SHOGUN
v3.0.0
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class MultitaskL12LogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library.
Definition at line 22 of file MultitaskL12LogisticRegression.h.
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 Member Functions | |
virtual bool | train_machine (CFeatures *data=NULL) |
virtual bool | train_locked_implementation (SGVector< index_t > *tasks) |
virtual SGVector< float64_t > | apply_get_outputs (CFeatures *data=NULL) |
SGVector< index_t > * | get_subset_tasks_indices () |
virtual void | store_model_features () |
virtual bool | is_label_valid (CLabels *lab) const |
virtual bool | train_require_labels () const |
virtual TParameter * | migrate (DynArray< TParameter * > *param_base, const SGParamInfo *target) |
virtual void | one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL) |
virtual void | load_serializable_pre () throw (ShogunException) |
virtual void | load_serializable_post () throw (ShogunException) |
virtual void | save_serializable_pre () throw (ShogunException) |
virtual void | save_serializable_post () throw (ShogunException) |
Protected Attributes | |
float64_t | m_rho1 |
float64_t | m_rho2 |
int32_t | m_regularization |
int32_t | m_termination |
int32_t | m_max_iter |
float64_t | m_tolerance |
float64_t | m_q |
float64_t | m_z |
int32_t | m_current_task |
CTaskRelation * | m_task_relation |
SGMatrix< float64_t > | m_tasks_w |
SGVector< float64_t > | m_tasks_c |
vector< set< index_t > > | m_tasks_indices |
SGVector< float64_t > | w |
float64_t | bias |
CDotFeatures * | features |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
default constructor
Definition at line 18 of file MultitaskL12LogisticRegression.cpp.
CMultitaskL12LogisticRegression | ( | float64_t | rho1, |
float64_t | rho2, | ||
CDotFeatures * | training_data, | ||
CBinaryLabels * | training_labels, | ||
CTaskGroup * | task_group | ||
) |
constructor
rho1 | rho1 regularization coefficient of L1/L2 term |
rho2 | rho2 regularization coefficient of L2 term |
training_data | training features |
training_labels | training labels |
task_group | task group |
Definition at line 24 of file MultitaskL12LogisticRegression.cpp.
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destructor
Definition at line 60 of file MultitaskL12LogisticRegression.cpp.
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
Definition at line 162 of file Machine.cpp.
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apply linear machine to data for binary classification problem
data | (test)data to be classified |
Reimplemented from CMachine.
Reimplemented in CDomainAdaptationSVMLinear.
Definition at line 57 of file LinearMachine.cpp.
apply get outputs
Reimplemented from CLinearMachine.
Definition at line 164 of file MultitaskLinearMachine.cpp.
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apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 242 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 197 of file Machine.cpp.
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applies on given indices
Reimplemented from CMachine.
Definition at line 138 of file MultitaskLinearMachine.cpp.
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applies a locked machine on a set of indices for latent problems
Definition at line 276 of file Machine.cpp.
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applies a locked machine on a set of indices for multiclass problems
Definition at line 262 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 255 of file Machine.cpp.
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applies a locked machine on a set of indices for structured problems
Definition at line 269 of file Machine.cpp.
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apply machine to data in means of multiclass classification problem
Reimplemented in CMulticlassMachine, CKNN, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CQDA, CRelaxedTree, and CBaggingMachine.
Definition at line 230 of file Machine.cpp.
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applies to one vector
Reimplemented from CMultitaskLinearMachine.
Definition at line 163 of file MultitaskLogisticRegression.cpp.
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apply linear machine to data for regression problem
data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 51 of file LinearMachine.cpp.
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apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 236 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 1196 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 1313 of file SGObject.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 122 of file Machine.cpp.
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Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 153 of file Machine.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 160 of file SGObject.h.
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) |
Definition at line 1217 of file SGObject.cpp.
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get bias
Reimplemented from CLinearMachine.
Definition at line 205 of file MultitaskLinearMachine.cpp.
