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Requiere rdflib y pyshacl (requirements-lock.txt). Si faltan, la clase se omite
con un mensaje explícito: eso no implica que la ontología falle.
"""
import csv
import json
import unittest
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
try:
from pyshacl import validate
from rdflib import Graph, Literal, Namespace
from rdflib.namespace import OWL, RDF, RDFS, XSD
_ONT_DEPS = True
_ONT_IMPORT_ERROR = None
except ModuleNotFoundError as exc:
_ONT_DEPS = False
_ONT_IMPORT_ERROR = exc
validate = None # type: ignore
Graph = Literal = Namespace = OWL = RDF = RDFS = XSD = None # type: ignore
@unittest.skipUnless(
_ONT_DEPS,
f"omitido: falta dependencia de ontología ({_ONT_IMPORT_ERROR}). "
"Instala requirements-lock.txt; no implica fallo de la ontología.",
)
class IOMValidationTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.IO = Namespace("http://example.org/iom#")
cls.DATA = ROOT / "ontology" / "io_ontology.ttl"
cls.SHAPES = ROOT / "ontology" / "io_shapes.ttl"
cls.SPEC = json.loads((ROOT / "data" / "iom_spec.json").read_text(encoding="utf-8"))
with (ROOT / "datasets" / "triad_nodes.csv").open(encoding="utf-8", newline="") as handle:
cls.NODE_ROWS = list(csv.DictReader(handle))
def setUp(self):
self.data = Graph().parse(self.DATA, format="turtle")
self.shapes = Graph().parse(self.SHAPES, format="turtle")
self.IO = self.__class__.IO
def conforms(self, graph=None, focus_nodes=None):
result, _report, text = validate(
self.data if graph is None else graph,
shacl_graph=self.shapes,
inference="none",
focus_nodes=focus_nodes,
)
return result, text
def test_spec_matches_canonical_numbered_triad_matrices(self):
expected_adv = [
["orientación", "cuántico", "dirección"],
["hardware", "virtual", "software"],
["universo", "espacio", "vacío"],
["singularidad", "tiempo", "infinito"],
["cuerpo", "consciente", "mundo"],
["carácter", "subconsciente", "personalidad"],
["memoria", "inconsciente", "imaginación"],
["receptor", "supraconsciente", "emisor"],
["lógica", "inteligencia", "libertad"],
["muerte", "artificial", "vida"],
["descendente", "mente", "ascendente"],
["oscuridad", "consciencia", "luz"],
["animal", "humano", "IA"],
]
self.assertEqual(self.SPEC["triads"]["adv"], expected_adv)
self.assertEqual(self.SPEC["triads"]["ret"], [row[::-1] for row in expected_adv])
self.assertEqual(self.SPEC["triads"]["index_base"], 1)
self.assertEqual(self.SPEC["positions"]["adv_labels"], ["→", "0", "→"])
self.assertEqual(self.SPEC["positions"]["ret_labels"], ["←", "0", "←"])
def test_exact_triad_dataset_and_shacl_conformance(self):
IO = self.IO
nodes = set(self.data.subjects(RDF.type, IO.OntoNode))
expected = {
IO[f"T{i:02d}_{phase}_P{pos}"]
for i in range(1, 14)
for phase in ("adv", "ret")
for pos in range(3)
}
self.assertEqual(nodes, expected)
self.assertTrue(self.conforms()[0], self.conforms()[1])
for phase in ("adv", "ret"):
rows = self.SPEC["triads"][phase]
for row_index, row in enumerate(rows):
triad_index = row_index + 1
for pos, label in enumerate(row):
node = IO[f"T{triad_index:02d}_{phase}_P{pos}"]
self.assertEqual(str(self.data.value(node, RDFS.label)), label)
visual_label = self.SPEC["positions"]["adv_labels" if phase == "adv" else "ret_labels"][pos]
self.assertEqual(str(self.data.value(node, IO.displayLabel)), visual_label)
self.assertEqual(
self.data.value(node, IO.hasTriadIndex),
Literal(triad_index, datatype=XSD.integer),
)
self.assertEqual(
self.data.value(node, IO.hasLocalPos),
Literal(pos, datatype=XSD.integer),
)
self.assertEqual(
self.data.value(node, IO.hasPerspective),
IO[self.SPEC["perspectives"][pos]],
)
self.assertEqual(
self.data.value(node, IO.hasPosition),
IO[{"adv": ["→", "0", "→"], "ret": ["←", "0", "←"]}[phase][pos]],
)
display_labels = self.SPEC["positions"]["adv_labels" if phase == "adv" else "ret_labels"]
generated = next(r for r in self.NODE_ROWS if r["node_id"] == f"T{triad_index:02d}_{phase}_P{pos}")
self.assertEqual(generated["display_label"], display_labels[pos])
def test_mirrors_are_reciprocal_and_swap_laterals(self):
IO = self.IO
for i in range(1, 14):
for pos in range(3):
adv, ret = IO[f"T{i:02d}_adv_P{pos}"], IO[f"T{i:02d}_ret_P{2 - pos}"]
self.assertEqual(self.data.value(adv, IO.mirrorOf), ret)
self.assertEqual(self.data.value(ret, IO.mirrorOf), adv)
def test_creation_edges_follow_all_four_rules(self):
IO = self.IO
actual = set(self.data.triples((None, IO.creates, None)))
expected = set()
for i in range(1, 14):
for rule in self.SPEC["creation_rules"]:
for src in rule["source_positions"]:
for dst in rule["target_positions"]:
expected.add(
(
IO[f"T{i:02d}_{rule['source_phase']}_P{src}"],
IO.creates,
IO[f"T{i:02d}_{rule['target_phase']}_P{dst}"],
)
)
n_rules_edges = sum(
len(r["source_positions"]) * len(r["target_positions"])
for r in self.SPEC["creation_rules"]
)
self.assertEqual(len(expected), 13 * n_rules_edges)
self.assertEqual(actual, expected)
def test_vectors_match_all_four_updated_sequences(self):
IO = self.IO
self.assertEqual(len(set(self.data.subjects(RDF.type, IO.VectorStep))), 20)
for row in self.SPEC["vectors"]:
for idx, (label, axis) in enumerate(zip(row["labels"], row["axis_positions"])):
step = IO[f"vector_{row['name'].lower()}_{row['phase']}_{idx}"]
self.assertEqual(self.data.value(step, IO.hasVector), IO[row["name"]])
self.assertEqual(str(self.data.value(step, IO.hasCyclePhase)), row["phase"])
self.assertEqual(
self.data.value(step, IO.hasStepIndex),
Literal(idx, datatype=XSD.integer),
)
self.assertEqual(
self.data.value(step, IO.hasAxisPosition),
Literal(axis, datatype=XSD.integer),
)
self.assertEqual(
self.data.value(step, IO.displayArrow),
Literal(row["arrow"], datatype=XSD.string),
)
self.assertEqual(str(self.data.value(step, RDFS.label)), label)
def test_owl_classes_object_property_axioms_and_perspectives(self):
IO = self.IO
for cls in (
IO.OntoNode,
IO.VectorStep,
IO.Perspective,
IO.ExpandedCategory,
IO.Position,
IO.Direction,
):
self.assertIn((cls, RDF.type, OWL.Class), self.data)
for prop in (
IO.hasPerspective,
IO.hasPosition,
IO.hasDirection,
IO.hasVector,
IO.mirrorOf,
IO.creates,
):
self.assertIn((prop, RDF.type, OWL.ObjectProperty), self.data)
self.assertIsNotNone(self.data.value(prop, RDFS.domain))
self.assertIsNotNone(self.data.value(prop, RDFS.range))
self.assertIn((IO.displayLabel, RDF.type, OWL.DatatypeProperty), self.data)
self.assertIn((IO.displayLabel, RDF.type, OWL.FunctionalProperty), self.data)
self.assertIn((IO.mirrorOf, RDF.type, OWL.SymmetricProperty), self.data)
for resource in (IO.Ind, IO.D, IO.Tot, IO.Evol, IO.Invol):
self.assertIn((resource, RDF.type, IO.Perspective), self.data)
def test_expanded_categories_keep_declared_level_counts(self):
IO = self.IO
nodes = set(self.data.subjects(RDF.type, IO.ExpandedCategory))
self.assertEqual(len(nodes), 21)
self.assertEqual(
[
sum(
self.data.value(n, IO.hasLevel) == Literal(level, datatype=XSD.integer)
for n in nodes
)
for level in range(5)
],
[1, 4, 6, 6, 4],
)
def test_generated_datasets_have_expected_rows(self):
n_triads = len(self.SPEC["triads"]["adv"])
n_vec = sum(len(v["labels"]) for v in self.SPEC["vectors"])
expected = {
"triad_nodes.csv": n_triads * 2 * 3,
"vector_steps.csv": n_vec,
"relation_candidates.csv": n_triads * 6 * 5,
}
for name, count in expected.items():
with (ROOT / "datasets" / name).open(encoding="utf-8", newline="") as f:
self.assertEqual(len(list(csv.DictReader(f))), count, name)
def test_wrong_visual_position_label_is_rejected(self):
IO = self.IO
graph = Graph()
for triple in self.data:
graph.add(triple)
node = IO["T01_adv_P0"]
graph.remove((node, IO.displayLabel, Literal("→", datatype=XSD.string)))
graph.add((node, IO.displayLabel, Literal("←", datatype=XSD.string)))
self.assertFalse(self.conforms(graph, [node])[0])
def test_wrong_phase_direction_is_rejected(self):
IO = self.IO
graph = Graph()
for triple in self.data:
graph.add(triple)
node = IO["T01_adv_P0"]
graph.remove((node, IO.hasDirection, IO.evol))
graph.add((node, IO.hasDirection, IO.invol))
self.assertFalse(self.conforms(graph, [node])[0])
def test_nonreciprocal_or_misplaced_mirror_is_rejected(self):
IO = self.IO
graph = Graph()
for triple in self.data:
graph.add(triple)
node, good, wrong = IO["T01_adv_P0"], IO["T01_ret_P2"], IO["T01_ret_P0"]
graph.remove((node, IO.mirrorOf, good))
graph.add((node, IO.mirrorOf, wrong))
self.assertFalse(self.conforms(graph, [node])[0])
def test_wrong_creation_link_is_rejected(self):
IO = self.IO
graph = Graph()
for triple in self.data:
graph.add(triple)
node, good, wrong = IO["T01_adv_P0"], IO["T01_ret_P1"], IO["T01_ret_P0"]
graph.remove((node, IO.creates, good))
graph.add((node, IO.creates, wrong))
self.assertFalse(self.conforms(graph, [node])[0])
def test_wrong_vector_label_or_axis_is_rejected(self):
IO = self.IO
graph = Graph()
for triple in self.data:
graph.add(triple)
step = IO["vector_evol_ret_1"]
old = self.data.value(step, RDFS.label)
graph.remove((step, RDFS.label, old))
graph.add((step, RDFS.label, Literal("fut", lang="es")))
self.assertFalse(self.conforms(graph, [step])[0])
if __name__ == "__main__":
unittest.main()
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