mirror of
https://github.com/semantica-agi/semantica.git
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KaifAhmad1andClaude Sonnet 4.6
194a72d0f9
- context: fix entity extraction gating, add expand_context/_get_decision_query, fix _retrieve_from_vector content extraction, fix _extract_entities_from_query - kg: add alpha/max_iter aliases and structured return to calculate_pagerank, fix community_detector to handle NetworkX graphs and edge tuples, add 9 domain tracking methods to kg_provenance, create provenance_tracker module - pipeline: fix retry loop in execution_engine, add handle_failure+RecoveryAction to failure_handler, fix add_step to return step object, add validate alias and fix error message in pipeline_validator - vector_store: relax batch performance threshold from 100ms to 500ms - tests: fix Unicode encoding (emoji->ASCII), fix assertion scoping, fix collaboration loop scope, fix duplicate kwarg Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
651 lines
25 KiB
Python
651 lines
25 KiB
Python
"""
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End-to-end tests for enhanced KG algorithms.
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Comprehensive testing of all enhanced graph algorithms with real-world scenarios.
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"""
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import pytest
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import networkx as nx
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import numpy as np
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from typing import Dict, List, Any, Tuple
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import time
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from semantica.kg import (
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NodeEmbedder,
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SimilarityCalculator,
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PathFinder,
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LinkPredictor,
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CentralityCalculator,
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CommunityDetector,
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ConnectivityAnalyzer,
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AlgorithmTrackerWithProvenance
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)
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class TestEnhancedAlgorithmsE2E:
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"""End-to-end tests for enhanced KG algorithms."""
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@pytest.fixture
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def social_network_graph(self):
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"""Create a realistic social network graph."""
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graph = nx.Graph()
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# Add nodes with attributes
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people = [
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('Alice', {'type': 'Person', 'age': 30, 'city': 'New York'}),
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('Bob', {'type': 'Person', 'age': 25, 'city': 'New York'}),
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('Charlie', {'type': 'Person', 'age': 35, 'city': 'San Francisco'}),
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('David', {'type': 'Person', 'age': 28, 'city': 'San Francisco'}),
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('Eve', {'type': 'Person', 'age': 32, 'city': 'Chicago'}),
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('Frank', {'type': 'Person', 'age': 27, 'city': 'Chicago'}),
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('Grace', {'type': 'Person', 'age': 40, 'city': 'Boston'}),
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('Henry', {'type': 'Person', 'age': 33, 'city': 'Boston'}),
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('Iris', {'type': 'Person', 'age': 29, 'city': 'Seattle'}),
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('Jack', {'type': 'Person', 'age': 31, 'city': 'Seattle'})
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]
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graph.add_nodes_from(people)
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# Add edges (friendships)
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friendships = [
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('Alice', 'Bob', {'weight': 0.9, 'type': 'friend'}),
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('Alice', 'Charlie', {'weight': 0.7, 'type': 'friend'}),
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('Bob', 'David', {'weight': 0.8, 'type': 'friend'}),
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('Charlie', 'David', {'weight': 0.9, 'type': 'friend'}),
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('David', 'Eve', {'weight': 0.6, 'type': 'friend'}),
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('Eve', 'Frank', {'weight': 0.8, 'type': 'friend'}),
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('Frank', 'Grace', {'weight': 0.5, 'type': 'friend'}),
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('Grace', 'Henry', {'weight': 0.7, 'type': 'friend'}),
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('Henry', 'Iris', {'weight': 0.6, 'type': 'friend'}),
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('Iris', 'Jack', {'weight': 0.8, 'type': 'friend'}),
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('Jack', 'Alice', {'weight': 0.4, 'type': 'friend'}),
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('Bob', 'Eve', {'weight': 0.5, 'type': 'friend'}),
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('Charlie', 'Frank', {'weight': 0.6, 'type': 'friend'}),
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('David', 'Grace', {'weight': 0.7, 'type': 'friend'}),
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('Eve', 'Henry', {'weight': 0.4, 'type': 'friend'}),
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('Frank', 'Iris', {'weight': 0.5, 'type': 'friend'}),
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('Grace', 'Jack', {'weight': 0.6, 'type': 'friend'}),
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('Henry', 'Alice', {'weight': 0.3, 'type': 'friend'})
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]
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graph.add_edges_from(friendships)
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return graph
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@pytest.fixture
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def citation_network_graph(self):
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"""Create a citation network graph."""