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get classifier type
Reimplemented in CLaRank, CSVMLight, CDualLibQPBMSOSVM, CCCSOSVM, CLeastAngleRegression, CKernelRidgeRegression, CLibLinearMTL, CBaggingMachine, CLibSVR, CLDA, CQDA, CLibLinear, CGaussianNaiveBayes, CSVRLight, CMCLDA, CLinearRidgeRegression, CScatterSVM, CKNN, CGaussianProcessRegression, CSGDQN, CSVMSGD, CSVMOcas, CLeastSquaresRegression, COnlineSVMSGD, CKMeans, CDomainAdaptationSVMLinear, CMKLRegression, CWDSVMOcas, CHierarchical, CLPBoost, CMKLMulticlass, CMKLClassification, CMKLOneClass, CLibSVM, CDomainAdaptationSVM, CLPM, CPerceptron, CAveragedPerceptron, CNewtonSVM, CSVMLightOneClass, CSVMLin, CGMNPSVM, CMulticlassLibSVM, CGPBTSVM, CLibSVMOneClass, CGNPPSVM, CMPDSVM, and CCPLEXSVM.
Definition at line 102 of file Machine.cpp.
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getter for current task
Definition at line 50 of file MultitaskLinearMachine.cpp.
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returns type of problem machine solves
Reimplemented in CBaseMulticlassMachine.
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get max iter
Definition at line 171 of file MultitaskLogisticRegression.cpp.
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Definition at line 1100 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 1124 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 1137 of file SGObject.cpp.
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get name
Reimplemented from CMultitaskLogisticRegression.
Definition at line 70 of file MultitaskL12LogisticRegression.h.
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get q
Definition at line 191 of file MultitaskLogisticRegression.cpp.
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get regularization
Definition at line 175 of file MultitaskLogisticRegression.cpp.
float64_t get_rho1 | ( | ) | const |
get rho1 regularization coefficient
Definition at line 50 of file MultitaskL12LogisticRegression.cpp.
float64_t get_rho2 | ( | ) | const |
get rho2 regularization coefficient
Definition at line 55 of file MultitaskL12LogisticRegression.cpp.
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subset mapped task indices
Definition at line 210 of file MultitaskLinearMachine.cpp.
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getter for task relation
Definition at line 62 of file MultitaskLinearMachine.cpp.
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get termination
Definition at line 179 of file MultitaskLogisticRegression.cpp.
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get tolerance
Definition at line 183 of file MultitaskLogisticRegression.cpp.
get w
Reimplemented from CLinearMachine.
Definition at line 186 of file MultitaskLinearMachine.cpp.
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get z
Definition at line 187 of file MultitaskLogisticRegression.cpp.
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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 268 of file SGObject.cpp.
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check whether the labels is valid.
Subclasses can override this to implement their check of label types.
lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented in CGaussianProcessRegression, and CBaseMulticlassMachine.
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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 673 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 514 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 345 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 1029 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.
Definition at line 1024 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 711 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 918 of file SGObject.cpp.
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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 858 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 1076 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 280 of file SGObject.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 286 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel.
Definition at line 1039 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.
Definition at line 1034 of file SGObject.cpp.
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set bias
b | new bias |
Reimplemented from CLinearMachine.
Definition at line 200 of file MultitaskLinearMachine.cpp.
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setter for current task
task | task index |
Definition at line 55 of file MultitaskLinearMachine.cpp.
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set features
feat | features to set |
Reimplemented in CLDA, CLPBoost, and CLPM.
Definition at line 105 of file LinearMachine.cpp.
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set generic type to T
Definition at line 41 of file SGObject.cpp.
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set the parallel object
parallel | parallel object to use |
Definition at line 220 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 255 of file SGObject.cpp.
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set labels
lab | labels |
Reimplemented in CGaussianProcessMachine, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.
Definition at line 75 of file Machine.cpp.
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set max iter
Definition at line 196 of file MultitaskLogisticRegression.cpp.
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set maximum training time
t | maximimum training time |
Definition at line 92 of file Machine.cpp.
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set q
Definition at line 220 of file MultitaskLogisticRegression.cpp.
