SHOGUN
4.2.0
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The class implements the Stochastic MIrror Descent mAde Sparse (SMIDAS) minimizer.
Reference: Shai Shalev-Shwartz and Ambuj Tewari, Stochastic methods for l1 regularized loss minimization. Proceedings of the 26th International Conference on Machine Learning, pages 929-936, 2009.
Definition at line 47 of file SMIDASMinimizer.h.
Public Member Functions | |
SMIDASMinimizer () | |
SMIDASMinimizer (FirstOrderStochasticCostFunction *fun) | |
virtual | ~SMIDASMinimizer () |
virtual float64_t | minimize () |
virtual const char * | get_name () const |
virtual void | set_mapping_function (MappingFunction *mapping_fun) |
virtual bool | supports_batch_update () const |
virtual void | set_gradient_updater (DescendUpdater *gradient_updater) |
virtual void | set_number_passes (int32_t num_passes) |
virtual void | set_learning_rate (LearningRate *learning_rate) |
virtual int32_t | get_iteration_counter () |
virtual void | set_cost_function (FirstOrderCostFunction *fun) |
virtual void | unset_cost_function (bool is_unref=true) |
virtual void | set_penalty_weight (float64_t penalty_weight) |
virtual void | set_penalty_type (Penalty *penalty_type) |
virtual CSGObject * | shallow_copy () const |
virtual CSGObject * | deep_copy () const |
virtual bool | is_generic (EPrimitiveType *generic) const |
template<class T > | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
void | unset_generic () |
virtual void | print_serializable (const char *prefix="") |
virtual bool | save_serializable (CSerializableFile *file, const char *prefix="") |
virtual bool | load_serializable (CSerializableFile *file, const char *prefix="") |
void | set_global_io (SGIO *io) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
SGStringList< char > | get_modelsel_names () |
void | print_modsel_params () |
char * | get_modsel_param_descr (const char *param_name) |
index_t | get_modsel_param_index (const char *param_name) |
void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
bool | has (const std::string &name) const |
template<typename T > | |
bool | has (const Tag< T > &tag) const |
template<typename T , typename U = void> | |
bool | has (const std::string &name) const |
template<typename T > | |
void | set (const Tag< T > &_tag, const T &value) |
template<typename T , typename U = void> | |
void | set (const std::string &name, const T &value) |
template<typename T > | |
T | get (const Tag< T > &_tag) const |
template<typename T , typename U = void> | |
T | get (const std::string &name) const |
virtual void | update_parameter_hash () |
virtual bool | parameter_hash_changed () |
virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
virtual CSGObject * | clone () |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
Protected Member Functions | |
virtual void | init_minimization () |
virtual void | do_proximal_operation (SGVector< float64_t >variable_reference) |
virtual float64_t | get_penalty (SGVector< float64_t > var) |
virtual void | update_gradient (SGVector< float64_t > gradient, SGVector< float64_t > var) |
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) |
template<typename T > | |
void | register_param (Tag< T > &_tag, const T &value) |
template<typename T > | |
void | register_param (const std::string &name, const T &value) |
Protected Attributes | |
SGVector< float64_t > | m_dual_variable |
MappingFunction * | m_mapping_fun |
DescendUpdater * | m_gradient_updater |
int32_t | m_num_passes |
int32_t | m_cur_passes |
int32_t | m_iter_counter |
LearningRate * | m_learning_rate |
FirstOrderCostFunction * | m_fun |
Penalty * | m_penalty_type |
float64_t | m_penalty_weight |
SMIDASMinimizer | ( | ) |
Default constructor
Definition at line 38 of file SMIDASMinimizer.cpp.
Constructor
fun | stochastic cost function |
Definition at line 48 of file SMIDASMinimizer.cpp.
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virtual |
Destructor
Definition at line 44 of file SMIDASMinimizer.cpp.
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inherited |
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 630 of file SGObject.cpp.
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virtualinherited |
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 747 of file SGObject.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 231 of file SGObject.cpp.
Do proximal update in place
variable_reference | variable_reference to be updated |
Definition at line 71 of file FirstOrderStochasticMinimizer.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 651 of file SGObject.cpp.
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inherited |
Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
Definition at line 367 of file SGObject.h.
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inherited |
Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
Definition at line 388 of file SGObject.h.
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inherited |
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inherited |
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virtualinherited |
How many samples/mini-batch does the minimizer use?
Definition at line 136 of file FirstOrderStochasticMinimizer.h.
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inherited |
Definition at line 531 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 555 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 568 of file SGObject.cpp.
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virtual |
returns the name of the class
Reimplemented from SMDMinimizer.
Definition at line 71 of file SMIDASMinimizer.h.
Get the penalty given target variables For L2 penalty, the target variable is \(w\) and the value of penalty is \(\lambda \frac{w^t w}{2}\), where \(\lambda\) is the weight of penalty
var | the variable used in regularization |
Definition at line 69 of file FirstOrderMinimizer.cpp.
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Checks if object has a class parameter identified by a name.
name | name of the parameter |
Definition at line 289 of file SGObject.h.
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inherited |
Checks if object has a class parameter identified by a Tag.
tag | tag of the parameter containing name and type information |
Definition at line 301 of file SGObject.h.
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inherited |
Checks if a type exists for a class parameter identified by a name.
name | name of the parameter |
Definition at line 312 of file SGObject.h.
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protectedvirtual |
init minimization
Reimplemented from SMDMinimizer.
