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remove switching methods
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@dilpath
dilpath committed Nov 30, 2023
1 parent c19bd9f commit efa17a5
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123 changes: 0 additions & 123 deletions petab_select/candidate_space.py
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Original file line number Diff line number Diff line change
Expand Up @@ -560,102 +560,6 @@ class BackwardCandidateSpace(ForwardCandidateSpace):
direction = -1


#class BidirectionalCandidateSpace(ForwardCandidateSpace):
# """The bidirectional method class.
#
# Attributes:
# method_history:
# The history of models that were found at each search.
# A list of dictionaries, where each dictionary contains keys for the `METHOD`
# and the list of `MODELS`.
# """
#
# # TODO refactor method to inherit from governing_method if not specified
# # by constructor argument -- remove from here.
# method = Method.BIDIRECTIONAL
# governing_method = Method.BIDIRECTIONAL
# retry_model_space_search_if_no_models = True
#
# def __init__(
# self,
# *args,
# initial_method: Method = Method.FORWARD,
# **kwargs,
# ):
# super().__init__(*args, **kwargs)
#
# # FIXME cannot access from CLI
# # FIXME probably fine to replace `self.initial_method`
# # with `self.method` here. i.e.:
# # 1. change `method` to `Method.FORWARD
# # 2. change signature to `initial_method: Method = None`
# # 3. change code here to `if initial_method is not None: self.method = initial_method`
# self.initial_method = initial_method
#
# self.method_history: List[Dict[str, Union[Method, List[Model]]]] = []
#
# def update_method(self, method: Method):
# if method == Method.FORWARD:
# self.direction = 1
# elif method == Method.BACKWARD:
# self.direction = -1
# else:
# raise NotImplementedError(
# f'Bidirectional direction must be either `Method.FORWARD` or `Method.BACKWARD`, not {method}.'
# )
#
# self.method = method
#
# def switch_method(self):
# if self.method == Method.FORWARD:
# method = Method.BACKWARD
# elif self.method == Method.BACKWARD:
# method = Method.FORWARD
#
# self.update_method(method=method)
#
# def setup_before_model_subspaces_search(self):
# # If previous search found no models, then switch method.
# previous_search = (
# None if not self.method_history else self.method_history[-1]
# )
# if previous_search is None:
# self.update_method(self.initial_method)
# return
#
# self.update_method(previous_search[METHOD])
# if not previous_search[MODELS]:
# self.switch_method()
# self.retry_model_space_search_if_no_models = False
#
# def setup_after_model_subspaces_search(self):
# current_search = {
# METHOD: self.method,
# MODELS: self.models,
# }
# self.method_history.append(current_search)
# self.method = self.governing_method
#
# def wrap_search_subspaces(self, search_subspaces):
# def wrapper():
# def iterate():
# self.setup_before_model_subspaces_search()
# search_subspaces()
# self.setup_after_model_subspaces_search()
#
# # Repeat until models are found or switching doesn't help.
# iterate()
# while (
# not self.models and self.retry_model_space_search_if_no_models
# ):
# iterate()
#
# # Reset flag for next time.
# self.retry_model_space_search_if_no_models = True
#
# return wrapper


class FamosCandidateSpace(CandidateSpace):
"""The FAMoS method class.
Expand Down Expand Up @@ -1256,33 +1160,6 @@ def wrapper():
return wrapper


## TODO rewrite so BidirectionalCandidateSpace inherits from ForwardAndBackwardCandidateSpace
## instead
#class ForwardAndBackwardCandidateSpace(BidirectionalCandidateSpace):
# method = Method.FORWARD_AND_BACKWARD
# governing_method = Method.FORWARD_AND_BACKWARD
# retry_model_space_search_if_no_models = False
#
# def __init__(self, *args, **kwargs):
# super().__init__(*args, **kwargs, initial_method=None)
#
# def wrap_search_subspaces(self, search_subspaces):
# def wrapper():
# for method in [Method.FORWARD, Method.BACKWARD]:
# self.update_method(method=method)
# search_subspaces()
# self.setup_after_model_subspaces_search()
#
# return wrapper
#
# # Disable unused interface
# setup_before_model_subspaces_search = None
# switch_method = None
#
# def setup_after_model_space_search(self):
# pass


class LateralCandidateSpace(CandidateSpace):
"""Find models with the same number of estimated parameters."""

Expand Down
28 changes: 0 additions & 28 deletions petab_select/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,12 +48,6 @@
# COMPARED_MODEL_ID = 'compared_'+MODEL_ID
YAML_FILENAME = 'yaml'

# FORWARD = 'forward'
# BACKWARD = 'backward'
# BIDIRECTIONAL = 'bidirectional'
# LATERAL = 'lateral'


# DISTANCES = {
# FORWARD: {
# 'l1': 1,
Expand All @@ -70,20 +64,6 @@
# }

CRITERIA = 'criteria'
# FIXME remove, change all uses to Enum below
# AIC = 'AIC'
# AICC = 'AICc'
# BIC = 'BIC'
# AKAIKE_INFORMATION_CRITERION = AIC
# CORRECTED_AKAIKE_INFORMATION_CRITERION = AICC
# BAYESIAN_INFORMATION_CRITERION = BIC
# LH = 'LH'
# LLH = 'LLH'
# NLLH = 'NLLH'
# LIKELIHOOD = LH
# LOG_LIKELIHOOD = LLH
# NEGATIVE_LOG_LIKELIHOOD = NLLH


PARAMETERS = 'parameters'
# PARAMETER_ESTIMATE = 'parameter_estimate'
Expand Down Expand Up @@ -119,11 +99,9 @@ class Method(str, Enum):
"""String literals for model selection methods."""

