neuralogic.core package

Subpackages

• neuralogic.core.builder package
  • Submodules
  • neuralogic.core.builder.builder module
    • Builder
      • Builder.build_model()
      • Builder.build_model_from_file()
      • Builder.get_builders()
      • Builder.ground_from_logic_samples()
      • Builder.ground_from_sources()
      • Builder.neuralize()
    • stream_to_list()
  • neuralogic.core.builder.components module
    • Atom
      • Atom.arity
      • Atom.node_type()
      • Atom.predicate
      • Atom.substitutions
    • Grounding
      • Grounding.atoms
      • Grounding.draw()
      • Grounding.get_atoms()
    • NeuralSample
      • NeuralSample.draw()
      • NeuralSample.get_fact()
      • NeuralSample.get_neurons()
      • NeuralSample.neurons
      • NeuralSample.set_fact_value()
      • NeuralSample.target
    • Neuron
      • Neuron.gradient
      • Neuron.substitutions
      • Neuron.value
    • NeuronType
      • NeuronType.Aggregation
      • NeuronType.Atom
      • NeuronType.Fact
      • NeuronType.Negation
      • NeuronType.Rule
      • NeuronType.SplittableAggregation
      • NeuronType.WeightedAtom
      • NeuronType.WeightedRule
  • neuralogic.core.builder.dataset module
    • BuiltDataset
    • GroundedDataset
      • GroundedDataset.neuralize()
  • neuralogic.core.builder.dataset_builder module
    • DatasetBuilder
      • DatasetBuilder.build_dataset()
      • DatasetBuilder.build_examples()
      • DatasetBuilder.build_queries()
      • DatasetBuilder.ground_dataset()
      • DatasetBuilder.merge_queries_with_examples()
    • samples_to_examples_and_queries()
  • Module contents
    • Atom
      • Atom.arity
      • Atom.node_type()
      • Atom.predicate
      • Atom.substitutions
    • Builder
      • Builder.build_model()
      • Builder.build_model_from_file()
      • Builder.get_builders()
      • Builder.ground_from_logic_samples()
      • Builder.ground_from_sources()
      • Builder.neuralize()
    • BuiltDataset
    • DatasetBuilder
      • DatasetBuilder.build_dataset()
      • DatasetBuilder.build_examples()
      • DatasetBuilder.build_queries()
      • DatasetBuilder.ground_dataset()
      • DatasetBuilder.merge_queries_with_examples()
    • GroundedDataset
      • GroundedDataset.neuralize()
    • Grounding
      • Grounding.atoms
      • Grounding.draw()
      • Grounding.get_atoms()
    • NeuralSample
      • NeuralSample.draw()
      • NeuralSample.get_fact()
      • NeuralSample.get_neurons()
      • NeuralSample.neurons
      • NeuralSample.set_fact_value()
      • NeuralSample.target
    • Neuron
      • Neuron.gradient
      • Neuron.substitutions
      • Neuron.value
    • NeuronType
      • NeuronType.Aggregation
      • NeuronType.Atom
      • NeuronType.Fact
      • NeuronType.Negation
      • NeuronType.Rule
      • NeuronType.SplittableAggregation
      • NeuronType.WeightedAtom
      • NeuronType.WeightedRule
• neuralogic.core.constructs package
  • Subpackages
    • neuralogic.core.constructs.function package
      • Submodules
      • neuralogic.core.constructs.function.concat module
      • neuralogic.core.constructs.function.enum module
      • neuralogic.core.constructs.function.function module
      • neuralogic.core.constructs.function.function_container module
      • neuralogic.core.constructs.function.function_graph module
      • neuralogic.core.constructs.function.reshape module
      • neuralogic.core.constructs.function.slice module
      • neuralogic.core.constructs.function.softmax module
      • Module contents
  • Submodules
  • neuralogic.core.constructs.factories module
    • AtomFactory
      • AtomFactory.get()
      • AtomFactory.get_predicate()
    • ConstantFactory
      • ConstantFactory.get()
    • HiddenPredicateFactory
      • HiddenPredicateFactory.get()
      • HiddenPredicateFactory.special
    • SpecialPredicateFactory
      • SpecialPredicateFactory.add()
      • SpecialPredicateFactory.add_eval()
      • SpecialPredicateFactory.alldiff()
      • SpecialPredicateFactory.anypred()
      • SpecialPredicateFactory.div_eval()
      • SpecialPredicateFactory.eq()
      • SpecialPredicateFactory.geq()
      • SpecialPredicateFactory.get()
      • SpecialPredicateFactory.gt()
      • SpecialPredicateFactory.hidden
      • SpecialPredicateFactory.leq()
      • SpecialPredicateFactory.lt()
