stop the algorithm. Comparable toall()ifstop_all = TRUEandany()ifstop_all = FALSE.early_stop(
logical(1))
Indicator whether early stopping is used or not.
Active bindings
offset(
numeric())
Offset of the estimated model.baselearner_list(
list())
Namedlistwith names$getBaselearnerNames(). Each elements contains-
"feature"(character(1)): The name of the feature fromdata. -
"factory"(Baselearner*): The raw base learner asfactoryobject. See?Baselearner*for details.
-
boost_intercept(
logical(1))
Logical value indicating whether an intercept base learner was added with$addIntercept()or not.logs(
data.frame)
Basic information such as risk, selected base learner etc. about each iteration. Ifoob_datais set, further information about the validation/oob risk is also logged. The same applies for time logging etc. Note: Using the fieldlogsinternally is set and updated after each call to$getLoggerData(). Hence, it cashes the logged data set instead of recalculating the data set as it is done for$getLoggerData().idx_oob(
integer())
An index vector used to splitdataintodata = data[idx_train, ]anddata_oob = data[idx_oob, ]. Note:oob_fractionis ignored if this argument is set.idx_train(
integer())
An index vector used to splitdataintodata = data[idx_train, ]anddata_oob = data[idx_oob, ]. Note:oob_fractionis ignored if this argument is set.
Methods
Public methods
Method new()
Creates a new instance of this R6 class.
Usage
Compboost$new( data = NULL, target = NULL, optimizer = NULL, loss = NULL, learning_rate = 0.05, positive = NULL, oob_fraction = NULL, early_stop = FALSE, idx_oob = NULL, stop_args = list(eps_for_break = 0, patience = 10L), file = NULL )
Arguments
data(
data.frame)
The data set to build the object. Note: This data set is completely used for training ifis.null(idx_oob). Otherwise, the data set is split intodata = data[idx_train, ]anddata_oob = data[idx_oob, ].target(
character(1))
Character indicating the name of the target variable.optimizer(OptimizerCoordinateDescent | OptimizerCoordinateDescentLineSearch | OptimizerAGBM | OptimizerCosineAnnealing)
An initializedS4optimizer object (requires to callOptimizer*.new(..). See the respective help page for further information.loss(LossQuadratic | LossBinomial | LossHuber | LossAbsolute | LossQuantile)
An initializedS4loss object (requires to callLoss*$new(...)). See the respective help page for further information.learning_rate(
numeric(1))
Learning rate of the model (default is0.05).positive(
character(1))
The name of the positive class (in the case of binary classification).oob_fraction(
numeric(1))
The fraction ofnrow(input data)defining the number of observations indata_oob. This argument is ignored ifidx_oobis set.early_stop(
logical(1))
Indicator whether early stopping should be used or not.idx_oob(
integer())
An index vector used to splitdataintodata = data[idx_train, ]anddata_oob = data[idx_oob, ]. Note:oob_fractionis ignored if this argument is set.stop_args(
list(integer(1), integer(1)))
listcontaining two elementspatienceandeps_for_breakwhich are used for early stopping.file(
character(1)
File from which a model should be loaded. IfNULL,dataandtargetmust be defined.
Method addLogger()
Add a logger to the model.
Usage
Compboost$addLogger(logger, use_as_stopper = FALSE, logger_id, ...)
Arguments
logger(LoggerIteration | LoggerTime | LoggerInbagRisk | LoggerOobRisk)
The uninitialized logger.use_as_stopper(
logical(1))
Indicator defining the logger as stopper considering it for early stopping.logger_id(
character(1))
The id of the logger. This allows to define two logger of the same type (e.g. risk logging) but with different arguments....Additional arguments passed to
loger$new(logger_id, use_as_stopper, ...).
Method getCurrentIteration()
Get the number of the current iteration.
Usage
Compboost$getCurrentIteration()
Returns
integer(1) value.
Method addIntercept()
This functions adds a base learner that adjusts the intercept (if selected). Adding an intercept base learner may be necessary, e.g., when adding linear effects without intercept.
Usage
Compboost$addIntercept(id = "intercept", data_source = InMemoryData)
Arguments
id(
character(1))
The id of the base learner (default is"intercept").data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory storing is supported, see?InMemorydatafor details.data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory storing is supported, see?InMemorydatafor details.
