AlgoMarkerInternal.h

#pragma once
 
#include <string>
#include <InfraMed/InfraMed/InfraMed.h>
#include <InfraMed/InfraMed/MedPidRepository.h>
#include <MedProcessTools/MedProcessTools/MedModel.h>
#include <MedProcessTools/MedProcessTools/ExplainWrapper.h>
#include <MedStat/MedStat/MedBootstrap.h>
#include "InputTesters.h"
#include "AlgoMarkerErr.h"
#include <cmath>
 
#undef LOCAL_SECTION
#define LOCAL_SECTION LOG_APP
#define LOCAL_LEVEL LOG_DEF_LEVEL
 
class Explainer_record_config : public SerializableObject {
public:
    string contributer_group_name = ""; 
    string signal_name = ""; 
    int max_count = 1; 
    int max_time_window = 0; 
    int time_channel = 0; 
    int time_unit = MedTime::Days; 
    int val_channel = 0; 
    vector<string> sets; 
 
    // Not to be initialized:
    vector<char> lut;
    int init(map<string, string>& mapper) {
        for (auto &it : mapper)
        {
            if (it.first == "contributer_group_name")
                contributer_group_name = it.second;
            else if (it.first == "signal_name")
                signal_name = it.second;
            else if (it.first == "max_count")
                max_count = med_stoi(it.second);
            else if (it.first == "max_time_window")
                max_time_window = med_stoi(it.second);
            else if (it.first == "time_channel")
                time_channel = med_stoi(it.second);
            else if (it.first == "val_channel")
                val_channel = med_stoi(it.second);
            else if (it.first == "time_unit")
                time_unit = med_time_converter.string_to_type(it.second);
            else if (it.first == "sets")
                boost::split(sets, it.second, boost::is_any_of(","));
            else
                HMTHROW_AND_ERR("Error in Explainer_record_config::init - unknown parameter \"%s\"\n",
                    it.first.c_str());
        }
        if (contributer_group_name.empty())
            HMTHROW_AND_ERR("Error in Explainer_record_config::init - contributer_group_name must be given\n");
        if (signal_name.empty())
            HMTHROW_AND_ERR("Error in Explainer_record_config::init - signal_name must be given\n");
        return  0;
    }
 
    ADD_CLASS_NAME(Explainer_record_config)
        ADD_SERIALIZATION_FUNCS(contributer_group_name, signal_name, max_count, max_time_window, time_channel, time_unit, val_channel, sets)
};
 
class Explainer_description_config : public SerializableObject {
public:
    map<string, Explainer_record_config> records;
 
    void read_cfg_file(const string &file);
 
    int init(map<string, string>& mapper) {
        for (auto &it : mapper)
        {
            if (it.first == "records")
                read_cfg_file(it.second);
            else
                HMTHROW_AND_ERR("Error in Explainer_description_config::init - unknown parameter \"%s\"\n",
                    it.first.c_str());
        }
        return  0;
    }
 
    ADD_CLASS_NAME(Explainer_description_config)
        ADD_SERIALIZATION_FUNCS(records)
};
 
class Explainer_parameters : public SerializableObject {
public:
    float max_threshold = 0; 
    int num_groups = 3; 
    bool use_perc = false; 
    Explainer_description_config cfg; 
    unordered_set<string> ignore_groups_list; 
    int total_max_reasons = -1; 
    int total_max_pos_reasons = -1; 
    int total_max_neg_reasons = -1; 
    float threshold_abs = -1; 
    float threshold_percentage = -1; 
    vector<string> static_features_info; 
 
    // to be init before:
    string base_dir = "";
 
