diff --git a/src/descriptor_identifier/Model/Model.cpp b/src/descriptor_identifier/Model/Model.cpp
index c90bdd3dc415fc75be1f1cc7aeeca639765d82bd..4b43809bb0cab5df6e4be138c2d6c164575208a3 100644
--- a/src/descriptor_identifier/Model/Model.cpp
+++ b/src/descriptor_identifier/Model/Model.cpp
@@ -1,14 +1,14 @@
#include <descriptor_identifier/Model/Model.hpp>
Model::Model(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept
) :
_n_samp_train(feats[0]->n_samp()),
_n_samp_test(feats[0]->n_test_samp()),
@@ -30,7 +30,7 @@ Model::Model() :
_task_eval(0)
{}
-double Model::eval(std::vector<double> x_in)
+double Model::eval(std::vector<double> x_in) const
{
int n_leaves_tot = std::accumulate(_feats.begin(), _feats.end(), 0, [](int tot, model_node_ptr feat){return tot + feat->n_leaves();});
if(x_in.size() != n_leaves_tot)
@@ -45,7 +45,7 @@ double Model::eval(std::vector<double> x_in)
return eval(x_in.data());
}
-double Model::eval(std::map<std::string, double> x_in_dct)
+double Model::eval(std::map<std::string, double> x_in_dct) const
{
std::vector<double> x_in;
for(auto& feat : _feats)
@@ -68,7 +68,7 @@ double Model::eval(std::map<std::string, double> x_in_dct)
}
-std::vector<double> Model::eval(std::vector<std::vector<double>> x_in)
+std::vector<double> Model::eval(std::vector<std::vector<double>> x_in) const
{
int n_leaves_tot = std::accumulate(_feats.begin(), _feats.end(), 0, [](int tot, model_node_ptr feat){return tot + feat->n_leaves();});
if(x_in.size() != n_leaves_tot)
@@ -91,7 +91,7 @@ std::vector<double> Model::eval(std::vector<std::vector<double>> x_in)
return eval(x_in.data());
}
-std::vector<double> Model::eval(std::map<std::string, std::vector<double>> x_in_dct)
+std::vector<double> Model::eval(std::map<std::string, std::vector<double>> x_in_dct) const
{
std::vector<std::vector<double>> x_in;
for(auto& feat :_feats)
diff --git a/src/descriptor_identifier/Model/Model.hpp b/src/descriptor_identifier/Model/Model.hpp
index 7a409abd6a5aa26694d3859fc5118f1b10c2c735..0022ef8e673db2f9f28fbde5749a64db6b5f2f3b 100644
--- a/src/descriptor_identifier/Model/Model.hpp
+++ b/src/descriptor_identifier/Model/Model.hpp
@@ -77,14 +77,14 @@ public:
* @param fix_intercept If true the intercept of the model is 0
*/
Model(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept
);
/**
@@ -121,7 +121,7 @@ public:
* @param x_in pointer to the new data point (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- virtual double eval(double* x_in) = 0;
+ virtual double eval(double* x_in) const = 0;
/**
* @brief Evaluate the model for a new point
@@ -129,7 +129,7 @@ public:
* @param x_in The data point to evaluate the model (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- double eval(std::vector<double> x_in);
+ double eval(std::vector<double> x_in) const;
/**
* @brief Evaluate the model for a new point
@@ -137,7 +137,7 @@ public:
* @param x_in_dct Dictionary describing the new point ("feature expr": value)
* @return The prediction of the model for a given data point
*/
- double eval(std::map<std::string, double> x_in_dct);
+ double eval(std::map<std::string, double> x_in_dct) const;
/**
* @brief Evaluate the model for a new set of new points
@@ -145,7 +145,7 @@ public:
* @param x_in a vector of pointers to the set of values for new data points (one pointer per each feature and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given set of data points
*/
- virtual std::vector<double> eval(std::vector<double>* x_in) = 0;
+ virtual std::vector<double> eval(std::vector<double>* x_in) const = 0;
/**
* @brief Evaluate the model for a set of new points
@@ -153,7 +153,7 @@ public:
* @param x_in The set data for a set of new data points (size of n_feature x n_points, and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- std::vector<double> eval(std::vector<std::vector<double>> x_in);
+ std::vector<double> eval(std::vector<std::vector<double>> x_in) const;
/**
* @brief Evaluate the model for a set of new points
@@ -161,7 +161,7 @@ public:
* @param x_in_dct The set of data points to evaluate the model. Keys must be strings representing feature expressions and vectors must be the same length
* @return The prediction of the model for a given data point
*/
- std::vector<double> eval(std::map<std::string, std::vector<double>> x_in_dct);
+ std::vector<double> eval(std::map<std::string, std::vector<double>> x_in_dct) const;
// DocString: model_set_task_eval
/**
@@ -177,25 +177,25 @@ public:
*
* @returns _task_eval Task to evaluate
*/
- inline int get_task_eval(){return _task_eval;}
+ inline int get_task_eval() const {return _task_eval;}
// DocString: model_n_samp_train
/**
* @brief Total number of samples being trained on
*/
- inline int n_samp_train(){return _n_samp_train;}
+ inline int n_samp_train() const {return _n_samp_train;}
// DocString: model_n_samp_test
/**
* @brief Total number of samples being tested
*/
- inline int n_samp_test(){return _n_samp_test;}
+ inline int n_samp_test() const {return _n_samp_test;}
// DocString: model_n_dim
/**
* @brief The dimensionality of the data
*/
- inline int n_dim(){return _n_dim;}
+ inline int n_dim() const {return _n_dim;}
// DocString: model_prop_unit
/**
@@ -209,7 +209,7 @@ public:
* @brief The label for the property
* @return The label for the property
*/
- inline std::string prop_label(){return _prop_label;}
+ inline std::string prop_label() const {return _prop_label;}
/**
* @brief Convert the Model into an output file
@@ -218,19 +218,19 @@ public:
* @param train If true output the training data
* @param test_inds The indexes of the test set
*/
- virtual void to_file(std::string filename, bool train=true, std::vector<int> test_inds={}) = 0;
+ virtual void to_file(const std::string filename, const bool train=true, const std::vector<int> test_inds={}) const = 0;
// DocString: model_fix_intercept
/**
* @brief Access whether the intercept is fixed at 0 or not
*/
- inline bool fix_intercept(){return _fix_intercept;}
+ inline bool fix_intercept() const {return _fix_intercept;}
/**
* @brief Accessor to the coefficients for the model
* @return The coefficients for the model for each task
*/
- inline std::vector<std::vector<double>> coefs(){return _coefs;}
+ inline std::vector<std::vector<double>> coefs() const {return _coefs;}
#ifdef PY_BINDINGS
// DocString: model_coefs
@@ -291,7 +291,7 @@ public:
* @param x_in The data point to evaluate the model (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- inline double eval_py(np::ndarray x_in){return eval(python_conv_utils::from_ndarray<double>(x_in));}
+ inline double eval_py(np::ndarray x_in) const {return eval(python_conv_utils::from_ndarray<double>(x_in));}
/**
* @brief Evaluate the model for a new point
@@ -299,7 +299,7 @@ public:
* @param x_in The data point to evaluate the model (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- inline double eval_py(py::list x_in){return eval(python_conv_utils::from_list<double>(x_in));}
+ inline double eval_py(py::list x_in) const {return eval(python_conv_utils::from_list<double>(x_in));}
/**
* @brief Evaluate the model for a new point
@@ -307,7 +307,7 @@ public:
* @param x_in_dct Dictionary describing the new point ("feature expr": value)
* @return The prediction of the model for a given data point
*/
- inline double eval_py(py::dict x_in){return eval(python_conv_utils::from_dict<std::string, double>(x_in));}
+ inline double eval_py(py::dict x_in) const {return eval(python_conv_utils::from_dict<std::string, double>(x_in));}
/**
* @brief Evaluate the model for a set of new points
@@ -315,7 +315,7 @@ public:
* @param x_in The set data for a set of new data points (size of n_feature x n_points, and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- np::ndarray eval_many_py(np::ndarray x_in);
+ np::ndarray eval_many_py(np::ndarray x_in) const ;
/**
* @brief Evaluate the model for a set of new points
@@ -323,7 +323,7 @@ public:
* @param x_in_dct The set of data points to evaluate the model. Keys must be strings representing feature expressions and vectors must be the same length
* @return The prediction of the model for a given data point
*/
- np::ndarray eval_many_py(py::dict x_in);
+ np::ndarray eval_many_py(py::dict x_in) const ;
#endif
};
diff --git a/src/descriptor_identifier/Model/ModelClassifier.cpp b/src/descriptor_identifier/Model/ModelClassifier.cpp
index 35a8fd4e2ab7e04ac7d21243c684d4d592a7387f..f1f33766e886380f7df00e2b96051f2b3c240e95 100644
--- a/src/descriptor_identifier/Model/ModelClassifier.cpp
+++ b/src/descriptor_identifier/Model/ModelClassifier.cpp
@@ -1,14 +1,14 @@
#include <descriptor_identifier/Model/ModelClassifier.hpp>
ModelClassifier::ModelClassifier(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept
) :
Model(prop_label, prop_unit, prop_train, prop_test, feats, task_sizes_train, task_sizes_test, fix_intercept),