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set regularization
Definition at line 201 of file MultitaskLogisticRegression.cpp.
void set_rho1 | ( | float64_t | rho1 | ) |
set rho1 regularization coefficient
rho1 | value |
Definition at line 40 of file MultitaskL12LogisticRegression.cpp.
void set_rho2 | ( | float64_t | rho2 | ) |
set rho2 regularization coefficient
rho2 | value |
Definition at line 45 of file MultitaskL12LogisticRegression.cpp.
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Setter for store-model-features-after-training flag
store_model | whether model should be stored after training |
Definition at line 117 of file Machine.cpp.
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setter for task relation
task_relation | task relation |
Definition at line 68 of file MultitaskLinearMachine.cpp.
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set termination
Definition at line 206 of file MultitaskLogisticRegression.cpp.
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set tolerance
Definition at line 211 of file MultitaskLogisticRegression.cpp.
set w
src_w | new w |
Reimplemented from CLinearMachine.
Definition at line 194 of file MultitaskLinearMachine.cpp.
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set z
Definition at line 216 of file MultitaskLogisticRegression.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 151 of file SGObject.h.
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Stores feature data of underlying model. Does nothing because Linear machines store the normal vector of the separating hyperplane and therefore the model anyway
Reimplemented from CMachine.
Definition at line 118 of file LinearMachine.cpp.
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Reimplemented from CMachine.
Definition at line 101 of file MultitaskLinearMachine.h.
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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, CSGDQN, and COnlineSVMSGD.
Definition at line 49 of file Machine.cpp.
train on given indices
Reimplemented from CMachine.
Definition at line 101 of file MultitaskLinearMachine.cpp.
train locked implementation
tasks | array of tasks indices |
Reimplemented from CMultitaskLogisticRegression.
Definition at line 64 of file MultitaskL12LogisticRegression.cpp.
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train machine
data | features to use for training |
Reimplemented from CMultitaskLogisticRegression.
Definition at line 91 of file MultitaskL12LogisticRegression.cpp.
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returns whether machine require labels for training
Reimplemented in COnlineLinearMachine, CKMeans, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 275 of file SGObject.cpp.
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Updates the hash of current parameter combination.
Definition at line 227 of file SGObject.cpp.
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bias
Definition at line 158 of file LinearMachine.h.
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features
Definition at line 160 of file LinearMachine.h.
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io
Definition at line 514 of file SGObject.h.
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current task index
Definition at line 137 of file MultitaskLinearMachine.h.
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parameters wrt which we can compute gradients
Definition at line 529 of file SGObject.h.
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Hash of parameter values
Definition at line 535 of file SGObject.h.
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max iteration
Definition at line 119 of file MultitaskLogisticRegression.h.
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model selection parameters
Definition at line 526 of file SGObject.h.
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map for different parameter versions
Definition at line 532 of file SGObject.h.
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parameters
Definition at line 523 of file SGObject.h.
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q of L1/Lq
Definition at line 125 of file MultitaskLogisticRegression.h.
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regularization type
Definition at line 113 of file MultitaskLogisticRegression.h.
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rho1, regularization coefficient of L1/L2 term
Definition at line 97 of file MultitaskL12LogisticRegression.h.
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rho2, regularization coefficient of L2 term
Definition at line 100 of file MultitaskL12LogisticRegression.h.
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feature tree
Definition at line 140 of file MultitaskLinearMachine.h.
tasks interceptss
Definition at line 146 of file MultitaskLinearMachine.h.
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vector of sets of indices
Definition at line 149 of file MultitaskLinearMachine.h.
tasks w's
Definition at line 143 of file MultitaskLinearMachine.h.
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termination criteria
Definition at line 116 of file MultitaskLogisticRegression.h.
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tolerance
Definition at line 122 of file MultitaskLogisticRegression.h.
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regularization coefficient
Definition at line 128 of file MultitaskLogisticRegression.h.
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parallel
Definition at line 517 of file SGObject.h.
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version
Definition at line 520 of file SGObject.h.
w
Definition at line 156 of file LinearMachine.h.