neurolang.NeurolangPDL¶

class neurolang.NeurolangPDL(chase_class: ~typing.Type[~neurolang.datalog.chase.Chase] = <class 'neurolang.datalog.chase.Chase'>, probabilistic_solvers: ~typing.Tuple[~typing.Callable] = (<function solve_succ_query>, <function solve_succ_query>, <function solve_succ_query_sdd_direct>), probabilistic_marg_solvers: ~typing.Tuple[~typing.Callable] = (<function solve_marg_query>, <function solve_marg_query>, <function solve_marg_query>), check_qbased_pfact_tuple_unicity=False)¶

Complements QueryBuilderDatalog class with probabilistic capabilities 1- add extensional probabilistic facts and choices 2- sove probabilistic queries

Attributes:

current_program: Returns the list of Front End Expressions that have
environment: Dynamic context that can be used to create symbols to write a Datalog program.
functions: Returns the list of symbols corresponding to callables
region_names: Returns the list of symbol names with Region type
region_set_names: Returns the list of symbol names with set_type
scope: Dynamic context that can be used to create symbols to write a Datalog program.
symbol_table: Projector to the program_ir’s symbol_table
symbols: Iterator through the symbol’s names
types: Returns a list of the types of the symbols currently

Methods

`add_atlas_set`(name, atlas_labels, spatial_image)	Creates an atlas set: 1- for each region specified by a label and name in atlas_labels, creates associated ExplicitVBR and symbols Tuple[region_name: str, Region] 2- groups regions in an AbstractSet[Tuple[str, Region]] symbol with specified name
`add_constraint`(antecedent, consequent)	Creates an right implication of the consequent by the antecedent and adds the rule to the current program: antecedent -> consequent
`add_probabilistic_choice_from_tuples`(iterable)	Add probabilistic choice from tuples whose first element contains the probability label attached to that tuple.
`add_probabilistic_facts_from_tuples`(iterable)	Add probabilistic facts from tuples whose first element contains the probability label attached to that tuple.
`add_region`(region[, name])	Adds region fe.Symbol to symbol_table
`add_region_set`(region_set[, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Region]] format
`add_symbol`(value[, name, type_])	Creates a symbol with given value and adds it to the current symbol_table.
`add_tuple_set`(iterable[, type_, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Any, ...]] format (see examples).
`add_uniform_probabilistic_choice_over_set`(...)	Add uniform probabilistic choice over values in the iterable.
`all`(quantified_variable, body)	Universal predicate on the body.
`create_region`(spatial_image[, label, ...])	Creates an ExplicitVBR out of the voxels of a dense spatial_image with specified label
`del_symbol`(name)	Deletes the symbol with parameter name from the symbol_table
`execute_datalog_program`(code)	Execute a Datalog program in classical syntax.
`exists`(quantified_variable, body)	Existential predicate on the body.
`get_symbol`(symbol_name)	Retrieves symbol via its name, either providing a fe.Expression with the correct name or the name itself
`load_neurosynth_mni_peaks_reported`(data_dir)	Load the coordinates for the peaks reported by studies in the Neurosynth dataset.
`load_neurosynth_study_ids`(data_dir[, name, ...])	Load all study ids (PMIDs) that are part of the Neurosynth database.
`load_neurosynth_term_study_associations`(data_dir)	Load TF-IDF values for each (term, study) association within the Neurosynth database.
`load_ontology`(paths[, connector_symbol_name])	Loads and parses ontology stored at the specified paths, and store them into attributes
`make_implicit_regions_explicit`(affine, dim)	Raises NotImplementedError for now
`new_region_symbol`([name])	Returns symbol with type Region
`new_symbol`([type_, name])	Creates a symbol and associated expression, optionally specifying it's type and/or name
`predicate_parameter_names`(predicate_name)	Get the names of the parameters for the given predicate
`query`(*args)	Performs an inferential query on the database.
`reset_program`()	Clears current symbol table
`solve_all`()	Returns a dictionary of "predicate_name": "Content" for all elements in the solution of the Datalog program.
`sphere`(center, radius[, name])	Creates a Region symbol associated with the spherical volume described by its center and volume

magic_sets_rewrite_program

__init__(chase_class: ~typing.Type[~neurolang.datalog.chase.Chase] = <class 'neurolang.datalog.chase.Chase'>, probabilistic_solvers: ~typing.Tuple[~typing.Callable] = (<function solve_succ_query>, <function solve_succ_query>, <function solve_succ_query_sdd_direct>), probabilistic_marg_solvers: ~typing.Tuple[~typing.Callable] = (<function solve_marg_query>, <function solve_marg_query>, <function solve_marg_query>), check_qbased_pfact_tuple_unicity=False) → NeurolangPDL¶

