diff --git a/Makefile b/Makefile index 2593dc2..eadd584 100644 --- a/Makefile +++ b/Makefile @@ -4,7 +4,7 @@ RUN = poetry run # Tests # ---------------------------------------- test: - $(RUN) python -m unittest discover -p 'test_*.py' + $(RUN) python -m unittest tests/test_*.py tests/models/%.py: tests/inputs/%.yaml $(RUN) gen-python $< > $@.tmp && mv $@.tmp $@ diff --git a/docs/basics.md b/docs/basics.md index 20550c5..279691c 100644 --- a/docs/basics.md +++ b/docs/basics.md @@ -1,23 +1,27 @@ -# Basics +# How it works + +## Overview The linkml-dl wrapper works by executing the following steps: - 1. The schema is compiled to Souffle DL problem (see generated schema.dl file) - 2. Any embedded logic program in the schema is also added - 3. Data is converted to generic triple-like tuples (see `*.facts`) - 4. Souffle is executed - 5. Inferred validation results turned into objects - 6. TODO: other inferred facts are incorporated back into objects +- The schema is compiled to Souffle DL problem (see generated schema.dl file) +- Any embedded logic program in the schema is also added +- Data is converted to generic triple-like tuples (see `*.facts`) +- Souffle is executed +- Inferred facts are collected: + - validation results are collected into a results object + - inferred facts are incorporated into new copy of the input object -## Compilation +## Compilation of schemas to Datalog Assuming input like this: ```yaml classes: Person: + class_uri: schema:Person attributes: - age: + age_in_years: range: integer maximum_value: 999 ``` @@ -25,6 +29,12 @@ classes: The generated souffle program will look like this: ```prolog +.decl Person(i: symbol) +.decl Person_asserted(i: identifier) +.output Person +Person_asserted(i) :- triple(i, RDF_TYPE, "http://schema.org/Person"). +Person(i) :- Person_asserted(i). + .decl Person_age_in_years_asserted(i: identifier, v: value) .decl Person_age_in_years(i: identifier, v: value) .output Person_age_in_years @@ -50,9 +60,9 @@ validation_result( (note that most users never need to see these programs, but if you want to write advanced rules it is useful to understand the structure) -## Facts +## Conversion of data to Facts -The linkml data file (which can be JSON, YAML, RDF, or TSV) is converted to a triple-like model following the souffle spec: +The LinkML data file is converted to a triple-like model following the souffle spec: ```prolog .decl triple(s:symbol, p:symbol, o:symbol) @@ -60,16 +70,27 @@ The linkml data file (which can be JSON, YAML, RDF, or TSV) is converted to a tr .decl literal_symbol(s:symbol, o:symbol) ``` +Under the hood, this is a two step process: + +1. convert the data to RDF using the [standard rdflib dumper](https://linkml.io/linkml/data/rdf.html) +2. convert triple to the tuples above + - each triple is mapped to triple/3 facts + - if the object is a literal: + - it is serialized as a json string + - an additional fact is added mapping this to a souffle number or symbol + Every slot-value assignment is turned into a triple. If the value is a literal/atom then an additional fact is added mapping the node to the number or symbol value. ## Execution `linkml_datalog.engines.datalog_engine` will do this compilation, translate your data to relational facts, then wrap calls to Souffle -## Parsing +Note that Souffle needs to be on the command line for this to work + +Generated programs and facts will be placed in a temporary working directory, unless `-d` is passed. -The engine will then read back all `validation_result` facts and translate these to the LinkML validation data model (influenced by SHACL) +## Parsing -## Inferences +The engine will then read back all `validation_result` facts and translate these to the LinkML validation data model, +and will walk the input object reading any new inferences -Currently other inferred facts are not read back in, but in future a new data object will be created. diff --git a/docs/index.md b/docs/index.md index cd5bbb4..c646b94 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,11 +1,69 @@ # linkml-datalog -Validation and inference over LinkML instance data using souffle +Validation and inference over LinkML instance data using Soufflé ![souffle logo](https://souffle-lang.github.io/img/logo-2x.png) ![linkml logo](https://avatars.githubusercontent.com/u/79337873?s=200&v=4) -## Caveats +=== "schema" + + ```yaml + Person: + attributes: + name: + identifier: true + friend_of: + multivalued: true + range: Person + symmetric: true + in_network_of: + range: Person + multivalued: true + annotations: + transitive_closure_of: friend_of + ``` + +=== "data" + + ```yaml + persons: + - name: p:akira + friend_of: [p:bill] + - name: p:bill + friend_of: [p:carrie] + - name: p:carrie + friend_of: + ``` + +=== "output" + + ```yaml + persons: + - name: p:akira + friend_of: + - p:bill + in_network_of: + - p:akira + - p:bill + - p:carrie + - name: p:bill + friend_of: + - p:carrie + - p:akira + in_network_of: + - p:akira + - p:bill + - p:carrie + - name: p:carrie + friend_of: + - p:bill + in_network_of: + - p:akira + - p:bill + - p:carrie + ``` + +__Caveats__ This is currently experimental/alpha software! diff --git a/docs/install.md b/docs/install.md index 50122ad..c951ea6 100644 --- a/docs/install.md +++ b/docs/install.md @@ -1,11 +1,28 @@ # Installation -This project requires [souffle](https://souffle-lang.github.io/) +## Install Soufflé -After installing souffle: +This project requires [Soufflé](https://souffle-lang.github.io/), a fast engine +for executing [datalog](https://en.wikipedia.org/wiki/Datalog) programs + + * [Install Souffle](https://souffle-lang.github.io/install) + +Make sure the souffle executable is on your command line: + +```bash +souffle --help +``` + +## Install LinkML-Datalog + +Python 3.9 is required. + +Install in the standard way from PyPi: ```bash pip install linkml-datalog ``` +## Docker + Docker containers will be provided in future diff --git a/docs/translations.md b/docs/translations.md index 4aa8704..6c76260 100644 --- a/docs/translations.md +++ b/docs/translations.md @@ -1,6 +1,6 @@ # Translations of LinkML to Datalog -LinkML is primarily a data modeling language in the mold of +LinkML is primarily a data modeling language in the vein of JSON-Schema, UML, or a shape language like SHACL. The core is deliberately simple and does not have complex semantics. @@ -68,6 +68,26 @@ This will generate sibling_of(i,j) :- sibling_of(j,i). ``` +## Transitive closures over + +LinkML 1.2 will introduce [transitive_form_of](https://w3id.org/linkml/transitive_form_of), +to declare that one slot (e.g. `ancestor_of`) is the transitive form of another slot (e.g `parent_of`) + +For now, you can get the same semantics from an annotation: + +```yaml +ancestor_of: + annotations: + transitive_closure_of: parent_of +``` + +This will generate + +```yaml +ancestor_of(i,j) :- parent_of(j,i). +ancestor_of(i,j) :- parent_of(j,z), ancestor_of(z,j). +``` + ## Association classes Compilation to datalog will also handle associative classes (e.g. reified statements). This is very useful when we want to be able to model diff --git a/docs/usage.md b/docs/usage.md index 44d1c45..340fe8b 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -1,139 +1,280 @@ # Usage -## Validation +## Execution Minimally you need to specify two inputs: -* A [linkml schema](https://linkml.io/linkml/schemas) -* A datafile in YAML, JSON, RDF, or TSV conformant with the schema +* A [linkml schema](https://linkml.io/linkml/schemas/) +* A [datafile](https://linkml.io/linkml/data/) datafile in YAML, JSON, RDF, or TSV conformant with the schema -The current main use case is data validation. Pass