    int init(map<string, string>& mapper) {
        for (auto &it : mapper)
        {
            if (it.first == "max_threshold")
                max_threshold = med_stof(it.second);
            else if (it.first == "num_groups")
                num_groups = med_stoi(it.second);
            else if (it.first == "total_max_reasons")
                total_max_reasons = med_stoi(it.second);
            else if (it.first == "total_max_pos_reasons")
                total_max_pos_reasons = med_stoi(it.second);
            else if (it.first == "total_max_neg_reasons")
                total_max_neg_reasons = med_stoi(it.second);
            else if (it.first == "threshold_abs")
                threshold_abs = med_stof(it.second);
            else if (it.first == "threshold_percentage")
                threshold_percentage = med_stof(it.second);
            else if (it.first == "use_perc")
                use_perc = med_stoi(it.second) > 0;
            else if (it.first == "static_features_info")
                boost::split(static_features_info, it.second, boost::is_any_of(","));
            else if (it.first == "ignore_groups_list") {
                vector<string> tokens;
                boost::split(tokens, it.second, boost::is_any_of(","));
                ignore_groups_list.insert(tokens.begin(), tokens.end());
            }
            else if (it.first == "cfg") {
                if (it.second != "" && it.second[0] != '/' && it.second[0] != '\\' && !base_dir.empty())
                    cfg.read_cfg_file(base_dir + path_sep() + it.second);
                else
                    cfg.read_cfg_file(it.second);
            }
            else
                HMTHROW_AND_ERR("Error in Explainer_parameters::init - unknown parameter \"%s\"\n",
                    it.first.c_str());
        }
        if (max_threshold < 0)
            HMTHROW_AND_ERR("Error in Explainer_parameters::init - max_threshold should be positive\n");
 
        return 0;
    }
 
    ADD_CLASS_NAME(Explainer_parameters)
        ADD_SERIALIZATION_FUNCS(max_threshold, num_groups, cfg, ignore_groups_list, total_max_reasons, total_max_pos_reasons, total_max_neg_reasons, threshold_abs, threshold_percentage, static_features_info)
};
 
//===============================================================================
// MedAlgoMarkerInternal - a mid-way API class : hiding all details of 
// implementation that are specific to the base classes (MedRepository, MedSamples, MedModel)
// that we use today.
// All functions assume c style to allow for easy export to C#/.NET
//===============================================================================
class  MedAlgoMarkerInternal {
private:
    // we force working ONLY using the API
 
    MedPidRepository rep;
    MedModel model;
    MedSamples samples;
    unordered_map<int, unordered_map<string, unordered_set<string>>> unknown_codes;
    Explainer_parameters explainer_params;
    //InputSanityTester ist;
    map<string, map<string, float>> mbr; 
    string default_threshold = ""; 
 
    string name;
    string model_fname;
    string rep_fname;
    vector<int> pids;
    int model_end_stage = MED_MDL_END;
    bool model_init_done = false;
    bool model_rep_done = false;
public:
 
    MedPidRepository & get_rep() { return rep; }
    //========================================================
    // Initializations
    //========================================================
 
    // init name
    void set_name(const char *_name) { name = string(_name); }
    void set_model_end_stage(int _model_end_stage) { model_end_stage = _model_end_stage; };
 
    // init repository config
    int init_rep_config(const char *config_fname) {
        rep.switch_to_in_mem_mode();
        if (rep.MedRepository::init(string(config_fname)) < 0) return -1;
 
        return 0;
    }
 
    // set time_unit env for repositories and models
    int set_time_unit_env(int time_unit) {
        global_default_time_unit = time_unit;
        return 0;
    }
 
    // init pids
    void set_pids(int *_pids, int npids) { pids.clear(); pids.assign(_pids, _pids + npids); }
 
    // init rep , model , samples
    int init_rep_with_file_data(const char *_rep_fname) {
        rep.clear();
        rep_fname = string(_rep_fname);
        vector<string> sigs = {};
        return (rep.read_all(rep_fname, pids, sigs));
    }
 
    // init model
    int init_model_from_file(const char *_model_fname) { model.clear(); model.verbosity = 0; return (model.read_from_file(string(_model_fname))); }
    int model_check_required_signals() {
        int ret = 0;
        vector<string> req_sigs;
        model.get_required_signal_names(req_sigs);
        for (const auto& s : req_sigs)
            if (0 == rep.sigs.Name2Sid.count(s)) {
                ret = -1;
                fprintf(stderr, "ERROR: AM model requires signal '%s' but signal does not exist in AM repository .signals file\n", s.c_str());
            }
        return ret;
    }
 
