LimeExplainer Class Reference

shapley-Lime explainer with gibbs, GAN or other sampler generator More...

#include <ExplainWrapper.h>

Inheritance diagram for LimeExplainer:

Public Member Functions
void	_learn (const MedFeatures &train_mat)
	overload function for ModelExplainer - easier API
void	explain (const MedFeatures &matrix, vector< map< string, float > > &sample_explain_reasons) const
	Virtual - return explain results in sample_feature_contrib.
void	load_GIBBS (MedPredictor *original_pred, const GibbsSampler< float > &gibbs, const GibbsSamplingParams &sampling_args)
void	load_GAN (MedPredictor *original_pred, const string &gan_path)
void	load_MISSING (MedPredictor *original_pred)
void	load_sampler (MedPredictor *original_pred, unique_ptr< SamplesGenerator< float > > &&generator)
void	post_deserialization ()
void	dprint (const string &pref) const
Public Member Functions inherited from ModelExplainer
virtual int	init (map< string, string > &mapper)
	Global init for general args in all explainers. initialize directly all args in GlobalExplainerParams.
virtual int	update (map< string, string > &mapper)
	Virtual to update object from parsed fields.
virtual void	Learn (const MedFeatures &train_mat)
	Learns from predictor and train_matrix (PostProcessor API)
void	Apply (MedFeatures &matrix)
	alias for explain
void	get_input_fields (vector< Effected_Field > &fields) const
	List of fields that are used by this post_processor.
void	get_output_fields (vector< Effected_Field > &fields) const
	List of fields that are being effected by this post_processor.
void	init_post_processor (MedModel &model)
	Init ModelExplainer from MedModel - copies predictor pointer, might save normalizers pointers.
virtual void	explain (MedFeatures &matrix) const
	Stores explain results in matrix.
void	dprint (const string &pref) const
Public Member Functions inherited from PostProcessor
void *	new_polymorphic (string dname)
	for polymorphic classes that want to be able to serialize/deserialize a pointer * to the derived class given its type one needs to implement this function to return a new to the derived class given its type (as in my_type)
virtual float	get_use_p ()
virtual int	get_use_split ()
Public Member Functions inherited from SerializableObject
virtual int	version () const
	Relevant for serializations.
virtual string	my_class_name () const
	For better handling of serializations it is highly recommended that each SerializableObject inheriting class will implement the next method.
virtual void	serialized_fields_name (vector< string > &field_names) const
	The names of the serialized fields.
virtual void	pre_serialization ()
virtual size_t	get_size ()
	Gets bytes sizes for serializations.
virtual size_t	serialize (unsigned char *blob)
	Serialiazing object to blob memory. return number ob bytes wrote to memory.
virtual size_t	deserialize (unsigned char *blob)
	Deserialiazing blob to object. returns number of bytes read.
size_t	serialize_vec (vector< unsigned char > &blob)
size_t	deserialize_vec (vector< unsigned char > &blob)
virtual size_t	serialize (vector< unsigned char > &blob)
virtual size_t	deserialize (vector< unsigned char > &blob)
virtual int	read_from_file (const string &fname)
	read and deserialize model
virtual int	write_to_file (const string &fname)
	serialize model and write to file
virtual int	read_from_file_unsafe (const string &fname)
	read and deserialize model without checking version number - unsafe read
int	init_from_string (string init_string)
	Init from string.
int	init_params_from_file (string init_file)
int	init_param_from_file (string file_str, string &param)
int	update_from_string (const string &init_string)
virtual string	object_json () const

Data Fields
GeneratorType	gen_type = GeneratorType::GIBBS
	generator type
string	generator_args = ""
	for learn
string	sampling_args = ""
	args for sampling
float	missing_value = MED_MAT_MISSING_VALUE
	missing value
float	p_mask = 0
	prob for 1 in mask, if 0 - mask generation done by first selecting # of 1's in mask (uniformly) and then selecting the 1's
medial::shapley::LimeWeightMethod	weighting = medial::shapley::LimeWeightSum
int	n_masks = 1250
	number of masks
Data Fields inherited from ModelExplainer
MedPredictor *	original_predictor = NULL
	predictor we're trying to explain
ExplainFilters	filters
	general filters of results
ExplainProcessings	processing
	processing of results, like groupings, COV
GlobalExplainerParams	global_explain_params
Data Fields inherited from PostProcessor
PostProcessorTypes	processor_type = PostProcessorTypes::FTR_POSTPROCESS_LAST
int	use_split = -1
float	use_p = 0.0

Additional Inherited Members
Static Public Member Functions inherited from ModelExplainer
static void	print_explain (MedSample &smp, int sort_mode=0)
Static Public Member Functions inherited from PostProcessor
static PostProcessor *	make_processor (const string &processor_name, const string &params="")
static PostProcessor *	make_processor (PostProcessorTypes type, const string &params="")
static PostProcessor *	create_processor (string &params)

Detailed Description

shapley-Lime explainer with gibbs, GAN or other sampler generator

Member Function Documentation

_learn()

void LimeExplainer::_learn ( const MedFeatures &  train_mat )

virtual

overload function for ModelExplainer - easier API

Implements ModelExplainer.

dprint()

void LimeExplainer::dprint ( const string &  pref ) const

virtual

Reimplemented from PostProcessor.

explain()

void LimeExplainer::explain ( const MedFeatures &  matrix, vector< map< string, float > > &  sample_explain_reasons  ) const

virtual

Virtual - return explain results in sample_feature_contrib.

Implements ModelExplainer.

post_deserialization()

void LimeExplainer::post_deserialization ( )

virtual

Reimplemented from SerializableObject.

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

• Internal/MedProcessTools/MedProcessTools/ExplainWrapper.h
• Internal/MedProcessTools/MedProcessTools/ExplainWrapper.cpp