in your schema and data: +Then run the `linkml-dl` script: ```bash linkml-dl -d tmp -s personinfo.yaml example_personinfo_data.yaml ``` -The output will be a ValidationReport object, in yaml +This produces two output: -For example, given a simple schema modeling Person objects, including an age slot with a specified range: +- a ValidationReport object +- a new version of the data file, with new inferences injected on + +## Inference Example + +The following schema models friend-of-a-friend networks. It introduces +a class *Person*, with attributes: + +- `friend_of` for stating one person is a friend of another +- `in_network_of` which is true if there is a chain of friendships between two people + +### Schema + +`foaf.yaml`: ```yaml -id: https://w3id.org/linkml/examples/personinfo -name: personinfo -description: |- - Simple demo person schema -default_curi_maps: - - semweb_context -imports: - - linkml:types +id: http://example.org/foaf prefixes: - personinfo: https://w3id.org/linkml/examples/personinfo/ + ex: http://example.org/foaf/ + p: http://example.org/person/ linkml: https://w3id.org/linkml/ - P: https://example.org/P/ -default_prefix: personinfo -default_range: string +default_prefix: ex +imports: + - linkml:types classes: - - Container: - tree_root: true + Database: attributes: persons: range: Person - inlined: true - inlined_as_list: true multivalued: true - + inlined_as_list: true Person: - description: >- - A person (alive, dead, undead, or fictional). attributes: - id: - identifier: true - slot_uri: schema:identifier name: - slot_uri: schema:name - required: true - age_in_years: - range: integer - minimum_value: 0 - maximum_value: 999 + identifier: true + friend_of: + multivalued: true + range: Person + symmetric: true + in_network_of: + range: Person + multivalued: true + annotations: + transitive_closure_of: friend_of ``` -Data a data object: +note that `friend_of` is declared *transitive*, and `in_network_of` has an annotation +stating it is the *transitive closure of* `friend_of` -```yaml -persons: - - id: P:003 - name: bobby - age_in_years: 100001 -``` +==Note in LinkML 1.2 there will be direct support for transitive_closure_of== -Then we will get a validation result object telling us we have violated a MaxValue constraint: +Don't worry about the `Database` class, this just acts as a holder for our +main data objects, which is a list of Persons -```yaml -- type: sh:MaxValue - subject: https://example.org/P/003 - instantiates: Person - predicate: age_in_years - object_str: '100001' - info: Maximum is 999 -``` +### Data -Note that this in itself is not so impressive - it is more -conventional to use something like jsonschema or the builtin linkml -validator to do this kind of task. +Data can be specified as YAML, JSON, or RDF. For simple flat schemas, +data can also be passed as TSV/CSVs. -However, because we are using a datalog engine behind the scenes we can do more powerful inferences and checks +`foaf_data.{yaml,json,tsv,ttl}`: -## Inferring new slot values +=== "YAML" -The above example demonstrates the validation use case. + ```yaml + persons: + - name: p:akira + friend_of: [p:bill] + - name: p:bill + friend_of: [p:carrie] + - name: p:carrie + friend_of: + ``` -We can also add information to the schema about transitive relations as well as custom rules. +=== "JSON" -For example: + ```json + { + "persons": [ + { + "name": "p:akira", + "friend_of": [ + "p:bill" + ] + }, + { + "name": "p:bill", + "friend_of": [ + "p:carrie" + ] + }, + { + "name": "p:carrie", + "friend_of": [] + } + ], + } + ``` + +=== "TSV" + + |name|friend_of| + |---|---| + |p:akira|p:bill| + |p:bill|p:carrie| + |p:carrie|| + +=== "Turtle" + + ```turtle + @prefix ns1: . + + a ns1:Person ; + ns1:friend_of . + a ns1:Person ; + ns1:friend_of . + a ns1:Person . + + [] a ns1:Database ; + ns1:persons , + , + . + ``` + +### Execution + +Once you have a schema and a data file, you can run the script: ```bash -linkml-dl -s tests/inputs/personinfo.yaml tests/inputs/example_personinfo_data.yaml -d tmp +linkml-dl -s foaf.yaml foaf_data.yaml ``` -If the input set of objects include: +This will generate an output file that is the input injected with inferred slot-values. ```yaml - - id: P:001 - - id: P:004 - has_familial_relationships: - - related_to: P:001 - type: PARENT_OF - - id: P:005 - has_familial_relationships: - - related_to: P:004 - type: PARENT_OF - gender: cisgender man +persons: +- name: p:akira + friend_of: + - p:bill + in_network_of: + - p:carrie + - p:bill + - p:akira +- name: p:bill + friend_of: + - p:carrie + - p:akira + in_network_of: + - p:carrie + - p:bill + - p:akira +- name: p:carrie + friend_of: + - p:bill + in_network_of: + - p:carrie + - p:bill + - p:akira ``` -and the schema has a schema-level rules: +## Validation Example + +For example, given a simple schema modeling Person objects, including an age slot with a specified range: + +### Schema ```yaml -annotations: - datalog: |- +id: http://example.org/foaf +prefixes: + ex: http://example.org/foaf/ + p: http://example.org/person/ + linkml: https://w3id.org/linkml/ +default_prefix: ex +imports: + - linkml:types +default_curi_maps: + - semweb_context - grandparent_of(i, j) :- - parent_of(i, z), - parent_of(z, j). +classes: + Database: + attributes: + persons: + range: Person + multivalued: true + inlined_as_list: true + Person: + attributes: + name: + identifier: true + age_in_years: + range: integer + minimum_value: 0 + maximum_value: 200 +``` + +### Data + +`foaf_data.{yaml,json,tsv,ttl}`: + +=== "YAML" + + ```yaml + persons: + - name: p:methuselah + age_in_years: 999 + ``` + +=== "JSON" + + ```json + { + "persons": [ + { + "name": "p:methuselah", + "age": 999 + } + ] + } + ``` + +=== "TSV" + + |name|age| + |---|---| + |p:methuselah|999| + +=== "Turtle" - grandfather_of(i, j) :- - grandparent_of(i, j), - is_man(i). + ```turtle + @prefix ns1: . + + a ns1:Person ; + ns1:age_in_years 999 . + + [] a ns1:Database ; + ns1:persons . + ``` + +### Execution + +You can execute in the same way. In this case we are not interested in new inferences, +so we can suppress the writing of the inferred object file with `--validate-only` + +```bash +linkml-dl --validate-only -s foaf_validation.yaml foaf_validation_data.yaml ``` -(TODO: this can be specified in a more compact way now with annotations on predicates) +This generates a validation report object conforming to the LinkML validation schema (modeled after SHACL): -the above exert elides some details but the basic idea is that we have transitive slot values inferred. Currently, to see these, consult the directory you specified in `-d` E.g. `tmp/Person_grandfather_of.csv` +```yaml +results: +- type: sh:MaxInclusiveConstraintComponent + subject: http://example.org/person/methuselah + instantiates: Person + predicate: age_in_years + object_str: '999' + info: Maximum is 200 +``` -|id|grandfather_of| -|---|---| -|https://example.org/P/005|https://example.org/P/001| +A result of type [sh:sh:MaxInclusiveConstraintComponent](https://www.w3.org/TR/shacl/#validator-MaxInclusiveConstraintComponent) +tells us that the subject (methuselah) has an age that exceeds the maximum specified in the schema -In future the inferences will be incorporated back into the core objects +== Note that this kind of simple validation can easily be done using frameworks like JSON-Schema, the advantage of LinkML datalog is the incoporation of expressive rules == \ No newline at end of file diff --git a/linkml_datalog/dumpers/jsonfactsdumper.py b/linkml_datalog/dumpers/jsonfactsdumper.py new file mode 100644 index 0000000..8099305 --- /dev/null +++ b/linkml_datalog/dumpers/jsonfactsdumper.py @@ -0,0 +1,226 @@ +import logging +import os +from abc import abstractmethod +from enum import Enum +from functools import lru_cache +from numbers import Number +from pathlib import Path +from typing import Optional, Any, Dict, List, Union, TextIO, Iterator, Tuple, Iterable, ClassVar, Callable +import json + +from linkml_runtime.dumpers import rdflib_dumper, yaml_dumper +from pydantic import BaseModel +from rdflib import Graph, URIRef +from rdflib.term import Node, BNode, Literal, Identifier +from rdflib.namespace import RDF + + +from linkml_runtime.dumpers.dumper_root import Dumper +from linkml_runtime.utils.schemaview import SchemaView, ElementName, PermissibleValue, PermissibleValueText +from linkml_runtime.utils.yamlutils import YAMLRoot + + +class Predicate(Enum): + PATH = 'path' + SV = 'sv' + NTH = 'nth' + LITERAL_NUMBER = 'literal_number' + LITERAL_BOOLEAN = 'literal_boolean' + LITERAL_STRING = 'literal_string' + LITERAL_NULL = 'literal_null' + + @staticmethod + def list() -> List[str]: + return list(map(lambda c: c.value, Predicate)) + +# TODO: replace with Atom +FACT = Tuple[Predicate, List[Any]] + +@lru_cache +def predicate_file(directory: str, predicate: Predicate) -> TextIO: + path = Path(directory) + path.mkdir(exist_ok=True, parents=True) + return open(path / f'{predicate}.facts', 'w', encoding='utf-8') + + +def emit_fact(directory: str, fact: FACT): + file = predicate_file(directory, fact[0]) + + +def tuple_as_str(fact: FACT) -> str: + """ + Convert a predicate and arguments to a TSV tuple + + >>> print(tuple_as_str((Predicate.SV, ['/persons', '/a']))) + sv(/persons, /a) + + :param predicate: + :param args: + :return: + """ + predicate = fact[0] + args = fact[1] + return f"{predicate.value}(" + ', '.join([str(a) for a in args]) + ")" + +def pp_facts(facts: Iterable[FACT], hide: Optional[List[Predicate]]=None) -> None: + """ + Pretty print a set of TSV tuples. + + >>> pp_facts([(Predicate.SV, ['/persons', '/a'])]) + sv(/persons, /a) + + :param facts: + :param hide: + :return: + """ + for fact in facts: + if hide and fact[0] in hide: + continue + print(tuple_as_str(fact)) + + +def object_to_tuples(obj: Any, directory: str=None, path=None) -> Iterator[FACT]: + """ + Convert a dictionary to a set of TSV tuples + + >>> pp_facts(object_to_tuples({'a': 1}), hide=[Predicate.PATH]) + sv(., a, ./a) + literal_number(./a, 1) + + >>> pp_facts(object_to_tuples({'a': True}), hide=[Predicate.PATH]) + sv(., a, ./a) + literal_boolean(./a, True) + + + >>> pp_facts(object_to_tuples({'a': "x"}), hide=[Predicate.PATH]) + sv(., a, ./a) + literal_string(./a, x) + + >>> pp_facts(object_to_tuples({'l': [1]}), hide=[Predicate.PATH]) + sv(., l, ./l) + nth(./l, 0, ./l/[0]) + literal_number(./l/[0], 1) + + >>> pp_facts(object_to_tuples({'persons': {"P1": {"name": "n1"}}}), hide=[Predicate.PATH]) + sv(., persons, ./persons) + sv(./persons, P1, ./persons/P1) + sv(./persons/P1, name, ./persons/P1/name) + literal_string(./persons/P1/name, n1) + + :param obj: + :param directory: + :param path: + :return: + """ + if path is None: + path = '.' + yield Predicate.PATH, [path, path] + if isinstance(obj, dict): + for k, v in obj.items(): + k = str(k).replace("/", "__SLASH__") + child_path = f"{path}/{k}" + yield Predicate.SV, [path, k, child_path] + yield from object_to_tuples(v, directory, child_path) + elif isinstance(obj, list): + for i, v in enumerate(obj): + child_path = f"{path}/[{i}]" + yield Predicate.NTH, [path, i, child_path] + yield from object_to_tuples(v, directory, child_path) + else: + if obj is None: + yield Predicate.LITERAL_NULL, [path] + elif isinstance(obj, str): + yield Predicate.LITERAL_STRING, [path, obj] + elif isinstance(obj, bool): + yield Predicate.LITERAL_BOOLEAN, [path, obj] + elif isinstance(obj, Number): + yield Predicate.LITERAL_NUMBER, [path, obj] + else: + raise ValueError(f'Cannot handle {type(obj)}') + + + +class JsonFactsDumper(Dumper): + """ + Dumps JSON objects as TSV tuples + """ + + def dump(self, element: Union[YAMLRoot, Dict, BaseModel], directory=None, **kwargs): + """ + Dump a LinkML instance as TSV tuples, saving to a directory + + :param element: + :param schemaview: + :param directory: + :param kwargs: + :return: + """ + if isinstance(element, BaseModel): + element = element.dict() + elif isinstance(element, YAMLRoot): + element = yaml_dumper.dumps(element) + elif not isinstance(element, dict): + raise ValueError(f'Cannot dump {type(element)}') + object_to_tuples(g, directory=directory) + + def dumps(self, *args, **kwargs) -> str: + """ + Dump a LinkML instance as TSV tuples, returning a string + :param args: + :param kwargs: + :return: + """ + # return self.dump(*args, **kwargs) + raise NotImplementedError + + def dict_to_tuples(self, graph: Graph, directory: str) -> None: + """ + Convert an RDF graph Tuple format + + :param graph: + :param directory: + :return: + """ + file_map = {} + for p in Predicate.list(): + file_map[p] = open(os.path.join(directory, f'{p}.facts'), 'w') + + def safe_str(v: str) -> str: + return str(v).replace('\t', '\\t').replace('\n', '\\n') + + def as_str(v: Identifier) -> str: + if isinstance(v, Literal): + v = v.toPython() + return json.dumps(v, default=str) + #if isinstance(v, Number) and not isinstance(v, bool): + # return str(v) + #else: + # v = safe_str(v) + # return f'"{v}"' + elif isinstance(v, BNode): + return f'_:b{v}' + else: + # TODO: rdflib_dumper should treat these as literals + if v == 'True': + return 'true' + elif v == 'False': + return 'false' + else: + return str(v) + + def emit(predicate: Predicate, *args): + file_map[predicate.value].write('\t'.join([as_str(a) for a in args])) + file_map[predicate.value].write('\n') + + for s, p, o in graph.triples((None, None, None)): + emit(Predicate.triple, s, p, o) + if isinstance(o, Literal): + v = o.toPython() + if isinstance(v, Number) and not isinstance(v, bool): + emit(Predicate.literal_number, as_str(o), v) + else: + emit(Predicate.literal_symbol, as_str(o), safe_str(v)) + + + + diff --git a/linkml_datalog/dumpers/tupledumper.py b/linkml_datalog/dumpers/tupledumper.py index 790fb8a..ca1f03b 100644 --- a/linkml_datalog/dumpers/tupledumper.py +++ b/linkml_datalog/dumpers/tupledumper.py @@ -4,6 +4,7 @@ from enum import Enum from numbers import Number from typing import Optional, Any, Dict, List, Union +import json from linkml_runtime.dumpers import rdflib_dumper from rdflib import Graph, URIRef @@ -27,20 +28,52 @@ def list() -> List[str]: class TupleDumper(Dumper): """ - Dumps LinkML instance data as TSV tuples + Dumps LinkML instance data as TSV tuples. + + Creates 3 triple files: + + - triple.facts + - literal_number.facts + - literal_symbol.facts + + The triples are plain RDF triples. In cases where the object is a literal, + it will also appear in either literal_number.facts or literal_symbol.facts. """ def dump(self, element: Union[YAMLRoot, Graph], schemaview: SchemaView = None, directory=None, **kwargs): + """ + Dump a LinkML instance as TSV tuples, saving to a directory + + :param element: + :param schemaview: + :param directory: + :param kwargs: + :return: + """ if isinstance(element, Graph): g = element else: g = rdflib_dumper.as_rdf_graph(element, schemaview, **kwargs) self.graph_to_tuples(g, directory=directory) - def dumps(self, *args, **kwargs): - return self.dump(*args, **kwargs) + def dumps(self, *args, **kwargs) -> str: + """ + Dump a LinkML instance as TSV tuples, returning a string + :param args: + :param kwargs: + :return: + """ + #return self.dump(*args, **kwargs) + raise NotImplementedError def graph_to_tuples(self, graph: Graph, directory: str) -> None: + """ + Convert