FeatureSpace.hpp 16 KB
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/** @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.
 */

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#ifndef FEATURE_SPACE
#define FEATURE_SPACE

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#include <mpi_interface/MPI_Interface.hpp>
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#include <feature_creation/node/FeatureNode.hpp>
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#include <feature_creation/node/ModelNode.hpp>
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#include <feature_creation/node/operator_nodes/allowed_ops.hpp>
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#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
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#include <utils/project.hpp>
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#include <boost/serialization/shared_ptr.hpp>
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#include <boost/filesystem.hpp>
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#include <iostream>
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#include <iomanip>
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#ifdef PY_BINDINGS
    namespace np = boost::python::numpy;
    namespace py = boost::python;
#endif
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// DocString: cls_feat_space
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/**
 * @brief Feature Space for SISSO calculations
 * @details Stores and performs all feature calculations for SIS
 *
 */
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class FeatureSpace
{
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    std::vector<node_ptr> _phi_selected; //!< selected features
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    std::vector<node_ptr> _phi; //!< all features
    std::vector<node_ptr> _phi_0; //!< initial feature space

    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> _scores; //!< projection scores for each feature

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    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
    std::string _feature_space_file; //!< File to store information about the selected features
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    std::string _feature_space_summary_file; //!< File to store information about the selected features
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    std::function<void(double*, double*, std::vector<node_ptr>&, std::vector<int>&, int)> _project; //!< Function used to calculate the scores for SIS
    std::shared_ptr<MPI_Interface> _mpi_comm; //!< MPI communicator
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    double _l_bound; //!< lower bound for absolute value of the features
    double _u_bound; //!< upper bound for absolute value of the features

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    int _max_phi; //!< Maximum rung for the feature creation
    int _n_sis_select; //!< Number of features to select for each dimensions
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    int _n_samp; //!< Number of samples (training data)
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    int _n_feat; //!< Total number of features
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    int _n_rung_store; //!< Total rungs stored
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    int _n_rung_generate; //!< Total number of rungs to generate on the fly
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public:
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    /**
     * @brief Constructor for the feature space
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     * @details constructs the feature space from an initial set of features and a list of allowed operators
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     *
     * @param mpi_comm MPI communicator for the calculations
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     * @param phi_0 The initial set of features to combine
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     * @param allowed_ops list of allowed operators
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     * @param prop The property to be learned (training data)
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     * @param max_phi highest rung value for the calculation
     * @param n_sis_select number of features to select during each SIS step
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     * @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 min_abs_feat_val minimum absolute feature value
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     * @param max_abs_feat_val maximum absolute feature value
     */
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    FeatureSpace(
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        std::shared_ptr<MPI_Interface> mpi_comm,
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        std::vector<node_ptr> phi_0,
        std::vector<std::string> allowed_ops,
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        std::vector<double> prop,
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        std::vector<int> task_sizes,
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        int max_phi=1,
        int n_sis_select=1,
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        int max_store_rung=-1,
        int n_rung_generate=0,
        double min_abs_feat_val=1e-50,
        double max_abs_feat_val=1e50
    );

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

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    /**
     * @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
     */
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    void generate_feature_space(std::vector<double>& prop);
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    /**
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     * @brief The selected feature space
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     */
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    inline std::vector<node_ptr> phi_selected(){return _phi_selected;};
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    /**
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     * @brief The full feature space
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     */
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    inline std::vector<node_ptr> phi(){return _phi;};
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    /**
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     * @brief The initial feature space
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     */
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    inline std::vector<node_ptr> phi0(){return _phi_0;};
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    /**
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     * @brief The vector of projection scores for SIS
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     */
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    inline std::vector<double> scores(){return _scores;}

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    /**
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     * @brief The MPI Communicator
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     */
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    inline std::shared_ptr<MPI_Interface> mpi_comm(){return _mpi_comm;}
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    /**
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     * @brief The vector storing the number of samples in each task
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     */
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    inline std::vector<int> task_sizes(){return _task_sizes;}
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    // DocString: feat_space_feature_space_file
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    /**
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     * @brief The feature space filename
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     */
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    inline std::string feature_space_file(){return _feature_space_file;}
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    // DocString: feat_space_l_bound
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    /**
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     * @brief The minimum absolute value of the feature
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     */
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    inline double l_bound(){return _l_bound;}
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    // DocString: feat_space_u_bound
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    /**
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     * @brief The maximum absolute value of the feature
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     */
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    inline double u_bound(){return _u_bound;}
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    // DocString: feat_space_max_phi
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    /**
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     * @brief The maximum rung of the feature space
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     */
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    inline int max_phi(){return _max_phi;}
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    // DocString: feat_space_n_sis_select
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    /**
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     * @brief The number of features selected in each SIS step
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     */
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    inline int n_sis_select(){return _n_sis_select;}
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    // DocString: feat_space_n_samp
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    /**
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     * @brief The number of samples per feature
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     */
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    inline int n_samp(){return _n_samp;}
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    // DocString: feat_space_n_feat
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    /**
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     * @brief The number of features in the feature space
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     */
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    inline int n_feat(){return _n_feat;}
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    // DocString: feat_space_n_rung_store
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    /**
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     * @brief The number of rungs whose feature training data is stored in memory
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     */
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    inline int n_rung_store(){return _n_rung_store;}
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    // DocString: feat_space_n_rung_generate
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    /**
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     * @brief The number of rungs to be generated on the fly during SIS
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     */
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    inline int n_rung_generate(){return _n_rung_generate;}
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    /**
     * @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
     */
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    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);

