Merge pull request #51 from Gromwud/main

Cumulitive update

Author: Maslyaev

epde/operators/common/coeff_calculation.py

@@ -82,9 +82,6 @@ def apply(self, objective : Equation, arguments : dict = None):
                 if weight_idx in nonzero_features_indexes:
                     weights[weight_idx] = valueable_weights[nonzero_features_indexes.index(weight_idx)]
             weights[-1] = valueable_weights[-1]
-            # nonzero_terms_mask = np.array([False if np.isclose(weight, 0) else True for weight in weights])
-            # weights = np.array([item if keep else 0 for item, keep in zip(weights, nonzero_terms_mask)])
-            # objective.weights_internal = np.array([item if keep else 0 for item, keep in zip(objective.weights_internal, nonzero_terms_mask[:-1])])
             objective.weights_final_evald = True
             objective.weights_final = weights
 

epde/operators/common/fitness.py

@@ -60,9 +60,7 @@ def apply(self, objective : Equation, arguments : dict):
                 if not (objective.structure[target_idx].contains_variable(objective.main_var_to_explain) and objective.structure[target_idx].contains_deriv(objective.main_var_to_explain)):
                     continue
                 objective.target_idx = target_idx
-                fitness = self.suboperators['fitness_calculation'].apply(objective,
-                                                                            arguments = subop_args['fitness_calculation'],
-                                                                            force_out_of_place = True)
+                fitness = self.suboperators['fitness_calculation'].apply(objective, arguments = subop_args['fitness_calculation'], force_out_of_place = True)
                 if fitness < min_fitness:
                     min_fitness = fitness
                     min_idx = target_idx
@@ -72,14 +70,10 @@ def apply(self, objective : Equation, arguments : dict):
 
             objective.weights_internal = weights_internal
             objective.target_idx = min_idx
-            # self.suboperators['fitness_calculation'].apply(objective, arguments = subop_args['fitness_calculation'])
-            # if not np.isclose(objective.fitness_value, max_fitness) and global_var.verbose.show_warnings:
-            #     warnings.warn('Reevaluation of fitness function for equation has obtained different result. Not an error, if ANN DE solver is used.')
             self.simplify_equation(objective)
             if objective.structure[objective.target_idx].contains_variable(objective.main_var_to_explain) and objective.structure[objective.target_idx].contains_deriv(objective.main_var_to_explain):
                 objective.is_correct_right_part = True
         else:
-            objective.reset_explaining_term(objective.target_idx)
             objective.right_part_selected = True
 
     def simplify_equation(self, objective: Equation):
@@ -88,18 +82,18 @@ def simplify_equation(self, objective: Equation):
         nonrs_terms = [term for i, term in enumerate(objective.structure) if i != objective.target_idx]
         nonzero_terms = [item for item, keep in zip(nonrs_terms, nonzero_terms_mask) if keep]
         nonzero_terms.append(objective.structure[objective.target_idx])
-        nonzero_terms_labels = [[term.cache_label[0]] if not isinstance(term.cache_label[0], tuple) else list(next(zip(*term.cache_label))) for term in nonzero_terms]
 
+        equation_terms = objective.described_variables
         # If amount nonzero terms is more than one -- get their intersection
-        if len(nonzero_terms) > 1:
-            common_factor = np.array(list(set.intersection(*map(set, nonzero_terms_labels)))).flatten()
+        if len(equation_terms) > 1:
+            common_factor = list(frozenset.intersection(*equation_terms))
             common_dim = []
             if len(common_factor) > 0:
                 # Find if this intersection in the same dimension (i.e. trigonometry functions) + it's minimal order
                 min_order = np.inf
                 for term in nonzero_terms:
                     for factor in term.structure:
-                        if factor.cache_label[0] == common_factor[0]:
+                        if factor.cache_label[0] == common_factor[0][0]:
                             if len(factor.params) > 1:
                                 common_dim.append(factor.params[-1])
                             if factor.cache_label[1][0] < min_order:
@@ -110,7 +104,7 @@ def simplify_equation(self, objective: Equation):
                         temp = deepcopy(term)
                         factors_simplified = []
                         for factor in term.structure:
-                            if factor.cache_label[0] == common_factor[0]:
+                            if factor.cache_label[0] == common_factor[0][0]:
                                 for i, value in enumerate(factor.params_description):
                                     if factor.params_description[i]["name"] == "power":
                                         factor.params[i] -= min_order