an RDF graph Tuple format + + :param graph: + :param directory: + :return: + """ file_map = {} for p in Predicate.list(): file_map[p] = open(os.path.join(directory, f'{p}.facts'), 'w') @@ -51,13 +84,22 @@ def safe_str(v: str) -> str: def as_str(v: Identifier) -> str: if isinstance(v, Literal): v = v.toPython() - if isinstance(v, Number) and not isinstance(v, bool): - return str(v) - else: - v = safe_str(v) - return f'"{v}"' + return json.dumps(v, default=str) + #if isinstance(v, Number) and not isinstance(v, bool): + # return str(v) + #else: + # v = safe_str(v) + # return f'"{v}"' + elif isinstance(v, BNode): + return f'_:b{v}' else: - return str(v) + # TODO: rdflib_dumper should treat these as literals + if v == 'True': + return 'true' + elif v == 'False': + return 'false' + else: + return str(v) def emit(predicate: Predicate, *args): file_map[predicate.value].write('\t'.join([as_str(a) for a in args])) diff --git a/linkml_datalog/engines/datalog_engine.py b/linkml_datalog/engines/datalog_engine.py index bd371db..777b112 100644 --- a/linkml_datalog/engines/datalog_engine.py +++ b/linkml_datalog/engines/datalog_engine.py @@ -2,13 +2,16 @@ import json import os import subprocess +import sys +import tempfile from dataclasses import dataclass -from typing import Any, Dict, List, Union, Tuple +from typing import Dict, List, Union, Tuple -import yaml import logging +from rdflib import Graph import click + from linkml.generators.pythongen import PythonGenerator from linkml_runtime.dumpers import yaml_dumper from linkml_runtime.linkml_model import SlotDefinitionName @@ -16,7 +19,8 @@ from linkml_runtime.utils.formatutils import underscore from linkml_runtime.utils.schemaview import SchemaView, ClassDefinitionName from linkml_runtime.utils.yamlutils import YAMLRoot -from rdflib import Graph + +#import linkml_dataops from linkml_datalog.dumpers.tupledumper import TupleDumper from linkml_datalog.generators.dataloggen import DatalogGenerator @@ -24,29 +28,31 @@ from linkml_datalog.model.validation import ValidationReport, ValidationResult -def runcmd(cmd: str) -> int: - status = os.system(cmd) - logging.info(f'{status} CMD: {cmd}') - if status != 0: - raise Exception(f'Error running" {cmd}') - return status - @dataclass class DatalogEngine: """ - Engine for performing datalog operations on linkml data - - ALPHA - - uses DatalogDumper - + Engine for performing datalog inference using LinkML schemas and data compiled to Datalog """ sv: SchemaView = None workdir: str = None def run(self, obj: Union[YAMLRoot, Graph], prefix_map: Dict[str, str] = None, strict=True): """ - Run datalog inference over a data object + Run datalog inference over a data object. + + This uses ref:`DatalogGenerator` to export schema to datalog rules (schema.dl). + These input from rdf/3 and rdfn/3-tuple format, and output to domain-specific predicates, + e.g. Person_name/2, as well as validation_result/6. + + The data is converted using ref:`TupleDumper` to dump object to CSVs for Souffle. + These follow a generic triple pattern. + + Souffle is executed on the resulting working directory. + + :param obj: + :param prefix_map: + :param strict: + :return: """ sv = self.sv workdir = self.workdir @@ -58,28 +64,39 @@ def run(self, obj: Union[YAMLRoot, Graph], prefix_map: Dict[str, str] = None, st dumper.dump(obj, sv, directory=workdir, prefix_map=prefix_map) result = subprocess.run(['souffle', f'-F{workdir}', f'-D{workdir}', f'{workdir}/schema.dl'], capture_output=True) - print(f'STDERR: {result.stderr}') + logging.warning(f'STDERR: {result.stderr}') if result.stderr: logging.error(f'STDERR: {result.stderr}') if result.stdout: - logging.error(f'STDOUT: {result.stdout}') + logging.info(f'STDOUT: {result.stdout}') result.check_returncode() if strict and result.stderr: raise Exception(f'Got warnings: {result.stderr}') - #runcmd(f'souffle -F{workdir} -D{workdir} {workdir}/schema.dl') def _parse_results(self, pred: str) -> List[List[str]]: + """ + Parses Souffle results, which are stored as tsvs in the working directory + + :param pred: + :return: + """ with open(os.path.join(self.workdir, f'{pred}.csv')) as csvfile: reader = csv.reader(csvfile, delimiter='\t', quotechar='|') return [row for row in reader] def validation_results(self) -> ValidationReport: """ - Retrieves validation results, after running souffle + Retrieves validation results + + Should be called after :ref:`run` """ + # The compile souffle program using the predicate validation_result/6 + # for results rows = self._parse_results('validation_result') results = [] for [typ, subject, cls, pred, val, info] in rows: + if subject.startswith("_:"): + subject = None result = ValidationResult(type=typ, subject=subject, instantiates=cls, @@ -90,13 +107,23 @@ def validation_results(self) -> ValidationReport: return ValidationReport(results=results) def inferred_slot_values(self, cn: ClassDefinitionName, sn: SlotDefinitionName) -> List[Tuple[str, str]]: + """ + Retrieve inferences as tuples. + + TODO: avoid reloading repeatedly + + :param cn: + :param sn: + :return: + """ return [(r[0], r[1]) for r in self._parse_results(f'{cn}_{sn}')] def materialize_inferences(self, obj: YAMLRoot) -> None: # TODO: potentially redo, get all inferred triples first - print(f'MAT {obj} {type(obj)}') + #print(f'MAT {obj} {type(obj)}') if obj is None: return + # recurse down tree if isinstance(obj, list): for x in obj: self.materialize_inferences(x) @@ -116,14 +143,52 @@ def materialize_inferences(self, obj: YAMLRoot) -> None: id_val = getattr(obj, id_slot.name) else: id_val = None + + def _curie_for(v: str) -> str: + if v.startswith("_:"): + return v + elif v.startswith("http"): + sv.namespaces().curie_for(v) + else: + return v + for islot in sv.class_induced_slots(cn): sn = underscore(islot.name) if id_val: + obj_uri = sv.expand_curie(id_val) + #print(f'CHECKINGx: {cn}.{id_val}.{sn} ({obj_uri}) {type(obj)} {obj}') # TODO: optimize - for row in self._parse_results(sn): + for row in self.inferred_slot_values(cn, sn): # TODO: CURIE expansion - if row[0] == id_val: - setattr(obj, sn, row[1]) + # print(f' TESTING: {row} // {row[0]} == {obj_uri}') + if row[0] == obj_uri: + v = row[1] + if islot.range in sv.all_classes(): + # contract + v = _curie_for(v) + elif islot.range in sv.all_enums(): + v = _curie_for(v) + else: + if v is not None: + v = json.loads(v) + ##if v.startswith('"'): + # # TODO + # v = v.replace('"', '') + #else: + # # TODO + # v = v + #print(f' ***Using inference: {sn} {row} v = {v}') + # TODO: lists + if islot.multivalued: + curr = getattr(obj, sn) + if isinstance(curr, list): + if v not in curr: + curr.append(v) + else: + raise ValueError(f'Cannot handle {cn}.{id_val}.