Compboost_internal: Internal Compboost Class
In schalkdaniel/compboost: Efficient Component-Wise Boosting Implementation
Compboost_internal R Documentation
Internal Compboost Class
Description
This class is the raw C++ pendant and still at a very high-level.
It is the base for the Compboost R6 class and provides
many convenient wrapper to access data and execute methods by calling
the C++ methods.
Arguments
oob_response
(ResponseRegr | ResponseBinaryClassif)
The response object containing the target variable.
learning_rate
(numeric(1))
The learning rate.
stop_if_all_stopper_fulfilled
(logical(1))
Boolean to indicate which stopping strategy is used. If TRUE,
the algorithm stops if the conditions of all loggers for stopping apply.
factory_list
(BlearnerFactoryList)
List of base learner factories from which one base learner is selected
in each iteration by using the
loss
(LossQuadratic | LossBinomial | LossHuber | LossAbsolute | LossQuantile)
An initialized S4 loss object (requires to call Loss*$new(...)).
See the respective help page for further information.
logger_list
(LoggerList)
The LoggerList object with all loggers.
optimizer
(OptimizerCoordinateDescent | OptimizerCoordinateDescentLineSearch | OptimizerAGBM | OptimizerCosineAnnealing)
An initialized S4 optimizer object (requires to call Optimizer*.new(..).
See the respective help page for further information.
Format
S4 object.
Usage
Compboost$new(response, learning_rate, stop_if_all_stopper_fulfilled,
factory_list, loss, logger_list, optimizer)
Fields
This class doesn't contain public fields.
Methods
-
$train(): () -> ()
-
$continueTraining(): () -> ()
-
$getLearningRate(): () -> numeric(1)
-
$getPrediction(): () -> matrix()
-
$getSelectedBaselearner(): () -> character()
-
$getLoggerData(): () -> list(character(), matrix())
-
$getEstimatedParameter(): () -> list(matrix())
-
$getParameterAtIteration(): () -> list(matrix())
-
$getParameterMatrix(): () -> matrix()
-
$predictFactoryTrainData(): () -> matrix()
-
$predictFactoryNewData(): list(Data*) -> matrix()
-
$predictIndividualTrainData(): () -> list(matrix()) Get the linear contribution of each base learner for the training data.
-
$predictIndividual(): list(Data*) -> list(matrix()) Get the linear contribution of each base learner for new data.
-
$predict(): list(Data*), logical(1) -> matrix()
-
$summarizeCompboost(): () -> ()
-
$isTrained(): () -> logical(1)
-
$setToIteration(): () -> ()
-
$saveJson(): () -> ()
-
$getOffset(): () -> numeric(1) | matrix()
-
$getRiskVector(): () -> numeric()
-
$getResponse(): () -> Response*
-
$getOptimizer(): () -> Optimizer*
-
$getLoss(): () -> Loss*
-
$getLoggerList(): () -> LoggerList
-
$getBaselearnerList(): () -> BlearnerFactoryList
-
$useGlobalStopping(): () -> logical(1)*
-
$getFactoryMap(): () -> list(Baselearner*)
-
$getDataMap(): () -> list(Data*)
Examples
# Some data:
df = mtcars
df$mpg_cat = ifelse(df$mpg > 20, "high", "low")
# # Create new variable to check the polynomial base learner with degree 2:
# df$hp2 = df[["hp"]]^2
# Data for the baselearner are matrices:
X_hp = as.matrix(df[["hp"]])
X_wt = as.matrix(df[["wt"]])
# Target variable:
response = ResponseBinaryClassif$new("mpg_cat", "high", df[["mpg_cat"]])
data_source_hp = InMemoryData$new(X_hp, "hp")
data_source_wt = InMemoryData$new(X_wt, "wt")
# List for oob logging:
oob_data = list(data_source_hp, data_source_wt)
# List to test prediction on newdata:
test_data = oob_data
# Factories:
linear_factory_hp = BaselearnerPolynomial$new(data_source_hp,
list(degree = 1, intercept = TRUE))
linear_factory_wt = BaselearnerPolynomial$new(data_source_wt,
list(degree = 1, intercept = TRUE))
quadratic_factory_hp = BaselearnerPolynomial$new(data_source_hp,
list(degree = 2, intercept = TRUE))
spline_factory_wt = BaselearnerPSpline$new(data_source_wt,
list(degree = 3, n_knots = 10, penalty = 2, differences = 2))
# Create new factory list:
factory_list = BlearnerFactoryList$new()
# Register factories:
factory_list$registerFactory(linear_factory_hp)
factory_list$registerFactory(linear_factory_wt)
factory_list$registerFactory(quadratic_factory_hp)
factory_list$registerFactory(spline_factory_wt)
# Define loss:
loss_bin = LossBinomial$new()
# Define optimizer:
optimizer = OptimizerCoordinateDescent$new()
## Logger
# Define logger. We want just the iterations as stopper but also track the
# time, inbag risk and oob risk:
log_iterations = LoggerIteration$new(" iteration_logger", TRUE, 500)
log_time = LoggerTime$new("time_logger", FALSE, 500, "microseconds")
# Define new logger list:
logger_list = LoggerList$new()
# Register the logger:
logger_list$registerLogger(log_iterations)
logger_list$registerLogger(log_time)
# Run compboost:
# --------------
# Initialize object:
cboost = Compboost_internal$new(
response = response,
learning_rate = 0.05,
stop_if_all_stopper_fulfilled = FALSE,
factory_list = factory_list,
loss = loss_bin,
logger_list = logger_list,
optimizer = optimizer
)
# Train the model (we want to print the trace):
cboost$train(trace = 50)
cboost
# Get estimated parameter:
cboost$getEstimatedParameter()
# Get trace of selected base learner:
cboost$getSelectedBaselearner()
# Set to iteration 200:
cboost$setToIteration(200, 30)
# Get new parameter values:
cboost$getEstimatedParameter()
schalkdaniel/compboost documentation built on April 15, 2023, 9:03 p.m.
