FeatureSpace.hpp

/** @file feature_creation/feature_space/FeatureSpace.hpp
 *  @brief Create a feature space from an initial set of features and algebraic operators
 *
 *  Use an initial set of features and combine them to generate more complicated algebraical features. SIS is also performed here
 *
 *  @author Thomas A. R. Purcell (tpurcell)
 *  @bug No known bugs.
 */

#ifndef FEATURE_SPACE
#define FEATURE_SPACE

#include <mpi_interface/MPI_Interface.hpp>
#include <mpi_interface/MPI_ops.hpp>
#include <mpi_interface/serialize_tuple.h>
#include <feature_creation/node/ModelNode.hpp>
#include <feature_creation/node/operator_nodes/allowed_ops.hpp>
#include <feature_creation/node/utils.hpp>
#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <utils/compare_features.hpp>
#include <utils/project.hpp>

#include <boost/serialization/shared_ptr.hpp>
#include <boost/filesystem.hpp>

#include <iostream>
#include <iomanip>
#include <utility>

#ifdef PY_BINDINGS
    namespace np = boost::python::numpy;
    namespace py = boost::python;
#endif

// DocString: cls_feat_space
/**
 * @brief Feature Space for SISSO calculations
 * @details Stores and performs all feature calculations for SIS
 *
 */
class FeatureSpace
{
    std::vector<node_ptr> _phi_selected; //!< selected features
    std::vector<node_ptr> _phi; //!< all features
    const std::vector<node_ptr> _phi_0; //!< initial feature space

    #ifdef PARAMETERIZE
        std::vector<un_param_op_node_gen> _un_param_operators; //!< list of all parameterized unary operators with free parameters
        std::vector<bin_param_op_node_gen> _com_bin_param_operators; //!< list of all parameterized commutable binary operators with free parameters
        std::vector<bin_param_op_node_gen> _bin_param_operators; //!< list of all parameterized binary operators with free parameters
    #endif

    std::vector<std::string> _allowed_param_ops; //!< Map of parameterization operator set (set of operators and non-linear parameters used for a non-linear least squares fit to property)
    std::vector<std::string> _allowed_ops; //!< list of all allowed operators strings
    std::vector<un_op_node_gen> _un_operators; //!< list of all unary operators
    std::vector<bin_op_node_gen> _com_bin_operators; //!< list of all commutable binary operators
    std::vector<bin_op_node_gen> _bin_operators; //!< list of all binary operators

    std::vector<double> _prop; //!< The property to fit
    std::vector<double> _scores; //!< projection scores for each feature

    const std::vector<int> _task_sizes; //!< The number of elements in each task (training data)
    std::vector<int> _start_gen; //!< list of the indexes where each generation starts in _phi
    const std::string _feature_space_file; //!< File to store information about the selected features
    const std::string _feature_space_summary_file; //!< File to store information about the selected features

    std::function<void(double*, double*, std::vector<node_ptr>&, const std::vector<int>&, int)> _project; //!< Function used to calculate the scores for SIS
    std::function<void(double*, double*, std::vector<node_ptr>&, const std::vector<int>&, int)> _project_no_omp; //!< Function used to calculate the scores for SIS without changing omp environment
    std::function<bool(double*, int, double, std::vector<double>&, double, int, int)> _is_valid; //!< Function used to calculate the scores for SIS
    std::function<bool(double*, int, double, std::vector<node_ptr>&, std::vector<double>&, double)> _is_valid_feat_list; //!< Function used to calculate the scores for SIS without changing omp environment

    std::shared_ptr<MPI_Interface> _mpi_comm; //!< MPI communicator

    const double _cross_cor_max; //!< Maximum cross-correlation used for selecting features
    const double _l_bound; //!< lower bound for absolute value of the features
    const double _u_bound; //!< upper bound for absolute value of the features

    int _n_rung_store; //!< Total rungs stored
    int _n_feat; //!< Total number of features
    int _max_phi; //!< Maximum rung for the feature creation

    const int _n_sis_select; //!< Number of features to select for each dimensions
    const int _n_samp; //!< Number of samples (training data)
    const int _n_rung_generate; //!< Total number of rungs to generate on the fly
public:

    /**
     * @brief Constructor for the feature space
     * @details constructs the feature space from an initial set of features and a list of allowed operators
     *
     * @param mpi_comm MPI communicator for the calculations
     * @param phi_0 The initial set of features to combine
     * @param allowed_ops list of allowed operators
     * @param allowed_param_ops dictionary of the parameterizable operators and their associated free parameters
     * @param prop The property to be learned (training data)
     * @param task_sizes The number of samples per task
     * @param project_type The projection operator to use
     * @param max_phi highest rung value for the calculation
     * @param n_sis_select number of features to select during each SIS step
     * @param max_store_rung number of rungs to calculate and store the value of the features for all samples
     * @param n_rung_generate number of rungs to generate on the fly during SIS (this must be 1 or 0 right now, possible to be higher with recursive algorithm)
     * @param cross_corr_max Maximum cross-correlation used for selecting features
     * @param min_abs_feat_val minimum absolute feature value
     * @param max_abs_feat_val maximum absolute feature value
     */
    FeatureSpace(
        std::shared_ptr<MPI_Interface> mpi_comm,
        std::vector<node_ptr> phi_0,
        std::vector<std::string> allowed_ops,
        std::vector<std::string> allowed_param_ops,
        std::vector<double> prop,
        std::vector<int> task_sizes,
        std::string project_type="regression",
        int max_phi=1,
        int n_sis_select=1,
        int max_store_rung=-1,
        int n_rung_generate=0,
        double cross_corr_max=1.0,
        double min_abs_feat_val=1e-50,
        double max_abs_feat_val=1e50
    );

    /**
     * @brief Initialize the feature set given a property vector
     *
     * @param prop The property trying to be learned
     */
    void initialize_fs(std::string project_type);

    /**
     * @brief Generate the full feature set from the allowed operators and initial feature set
     * @details populates phi with all features from an initial set and the allowed operators
     */
    void generate_feature_space();

    /**
     * @brief The selected feature space
     */
    inline std::vector<node_ptr> phi_selected(){return _phi_selected;};

    /**
     * @brief The full feature space
     */
    inline std::vector<node_ptr> phi(){return _phi;};

    /**
     * @brief The initial feature space
     */
    inline std::vector<node_ptr> phi0(){return _phi_0;};

    /**
     * @brief The vector of projection scores for SIS
     */
    inline std::vector<double> scores(){return _scores;}

    /**
     * @brief The MPI Communicator
     */
    inline std::shared_ptr<MPI_Interface> mpi_comm(){return _mpi_comm;}

    /**
     * @brief The vector storing the number of samples in each task
     */
    inline std::vector<int> task_sizes(){return _task_sizes;}

    // DocString: feat_space_feature_space_file
    /**
     * @brief The feature space filename
     */
    inline std::string feature_space_file(){return _feature_space_file;}

    // DocString: feat_space_l_bound
    /**
     * @brief The minimum absolute value of the feature
     */
    inline double l_bound(){return _l_bound;}

    // DocString: feat_space_u_bound
    /**
     * @brief The maximum absolute value of the feature
     */
    inline double u_bound(){return _u_bound;}

    // DocString: feat_space_max_phi
    /**
     * @brief The maximum rung of the feature space
     */
    inline int max_phi(){return _max_phi;}

    // DocString: feat_space_n_sis_select
    /**
     * @brief The number of features selected in each SIS step
     */
    inline int n_sis_select(){return _n_sis_select;}

    // DocString: feat_space_n_samp
    /**
     * @brief The number of samples per feature
     */
    inline int n_samp(){return _n_samp;}

    // DocString: feat_space_n_feat
    /**
     * @brief The number of features in the feature space
     */
    inline int n_feat(){return _n_feat;}

    // DocString: feat_space_n_rung_store
    /**
     * @brief The number of rungs whose feature training data is stored in memory
     */
    inline int n_rung_store(){return _n_rung_store;}

    // DocString: feat_space_n_rung_generate
    /**
     * @brief The number of rungs to be generated on the fly during SIS
     */
    inline int n_rung_generate(){return _n_rung_generate;}

