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OffsprinUpdater update #40

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622a2d0
sparsity initial value is set to 0
Gromwud Sep 19, 2025
f8aeac4
OffspringUpdater update
Gromwud Sep 19, 2025
32f8b04
Merge remote-tracking branch 'origin/main'
Gromwud Sep 19, 2025
51b950b
Division by zero bugfix
Gromwud Sep 19, 2025
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6 changes: 3 additions & 3 deletions epde/interface/equation_translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@ def _(text_form : str, pool, all_vars: List[str], use_pic: bool = False):
metaparameters={'terms_number': {'optimizable': False, 'value': len(term_list)},
'max_factors_in_term': {'optimizable': False, 'value': max_factors}}
for var_key in all_vars:
metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 1.}
metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 0.}


equation = Equation(pool=pool, basic_structure=term_list, var_to_explain = all_vars[0],
Expand Down Expand Up @@ -95,7 +95,7 @@ def _(text_form : dict, pool, all_vars: List[str], use_pic: bool = False):
metaparameters={'terms_number': {'optimizable': False, 'value': len(term_list)},
'max_factors_in_term': {'optimizable': False, 'value': max_factors}}
for var_key in all_vars:
metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 1.}
metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 0.}

equation = Equation(pool = pool, basic_structure = term_list, var_to_explain = var_key,
metaparameters = metaparameters)
Expand Down Expand Up @@ -185,7 +185,7 @@ def __init__(self, lp_terms : Union[list, tuple, dict], rp_term : Union[list, tu
metaparameters={'terms_number': {'optimizable': False, 'value': len(term_list)},
'max_factors_in_term': {'optimizable': False, 'value': max_factors}}
for var_key in all_vars:
metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 1.}
metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 0.}

equation = Equation(pool=pool, basic_structure=terms_aggregated,
metaparameters=metaparameters)
Expand Down
37 changes: 31 additions & 6 deletions epde/operators/common/fitness.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,7 +200,11 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
for start_idx in range(0, num_horizons, step_size):
end_idx = start_idx + window_size
target_window = target_vals[start_idx:end_idx]
eq_window_weights.append(np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2)))))
if np.isclose(np.sqrt(np.mean(np.power(_, 2))), 0, atol=1e-10):
window_stability = np.abs(np.std(target_window))
else:
window_stability = np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2))))
eq_window_weights.append(window_stability)
lr = np.mean(eq_window_weights)
else:
features = self.feature_reshape(features_vals)
Expand All @@ -212,7 +216,11 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
estimator.fit(feature_window, target_window, sample_weight=self.g_fun_vals[start_idx:end_idx])
valuable_weights = estimator.coef_[:-1]
eq_window_weights.append(valuable_weights)
eq_cv = np.array([np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2)))) for _ in zip(*eq_window_weights)])
eq_cv = np.array([
np.abs(np.std(_)) if np.isclose(np.sqrt(np.mean(np.power(_, 2))), 0, atol=1e-10)
else np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2))))
for _ in zip(*eq_window_weights)
])
lr = eq_cv.mean()

elif target_vals.ndim == 2:
Expand All @@ -233,7 +241,11 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
target_window = target_vals[start_idx:end_idx, :].reshape(-1)
else:
target_window = target_vals[:, start_idx:end_idx].reshape(-1)
eq_window_weights.append(np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2)))))
if np.isclose(np.sqrt(np.mean(np.power(_, 2))), 0, atol=1e-10):
window_stability = np.abs(np.std(target_window))
else:
window_stability = np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2))))
eq_window_weights.append(window_stability)
lr += np.mean(eq_window_weights)
else:
features = self.feature_reshape(features_vals)
Expand All @@ -250,7 +262,11 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
estimator.fit(feature_window, target_window, sample_weight=self.g_fun_vals.reshape(*data_shape, -1)[:, start_idx:end_idx].reshape(-1))
valuable_weights = estimator.coef_[:-1]
eq_window_weights.append(valuable_weights)
eq_cv = np.array([np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2)))) for _ in zip(*eq_window_weights)])
eq_cv = np.array([
np.abs(np.std(_)) if np.isclose(np.sqrt(np.mean(np.power(_, 2))), 0, atol=1e-10)
else np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2))))
for _ in zip(*eq_window_weights)
])
lr += eq_cv.mean()