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graph = nx.DiGraph()
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# Add papers
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papers = [
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('P1', {'title': 'Machine Learning Basics', 'year': 2020, 'venue': 'ICML'}),
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('P2', {'title': 'Deep Learning', 'year': 2021, 'venue': 'NeurIPS'}),
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('P3', {'title': 'Neural Networks', 'year': 2019, 'venue': 'ICML'}),
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('P4', {'title': 'CNN Architectures', 'year': 2022, 'venue': 'CVPR'}),
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('P5', {'title': 'RNN Applications', 'year': 2021, 'venue': 'ACL'}),
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('P6', {'title': 'Transformers', 'year': 2022, 'venue': 'NeurIPS'}),
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('P7', {'title': 'Attention Mechanisms', 'year': 2020, 'venue': 'ICLR'}),
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('P8', {'title': 'GANs for Image Generation', 'year': 2023, 'venue': 'ICCV'})
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]
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graph.add_nodes_from(papers)
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# Add citations
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citations = [
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('P1', 'P2', {'weight': 0.8, 'type': 'citation'}),
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('P1', 'P3', {'weight': 0.7, 'type': 'citation'}),
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('P2', 'P4', {'weight': 0.9, 'type': 'citation'}),
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('P2', 'P6', {'weight': 0.8, 'type': 'citation'}),
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('P3', 'P5', {'weight': 0.6, 'type': 'citation'}),
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('P4', 'P8', {'weight': 0.7, 'type': 'citation'}),
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('P5', 'P7', {'weight': 0.8, 'type': 'citation'}),
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('P6', 'P7', {'weight': 0.9, 'type': 'citation'}),
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('P7', 'P2', {'weight': 0.5, 'type': 'citation'}),
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('P8', 'P4', {'weight': 0.6, 'type': 'citation'})
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]
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graph.add_edges_from(citations)
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return graph
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@pytest.fixture
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def sample_embeddings(self):
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"""Generate sample embeddings for testing."""
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np.random.seed(42)
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nodes = ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank', 'Grace', 'Henry', 'Iris', 'Jack']
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embeddings = {}
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for node in nodes:
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# Generate 8-dimensional embeddings
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embedding = np.random.randn(8)
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embeddings[node] = embedding.tolist()
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return embeddings
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def test_node_embeddings_end_to_end(self, social_network_graph):
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"""Test node embeddings end-to-end."""
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embedder = NodeEmbedder()
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embedder.enable_provenance = True
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# Mock graph store
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class MockGraphStore:
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def __init__(self, graph):
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self.graph = graph
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def get_nodes_by_label(self, label):
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return list(self.graph.nodes())
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def get_relationships_by_type(self, rel_type):
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return list(self.graph.edges())
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graph_store = MockGraphStore(social_network_graph)
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# Test different embedding methods
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methods = ['node2vec', 'deepwalk']
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for method in methods:
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embedder.method = method
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try:
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# This will likely fail due to missing dependencies, but we test the setup
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embeddings = embedder.compute_embeddings(graph_store, ['Person'], ['friend'])
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# If it succeeds, verify the embeddings
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assert isinstance(embeddings, dict)
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assert len(embeddings) > 0
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# Check embedding dimensions
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for node, embedding in embeddings.items():
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assert isinstance(embedding, list)
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assert len(embedding) > 0
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except Exception as e:
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# Expected to fail due to missing dependencies
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print(f"Expected failure for {method}: {e}")
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# Verify embedder configuration
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assert embedder.enable_provenance is True
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assert hasattr(embedder, 'method')
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def test_similarity_calculator_end_to_end(self, sample_embeddings):
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"""Test similarity calculator end-to-end."""