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    /**
     * @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
     */
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    void project_generated(double* prop, int size, std::vector<node_ptr>& phi_selected, std::vector<double>& scores_selected, std::vector<double>& scores_comp);
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    /**
     * @brief Check if a feature overlaps with a feature previously selected in earlier SIS iterations
     * @details Compares the projection score of the current candidate feature with all those of previously selected features (using the current prop) and
     *          if they are within 1e-10, then check the correlation between the features themselves
     *
     * @param val_ptr pointer to the candidate feature's data
     * @param cur_score the projection score of the candidate feature
     * @param scores_past The projection scores of the previous features
     * @param scores_comp vector to temporarily store the comparison of projection scores
     * @return True if the feature does not overlap with any previously selected
     */
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    bool valid_score_against_past(double* val_ptr, double cur_score, std::vector<double> scores_past, std::vector<double>& scores_comp);
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    /**
     * @brief Check if a feature overlaps with a feature previously selected in this SIS iterations
     * @details CCompares the projection score of the current candidate feature with all those of previously selected features in this iteration and
     *          if they are within 1e-10, then check the correlation between the features themselves
     *
     * @param end_check the end point to stop the comparison (the same as the current number of selected features)
     * @param val_ptr pointer to the candidate feature's data
     * @param cur_score the projection score of the candidate feature
     * @param scores_selected The projection scores of the previous features
     * @param scores_comp vector to temporarily store the comparison of projection scores
     * @return True if the feature does not overlap with any previously selected
     */
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    bool valid_score_against_current(int end_check, double* val_ptr, double cur_score, std::vector<double>& scores_selected, std::vector<double>& scores_comp);
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    /**
     * @brief Perform SIS on a feature set with a specified property
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     * @details Perform sure-independence screening with either the correct property or the error
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     *
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     * @param prop The property to perform SIS over
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     */
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    void sis(std::vector<double>& prop);
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    // DocString: feat_space_feat_in_phi
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    /**
     * @brief Is a feature in this process' _phi?
     *
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     * @param ind The index of the feature
     * @return True if feature is in this rank's _phi
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     */
    inline bool feat_in_phi(int ind){return (ind >= _phi[0]->feat_ind()) && (ind <= _phi.back()->feat_ind());}

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    // Python Interface Functions
    #ifdef PY_BINDINGS
        /**
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         * @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>)
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         *
         * @param mpi_comm MPI communicator for the calculations
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         * @param phi_0 The initial set of features to combine
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         * @param allowed_ops list of allowed operators
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         * @param prop The property to be learned (training data)
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         * @param max_phi highest rung value for the calculation
         * @param n_sis_select number of features to select during each SIS step
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         * @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 min_abs_feat_val minimum absolute feature value
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         * @param max_abs_feat_val maximum absolute feature value
         */
        FeatureSpace(
            py::list phi_0,
            py::list allowed_ops,
            py::list prop,
            py::list task_sizes,
            int max_phi=1,
            int n_sis_select=1,
            int max_store_rung=-1,
            int n_rung_generate=0,
            double min_abs_feat_val=1e-50,
            double max_abs_feat_val=1e50
        );

        /**
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         * @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>)
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         *
         * @param mpi_comm MPI communicator for the calculations
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         * @param phi_0 The initial set of features to combine
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         * @param allowed_ops list of allowed operators
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         * @param prop The property to be learned (training data)
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         * @param max_phi highest rung value for the calculation
         * @param n_sis_select number of features to select during each SIS step
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         * @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 min_abs_feat_val minimum absolute feature value
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         * @param max_abs_feat_val maximum absolute feature value
         */
        FeatureSpace(
            py::list phi_0,
            py::list allowed_ops,
            np::ndarray prop,
            py::list task_sizes,
            int max_phi=1,
            int n_sis_select=1,
            int max_store_rung=-1,
            int n_rung_generate=0,
            double min_abs_feat_val=1e-50,
            double max_abs_feat_val=1e50
        );

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

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        // DocString: feat_space_phi_selected_py
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        /**
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         * @brief The selected feature space (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
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         * @return _phi_selected as a python list
         */
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        py::list phi_selected_py();
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        // DocString: feat_space_phi0_py
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        /**
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         * @brief The initial feature space (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
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         * @return _phi0 as a python list
         */
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        py::list phi0_py();
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        // DocString: feat_space_scores_py
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        /**
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         * @brief The vector of projection scores for SIS
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         * @return _scores as a numpy array
         */
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        inline np::ndarray scores_py(){return python_conv_utils::to_ndarray<double>(_scores);};
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        // DocString: feat_space_task_sizes_py
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        /**
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         * @brief The vector storing the number of samples in each task
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         * @return _task_sizes as a python list
         */
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        inline py::list task_sizes_py(){return python_conv_utils::to_list<int>(_task_sizes);};
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        // DocString: feat_space_allowed_ops_py
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        /**
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         * @brief The list of allowed operator nodes
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         * @return _allowed_ops as a python list
         */
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        inline py::list allowed_ops_py(){return python_conv_utils::to_list<std::string>(_allowed_ops);}
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        // DocString: feat_space_start_gen_py
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        /**
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         * @brief The index in _phi where each generation starts
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         * @return _start_gen as a python list
         */
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        inline py::list start_gen_py(){return python_conv_utils::to_list<int>(_start_gen);}
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    #endif
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};

#endif