    // init model for apply
    int init_model_for_apply() {
        global_logger.log(LOG_APP, LOG_DEF_LEVEL, "Init MedModel for Apply\n");
        model_init_done = true;
        return model.init_model_for_apply(rep, MED_MDL_APPLY_FTR_GENERATORS, MED_MDL_END);
    }
 
    void fit_model_to_rep() {
        model.fit_for_repository(rep);
    }
 
    int init_model_for_rep() {
        //global_logger.log(LOG_APP, LOG_DEF_LEVEL, "Init MedModel for Rep\n");
        if (!model_rep_done) {
            model_rep_done = true;
            return model.init_model_for_apply(rep, MED_MDL_APPLY_FTR_GENERATORS, MED_MDL_APPLY_FTR_PROCESSORS);
        }
        return 0;
    }
 
    unordered_map<string, unordered_set<string>> *get_unknown_codes(int pid) {
        return &unknown_codes[pid];
    }
    // init samples
    int init_samples(int *pids, int *times, int n_samples) { clear_samples(); int rc = insert_samples(pids, times, n_samples); samples.normalize(); return rc; }
    int init_samples(int pid, int time) { return init_samples(&pid, &time, 1); }  // single prediction point initiation 
 
    // init input_tester
    //int init_input_tester(const char *_fname) { return ist.read_config(string(_fname)); }
 
    void add_json_dict(json &js) { rep.dict.add_json(js); }
 
    bool model_initiated() { return model_init_done; }
 
    //========================================================
    // Loading data to rep
    //========================================================
 
     // init loading : actions that must be taken BEFORE any loading starts
    int data_load_init() { unknown_codes.clear(); rep.switch_to_in_mem_mode(); return 0; }
 
    // load n_elems for a pid,sig
    int data_load_pid_sig(int pid, const char *sig_name, int *times, float *vals, int n_elems) {
        int sid = rep.sigs.Name2Sid[string(sig_name)];
        if (sid < 0) return -1; // no such signal
        int n_times = n_elems * rep.sigs.Sid2Info[sid].n_time_channels, n_vals = n_elems * rep.sigs.Sid2Info[sid].n_val_channels;
        if (times == NULL) n_times = 0;
        if (vals == NULL) n_vals = 0;
        return rep.in_mem_rep.insertData(pid, sid, times, vals, n_times, n_vals);
    }
 
    // load pid,sig with vectors of times and vals
    int data_load_pid_sig(int pid, const char *sig_name, int *times, int n_times, float *vals, int n_vals,
    map<pair<int, int>, pair<int, vector<char>>> *data = NULL) {
        int sid = rep.sigs.Name2Sid[string(sig_name)];
        if (sid < 0) return -1; // no such signal
        if (data == NULL)
            data = &rep.in_mem_rep.data;
        return rep.in_mem_rep.insertData_to_buffer(pid, sid, times, vals, n_times, n_vals, rep.sigs, *data);
    }
 
    // load a single element for a pid,sig
    int data_load_pid_sig(int pid, const char *sig_name, int *times, float *vals) { return data_load_pid_sig(pid, sig_name, times, vals, 1); }
 
    // end loading : actions that must be taken AFTER all loading was done, and BEFORE we calculate the predictions
    int data_load_end() { return rep.in_mem_rep.sortData(); }
 
    void get_rep_signals(unordered_set<string> &sigs)
    {
        for (auto &sig : rep.sigs.signals_names)
        {
            sigs.insert(sig);
        }
    }
    // returns the available signals
 
    //========================================================
    // Samples
    //========================================================
 
    // clear prediction points BEFORE a new set of predictions is done using the same instance
    void clear_samples() { samples.clear(); }
 
    // insert prediction points
    int insert_samples(int *pids, int *times, int n_samples) {
        for (int i = 0; i < n_samples; i++)
            samples.insertRec(pids[i], times[i]);
        return 0;
    }
 
    int insert_sample(int pid, int time) { return insert_samples(&pid, &time, 1); }
 
    //MedSample *get_sample(int idx) { if (idx>=0 && idx<samples.get_size()) return & }
 
    // normalize samples must be called after finishing inserting all samples.
    int normalize_samples() { samples.normalize(); return 0; }
 
    MedSamples *get_samples_ptr() { return &samples; }
 
    //========================================================
    // Calculate predictions
    //========================================================
    // note that if (_pids,times) are not sorted, they will be changed and sorted.
    int get_preds(int *_pids, int *times, float *preds, int n_samples) {
 
        // init_samples
        init_samples(_pids, times, n_samples);
 
        return get_raw_preds(_pids, times, preds);
    }
 
    int get_preds(int *_pids, int *times, float *preds, int n_samples,
        const vector<Effected_Field> &requested_fields, MedPidRepository *_rep=NULL) {
 