Definition at line 98 of file SMIDASMinimizer.cpp.
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virtualinherited |
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 329 of file SGObject.cpp.
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virtualinherited |
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 |
Definition at line 402 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 459 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 454 of file SGObject.cpp.
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Do minimization and get the optimal value
Reimplemented from SMDMinimizer.
Definition at line 54 of file SMIDASMinimizer.cpp.
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virtualinherited |
Definition at line 295 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 507 of file SGObject.cpp.
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virtualinherited |
prints registered parameters out
prefix | prefix for members |
Definition at line 341 of file SGObject.cpp.
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protectedinherited |
Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 439 of file SGObject.h.
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protectedinherited |
Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 452 of file SGObject.h.
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virtualinherited |
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 |
Definition at line 347 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 469 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 464 of file SGObject.cpp.
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Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 328 of file SGObject.h.
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inherited |
Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 354 of file SGObject.h.
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virtualinherited |
Set cost function used in the minimizer
fun | the cost function |
Definition at line 42 of file FirstOrderMinimizer.cpp.
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inherited |
Definition at line 74 of file SGObject.cpp.
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Definition at line 79 of file SGObject.cpp.
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Definition at line 84 of file SGObject.cpp.
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Definition at line 89 of file SGObject.cpp.
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Definition at line 94 of file SGObject.cpp.
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Definition at line 99 of file SGObject.cpp.
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Definition at line 104 of file SGObject.cpp.
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Definition at line 109 of file SGObject.cpp.
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Definition at line 114 of file SGObject.cpp.
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Definition at line 119 of file SGObject.cpp.
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Definition at line 124 of file SGObject.cpp.
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Definition at line 129 of file SGObject.cpp.
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Definition at line 134 of file SGObject.cpp.
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Definition at line 139 of file SGObject.cpp.
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Definition at line 144 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 274 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 316 of file SGObject.cpp.
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Set a gradient updater
gradient_updater | the gradient_updater |
Definition at line 38 of file FirstOrderStochasticMinimizer.cpp.
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Set the learning rate of a minimizer
learning_rate | learn rate or step size |
Definition at line 61 of file FirstOrderStochasticMinimizer.cpp.
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Set projection function
mapping_fun | mapping/projection function |
Definition at line 86 of file SMDMinimizer.cpp.
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Set the number of times to go through all data points (samples) For example, num_passes=1 means go through all data points once.
Recall that a stochastic cost function \(f(w)\) can be written as \(\sum_i{ f_i(w) }\), where \(f_i(w)\) is the differentiable function for the i-th sample.
num_passes | the number of times |
Definition at line 55 of file FirstOrderStochasticMinimizer.cpp.
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Set the type of penalty For example, L2 penalty
penalty_type | the type of penalty. If NULL is given, regularization is not enabled. |
Definition at line 53 of file FirstOrderMinimizer.cpp.
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Set the weight of penalty
penalty_weight | the weight of penalty, which is positive |
Definition at line 63 of file FirstOrderMinimizer.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 225 of file SGObject.cpp.
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Does minimizer support batch update
Implements FirstOrderMinimizer.
Definition at line 102 of file FirstOrderStochasticMinimizer.h.
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Unset cost function used in the minimizer
Definition at line 94 of file FirstOrderMinimizer.h.
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inherited |
unset generic type
this has to be called in classes specializing a template class
Definition at line 336 of file SGObject.cpp.
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protectedvirtualinherited |
Add gradient of the penalty wrt target variables to unpenalized gradient For least sqaure with L2 penalty,
\[ L2f(w)=f(w) + L2(w) \]
where \( f(w)=\sum_i{(y_i-w^T x_i)^2}\) is the least sqaure cost function and \(L2(w)=\lambda \frac{w^t w}{2}\) is the L2 penalty
Target variables is \(w\) Unpenalized gradient is \(\frac{\partial f(w) }{\partial w}\) Gradient of the penalty wrt target variables is \(\frac{\partial L2(w) }{\partial w}\)
gradient | unpenalized gradient wrt its target variable |
var | the target variable |
Definition at line 81 of file FirstOrderMinimizer.cpp.
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Updates the hash of current parameter combination
Definition at line 281 of file SGObject.cpp.
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io
Definition at line 537 of file SGObject.h.
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protectedinherited |
current iteration to go through data
Definition at line 156 of file FirstOrderStochasticMinimizer.h.
dual variable
Definition at line 78 of file SMIDASMinimizer.h.
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Cost function
Definition at line 146 of file FirstOrderMinimizer.h.
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parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
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the gradient update step
Definition at line 150 of file FirstOrderStochasticMinimizer.h.
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Hash of parameter values
Definition at line 555 of file SGObject.h.
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number of used samples/mini-batches
Definition at line 159 of file FirstOrderStochasticMinimizer.h.
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learning_rate object
Definition at line 162 of file FirstOrderStochasticMinimizer.h.
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mapping function
Definition at line 79 of file SMDMinimizer.h.
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model selection parameters
Definition at line 549 of file SGObject.h.
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iteration to go through data
Definition at line 153 of file FirstOrderStochasticMinimizer.h.
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parameters
Definition at line 546 of file SGObject.h.
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the type of penalty
Definition at line 149 of file FirstOrderMinimizer.h.
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the weight of penalty
Definition at line 152 of file FirstOrderMinimizer.h.
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parallel
Definition at line 540 of file SGObject.h.
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version
Definition at line 543 of file SGObject.h.