_prop_train_est(_prop_train),
@@ -72,7 +72,7 @@ ModelClassifier::ModelClassifier(
_test_n_svm_misclassified = std::accumulate(test_misclassified.begin(), test_misclassified.end(), 0);
}
-ModelClassifier::ModelClassifier(std::string train_file)
+ModelClassifier::ModelClassifier(const std::string train_file)
{
_n_samp_test = 0;
@@ -128,7 +128,7 @@ ModelClassifier::ModelClassifier(std::string train_file)
throw std::logic_error("The file does not have the same convex overlap (" + std::to_string(file_train_n_convex_overlap) + ") as calculated here (" + std::to_string(_train_n_convex_overlap) + ").");
}
-ModelClassifier::ModelClassifier(std::string train_file, std::string test_file)
+ModelClassifier::ModelClassifier(const std::string train_file, const std::string test_file)
{
std::vector<std::string> split_str;
std::vector<std::string> feature_expr_train = populate_model(train_file, true);
@@ -203,21 +203,21 @@ ModelClassifier::ModelClassifier(std::string train_file, std::string test_file)
}
}
-double ModelClassifier::eval(double* x_in)
+double ModelClassifier::eval(double* x_in) const
{
double result = 0.0;
throw std::logic_error("Eval not defined for classifier.");
return result;
}
-std::vector<double> ModelClassifier::eval(std::vector<double>* x_in)
+std::vector<double> ModelClassifier::eval(std::vector<double>* x_in) const
{
std::vector<double> result(0, 0.0);
throw std::logic_error("Eval not defined for classifier.");
return result;
}
-std::vector<std::string> ModelClassifier::populate_model(std::string filename, bool train)
+std::vector<std::string> ModelClassifier::populate_model(const std::string filename, const bool train)
{
std::ifstream file_stream;
file_stream.open(filename, std::ios::in);
@@ -578,7 +578,7 @@ std::ostream& operator<< (std::ostream& outStream, const ModelClassifier& model)
return outStream;
}
-void ModelClassifier::to_file(std::string filename, bool train, std::vector<int> test_inds)
+void ModelClassifier::to_file(std::string filename, bool train, std::vector<int> test_inds) const
{
boost::filesystem::path p(filename.c_str());
boost::filesystem::path parent = p.remove_filename();
diff --git a/src/descriptor_identifier/Model/ModelClassifier.hpp b/src/descriptor_identifier/Model/ModelClassifier.hpp
index 9f3eb80f552a32558fba5fa6cb6d0e25e80b0229..04e4ffd65652bd58ab3c360f26d6675168fc6215 100644
--- a/src/descriptor_identifier/Model/ModelClassifier.hpp
+++ b/src/descriptor_identifier/Model/ModelClassifier.hpp
@@ -66,14 +66,14 @@ public:
* @param fix_intercept if True then the intercept is 0.0
*/
ModelClassifier(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept
);
// DocString: model_class_init_str
@@ -83,7 +83,7 @@ public:
*
* @param train_file Previously generated model file
*/
- ModelClassifier(std::string train_file);
+ ModelClassifier(const std::string train_file);
// DocString: model_class_init_str_str
/**
@@ -93,7 +93,7 @@ public:
* @param train_file Previously generated training model output file
* @param train_file Previously generated testing model output file
*/
- ModelClassifier(std::string train_file, std::string test_file);
+ ModelClassifier(const std::string train_file, const std::string test_file);
/**
* @brief Copy Constructor
@@ -129,7 +129,7 @@ public:
* @param x_in pointer to the new data point (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- double eval(double* x_in);
+ double eval(double* x_in) const;
/**
* @brief Evaluate the model for a new set of new points
@@ -137,7 +137,7 @@ public:
* @param x_in a vector of pointers to the set of values for new data points (one pointer per each feature and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given set of data points
*/
- std::vector<double> eval(std::vector<double>* x_in);
+ std::vector<double> eval(std::vector<double>* x_in) const;
/**
* @brief Read an output file and extract all relevant information
@@ -171,7 +171,7 @@ public:
*
* @param res pointer to the beginning of the vector to store the residual
*/
- inline void copy_error(double* res){std::copy_n(_train_error.data(), _n_samp_train, res);}
+ inline void copy_error(double* res) const {std::copy_n(_train_error.data(), _n_samp_train, res);}
/**
* @brief Convert the ModelClassifier into an output file
@@ -180,7 +180,7 @@ public:
* @param train If true output the training data
* @param test_inds The indexes of the test set
*/
- void to_file(std::string filename, bool train=true, std::vector<int> test_inds={});
+ void to_file(const std::string filename, const bool train=true, const std::vector<int> test_inds={}) const;
/**
* @brief Set the train/test error of the model using linear programming
@@ -191,31 +191,31 @@ public:
/**
* @brief The number of samples in overlapping convex hull regions (training data)
*/
- inline int n_convex_overlap_train(){return _train_n_convex_overlap;}
+ inline int n_convex_overlap_train() const {return _train_n_convex_overlap;}
// DocString: model_classn_convex_overlap_test
/**
* @brief The number of samples in overlapping convex hull regions (test data)
*/
- inline int n_convex_overlap_test(){return _test_n_convex_overlap;}
+ inline int n_convex_overlap_test() const {return _test_n_convex_overlap;}
// DocString: model_classn_svm_misclassified_train
/**
* @brief The number of samples misclassified by SVM (training data)
*/
- inline int n_svm_misclassified_train(){return _train_n_svm_misclassified;}
+ inline int n_svm_misclassified_train() const {return _train_n_svm_misclassified;}
// DocString: model_classn_svm_misclassified_test
/**
* @brief The number of samples misclassified by SVM (training data)
*/
- inline int n_svm_misclassified_test(){return _test_n_svm_misclassified;}
+ inline int n_svm_misclassified_test() const {return _test_n_svm_misclassified;}
// DocString: model_class_precent_train_error
/**
* @brief Percent of all training samples misclassified
*/
- inline double percent_train_error()
+ inline double percent_train_error() const
{
return 100.0 * static_cast<double>(_train_n_convex_overlap) / static_cast<double>(_n_samp_train);
}
@@ -224,7 +224,7 @@ public:
/**
* @brief Percent of all tset samples misclassified
*/
- inline double percent_test_error()
+ inline double percent_test_error() const
{
return 100.0 * static_cast<double>(_test_n_convex_overlap) / static_cast<double>(_n_samp_test);
}
diff --git a/src/descriptor_identifier/Model/ModelLogRegressor.cpp b/src/descriptor_identifier/Model/ModelLogRegressor.cpp
index ddb1550e461a1bd7f78823c61575e4186b05efee..652d2975dd427b5a72f8e53eebbe45b1f942a428 100644
--- a/src/descriptor_identifier/Model/ModelLogRegressor.cpp
+++ b/src/descriptor_identifier/Model/ModelLogRegressor.cpp
@@ -4,14 +4,14 @@ ModelLogRegressor::ModelLogRegressor()
{}
ModelLogRegressor::ModelLogRegressor(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept
) :
ModelRegressor(prop_label, prop_unit, prop_train, prop_test, feats, task_sizes_train, task_sizes_test, fix_intercept, false),
_log_prop_train(_n_samp_train, 0.0),
@@ -119,7 +119,7 @@ ModelLogRegressor::ModelLogRegressor(
std::transform(_prop_test.begin(), _prop_test.end(), _prop_test_est.begin(), _test_error.begin(), [](double pt, double pt_est){return pt - pt_est;});
}
-ModelLogRegressor::ModelLogRegressor(std::string train_file) :
+ModelLogRegressor::ModelLogRegressor(const std::string train_file) :
ModelRegressor(train_file),
_log_prop_train(_n_samp_train, 0.0),
_log_prop_test(_n_samp_test, 0.0),
@@ -141,7 +141,7 @@ ModelLogRegressor::ModelLogRegressor(std::string train_file) :
);
}
-ModelLogRegressor::ModelLogRegressor(std::string train_file, std::string test_file) :
+ModelLogRegressor::ModelLogRegressor(const std::string train_file, const std::string test_file) :
ModelRegressor(train_file, test_file),
_log_prop_train(_n_samp_train, 0.0),
_log_prop_test(_n_samp_test, 0.0),
@@ -178,7 +178,7 @@ ModelLogRegressor::ModelLogRegressor(std::string train_file, std::string test_fi
);
}
-double ModelLogRegressor::eval(double* x_in)
+double ModelLogRegressor::eval(double* x_in) const
{
double result = _coefs[_task_eval].back();
int ff = 0;
@@ -191,7 +191,7 @@ double ModelLogRegressor::eval(double* x_in)
return result;
}
-std::vector<double> ModelLogRegressor::eval(std::vector<double>* x_in)
+std::vector<double> ModelLogRegressor::eval(std::vector<double>* x_in) const
{
std::vector<double> result(x_in->size(), _coefs[_task_eval].back());
int ff = 0;
diff --git a/src/descriptor_identifier/Model/ModelLogRegressor.hpp b/src/descriptor_identifier/Model/ModelLogRegressor.hpp
index 14410372cf76dccc8e36ca309c797e55f883cfcc..ef9b5109d6c06fbe4333f90a0a71932cf5bdcb02 100644
--- a/src/descriptor_identifier/Model/ModelLogRegressor.hpp
+++ b/src/descriptor_identifier/Model/ModelLogRegressor.hpp
@@ -56,14 +56,14 @@ public:
* @param fix_intercept if True then the intercept is 0.0
*/