        // init_samples
        init_samples(_pids, times, n_samples);
        if (_rep == NULL)
            _rep = &this->rep;
        return get_raw_preds(_pids, times, preds, requested_fields, _rep);
    }
 
    int get_raw_preds(int *_pids, int *times, float *preds,
        const vector<Effected_Field> &requested_fields, MedPidRepository *_rep) {
 
        try {
 
            try {
                // run model to calculate predictions
                if (!samples.idSamples.empty())
                    model.no_init_apply_partial(*_rep, samples, requested_fields);
            }
            catch (...) {
                fprintf(stderr, "Caught an exception in no_init_apply_partial\n");
                return -1;
            }
 
            // export pids, times and preds to c arrays
            int j = 0;
            if (preds != NULL) {
                for (auto& idSample : samples.idSamples)
                    for (auto& sample : idSample.samples) {
                        _pids[j] = sample.id;
                        times[j] = sample.time;
                        preds[j] = sample.prediction.size() > 0 ? sample.prediction[0] : (float)AM_UNDEFINED_VALUE; // This is Naive - but works for simple predictors giving the Raw score.
                        j++;
                    }
            }
 
            return 0;
        }
        catch (int &exception_code) {
            fprintf(stderr, "Caught an exception code: %d\n", exception_code);
            return -1; // exception_code;
        }
        catch (...) {
            fprintf(stderr, "Caught Something...\n");
            return -1;
        }
    }
 
 
    int get_raw_preds(int *_pids, int *times, float *preds) {
 
        try {
 
            try {
                // run model to calculate predictions
                if (!samples.idSamples.empty())
                    if (model.no_init_apply(rep, samples, (MedModelStage)0, (MedModelStage)model_end_stage) < 0) {
                        fprintf(stderr, "ERROR: MedAlgoMarkerInternal::get_preds FAILED.");
                        return -1;
                    }
            }
            catch (...) {
                fprintf(stderr, "Caught an exception in no_init_apply\n");
                return -1;
            }
 
            // export pids, times and preds to c arrays
            int j = 0;
            if (preds != NULL) {
                for (auto& idSample : samples.idSamples)
                    for (auto& sample : idSample.samples) {
                        _pids[j] = sample.id;
                        times[j] = sample.time;
                        preds[j] = sample.prediction.size() > 0 ? sample.prediction[0] : (float)AM_UNDEFINED_VALUE; // This is Naive - but works for simple predictors giving the Raw score.
                        j++;
                    }
            }
 
            return 0;
        }
        catch (int &exception_code) {
            fprintf(stderr, "Caught an exception code: %d\n", exception_code);
            return -1; // exception_code;
        }
        catch (...) {
            fprintf(stderr, "Caught Something...\n");
            return -1;
        }
    }
 
    int get_preds(MedSamples &_samples, float *preds) {
 
        samples = _samples;
 
        // run model to calculate predictions
        if (model.no_init_apply(rep, samples, (MedModelStage)0, (MedModelStage)model_end_stage) < 0) {
            fprintf(stderr, "ERROR: MedAlgoMarkerInternal::get_preds FAILED.");
            return -1;
        }
 
        // export pids, times and preds to c arrays
        int j = 0;
        for (auto& idSample : samples.idSamples)
            for (auto& sample : idSample.samples) {
                preds[j++] = sample.prediction[0]; // This is Naive - but works for simple predictors giving the Raw score.
            }
        return 0;
    }
 
    int get_pred(int *pid, int *time, float *pred) { return get_preds(pid, time, pred, 1); }
 
 
    //========================================================
    // Clearing - freeing mem
    //========================================================
    void clear() { unknown_codes.clear(); pids.clear(); model.clear(); samples.clear(); rep.in_mem_rep.clear(); rep.clear(); }
 
    // clear_data() : leave model up, leave repository config up, but get rid of data and samples
    void clear_data() {
        samples.clear(); rep.in_mem_rep.clear(); unknown_codes.clear();
    }
 
