SHOGUN
4.1.0
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Balanced Conditional Probability Tree.
The tree is constructed to trade-off the existing regressor's prediction and the balance (depth) of the tree. The parameter alpha in [0,1] control the trade-off.
more balanced tree means better computational efficiency, but usually worse performance. See the following paper for more details:
Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alex Strehl. Conditional Probability Tree Estimation Analysis and Algorithms. UAI 2009.
Definition at line 37 of file BalancedConditionalProbabilityTree.h.
Public Types | |
typedef CTreeMachineNode< ConditionalProbabilityTreeNodeData > | node_t |
typedef CBinaryTreeMachineNode< ConditionalProbabilityTreeNodeData > | bnode_t |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
ParameterMap * | m_parameter_map |
uint32_t | m_hash |
Protected Attributes | |
int32_t | m_num_passes |
number of passes for online training More... | |
std::map< int32_t, bnode_t * > | m_leaves |
class => leaf mapping More... | |
CStreamingDenseFeatures< float32_t > * | m_feats |
online features More... | |
CTreeMachineNode< ConditionalProbabilityTreeNodeData > * | m_root |
CDynamicObjectArray * | m_machines |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
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bnode_t type- Tree node with max 2 possible children
Definition at line 55 of file TreeMachine.h.
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node_t type- Tree node with many possible children
Definition at line 52 of file TreeMachine.h.
constructor
Definition at line 15 of file BalancedConditionalProbabilityTree.cpp.
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destructor
Definition at line 44 of file BalancedConditionalProbabilityTree.h.
accumulate along the path to the root the conditional probability for a particular leaf node.
Definition at line 80 of file ConditionalProbabilityTree.cpp.
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
Definition at line 160 of file Machine.cpp.
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apply machine to data in means of binary classification problem
Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CPluginEstimate, and CBaggingMachine.
Definition at line 216 of file Machine.cpp.
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apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 240 of file Machine.cpp.
Applies a locked machine on a set of indices. Error if machine is not locked
indices | index vector (of locked features) that is predicted |
Definition at line 195 of file Machine.cpp.
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applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
Definition at line 246 of file Machine.cpp.
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applies a locked machine on a set of indices for latent problems
Definition at line 274 of file Machine.cpp.
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applies a locked machine on a set of indices for multiclass problems
Definition at line 260 of file Machine.cpp.
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applies a locked machine on a set of indices for regression problems
Reimplemented in CKernelMachine.
Definition at line 253 of file Machine.cpp.
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applies a locked machine on a set of indices for structured problems
Definition at line 267 of file Machine.cpp.
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apply machine to data in means of multiclass classification problem
Reimplemented from CMachine.
Definition at line 21 of file ConditionalProbabilityTree.cpp.
apply machine one single example.
ex | a vector to be applied |
Definition at line 49 of file ConditionalProbabilityTree.cpp.
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applies to one vector
Reimplemented in CKernelMachine, CRelaxedTree, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CMultitaskLinearMachine, CMulticlassMachine, CKNN, CDistanceMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, CScatterSVM, CGaussianNaiveBayes, CPluginEstimate, and CFeatureBlockLogisticRegression.
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apply machine to data in means of regression problem
Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CNeuralNetwork, CCHAIDTree, CStochasticGBMachine, CCARTree, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.
Definition at line 222 of file Machine.cpp.
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apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 234 of file Machine.cpp.
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Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
dict | dictionary of parameters to be built. |
Definition at line 1244 of file SGObject.cpp.
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Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
Definition at line 1361 of file SGObject.cpp.
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compute conditional probabilities for ex along the whole tree for predicting
Definition at line 60 of file ConditionalProbabilityTree.cpp.
create a new OnlineLinear machine for a node
ex | the Example instance for training the new machine |
Definition at line 248 of file ConditionalProbabilityTree.cpp.
Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called
Only possible if supports_locking() returns true
labs | labels used for locking |
features | features used for locking |
Reimplemented in CKernelMachine.
Definition at line 120 of file Machine.cpp.
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Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 151 of file Machine.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 200 of file SGObject.cpp.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
Definition at line 1265 of file SGObject.cpp.
float64_t get_alpha | ( | ) | const |
get alpha
Definition at line 53 of file BalancedConditionalProbabilityTree.h.
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get classifier type
Reimplemented in CLaRank, CDualLibQPBMSOSVM, CNeuralNetwork, CCCSOSVM, CLeastAngleRegression, CLDA, CKernelRidgeRegression, CLibLinearMTL, CBaggingMachine, CLibLinear, CGaussianProcessClassification, CKMeans, CLibSVR, CQDA, CGaussianNaiveBayes, CMCLDA, CLinearRidgeRegression, CKNN, CGaussianProcessRegression, CScatterSVM, CSGDQN, CSVMSGD, CSVMOcas, COnlineSVMSGD, CLeastSquaresRegression, CMKLRegression, CDomainAdaptationSVMLinear, CMKLMulticlass, CWDSVMOcas, CHierarchical, CMKLOneClass, CLibSVM, CStochasticSOSVM, CMKLClassification, CLPBoost, CPerceptron, CAveragedPerceptron, CFWSOSVM, CNewtonSVM, CLPM, CGMNPSVM, CSVMLin, CMulticlassLibSVM, CLibSVMOneClass, CMPDSVM, CGPBTSVM, CGNPPSVM, and CCPLEXSVM.
Definition at line 100 of file Machine.cpp.
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get problem type
Reimplemented from CMachine.
Reimplemented in CCHAIDTree, and CCARTree.
Definition at line 32 of file BaseMulticlassMachine.cpp.
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Definition at line 1136 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 1160 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 1173 of file SGObject.cpp.
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get name
Reimplemented from CConditionalProbabilityTree.
Definition at line 47 of file BalancedConditionalProbabilityTree.h.
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get number of machines
Definition at line 27 of file BaseMulticlassMachine.cpp.
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get number of passes
Definition at line 55 of file ConditionalProbabilityTree.h.
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If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 298 of file SGObject.cpp.
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check whether the labels is valid.
lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented from CMachine.
Reimplemented in CCARTree, and CCHAIDTree.
Definition at line 37 of file BaseMulticlassMachine.cpp.
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inherited |
maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)
file_version | parameter version of the file |
current_version | version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
Definition at line 705 of file SGObject.cpp.
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loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
param_info | information of parameter |
file_version | parameter version of the file, must be <= provided parameter version |
file | file to load from |
prefix | prefix for members |
Definition at line 546 of file SGObject.cpp.
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Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
file | where to load from |
prefix | prefix for members |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 375 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 1063 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 1058 of file SGObject.cpp.
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Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match
param_base | set of TParameter instances that are mapped to the provided target parameter infos |
base_version | version of the parameter base |
target_param_infos | set of SGParamInfo instances that specify the target parameter base |
Definition at line 743 of file SGObject.cpp.
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creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.
If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
Definition at line 950 of file SGObject.cpp.
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protectedvirtualinherited |
This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
replacement | (used as output) here the TParameter instance which is returned by migration is created into |
to_migrate | the only source that is used for migration |
old_name | with this parameter, a name change may be specified |
Definition at line 890 of file SGObject.cpp.
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Definition at line 264 of file SGObject.cpp.
predict a single node
ex | the example being predicted |
node | the node |
Definition at line 237 of file ConditionalProbabilityTree.cpp.
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prints all parameter registered for model selection and their type
Definition at line 1112 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 310 of file SGObject.cpp.
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print the tree structure for debug purpose
Definition at line 144 of file ConditionalProbabilityTree.cpp.
