PredictorImputer Class Reference

Predictor Imputer - use all features in the matrix to predict value to impute selects randomly a value based on probability to get that value (similar to our gibbs) More...

#include <PredictorImputer.h>

Inheritance diagram for PredictorImputer:

Public Member Functions
void	init_defaults ()
void	post_deserialization ()
void	load_GIBBS (const GibbsSampler< float > &gibbs, const GibbsSamplingParams &sampling_args)
void	load_GAN (const string &gan_path)
void	load_MISSING ()
void	load_sampler (unique_ptr< SamplesGenerator< float > > &&generator)
int	init (map< string, string > &mapper)
	The parsed fields from init command.
int	Learn (MedFeatures &features, unordered_set< int > &ids)
int	_apply (MedFeatures &features, unordered_set< int > &ids)
Public Member Functions inherited from FeatureProcessor
virtual string	select_learn_matrix (const vector< string > &matrix_tags) const
	Will be called before learn to create new version for the matrix if needed - in parallel of existing matrix.
virtual void	clear ()
void	init_defaults ()
virtual void	copy (FeatureProcessor *processor)
virtual void	set_feature_name (const string &feature_name)
virtual string	get_feature_name ()
virtual void	get_feature_names (vector< string > &feature_names)
int	learn (MedFeatures &features)
	PostProcess of MedFeatures - on all ids.
int	learn (MedFeatures &features, unordered_set< int > &ids)
virtual int	_apply (MedFeatures &features, unordered_set< int > &ids, bool learning)
virtual int	_conditional_apply (MedFeatures &features, unordered_set< int > &ids, unordered_set< string > &req_features, bool learning)
int	apply (MedFeatures &features, bool learning)
	PostProcess of MedFeatures - on all or a subset of the ids calls virtaul function "_apply/_conditional_apply" for the specific implementation.
int	apply (MedFeatures &features, unordered_set< string > &req_features, bool learning)
int	apply (MedFeatures &features, unordered_set< int > &ids, bool learning)
int	apply (MedFeatures &features, unordered_set< int > &ids, unordered_set< string > &req_features, bool learning)
int	apply (MedFeatures &features)
int	apply (MedFeatures &features, unordered_set< string > &req_features)
int	apply (MedFeatures &features, unordered_set< int > &ids)
int	apply (MedFeatures &features, unordered_set< int > &ids, unordered_set< string > &req_features)
virtual int	init (void *processor_params)
virtual int	filter (unordered_set< string > &features)
	Filter according to a subset of features.
string	resolve_feature_name (MedFeatures &features, string substr)
	Utility : get corresponding name in MedFeatures.
virtual bool	are_features_affected (unordered_set< string > &out_req_features)
	check if a set of features is affected by the current processor
virtual void	update_req_features_vec (unordered_set< string > &out_req_features, unordered_set< string > &in_req_features)
	update sets of required as input according to set required as output to processor Empty sets = require everything.
virtual bool	is_selector ()
	allows testing if this feature processor is a selector
void *	new_polymorphic (string derived_class_name)
	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)
size_t	get_processor_size ()
size_t	processor_serialize (unsigned char *blob)
virtual void	dprint (const string &pref, int rp_flag)
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 int	update (map< string, string > &map)
	Virtual to update object from parsed fields.
virtual string	object_json () const

Data Fields
float	missing_value
	missing value to look for to impute
bool	verbose_learn
	if true will output more info when learning
bool	verbose_apply
	if true will output verbose output in apply
string	tag_search
	feature tag search
GeneratorType	gen_type
	generator type
string	generator_args
	for learn
string	sampling_args
	args for sampling
Data Fields inherited from FeatureProcessor
string	feature_name = "unset_feature_name"
	Feature name ( + name as appears in MedFeatures) ;.
string	resolved_feature_name
FeatureProcessorTypes	processor_type = FTR_PROCESS_LAST
int	learn_nthreads
int	clean_nthreads

Additional Inherited Members
Static Public Member Functions inherited from FeatureProcessor
static FeatureProcessor *	make_processor (string processor_name)
static FeatureProcessor *	make_processor (FeatureProcessorTypes type)
static FeatureProcessor *	make_processor (string processor_name, string params)
static FeatureProcessor *	make_processor (FeatureProcessorTypes type, string params)

Detailed Description

Predictor Imputer - use all features in the matrix to predict value to impute selects randomly a value based on probability to get that value (similar to our gibbs)

Member Function Documentation

_apply()

int PredictorImputer::_apply ( MedFeatures &  features, unordered_set< int > &  ids  )

virtual

Reimplemented from FeatureProcessor.

init()

int PredictorImputer::init ( map< string, string > &  mapper )

virtual

The parsed fields from init command.

        if (it.first == "missing_value")
            missing_value = med_stof(it.second);
        else if (it.first == "generator_args")
            generator_args = it.second;
        else if (it.first == "verbose_learn")
            verbose_learn = med_stoi(it.second) > 0;
        else if (it.first == "sampling_args")
            sampling_args = it.second;
        else if (it.first == "verbose_apply")
            verbose_apply = med_stoi(it.second) > 0;
        else if (it.first == "tag_search" || it.first == "tag")
            tag_search = it.second;
        else if (it.first == "gen_type")
            gen_type = GeneratorType_fromStr(it.second);
        else if (it.first == "fp_type" || it.first == "use_parallel_learn" || it.first == "use_parallel_apply") {}
        else
            MTHROW_AND_ERR("Error in PredictorImputer::init - unsupported argument %s\n", it.first.c_str());

[PredictorImputer::init]

[PredictorImputer::init]

Reimplemented from FeatureProcessor.

Learn()

int PredictorImputer::Learn ( MedFeatures &  features, unordered_set< int > &  ids  )

virtual

Reimplemented from FeatureProcessor.

post_deserialization()

void PredictorImputer::post_deserialization ( )

virtual

Reimplemented from SerializableObject.

---

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

• Internal/MedProcessTools/MedProcessTools/PredictorImputer.h
• Internal/MedProcessTools/MedProcessTools/PredictorImputer.cpp