 
    //========================================================
    // a few more needed APIs
    //========================================================
    const char *get_name() { return name.c_str(); }
 
    void write_features_mat(const string &feat_mat) { model.write_feature_matrix(feat_mat); }
    void add_features_mat(const string &feat_mat) { model.write_feature_matrix(feat_mat, false, true); }
 
    void get_signal_structure(string &sig, int &n_time_channels, int &n_val_channels, int* &is_categ)
    {
        int sid = this->rep.sigs.sid(sig);
        if (sid <= 0) {
            n_time_channels = 0;
            n_val_channels = 0;
        }
        else {
            n_time_channels = this->rep.sigs.Sid2Info[sid].n_time_channels;
            n_val_channels = this->rep.sigs.Sid2Info[sid].n_val_channels;
            is_categ = &(this->rep.sigs.Sid2Info[sid].is_categorical_per_val_channel[0]);
        }
    }
 
    void model_apply_verbose(bool flag) {
        if ((model.verbosity > 0) ^ flag) {
            model.verbosity = int(flag);
 
            string full_log_format = "$timestamp\t$level\t$section\t%s";
            global_logger.init_format(LOG_APP, full_log_format);
            global_logger.init_format(LOG_DEF, full_log_format);
            global_logger.init_format(LOG_MED_MODEL, full_log_format);
            global_logger.init_format(LOG_MEDALGO, full_log_format);
            MLOG("Activated logging, Version Info:\n%s\n", medial::get_git_version().c_str());
        }
    }
 
    string model_version_info() const {
        return model.version_info;
    }
 
    void get_model_signals_info(vector<string> &sigs,
        unordered_map<string, vector<string>> &res_categ) const {
        model.get_required_signal_names(sigs);
        model.get_required_signal_categories(res_categ);
    }
 
    void get_explainer_params(Explainer_parameters &out) const {
        out = explainer_params;
    }
 
    void get_explainer_output_options(vector<string> &opts) {
        vector<const PostProcessor *> flat;
        for (const PostProcessor *pp : model.post_processors) {
            if (pp->processor_type == PostProcessorTypes::FTR_POSTPROCESS_MULTI)
            {
                const MultiPostProcessor *multi = static_cast<const MultiPostProcessor *>(pp);
                for (const PostProcessor *m_pp : multi->post_processors)
                    flat.push_back(m_pp);
            }
            else
                flat.push_back(pp);
        }
 
        for (const PostProcessor *pp : flat)
        {
            const ModelExplainer *explainer_m = dynamic_cast<const ModelExplainer *>(pp);
            if (explainer_m != NULL) {
                for (const string &grp : explainer_m->processing.groupNames)
                    opts.push_back(grp);
                break;
            }
        }
    }
 
    void set_explainer_params(const string &params, const string &base_dir) {
        explainer_params.base_dir = base_dir;
        explainer_params.init_from_string(params);
    }
 
    void set_threshold_leaflet(const string &init_string, const string &base_dir) {
        map<string, string> params;
        if (MedSerialize::init_map_from_string(init_string, params) < 0)
            MTHROW_AND_ERR("Error Init from String %s\n", init_string.c_str());
        string bt_file_path = "";
        map<string, string> rename_cohorts;
        for (const auto &it : params)
        {
            if (it.first == "bootstrap_file_path")
                bt_file_path = it.second;
            else if (it.first == "rename_cohorts") {
                vector<string> tokens;
                boost::split(tokens, it.second, boost::is_any_of("#"));
                for (const string &tk : tokens)
                {
                    vector<string> src_target;
                    boost::split(src_target, tk, boost::is_any_of("|"));
                    if (src_target.size() != 2)
                        MTHROW_AND_ERR("Error expecting 2 tokens, recieved \"%s\"\n", tk.c_str());
                    mes_trim(src_target[1]);
                    mes_trim(src_target[1]);
                    rename_cohorts[src_target[0]] = src_target[1];
                }
            }
            else if (it.first == "default_threshold") {
                default_threshold = it.second;
                mes_trim(default_threshold);
            }
            else
                MTHROW_AND_ERR("Error unknown param %s\n", it.first.c_str());
        }
        if (bt_file_path.empty())
            MTHROW_AND_ERR("Error must provide bootstrap_file_path in THRESHOLD_LEAFLET\n");
 
        if (bt_file_path != "" && bt_file_path[0] != '/' && bt_file_path[0] != '\\' && !base_dir.empty())
            bt_file_path = base_dir + path_sep() + bt_file_path;
 
        if (default_threshold.empty())
            MTHROW_AND_ERR("Error - must have default_threshold\n");
 
        map<string, map<string, float>> mbr_before;
        read_pivot_bootstrap_results(bt_file_path, mbr_before);
 