Method addBaselearner()
Add a base learner of one feature to the model that is considered in each iteration.
Using $addBaselearner() just allows including univariate features. See $addTensor() for
bivariate effect modelling and $addComponents() for an effect decomposition.
Usage
Compboost$addBaselearner( feature, id, bl_factory, data_source = InMemoryData, ... )
Arguments
feature(
character(1))
Name of the feature, must be a column indata.feature(
character(1))
Name of the feature, must be a column indata.id(
character(1))
The name of the base learner.bl_factory(BaselearnerPolynomial | BaselearnerPSpline | BaselearnerCategoricalBinary | BaselearnerCategoricalRidge)
Uninitialized base learner class. See the respective help page for details.data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory storing is supported, see?InMemorydatafor details.data_source(InMemoryData)
Uninitialized data object used to store the meta data. Note: At the moment, just in memory storing is supported, see?InMemorydatafor details....Further argument spassed to the
$new(...)constructor ofbl_factory.
Method rmBaselearner()
Remove a base learner from the model.
Usage
Compboost$rmBaselearner(blname)
Arguments
blname(
character(1))
Name of the base learner that should be removed. Must be an element of$getBaselearnerNames().
Method addTensor()
Add a row-wise tensor product of features. Note: The base learner are pre-defined
by the type of the feature. Numerical features uses a BaselearnerPSpline while categorical
features are included using a BaselearnerCategoricalRidge base learner.
To include an arbitrary tensor product requires to use the S4 API with using
BaselearnerTensor on two base learners of any type.
Usage
Compboost$addTensor( feature1, feature2, df = NULL, df1 = NULL, df2 = NULL, isotrop = FALSE, ... )
Arguments
feature1(
character(1))
Name of the first feature. Must be an element ofnames(data).feature2(
character(1))
Name of the second feature. Must be an element ofnames(data).df(
numeric(1))
The degrees of freedom used for both base learner (this parameter overwritesdf1anddf2).df1(
numeric(1))
The degrees of freedom used for the first base learner.df2(
numeric(1))
The degrees of freedom used for the first base learner.isotrop(
logical(1))
Indicator how the two penalties should be combined, ifisotrop == TRUE, the total degrees of freedom are uniformly distributed over the dimensions whileisotrop == FALSEallows to define how strong each of the two dimensions is penalized....Additional arguments passed to the
$new()constructor of the BaselearnerPSpline class.
Method addComponents()
Add an effect with individual components. A linear term is added as well as a non-linear term without the linear effect. This ensures that the linear component is selected prior to the non-linear effect. The non-linear effect is only included if a deviation from a linear effect is required.
Note: Internally, a BaselearnerPolynomial with degree one and a BaselearnerCentered is added. Centering a base learner makes the design matrix dense and hence memory is filled very fast. Considering binning may be an option to reduce the memory consumption.
Usage
Compboost$addComponents(feature, ...)
Arguments
feature(
character(1))
Name of the feature, must be a column indata.feature(
character(1))
Name of the feature, must be a column indata....Additional arguments passed to the
$new()constructor of the BaselearnerPSpline class.
Method train()
Start fitting a model.
Usage
Compboost$train(iteration = 100, trace = -1)
Arguments
iteration(
integer(1))
The maximal number of iteration. The algorithm can be stopped earlier if early stopping is active.trace(
integer(1))
The number of integers after which the status of the fitting is printed to the screen. The defaulttrace = -1internally usestrace = round(iteration / 40). To silently fit the model usetrace = 0.
Method prepareData()
Internally, each base learner is build on a InMemoryData object. Some
methods (e.g. adding a LoggerOobRisk) requires to pass the data as
list(InMemoryData | CategoricalDataRaw) with data objects as elements.
This function converts the given data.frame into that format.
Usage
Compboost$prepareData(newdata)
Arguments
newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.
Returns
list(InMemoryData | CategoricalDataRaw) with data container as elements.
Numeric features are wrapped by InMemoryData while categorical features
are included with CategoricalDataRaw.
Method prepareResponse()
Same as for $prepareData() but for the response. Internally, vectorToResponse() is
used to generate a ResponseRegr or ResponseBinaryClassif object.
Usage
Compboost$prepareResponse(response)
Arguments
response(
vector())
A vector of typenumbericorcategoricalthat is transformed to an response object.
Returns
ResponseRegr | ResponseBinaryClassif object.