epde/operators/common/right_part_selection.py

@@ -8,7 +8,8 @@
 
 from typing import Union, Callable
 import numpy as np
-from sklearn.linear_model import Lasso
+from sklearn.linear_model import Lasso, LassoLars
+from pysindy import STLSQ
 
 import epde.globals as global_var
 from epde.operators.utils.template import CompoundOperator
@@ -60,15 +61,17 @@ def apply(self, objective : Equation, arguments : dict):
         # print(f'Metaparameter: {objective.metaparameters}, objective.metaparameters[("sparsity", objective.main_var_to_explain)]')
         self_args, subop_args = self.parse_suboperator_args(arguments = arguments)
 
-        estimator = Lasso(alpha = objective.metaparameters[('sparsity', objective.main_var_to_explain)]['value'],
-                          copy_X=True, fit_intercept=True, max_iter=1000,
-                          positive=False, precompute=False, random_state=None,
-                          selection='random', tol=0.0001, warm_start=False)
+        # estimator = Lasso(alpha = objective.metaparameters[('sparsity', objective.main_var_to_explain)]['value'],
+        #                   copy_X=True, fit_intercept=True, max_iter=1000,
+        #                   positive=False, precompute=False, random_state=None,
+        #                   selection='random', tol=0.0001, warm_start=False)
+        estimator = STLSQ(threshold=objective.metaparameters[('sparsity', objective.main_var_to_explain)]['value'],
+                          copy_X=True, unbias=True, max_iter=1000, alpha=0.05)
         _, target, features = objective.evaluate(normalize = True, return_val = False)
         self.g_fun_vals = global_var.grid_cache.g_func.reshape(-1)
 
         estimator.fit(features, target, sample_weight = self.g_fun_vals)
-        objective.weights_internal = estimator.coef_
+        objective.weights_internal = estimator.coef_[-1]
 
     def use_default_tags(self):
         self._tags = {'sparsity', 'gene level', 'no suboperators', 'inplace'}

epde/operators/common/sparsity.py

@@ -190,8 +190,7 @@ def apply(self, objective : Tuple[Union[SoEq, ParetoLevels]], arguments : dict):
                 most_crowded_domain = crowded_domains[np.argmax(PBIS)]
 
             if len(last_level_by_domains[most_crowded_domain]) == 1:
-                worst_solution = locate_pareto_worst(objective[1], self_args['weights'],
-                                                     self_args['best_obj'], self.params['PBI_penalty'])
+                worst_solution = last_level_by_domains[most_crowded_domain][0]
             else:
                 PBIS = np.fromiter(map(lambda solution: penalty_based_intersection(solution,
                                                                                    self_args['weights'][most_crowded_domain],
@@ -367,31 +366,34 @@ def apply(self, objective: ParetoLevels, arguments: dict):
 
         while objective.unplaced_candidates:
             offspring = objective.unplaced_candidates.pop()
-            attempt = 1
-            attempt_limit = self.params['attempt_limit']
+            attempt = 0
+            mutation_attempt_limit = self.params['mutation_attempt_limit']
+            offspring_attempt_limit = self.params['offspring_attempt_limit']
             temp_offspring = deepcopy(offspring)
             replaced = 0
             while True:
-                temp_offspring = self.suboperators['chromosome_mutation'].apply(objective=temp_offspring,
-                                                                                arguments=subop_args['chromosome_mutation'])
                 self.suboperators['right_part_selector'].apply(objective=temp_offspring,
                                                                arguments=subop_args['right_part_selector'])
-                self.suboperators['chromosome_fitness'].apply(objective=temp_offspring,
-                                                              arguments=subop_args['chromosome_fitness'])
-
-                if tuple(temp_offspring.obj_fun) not in objective.history:
+                temp_offspring.reset_state()
+                system = temp_offspring.described_variables
+                if system not in objective.history:
+                    self.suboperators['chromosome_fitness'].apply(objective=temp_offspring,
+                                                                  arguments=subop_args['chromosome_fitness'])
                     self.suboperators['pareto_level_updater'].apply(objective=(temp_offspring, objective),
                                                                     arguments=subop_args['pareto_level_updater'])
-                    objective.history.add(tuple(temp_offspring.obj_fun))
-                    # print(tuple(temp_offspring.obj_fun))
+                    objective.history.add(system)
+                    print(temp_offspring.obj_fun)
                     break
-                elif replaced == attempt_limit:
+                elif replaced == offspring_attempt_limit:
                     print("Could not generate unique offspring")
                     break
-                elif attempt == attempt_limit:
+                elif attempt == mutation_attempt_limit:
                     temp_offspring = deepcopy(offspring)
                     replaced += 1
                     attempt = 0
+                temp_offspring = self.suboperators['chromosome_mutation'].apply(objective=temp_offspring,
+                                                                                arguments=subop_args[
+                                                                                    'chromosome_mutation'])
                 attempt += 1
         return objective
 