BACKWARD = 'backward'
#BIDIRECTIONAL = 'bidirectional'
BRUTE_FORCE = 'brute_force'
FAMOS = 'famos'
FORWARD = 'forward'
#FORWARD_AND_BACKWARD = 'forward_and_backward'
LATERAL = 'lateral'
MOST_DISTANT = 'most_distant'

Expand All @@ -142,25 +120,19 @@ class Criterion(str, Enum):
# Methods that move through model space by taking steps away from some model.
STEPWISE_METHODS = [
Method.BACKWARD,
#Method.BIDIRECTIONAL,
Method.FORWARD,
#Method.FORWARD_AND_BACKWARD,
Method.LATERAL,
]
# Methods that require an initial model.
INITIAL_MODEL_METHODS = [
Method.BACKWARD,
#Method.BIDIRECTIONAL,
Method.FORWARD,
#Method.FORWARD_AND_BACKWARD,
Method.LATERAL,
]

# Virtual initial models can be used to initialize some initial model methods.
VIRTUAL_INITIAL_MODEL = 'virtual_initial_model'
VIRTUAL_INITIAL_MODEL_METHODS = [
Method.BACKWARD,
#Method.BIDIRECTIONAL,
Method.FORWARD,
#Method.FORWARD_AND_BACKWARD,
]
126 changes: 0 additions & 126 deletions test/candidate_space/test_candidate_space.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,129 +107,3 @@ def model_space(calibrated_model_space) -> pd.DataFrame:
df = get_model_space_df(df)
model_space = ModelSpace.from_df(df)
return model_space


#def test_bidirectional(
# model_space, calibrated_model_space, ordered_model_parameterizations
#):
# criterion = Criterion.AIC
# model_id_length = one(
# set([len(model_id) for model_id in calibrated_model_space])
# )
#
# candidate_space = BidirectionalCandidateSpace()
# calibrated_models = []
#
# # Perform bidirectional search until no more models are found.
# search_iterations = 0
# while True:
# new_calibrated_models = []
# search_iterations += 1
#
# # Get models.
# model_space.search(candidate_space)
#
# # Calibrate models.
# for model in candidate_space.models:
# model_id = model.model_subspace_id[-model_id_length:]
# model.set_criterion(
# criterion=criterion, value=calibrated_model_space[model_id]
# )
# new_calibrated_models.append(model)
#
# # End if no more models were found.
# if not new_calibrated_models:
# break
#
# # Get next predecessor model as best of new models.
# best_new_model = None
# for model in new_calibrated_models:
# if best_new_model is None:
# best_new_model = model
# continue
# if default_compare(
# model0=best_new_model, model1=model, criterion=criterion
# ):
# best_new_model = model
#
# # Set next predecessor and exclusions.
# calibrated_model_hashes = [
# model.get_hash() for model in calibrated_models
# ]
# candidate_space.reset(
# predecessor_model=best_new_model,
# exclusions=calibrated_model_hashes,
# )
#
# # exclude calibrated model hashes from model space too?
# model_space.exclude_model_hashes(model_hashes=calibrated_model_hashes)
#
# # Check that expected models are found at each iteration.
# (
# good_model_parameterizations_ascending,
# bad_model_parameterizations,
# ) = ordered_model_parameterizations
# search_iteration = 0
# for history_item in candidate_space.method_history:
# models = history_item[MODELS]
# if not models:
# continue
# model_parameterizations = [
# model.model_subspace_id[-5:] for model in models
# ]
#
# good_model_parameterization = good_model_parameterizations_ascending[
# search_iteration
# ]
# # The expected good model was found.
# assert good_model_parameterization in model_parameterizations
# model_parameterizations.remove(good_model_parameterization)
#
# if search_iteration == 0:
# # All parameterizations have been correctly identified and removed.
# assert not model_parameterizations
# search_iteration += 1
# continue
#
# previous_model_parameterization = (
# good_model_parameterizations_ascending[search_iteration - 1]
# )
#
# # The expected bad model is also found.
# # If a bad model is the same dimension and also represents a similar stepwise move away from the previous
# # model parameterization, it should also be in the parameterizations.
# for bad_model_parameterization in bad_model_parameterizations:
# # Skip if different dimensions
# if sum(map(int, bad_model_parameterization)) != sum(
# map(int, good_model_parameterization)
# ):
# continue
# # Skip if different distances from previous model parameterization
# if sum(
# [
# a != b
# for a, b in zip(
# bad_model_parameterization,
# previous_model_parameterization,
# )
# ]
# ) != sum(
# [
# a != b
# for a, b in zip(
# good_model_parameterization,
# previous_model_parameterization,
# )
# ]
# ):
# continue
# assert bad_model_parameterization in model_parameterizations
# model_parameterizations.remove(bad_model_parameterization)
#
# # All parameterizations have been correctly identified and removed.
# assert not model_parameterizations
# search_iteration += 1
#
# # End test if all good models were found in the correct order.
# if search_iteration >= len(good_model_parameterizations_ascending):
# break

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