    /**
     * @brief Generate a new set of features from a single feature
     * @details Take in the feature and perform all valid algebraic operations on it.
     *
     * @param feat The feature to spawn new features from
     * @param feat_set The feature set to pull features from for combinations
     * @param feat_ind starting index for the next feature generated
     * @param l_bound lower bound for the absolute value of the feature
     * @param u_bound upper bound for the abosulte value of the feature
     */
    void generate_new_feats(std::vector<node_ptr>::iterator& feat, std::vector<node_ptr>& feat_set, int& feat_ind, double l_bound=1e-50, double u_bound=1e50);

    /**
     * @brief Calculate the SIS Scores for feature generated on the fly
     * @details Create the next rung of features and calculate their projection scores. Only keep those that can be selected by SIS.
     *
     * @param prop Pointer to the start of the vector storing the data to project the features onto
     * @param size The size of the data to project over
     * @param phi_selected The features that would be selected from the previous rungs
     * @param scores_selected The projection scores of the features that would be selected from the previous rungs
     * @param scores_comp vector to store temporary score comparisons
     */
    void project_generated(double* prop, int size, std::vector<node_ptr>& phi_selected, std::vector<double>& scores_selected);

    /**
     * @brief Perform SIS on a feature set with a specified property
     * @details Perform sure-independence screening with either the correct property or the error
     *
     * @param prop The property to perform SIS over
     */
    void sis(std::vector<double>& prop);

    // DocString: feat_space_feat_in_phi
    /**
     * @brief Is a feature in this process' _phi?
     *
     * @param ind The index of the feature
     * @return True if feature is in this rank's _phi
     */
    inline bool feat_in_phi(int ind){return (ind >= _phi[0]->feat_ind()) && (ind <= _phi.back()->feat_ind());}

    // DocString: feat_space_remove_feature
    /**
     * @brief Remove a feature from phi
     *
     * @param ind index of feature to remove
     */
    void remove_feature(int ind);

    // Python Interface Functions
    #ifdef PY_BINDINGS
        /**
         * @brief Constructor for the feature space that takes in python objects
         * @details constructs the feature space from an initial set of features and a list of allowed operators (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
         *
         * @param phi_0 The initial set of features to combine
         * @param allowed_ops list of allowed operators
         * @param allowed_param_ops dictionary of the parameterizable operators and their associated free parameters
         * @param prop The property to be learned (training data)
         * @param task_sizes The number of samples per task
         * @param project_type The projection operator to use
         * @param max_phi highest rung value for the calculation
         * @param n_sis_select number of features to select during each SIS step
         * @param max_store_rung number of rungs to calculate and store the value of the features for all samples
         * @param n_rung_generate number of rungs to generate on the fly during SIS (this must be 1 or 0 right now, possible to be higher with recursive algorithm)
         * @param cross_corr_max Maximum cross-correlation used for selecting features
         * @param min_abs_feat_val minimum absolute feature value
         * @param max_abs_feat_val maximum absolute feature value
         */
        FeatureSpace(
            py::list phi_0,
            py::list allowed_ops,
            py::list allowed_param_ops,
            py::list prop,
            py::list task_sizes,
            std::string project_type="regression",
            int max_phi=1,
            int n_sis_select=1,
            int max_store_rung=-1,
            int n_rung_generate=0,
            double cross_corr_max=1.0,
            double min_abs_feat_val=1e-50,
            double max_abs_feat_val=1e50
        );