{sn} {type(curr)} == {curr}') + else: + if getattr(obj, sn, None) != v: + setattr(obj, sn, v) if islot.range in sv.all_classes(): self.materialize_inferences(getattr(obj, sn)) @@ -132,22 +197,32 @@ def materialize_inferences(self, obj: YAMLRoot) -> None: - - - - @click.command() -@click.option('--dir', '-d', required=True, help='Directory to export to') -@click.option('--schema', '-s', required=True, help='Path to schema') +@click.option("--workdir", "-d", required=False, + help="Working directory in which intermediate results are stored") +@click.option("--schema", "-s", required=True, + help="Path to LinkML schema in YAML") +@click.option("--validate-only/--no-validate-only", default=False, show_default=True, + help="Do not materialize inferences, only generate validation results") +@click.option("--output", "-o", + type=click.File(mode="w"), + default=sys.stdout, + help="Path to output file") +@click.option("--report", "-e", + type=click.File(mode="w"), + default=sys.stderr, + show_default=True, + help="Path to validation report file") @click.option("--input-format", "-f", type=click.Choice(list(dumpers_loaders.keys())), help="Input format. Inferred from input suffix if not specified") @click.option("--target-class", "-C", - help="name of class in datamodel that the root node instantiates") + help="name of class in datamodel that the root node instantiates. Inferred if not present") @click.option("--module", "-m", - help="Path to python datamodel module") -@click.argument('input') -def cli(input, schema, module, target_class, input_format, dir): + help="Path to pre-compiled python datamodel module. If not specified, will be compiled on the fly") +@click.option("-v", "--verbose", count=True) +@click.argument("input") +def cli(input, schema, module, target_class, input_format, validate_only: bool, output, report, workdir, verbose): """ Performs inference and validation over input files using a linkml schema @@ -156,8 +231,14 @@ def cli(input, schema, module, target_class, input_format, dir): - translate instance data to tuples - collect above in working directory - run souffle + - collect results: validation and new inferences """ - logging.basicConfig(level=logging.INFO) + if verbose >= 2: + logging.basicConfig(level=logging.DEBUG) + elif verbose == 1: + logging.basicConfig(level=logging.INFO) + else: + logging.basicConfig(level=logging.WARNING) if module is None: if schema is None: raise Exception('must pass one of module OR schema') @@ -168,6 +249,7 @@ def cli(input, schema, module, target_class, input_format, dir): sv = SchemaView(schema) input_format = _get_format(input, input_format) loader = get_loader(input_format) + logging.info(f'Using {loader} to load data from {input}') if target_class is None: target_class = infer_root_class(sv) if target_class is None: @@ -175,10 +257,19 @@ def cli(input, schema, module, target_class, input_format, dir): py_target_class = python_module.__dict__[target_class] obj = loader.load(source=input, target_class=py_target_class) - engine = DatalogEngine(sv, workdir=dir) - engine.run(obj) - rpt = engine.validation_results() - print(yaml_dumper.dumps(rpt)) + with tempfile.TemporaryDirectory() as tmpdir: + if workdir is None: + workdir = tmpdir + logging.info(f'Working dir: {workdir}') + engine = DatalogEngine(sv, workdir=workdir) + engine.run(obj) + validation_results = engine.validation_results() + report.write(yaml_dumper.dumps(validation_results)) + if not validate_only: + engine.materialize_inferences(obj) + output.write(yaml_dumper.dumps(obj)) + + if __name__ == '__main__': diff --git a/linkml_datalog/engines/jsonlog_engine.py b/linkml_datalog/engines/jsonlog_engine.py new file mode 100644 index 0000000..4083d98 --- /dev/null +++ b/linkml_datalog/engines/jsonlog_engine.py @@ -0,0 +1,58 @@ +import json +from dataclasses import dataclass, field +from typing import Union, List, Optional, Type + +import yaml + +import linkml_datalog.pydanticlog.pydanticlog +from linkml_datalog.pydanticlog import pydanticlog +from linkml_datalog.pydanticlog.pydanticlog import Fact +from linkml_datalog.dumpers.jsonfactsdumper import object_to_tuples +import linkml_datalog.lp.jsonlog as jsonlog + +from snakelog.common import * +from snakelog.litelog import Solver, TEXT + +def print_schema(cursor): + cursor.execute("SELECT name FROM sqlite_master WHERE type='table';") + for table in cursor.fetchall(): + print(f'\nTable name: {table[0]}') + cursor.execute(f"PRAGMA table_info({table[0]})") + print("Column_Info:") + print(f"{'CID':<5}{'Name':<15}{'Type':<10}{'Notnull':<10}{'Dflt_value':<15}{'Pk':<5}") + for row in cursor.fetchall(): + print(row) + + +@dataclass +class JsonLogEngine: + """ + An engine that reasons over JSON documents + """ + solver: Solver = field(default_factory=Solver) + + def __post_init__(self): + linkml_datalog.pydanticlog.pydanticlog.load_module(self.solver, jsonlog) + + + def infer(self, predicate: Optional[Union[str, Type]] = None): + """ + Infer facts from the JSON documents + + :param predicate: + :return: + """ + return pydanticlog.query(self.solver, predicate, run=True) + + def load(self, sources: Union[str, List[str]]): + """ + Load JSON documents into the engine. + + >>> engine = JsonLogEngine() + >>> engine.load('tests/inputs/minimal.yaml') + + :param sources: + :return: + """ + jsonlog.load(self.solver, sources) + diff --git a/linkml_datalog/generators/dataloggen.py b/linkml_datalog/generators/dataloggen.py index 38f1465..89c9a0c 100644 --- a/linkml_datalog/generators/dataloggen.py +++ b/linkml_datalog/generators/dataloggen.py @@ -114,6 +114,7 @@ // DOMAIN AND RANGE +{% if gen.domains(s) %} validation_result( "sh:ClosedConstraintComponent", i, @@ -126,6 +127,7 @@ {%- for domain in gen.domains(s) -%} , ! {{ gen.pred(domain) }}(i) {%- endfor %} . +{% endif %} {% if s.range and not s.range in schemaview.all_types() %} validation_result( @@ -334,7 +336,7 @@ class Reification: class DatalogGenerator(Generator): """ - Generates Souffle datalog from a LinkML schema where the domain of discourse is RDF triples + Generates Souffle datalog from a LinkML schema where the domain of discourse is RDF triples. """ generatorname = os.path.basename(__file__) @@ -462,7 +464,10 @@ def type_is_numeric(self, t: Union[TypeDefinition, TypeDefinitionName]) -> bool: def reification_of(self, cn: ClassDefinitionName) -> Optional[Reification]: """ - TODO: move to schemaview + If cn is a reification, return the reification structure. + + :param cn: + :return: """ sv = self.schemaview if sv.is_relationship(cn): @@ -496,7 +501,7 @@ def reification_of(self, cn: ClassDefinitionName) -> Optional[Reification]: @shared_arguments(DatalogGenerator) @click.command() -def cli(yamlfile, dir, **kwargs): +def cli(yamlfile, **kwargs): """ Generate Souffle datalog from a LinkML schema """ print(DatalogGenerator(yamlfile, **kwargs).serialize(**kwargs)) diff --git a/linkml_datalog/lp/__init__.py b/linkml_datalog/lp/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/linkml_datalog/lp/jsonlog.py b/linkml_datalog/lp/jsonlog.py new file mode 100644 index 0000000..e37c4de --- /dev/null +++ b/linkml_datalog/lp/jsonlog.py @@ -0,0 +1,152 @@ +import json +from typing import ClassVar, Union, List, Any, Iterable + +import yaml +from snakelog.common import Vars, Atom, Clause, Var +from snakelog.litelog import Solver + +from linkml_datalog.dumpers.jsonfactsdumper import object_to_tuples +from linkml_datalog.pydanticlog.pydanticlog import Fact, rule + + +REF = Var("ref") +VAL = Var("val") + +class RefPath(Fact): + """ + A path to a reference + """ + signature: ClassVar[str] = ["path", "TEXT", "TEXT"] + + ref: str + path: str + + +class Link(Fact): + """ + A slot and its value + """ + signature: ClassVar[str] = ["sv", "TEXT", "TEXT", "TEXT"] + + ref: str + att: str + value: str + + +class Member(Fact): + """ + A member of a list + """ + signature: ClassVar[str] = ["nth", "TEXT", "INTEGER", "TEXT"] + + ref: str + offset: int + value: str + + +class RefNumber(Fact): + """ + A reference to a number + """ + signature: ClassVar[str] = ["literal_number", "TEXT", "NUMBER"] + + ref: str + value: Union[int, float] + + +class RefBoolean(Fact): + """ + A reference to a number + """ + signature: ClassVar[str] = ["literal_boolean", "TEXT", "BOOLEAN"] + + ref: str + value: bool + + +class RefString(Fact): + """ + A reference to a string + """ + signature: ClassVar[str] = ["literal_string", "TEXT", "TEXT"] + + ref: str + value: str + + +class RefNull(Fact): + """ + A reference to a null + """ + signature: ClassVar[str] = ["literal_null", "TEXT"] + + ref: str + + +class RefValue(Fact): + """ + A reference to a value (any type) + """ + signature: ClassVar[str] = ["ref_value", "TEXT", "TEXT", "TEXT", "NUMBER", "BOOLEAN"] + ref: Any + datatype: str + value_str: str + value_number: Union[int, float] + value_boolean: bool + + @classmethod + def rules(cls) -> Iterable[Clause]: + this = cls.