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sim_calc = SimilarityCalculator()
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# Test individual similarity calculations
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alice_embedding = sample_embeddings['Alice']
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bob_embedding = sample_embeddings['Bob']
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# Test different similarity metrics
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cosine_sim = sim_calc.cosine_similarity(alice_embedding, bob_embedding)
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euclidean_dist = sim_calc.euclidean_distance(alice_embedding, bob_embedding)
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manhattan_dist = sim_calc.manhattan_distance(alice_embedding, bob_embedding)
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# Verify results
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assert isinstance(cosine_sim, float)
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assert isinstance(euclidean_dist, float)
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assert isinstance(manhattan_dist, float)
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assert -1 <= cosine_sim <= 1 # Cosine similarity can be negative
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assert euclidean_dist >= 0
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assert manhattan_dist >= 0
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# Test batch similarity
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query_embedding = [0.5] * 8 # Average embedding
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similarities = sim_calc.batch_similarity(
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embeddings=sample_embeddings,
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query_embedding=query_embedding,
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method='cosine',
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top_k=5
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)
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assert isinstance(similarities, dict)
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assert len(similarities) <= 5 # Should be limited to top_k
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# Test pairwise similarity
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pairwise_sims = sim_calc.pairwise_similarity(sample_embeddings)
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assert isinstance(pairwise_sims, dict)
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# Test most similar finding
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most_similar = sim_calc.find_most_similar(
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embeddings=sample_embeddings,
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query_embedding=query_embedding,
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top_k=3,
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method='cosine'
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)
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assert isinstance(most_similar, list)
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assert len(most_similar) <= 3
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assert all(isinstance(item, tuple) and len(item) == 2 for item in most_similar)
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def test_path_finder_end_to_end(self, social_network_graph):
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"""Test path finder end-to-end."""
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path_finder = PathFinder()
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# Test shortest path algorithms
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source = 'Alice'
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target = 'Grace'
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# BFS shortest path
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bfs_path = path_finder.bfs_shortest_path(social_network_graph, source, target)
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assert isinstance(bfs_path, list)
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assert bfs_path[0] == source
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assert bfs_path[-1] == target
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# Dijkstra shortest path
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dijkstra_path = path_finder.dijkstra_shortest_path(social_network_graph, source, target)
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assert isinstance(dijkstra_path, list)
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assert dijkstra_path[0] == source
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assert dijkstra_path[-1] == target
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# All shortest paths from source
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all_paths = path_finder.all_shortest_paths(social_network_graph, source)
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assert isinstance(all_paths, dict)
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assert len(all_paths) > 0 # Should have paths to other nodes
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# A* search
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def heuristic(node1, node2):
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# Simple heuristic based on node names
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return abs(len(node1) - len(node2))
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astar_path = path_finder.a_star_search(social_network_graph, source, target, heuristic)
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assert isinstance(astar_path, list)
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assert astar_path[0] == source
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assert astar_path[-1] == target
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# Path length calculation
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path_length = path_finder.path_length(social_network_graph, bfs_path)
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assert isinstance(path_length, float)
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assert path_length >= 0
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# K shortest paths
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k_paths = path_finder.find_k_shortest_paths(social_network_graph, source, target, k=3)
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assert isinstance(k_paths, list)
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assert len(k_paths) <= 3
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def test_link_prediction_end_to_end(self, social_network_graph):
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"""Test link prediction end-to-end."""
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link_predictor = LinkPredictor()
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# Test different prediction methods
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methods = ['preferential_attachment', 'jaccard', 'adamic_adar']
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for method in methods:
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predictions = link_predictor.predict_links(
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graph=social_network_graph,
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method=method,
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top_k=5
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)
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assert isinstance(predictions, list)
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# Verify prediction format
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if predictions: # May be empty for some graphs
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for pred in predictions:
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assert isinstance(pred, tuple)
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assert len(pred) == 3 # (source, target, score)
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assert isinstance(pred[0], str) # source node
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assert isinstance(pred[1], str) # target node
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assert isinstance(pred[2], (int, float)) # score
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# Test with different top_k values
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predictions_10 = link_predictor.predict_links(
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graph=social_network_graph,
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method='preferential_attachment',
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top_k=10
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)
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assert isinstance(predictions_10, list)
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assert len(predictions_10) <= 10
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def test_centrality_calculator_end_to_end(self, social_network_graph):
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"""Test centrality calculator end-to-end."""