ModelLogRegressor(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept
);
// DocString: model_log_reg_init_str
@@ -119,7 +119,7 @@ public:
* @param x_in pointer to the new data point (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- double eval(double* x_in);
+ double eval(double* x_in) const;
/**
* @brief Evaluate the model for a new set of new points
@@ -127,7 +127,7 @@ public:
* @param x_in a vector of pointers to the set of values for new data points (one pointer per each feature and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given set of data points
*/
- std::vector<double> eval(std::vector<double>* x_in);
+ std::vector<double> eval(std::vector<double>* x_in) const;
// DocString: model_log_reg_str
/**
@@ -150,7 +150,7 @@ public:
*
* @param res pointer to the beginning of the vector to store the residual
*/
- virtual inline void copy_error(double* res){std::copy_n(_log_train_error.data(), _n_samp_train, res);}
+ virtual inline void copy_error(double* res) const {std::copy_n(_log_train_error.data(), _n_samp_train, res);}
};
/**
diff --git a/src/descriptor_identifier/Model/ModelRegressor.cpp b/src/descriptor_identifier/Model/ModelRegressor.cpp
index 4d68e0f1a59beb7583a00d70a32c6e36baaa99d6..dd38fc2579b1177edbfdc421b14d03ea668d5832 100644
--- a/src/descriptor_identifier/Model/ModelRegressor.cpp
+++ b/src/descriptor_identifier/Model/ModelRegressor.cpp
@@ -4,15 +4,15 @@ ModelRegressor::ModelRegressor()
{}
ModelRegressor::ModelRegressor(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept,
- bool fill_vecs
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept,
+ const bool fill_vecs
) :
Model(prop_label, prop_unit, prop_train, prop_test, feats, task_sizes_train, task_sizes_test, fix_intercept),
_prop_train_est(_n_samp_train, 0.0),
@@ -85,7 +85,7 @@ ModelRegressor::ModelRegressor(
}
}
-ModelRegressor::ModelRegressor(std::string train_file)
+ModelRegressor::ModelRegressor(const std::string train_file)
{
_n_samp_test = 0;
@@ -132,7 +132,7 @@ ModelRegressor::ModelRegressor(std::string train_file)
}
}
-ModelRegressor::ModelRegressor(std::string train_file, std::string test_file)
+ModelRegressor::ModelRegressor(const std::string train_file, std::string test_file)
{
std::vector<std::string> split_str;
std::vector<std::string> feature_expr_train = populate_model(train_file, true);
@@ -185,7 +185,7 @@ ModelRegressor::ModelRegressor(std::string train_file, std::string test_file)
}
}
-double ModelRegressor::eval(double* x_in)
+double ModelRegressor::eval(double* x_in) const
{
double result = _coefs[_task_eval].back();
int ff = 0;
@@ -198,7 +198,7 @@ double ModelRegressor::eval(double* x_in)
return result;
}
-std::vector<double> ModelRegressor::eval(std::vector<double>* x_in)
+std::vector<double> ModelRegressor::eval(std::vector<double>* x_in) const
{
std::vector<double> result(x_in->size(), _coefs[_task_eval].back());
int ff = 0;
@@ -213,7 +213,7 @@ std::vector<double> ModelRegressor::eval(std::vector<double>* x_in)
return result;
}
-std::vector<std::string> ModelRegressor::populate_model(std::string filename, bool train)
+std::vector<std::string> ModelRegressor::populate_model(const std::string filename, const bool train)
{
std::ifstream file_stream;
file_stream.open(filename, std::ios::in);
@@ -420,7 +420,7 @@ std::ostream& operator<< (std::ostream& outStream, const ModelRegressor& model)
return outStream;
}
-void ModelRegressor::to_file(std::string filename, bool train, std::vector<int> test_inds)
+void ModelRegressor::to_file(const std::string filename, const bool train, const std::vector<int> test_inds) const
{
boost::filesystem::path p(filename.c_str());
boost::filesystem::path parent = p.remove_filename();
@@ -540,7 +540,7 @@ void ModelRegressor::to_file(std::string filename, bool train, std::vector<int>
out_file_stream.close();
}
-std::vector<double> ModelRegressor::sorted_error()
+std::vector<double> ModelRegressor::sorted_error() const
{
std::vector<double> sorted_error(_train_error.size(), 0.0);
std::copy_n(_train_error.data(), _train_error.size(), sorted_error.data());
@@ -549,7 +549,7 @@ std::vector<double> ModelRegressor::sorted_error()
return sorted_error;
}
-std::vector<double> ModelRegressor::sorted_test_error()
+std::vector<double> ModelRegressor::sorted_test_error() const
{
std::vector<double> sorted_error(_test_error.size(), 0.0);
std::copy_n(_test_error.data(), _test_error.size(), sorted_error.data());
@@ -558,14 +558,14 @@ std::vector<double> ModelRegressor::sorted_test_error()
return sorted_error;
}
-double ModelRegressor::mape()
+double ModelRegressor::mape() const
{
std::vector<double> percent_error(_train_error.size(), 0.0);
std::transform(_train_error.begin(), _train_error.end(), _prop_train.begin(), percent_error.begin(), [](double e, double p){return std::abs(e / p);});
return util_funcs::mean<double>(percent_error);
}
-double ModelRegressor::test_mape()
+double ModelRegressor::test_mape() const
{
std::vector<double> percent_error(_test_error.size(), 0.0);
std::transform(_test_error.begin(), _test_error.end(), _prop_test.begin(), percent_error.begin(), [](double e, double p){return std::abs(e / p);});
diff --git a/src/descriptor_identifier/Model/ModelRegressor.hpp b/src/descriptor_identifier/Model/ModelRegressor.hpp
index 7bdfb8e47ed441f9653e89eb8f8e6857edc0e3c4..a70182eae97e4700c6ba7d26256f9037940f7f8d 100644
--- a/src/descriptor_identifier/Model/ModelRegressor.hpp
+++ b/src/descriptor_identifier/Model/ModelRegressor.hpp
@@ -62,15 +62,15 @@ public:
* @param fill_vecs if true perform least squares regression (used because of log regressors need to use this)
*/
ModelRegressor(
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop_train,
- std::vector<double> prop_test,
- std::vector<model_node_ptr> feats,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- bool fix_intercept,
- bool fill_vecs=true
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop_train,
+ const std::vector<double> prop_test,
+ const std::vector<model_node_ptr> feats,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const bool fix_intercept,
+ const bool fill_vecs=true
);
// DocString: model_reg_init_str
@@ -80,7 +80,7 @@ public:
*
* @param train_file Previously generated model file
*/
- ModelRegressor(std::string train_file);
+ ModelRegressor(const std::string train_file);
// DocString: model_reg_init_str_str
/**
@@ -90,7 +90,7 @@ public:
* @param train_file Previously generated training model output file
* @param train_file Previously generated testing model output file
*/
- ModelRegressor(std::string train_file, std::string test_file);
+ ModelRegressor(const std::string train_file, const std::string test_file);
/**
* @brief Copy Constructor
@@ -112,7 +112,7 @@ public:
* @param x_in pointer to the new data point (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
- virtual double eval(double* x_in);
+ virtual double eval(double* x_in) const;
/**
* @brief Evaluate the model for a new set of new points
@@ -120,7 +120,7 @@ public:
* @param x_in a vector of pointers to the set of values for new data points (one pointer per each feature and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given set of data points
*/
- virtual std::vector<double> eval(std::vector<double>* x_in);
+ virtual std::vector<double> eval(std::vector<double>* x_in) const;
/**
* @brief Copy Assignment operator
@@ -145,7 +145,7 @@ public:
*
* @return Vector of strings describing feature meta data
*/
- virtual std::vector<std::string> populate_model(std::string filename, bool train);
+ virtual std::vector<std::string> populate_model(const std::string filename, const bool train);
// DocString: model_reg_str
/**
@@ -168,7 +168,7 @@ public:
*
* @param res pointer to the beginning of the vector to store the residual
*/
- virtual inline void copy_error(double* res)
+ virtual inline void copy_error(double* res) const
{
std::copy_n(_train_error.data(), _n_samp_train, res);
}
@@ -177,7 +177,7 @@ public:
/**
* @brief The training rmse of the model
*/
- inline double rmse()
+ inline double rmse() const
{
return util_funcs::norm(_train_error.data(), _n_samp_train) / std::sqrt(static_cast<double>(_n_samp_train));
}
@@ -186,7 +186,7 @@ public:
/**
* @brief The test rmse of the model
*/
- inline double test_rmse()
+ inline double test_rmse() const
{
return util_funcs::norm(_test_error.data(), _n_samp_test) / std::sqrt(static_cast<double>(_n_samp_test));
}
@@ -195,7 +195,7 @@ public:
/**
* @brief The training R^2 of the model
*/
- inline double r2()
+ inline double r2() const
{
return util_funcs::r2(_prop_train.data(), _prop_train_est.data(), _n_samp_train);
}
@@ -204,7 +204,7 @@ public:
/**
* @brief The test R^2 of the model
*/
- inline double test_r2()
+ inline double test_r2() const
{
return util_funcs::r2(_prop_test.data(), _prop_test_est.data(), _n_samp_test);
}
@@ -213,7 +213,7 @@ public:
/**
