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semantica/benchmarks/visualization/conftest.py
T

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2.3 KiB
Python

import random
from typing import Any, Dict, List
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
# Data Generators
@pytest.fixture
def generate_embeddings():
"""Generates synthetic high-dim embeddings."""
def _gen(n_samples: int, n_features: int = 768):
return np.random.rand(n_samples, n_features).astype(np.float32)
return _gen
@pytest.fixture
def generate_knowledge_graph():
"""Generates synthetic Knowledge Graph dictionary."""
def _gen(n_nodes: int, density: float = 0.05):
entities = [
{
"id": f"e_{i}",
"label": f"Entity_{i}",
"type": random.choice(["Person", "Organization", "Location", "Event"]),
"metadata": {"score": random.random()},
}
for i in range(n_nodes)
]
relationships = []
n_edges = int(n_nodes * (n_nodes - 1) * density)
# Capping edges for safety
n_edges = min(n_edges, n_nodes * 5)
for i in range(n_edges):
src = random.randint(0, n_nodes - 1)
tgt = random.randint(0, n_nodes - 1)
if src != tgt:
relationships.append(
{
"source": f"e_{src}",
"target": f"e_{tgt}",
"type": "related_to",
"metadata": {"weight": random.random()},
}
)
return {"entities": entities, "relationships": relationships}
return _gen
@pytest.fixture
def generate_temporal_data(generate_knowledge_graph):
"""Generates synthetic temporal graph snapshots."""
def _gen(n_snapshots: int, n_nodes: int):
timestamps_map = {}
base_kg = generate_knowledge_graph(n_nodes)
entities = base_kg["entities"]
all_years = list(range(2020, 2020 + n_snapshots))
for ent in entities:
start = random.randint(0, len(all_years) - 2)
duration = random.randint(1, len(all_years) - start)
timestamps_map[ent["id"]] = all_years[start : start + duration]
return {
"entities": entities,
"relationships": base_kg["relationships"],
"timestamps": timestamps_map,
}
return _gen