      • SpecialPredicateFactory.max_eval()
      • SpecialPredicateFactory.maxcard()
      • SpecialPredicateFactory.min_eval()
      • SpecialPredicateFactory.mod()
      • SpecialPredicateFactory.mod_eval()
      • SpecialPredicateFactory.mul_eval()
      • SpecialPredicateFactory.neq()
      • SpecialPredicateFactory.next()
      • SpecialPredicateFactory.sub()
      • SpecialPredicateFactory.sub_eval()
      • SpecialPredicateFactory.truepred()
    • VariableFactory
      • VariableFactory.get()
  • neuralogic.core.constructs.java_objects module
    • JavaFactory
      • JavaFactory.add_metadata_function()
      • JavaFactory.add_parametrized_function()
      • JavaFactory.atom_to_clause()
      • JavaFactory.get_conjunction()
      • JavaFactory.get_generic_relation()
      • JavaFactory.get_lifted_example()
      • JavaFactory.get_metadata()
      • JavaFactory.get_new_weight_factory()
      • JavaFactory.get_predicate()
      • JavaFactory.get_predicate_metadata_pair()
      • JavaFactory.get_query()
      • JavaFactory.get_relation()
      • JavaFactory.get_rule()
      • JavaFactory.get_term()
      • JavaFactory.get_valued_fact()
      • JavaFactory.get_variable_factory()
      • JavaFactory.get_weight()
      • JavaFactory.to_clause()
    • ValueFactory
      • ValueFactory.from_java()
      • ValueFactory.get_value()
  • neuralogic.core.constructs.metadata module
    • Metadata
      • Metadata.aggregation
      • Metadata.combination
      • Metadata.combine()
      • Metadata.copy()
      • Metadata.duplicate_grounding
      • Metadata.from_iterable()
      • Metadata.learnable
      • Metadata.transformation
  • neuralogic.core.constructs.predicate module
    • Predicate
      • Predicate.arity
      • Predicate.hidden
      • Predicate.name
      • Predicate.set_arity()
      • Predicate.special
      • Predicate.to_str()
    • PredicateMetadata
      • PredicateMetadata.metadata
      • PredicateMetadata.predicate
  • neuralogic.core.constructs.relation module
    • BaseRelation
      • BaseRelation.T
      • BaseRelation.attach_activation_function()
      • BaseRelation.function
      • BaseRelation.negated
      • BaseRelation.predicate
      • BaseRelation.terms
      • BaseRelation.to_str()
    • WeightedRelation
      • WeightedRelation.T
      • WeightedRelation.attach_activation_function()
      • WeightedRelation.fixed()
      • WeightedRelation.is_fixed
      • WeightedRelation.to_str()
      • WeightedRelation.weight
      • WeightedRelation.weight_name
  • neuralogic.core.constructs.rule module
    • Rule
      • Rule.body
      • Rule.head
      • Rule.metadata
      • Rule.to_str()
    • RuleBody
      • RuleBody.literals
      • RuleBody.metadata
  • neuralogic.core.constructs.term module
    • Constant
      • Constant.name
      • Constant.type
    • Variable
      • Variable.name
      • Variable.type
  • Module contents
• neuralogic.core.settings package
  • Submodules
  • neuralogic.core.settings.settings_proxy module
    • SettingsProxy
      • SettingsProxy.chain_pruning
      • SettingsProxy.debug_exporting
      • SettingsProxy.default_fact_value
      • SettingsProxy.error_function
      • SettingsProxy.get_aggregation_function()
      • SettingsProxy.get_combination_function()
      • SettingsProxy.get_transformation_function()
      • SettingsProxy.grounder
      • SettingsProxy.initializer
      • SettingsProxy.initializer_const
      • SettingsProxy.initializer_uniform_scale
      • SettingsProxy.iso_value_compression
      • SettingsProxy.optimizer
      • SettingsProxy.prune_only_identities
      • SettingsProxy.relation_combination
      • SettingsProxy.relation_transformation
      • SettingsProxy.rule_aggregation
      • SettingsProxy.rule_combination
      • SettingsProxy.rule_transformation
      • SettingsProxy.to_json()
  • Module contents
    • Settings
      • Settings.chain_pruning
      • Settings.create_disconnected_proxy()
      • Settings.create_proxy()
      • Settings.error_function
      • Settings.grounder
      • Settings.initializer
      • Settings.iso_value_compression
      • Settings.optimizer
      • Settings.prune_only_identities
• neuralogic.core.torch package
  • Submodules
  • neuralogic.core.torch.network_output module
  • neuralogic.core.torch.neural_module module
  • neuralogic.core.torch.tensor module
  • Module contents