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Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 316 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 1073 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 1068 of file SGObject.cpp.
void set_alpha | ( | float64_t | alpha | ) |
set alpha
Definition at line 21 of file BalancedConditionalProbabilityTree.cpp.
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Definition at line 42 of file SGObject.cpp.
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Definition at line 47 of file SGObject.cpp.
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Definition at line 52 of file SGObject.cpp.
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Definition at line 57 of file SGObject.cpp.
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Definition at line 62 of file SGObject.cpp.
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Definition at line 67 of file SGObject.cpp.
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Definition at line 72 of file SGObject.cpp.
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Definition at line 77 of file SGObject.cpp.
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Definition at line 82 of file SGObject.cpp.
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Definition at line 87 of file SGObject.cpp.
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Definition at line 92 of file SGObject.cpp.
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Definition at line 97 of file SGObject.cpp.
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Definition at line 102 of file SGObject.cpp.
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Definition at line 107 of file SGObject.cpp.
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Definition at line 112 of file SGObject.cpp.
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set generic type to T
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set the parallel object
parallel | parallel object to use |
Definition at line 243 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 285 of file SGObject.cpp.
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set labels
lab | labels |
Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.
Definition at line 73 of file Machine.cpp.
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set maximum training time
t | maximimum training time |
Definition at line 90 of file Machine.cpp.
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set number of passes
Definition at line 49 of file ConditionalProbabilityTree.h.
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Setter for store-model-features-after-training flag
store_model | whether model should be stored after training |
Definition at line 115 of file Machine.cpp.
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 194 of file SGObject.cpp.
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enable unlocked cross-validation - no model features to store
Reimplemented from CMachine.
Definition at line 152 of file TreeMachine.h.
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Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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train machine
data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training. |
Reimplemented in CRelaxedTree, CAutoencoder, CSGDQN, and COnlineSVMSGD.
Definition at line 47 of file Machine.cpp.
train on a single example (online learning)
ex | the example being trained |
label | the label of this training example |
Definition at line 152 of file ConditionalProbabilityTree.cpp.
Trains a locked machine on a set of indices. Error if machine is not locked
NOT IMPLEMENTED
indices | index vector (of locked features) that is used for training |
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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train machine
data | training data |
Reimplemented from CMachine.
Definition at line 98 of file ConditionalProbabilityTree.cpp.
train a single node
ex | the example being trained |
label | label |
node | the node |
Definition at line 228 of file ConditionalProbabilityTree.cpp.
train on a path from a node up to the root
ex | the instance of the training example |
node | the leaf node |
Definition at line 209 of file ConditionalProbabilityTree.cpp.
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the labels will be embedded in the streaming features
Reimplemented from CMachine.
Definition at line 82 of file ConditionalProbabilityTree.h.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 305 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 250 of file SGObject.cpp.
decide which subtree to go, when training the tree structure.
node | the node being decided |
ex | the example being decided |
Definition at line 28 of file BalancedConditionalProbabilityTree.cpp.
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decide which subtree to go, when training the tree structure.
node | the node being decided |
ex | the example being decided |
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io
Definition at line 496 of file SGObject.h.
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online features
Definition at line 139 of file ConditionalProbabilityTree.h.
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parameters wrt which we can compute gradients
Definition at line 511 of file SGObject.h.
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Hash of parameter values
Definition at line 517 of file SGObject.h.
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class => leaf mapping
Definition at line 138 of file ConditionalProbabilityTree.h.
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machines
Definition at line 56 of file BaseMulticlassMachine.h.
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model selection parameters
Definition at line 508 of file SGObject.h.
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number of passes for online training
Definition at line 137 of file ConditionalProbabilityTree.h.
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map for different parameter versions
Definition at line 514 of file SGObject.h.
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parameters
Definition at line 505 of file SGObject.h.
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tree root
Definition at line 156 of file TreeMachine.h.
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parallel
Definition at line 499 of file SGObject.h.
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version
Definition at line 502 of file SGObject.h.