* @brief The max Absolute error of the training data
*/
- inline double max_ae()
+ inline double max_ae() const
{
return std::abs(
*std::max_element(
@@ -228,7 +228,7 @@ public:
/**
* @brief The max Absolute error of the testing data
*/
- inline double test_max_ae()
+ inline double test_max_ae() const
{
return std::abs(
*std::max_element(
@@ -244,7 +244,7 @@ public:
* @brief The mean absolute error of the model
* @return The mean absolute error of the training data
*/
- inline double mae()
+ inline double mae() const
{
return 1.0 / static_cast<double>(_n_samp_train) * std::accumulate(
_train_error.begin(),
@@ -259,7 +259,7 @@ public:
* @brief The mean absolute test error of the model
* @return The mean absolute error of the test data
*/
- inline double test_mae()
+ inline double test_mae() const
{
return 1.0 / static_cast<double>(_n_samp_test) * std::accumulate(
_test_error.begin(),
@@ -274,33 +274,33 @@ public:
* @brief The mean absolute error of the model
* @return The mean absolute error of the training data
*/
- double mape();
+ double mape() const;
// DocString: model_reg_test_mape
/**
* @brief The mean absolute test error of the model
* @return The mean absolute error of the test data
*/
- double test_mape();
+ double test_mape() const;
/**
* @brief Sort the training error based on magnitude
* @return The error vector sorted
*/
- std::vector<double> sorted_error();
+ std::vector<double> sorted_error() const;
/**
* @brief Sort the training test_error based on magnitude
* @return The test_error vector sorted
*/
- std::vector<double> sorted_test_error();
+ std::vector<double> sorted_test_error() const;
// DocString: model_reg_percentile_25_ae
/**
* @brief The the 25 percentile error of the model
* @return The the 25 percentile error of the training data
*/
- inline double percentile_25_ae()
+ inline double percentile_25_ae() const
{
return sorted_error()[static_cast<int>(floor(_n_samp_train * 0.25))];
}
@@ -310,7 +310,7 @@ public:
* @brief The 25 percentile test error of the model
* @return The 25 percentile error of the test data
*/
- inline double percentile_25_test_ae()
+ inline double percentile_25_test_ae() const
{
return sorted_test_error()[static_cast<int>(floor(_n_samp_test * 0.25))];
}
@@ -320,7 +320,7 @@ public:
* @brief The 50 percentile error of the model
* @return The 50 percentile error of the training data
*/
- inline double percentile_50_ae()
+ inline double percentile_50_ae() const
{
return sorted_error()[static_cast<int>(floor(_n_samp_train * 0.50))];
}
@@ -330,7 +330,7 @@ public:
* @brief The 50 percentile test error of the model
* @return The 50 percentile error of the test data
*/
- inline double percentile_50_test_ae()
+ inline double percentile_50_test_ae() const
{
return sorted_test_error()[static_cast<int>(floor(_n_samp_test * 0.50))];
}
@@ -340,7 +340,7 @@ public:
* @brief The 75 percentile error of the model
* @return The 75 percentile error of the training data
*/
- inline double percentile_75_ae()
+ inline double percentile_75_ae() const
{
return sorted_error()[static_cast<int>(floor(_n_samp_train * 0.75))];
}
@@ -350,7 +350,7 @@ public:
* @brief The 75 percentile test error of the model
* @return The 75 percentile error of the test data
*/
- inline double percentile_75_test_ae()
+ inline double percentile_75_test_ae() const
{
return sorted_test_error()[static_cast<int>(floor(_n_samp_test * 0.75))];
}
@@ -360,7 +360,7 @@ public:
* @brief The 95 percentile error of the model
* @return The 95 percentile error of the training data
*/
- inline double percentile_95_ae()
+ inline double percentile_95_ae() const
{
return sorted_error()[static_cast<int>(floor(_n_samp_train * 0.95))];
}
@@ -370,7 +370,7 @@ public:
* @brief The 95 percentile test error of the model
* @return The 95 percentile error of the test data
*/
- inline double percentile_95_test_ae()
+ inline double percentile_95_test_ae() const
{
return sorted_test_error()[static_cast<int>(floor(_n_samp_test * 0.95))];
}
@@ -382,7 +382,7 @@ public:
* @param train If true output the training data
* @param test_inds The indexes of the test set
*/
- virtual void to_file(std::string filename, bool train=true, std::vector<int> test_inds={});
+ virtual void to_file(const std::string filename, const bool train=true, const std::vector<int> test_inds={}) const;
#ifdef PY_BINDINGS
// DocString: model_reg_prop_train_est
diff --git a/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp b/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp
index 75678fef4988e6c383ddd790a5b3fd9f2a7addff..7ac3b5d2b6009b9fb2fa601b35db177dc4f1cd47 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp
@@ -1,17 +1,17 @@
#include <descriptor_identifier/SISSO_DI/SISSOClassifier.hpp>
SISSOClassifier::SISSOClassifier(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store
):
SISSO_DI(feat_space, prop_label, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, false),
_c(1000.0),
@@ -22,7 +22,7 @@ SISSOClassifier::SISSOClassifier(
setup_prop(prop, prop_test);
}
-void SISSOClassifier::check_prop_test(std::vector<double> prop, std::vector<double> prop_test)
+void SISSOClassifier::check_prop_test(const std::vector<double> prop, const std::vector<double> prop_test) const
{
for(auto& pt : prop_test)
{
@@ -33,7 +33,6 @@ void SISSOClassifier::check_prop_test(std::vector<double> prop, std::vector<doub
}
}
-
void SISSOClassifier::setup_prop(std::vector<double> prop, std::vector<double> prop_test)
{
std::vector<int> inds = util_funcs::argsort<double>(prop);
@@ -72,7 +71,7 @@ void SISSOClassifier::setup_prop(std::vector<double> prop, std::vector<double> p
std::copy_n(prop_test.begin(), prop_test.size(), _prop_test.begin());
}
-std::vector<LPWrapper> SISSOClassifier::setup_lp(int n_dim)
+std::vector<LPWrapper> SISSOClassifier::setup_lp(const int n_dim)
{
std::vector<int> n_samp_per_class;
int task_start = 0;
@@ -112,7 +111,7 @@ std::vector<LPWrapper> SISSOClassifier::setup_lp(int n_dim)
return lp;
}
-std::array<double, 2> SISSOClassifier::svm_error(std::vector<SVMWrapper>& svm, std::vector<int>& feat_inds)
+std::array<double, 2> SISSOClassifier::svm_error(std::vector<SVMWrapper>& svm, const std::vector<int>& feat_inds) const
{
double error = 0.0;
double dist_error = 0.0;
@@ -129,12 +128,15 @@ std::array<double, 2> SISSOClassifier::svm_error(std::vector<SVMWrapper>& svm, s
return {error, dist_error};
}
-int SISSOClassifier::get_max_error_ind(int n_models, int* n_convex_overlap, double* svm_score, double* svm_margin, double* scores)
+int SISSOClassifier::get_max_error_ind(
+ const int n_models, const int* n_convex_overlap, const double* svm_score, const double* svm_margin, double* scores
+) const
{
std::transform(
n_convex_overlap,
n_convex_overlap + n_models,
- svm_score, scores,
+ svm_score,
+ scores,
[this](int n_overlap, double score){return static_cast<double>(n_overlap * _n_samp * _n_class) + score;}
);
@@ -150,7 +152,7 @@ int SISSOClassifier::get_max_error_ind(int n_models, int* n_convex_overlap, doub
return std::max_element(scores, scores + n_models) - scores;
}
-void SISSOClassifier::transfer_d_mat_to_sorted()
+void SISSOClassifier::transfer_d_mat_to_sorted() const
{
prop_sorted_d_mat::resize_sroted_d_matrix_arr(node_value_arrs::N_SELECTED);
@@ -165,7 +167,7 @@ void SISSOClassifier::transfer_d_mat_to_sorted()
}
}
-void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim)
+void SISSOClassifier::l0_norm(std::vector<double>& prop, const int n_dim)
{
int n_get_models = std::max(_n_residual, _n_models_store);
diff --git a/src/descriptor_identifier/SISSO_DI/SISSOClassifier.hpp b/src/descriptor_identifier/SISSO_DI/SISSOClassifier.hpp
index 39cbf97211ffdc445fa5b1ef1b1fe732a1acce22..1a63af0b306d6b812767cad54ed6e9d0cc9be9c2 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSOClassifier.hpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSOClassifier.hpp
@@ -37,8 +37,8 @@ protected:
std::map<int, int> _sample_inds_to_sorted_dmat_inds; //!< map from input sample inds to the SORTED_D_MATRIX_INDS
- double _c; //!< the C value for the SVM results
- double _width; //!< The tolerance used for calculating overlap (data point to check, 1.0 +/- width for the row constraints)
+ const double _c; //!< the C value for the SVM results
+ const double _width; //!< The tolerance used for calculating overlap (data point to check, 1.0 +/- width for the row constraints)
int _n_class; //!< The number of classes in the calculation
@@ -58,16 +58,16 @@ public:
* @param n_models_store Number of features to store in files
*/
SISSOClassifier(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label, Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label, Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store
);
/**
@@ -76,8 +76,7 @@ public:
* @param prop The property vector for the training data
* @param prop_test The property vector for the test data