elif target_vals.ndim == 3:
Expand All @@ -269,7 +285,11 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
target_window = target_vals[:, start_idx:end_idx, :].reshape(-1)
else:
target_window = target_vals[:, :, start_idx:end_idx].reshape(-1)
eq_window_weights.append(np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2)))))
if np.isclose(np.sqrt(np.mean(np.power(_, 2))), 0, atol=1e-10):
window_stability = np.abs(np.std(target_window))
else:
window_stability = np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2))))
eq_window_weights.append(window_stability)
lr += np.mean(eq_window_weights)
else:
features = self.feature_reshape(features_vals)
Expand All @@ -290,7 +310,12 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
estimator.fit(feature_window, target_window, sample_weight=self.g_fun_vals.reshape(*data_shape, -1)[:, :, start_idx:end_idx].reshape(-1))
valuable_weights = estimator.coef_[:-1]
eq_window_weights.append(valuable_weights)
eq_cv = np.array([np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2)))) for _ in zip(*eq_window_weights)])
eq_cv = np.array([
np.abs(np.std(_)) if np.isclose(np.sqrt(np.mean(np.power(_, 2))), 0, atol=1e-10)
else np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2))))
for _ in zip(*eq_window_weights)
])

lr += eq_cv.mean()

objective.fitness_calculated = True
Expand Down
47 changes: 21 additions & 26 deletions epde/operators/multiobjective/moeadd_specific.py
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Original file line number Diff line number Diff line change
Expand Up @@ -343,38 +343,33 @@ def best_obj_values(levels : ParetoLevels):

class OffspringUpdater(CompoundOperator):
key = 'ParetoLevelUpdater'
def apply(self, objective : ParetoLevels, arguments : dict):
self_args, subop_args = self.parse_suboperator_args(arguments = arguments)

def apply(self, objective: ParetoLevels, arguments: dict):
self_args, subop_args = self.parse_suboperator_args(arguments=arguments)

while objective.unplaced_candidates:
offspring = objective.unplaced_candidates.pop()
attempt = 1; attempt_limit = self.params['attempt_limit']
attempt = 1;
attempt_limit = self.params['attempt_limit']
temp_offspring = self.suboperators['chromosome_mutation'].apply(objective=offspring,
arguments=subop_args['chromosome_mutation'])
while True:
temp_offspring = self.suboperators['chromosome_mutation'].apply(objective = 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 all([temp_offspring != solution for solution in objective.population]):
self.suboperators['pareto_level_updater'].apply(objective = (temp_offspring, objective),
arguments = subop_args['pareto_level_updater'])
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 all([not np.allclose(temp_offspring.obj_fun, solution.obj_fun) for solution in objective.population]):
self.suboperators['pareto_level_updater'].apply(objective=(temp_offspring, objective),
arguments=subop_args['pareto_level_updater'])
break
elif attempt >= attempt_limit:
# print(temp_offspring.text_form)
# print('-----------------------')
# for idx, individual in enumerate(objective.population):
# print(f'Individual {idx}')
# print(individual.text_form)
# print('-----------------------')
# raise Exception('Can not place individual into the population. Try decreasing population size or increasing token variety. ')
print('The algorithm had issues with generating unique offsprings, allowed replication.')
self.suboperators['pareto_level_updater'].apply(objective = (temp_offspring, objective),
arguments = subop_args['pareto_level_updater'])

break
# print('The algorithm had issues with generating unique offsprings.')
temp_offspring.create()
# temp_offspring.reset_state()
attempt = 1
self.suboperators['chromosome_mutation'].apply(objective=temp_offspring,
arguments=subop_args['chromosome_mutation'])
attempt += 1
return objective

Expand Down