Submodules

neuralogic.core.enums module

class Grounder(value)

Bases: Enum

BUP = 'BUP'

GRINGO = 'GRINGO'

neuralogic.core.model module

class Model(*, model_file: str | None = None)

Bases: NeuralModule

Model is a collection of rules and relations that define the structure of the neural model.

add_module(module: Module) → None

Expands the module into rules and adds them into the model.

Parameters:

module (Module) – The module to expand and add.

add_rule(rule: BaseRelation | WeightedRelation | Rule | PredicateMetadata) → None

Adds one rule to the model.

Parameters:

rule (ModelEntries) – The rule to add.

add_rules(rules: list[BaseRelation | WeightedRelation | Rule | PredicateMetadata]) → None

Adds multiple rules to the model.

Parameters:

rules (list[ModelEntries]) – The rules to add.

build(settings: Settings | None = None, torch: bool = False) → Model

Builds the model into a neural model.

Parameters:

• settings (Settings, optional) – The settings for building. Default: None.

• torch (bool) – Whether to use PyTorch backend. Default: False.

Returns:

The built model (self).

Return type:

Model

clone() → Model

derivable_queries(example: list[BaseRelation | Rule] | None = None) → list[BaseRelation] | dict

Returns all derivable queries for the provided example.

Parameters:

example (list[BaseRelation | Rule], optional) – The example to derive queries from. Default: None.

Returns:

The list of derivable queries.

Return type:

list[BaseRelation] | dict

load(path: str | Path) → None

Load model weights from a file saved by save().

The model must already be built with the same architecture that produced the saved file.

Parameters:

path (str | Path) – Path to the saved file.

q(query: BaseRelation, examples: list[BaseRelation | Rule] | None = None) → list[dict] | dict

query(query: BaseRelation, examples: list[BaseRelation | Rule] | None = None) → list[dict] | dict

Performs a query on the model with the provided examples.

Parameters:

• query (BaseRelation) – The query to perform.

• examples (list[BaseRelation | Rule], optional) – The examples to use for the query. Default: None.

Returns:

The list of query results (substitutions).

Return type:

list[dict] | dict

remove_duplicates() → None

Removes duplicates from the model.

save(path: str | Path) → None

Save the model weights to a file.

The model must be built before calling save(). To restore, rebuild the same architecture and call load().

Parameters:

path (str | Path) – Path to the output file (.pkl extension recommended).

neuralogic.core.neural_module module

class NeuralModule

Bases: object

NeuralModule is the base class for all neural models. It provides methods for grounding, building, training, and testing.

build_dataset(dataset: BaseDataset | GroundedDataset, *, batch_size: int = 1, learnable_facts: bool = False, progress: bool = False) → BuiltDataset

Builds (ground and neuralize) the provided dataset.