Method predict()
Calculate predictions.
Usage
Compboost$predict(newdata = NULL, as_response = FALSE)
Arguments
newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.as_response(
logical(1))
In the case of binary classification,as_response = TRUEreturns predictions as response, i.e. classes.
Returns
Vector of predictions.
Method predictIndividual()
While $predict() returns the sum of all base learner predictions, this function
returns a list with the predictions for each base learner.
Usage
Compboost$predictIndividual(newdata)
Arguments
newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.
Returns
Named list() with the included base learner names as names and the base learner
predictions as elements.
Method transformData()
Get design matrices of all (or a subset) base learners for a new data.frame.
Usage
Compboost$transformData(newdata, blnames = NULL)
Arguments
newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.newdata(
data.frame)
New data set of the same structure asdata.blnames(
character())
Names of the base learners for which the design matrices are returned. Ifis.null(blnames), compboost tries to guess all base learners that were constructed based on the feature names ofnewdata.
Returns
list(matrix | Matrix::Matrix) matrices as elements.
Method getInbagRisk()
Return the training risk of each iteration.
Usage
Compboost$getInbagRisk()
Returns
numeric() vector of risk values or NULL if $train() was not called previously.
Method getSelectedBaselearner()
Get a vector with the name of the selected base learner of each iteration.
Usage
Compboost$getSelectedBaselearner()
Returns
character() vector of base learner names.
Method print()
Printer of the object.
Usage
Compboost$print()
Returns
Invisibly returns the object.
Method getCoef()
Get the estimated coefficients.
Usage
Compboost$getCoef()
Returns
list(pars, offset) with estimated coefficients/parameters and intercept/offset.
Method getEstimatedCoef()
DEPRICATED use $getCoef() instead.
Get the estimated coefficients.
Usage
Compboost$getEstimatedCoef()
Returns
list(pars, offset) with estimated coefficients/parameters and intercept/offset.
Method getBaselearnerNames()
Get the names of the registered base learners.
Usage
Compboost$getBaselearnerNames()
Returns
charcter() of base learner names.
Method getLoggerData()
Get the logged information.
Usage
Compboost$getLoggerData()
Returns
data.frame of logging information.
Method calculateFeatureImportance()
Calculate feature important based on the training risk. Note that early stopping should be used to get adequate importance measures.
Usage
Compboost$calculateFeatureImportance( num_feats = NULL, aggregate_bl_by_feat = FALSE )
Arguments
num_feats(
integer(1))
The number considered features, thenum_featsmost important feature names and the respective value is returned. Ifnum_feats = NULL, all features are considered.aggregate_bl_by_feat(
logical(1))
Indicator whether the importance is aggregated based on feature level. For example, adding components included two different base learners for the same feature. Ifaggregate_bl_by_feat == TRUE, the importance of these two base learners is aggregated instead of considering them individually.
Returns
Named numeric() vector of length num_feats (if at least num_feats were selected)
with importance values as elements.
Method saveToJson()
Save a Compboost object to a JSON file. Because of the underlying C++ objects,
it is not possible to use R's native load and save methods.
Usage
Compboost$saveToJson(file, rm_data = FALSE)
Arguments
file(
character(1))
Name/path to the file.rm_data(
logical(1))
Remove all data from the model. This applies to the training data, response, as well as the test data and response used for the test risk logging. Note: If data is removed, no continuation of the training is possible after reloading. Also, everything related to predictions based on the training data throws an error.
Method clone()
The objects of this class are cloneable with this method.
Usage
Compboost$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
References
Buehlmann, Peter, Yu, Bin (2003). “Boosting with the L2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/016214503000125")}.
Examples
cboost = Compboost$new(mtcars, "mpg", loss = LossQuadratic$new(), oob_fraction = 0.3)
cboost$addBaselearner("hp", "spline", BaselearnerPSpline, degree = 3,
n_knots = 10, df = 3, differences = 2)
cboost$addBaselearner("wt", "spline", BaselearnerPSpline)
cboost$train(1000, 0)
table(cboost$getSelectedBaselearner())
head(cboost$logs)
names(cboost$baselearner_list)
# Access information about the a base learner in the list:
cboost$baselearner_list$hp_spline$factory$getDF()
cboost$baselearner_list$hp_spline$factory$getPenalty()
plotBaselearner(cboost, "hp_spline")
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