@@ -437,15 +439,16 @@ def apply(self, objective : ParetoLevels, arguments : dict):
             for idx, candidate in enumerate(objective.unplaced_candidates):
                 self.suboperators['right_part_selector'].apply(objective = candidate,
                                                                 arguments = subop_args['right_part_selector'])
-                self.suboperators['chromosome_fitness'].apply(objective = objective.unplaced_candidates[idx],
-                                                              arguments = subop_args['chromosome_fitness'])
-                while tuple(candidate.obj_fun) in objective.history:
+                system = candidate.described_variables
+                while system in objective.history:
                     candidate.create()
                     self.suboperators['right_part_selector'].apply(objective=candidate,
                                                                    arguments=subop_args['right_part_selector'])
-                    self.suboperators['chromosome_fitness'].apply(objective=objective.unplaced_candidates[idx],
-                                                                  arguments=subop_args['chromosome_fitness'])
-                objective.history.add(tuple(candidate.obj_fun))
+                    system = candidate.described_variables
+                self.suboperators['chromosome_fitness'].apply(objective=candidate,
+                                                              arguments=subop_args['chromosome_fitness'])
+                objective.history.add(system)
+                print(candidate.obj_fun)
             objective.initial_placing()
 
             # TODO: consider carefully, where normalizer init shall be held. If here, only the initial values are employed

epde/operators/multiobjective/moeadd_specific.py

@@ -57,13 +57,13 @@ class EquationMutation(CompoundOperator):
     def apply(self, objective : Equation, arguments : dict):
         self_args, subop_args = self.parse_suboperator_args(arguments = arguments)
 
-        for term_idx in range(objective.n_immutable, len(objective.structure)):
-            if np.random.uniform(0, 1) <= self.params['r_mutation']:
-                objective.structure[term_idx] = self.suboperators['mutation'].apply(objective = (term_idx, objective),
-                                                                                    arguments = subop_args['mutation'])
-        # term_idx = np.random.choice(len(objective.structure))
-        # objective.structure[term_idx] = self.suboperators['mutation'].apply(objective=(term_idx, objective),
-        #                                                                     arguments=subop_args['mutation'])
+        # for term_idx in range(objective.n_immutable, len(objective.structure)):
+        #     if np.random.uniform(0, 1) <= self.params['r_mutation']:
+        #         objective.structure[term_idx] = self.suboperators['mutation'].apply(objective = (term_idx, objective),
+        #                                                                             arguments = subop_args['mutation'])
+        term_idx = np.random.choice(len(objective.structure))
+        objective.structure[term_idx] = self.suboperators['mutation'].apply(objective=(term_idx, objective),
+                                                                            arguments=subop_args['mutation'])
         return objective
 
     def use_default_tags(self):
@@ -76,11 +76,11 @@ class MetaparameterMutation(CompoundOperator):
     def apply(self, objective : Union[int, float], arguments : dict):
         self_args, subop_args = self.parse_suboperator_args(arguments = arguments)
 