        //commit rename:
        for (auto &it : mbr_before)
        {
            string cohort = it.first;
            if (rename_cohorts.find(cohort) != rename_cohorts.end())
                cohort = rename_cohorts[cohort];
            //Filter to take only "SCORE@" prefix - and SKIP missing values
            map<string, float> &filt = mbr[cohort];
            for (const auto &jt : it.second)
                if (boost::starts_with(jt.first, "SCORE@") && boost::ends_with(jt.first, "_Mean") && jt.second != MED_MAT_MISSING_VALUE)
                    filt[jt.first.substr(6, jt.first.length() - 11)] = jt.second;
        }
 
        //Test default is OK:
        string err_c;
        fetch_threshold(default_threshold, err_c);
        if (!err_c.empty()) {
            vector<string> opts;
            fetch_all_thresholds(opts);
            for (const string & s : opts)
                MLOG("Option: \"%s\"\n", s.c_str());
            MTHROW_AND_ERR("Error default_threshold is invalid - please select one in format as COHORT$MEASURE_NUMERIC\n");
        }
    }
 
    bool has_threshold_settings() const {
        return !mbr.empty();
    }
 
    string get_default_threshold() const { return default_threshold; }
 
    void fetch_all_thresholds(vector<string> &opts) const {
        for (const auto &it : mbr)
        {
            for (const auto &jt : it.second)
            {
                string res = it.first + "$" + jt.first;
                opts.push_back(res);
            }
        }
    }
 
    float fetch_threshold(const string &threshold, string &err_msg) const {
        vector<string> tokens;
        err_msg = "";
        boost::split(tokens, threshold, boost::is_any_of("$"));
        if (tokens.size() != 2) {
            err_msg = "(" + to_string(AM_THRESHOLD_ERROR_NON_FATAL) + ")Error flag_threshold should contain $";
            return MED_MAT_MISSING_VALUE;
        }
        mes_trim(tokens[0]);
        mes_trim(tokens[1]);
        if (mbr.find(tokens[0]) == mbr.end()) {
            err_msg = "(" + to_string(AM_THRESHOLD_ERROR_NON_FATAL) + ")Error flag_threshold doesn't contain threshold settings for " + tokens[0];
            return MED_MAT_MISSING_VALUE;
        }
        const map<string, float> &fnd = mbr.at(tokens[0]);
        //Search numericly:
        vector<string> meas_tokens;
        boost::split(meas_tokens, tokens[1], boost::is_any_of("_"));
        if (meas_tokens.size() != 2) {
            err_msg = "(" + to_string(AM_THRESHOLD_ERROR_NON_FATAL) + ")Error flag_threshold doesn't should contain _ in the cutoff setting part";
            return MED_MAT_MISSING_VALUE;
        }
        float num_val;
        try {
            num_val = stof(meas_tokens[1]);
        }
        catch (...) {
            err_msg = "(" + to_string(AM_THRESHOLD_ERROR_NON_FATAL) + ")Error flag_threshold search cutoff isn't numeric";
            return MED_MAT_MISSING_VALUE;
        }
 
        float res = MED_MAT_MISSING_VALUE;
        for (const auto &jt : fnd)
        {
            string cand = jt.first;
            vector<string> cand_tokens;
            boost::split(cand_tokens, cand, boost::is_any_of("_"));
            if (cand_tokens.size() != 2)
                continue;
            if (cand_tokens[0] != meas_tokens[0])
                continue;
            //Need to compare numericaly: cand_tokens[1] == meas_tokens[1]
            float num_val_cmp;
            try {
                num_val_cmp = stof(cand_tokens[1]);
            }
            catch (...) {
                continue;
            }
            if (abs(num_val_cmp - num_val) <= 1e-6) {
                res = jt.second;
                break; //found
            }
        }
 
        if (res == MED_MAT_MISSING_VALUE)
            err_msg = "(" + to_string(AM_THRESHOLD_ERROR_NON_FATAL) + ")Error flag_threshold doesn't contain threshold for " + tokens[1];
        return res;
    }
};
 
MEDSERIALIZE_SUPPORT(Explainer_record_config)
MEDSERIALIZE_SUPPORT(Explainer_description_config)
MEDSERIALIZE_SUPPORT(Explainer_parameters)