refactor base_pipeline forbidden conditions

Author: ravinkohli

autoPyTorch/pipeline/base_pipeline.py

@@ -1,10 +1,12 @@
+from copy import copy
 import warnings
 from abc import ABCMeta
 from collections import Counter
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from ConfigSpace import Configuration
 from ConfigSpace.configuration_space import ConfigurationSpace
+from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
 
 import numpy as np
 
@@ -295,6 +297,67 @@ def _get_hyperparameter_search_space(self,
         """
         raise NotImplementedError()
 
+    def _add_forbidden_conditions(self, cs):
+        """
+        Add forbidden conditions to ensure valid configurations.
+        Currently, Learned Entity Embedding is only valid when encoder is one hot encoder
+        and CyclicLR is disabled when using stochastic weight averaging and snapshot
+        ensembling.
+
+        Args:
+            cs (ConfigurationSpace):
+                Configuration space to which forbidden conditions are added.
+
+        """
+
+        # Learned Entity Embedding is only valid when encoder is one hot encoder
+        if 'network_embedding' in self.named_steps.keys() and 'encoder' in self.named_steps.keys():
+            embeddings = cs.get_hyperparameter('network_embedding:__choice__').choices
+            if 'LearnedEntityEmbedding' in embeddings:
+                encoders = cs.get_hyperparameter('encoder:__choice__').choices
+                possible_default_embeddings = copy(list(embeddings))
+                del possible_default_embeddings[possible_default_embeddings.index('LearnedEntityEmbedding')]
+
+                for encoder in encoders:
+                    if encoder == 'OneHotEncoder':
+                        continue
+                    while True:
+                        try:
+                            cs.add_forbidden_clause(ForbiddenAndConjunction(
+                                ForbiddenEqualsClause(cs.get_hyperparameter(
+                                    'network_embedding:__choice__'), 'LearnedEntityEmbedding'),
+                                ForbiddenEqualsClause(cs.get_hyperparameter('encoder:__choice__'), encoder)
+                            ))
+                            break
+                        except ValueError:
+                            # change the default and try again
+                            try:
+                                default = possible_default_embeddings.pop()
+                            except IndexError:
+                                raise ValueError("Cannot find a legal default configuration")
+                            cs.get_hyperparameter('network_embedding:__choice__').default_value = default
+
+        # Disable CyclicLR until todo is completed.
+        if 'lr_scheduler' in self.named_steps.keys() and 'trainer' in self.named_steps.keys():
+            trainers = cs.get_hyperparameter('trainer:__choice__').choices
+            for trainer in trainers:
+                available_schedulers = cs.get_hyperparameter('lr_scheduler:__choice__').choices
+                # TODO: update cyclic lr to use n_restarts and adjust according to batch size
+                cyclic_lr_name = 'CyclicLR'
+                if cyclic_lr_name in available_schedulers:
+                    # disable snapshot ensembles and stochastic weight averaging
+                    cs.add_forbidden_clause(ForbiddenAndConjunction(
+                        ForbiddenEqualsClause(cs.get_hyperparameter(
+                            f'trainer:{trainer}:use_snapshot_ensemble'), True),
+                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
+                    ))
+                    cs.add_forbidden_clause(ForbiddenAndConjunction(
+                        ForbiddenEqualsClause(cs.get_hyperparameter(
+                            f'trainer:{trainer}:use_stochastic_weight_averaging'), True),
+                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
+                    ))
+        return cs
+
     def __repr__(self) -> str:
         """Retrieves a str representation of the current pipeline
 

autoPyTorch/pipeline/image_classification.py

@@ -156,6 +156,7 @@ def _get_hyperparameter_search_space(self,
 
         # Here we add custom code, like this with this
         # is not a valid configuration
+        cs = self._add_forbidden_conditions(cs)
 
         self.configuration_space = cs
         self.dataset_properties = dataset_properties

autoPyTorch/pipeline/tabular_classification.py

@@ -3,7 +3,6 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
-from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
 
 import numpy as np
 
@@ -261,56 +260,9 @@ def _get_hyperparameter_search_space(self,
             cs=cs, dataset_properties=dataset_properties,
             exclude=exclude, include=include, pipeline=self.steps)
 
