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>
951 lines
40 KiB
Python
951 lines
40 KiB
Python
"""
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Test suite for Link Predictor module.
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This module tests the LinkPredictor class and its various
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link prediction algorithms for knowledge graphs.
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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 unittest.mock import Mock
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from semantica.kg.link_predictor import LinkPredictor
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class TestLinkPredictor:
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"""Test cases for LinkPredictor class."""
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def setup_method(self):
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"""Set up test fixtures."""
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self.predictor = LinkPredictor()
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# Create test graphs
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self.simple_graph = nx.Graph()
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self.simple_graph.add_edges_from([
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("A", "B"), ("B", "C"), ("C", "D"), ("A", "E")
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])
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self.complete_graph = nx.complete_graph(4)
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self.complete_graph = nx.relabel_nodes(self.complete_graph, {i: str(i) for i in range(4)})
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# Mock graph store
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self.mock_graph_store = Mock()
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self.mock_graph_store.get_nodes_by_label.return_value = ["A", "B", "C", "D", "E"]
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self.mock_graph_store.get_all_nodes.return_value = ["A", "B", "C", "D", "E"]
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self.mock_graph_store.has_node.return_value = True
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self.mock_graph_store.has_edge.return_value = False
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self.mock_graph_store.get_neighbors.return_value = []
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self.mock_graph_store.get_edge_data.return_value = {}
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def test_init_default(self):
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"""Test LinkPredictor initialization with default parameters."""
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predictor = LinkPredictor()
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assert predictor.method == "preferential_attachment"
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def test_init_custom_method(self):
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"""Test LinkPredictor initialization with custom method."""
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predictor = LinkPredictor(method="common_neighbors")
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assert predictor.method == "common_neighbors"
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def test_init_invalid_method(self):
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"""Test LinkPredictor initialization with invalid method."""
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with pytest.raises(ValueError, match="Unsupported prediction method"):
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LinkPredictor(method="invalid_method")
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def test_preferential_attachment(self):
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"""Test preferential attachment scoring."""
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# In simple graph: A(2), B(2), C(2), D(1), E(1)
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score = self.predictor._preferential_attachment(self.simple_graph, "A", "D")
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assert score == 2.0 # degree(A) * degree(D) = 2 * 1
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score = self.predictor._preferential_attachment(self.simple_graph, "A", "B")
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assert score == 4.0 # degree(A) * degree(B) = 2 * 2
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def test_common_neighbors(self):
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"""Test common neighbors counting."""
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# A and C have B as common neighbor
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score = self.predictor._common_neighbors(self.simple_graph, "A", "C")
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assert score == 1.0
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# A and D have no common neighbors
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score = self.predictor._common_neighbors(self.simple_graph, "A", "D")
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assert score == 0.0
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def test_jaccard_coefficient(self):
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"""Test Jaccard coefficient calculation."""
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# A and C: intersection={B}, union={B,E,D}
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score = self.predictor._jaccard_coefficient(self.simple_graph, "A", "C")
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assert abs(score - 1.0/3.0) < 1e-10 # |intersection|/|union| = 1/3
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# A and D: intersection={}, union={B,E,D}
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score = self.predictor._jaccard_coefficient(self.simple_graph, "A", "D")
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assert score == 0.0
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def test_adamic_adar_index(self):
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"""Test Adamic-Adar index calculation."""
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# A and C have B as common neighbor (degree 2)
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score = self.predictor._adamic_adar_index(self.simple_graph, "A", "C")
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expected = 1.0 / (2.0 * np.log(2.0)) # 1/(deg(B) * log(deg(B)))
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assert abs(score - expected) < 1e-10
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# A and D have no common neighbors
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score = self.predictor._adamic_adar_index(self.simple_graph, "A", "D")
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assert score == 0.0
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def test_resource_allocation_index(self):
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"""Test resource allocation index calculation."""
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# A and C have B as common neighbor (degree 2)
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score = self.predictor._resource_allocation_index(self.simple_graph, "A", "C")
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assert score == 0.5 # 1/deg(B) = 1/2
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# A and D have no common neighbors
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score = self.predictor._resource_allocation_index(self.simple_graph, "A", "D")
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assert score == 0.0
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def test_score_link_preferential_attachment(self):
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"""Test scoring a specific link with preferential attachment."""
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score = self.predictor.score_link(self.simple_graph, "A", "D")
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assert score == 2.0 # degree(A) * degree(D)
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def test_score_link_common_neighbors(self):
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"""Test scoring a specific link with common neighbors."""
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score = self.predictor.score_link(self.simple_graph, "A", "C", method="common_neighbors")
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assert score == 1.0
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def test_score_link_existing_edge(self):
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"""Test scoring an existing edge (should return 0)."""
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score = self.predictor.score_link(self.simple_graph, "A", "B")
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assert score == 0.0
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def test_score_link_node_not_found(self):
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"""Test scoring with non-existent node."""
