Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

Public Member Functions | Static Public Attributes | List of all members
Batch Class Reference

Trains model of the Gradient Boosted Trees algorithms in the batch processing mode. More...

Public Member Functions

def __init__
 
def parameter
 
def getInput
 
def getMethod
 
def getResult
 
def resetResult
 
def clone
 
def checkComputeParams
 
def compute
 
- Public Member Functions inherited from Batch
def getInput
 
def setResult
 
def getResult
 
def resetResult
 
def clone
 
def compute
 
- Public Member Functions inherited from AlgorithmImpl
def computeNoThrow
 
def compute
 
def checkComputeParams
 
def checkResult
 
def setupCompute
 
def resetCompute
 
def enableResetOnCompute
 
def hostApp
 
def setHostApp
 
- Public Member Functions inherited from Algorithm
def checkComputeParams
 
def getBaseParameter
 
- Public Member Functions inherited from AlgorithmIfaceImpl
def enableChecks
 
def isChecksEnabled
 
- Public Member Functions inherited from AlgorithmIface
def checkComputeParams
 
def checkResult
 
def getMethod
 

Static Public Attributes

 input = ...
 

Detailed Description

Deprecated:
This item will be removed in a future release.
Parameters
fptypeData type to use in intermediate computations for Gradient Boosted Trees, double or float
methodGradient Boosted Trees computation method, daal.algorithms.gbt.classification.training.Method
Enumerations
  • Method Gradient Boosted Trees training methods
  • classifier.training.InputId Identifiers of input objects for the Gradient Boosted Trees training algorithm
  • classifier.training.ResultId Identifiers of Gradient Boosted Trees training results
References
Aliases
  • Batch_Float64Xboost is an alias of Batch(fptype=float64, method=daal.algorithms.gbt.classification.training.xboost)
  • Batch_Float32Xboost is an alias of Batch(fptype=float32, method=daal.algorithms.gbt.classification.training.xboost)

Constructor & Destructor Documentation

def __init__ (   self,
  args 
)

Variant 1

Constructs the Gradient Boosted Trees training algorithm

Parameters
nClassesNumber of classes

Variant 2

Constructs a Gradient Boosted Trees training algorithm by copying input objects and parameters of another Gradient Boosted Trees training algorithm

Parameters
otherAn algorithm to be used as the source to initialize the input objects and parameters of the algorithm

Member Function Documentation

def checkComputeParams (   self)

checkComputeParams(Batch self) -> Status

def clone (   self)

Returns a pointer to the newly allocated Gradient Boosted Trees training algorithm with a copy of input objects and parameters of this Gradient Boosted Trees training algorithm

Returns
Pointer to the newly allocated algorithm
def compute (   self)

Invokes computations

def getInput (   self)

Get input objects for the Gradient Boosted Trees training algorithm

Returns
Input objects for the Gradient Boosted Trees training algorithm
def getMethod (   self)

Returns the method of the algorithm

Returns
Method of the algorithm
def getResult (   self)

Returns the structure that contains results of Gradient Boosted Trees training

Returns
Structure that contains results of Gradient Boosted Trees training
def parameter (   self,
  args 
)

Variant 1

Gets parameter of the algorithm

Returns
parameter of the algorithm

Variant 2

Gets parameter of the algorithm

Returns
parameter of the algorithm
def resetResult (   self)

Resets the training results of the classification algorithm

Member Data Documentation

input = ...
static

Input data structure


The documentation for this class was generated from the following file:

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