-        # Here we add custom code, that is used to ensure valid configurations, For example
-        # Learned Entity Embedding is only valid when encoder is one hot encoder
-        if 'network_embedding' in self.named_steps.keys() and 'encoder' in self.named_steps.keys():
-            embeddings = cs.get_hyperparameter('network_embedding:__choice__').choices
-            if 'LearnedEntityEmbedding' in embeddings:
-                encoders = cs.get_hyperparameter('encoder:__choice__').choices
-                possible_default_embeddings = copy.copy(list(embeddings))
-                del possible_default_embeddings[possible_default_embeddings.index('LearnedEntityEmbedding')]
-
-                for encoder in encoders:
-                    if encoder == 'OneHotEncoder':
-                        continue
-                    while True:
-                        try:
-                            cs.add_forbidden_clause(ForbiddenAndConjunction(
-                                ForbiddenEqualsClause(cs.get_hyperparameter(
-                                    'network_embedding:__choice__'), 'LearnedEntityEmbedding'),
-                                ForbiddenEqualsClause(cs.get_hyperparameter('encoder:__choice__'), encoder)
-                            ))
-                            break
-                        except ValueError:
-                            # change the default and try again
-                            try:
-                                default = possible_default_embeddings.pop()
-                            except IndexError:
-                                raise ValueError("Cannot find a legal default configuration")
-                            cs.get_hyperparameter('network_embedding:__choice__').default_value = default
-
-        # Disable CyclicLR until todo is completed.
-        if 'lr_scheduler' in self.named_steps.keys() and 'trainer' in self.named_steps.keys():
-            trainers = cs.get_hyperparameter('trainer:__choice__').choices
-            for trainer in trainers:
-                available_schedulers = self.named_steps['lr_scheduler'].get_available_components(
-                    dataset_properties=dataset_properties,
-                    exclude=exclude if bool(exclude) else None,
-                    include=include if bool(include) else None)
-                # TODO: update cyclic lr to use n_restarts and adjust according to batch size
-                cyclic_lr_name = 'CyclicLR'
-                if cyclic_lr_name in available_schedulers:
-                    # disable snapshot ensembles and stochastic weight averaging
-                    cs.add_forbidden_clause(ForbiddenAndConjunction(
-                        ForbiddenEqualsClause(cs.get_hyperparameter(
-                            f'trainer:{trainer}:use_snapshot_ensemble'), True),
-                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
-                    ))
-                    cs.add_forbidden_clause(ForbiddenAndConjunction(
-                        ForbiddenEqualsClause(cs.get_hyperparameter(
-                            f'trainer:{trainer}:use_stochastic_weight_averaging'), True),
-                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
-                    ))
+        # Here we add custom code, like this with this
+        # is not a valid configuration
+        cs = self._add_forbidden_conditions(cs)
 
         self.configuration_space = cs
         self.dataset_properties = dataset_properties

autoPyTorch/pipeline/tabular_regression.py

@@ -3,7 +3,6 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
-from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
 
 import numpy as np
 
@@ -210,56 +209,7 @@ def _get_hyperparameter_search_space(self,
 
         # Here we add custom code, like this with this
         # is not a valid configuration
-        # Learned Entity Embedding is only valid when encoder is one hot encoder
-        if 'network_embedding' in self.named_steps.keys() and 'encoder' in self.named_steps.keys():
-            embeddings = cs.get_hyperparameter('network_embedding:__choice__').choices
-            if 'LearnedEntityEmbedding' in embeddings:
-                encoders = cs.get_hyperparameter('encoder:__choice__').choices
-                default = cs.get_hyperparameter('network_embedding:__choice__').default_value
-                possible_default_embeddings = copy.copy(list(embeddings))
-                del possible_default_embeddings[possible_default_embeddings.index(default)]
-
-                for encoder in encoders:
-                    if encoder == 'OneHotEncoder':
-                        continue
-                    while True:
-                        try:
-                            cs.add_forbidden_clause(ForbiddenAndConjunction(
-                                ForbiddenEqualsClause(cs.get_hyperparameter(
-                                    'network_embedding:__choice__'), 'LearnedEntityEmbedding'),
-                                ForbiddenEqualsClause(cs.get_hyperparameter('encoder:__choice__'), encoder)
-                            ))
-                            break
-                        except ValueError:
-                            # change the default and try again
-                            try:
-                                default = possible_default_embeddings.pop()
-                            except IndexError:
-                                raise ValueError("Cannot find a legal default configuration")
-                            cs.get_hyperparameter('network_embedding:__choice__').default_value = default
-
-        # Disable CyclicLR until todo is completed.
-        if 'lr_scheduler' in self.named_steps.keys() and 'trainer' in self.named_steps.keys():
-            trainers = cs.get_hyperparameter('trainer:__choice__').choices
-            for trainer in trainers:
-                available_schedulers = self.named_steps['lr_scheduler'].get_available_components(
-                    dataset_properties=dataset_properties,
-                    exclude=exclude if bool(exclude) else None,
-                    include=include if bool(include) else None)
-                # TODO: update cyclic lr to use n_restarts and adjust according to batch size
-                cyclic_lr_name = 'CyclicLR'
-                if cyclic_lr_name in available_schedulers:
-                    # disable snapshot ensembles and stochastic weight averaging
-                    cs.add_forbidden_clause(ForbiddenAndConjunction(
-                        ForbiddenEqualsClause(cs.get_hyperparameter(
-                            f'trainer:{trainer}:use_snapshot_ensemble'), True),
-                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
-                    ))
-                    cs.add_forbidden_clause(ForbiddenAndConjunction(
-                        ForbiddenEqualsClause(cs.get_hyperparameter(
-                            f'trainer:{trainer}:use_stochastic_weight_averaging'), True),
-                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
-                    ))
+        cs = self._add_forbidden_conditions(cs)
 
         self.configuration_space = cs
         self.dataset_properties = dataset_properties