-        altered_objective = np.random.normal(objective, scale = self.params['std'])
+        altered_objective = np.random.normal(objective, objective)
         if altered_objective < 0:
             altered_objective = - altered_objective
-        
-        return altered_objective
+
+        return np.float64(altered_objective)
 
     def use_default_tags(self):
         self._tags = {'mutation', 'gene level', 'no suboperators'}

epde/operators/multiobjective/mutations.py

@@ -10,7 +10,8 @@
         "number_of_neighbors" : 4
  	},
 	"ParetoLevelUpdater" : {
-    	"attempt_limit" : 5
+    	"mutation_attempt_limit" : 5,
+		"offspring_attempt_limit" : 5
 	},
 	"InitialParetoLevelSorting" : {
 

epde/operators/utils/parameters/default_parameters_multi_objective.json

@@ -175,25 +175,17 @@ def delete_point(self, point):
             None
         """
         new_levels = []
-        deleted = False
+        population_cleared = []
+        point_system = point.described_variables
         for level in self.levels:
             temp = []
             for element in level:
-                if not np.allclose(element.obj_fun, point.obj_fun) or deleted:
+                if element.described_variables != point_system:
                     temp.append(element)
-                else:
-                    deleted = True
+                    population_cleared.append(element)
             if not len(temp) == 0:
                 new_levels.append(temp)
 
-        population_cleared = []
-        deleted = False
-        for elem in self.population:
-            if not np.allclose(elem.obj_fun, point.obj_fun) or deleted:
-                population_cleared.append(elem)
-            else:
-                deleted = True
-                
         if len(population_cleared) != sum([len(level) for level in new_levels]):
             print(len(population_cleared), len(self.population), sum([len(level) for level in new_levels]))
             print('initial population', [solution.vals for solution in self.population], len([solution.vals for solution in self.population]), '\n')

epde/optimizers/moeadd/moeadd.py

@@ -215,7 +215,9 @@ def evaluate(self, structural, grids=None):
             self.prev_normalized = normalize
             value = super().evaluate(structural)
             if normalize:
-                value = value / np.linalg.norm(value, 2)
+                value = (value - np.mean(value)) / np.std(value)
+                # value = value / np.linalg.norm(value, 2)
+
             if np.all([len(factor.params) == 1 for factor in self.structure]) and grids is None:
                 # Место возможных проблем: сохранение/загрузка нормализованных данных
                 self.saved[normalize] = global_var.tensor_cache.add(self.cache_label, value, normalized=normalize)
@@ -580,7 +582,7 @@ def shifted_idx(idx):
     def reset_state(self, reset_right_part: bool = True):
         if reset_right_part:
             self.right_part_selected = False
-        self.weights_internal_evald = False
+        # self.weights_internal_evald = False
         self.weights_final_evald = False
         self.fitness_calculated = False
         self.stability_calculated = False
@@ -744,17 +746,28 @@ def state(self):
 
     @property
     def described_variables(self):
-        eps = 1e-7
         described = set()
         for term_idx, term in enumerate(self.structure):
+            cache_label = set()
             if term_idx == self.target_idx:
-                described.update({factor.family_type for factor in term.structure
-                                  if factor.is_deriv and factor.deriv_code != [None]})
+                for factor in term.structure:
+                    if len(factor.params) == 1:
+                        factor_label = (factor.cache_label[0])
+                    else:
+                        factor_label = (factor.cache_label[0], (factor.cache_label[1][-1]))
+                    cache_label.add(factor_label)
             else:
-                weight_idx = term_idx if term_idx < term_idx else term_idx - 1
-                if np.abs(self.weights_final[weight_idx]) > eps:
-                    described.update({factor.family_type for factor in term.structure
-                                      if factor.is_deriv and factor.deriv_code != [None]})
+                weight_idx = term_idx if term_idx < self.target_idx else term_idx - 1
+                if not np.isclose(self.weights_internal[weight_idx], 0):
+                    for factor in term.structure:
+                        if len(factor.params) == 1:
+                            factor_label = (factor.cache_label[0])
+                        else:
+                            factor_label = (factor.cache_label[0], (factor.cache_label[1][-1]))
+                        cache_label.add(factor_label)
+            if len(cache_label) > 0:
+                cache_label = frozenset(cache_label)
+                described.add(cache_label)
         described = frozenset(described)
         return described
 
@@ -1110,6 +1123,13 @@ def __iter__(self):
     def fitness_calculated(self):
         return all([equation.fitness_calculated for equation in self.vals])
 
+    @property
+    def described_variables(self):
+        equations_caches = set()
+        for equation in self.vals:
+            equations_caches.add(equation.described_variables)
+        return frozenset(equations_caches)
+
 
 class SoEqIterator(object):
     def __init__(self, system: SoEq):