Parameters:

• dataset (Union[BaseDataset, GroundedDataset]) – The dataset to build.

• batch_size (int) – The batch size. Default: 1.

• learnable_facts (bool) – Whether facts are learnable. Default: False.

• progress (bool) – Whether to show progress. Default: False.

Returns:

The built dataset.

Return type:

BuiltDataset

draw(filename: str | None = None, show=True, img_type='png', value_detail: int = 0, graphviz_path: str | None = None, *args, **kwargs)

forward(dataset)

ground(dataset: BaseDataset, *, batch_size: int = 1, learnable_facts: bool = False, progress: bool = False) → GroundedDataset

Grounds the provided dataset using the model’s settings.

Parameters:

• dataset (BaseDataset) – The dataset to ground.

• batch_size (int) – The batch size for grounding. Default: 1.

• learnable_facts (bool) – Whether facts are learnable. Default: False.

• progress (bool) – Whether to show progress. Default: False.

Returns:

The grounded dataset.

Return type:

GroundedDataset

load_state_dict(state_dict: dict)

parameters() → dict

Returns the model parameters.

Returns:

The model parameters.

Return type:

dict

reset_parameters()

state_dict() → dict

Returns the state dictionary of the model.

Returns:

The state dictionary (weights and weight names).

Return type:

dict

tensor_parameters()

test(dataset) → list | float

Tests the model on the provided dataset.

Parameters:

dataset (Any) – The dataset to test on.

Returns:

The test results (outputs).

Return type:

Union[Value, List[Value]]

train(dataset, epochs: int = 1) → list | float

Trains the model on the provided dataset.

Parameters:

• dataset (Any) – The dataset to train on. Can be a Dataset, GroundedDataset, BuiltDataset, or a list of samples.

• epochs (int) – The number of epochs to train. Default: 1.

Returns:

The training results (target, output, error).

Return type:

Union[Tuple[Value, Value, Value], List[Tuple[Value, Value, Value]]]

neuralogic.core.sources module

class Sources(sources: Any)

Bases: object

Represents the logic sources (models, examples, queries) for the NeuraLogic backend.

static from_args(args: list[str], settings: SettingsProxy) → Sources

Creates Sources from command line arguments and settings.

Parameters:

• args (List[str]) – The command line arguments.

• settings (SettingsProxy) – The settings proxy.

Returns:

The created Sources object.

Return type:

Sources

static from_settings(settings: SettingsProxy) → Sources

Creates Sources from the provided settings.

Parameters:

settings (SettingsProxy) – The settings proxy.

Returns:

The created Sources object.

Return type:

Sources

to_json() → str

Exports the sources to a JSON string.

Returns:

The JSON representation of the sources.

Return type:

str

Module contents

class Aggregation

Bases: object

Collection of aggregation functions. Aggregation functions are used to aggregate multiple inputs, typically from different groundings of the same rule.

AVG: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

CONCAT: ConcatAggregation = <neuralogic.core.constructs.function.concat.ConcatAggregation object>

COUNT: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

MAX: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

MIN: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

SOFTMAX: SoftmaxAggregation = <neuralogic.core.constructs.function.softmax.SoftmaxAggregation object>

SUM: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

class BuiltDataset(samples: list[NeuralSample], batch_size: int)

Bases: object

BuiltDataset represents an already built dataset - that is, a dataset that has been grounded and neuralized.

class Combination

Bases: object

Collection of combination functions. Combination functions are used to combine multiple inputs into a single output, often as part of a rule body or for combining multiple rules.