*/
- void check_prop_test(std::vector<double> prop, std::vector<double> prop_test);
-
+ void check_prop_test(const std::vector<double> prop, const std::vector<double> prop_test) const;
/**
* @brief Convert the property vectors into something that SharkML's SVM expects
@@ -90,12 +89,12 @@ public:
/**
* @brief set up the data structures for finding the number of overlap points for a linear programming problem
*/
- std::vector<LPWrapper> setup_lp(int n_dim = 1);
+ std::vector<LPWrapper> setup_lp(const int n_dim = 1);
/**
* @brief Transfer data from the node value array D-matrix into a property sorted array
*/
- void transfer_d_mat_to_sorted();
+ void transfer_d_mat_to_sorted() const;
/**
* @brief Find the SVM error and margin for a given set of indexes
@@ -104,7 +103,7 @@ public:
* @param feat_inds The indexes to develop the SVM model for
* @return {The number of misclassified points, the SVM margin}
*/
- std::array<double, 2> svm_error(std::vector<SVMWrapper>& svm, std::vector<int>& feat_inds);
+ std::array<double, 2> svm_error(std::vector<SVMWrapper>& svm, const std::vector<int>& feat_inds) const;
/**
* @brief Gets the max error index for the classification problem
@@ -117,7 +116,7 @@ public:
* @param scores the pointer to the scores array
* @return Get the index with the maximum error
*/
- int get_max_error_ind(int n_models, int* n_convex_overlap, double* svm_score, double* svm_margin, double* scores);
+ int get_max_error_ind(const int n_models, const int* n_convex_overlap, const double* svm_score, const double* svm_margin, double* scores) const;
/**
* @brief Preform the l0 normalization for a property or the residual
@@ -125,7 +124,7 @@ public:
* @param prop The property to fit
* @param n_dim the dimensionality of the model
*/
- void l0_norm(std::vector<double>& prop, int n_dim);
+ void l0_norm(std::vector<double>& prop, const int n_dim);
// DocString: sisso_class_fit
/**
@@ -137,7 +136,7 @@ public:
/**
* @brief Acessor function for models
*/
- inline std::vector<std::vector<ModelClassifier>> models(){return _models;}
+ inline std::vector<std::vector<ModelClassifier>> models() const {return _models;}
// Python interface functions
#ifdef PY_BINDINGS
diff --git a/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.cpp b/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.cpp
index 855ab6c71311c805d6e9d5354e2ea7654c4cca46..bb5d72e5a2c5936aab87882f280e56fe407f9d86 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.cpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.cpp
@@ -1,18 +1,18 @@
#include <descriptor_identifier/SISSO_DI/SISSOLogRegressor.hpp>
SISSOLogRegressor::SISSOLogRegressor(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store,
- bool fix_intercept
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store,
+ const bool fix_intercept
):
SISSORegressor(feat_space, prop_label, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, fix_intercept),
_prop_no_log(prop)
@@ -20,7 +20,7 @@ SISSOLogRegressor::SISSOLogRegressor(
std::transform(_prop.begin(), _prop.end(), _prop.begin(), [](double p){return std::log(p);});
}
-void SISSOLogRegressor::set_a(std::vector<int>& inds, int start, int n_samp, double* a)
+void SISSOLogRegressor::set_a(const std::vector<int>& inds, const int start, const int n_samp, double* a)
{
for(int ii = 0; ii < inds.size(); ++ii)
{
@@ -38,7 +38,7 @@ void SISSOLogRegressor::set_a(std::vector<int>& inds, int start, int n_samp, do
}
}
-void SISSOLogRegressor::set_error(std::vector<int>& inds, double* coefs, int start, int n_samp, double* error)
+void SISSOLogRegressor::set_error(const std::vector<int>& inds, const double* coefs, const int start, const int n_samp, double* error) const
{
std::fill_n(error + start, n_samp, -1 * (!_fix_intercept) * coefs[inds.size()]);
for(int ii = 0; ii < inds.size(); ++ii)
@@ -76,7 +76,7 @@ void SISSOLogRegressor::output_models(std::vector<double>& residual)
}
}
-void SISSOLogRegressor::add_model(std::vector<int> indexes)
+void SISSOLogRegressor::add_model(const std::vector<int> indexes)
{
if(_models.size() < indexes.size())
{
diff --git a/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.hpp b/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.hpp
index 6a06c9b0533250d0dd3928d785b5547e452094df..bd54aaddee7560ad6ce46d052765eac3f65e30d3 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.hpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSOLogRegressor.hpp
@@ -45,18 +45,18 @@ public:
* @param fix_intrecept If true fix intercept to 0
*/
SISSOLogRegressor(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store,
- bool fix_intercept=false
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store,
+ const bool fix_intercept=false
);
/**
@@ -66,7 +66,7 @@ public:
* @param start The index in the property and feature vectors start copying into _b and _a
* @param n_samp number of samples to perform least squares optimization on
*/
- void set_a(std::vector<int>& inds, int start, int n_samp, double* a);
+ void set_a(const std::vector<int>& inds, const int start, const int n_samp, double* a);
/**
* @brief Set the residual for the next step
@@ -77,14 +77,14 @@ public:
* @param n_samp number of samples to perform least squares optimization on
* @param error pointer to the error vector
*/
- virtual void set_error(std::vector<int>& inds, double* coefs, int start, int n_samp, double* error);
+ virtual void set_error(const std::vector<int>& inds, const double* coefs, const int start, const int n_samp, double* error) const;
/**
* @brief Adds a model to the model list
*
* @param indexes Vector storing all of the indexes of features in _phi_selected to use for the model
*/
- void add_model(std::vector<int> indexes);
+ void add_model(const std::vector<int> indexes);
/**
* @brief Output the models to files and copy the residuals
@@ -104,7 +104,7 @@ public:
/**
* @brief Acessor function for models
*/
- inline std::vector<std::vector<ModelLogRegressor>> models(){return _models;}
+ inline std::vector<std::vector<ModelLogRegressor>> models() const {return _models;}
// Python interface functions
#ifdef PY_BINDINGS
diff --git a/src/descriptor_identifier/SISSO_DI/SISSORegressor.cpp b/src/descriptor_identifier/SISSO_DI/SISSORegressor.cpp
index 35ed8000699c4f2789a1e7d91f492b43c82e6645..56484b0864389f0c926769df7eb791bd64101f61 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSORegressor.cpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSORegressor.cpp
@@ -1,18 +1,18 @@
#include <descriptor_identifier/SISSO_DI/SISSORegressor.hpp>
SISSORegressor::SISSORegressor(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store,
- bool fix_intercept
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store,
+ const bool fix_intercept
):
SISSO_DI(
feat_space,
@@ -30,7 +30,7 @@ SISSORegressor::SISSORegressor(
)
{}
-void SISSORegressor::set_a(std::vector<int>& inds, int start, int n_samp, double* a)
+void SISSORegressor::set_a(const std::vector<int>& inds, int start, int n_samp, double* a)
{
for(int ii = 0; ii < inds.size(); ++ii)
{
@@ -43,7 +43,7 @@ void SISSORegressor::set_a(std::vector<int>& inds, int start, int n_samp, doubl
}
}
-void SISSORegressor::least_squares(std::vector<int>& inds, double* coeffs, int start, int n_samp, double* a, double*b, double* work)
+void SISSORegressor::least_squares(const std::vector<int>& inds, double* coeffs, const int start, const int n_samp, double* a, double*b, double* work)
{
int info;
int n_dim = inds.size() + (1 - _fix_intercept);
@@ -79,7 +79,7 @@ void SISSORegressor::least_squares(std::vector<int>& inds, double* coeffs, int s
}
}
-int SISSORegressor::get_opt_lwork(int n_dim, double* a, double* b)
+int SISSORegressor::get_opt_lwork(const int n_dim, double* a, double* b)
{
std::vector<double> work(1, 0.0);
int info = 0, rank = 0;
@@ -96,7 +96,7 @@ int SISSORegressor::get_opt_lwork(int n_dim, double* a, double* b)
}
}
-void SISSORegressor::set_error(std::vector<int>& inds, double* coefs, int start, int n_samp, double* error)
+void SISSORegressor::set_error(const std::vector<int>& inds, const double* coefs, const int start, const int n_samp, double* error) const
{
std::fill_n(error + start, n_samp, -1 * (!_fix_intercept) * coefs[inds.size()]);
for(int ii = 0; ii < inds.size(); ++ii)
@@ -107,7 +107,7 @@ void SISSORegressor::set_error(std::vector<int>& inds, double* coefs, int start,
}
-void SISSORegressor::add_model(std::vector<int> indexes)
+void SISSORegressor::add_model(const std::vector<int> indexes)
{
if(_models.size() < indexes.size())
_models.push_back({});
@@ -142,7 +142,7 @@ void SISSORegressor::output_models(std::vector<double>& residual)
}
}
-void SISSORegressor::l0_norm(std::vector<double>& prop, int n_dim)
+void SISSORegressor::l0_norm(std::vector<double>& prop, const int n_dim)
{
int n_get_models = std::max(_n_residual, _n_models_store);