__rfunc__() + ref_string = RefString.__rfunc__() + ref_number = RefNumber.__rfunc__() + ref_boolean = RefBoolean.__rfunc__() + ref_null = RefNull.__rfunc__() + yield (this(REF, "string", VAL, 0, False) <= ref_string(REF, VAL)) + yield (this(REF, "number", "", VAL, False) <= ref_number(REF, VAL)) + yield (this(REF, "boolean", "", 0, VAL) <= ref_boolean(REF, VAL)) + yield (this(REF, "null", "", 0, False) <= ref_null(REF, VAL)) + + + +class AncestorOf(Fact): + """ + Entailed fact + """ + ancestor: str + descendant: str + + signature: ClassVar[str] = ["ancestor_of", "TEXT", "TEXT"] + + +def load(solver: Solver, sources: Union[str, List[str]]): + """ + Load JSON documents into the solves. + + :param sources: + :return: + """ + if not isinstance(sources, list): + sources = [sources] + for source in sources: + if not isinstance(source, str): + doc = source + elif source.endswith(".json"): + doc = json.load(open(source)) + elif source.endswith(".yaml"): + doc = yaml.safe_load(open(source)) + else: + doc = json.loads(source) + fact_iter = object_to_tuples(doc) + for fact in fact_iter: + atom = Atom(fact[0].value, fact[1]) + solver.add(atom) + + + +@rule +def json_rules(s: Solver): + x, y, z, u_ = Vars("x y z u") + link = Link.__relation__(s) + ancestor_of = AncestorOf.__relation__(s) + s.add(ancestor_of(x, y) <= link(x, u_, y)) + s.add(ancestor_of(x, y) <= ancestor_of(x, z) & ancestor_of(z, y)) diff --git a/linkml_datalog/lp/linkmllog.py b/linkml_datalog/lp/linkmllog.py new file mode 100644 index 0000000..35af5cb --- /dev/null +++ b/linkml_datalog/lp/linkmllog.py @@ -0,0 +1,225 @@ +from typing import ClassVar, Union, Iterable, Any + +from snakelog.common import Vars, Var, Clause, Not +from snakelog.litelog import Solver + +from linkml_datalog.lp.jsonlog import Link, RefString, Member +from linkml_datalog.pydanticlog.pydanticlog import Fact, rule, RULE + + +REF = Var("ref") +NAME = Var("name") +CDICT = Var("cdict") +CN = Var("cn") +SN = Var("sn") +ATT = Var("att") +VAL = Var("val") +ADICT = Var("adict") +VALREF = Var("valref") +L = Var("l") +MREF = Var("mref") +SREF = Var("sref") +U_ = Var("u") + +link = Link.__rfunc__() +member = Member.__rfunc__() +ref_string = RefString.__rfunc__() + +class ClassRefName(Fact): + ref: str + name: str + + signature: ClassVar[str] = ["class_ref_name", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + element_ref_name = ClassRefName.__rfunc__() + yield (element_ref_name(REF, NAME) <= link(U_, "classes", CDICT) & link(CDICT, NAME, REF)) + + + +class SlotRefName(Fact): + ref: str + name: str + + signature: ClassVar[str] = ["slot_ref_name", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + element_ref_name = cls.__rfunc__() + yield (element_ref_name(REF, NAME) <= + link(U_, "slots", CDICT) & link(CDICT, NAME, REF)) + + +class ClassInducedSlot(Fact): + class_definition: str + slot_name: str + + signature: ClassVar[str] = ["class_induced_slot", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + this = cls.__rfunc__() + class_ref_name = ClassRefName.__rfunc__() + + yield (this(CN, SN) <= + link(REF, "attributes", CDICT) & + class_ref_name(REF, CN) & + link(CDICT, SN, ADICT)) + yield (this(CN, SN) <= + link(REF, "slots", L) & + member(L, U_, MREF) & + ref_string(MREF, SN) & + class_ref_name(REF, CN)) + +class ClassInducedSlotValueRef(Fact): + class_definition: str + slot_name: str + att: str + valref: str + + signature: ClassVar[str] = ["class_induced_slot_value_ref", "TEXT", "TEXT", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + classdef_slot_av = cls.__rfunc__() + class_ref_name = ClassRefName.__rfunc__() + slot_ref_name = SlotRefName.__rfunc__() + + yield (classdef_slot_av(CN, SN, ATT, VALREF) <= + link(REF, "slot_usage", CDICT) & + class_ref_name(REF, CN) & + link(CDICT, SN, ADICT) & + link(ADICT, ATT, VALREF)) + yield (classdef_slot_av(CN, SN, ATT, VALREF) <= + link(REF, "attributes", CDICT) & + class_ref_name(REF, CN) & + link(CDICT, SN, ADICT) & + link(ADICT, ATT, VALREF)) + yield (classdef_slot_av(CN, SN, ATT, VALREF) <= + link(REF, "slots", L) & + class_ref_name(REF, CN) & + member(L, U_, MREF) & + ref_string(MREF, SN) & + slot_ref_name(SREF, SN) & + link(SREF, ATT, VALREF)) + +class ClassInducedSlotValueString(Fact): + class_definition: str + slot_name: str + att: str + val: str + + signature: ClassVar[str] = ["class_induced_slot_value_string", "TEXT", "TEXT", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + classdef_slot_av = cls.__rfunc__() + class_ref_name = ClassRefName.__rfunc__() + slot_ref_name = SlotRefName.__rfunc__() + + yield (classdef_slot_av(CN, SN, ATT, VAL) <= + link(REF, "slot_usage", CDICT) & + class_ref_name(REF, CN) & + link(CDICT, SN, ADICT) & + link(ADICT, ATT, VALREF) & + ref_string(VALREF, VAL)) + yield (classdef_slot_av(CN, SN, ATT, VAL) <= + link(REF, "attributes", CDICT) & + class_ref_name(REF, CN) & + link(CDICT, SN, ADICT) & + link(ADICT, ATT, VALREF) & + ref_string(VALREF, VAL)) + yield (classdef_slot_av(CN, SN, ATT, VAL) <= + link(REF, "slots", L) & + class_ref_name(REF, CN) & + member(L, U_, MREF) & + ref_string(MREF, SN) & + slot_ref_name(SREF, SN) & + link(SREF, ATT, VALREF) & + ref_string(VALREF, VAL)) + +class PrimaryKey(Fact): + class_definition: str + slot_name: str + + signature: ClassVar[str] = ["primary_key", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + csvr = ClassInducedSlotValueRef.__rfunc__() + this = cls.__rfunc__() + yield this(CN, SN) <= csvr(CN, SN, "identifier", U_) + + +class TypeDefinition(Fact): + element_name: str + + signature: ClassVar[str] = ["type_definition", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + element_ref_name = ClassRefName.__rfunc__() + this = cls.__rfunc__() + yield (this(REF) <= link(REF, "types", CDICT) & element_ref_name(CDICT, REF)) + yield this("string") ## TODO + + +class AssertedRange(Fact): + class_definition: str + slot_name: str + val: str + + signature: ClassVar[str] = ["asserted_range", "TEXT", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + csvr = ClassInducedSlotValueString.__rfunc__() + this = cls.__rfunc__() + yield (this(CN, SN, VAL) <= csvr(CN, SN, "range", VAL)) + + +class HasAssertedRange(Fact): + class_definition: str + slot_name: str + + signature: ClassVar[str] = ["has_asserted_range", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + asserted_range = AssertedRange.__rfunc__() + this = cls.__rfunc__() + yield (this(CN, SN) <= asserted_range(CN, SN, U_)) + + +class DefaultRange(Fact): + val: str + + signature: ClassVar[str] = ["default_range", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + this = cls.__rfunc__() + yield (this(VAL) <= link(U_, "default_range", REF) & ref_string(REF, VAL)) + + +class Range(Fact): + class_definition: str + slot_name: str + val: str + + signature: ClassVar[str] = ["range", "TEXT", "TEXT", "TEXT"] + + @classmethod + def rules(cls) -> Iterable[Clause]: + cs = ClassInducedSlot.__rfunc__() + asserted_range = AssertedRange.__rfunc__() + has_asserted_range = HasAssertedRange.__rfunc__() + type_definition = TypeDefinition.__rfunc__() + default_range = DefaultRange.__rfunc__() + this = cls.