data.h

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/* Brush
copyright 2020 William La Cava
license: GNU/GPL v3
*/
 
#ifndef DATA_H
#define DATA_H
 
// internal includes
#include "../init.h"
#include "../util/utils.h"
#include "../util/error.h"
#include "../util/logger.h"
#include "../util/rnd.h"
#include "timeseries.h"
//external includes
#include <variant>
#include <optional> 
 
namespace Brush
{
 
extern map<DataType,string>  DataTypeName; 
extern map<string,DataType>  DataNameType; 
ostream& operator<<(ostream& os, DataType n);
 
 
namespace Data
{
State check_type(const ArrayXf& x);
DataType StateType(const State& arg);
 
template<typename StateRef>
State cast_type(const ArrayXf& x, const StateRef& x_ref);
 
 
class Dataset 
{
    //TODO: make this a json object that allows elements to be fetched by
    //name 
    //Dataset(ArrayXXf& X, ArrayXf& y, std::map<string, 
    //std::pair<vector<ArrayXf>, vector<ArrayXf>>>& Z): X(X), y(y), Z(Z){}
    private:
        vector<size_t> training_data_idx;
        vector<size_t> validation_data_idx;
 
    public:
        std::vector<DataType> unique_data_types;
 
        std::vector<DataType> feature_types;
 
        std::unordered_map<DataType,vector<string>> features_of_type;
        
        std::map<string, State> features;
        // TODO: this should probably be a more complex type to include feature type 
        // and potentially other info, like arbitrary relations between features
 
        ArrayXf y;
 
        bool classification;
        std::optional<std::reference_wrapper<const ArrayXXf>> Xref;
 
        float validation_size; 
        bool use_validation; // TODO: shuffle before validation (this should be a parameter)
 
        float batch_size;
        bool use_batch;
 
        Dataset operator()(const vector<size_t>& idx) const;
        void init();
 
        map<string,State> make_features(const ArrayXXf& X,
                                        const map<string, State>& Z = {},
                                        const vector<string>& vn = {}
                                       );
 
        // TODO: let the user specify the datatypes 
 
        map<string,State> copy_and_make_features(const ArrayXXf& X,
                                                 const Dataset& ref_dataset,
                                                 const vector<string>& vn = {}
                                                );
 
        Dataset(std::map<string, State>& d, 
             const Ref<const ArrayXf>& y_ = ArrayXf(), 
             bool c = false,
             float validation_size = 0.0,
             float batch_size = 1.0
             ) 
             : features(d) 
             , y(y_)
             , classification(c) 
             , validation_size(validation_size)
             , use_validation(validation_size > 0.0 && validation_size < 1.0)
             , batch_size(batch_size)
             , use_batch(batch_size > 0.0 && batch_size < 1.0)
             {init();};
 
        Dataset(const ArrayXXf& X, 
             const Ref<const ArrayXf>& y_ = ArrayXf(), 
             const vector<string>& vn = {}, 
             const map<string, State>& Z = {},
             bool c = false,
             float validation_size = 0.0,
             float batch_size = 1.0
            ) 
            : features(make_features(X,Z,vn))
            , y(y_)
            , classification(c)
            , validation_size(validation_size)
            , use_validation(validation_size > 0.0 && validation_size < 1.0)
            , batch_size(batch_size)
            , use_batch(batch_size > 0.0 && batch_size < 1.0)
            {
                init();
                Xref = optional<reference_wrapper<const ArrayXXf>>{X};
            } 
 
        Dataset(const ArrayXXf& X, const vector<string>& vn,
             bool c = false,
             float validation_size = 0.0,
             float batch_size = 1.0
            ) 
            : classification(c)
            , features(make_features(X,map<string, State>{},vn))
            , validation_size(validation_size)
            , use_validation(validation_size > 0.0 && validation_size < 1.0)
            , batch_size(batch_size)
            , use_batch(batch_size > 0.0 && batch_size < 1.0)
            {
                init();
                Xref = optional<reference_wrapper<const ArrayXXf>>{X};
            }
 
        Dataset(const ArrayXXf& X, const Dataset& ref_dataset,
             const vector<string>& vn,
             bool c = false
            )
            : classification(c)
            , features(copy_and_make_features(X,ref_dataset,vn))
            , validation_size(0.0)
            , use_validation(false)
            , batch_size(1.0)
            , use_batch(false)
            {
                init();
                Xref = optional<reference_wrapper<const ArrayXXf>>{X};
            } 
 
        void print() const
        {
            fmt::print("Dataset contains {} samples and {} features\n",
                get_n_samples(), get_n_features()
            );
            for (auto& [key, value] : this->features) 
            {
                if (std::holds_alternative<ArrayXf>(value))
                    fmt::print("{} <ArrayXf>: {}\n", key, std::get<ArrayXf>(value));
                else if (std::holds_alternative<ArrayXi>(value))
                    fmt::print("{} <ArrayXi>: {}\n", key, std::get<ArrayXi>(value));
                else if (std::holds_alternative<ArrayXb>(value))
                    fmt::print("{} <ArrayXb>: {}\n", key, std::get<ArrayXb>(value));
            }
 
        };
        auto get_X() const
        {
            if (!Xref.has_value())
                HANDLE_ERROR_THROW("Dataset does not hold a reference to X.");
            return this->Xref.value().get();
        }
        
        // inner partition of original dataset for train and validation.
        // if split is not set, then training = validation.
        Dataset get_training_data() const;
        Dataset get_validation_data() const;
        // TODO: shuffle split
        inline int get_n_samples() const { 
            return std::visit(
                [&](auto&& arg) -> int { return int(arg.size());}, 
                features.begin()->second
            );
        };
        inline int get_n_features() const { return this->features.size(); };
        Dataset get_batch() const;
 
        float get_batch_size();
        void set_batch_size(float new_size);
 
        std::array<Dataset, 2> split(const ArrayXb& mask) const;
 
        State operator[](std::string name) const 
        {
            if (this->features.find(name) == features.end())
                HANDLE_ERROR_THROW(fmt::format("Couldn't find feature {} in data\n",name));
            return this->features.at(name);
        };
 
        /* template<> ArrayXb get<ArrayXb>(std::string name) */
}; // class data
 
// // read csv
// Dataset read_csv(const std::string & path, MatrixXf& X, VectorXf& y, 
//     vector<string>& names, vector<char> &dtypes, bool& binary_endpoint, char sep) ;
 
} // data
 
extern const map<DataType,std::type_index>  DataTypeID;
extern map<std::type_index,DataType>  DataIDType;
 
} // Brush
 
// format overload for DataType
template <> struct fmt::formatter<Brush::DataType>: formatter<string_view> {
  template <typename FormatContext>
  auto format(Brush::DataType x, FormatContext& ctx) const {
    return formatter<string_view>::format(Brush::DataTypeName.at(x), ctx);
  }
};
 
// TODO: fmt overload for Data
// template <> struct fmt::formatter<Brush::Data::Dataset>: formatter<string_view> {
//   template <typename FormatContext>
//   auto format(Brush::Data::Dataset& x, FormatContext& ctx) const {
    // return formatter<string_view>::format(Brush::DataTypeName.at(x), ctx);
//   }
// };
 
#endif