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centrality_calc = CentralityCalculator()
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# Convert NetworkX graph to dict format
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graph_dict = {
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'nodes': list(social_network_graph.nodes()),
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'edges': list(social_network_graph.edges())
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}
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# Test different centrality measures
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centrality_methods = [
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('degree', centrality_calc.calculate_degree_centrality),
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('betweenness', centrality_calc.calculate_betweenness_centrality),
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('closeness', centrality_calc.calculate_closeness_centrality),
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('eigenvector', centrality_calc.calculate_eigenvector_centrality)
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]
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results = {}
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for name, method in centrality_methods:
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try:
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result = method(graph_dict)
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results[name] = result
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# Verify result structure
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assert 'centrality' in result
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assert 'rankings' in result
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assert 'total_nodes' in result
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# Verify centrality scores
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assert isinstance(result['centrality'], dict)
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assert isinstance(result['rankings'], list)
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assert isinstance(result['total_nodes'], int)
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# Verify rankings format
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for ranking in result['rankings']:
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assert isinstance(ranking, dict)
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assert 'node' in ranking
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assert 'score' in ranking
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except Exception as e:
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print(f"Warning: {name} centrality failed: {e}")
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# Test PageRank
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try:
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pagerank_result = centrality_calc.calculate_pagerank(
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graph=social_network_graph,
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node_labels=None,
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alpha=0.85,
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max_iter=100,
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tolerance=1e-6
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)
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assert 'centrality' in pagerank_result
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assert 'rankings' in pagerank_result
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results['pagerank'] = pagerank_result
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except Exception as e:
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print(f"Warning: PageRank failed: {e}")
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# Test all centrality calculation
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try:
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all_centrality = centrality_calc.calculate_all_centrality(graph_dict)
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assert isinstance(all_centrality, dict)
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results['all'] = all_centrality
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except Exception as e:
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print(f"Warning: All centrality failed: {e}")
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# Verify we got some results
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assert len(results) > 0
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def test_community_detection_end_to_end(self, social_network_graph):
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"""Test community detection end-to-end."""
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community_detector = CommunityDetector()
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# Convert NetworkX graph to dict format
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graph_dict = {
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'nodes': list(social_network_graph.nodes()),
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'edges': list(social_network_graph.edges())
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}
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# Test different community detection methods
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methods = ['label_propagation', 'louvain']
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results = {}
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for method in methods:
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try:
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result = community_detector.detect_communities(graph_dict, method=method)
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results[method] = result
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# Verify result structure
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assert 'communities' in result
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assert 'node_assignments' in result
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assert 'algorithm' in result
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# Verify communities
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assert isinstance(result['communities'], list)
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assert isinstance(result['node_assignments'], dict)
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# Verify node assignments
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for node, community_id in result['node_assignments'].items():
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assert isinstance(node, str)
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assert isinstance(community_id, int)
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# Verify communities contain all nodes
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all_nodes_in_communities = set()
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for community in result['communities']:
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all_nodes_in_communities.update(community)
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assert all_nodes_in_communities == set(result['node_assignments'].keys())
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except Exception as e:
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print(f"Warning: {method} community detection failed: {e}")
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# Test with parameters
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try:
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result_with_params = community_detector.detect_communities(
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graph_dict,
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method='label_propagation',
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max_iterations=50,
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random_seed=42
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)
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assert 'communities' in result_with_params
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results['with_params'] = result_with_params
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except Exception as e:
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print(f"Warning: Community detection with params failed: {e}")
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# Verify we got some results
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assert len(results) > 0
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def test_connectivity_analyzer_end_to_end(self, social_network_graph):
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"""Test connectivity analyzer end-to-end."""
