Processings of explanations - grouping, Using covariance matrix for taking feature correlations into account. More...
#include <ExplainWrapper.h>
Inheritance diagram for ExplainProcessings:
Public Member Functions | |
| int | init (map< string, string > &map) |
| Virtual to init object from parsed fields. | |
| void | learn (const MedFeatures &train_mat) |
| Learns process - for example cov matrix. | |
| void | process (map< string, float > &explain_list) const |
| commit processings | |
| void | process (map< string, float > &explain_list, unsigned char *missing_value_mask) const |
| float | get_group_normalized_contrib (const vector< int > &group_inds, vector< float > &contribs, float total_normalization_factor) const |
| helper func: returns the normalized contribution for a specific group given original contributions | |
| void | post_deserialization () |
 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 * | new_polymorphic (string derived_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) | |
| 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 ¶m) |
| 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 |
Static Public Member Functions | |
| static void | read_feature_grouping (const string &file_name, const vector< string > &features, vector< vector< int > > &group2index, vector< string > &group_names, bool verbose=true) |
| Creates the feature groups from the argument file_name and by existing features. | |
Data Fields | |
| bool | group_by_sum = false |
| If true will do grouping by sum of each feature, otherwise will use internal special implementation. | |
| bool | learn_cov_matrix = false |
| If true will learn cov_matrix. | |
| int | normalize_vals = 0 |
| If != 0 will normalize contributions. 1: normalize by sum of (non b0) abs of all contributions 2: same, but also corrects for groups. | |
| int | zero_missing = 0 |
| if != 0 will throw bias terms and zero all contributions of missing values and groups of missing values | |
| bool | keep_b0 = false |
| if true will keep b0 prior | |
| bool | iterative = false |
| if true will add explainers iteratively, conditioned on those already selected | |
| int | iteration_cnt = 0 |
| if >0 the maximal number of iterations | |
| bool | use_max_cov = false |
| If true will use max cov logic. | |
| bool | use_mutual_information |
| if true will use mutual information instead of covariance | |
| BinSettings | mutual_inf_bin_setting |
| the bin setting for mutual information | |
| MedMat< float > | abs_cov_features |
| absolute values of covariance features for matrix.either read from file (and then apply absolute value), or learn if learn_cov_matrix is on , | |
| string | grouping |
| grouping file or "BY_SIGNAL" keyword to group by signal or "BY_SIGNAL_CATEG" - for category signal to split by values (aggreagates time windows) or "BY_SIGNAL_CATEG_TREND" - also splitby TRENDS | |
| vector< vector< int > > | group2Inds |
| vector< string > | groupNames |
| map< string, vector< int > > | groupName2Inds |
Detailed Description
Processings of explanations - grouping, Using covariance matrix for taking feature correlations into account.
Member Function Documentation
◆ init()
|
virtual |
Virtual to init object from parsed fields.
Reimplemented from SerializableObject.
◆ post_deserialization()
|
virtual |
Reimplemented from SerializableObject.
◆ read_feature_grouping()
|
static |
Creates the feature groups from the argument file_name and by existing features.
format TAB delim, 2 tokens: [Feature_name [TAB] group_name]
The documentation for this class was generated from the following files:
- Internal/MedProcessTools/MedProcessTools/ExplainWrapper.h
- Internal/MedProcessTools/MedProcessTools/ExplainWrapper.cpp