__rfunc__() + yield (this(CN, SN, VAL) <= asserted_range(CN, SN, VAL)) + # TODO + yield (this(CN, SN, VAL) <= default_range(VAL) & cs(CN, SN) & Not(has_asserted_range(CN, SN))) + #yield (this(CN, SN, VAL) <= default_range(VAL) & cs(CN, SN) & Not((type_definition(U_) & asserted_range(CN, SN, U_)))) + #yield (this(CN, SN, VAL) <= default_range(VAL) & cs(CN, SN)) diff --git a/linkml_datalog/pydanticlog/README.md b/linkml_datalog/pydanticlog/README.md new file mode 100644 index 0000000..f405ea0 --- /dev/null +++ b/linkml_datalog/pydanticlog/README.md @@ -0,0 +1,5 @@ +# pydanticlog + +Logic programming using pydantic structured clauses. + +This is a thin wrapper around snakelog. \ No newline at end of file diff --git a/linkml_datalog/pydanticlog/__init__.py b/linkml_datalog/pydanticlog/__init__.py new file mode 100644 index 0000000..4bea0fe --- /dev/null +++ b/linkml_datalog/pydanticlog/__init__.py @@ -0,0 +1,7 @@ +from linkml_datalog.pydanticlog.pydanticlog import Fact, rule, load_module + +__all__ = [ + 'Fact', + 'load_module', + 'rule' +] \ No newline at end of file diff --git a/linkml_datalog/pydanticlog/pydanticlog.py b/linkml_datalog/pydanticlog/pydanticlog.py new file mode 100644 index 0000000..efb0831 --- /dev/null +++ b/linkml_datalog/pydanticlog/pydanticlog.py @@ -0,0 +1,115 @@ +import json +from types import ModuleType +from typing import ClassVar, Optional, Union, Type, Callable, Iterable, Tuple, List + +from pydantic import BaseModel +from snakelog.common import Atom, Var, Clause +from snakelog.litelog import Solver + + +RULE = Tuple[Atom, List[Atom]] + +class Fact(BaseModel): + """ + Base class for all Fact models + """ + predicate_name: ClassVar[Optional[str]] = None + signature: ClassVar[str] = [] + + @classmethod + def get_signature(cls): + return cls.signature + + @classmethod + def __relation__(cls, solver: Solver) -> Callable: + sig = cls.get_signature() + return solver.Relation(*sig) + + @classmethod + def predicate(cls) -> str: + sig = cls.get_signature() + return sig[0] + + @classmethod + def __rfunc__(cls) -> Callable[..., Atom]: + return lambda *args: Atom(cls.predicate(), args) + + @classmethod + def query(cls, solver: Solver, **kwargs): + return query(solver, cls, **kwargs) + + @classmethod + def rules(cls) -> Iterable[Clause]: + pass + + +def rule(func): + """ + Decorator to mark a function as a rule + + :param func: + :return: + """ + func.is_rule = True + return func + + +def load_module(solver: Solver, module: Optional[ModuleType] = None): + if module is None: + module = globals() + # iterate over all classes in the module + for c in module.__dict__.values(): + if isinstance(c, type) and issubclass(c, Fact): + sig = c.get_signature() + if sig: + solver.Relation(*sig) + rules = c.rules() + if rules: + for r in rules: + solver.add(r) + # iterate over all functions in the module, find ones decorated as rules + for f in module.__dict__.values(): + if hasattr(f, 'is_rule'): + f(solver) + + +def query(solver: Solver, predicate: Optional[Union[str, Type]] = None, run=False): + """ + Infer facts from the JSON documents + + :param predicate: + :return: + """ + if run: + solver.run() + if isinstance(predicate, Type): + # test if the class is a subclass of Fact + if issubclass(predicate, Fact): + sig = predicate.get_signature() + predicate = sig[0] + else: + predicate = predicate.__name__ + solver.cur.execute(f"SELECT * FROM {predicate}") + return solver.cur.fetchall() + + +def solver_print_rules(solver: Solver): + for rule in solver.rules: + if rule[1]: + print(as_prolog(rule)) + +def as_prolog(rule): + head, body = rule + argstr = ',\n '.join([as_prolog_atom(b) for b in body]) + return f"{as_prolog_atom(head)} :-\n {argstr}." + + +def as_prolog_atom(atom: Atom): + return f"{atom.name}({', '.join([as_prolog_arg(a) for a in atom.args])})" + + +def as_prolog_arg(arg): + if isinstance(arg, Var): + return str(arg.name).upper() + else: + return json.dumps(arg) diff --git a/linkml_datalog/utils/json_to_eav.py b/linkml_datalog/utils/json_to_eav.py index 5d04bdd..7beef20 100644 --- a/linkml_datalog/utils/json_to_eav.py +++ b/linkml_datalog/utils/json_to_eav.py @@ -10,6 +10,8 @@ from enum import Enum from typing import Dict, List, Any, Union, Tuple +from deprecated import deprecated + DICT_OR_LIST = Union[Dict, List] class ObjType(Enum): @@ -24,6 +26,7 @@ class ObjType(Enum): HDR = ['id', 'index', 'key', 'val_s', 'val_i', 'type'] +@deprecated("rdf-like export used instead") @dataclass class EavDumper: """ diff --git a/mkdocs.yml b/mkdocs.yml index 5ae0730..8d322f0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -6,8 +6,23 @@ theme: palette: scheme: slate primary: cyan + features: + - content.tabs.link plugins: - search +markdown_extensions: + - pymdownx.highlight: + anchor_linenums: true + - pymdownx.inlinehilite + - pymdownx.snippets + - pymdownx.superfences + - 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linkml:types default_prefix: ex +default_range: string subsets: mysubset: {} diff --git a/tests/test_dataloggen.py b/tests/test_dataloggen.py index 223e606..cfce82a 100644 --- a/tests/test_dataloggen.py +++ b/tests/test_dataloggen.py @@ -6,19 +6,37 @@ OUTPUTS_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'outputs') +BIOLINK_LINES = [ + ".decl Gene(i: symbol)", + ".decl Gene_asserted(i: identifier)", + ".output Gene", + 'Gene_asserted(i) :- triple(i, RDF_TYPE, "https://w3id.org/biolink/vocab/Gene").', + "Gene(i) :- Gene_asserted(i).", +] + class DatalogGeneratorTestCase(unittest.TestCase): + """ + Tests the output of the DatalogGenerator. + + TODO: Avoid TestByGuru + """ def test_gen(self): fn = os.path.join(INPUTS_DIR, "personinfo.yaml") print(f'Loading {fn}') gen = DatalogGenerator(fn) - print(gen.serialize()) + prog = gen.serialize() + print(prog) + assert ".decl name(i: identifier, v: identifier)" in prog def test_biolink(self): fn = os.path.join(INPUTS_DIR, "biolink-model.yaml") print(f'Loading {fn}') gen = DatalogGenerator(fn) + prog = gen.serialize() with open(os.path.join(OUTPUTS_DIR, 'biolink.dl'), 'w') as stream: - stream.write(gen.serialize()) + stream.write(prog) + for line in BIOLINK_LINES: + assert line in prog if __name__ == '__main__': diff --git a/tests/test_engine.py b/tests/test_engine.py index 9d4120e..531e934 100644 --- a/tests/test_engine.py +++ b/tests/test_engine.py @@ -2,8 +2,7 @@ from pathlib import Path from typing import Type, Tuple, List -import yaml -from linkml_runtime.dumpers import rdflib_dumper, yaml_dumper +from linkml_runtime.dumpers import yaml_dumper from linkml_runtime.loaders import yaml_loader from linkml_runtime.utils.schemaview import SchemaView @@ -11,9 +10,8 @@ from linkml_runtime.utils.slot import Slot from linkml_runtime.utils.yamlutils import YAMLRoot -from rdflib import Graph, ConjunctiveGraph, RDF, Namespace, URIRef, RDFS +from rdflib import ConjunctiveGraph, RDF, Namespace, URIRef, RDFS -from linkml_datalog.dumpers.tupledumper import TupleDumper from linkml_datalog.engines.datalog_engine import DatalogEngine from tests.models.personinfo import Container, Person @@ -31,20 +29,29 @@ class DatalogEngineTestCase(unittest.TestCase): + """ + Tests end-to-end compilation to datalog and execution. + """ def test_engine(self): - """tests souffle engine""" + """ + End-to-end test of the datalog engine. + + Compiles a standard personinfo schema and data and runs the engine. + Uses example_personinfo_data, this is expected to have validation errors. + """ schema_fn = os.path.join(INPUTS_DIR, "personinfo.yaml") data_fn = os.path.join(INPUTS_DIR, "example_personinfo_data.yaml") - data = yaml_loader.load(data_fn, target_class=Container) + data: Container = yaml_loader.load(data_fn, target_class=Container) directory = os.path.join(OUTPUT_DIR, 'persondata') Path(directory).mkdir(exist_ok=True) sv = SchemaView(schema_fn) e = DatalogEngine(sv, workdir=os.path.join(OUTPUT_DIR, 'tmp')) e.run(data, prefix_map=prefixes) rpt = e.validation_results() - for result in rpt.results: - print(f' * {result}') + assert len(rpt.results) > 1 + #for result in rpt.results: + # print(f' * {result}') def has_type(t): matches = [result for result in rpt.results if result.type == t] return matches != [] @@ -67,9 +74,13 @@ def has_type(t): contains=[('https://example.org/P/001', 'http://purl.obolibrary.org/obo/HsapDv_0000087'), ('https://example.org/P/006', 'http://purl.obolibrary.org/obo/HsapDv_0000086'), ]) - #e.materialize_inferences(data) - #ys = yaml_dumper.dumps(data) - #print(ys) + e.materialize_inferences(data) + ys = yaml_dumper.dumps(data) + print(ys) + p1 = next(p for p in data.persons if p.id == 'P:001') + p4 = next(p for p in data.persons if p.id == 'P:004') + p5 = next(p for p in data.persons if p.id == 'P:005') + self.assertCountEqual(p5.ancestor_of, [p1.id, p4.id]) def test_engine_rdf(self): """uses a collection of annotated named graphs as test """ diff --git a/tests/test_jsonlog.py b/tests/test_jsonlog.py new file mode 100644 index 0000000..cfbb537 --- /dev/null +++ b/tests/test_jsonlog.py @@ -0,0 +1,48 @@ +from typing import Iterable, Tuple + +import pytest +from snakelog.common import * +from snakelog.litelog import Solver, TEXT + +from linkml_datalog.engines.jsonlog_engine import JsonLogEngine +from linkml_datalog.lp.jsonlog import AncestorOf, RefValue + + +@pytest.fixture +def solver(): + return Solver() + + +@pytest.fixture +def engine(): + return JsonLogEngine() + + +@pytest.fixture +def data(): + return { + "persons": { + "p1": { + "name": "Alice", + "age": 25, + "aliases": ["A", "Ally"], + "address": None, + "alive": True, + }, + }, + } + +def test_jsonlog(solver, engine, data): + engine.load(data) + #results = list(engine.infer(AncestorOf)) + results = list(AncestorOf.query(engine.solver, run=True)) + for r in results: + print(r) + assert len(results) > 0 + assert ('.', './persons/p1/aliases') in results + results = list(RefValue.query(engine.solver, run=False)) + for r in results: + print("RV", r) + assert len(results) > 0 + #assert ('.', './persons/p1/aliases') in results + diff --git a/tests/test_linkmllog.py b/tests/test_linkmllog.py new file mode 100644 index 0000000..dff9a83 --- /dev/null +++ b/tests/test_linkmllog.py @@ -0,0 +1,82 @@ +from pathlib import Path +from typing import Iterable, Tuple + +import pytest +import yaml +from snakelog.common import * +from snakelog.litelog import Solver, TEXT + +from linkml_datalog.engines.jsonlog_engine import JsonLogEngine +from linkml_datalog.lp import jsonlog, linkmllog +from linkml_datalog.lp.jsonlog import AncestorOf, Link, RefString +from linkml_datalog.lp.linkmllog import ClassRefName, SlotRefName, ClassInducedSlotValueString, PrimaryKey, Range, \ + AssertedRange, TypeDefinition +from linkml_datalog.pydanticlog import load_module +from linkml_datalog.pydanticlog.pydanticlog import solver_print_rules + +THIS = Path(__file__).parent +INPUT_DIR = THIS / "inputs" + + + +@pytest.fixture +def solver(): + return Solver() + + +@pytest.fixture +def init_module(solver): + load_module(solver, jsonlog) + load_module(solver, linkmllog) + +@pytest.fixture +def data(): + return yaml.safe_load(open(INPUT_DIR / "employee.yaml")) + + +def test_linkmllog(solver, init_module, data): + jsonlog.load(solver, data) + trules = list(ClassRefName.rules()) + print(len(trules)) + for r in trules: + print("RULE", r) + #assert False + solver.run() + results = list(ClassRefName.query(solver)) + for r in results: + print(r) + assert len(results) > 0 + assert ('./classes/organization', 'organization') in results + results = list(SlotRefName.query(solver)) + for r in results: + print(r) + assert len(results) > 0 + assert ('./slots/employees', 'employees') in results + print("ClassDefinitionSlotDefinition") + results = list(ClassInducedSlotValueString.query(solver)) + for r in results: + print(r) + assert len(results) > 0 + assert ('employee', 'scores', 'range', 'float') in results + assert ('person', 'id', 'pattern', '^\\S+$') in results + assert ('person', 'category', 'is_a', 'type') in results + results = list(PrimaryKey.query(solver)) + for r in results: + print("PK", r) + assert len(results) > 0 + #solver_print_rules(solver) + results = list(AssertedRange.query(solver)) + for r in results: + print("ASSERTED_RANGE", r) + assert len(results) > 0 + assert ('employee', 'scores', 'float') in results + results = list(Range.query(solver)) + for r in results: + print("INF RANGE", r) + assert len(results) > 0 + assert ('person', 'id', 'string') in results + results = list(TypeDefinition.query(solver)) + for r in results: + print("TD", r) + assert len(results) > 0 + diff --git a/tests/test_snakelog.py b/tests/test_snakelog.py new file mode 100644 index 0000000..e77e4e8 --- /dev/null +++ b/tests/test_snakelog.py @@ -0,0 +1,104 @@ +from typing import Iterable, Tuple + +import pytest +from snakelog.common import * +from snakelog.litelog import Solver, TEXT + +@pytest.fixture +def solver(): + return Solver() + + +def tree_edges(node: str, depth: int, num_children: int = 3) -> Iterable[Tuple[str, str]]: + if depth == 0: + return + for i in range(num_children): + child = f"{node}.{i}" + yield node, child + yield from tree_edges(child, depth-1, num_children) + + +@pytest.mark.parametrize("depth", [1, 2, 5, 10]) +@pytest.mark.parametrize("num_children", [2, 3]) +def test_snakelog(solver, depth, num_children): + """ + TODO: change from benchmark to test + + :param solver: + :param depth: + :param num_children: + :return: + """ + s = solver + x, y, z = Vars("x y z") + edge = s.Relation("edge", TEXT, TEXT) + path = s.Relation("path", TEXT, TEXT) + indirect_path = s.Relation("indirect_path", TEXT, TEXT) + + for e in tree_edges("a", depth, num_children): + s.add(Atom("edge", e)) + s.add(path(x, y) <= edge(x, y)) + s.add(path(x, z) <= path(x, y) & path(y, z)) + s.add(indirect_path(x, y) <= path(x, y) & Not(edge(x, y))) + s.run() + + s.cur.execute("SELECT * FROM indirect_path") + paths = list(s.cur.fetchall()) + print(f"\nDepth: {depth}, NChildren: {num_children} Num paths: {len(paths)}") + + +def test_unbound(solver): + """ + Tests unbounded + + :param solver: + :param depth: + :param num_children: + :return: + """ + s = solver + x, y, z = Vars("x y z") + (u,) = Vars("u") + edge = s.Relation("edge", TEXT, TEXT, TEXT) + path = s.Relation("path", TEXT, TEXT) + + for e in tree_edges("a", 2, 2): + s.add(Atom("edge", [e[0], "parent", e[1]])) + s.add(path(x, y) <= edge(x, Var("u"), y)) + s.add(path(x, z) <= edge(x, u, y) & path(y, z)) + s.run() + + s.cur.execute("SELECT * FROM path") + paths = list(s.cur.fetchall()) + print(len(paths)) + for p in paths: + print(p) + assert len(paths) == 10 + + +def test_bound(solver): + """ + Tests bounded in args + + :param solver: + :param depth: + :param num_children: + :return: + """ + s = solver + x, y, z, u = Vars("x y z u") + edge = s.Relation("edge", TEXT, TEXT, TEXT) + path = s.Relation("path", TEXT, TEXT) + + for e in tree_edges("a", 2, 2): + s.add(Atom("edge", [e[0], "parent", e[1]])) + s.add(path(x, y) <= edge(x, "parent", y)) + s.add(path(x, z) <= edge(x, "parent", y) & path(y, z)) + s.run() + + s.cur.execute("SELECT * FROM path") + paths = list(s.cur.fetchall()) + print(len(paths)) + for p in paths: + print(p) + assert len(paths) == 10 diff --git a/tests/test_tupledumper.py b/tests/test_tupledumper.py index eb36f50..737053b 100644 --- a/tests/test_tupledumper.py +++ b/tests/test_tupledumper.py @@ -2,6 +2,7 @@ from pathlib import Path import yaml +from deprecated.classic import deprecated from linkml_runtime.loaders import yaml_loader from linkml_runtime.utils.schemaview import SchemaView