AVG: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

CONCAT: ConcatCombination = <neuralogic.core.constructs.function.concat.ConcatCombination object>

COSSIM: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

COUNT: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

CROSSSUM: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

ELPRODUCT: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

MAX: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

MIN: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

PRODUCT: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

SOFTMAX: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

SPARSEMAX: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

SUM: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

class F

Bases: object

Utility class providing a flat namespace for common functions. It includes transformation, combination, and aggregation functions.

avg: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

avg_agg: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

concat: ConcatCombination = <neuralogic.core.constructs.function.concat.ConcatCombination object>

concat_agg: ConcatAggregation = <neuralogic.core.constructs.function.concat.ConcatAggregation object>

cossim: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

count: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

count_agg: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

crossum: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

elproduct: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

exp: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

identity: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

inverse: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

leaky_relu: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

log: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

lukasiewicz: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

max: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

max_agg: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

min: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

min_agg: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

norm: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

product: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

relu: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

reshape: Reshape = <neuralogic.core.constructs.function.reshape.Reshape object>

reverse: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

sigmoid: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

signum: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

slice: Slice = <neuralogic.core.constructs.function.slice.Slice object>

softmax: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

softmax_agg: SoftmaxAggregation = <neuralogic.core.constructs.function.softmax.SoftmaxAggregation object>

softmax_comb: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

sparsemax: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

sparsemax_comb: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

sqrt: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

sum: CombinationFunction = <neuralogic.core.constructs.function.function.CombinationFunction object>

sum_agg: AggregationFunction = <neuralogic.core.constructs.function.function.AggregationFunction object>

tanh: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

transp: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

class GroundedDataset(groundings, builder: Builder)

Bases: object

GroundedDataset represents grounded examples that are not neuralized yet.

neuralize(*, batch_size: int = 1, progress: bool = False) → BuiltDataset

class Grounder(value)

Bases: Enum

BUP = 'BUP'

GRINGO = 'GRINGO'

class Metadata(learnable: bool | None = None, transformation: TransformationFunction | CombinationFunction | None = None, combination: CombinationFunction | None = None, aggregation: AggregationFunction | None = None, duplicate_grounding: bool | None = None)

Bases: object

Represents metadata for a logic construct (e.g., rule, predicate).

Metadata can specify properties like learnability, transformation functions, aggregation functions, and combination functions.

aggregation

combination

combine(other: Metadata) → Metadata

Combines this metadata with another Metadata object. Values from the other object take precedence.

Parameters:

other (Metadata) – The other Metadata object to combine with.

Returns:

A new combined Metadata object.

Return type:

Metadata

copy() → Metadata

Returns a shallow copy of the metadata.

Returns:

The copy of the metadata.

Return type:

Metadata

duplicate_grounding

static from_iterable(iterable: Iterable[Any]) → Metadata

Creates a Metadata object from an iterable of functions or values.

Parameters:

iterable (Iterable) – The iterable containing metadata entries.

Returns:

The created Metadata object.

Return type:

Metadata

learnable

transformation

class Model(*, model_file: str | None = None)

Bases: NeuralModule

Model is a collection of rules and relations that define the structure of the neural model.

add_module(module: Module) → None

Expands the module into rules and adds them into the model.

Parameters:

module (Module) – The module to expand and add.

add_rule(rule: BaseRelation | WeightedRelation | Rule | PredicateMetadata) → None

Adds one rule to the model.

Parameters:

rule (ModelEntries) – The rule to add.

add_rules(rules: list[BaseRelation | WeightedRelation | Rule | PredicateMetadata]) → None

Adds multiple rules to the model.

Parameters:

rules (list[ModelEntries]) – The rules to add.

build(settings: Settings | None = None, torch: bool = False) → Model

Builds the model into a neural model.

Parameters:

• settings (Settings, optional) – The settings for building. Default: None.

• torch (bool) – Whether to use PyTorch backend. Default: False.

Returns:

The built model (self).

Return type:

Model

clone() → Model

derivable_queries(example: list[BaseRelation | Rule] | None = None) → list[BaseRelation] | dict

Returns all derivable queries for the provided example.

Parameters:

example (list[BaseRelation | Rule], optional) – The example to derive queries from. Default: None.

Returns:

The list of derivable queries.

Return type:

list[BaseRelation] | dict

load(path: str | Path) → None

Load model weights from a file saved by save().

The model must already be built with the same architecture that produced the saved file.

Parameters:

path (str | Path) – Path to the saved file.

q(query: BaseRelation, examples: list[BaseRelation | Rule] | None = None) → list[dict] | dict

query(query: BaseRelation, examples: list[BaseRelation | Rule] | None = None) → list[dict] | dict

Performs a query on the model with the provided examples.

