neuralogic.core.builder

class Builder(settings: SettingsProxy)

Bases: object

build_model(parsed_template, settings: SettingsProxy)

build_template_from_file(settings: SettingsProxy, filename: str)

static get_builders(settings: SettingsProxy)

ground_from_logic_samples(parsed_template, logic_samples)

ground_from_sources(parsed_template, sources: Sources)

neuralize(groundings, progress: bool, length: int | None) → List[NeuralSample]

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.

batch_size

samples

class DatasetBuilder(parsed_template, java_factory: JavaFactory)

Bases: object

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

Builds the dataset (does grounding and neuralization)

Parameters:

• dataset

• settings

• batch_size

• learnable_facts

• progress

Returns:

build_examples(examples, examples_builder, learnable_facts=False)

build_queries(queries, query_builder)

ground_dataset(dataset: BaseDataset, settings: SettingsProxy, *, batch_size: int = 1, learnable_facts: bool = False) → GroundedDataset

Grounds the dataset

Parameters:

• dataset

• settings

• batch_size

• learnable_facts

Returns:

static merge_queries_with_examples(queries, examples, one_query_per_example, example_queries=True)

static samples_to_examples_and_queries(samples: List)

class GroundedDataset(groundings, builder)

Bases: object

GroundedDataset represents grounded examples that are not neuralized yet.

neuralize(progress: bool)

class Neuron(neuron: Dict[str, Any], index)

Bases: object

static parse_hook_name(name: str)

class Weight(weight)

Bases: object

static get_unit_weight() → Weight