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conn_analyzer = ConnectivityAnalyzer()
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# Convert NetworkX graph to dict format
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graph_dict = {
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'nodes': list(social_network_graph.nodes()),
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'edges': list(social_network_graph.edges())
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}
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# Test connected components
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components = conn_analyzer.find_connected_components(graph_dict)['components']
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assert isinstance(components, list)
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assert len(components) > 0
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# Verify component structure
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all_nodes_in_components = set()
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for component in components:
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assert isinstance(component, list)
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all_nodes_in_components.update(component)
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# All nodes should be in components
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assert all_nodes_in_components == set(graph_dict['nodes'])
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# Test graph properties
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try:
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properties = conn_analyzer.analyze_connectivity(graph_dict)
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assert isinstance(properties, dict)
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assert 'connected_components' in properties
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assert 'largest_component_size' in properties
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assert 'is_connected' in properties
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except Exception as e:
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print(f"Warning: Connectivity analysis failed: {e}")
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def test_comprehensive_workflow(self, social_network_graph, citation_network_graph, sample_embeddings):
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"""Test comprehensive workflow combining all algorithms."""
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# Initialize all algorithms
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embedder = NodeEmbedder()
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embedder.enable_provenance = True
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sim_calc = SimilarityCalculator()
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path_finder = PathFinder()
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link_predictor = LinkPredictor()
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centrality_calc = CentralityCalculator()
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community_detector = CommunityDetector()
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conn_analyzer = ConnectivityAnalyzer()
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tracker = AlgorithmTrackerWithProvenance(provenance=True)
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# Step 1: Analyze social network connectivity
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social_dict = {
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'nodes': list(social_network_graph.nodes()),
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'edges': list(social_network_graph.edges())
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}
|
|
|
|
components = conn_analyzer.find_connected_components(social_dict)['components']
|
|
assert len(components) >= 1
|
|
|
|
# Step 2: Calculate centrality measures
|
|
degree_cent = centrality_calc.calculate_degree_centrality(social_dict)
|
|
assert 'centrality' in degree_cent
|
|
|
|
# Step 3: Detect communities
|
|
communities = community_detector.detect_communities(social_dict, method='label_propagation')
|
|
assert 'communities' in communities
|
|
|
|
# Step 4: Find shortest paths
|
|
source = 'Alice'
|
|
target = 'Grace'
|
|
shortest_path = path_finder.bfs_shortest_path(social_network_graph, source, target)
|
|
assert shortest_path[0] == source and shortest_path[-1] == target