Query builder with probabilistic capabilities

Parameters:

chase_classType[Chase], optional: used to compute deterministic solutions, by default Chase
probabilistic_solversTuple[Callable], optional: used to compute probabilistic solutions, the order of the elements indicates the priority of usage of the solver. by default (lifted_solve_succ_query, wmc_solve_succ_query)
probabilistic_marg_solversTuple[Callable], optional: used to compute probabilistic solutions, by default (lifted_solve_marg_query, wmc_solve_marg_query)

Returns:

NeurolangPDL: see description

Methods

`__init__`([chase_class, ...])	Query builder with probabilistic capabilities
`add_atlas_set`(name, atlas_labels, spatial_image)	Creates an atlas set: 1- for each region specified by a label and name in atlas_labels, creates associated ExplicitVBR and symbols Tuple[region_name: str, Region] 2- groups regions in an AbstractSet[Tuple[str, Region]] symbol with specified name
`add_constraint`(antecedent, consequent)	Creates an right implication of the consequent by the antecedent and adds the rule to the current program: antecedent -> consequent
`add_probabilistic_choice_from_tuples`(iterable)	Add probabilistic choice from tuples whose first element contains the probability label attached to that tuple.
`add_probabilistic_facts_from_tuples`(iterable)	Add probabilistic facts from tuples whose first element contains the probability label attached to that tuple.
`add_region`(region[, name])	Adds region fe.Symbol to symbol_table
`add_region_set`(region_set[, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Region]] format
`add_symbol`(value[, name, type_])	Creates a symbol with given value and adds it to the current symbol_table.
`add_tuple_set`(iterable[, type_, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Any, ...]] format (see examples).
`add_uniform_probabilistic_choice_over_set`(...)	Add uniform probabilistic choice over values in the iterable.
`all`(quantified_variable, body)	Universal predicate on the body.
`create_region`(spatial_image[, label, ...])	Creates an ExplicitVBR out of the voxels of a dense spatial_image with specified label
`del_symbol`(name)	Deletes the symbol with parameter name from the symbol_table
`execute_datalog_program`(code)	Execute a Datalog program in classical syntax.
`exists`(quantified_variable, body)	Existential predicate on the body.
`get_symbol`(symbol_name)	Retrieves symbol via its name, either providing a fe.Expression with the correct name or the name itself
`load_neurosynth_mni_peaks_reported`(data_dir)	Load the coordinates for the peaks reported by studies in the Neurosynth dataset.
`load_neurosynth_study_ids`(data_dir[, name, ...])	Load all study ids (PMIDs) that are part of the Neurosynth database.
`load_neurosynth_term_study_associations`(data_dir)	Load TF-IDF values for each (term, study) association within the Neurosynth database.
`load_ontology`(paths[, connector_symbol_name])	Loads and parses ontology stored at the specified paths, and store them into attributes
`magic_sets_rewrite_program`(query_expression)
`make_implicit_regions_explicit`(affine, dim)	Raises NotImplementedError for now
`new_region_symbol`([name])	Returns symbol with type Region
`new_symbol`([type_, name])	Creates a symbol and associated expression, optionally specifying it's type and/or name
`predicate_parameter_names`(predicate_name)	Get the names of the parameters for the given predicate
`query`(*args)	Performs an inferential query on the database.
`reset_program`()	Clears current symbol table
`solve_all`()	Returns a dictionary of "predicate_name": "Content" for all elements in the solution of the Datalog program.
`sphere`(center, radius[, name])	Creates a Region symbol associated with the spherical volume described by its center and volume

Attributes

`current_program`	Returns the list of Front End Expressions that have currently been declared in the program, or through the program's constraints
`environment`	Dynamic context that can be used to create symbols to write a Datalog program.
`functions`	Returns the list of symbols corresponding to callables
`region_names`	Returns the list of symbol names with Region type
`region_set_names`	Returns the list of symbol names with set_type
`scope`	Dynamic context that can be used to create symbols to write a Datalog program.
`symbol_table`	Projector to the program_ir's symbol_table
`symbols`	Iterator through the symbol's names
`types`	Returns a list of the types of the symbols currently in the table (type can be Unknown)