        /**
         * @brief Constructor for the feature space that takes in python and numpy objects
         * @details constructs the feature space from an initial set of features and a list of allowed operators (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
         *
         * @param phi_0 The initial set of features to combine
         * @param allowed_ops list of allowed operators
         * @param allowed_param_ops dictionary of the parameterizable operators and their associated free parameters
         * @param prop The property to be learned (training data)
         * @param task_sizes The number of samples per task
         * @param project_type The projection operator to use
         * @param max_phi highest rung value for the calculation
         * @param n_sis_select number of features to select during each SIS step
         * @param max_store_rung number of rungs to calculate and store the value of the features for all samples
         * @param n_rung_generate number of rungs to generate on the fly during SIS (this must be 1 or 0 right now, possible to be higher with recursive algorithm)
         * @param cross_corr_max Maximum cross-correlation used for selecting features
         * @param min_abs_feat_val minimum absolute feature value
         * @param max_abs_feat_val maximum absolute feature value
         */
        FeatureSpace(
            py::list phi_0,
            py::list allowed_ops,
            py::list allowed_param_ops,
            np::ndarray prop,
            py::list task_sizes,
            std::string project_type="regression",
            int max_phi=1,
            int n_sis_select=1,
            int max_store_rung=-1,
            int n_rung_generate=0,
            double cross_corr_max=1.0,
            double min_abs_feat_val=1e-50,
            double max_abs_feat_val=1e50
        );

        /**
         * @brief Constructor for the feature space that takes in python and numpy objects
         * @details constructs the feature space from an initial set of features and a file containing postfix expressions for the features (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
         *
         * @param feature_file The file with the postfix expressions for the feature space
         * @param phi_0 The initial set of features to combine
         * @param task_sizes The number of samples per task
         * @param project_type The projection operator to use
         * @param n_sis_select number of features to select during each SIS step
         * @param cross_corr_max Maximum cross-correlation used for selecting features
         */
        FeatureSpace(
            std::string feature_file,
            py::list phi_0,
            py::list task_sizes,
            std::string project_type="pearson",
            int n_sis_select=1,
            double cross_corr_max=1.0
        );

        // DocString: feat_space_sis_arr
        /**
         * @brief Wrapper function for SIS using a numpy array
         *
         * @param prop(np.ndarray) The property to perform SIS over as a numpy array
         */
        inline void sis(np::ndarray prop)
        {
            std::vector<double> prop_vec = python_conv_utils::from_ndarray<double>(prop);
            sis(prop_vec);
        }

        // DocString: feat_space_sis_list
        /**
         * @brief Wrapper function for SIS using a python list
         *
         * @param prop(list) The property to perform SIS over as a python list
         */
        inline void sis(py::list prop)
        {
            std::vector<double> prop_vec = python_conv_utils::from_list<double>(prop);
            sis(prop_vec);
        }

        // DocString: feat_space_phi_selected_py
        /**
         * @brief The selected feature space (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
         * @return _phi_selected as a python list
         */
        py::list phi_selected_py();

        // DocString: feat_space_phi0_py
        /**
         * @brief The initial feature space (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
         * @return _phi0 as a python list
         */
        py::list phi0_py();

        // DocString: feat_space_phi_py
        /**
         * @brief The feature space (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
         * @return _phi as a python list
         */
        py::list phi_py();

        // DocString: feat_space_scores_py
        /**
         * @brief The vector of projection scores for SIS
         * @return _scores as a numpy array
         */
        inline np::ndarray scores_py(){return python_conv_utils::to_ndarray<double>(_scores);};

        // DocString: feat_space_task_sizes_py
        /**
         * @brief The vector storing the number of samples in each task
         * @return _task_sizes as a python list
         */
        inline py::list task_sizes_py(){return python_conv_utils::to_list<int>(_task_sizes);};

        // DocString: feat_space_allowed_ops_py
        /**
         * @brief The list of allowed operator nodes
         * @return _allowed_ops as a python list
         */
        inline py::list allowed_ops_py(){return python_conv_utils::to_list<std::string>(_allowed_ops);}

        // DocString: feat_space_start_gen_py
        /**
         * @brief The index in _phi where each generation starts
         * @return _start_gen as a python list
         */
        inline py::list start_gen_py(){return python_conv_utils::to_list<int>(_start_gen);}

        // DocString: feat_space_get_feature
        /**
         * @brief Return a feature at a specified index
         *
         * @param ind index of the feature to get
         * @return A ModelNode of the feature at index ind
         */
        inline ModelNode get_feature(int ind){return ModelNode(_phi[ind]->d_mat_ind(), _phi[ind]->rung(), _phi[ind]->expr(), _phi[ind]->postfix_expr(), _phi[ind]->value(), _phi[ind]->test_value(), _phi[ind]->domain(), _phi[ind]->unit());}
    #endif
};

#endif