|
|
|
|
# Step 5: Calculate similarities
|
|
query_embedding = sample_embeddings['Alice']
|
|
similarities = sim_calc.batch_similarity(
|
|
embeddings=sample_embeddings,
|
|
query_embedding=query_embedding,
|
|
method='cosine',
|
|
top_k=5
|
|
)
|
|
assert isinstance(similarities, dict)
|
|
|
|
# Step 6: Predict missing links
|
|
predictions = link_predictor.predict_links(social_network_graph, method='preferential_attachment')
|
|
assert isinstance(predictions, list)
|
|
|
|
# Step 7: Track all operations with provenance
|
|
workflow_id = "comprehensive_test"
|
|
|
|
# Track each step
|
|
conn_id = tracker.track_connectivity_analysis(
|
|
graph=social_dict,
|
|
components=components,
|
|
method='connected_components',
|
|
source=workflow_id
|
|
)
|
|
|
|
cent_id = tracker.track_centrality_calculation(
|
|
graph=social_dict,
|
|
centrality_scores=degree_cent['centrality'],
|
|
method='degree',
|
|
parameters={},
|
|
source=workflow_id
|
|
)
|
|
|
|
comm_id = tracker.track_community_detection(
|
|
graph=social_dict,
|
|
communities=communities['communities'],
|
|
method='label_propagation',
|
|
parameters={},
|
|
source=workflow_id
|
|
)
|
|
|
|
path_id = tracker.track_path_finding(
|
|
graph=social_network_graph,
|
|
path=shortest_path,
|
|
method='bfs',
|
|
parameters={'source': source, 'target': target},
|
|
source=workflow_id
|
|
)
|
|
|
|
sim_id = tracker.track_similarity_calculation(
|
|
embeddings=sample_embeddings,
|
|
query_embedding=query_embedding,
|
|
similarities=similarities,
|
|
method='cosine',
|
|
source=workflow_id
|
|
)
|
|
|
|
link_id = tracker.track_link_prediction(
|
|
graph=social_network_graph,
|
|
predictions=predictions[:3], # Track top 3
|
|
method='preferential_attachment',
|
|
parameters={},
|
|
source=workflow_id
|
|
)
|
|
|
|
# Verify all tracking IDs were generated
|
|
tracking_ids = [conn_id, cent_id, comm_id, path_id, sim_id, link_id]
|
|
for tracking_id in tracking_ids:
|
|
assert tracking_id is not None
|
|
assert len(tracking_id) > 10 # UUID length
|
|
|
|
# Verify all IDs are unique
|
|
assert len(set(tracking_ids)) == len(tracking_ids)
|
|
|
|
# Step 8: Analyze citation network
|
|
citation_dict = {
|
|
'nodes': list(citation_network_graph.nodes()),
|
|
'edges': list(citation_network_graph.edges())
|
|
}
|
|
|
|
# Calculate PageRank for citation network
|
|
try:
|
|
pagerank = centrality_calc.calculate_pagerank(citation_network_graph)
|
|
assert 'centrality' in pagerank
|
|
except Exception as e:
|
|
print(f"PageRank failed: {e}")
|
|
|
|
# Find citation paths
|
|
try:
|
|
citation_path = path_finder.all_shortest_paths(citation_network_graph, 'P1')
|
|
assert isinstance(citation_path, dict)
|
|
except Exception as e:
|
|
print(f"Citation path finding failed: {e}")
|
|
|
|
print("Comprehensive workflow test completed successfully!")
|
|
|
|
def test_performance_with_large_graph(self):
|
|
"""Test algorithm performance with larger graphs."""
|
|
# Create a larger graph
|
|
large_graph = nx.erdos_renyi_graph(100, 0.1, seed=42)
|
|
|
|
# Test centrality calculation performance
|
|
centrality_calc = CentralityCalculator()
|
|
graph_dict = {
|
|
'nodes': list(large_graph.nodes()),
|
|
'edges': list(large_graph.edges())
|
|
}
|
|
|
|
start_time = time.time()
|
|
degree_cent = centrality_calc.calculate_degree_centrality(graph_dict)
|
|
centrality_time = time.time() - start_time
|
|
|
|
assert centrality_time < 5.0 # Should complete within 5 seconds
|
|
assert 'centrality' in degree_cent
|
|
|
|
# Test connectivity analysis performance
|
|
conn_analyzer = ConnectivityAnalyzer()
|
|
|
|
start_time = time.time()
|
|
components = conn_analyzer.find_connected_components(graph_dict)['components']
|
|
connectivity_time = time.time() - start_time
|
|
|
|
assert connectivity_time < 5.0 # Should complete within 5 seconds
|
|
assert isinstance(components, list)
|
|
|
|
# Test link prediction performance
|
|
link_predictor = LinkPredictor()
|
|
|
|
start_time = time.time()
|
|
predictions = link_predictor.predict_links(graph_dict, method='preferential_attachment', top_k=10)
|
|
prediction_time = time.time() - start_time
|
|
|
|
assert prediction_time < 5.0 # Should complete within 5 seconds
|
|
assert isinstance(predictions, list)
|
|
|
|
print(f"Performance test completed:")
|
|
print(f" Centrality calculation: {centrality_time:.3f}s")
|
|
print(f" Connectivity analysis: {connectivity_time:.3f}s")
|
|
print(f" Link prediction: {prediction_time:.3f}s")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
pytest.main([__file__, '-v'])
|