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| Compboost_internal | R Documentation |
Internal Compboost Class
Description
This class is the raw C++ pendant and still at a very high-level.
It is the base for the Compboost R6 class and provides
many convenient wrapper to access data and execute methods by calling
the C++ methods.
Arguments
oob_response |
(ResponseRegr | ResponseBinaryClassif) |
learning_rate |
( |
stop_if_all_stopper_fulfilled |
( |
factory_list |
(BlearnerFactoryList) |
loss |
(LossQuadratic | LossBinomial | LossHuber | LossAbsolute | LossQuantile) |
logger_list |
(LoggerList) |
optimizer |
(OptimizerCoordinateDescent | OptimizerCoordinateDescentLineSearch | OptimizerAGBM | OptimizerCosineAnnealing) |
Format
S4 object.
Usage
Compboost$new(response, learning_rate, stop_if_all_stopper_fulfilled, factory_list, loss, logger_list, optimizer)
Fields
This class doesn't contain public fields.
Methods
-
$train():() -> () -
$continueTraining():() -> () -
$getLearningRate():() -> numeric(1) -
$getPrediction():() -> matrix() -
$getSelectedBaselearner():() -> character() -
$getLoggerData():() -> list(character(), matrix()) -
$getEstimatedParameter():() -> list(matrix()) -
$getParameterAtIteration():() -> list(matrix()) -
$getParameterMatrix():() -> matrix() -
$predictFactoryTrainData():() -> matrix() -
$predictFactoryNewData():list(Data*) -> matrix() -
$predictIndividualTrainData():() -> list(matrix())Get the linear contribution of each base learner for the training data. -
$predictIndividual():list(Data*) -> list(matrix())Get the linear contribution of each base learner for new data. -
$predict():list(Data*), logical(1) -> matrix() -
$summarizeCompboost():() -> () -
$isTrained():() -> logical(1) -
$setToIteration():() -> () -
$saveJson():() -> () -
$getOffset():() -> numeric(1) | matrix() -
$getRiskVector():() -> numeric() -
$getResponse():() -> Response* -
$getOptimizer():() -> Optimizer* -
$getLoss():() -> Loss* -
$getLoggerList():() -> LoggerList -
$getBaselearnerList():() -> BlearnerFactoryList -
$useGlobalStopping():() -> logical(1)* -
$getFactoryMap():() -> list(Baselearner*) -
$getDataMap():() -> list(Data*)
Examples
# Some data:
df = mtcars
df$mpg_cat = ifelse(df$mpg > 20, "high", "low")
# # Create new variable to check the polynomial base learner with degree 2:
# df$hp2 = df[["hp"]]^2
# Data for the baselearner are matrices:
X_hp = as.matrix(df[["hp"]])
X_wt = as.matrix(df[["wt"]])
# Target variable:
response = ResponseBinaryClassif$new("mpg_cat", "high", df[["mpg_cat"]])
data_source_hp = InMemoryData$new(X_hp, "hp")
data_source_wt = InMemoryData$new(X_wt, "wt")
# List for oob logging:
oob_data = list(data_source_hp, data_source_wt)
# List to test prediction on newdata:
test_data = oob_data
# Factories:
linear_factory_hp = BaselearnerPolynomial$new(data_source_hp,
list(degree = 1, intercept = TRUE))
linear_factory_wt = BaselearnerPolynomial$new(data_source_wt,
list(degree = 1, intercept = TRUE))
quadratic_factory_hp = BaselearnerPolynomial$new(data_source_hp,
list(degree = 2, intercept = TRUE))
spline_factory_wt = BaselearnerPSpline$new(data_source_wt,
list(degree = 3, n_knots = 10, penalty = 2, differences = 2))
# Create new factory list:
factory_list = BlearnerFactoryList$new()
# Register factories:
factory_list$registerFactory(linear_factory_hp)
factory_list$registerFactory(linear_factory_wt)
factory_list$registerFactory(quadratic_factory_hp)
factory_list$registerFactory(spline_factory_wt)
# Define loss:
loss_bin = LossBinomial$new()
# Define optimizer:
optimizer = OptimizerCoordinateDescent$new()
## Logger
# Define logger. We want just the iterations as stopper but also track the
# time, inbag risk and oob risk:
log_iterations = LoggerIteration$new(" iteration_logger", TRUE, 500)
log_time = LoggerTime$new("time_logger", FALSE, 500, "microseconds")
# Define new logger list:
logger_list = LoggerList$new()
# Register the logger:
logger_list$registerLogger(log_iterations)
logger_list$registerLogger(log_time)
# Run compboost:
# --------------
# Initialize object:
cboost = Compboost_internal$new(
response = response,
learning_rate = 0.05,
stop_if_all_stopper_fulfilled = FALSE,
factory_list = factory_list,
loss = loss_bin,
logger_list = logger_list,
optimizer = optimizer
)
# Train the model (we want to print the trace):
cboost$train(trace = 50)
cboost
# Get estimated parameter:
cboost$getEstimatedParameter()
# Get trace of selected base learner:
cboost$getSelectedBaselearner()
# Set to iteration 200:
cboost$setToIteration(200, 30)
# Get new parameter values:
cboost$getEstimatedParameter()
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