ModelFeatGenerator Class Reference

Use a model to generate predictions to be used as features. More...

#include <FeatureGenerator.h>

Inheritance diagram for ModelFeatGenerator:

Public Member Functions
void	set_names ()
	Naming.
int	init (map< string, string > &mapper)
	The parsed fields from init command.
int	init_from_model ()
void	override_predictions (MedSamples &inSamples, MedSamples &modelSamples)
	Use a given vector of predictions instead of applying model.
void	prepare (MedFeatures &features, MedPidRepository &rep, MedSamples &samples)
	Do the actual prediction prior to feature generation ...
int	_learn (MedPidRepository &rep, const MedSamples &samples, vector< RepProcessor * > processors)
	learn method
int	_generate (PidDynamicRec &rec, MedFeatures &features, int index, int num, vector< float * > &_p_data)
	generate a new feature
void	modifySampleTime (MedSamples &samples, int time)
Public Member Functions inherited from FeatureGenerator
virtual void	get_p_data (MedFeatures &features, vector< float * > &_p_data)
void	get_p_data (MedFeatures &features)
virtual void	clear ()
void	get_required_signal_names (unordered_set< string > &signalNames)
virtual void	set_required_signal_ids (MedDictionarySections &dict)
void	get_required_signal_ids (unordered_set< int > &signalIds)
virtual void	get_generated_features (unordered_set< string > &names_list)
virtual void	set_signal_ids (MedSignals &sigs)
virtual void	init_tables (MedDictionarySections &dict)
virtual void	fit_for_repository (MedPidRepository &rep)
	Prepartion and adjustment for model based on repository.
int	learn (MedPidRepository &rep, const MedSamples &samples, vector< RepProcessor * > processors)
int	learn (MedPidRepository &rep, const MedSamples &samples)
int	_generate (PidDynamicRec &in_rep, MedFeatures &features, int index, int num)
int	generate (PidDynamicRec &in_rep, MedFeatures &features, int index, int num)
int	generate (PidDynamicRec &in_rep, MedFeatures &features)
int	generate (MedPidRepository &rep, int id, MedFeatures &features)
int	generate (MedPidRepository &rep, int id, MedFeatures &features, int index, int num)
virtual int	_generate (MedFeatures &features)
int	generate (MedFeatures &features)
virtual int	init (void *generator_params)
virtual void	init_defaults ()
virtual void	copy (FeatureGenerator *generator)
virtual int	nfeatures ()
virtual void	get_required_signal_categories (unordered_map< string, vector< string > > &signal_categories_in_use) const
	returns for each used signal it's used categories
virtual int	filter_features (unordered_set< string > &validFeatures)
	summary> prints summary of generator job.
virtual void	make_summary ()
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_generator_size ()
size_t	generator_serialize (unsigned char *blob)
virtual void	print ()
virtual void	dprint (const string &pref, int fg_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 void	post_deserialization ()
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
string	modelFile = ""
	File for serialized model.
MedModel *	model = NULL
	model
string	modelName = ""
	name of final feature
string	model_json = ""
	path load json and train model for this.
string	model_train_samples = ""
	path train model samples.
bool	ensure_patient_ids = true
	if true will ensure the ids are the same as curretn training samples
int	n_preds = 1
	how many features to create
int	impute_existing_feature = 0
	If true will use model to impute an existing feature (determined by model name. Otherwise - generate new feature(s)
int	use_overriden_predictions = 0
	Use a given vector of predictions instead of applying model.
int	time_unit_win = global_default_windows_time_unit
	the time unit in which the times are given. Default: global_default_windows_time_unit
int	time_unit_sig = global_default_windows_time_unit
	the time init in which the signal is given. (set correctly from Repository in learn and Generate)
vector< int >	times
Data Fields inherited from FeatureGenerator
FeatureGeneratorTypes	generator_type = FTR_GEN_LAST
	Type.
vector< string >	names
	Feature name.
int	learn_nthreads = 16
int	pred_nthreads = 16
float	missing_val = (float)MED_MAT_MISSING_VALUE
	Missing value.
vector< string >	tags
	Tags - for defining labels or groups. may be used later for filtering for example.
int	iGenerateWeights = 0
	Feature/Weights generator.
vector< float * >	p_data
vector< string >	req_signals
vector< int >	req_signal_ids
int	serial_id

Additional Inherited Members
Static Public Member Functions inherited from FeatureGenerator
static FeatureGenerator *	make_generator (string name)
static FeatureGenerator *	make_generator (string name, string params)
static FeatureGenerator *	make_generator (FeatureGeneratorTypes type)
static FeatureGenerator *	make_generator (FeatureGeneratorTypes type, string params)
static FeatureGenerator *	create_generator (string &params)

Detailed Description

Use a model to generate predictions to be used as features.

Member Function Documentation

_generate()

int ModelFeatGenerator::_generate ( PidDynamicRec &  rec, MedFeatures &  features, int  index, int  num, vector< float * > &  _p_data  )

virtual

generate a new feature

Reimplemented from FeatureGenerator.

_learn()

int ModelFeatGenerator::_learn ( MedPidRepository &  rep, const MedSamples &  samples, vector< RepProcessor * >  processors  )

virtual

learn method

Reimplemented from FeatureGenerator.

init()

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

virtual

The parsed fields from init command.

        if (field == "name") modelName = entry.second;
        else if (field == "file") modelFile = entry.second;
        else if (field == "impute_existing_feature") impute_existing_feature = med_stoi(entry.second);
        else if (field == "n_preds") n_preds = med_stoi(entry.second);
        else if (field == "model_json") model_json = entry.second;
        else if (field == "model_train_samples") model_train_samples = entry.second;
        else if (field == "ensure_patient_ids") ensure_patient_ids = med_stoi(entry.second) > 0;
        else if (field == "tags") { boost::split(tags, entry.second, boost::is_any_of(",")); }
        else if (field == "time_unit_win") time_unit_win = med_time_converter.string_to_type(entry.second);
        else if (field == "time_unit_sig") time_unit_sig = med_time_converter.string_to_type(entry.second);
        else if (field == "times") {
            vector<string> stringTimes;
            boost::split(stringTimes, entry.second, boost::is_any_of(","));
            for (auto stringTime : stringTimes) times.push_back(med_stoi(stringTime));
        }
        else if (field != "fg_type")
            MTHROW_AND_ERR("Unknown parameter \'%s\' for ModelFeatureGenerator\n", field.c_str());

[ModelFeatGenerator::init]

[ModelFeatGenerator::init]

Reimplemented from FeatureGenerator.

override_predictions()

void ModelFeatGenerator::override_predictions	(	MedSamples &	inSamples,
		MedSamples &	modelSamples
	)

Use a given vector of predictions instead of applying model.

Load predictions from a MedSamples object. Compare to the models MedSamples (unless empty)

prepare()

void ModelFeatGenerator::prepare ( MedFeatures &  features, MedPidRepository &  rep, MedSamples &  samples  )

virtual

Do the actual prediction prior to feature generation ...

Reimplemented from FeatureGenerator.

set_names()

void ModelFeatGenerator::set_names ( )

virtual

Naming.

Reimplemented from FeatureGenerator.

---

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

• Internal/MedProcessTools/MedProcessTools/FeatureGenerator.h
• Internal/MedProcessTools/MedProcessTools/FeatureGenerator.cpp