std::vector<double> coefs(n_dim + 1, 0.0);
diff --git a/src/descriptor_identifier/SISSO_DI/SISSORegressor.hpp b/src/descriptor_identifier/SISSO_DI/SISSORegressor.hpp
index 32ff50e31a7848d92e4ef260f2c5470e28675ae0..105e72de25c3ca348ed7dc672d0c2aa7a6e5eb68 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSORegressor.hpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSORegressor.hpp
@@ -49,18 +49,18 @@ public:
* @param fix_intrecept If true fix intercept to 0
*/
SISSORegressor(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store,
- bool fix_intercept=false
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store,
+ const bool fix_intercept=false
);
/**
@@ -72,7 +72,7 @@ public:
* @param n_samp number of samples to perform least squares optimization on
* @param error pointer to the error vector
*/
- virtual void set_error(std::vector<int>& inds, double* coefs, int start, int n_samp, double* error);
+ virtual void set_error(const std::vector<int>& inds, const double* coefs, const int start, const int n_samp, double* error) const;
/**
* @brief Get the optimal size of the working array for dgels (solve Ax = b)
@@ -82,7 +82,7 @@ public:
* @param b the pointer to the b vector
* @return Optimal size of the working array
*/
- int get_opt_lwork(int n_dim, double* a, double* b);
+ int get_opt_lwork(const int n_dim, double* a, double* b);
/**
* @brief Preform Least squares optimization using dgels (solve Ax = b)
@@ -95,7 +95,7 @@ public:
* @param b the pointer to the b vector
* @param work pointer to the working space for dgels
*/
- void least_squares(std::vector<int>& inds, double* coefs, int start, int n_samp, double* a, double*b, double* work);
+ void least_squares(const std::vector<int>& inds, double* coefs, const int start, const int n_samp, double* a, double*b, double* work);
/**
* @brief Set the A matrix for the least squares problem
@@ -105,14 +105,14 @@ public:
* @param n_samp number of samples to perform least squares optimization on
* @param a the pointer to the A matrix
*/
- virtual void set_a(std::vector<int>& inds, int start, int n_samp, double* a);
+ virtual void set_a(const std::vector<int>& inds, int start, int n_samp, double* a);
/**
* @brief Adds a model to the model list
*
* @param indexes Vector storing all of the indexes of features in _phi_selected to use for the model
*/
- virtual void add_model(std::vector<int> indexes);
+ virtual void add_model(const std::vector<int> indexes);
/**
* @brief Output the models to files and copy the residuals
@@ -127,7 +127,7 @@ public:
* @param prop The property to fit
* @param n_dim the dimensionality of the model
*/
- virtual void l0_norm(std::vector<double>& prop, int n_dim);
+ virtual void l0_norm(std::vector<double>& prop, const int n_dim);
// DocString: sisso_reg_fit
/**
@@ -139,7 +139,7 @@ public:
/**
* @brief Acessor function for models
*/
- inline std::vector<std::vector<ModelRegressor>> models(){return _models;}
+ inline std::vector<std::vector<ModelRegressor>> models() const {return _models;}
// Python interface functions
#ifdef PY_BINDINGS
diff --git a/src/descriptor_identifier/SISSO_DI/SISSO_DI.cpp b/src/descriptor_identifier/SISSO_DI/SISSO_DI.cpp
index d8e2cf59bd381d3b32ed9bdaffbac40423b206f3..bbfff6f844527776e8e05406711dc8ec36493a84 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSO_DI.cpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSO_DI.cpp
@@ -1,18 +1,18 @@
#include <descriptor_identifier/SISSO_DI/SISSO_DI.hpp>
SISSO_DI::SISSO_DI(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store,
- bool fix_intercept
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store,
+ const bool fix_intercept
):
_prop(prop),
_prop_test(prop_test),
diff --git a/src/descriptor_identifier/SISSO_DI/SISSO_DI.hpp b/src/descriptor_identifier/SISSO_DI/SISSO_DI.hpp
index 349d1a50e65181a0ebc25d7e46bdebeb14bc2ba2..7b123c5142b8903cf1087c1fa32a7c6318b4302a 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSO_DI.hpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSO_DI.hpp
@@ -31,23 +31,23 @@ protected:
std::vector<double> _prop_test; //!< Property array (testing data)
std::vector<double> _error; //!< Array to calculate the residuals for the models (training data)
- std::vector<int> _task_sizes_train; //!< Number of training samples per task
- std::vector<int> _task_sizes_test; //!< Number of testing samples per task
- std::vector<int> _leave_out_inds; //!< List of indexes from the initial data file in the test set
+ const std::vector<int> _task_sizes_train; //!< Number of training samples per task
+ const std::vector<int> _task_sizes_test; //!< Number of testing samples per task
+ const std::vector<int> _leave_out_inds; //!< List of indexes from the initial data file in the test set
- Unit _prop_unit; //!< The Unit for the property
- std::string _prop_label; //!< The label for the property
+ const Unit _prop_unit; //!< The Unit for the property
+ const std::string _prop_label; //!< The label for the property
- std::shared_ptr<FeatureSpace> _feat_space; //!< Feature Space for the problem
+ const std::shared_ptr<FeatureSpace> _feat_space; //!< Feature Space for the problem
std::shared_ptr<MPI_Interface> _mpi_comm; //!< MPI Communicator
- int _n_task; //!< The number of tasks
- int _n_samp; //!< the number of samples per feature
- int _n_dim; //!< Number of dimensions to calculate
- int _n_residual; //!< Number of residuals to pass to the next sis model
- int _n_models_store; //!< The number of models to store in files
+ const int _n_task; //!< The number of tasks
+ const int _n_samp; //!< the number of samples per feature
+ const int _n_dim; //!< Number of dimensions to calculate
+ const int _n_residual; //!< Number of residuals to pass to the next sis model
+ const int _n_models_store; //!< The number of models to store in files
- bool _fix_intercept; //!< If true fix intercept to 0
+ const bool _fix_intercept; //!< If true fix intercept to 0
public:
/**
* @brief Constructor for the Regressor
@@ -66,18 +66,18 @@ public:
* @param fix_intrecept If true fix intercept to 0
*/
SISSO_DI(
- std::shared_ptr<FeatureSpace> feat_space,
- std::string prop_label,
- Unit prop_unit,
- std::vector<double> prop,
- std::vector<double> prop_test,
- std::vector<int> task_sizes_train,
- std::vector<int> task_sizes_test,
- std::vector<int> leave_out_inds,
- int n_dim,
- int n_residual,
- int n_models_store,
- bool fix_intercept=false
+ const std::shared_ptr<FeatureSpace> feat_space,
+ const std::string prop_label,
+ const Unit prop_unit,
+ const std::vector<double> prop,
+ const std::vector<double> prop_test,
+ const std::vector<int> task_sizes_train,
+ const std::vector<int> task_sizes_test,
+ const std::vector<int> leave_out_inds,
+ const int n_dim,
+ const int n_residual,
+ const int n_models_store,
+ const bool fix_intercept=false
);
/**
@@ -92,51 +92,51 @@ public:
* @param prop The property to fit
* @param n_dim the dimensionality of the model
*/
- virtual void l0_norm(std::vector<double>& prop, int n_dim) = 0;
+ virtual void l0_norm(std::vector<double>& prop, const int n_dim) = 0;
/**
* @brief Acessor function for feat_space
*/
- inline std::shared_ptr<FeatureSpace> feat_space(){return _feat_space;}
+ inline std::shared_ptr<FeatureSpace> feat_space() const {return _feat_space;}
/**
* @brief Acessor function for prop
*/
- inline std::vector<double> prop(){return _prop;}
+ inline std::vector<double> prop() const {return _prop;}
/**
* @brief Acessor function for prop
*/
- inline std::vector<double> prop_test(){return _prop_test;}
+ inline std::vector<double> prop_test() const {return _prop_test;}
/**
* @brief Acessor function for the error vector
*/
- inline std::vector<double> error(){return _error;}
+ inline std::vector<double> error() const {return _error;}
// DocString: sisso_di_n_samp
/**
* @brief The number of samples per feature
*/
- inline int n_samp(){return _n_samp;}
+ inline int n_samp() const {return _n_samp;}
// DocString: sisso_di_n_dim
/**
* @brief The maximum model dimensionality
*/
- inline int n_dim(){return _n_dim;}
+ inline int n_dim() const {return _n_dim;}
// DocString: sisso_di_n_residual
/**
* @brief The number of residuals each iteration of SIS acts on
*/
- inline int n_residual(){return _n_residual;}
+ inline int n_residual() const {return _n_residual;}
// DocString: sisso_di_n_modles_store
/**
* @brief The number of models to store in output files
*/
- inline int n_models_store(){return _n_models_store;}
+ inline int n_models_store() const {return _n_models_store;}
// Python interface functions
#ifdef PY_BINDINGS
diff --git a/src/feature_creation/feature_space/FeatureSpace.cpp b/src/feature_creation/feature_space/FeatureSpace.cpp
index 7cac0863a55b362c1b39d724d8120d1ab3a84e7a..938abef653bd56874b85b21f7326206d439cdd6a 100644
--- a/src/feature_creation/feature_space/FeatureSpace.cpp
