wrapper for MedPredictor for certian groups - routes the input to correct model group. More...
#include <MedSpecificGroupModels.h>
Inheritance diagram for MedSpecificGroupModels:
Public Member Functions | |
| int | Learn (float *x, float *y, const float *w, int nsamples, int nftrs) |
| Learn should be implemented for each model. | |
| int | Predict (float *x, float *&preds, int nsamples, int nftrs) const |
| Predict should be implemented for each model. | |
| MedSpecificGroupModels * | clone () const |
| void | set_predictors (const vector< MedPredictor * > &predictors) |
| void | set_group_selection (int featNum, const vector< float > &feat_ths) |
| MedPredictor * | get_model (int ind) |
| int | model_cnt () const |
 Public Member Functions inherited from MedPredictor | |
| virtual int | init (void *classifier_params) |
| int | init_from_string (string initialization_text) |
| virtual int | init (map< string, string > &mapper) |
| Virtual to init object from parsed fields. | |
| virtual int | set_params (map< string, string > &mapper) |
| virtual void | init_defaults () |
| virtual void | print (FILE *fp, const string &prefix, int level=0) const |
| virtual int | n_preds_per_sample () const |
| Number of predictions per sample. typically 1 - but some models return several per sample (for example a probability vector) | |
| virtual int | denormalize_model (float *f_avg, float *f_std, float label_avg, float label_std) |
| int | learn (float *x, float *y, int nsamples, int nftrs) |
| simple no weights call | |
| virtual int | learn (MedMat< float > &x, MedMat< float > &y, const vector< float > &wgts) |
| MedMat x,y : will transpose/normalize x,y if needed by algorithm The convention is that untransposed mats are always samples x features, and transposed are features x samples. | |
| int | learn (MedMat< float > &x, MedMat< float > &y) |
| MedMat x,y : will transpose/normalize x,y if needed by algorithm The convention is that untransposed mats are always samples x features, and transposed are features x samples. | |
| int | learn (MedMat< float > &x, vector< float > &y, const vector< float > &wgts) |
| MedMat x, vector y: will transpose normalize x if needed (y assumed to be normalized) | |
| int | learn (MedMat< float > &x, vector< float > &y) |
| MedMat x, vector y: will transpose normalize x if needed (y assumed to be normalized) | |
| int | learn (vector< float > &x, vector< float > &y, const vector< float > &wgts, int n_samples, int n_ftrs) |
| vector x,y: transpose/normalizations not done. | |
| int | learn (vector< float > &x, vector< float > &y, int n_samples, int n_ftrs) |
| vector x,y: transpose/normalizations not done. | |
| virtual int | predict (MedMat< float > &x, vector< float > &preds) const |
| int | predict (vector< float > &x, vector< float > &preds, int n_samples, int n_ftrs) const |
| int | threaded_predict (MedMat< float > &x, vector< float > &preds, int nthreads) const |
| int | learn (const MedFeatures &features) |
| int | learn (const MedFeatures &features, vector< string > &names) |
| virtual int | predict (MedFeatures &features) const |
| virtual void | calc_feature_importance (vector< float > &features_importance_scores, const string &general_params) |
| Feature Importance - assume called after learn. | |
| virtual void | calc_feature_importance (vector< float > &features_importance_scores, const string &general_params, const MedFeatures *features) |
| virtual void | calc_feature_contribs (MedMat< float > &x, MedMat< float > &contribs) |
| Feature contributions explains the prediction on each sample (aka BUT_WHY) | |
| virtual void | calc_feature_contribs_conditional (MedMat< float > &mat_x_in, unordered_map< string, float > &contiditional_variables, MedMat< float > &mat_x_out, MedMat< float > &mat_contribs) |
| virtual void | export_predictor (const string &output_fname) |
| int | learn_prob_calibration (MedMat< float > &x, vector< float > &y, vector< float > &min_range, vector< float > &max_range, vector< float > &map_prob, int min_bucket_size=10000, float min_score_jump=0.001, float min_prob_jump=0.005, bool fix_prob_order=false) |
| calibration for probability using training data | |
| int | convert_scores_to_prob (const vector< float > &preds, const vector< float > &min_range, const vector< float > &max_range, const vector< float > &map_prob, vector< float > &probs) const |
| If you have ran learn_prob_calibration before, you have min_range,max_range,map_prob from This function - that is used to convert preds to probs. | |
| int | learn_prob_calibration (MedMat< float > &x, vector< float > &y, int poly_rank, vector< double > ¶ms, int min_bucket_size=10000, float min_score_jump=0.001) |
| Will create probability bins using Platt scale method. | |
| template<class T , class L > | |
| int | convert_scores_to_prob (const vector< T > &preds, const vector< double > ¶ms, vector< L > &converted) const |
| Converts probability from Platt scale model. | |
| virtual bool | predict_single_not_implemented () |
| Prepartion function for fast prediction on single item each time. | |
| virtual void | prepare_predict_single () |
| virtual void | predict_single (const vector< float > &x, vector< float > &preds) const |
| virtual void | predict_single (const vector< double > &x, vector< double > &preds) const |
| virtual void | calc_feature_importance_shap (vector< float > &features_importance_scores, string &importance_type, const MedFeatures *features) |
| 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_predictor_size () |
| size_t | predictor_serialize (unsigned char *blob) |
| 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 ¶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 |
Data Fields | |
| int | nsamples |
| int | nftrs |
| Data Fields inherited from MedPredictor | |
| MedPredictorTypes | classifier_type |
| The Predicotr enum type. | |
| bool | transpose_for_learn |
| True if need to transpose before learn. | |
| bool | normalize_for_learn |
| True if need to normalize before learn. | |
| bool | normalize_y_for_learn |
| True if need to normalize labels before learn. | |
| bool | transpose_for_predict |
| True if need to transpose before predict. | |
| bool | normalize_for_predict |
| True if need to normalize before predict. | |
| vector< string > | model_features |
| The model features used in Learn, to validate when caling predict. | |
| int | features_count = 0 |
| The model features count used in Learn, to validate when caling predict. | |
Additional Inherited Members | |
| Static Public Member Functions inherited from MedPredictor | |
| static MedPredictor * | make_predictor (string model_type) |
| static MedPredictor * | make_predictor (MedPredictorTypes model_type) |
| static MedPredictor * | make_predictor (string model_type, string params) |
| static MedPredictor * | make_predictor (MedPredictorTypes model_type, string params) |
| Protected Member Functions inherited from MedPredictor | |
| void | prepare_x_mat (MedMat< float > &x, const vector< float > &wgts, int &nsamples, int &nftrs, bool transpose_needed) const |
| void | predict_thread (void *p) const |
Detailed Description
wrapper for MedPredictor for certian groups - routes the input to correct model group.
for example may be used to train specific model for each age group
Member Function Documentation
◆ Learn()
|
virtual |
Learn should be implemented for each model.
This API always assumes the data is already normalized/transposed as needed, and never changes data in x,y,w. method should support calling with w=NULL.
Reimplemented from MedPredictor.
◆ Predict()
|
virtual |
Predict should be implemented for each model.
This API assumes x is normalized/transposed if needed. preds should either be pre-allocated or NULL - in which case the predictor should allocate it to the right size.
Reimplemented from MedPredictor.
The documentation for this class was generated from the following files:
- Internal/MedAlgo/MedAlgo/MedSpecificGroupModels.h
- Internal/MedAlgo/MedAlgo/MedSpecificGroupModels.cpp