Parameters:

• query (BaseRelation) – The query to perform.

• examples (list[BaseRelation | Rule], optional) – The examples to use for the query. Default: None.

Returns:

The list of query results (substitutions).

Return type:

list[dict] | dict

remove_duplicates() → None

Removes duplicates from the model.

save(path: str | Path) → None

Save the model weights to a file.

The model must be built before calling save(). To restore, rebuild the same architecture and call load().

Parameters:

path (str | Path) – Path to the output file (.pkl extension recommended).

class Rule(head: BaseRelation, body: RuleBody | Iterable[BodyItem] | BodyItem)

Bases: object

Represents a rule in the model, consisting of a head and a body.

body: list[BodyItem] | FContainer

head

metadata: Metadata | None

to_str(_: bool = False) → str

class RuleBody(lit1: BaseRelation, lit2: BaseRelation)

Bases: object

Represents the body of a rule, which is a collection of literals.

literals: list['BaseRelation' | FContainer]

metadata: Metadata | None

class Settings(*, optimizer: Optimizer = <neuralogic.nn.optim.adam.Adam object>, error_function: ErrorFunction = <neuralogic.nn.loss.MSE object>, initializer: Initializer = <neuralogic.nn.init.Uniform object>, iso_value_compression: bool = True, chain_pruning: bool = True, prune_only_identities: bool = False, grounder: Grounder = Grounder.BUP)

Bases: object

property chain_pruning: bool

create_disconnected_proxy() → SettingsProxy

create_proxy() → SettingsProxy

property error_function: ErrorFunction

property grounder: Grounder

property initializer: Initializer

property iso_value_compression: bool

property optimizer: Optimizer

property prune_only_identities: bool

class SettingsProxy(*, optimizer: Optimizer, error_function: ErrorFunction, initializer: Initializer, iso_value_compression: bool, chain_pruning: bool, prune_only_identities: bool, grounder: Grounder)

Bases: object

Proxy class for the Java Settings object.

It provides a Pythonic interface to configure various parameters of the NeuraLogic backend, such as optimizers, initializers, error functions, and grounding algorithms.

property chain_pruning: bool

Whether to use chain pruning (reducing redundant chains of operations).

property debug_exporting: bool

property default_fact_value: float

property error_function: Any

The error function used for training.

get_aggregation_function(aggregation: Aggregation) → Any

get_combination_function(combination: Combination) → Any

Returns the Java combination function for the given Python enum value.

Parameters:

combination (Combination) – The combination function enum value.

Returns:

The Java combination function object.

Return type:

Any

get_transformation_function(transformation: Transformation) → Any

property grounder: Any

The grounding algorithm to use.

property initializer: Any

The weight initializer used for model parameters.

property initializer_const: float

property initializer_uniform_scale: float

property iso_value_compression: bool

Whether to use iso-value compression.

property optimizer: Optimizer

The optimizer used for training.

property prune_only_identities: bool

property relation_combination: CombinationFunction

property relation_transformation: TransformationFunction

property rule_aggregation: AggregationFunction

property rule_combination: CombinationFunction

property rule_transformation: TransformationFunction

to_json() → str

Exports the settings to a JSON string.

Returns:

The JSON representation of the settings.

Return type:

str

class Transformation

Bases: object

Collection of transformation functions. Transformation functions are applied element-wise or as join operations (e.g., Softmax, Transpose).

EXP: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

IDENTITY: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

INVERSE: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

LEAKY_RELU: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

LOG: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

LUKASIEWICZ: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

NORM: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

RELU: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

RESHAPE: Reshape = <neuralogic.core.constructs.function.reshape.Reshape object>

REVERSE: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

SIGMOID: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

SIGNUM: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

SLICE: Slice = <neuralogic.core.constructs.function.slice.Slice object>

SOFTMAX: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

SPARSEMAX: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

SQRT: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

TANH: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>

TRANSP: TransformationFunction = <neuralogic.core.constructs.function.function.TransformationFunction object>