+++ b/src/feature_creation/feature_space/FeatureSpace.cpp
@@ -38,7 +38,6 @@ BOOST_CLASS_EXPORT_GUID(AbsParamNode, "AbsParamNode")
BOOST_CLASS_EXPORT_GUID(InvParamNode, "InvParamNode")
BOOST_CLASS_EXPORT_GUID(SinParamNode, "SinParamNode")
BOOST_CLASS_EXPORT_GUID(CosParamNode, "CosParamNode")
-#endif
FeatureSpace::FeatureSpace(
std::shared_ptr<MPI_Interface> mpi_comm,
@@ -82,6 +81,46 @@ FeatureSpace::FeatureSpace(
{
initialize_fs();
}
+#else
+FeatureSpace::FeatureSpace(
+ std::shared_ptr<MPI_Interface> mpi_comm,
+ std::vector<node_ptr> phi_0,
+ std::vector<std::string> allowed_ops,
+ std::vector<double> prop,
+ std::vector<int> task_sizes,
+ std::string project_type,
+ int max_phi,
+ int n_sis_select,
+ int max_store_rung,
+ int n_rung_generate,
+ double cross_corr_max,
+ double min_abs_feat_val,
+ double max_abs_feat_val
+):
+ _phi(phi_0),
+ _phi_0(phi_0),
+ _allowed_ops(allowed_ops),
+ _prop(prop),
+ _scores(phi_0.size(), 0.0),
+ _task_sizes(task_sizes),
+ _start_gen(1, 0),
+ _feature_space_file("feature_space/selected_features.txt"),
+ _feature_space_summary_file("feature_space/SIS_summary.txt"),
+ _project_type(project_type),
+ _mpi_comm(mpi_comm),
+ _cross_cor_max(cross_corr_max),
+ _l_bound(min_abs_feat_val),
+ _u_bound(max_abs_feat_val),
+ _max_phi(max_phi),
+ _n_sis_select(n_sis_select),
+ _n_samp(phi_0[0]->n_samp()),
+ _n_feat(phi_0.size()),
+ _n_rung_store(max_store_rung),
+ _n_rung_generate(n_rung_generate)
+{
+ initialize_fs();
+}
+#endif
void FeatureSpace::initialize_fs()
{
@@ -179,7 +218,7 @@ void FeatureSpace::set_op_lists()
#endif
}
-void FeatureSpace::initialize_fs_output_files()
+void FeatureSpace::initialize_fs_output_files() const
{
if(_mpi_comm->rank() == 0)
{
@@ -200,8 +239,8 @@ void FeatureSpace::generate_new_feats(
std::vector<node_ptr>& feat_set,
unsigned long int& feat_ind,
std::shared_ptr<NLOptimizer> optimizer,
- double l_bound,
- double u_bound
+ const double l_bound,
+ const double u_bound
)
{
@@ -255,8 +294,8 @@ void FeatureSpace::generate_new_feats(
std::vector<node_ptr>::iterator& feat,
std::vector<node_ptr>& feat_set,
unsigned long int& feat_ind,
- double l_bound,
- double u_bound
+ const double l_bound,
+ const double u_bound
)
{
unsigned long int phi_ind = feat - _phi.begin();
@@ -640,7 +679,7 @@ void FeatureSpace::generate_feature_space()
_n_feat = _phi.size();
}
-void FeatureSpace::project_generated(double* prop, int size, std::vector<node_ptr>& phi_sel, std::vector<double>& scores_sel)
+void FeatureSpace::project_generated(const double* prop, const int size, std::vector<node_ptr>& phi_sel, std::vector<double>& scores_sel)
{
std::vector<double> scores_sel_all(node_value_arrs::N_SELECTED);
if(node_value_arrs::N_SELECTED > _n_sis_select)
@@ -776,7 +815,7 @@ void FeatureSpace::project_generated(double* prop, int size, std::vector<node_pt
}
}
-void FeatureSpace::sis(std::vector<double>& prop)
+void FeatureSpace::sis(const std::vector<double>& prop)
{
// Create output directories if needed
boost::filesystem::path p(_feature_space_file.c_str());
@@ -993,7 +1032,7 @@ void FeatureSpace::sis(std::vector<double>& prop)
}
}
-void FeatureSpace::remove_feature(int ind)
+void FeatureSpace::remove_feature(const int ind)
{
if(_phi[ind]->selected())
{
diff --git a/src/feature_creation/feature_space/FeatureSpace.hpp b/src/feature_creation/feature_space/FeatureSpace.hpp
index 557fb2cfbc8dd7d544a55475fbd10062a6ac12eb..e773d451ce47c4e469495d1d598f93ec77445ca5 100644
--- a/src/feature_creation/feature_space/FeatureSpace.hpp
+++ b/src/feature_creation/feature_space/FeatureSpace.hpp
@@ -48,9 +48,9 @@ class FeatureSpace
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
+ 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)
#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
@@ -88,6 +88,7 @@ class FeatureSpace
public:
+ #ifdef PARAMETERIZE
/**
* @brief Constructor for the feature space
* @details constructs the feature space from an initial set of features and a list of allowed operators
@@ -125,7 +126,43 @@ public:
double max_abs_feat_val=1e50,
int max_param_depth = -1
);
-
+ #else
+ /**
+ * @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
+ * @param max_param_depth the maximum paremterization depths for features
+ */
+ FeatureSpace(
+ std::shared_ptr<MPI_Interface> mpi_comm,
+ std::vector<node_ptr> phi_0,
+ std::vector<std::string> allowed_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
+ );
+ #endif
/**
* @brief Initialize the feature set given a property vector
*/
@@ -139,7 +176,7 @@ public:
/**
* @brief Initializes the output files for SIS
*/
- void initialize_fs_output_files();
+ void initialize_fs_output_files() const;
/**
* @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
@@ -149,86 +186,86 @@ public:
/**
* @brief The selected feature space
*/
- inline std::vector<node_ptr> phi_selected(){return _phi_selected;};
+ inline std::vector<node_ptr> phi_selected() const {return _phi_selected;};
/**
* @brief The full feature space
*/
- inline std::vector<node_ptr> phi(){return _phi;};
+ inline std::vector<node_ptr> phi() const {return _phi;};
/**
* @brief The initial feature space
*/
- inline std::vector<node_ptr> phi0(){return _phi_0;};
+ inline std::vector<node_ptr> phi0() const {return _phi_0;};
/**
* @brief The vector of projection scores for SIS
*/
- inline std::vector<double> scores(){return _scores;}
+ inline std::vector<double> scores() const {return _scores;}
/**
* @brief The MPI Communicator
*/
- inline std::shared_ptr<MPI_Interface> mpi_comm(){return _mpi_comm;}
+ inline std::shared_ptr<MPI_Interface> mpi_comm() const {return _mpi_comm;}
/**
* @brief The vector storing the number of samples in each task
*/
- inline std::vector<int> task_sizes(){return _task_sizes;}
+ inline std::vector<int> task_sizes() const {return _task_sizes;}
// DocString: feat_space_feature_space_file
/**
* @brief The feature space filename
*/
- inline std::string feature_space_file(){return _feature_space_file;}
+ inline std::string feature_space_file() const {return _feature_space_file;}
// DocString: feat_space_l_bound
/**
* @brief The minimum absolute value of the feature
*/
- inline double l_bound(){return _l_bound;}
+ inline double l_bound() const {return _l_bound;}
// DocString: feat_space_u_bound
/**
* @brief The maximum absolute value of the feature
*/
- inline double u_bound(){return _u_bound;}
+ inline double u_bound() const {return _u_bound;}
// DocString: feat_space_max_phi
/**
* @brief The maximum rung of the feature space
*/
- inline int max_phi(){return _max_phi;}
+ inline int max_phi() const {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;}
+ inline int n_sis_select() const {return _n_sis_select;}
// DocString: feat_space_n_samp
/**
* @brief The number of samples per feature
*/
- inline int n_samp(){return _n_samp;}
+ inline int n_samp() const {return _n_samp;}
// DocString: feat_space_n_feat
/**
* @brief The number of features in the feature space
*/
- inline int n_feat(){return _n_feat;}
+ inline int n_feat() const {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;}
+ inline int n_rung_store() const {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;}
+ inline int n_rung_generate() const {return _n_rung_generate;}
#ifdef PARAMETERIZE
/**
@@ -247,8 +284,8 @@ public:
std::vector<node_ptr>& feat_set,
unsigned long int& feat_ind,
std::shared_ptr<NLOptimizer> optimizer,
- double l_bound=1e-50,
- double u_bound=1e50
+ const double l_bound=1e-50,
+ const double u_bound=1e50
);
#else
/**
@@ -265,8 +302,8 @@ public:
std::vector<node_ptr>::iterator& feat,
std::vector<node_ptr>& feat_set,
unsigned long int& feat_ind,
- double l_bound=1e-50,
- double u_bound=1e50
+ const double l_bound=1e-50,
+ const double u_bound=1e50
);
#endif
@@ -279,7 +316,7 @@ public:
* @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
*/
- void project_generated(double* prop, int size, std::vector<node_ptr>& phi_selected, std::vector<double>& scores_selected);
+ void project_generated(const double* prop, const int size, std::vector<node_ptr>& phi_selected, std::vector<double>& scores_selected);
/**
* @brief Perform SIS on a feature set with a specified property
@@ -287,7 +324,7 @@ public:
*
* @param prop The property to perform SIS over
*/
- void sis(std::vector<double>& prop);
+ void sis(const std::vector<double>& prop);
// DocString: feat_space_feat_in_phi
/**
@@ -296,7 +333,7 @@ public:
* @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());}
+ inline bool feat_in_phi(int ind) const {return (ind >= _phi[0]->feat_ind()) && (ind <= _phi.back()->feat_ind());}
// DocString: feat_space_remove_feature
/**
@@ -304,10 +341,11 @@ public:
*
* @param ind index of feature to remove
*/
- void remove_feature(int ind);
+ void remove_feature(const int ind);
// Python Interface Functions
#ifdef PY_BINDINGS
+ #ifdef PARAMETERIZE
/**
* @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>)
@@ -379,7 +417,71 @@ public:
double max_abs_feat_val=1e50,
int max_param_depth = -1
);
+ #else
+ /**
+ * @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 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 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 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,
+ 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
+ );
+ #endif
/**
* @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>)
@@ -504,7 +606,7 @@ public:
* @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]);}
+ inline ModelNode get_feature(const int ind) const {return ModelNode(_phi[ind]);}
#endif
};
diff --git a/src/python/bindings_docstring_keyed.cpp b/src/python/bindings_docstring_keyed.cpp
index e6b7c010bd70c324cc6eeb0eae82b3dfb00ecd76..91ddcc9c0b1a9d7651244e8812904200a622ff90 100644
--- a/src/python/bindings_docstring_keyed.cpp
+++ b/src/python/bindings_docstring_keyed.cpp
@@ -105,6 +105,7 @@ void sisso::feature_creation::registerFeatureSpace()
void (FeatureSpace::*sis_list)(list) = &FeatureSpace::sis;
void (FeatureSpace::*sis_ndarray)(np::ndarray) = &FeatureSpace::sis;
+ #ifdef PARAMETERIZE
class_<FeatureSpace>("FeatureSpace", init<list, list, list, np::ndarray, list, optional<std::string, int, int, int, int, double, double, double, int>>())
.def(init<list, list, list, list, list, optional<std::string, int, int, int, int, double, double, double, int>>())
.def(init<std::string, list, list, list, optional<std::string, int, double>>())
@@ -131,6 +132,34 @@ void sisso::feature_creation::registerFeatureSpace()
.add_property("n_rung_store", &FeatureSpace::n_rung_store, "@DocString_feat_space_n_rung_store@")
.add_property("n_rung_generate", &FeatureSpace::n_rung_generate, "@DocString_feat_space_n_rung_generate@")
;
+ #else
+ class_<FeatureSpace>("FeatureSpace", init<list, list, np::ndarray, list, optional<std::string, int, int, int, int, double, double, double>>())
+ .def(init<list, list, list, list, optional<std::string, int, int, int, int, double, double, double>>())
+ .def(init<std::string, list, list, list, optional<std::string, int, double>>())
+ .def(init<std::string, list, np::ndarray, list, optional<std::string, int, double>>())
+ .def("sis", sis_list, "@DocString_feat_space_sis_list@")
+ .def("sis", sis_ndarray, "@DocString_feat_space_sis_arr@")
+ .def("feat_in_phi", &FeatureSpace::feat_in_phi, "@DocString_feat_space_feat_in_phi@")
+ .def("remove_feature", &FeatureSpace::remove_feature, "@DocString_feat_space_remove_feature@")
+ .def("get_feature", &FeatureSpace::get_feature, "@DocString_feat_space_get_feature@")
+ .add_property("phi_selected", &FeatureSpace::phi_selected_py, "@DocString_feat_space_phi_selected_py@")
+ .add_property("phi0", &FeatureSpace::phi0_py, "@DocString_feat_space_phi0_py@")
+ .add_property("phi", &FeatureSpace::phi_py, "@DocString_feat_space_phi_py@")
+ .add_property("scores", &FeatureSpace::scores_py, "@DocString_feat_space_scores_py@")
+ .add_property("task_sizes", &FeatureSpace::task_sizes_py, "@DocString_feat_space_task_sizes_py@")
+ .add_property("allowed_ops", &FeatureSpace::allowed_ops_py, "@DocString_feat_space_allowed_ops_py@")
+ .add_property("start_gen", &FeatureSpace::start_gen_py, "@DocString_feat_space_start_gen_py@")
+ .add_property("feature_space_file", &FeatureSpace::feature_space_file, "@DocString_feat_space_feature_space_file@")
+ .add_property("l_bound", &FeatureSpace::l_bound, "@DocString_feat_space_l_bound@")
+ .add_property("u_bound", &FeatureSpace::u_bound, "@DocString_feat_space_u_bound@")
+ .add_property("max_phi", &FeatureSpace::max_phi, "@DocString_feat_space_max_phi@")
+ .add_property("n_sis_select", &FeatureSpace::n_sis_select, "@DocString_feat_space_n_sis_select@")
+ .add_property("n_samp", &FeatureSpace::n_samp, "@DocString_feat_space_n_samp@")
+ .add_property("n_feat", &FeatureSpace::n_feat, "@DocString_feat_space_n_feat@")
+ .add_property("n_rung_store", &FeatureSpace::n_rung_store, "@DocString_feat_space_n_rung_store@")
+ .add_property("n_rung_generate", &FeatureSpace::n_rung_generate, "@DocString_feat_space_n_rung_generate@")
+ ;
+ #endif
}
void sisso::feature_creation::registerUnit()
@@ -801,12 +830,12 @@ void sisso::feature_creation::node::registerSixPowNode()
void sisso::descriptor_identifier::registerModel()
{
- np::ndarray (Model::*eval_many_dict)(py::dict) = &Model::eval_many_py;
- np::ndarray (Model::*eval_many_ndarr)(np::ndarray) = &Model::eval_many_py;
+ np::ndarray (Model::*eval_many_dict)(py::dict) const = &Model::eval_many_py;
+ np::ndarray (Model::*eval_many_ndarr)(np::ndarray) const = &Model::eval_many_py;
- double (Model::*eval_ndarr)(np::ndarray) = &Model::eval_py;
- double (Model::*eval_list)(py::list) = &Model::eval_py;
- double (Model::*eval_dict)(py::dict) = &Model::eval_py;
+ double (Model::*eval_ndarr)(np::ndarray) const = &Model::eval_py;
+ double (Model::*eval_list)(py::list) const = &Model::eval_py;
+ double (Model::*eval_dict)(py::dict) const = &Model::eval_py;
class_<sisso::descriptor_identifier::Model_Wrap, boost::noncopyable>("Model", no_init)
.def("eval_many", eval_many_dict)
.def("eval_many", eval_many_ndarr)
diff --git a/src/python/descriptor_identifier/Model.cpp b/src/python/descriptor_identifier/Model.cpp
index e0a4231dde7cb7a2577ac6d56fa29021e42a1018..2af0586f21823c2a5f8c95fdc7418a3cc2658427 100644
--- a/src/python/descriptor_identifier/Model.cpp
+++ b/src/python/descriptor_identifier/Model.cpp
@@ -1,6 +1,6 @@
#include <descriptor_identifier/Model/Model.hpp>
-np::ndarray Model::eval_many_py(np::ndarray x_in)
+np::ndarray Model::eval_many_py(np::ndarray x_in) const
{
if(x_in.get_nd() != 2)
{
@@ -36,7 +36,7 @@ np::ndarray Model::eval_many_py(np::ndarray x_in)
return python_conv_utils::to_ndarray<double>(eval(x));
}
-np::ndarray Model::eval_many_py(py::dict x_in)
+np::ndarray Model::eval_many_py(py::dict x_in) const
{
std::map<std::string, std::vector<double>> x;
py::list keys = x_in.keys();
diff --git a/src/python/feature_creation/FeatureSpace.cpp b/src/python/feature_creation/FeatureSpace.cpp
index 4fa6f9147351c23019e4623f989c26092c0cea97..1bf6218883278acfb7006e3d0c5e72e788a3cb2c 100644
--- a/src/python/feature_creation/FeatureSpace.cpp
+++ b/src/python/feature_creation/FeatureSpace.cpp
@@ -1,5 +1,6 @@
#include <feature_creation/feature_space/FeatureSpace.hpp>
+#ifdef PARAMETERIZE
FeatureSpace::FeatureSpace(
py::list phi_0,
py::list allowed_ops,
@@ -83,6 +84,83 @@ FeatureSpace::FeatureSpace(
{
initialize_fs();
}
+#else
+FeatureSpace::FeatureSpace(
+ py::list phi_0,
+ py::list allowed_ops,
+ py::list prop,
+ py::list task_sizes,
+ std::string project_type,
+ int max_phi,
+ int n_sis_select,
+ int max_store_rung,
+ int n_rung_generate,
+ double cross_corr_max,
+ double min_abs_feat_val,
+ double max_abs_feat_val
+):
+ _phi(python_conv_utils::shared_ptr_vec_from_list<Node, FeatureNode>(phi_0)),
+ _phi_0(_phi),
+ _allowed_ops(python_conv_utils::from_list<std::string>(allowed_ops)),
+ _prop(python_conv_utils::from_list<double>(prop)),
+ _scores(py::len(phi_0), 0.0),
+ _task_sizes(python_conv_utils::from_list<int>(task_sizes)),
+ _start_gen(1, 0),
+ _feature_space_file("feature_space/selected_features.txt"),
+ _feature_space_summary_file("feature_space/SIS_summary.txt"),
+ _project_type(project_type),
+ _mpi_comm(mpi_setup::comm),
+ _cross_cor_max(cross_corr_max),
+ _l_bound(min_abs_feat_val),
+ _u_bound(max_abs_feat_val),
+ _max_phi(max_phi),
+ _n_sis_select(n_sis_select),
+ _n_feat(py::len(phi_0)),
+ _n_rung_store(max_store_rung),
+ _n_rung_generate(n_rung_generate),
+ _n_samp(_phi[0]->n_samp())
+{
+ initialize_fs();
+}
+
+FeatureSpace::FeatureSpace(
+ py::list phi_0,
+ py::list allowed_ops,
+ np::ndarray prop,
+ py::list task_sizes,
+ std::string project_type,
+ int max_phi,
+ int n_sis_select,
+ int max_store_rung,
+ int n_rung_generate,
+ double cross_corr_max,
+ double min_abs_feat_val,
+ double max_abs_feat_val
+):
+ _phi(python_conv_utils::shared_ptr_vec_from_list<Node, FeatureNode>(phi_0)),
+ _phi_0(_phi),
+ _allowed_ops(python_conv_utils::from_list<std::string>(allowed_ops)),
+ _prop(python_conv_utils::from_ndarray<double>(prop)),
+ _scores(py::len(phi_0), 0.0),
+ _task_sizes(python_conv_utils::from_list<int>(task_sizes)),
+ _start_gen(1, 0),
+ _feature_space_file("feature_space/selected_features.txt"),
+ _feature_space_summary_file("feature_space/SIS_summary.txt"),
+ _project_type(project_type),
+ _mpi_comm(mpi_setup::comm),
+ _cross_cor_max(cross_corr_max),
+ _l_bound(min_abs_feat_val),
+ _u_bound(max_abs_feat_val),
+ _max_phi(max_phi),
+ _n_sis_select(n_sis_select),
+ _n_feat(py::len(phi_0)),
+ _n_rung_store(max_store_rung),
+ _n_rung_generate(n_rung_generate),
+ _n_samp(_phi[0]->n_samp())
+{
+ initialize_fs();
+}
+#endif
FeatureSpace::FeatureSpace(
std::string feature_file,
py::list phi_0,