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with pytest.raises(ValueError, match="Node X not found"):
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self.predictor.score_link(self.simple_graph, "X", "A")
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with pytest.raises(ValueError, match="Node X not found"):
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self.predictor.score_link(self.simple_graph, "A", "X")
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def test_predict_links_simple(self):
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"""Test link prediction on simple graph."""
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links = self.predictor.predict_links(self.simple_graph, top_k=5)
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assert len(links) <= 5
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for link in links:
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assert len(link) == 3 # (node1, node2, score)
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assert isinstance(link[0], str)
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assert isinstance(link[1], str)
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assert isinstance(link[2], (int, float))
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assert link[2] > 0 # Only positive scores
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def test_predict_links_exclude_existing(self):
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"""Test link prediction excluding existing edges."""
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links = self.predictor.predict_links(
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self.simple_graph,
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top_k=10,
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exclude_existing=True
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)
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# Should not include existing edges
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existing_edges = set(self.simple_graph.edges())
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for node1, node2, _ in links:
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assert (node1, node2) not in existing_edges
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assert (node2, node1) not in existing_edges
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def test_predict_links_include_existing(self):
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"""Test link prediction including existing edges."""
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links = self.predictor.predict_links(
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self.simple_graph,
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top_k=10,
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exclude_existing=False
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)
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# Should include existing edges (though they might have score 0)
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# This test mainly ensures the parameter is respected
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assert isinstance(links, list)
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def test_predict_links_with_node_labels(self):
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"""Test link prediction with node label filtering."""
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# Mock graph store with node labels
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self.mock_graph_store.get_nodes_by_label.return_value = ["A", "B", "C"]
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self.mock_graph_store.get_edges.return_value = [
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{"source": "A", "target": "B"},
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{"source": "B", "target": "C"}
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]
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links = self.predictor.predict_links(
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self.mock_graph_store,
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node_labels=["Entity"],
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top_k=5
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)
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assert isinstance(links, list)
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assert len(links) <= 5
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def test_predict_links_with_relationship_types(self):
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"""Test link prediction with relationship type filtering."""
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# Mock graph store with relationship types
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self.mock_graph_store.get_nodes_by_label.return_value = ["A", "B", "C", "D"]
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self.mock_graph_store.get_edges.return_value = [
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{"source": "A", "target": "B", "type": "RELATED"},
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{"source": "B", "target": "C", "type": "RELATED"}
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]
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links = self.predictor.predict_links(
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self.mock_graph_store,
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relationship_types=["RELATED"],
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top_k=5
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)
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assert isinstance(links, list)
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def test_predict_top_links(self):
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"""Test predicting top links for a specific node."""
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top_links = self.predictor.predict_top_links(self.simple_graph, "A", top_k=3)
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assert len(top_links) <= 3
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for link in top_links:
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assert len(link) == 2 # (node_id, score)
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assert isinstance(link[0], str)
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assert isinstance(link[1], (int, float))
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assert link[1] > 0 # Only positive scores
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assert link[0] != "A" # Should not include self
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def test_predict_top_links_node_not_found(self):
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"""Test predicting top links for non-existent node."""
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with pytest.raises(ValueError, match="Node X not found"):
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self.predictor.predict_top_links(self.simple_graph, "X", top_k=3)
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def test_batch_score_links(self):
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"""Test batch scoring of node pairs."""
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node_pairs = [("A", "D"), ("A", "C"), ("B", "D")]
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scores = self.predictor.batch_score_links(self.simple_graph, node_pairs)
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assert len(scores) == 3
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for node1, node2, score in scores:
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assert (node1, node2) in node_pairs
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assert isinstance(score, (int, float))
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def test_batch_score_links_invalid_pairs(self):
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"""Test batch scoring with invalid node pairs."""
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node_pairs = [("A", "X"), ("Y", "B"), ("A", "C")] # X and Y don't exist
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scores = self.predictor.batch_score_links(self.simple_graph, node_pairs)
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assert len(scores) == 3
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# Invalid pairs should have score 0
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assert scores[0][2] == 0.0 # A-X
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assert scores[1][2] == 0.0 # Y-B
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assert scores[2][2] > 0.0 # A-C (valid)
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def test_get_candidate_nodes_all(self):
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"""Test getting all candidate nodes."""
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nodes = self.predictor._get_candidate_nodes(self.simple_graph, None)
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expected_nodes = set(self.simple_graph.nodes())
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assert set(nodes) == expected_nodes
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def test_get_candidate_nodes_filtered(self):
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"""Test getting candidate nodes with label filtering."""
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# Mock graph store
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self.mock_graph_store.get_nodes_by_label.return_value = ["A", "B", "C"]
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nodes = self.predictor._get_candidate_nodes(self.mock_graph_store, ["Entity"])
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assert set(nodes) == {"A", "B", "C"}
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def test_get_existing_edges(self):
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"""Test getting existing edges."""
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edges = self.predictor._get_existing_edges(self.simple_graph, None)
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expected_edges = set()
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for u, v in self.simple_graph.edges():
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expected_edges.add((u, v))
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expected_edges.add((v, u)) # Add both directions
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assert edges == expected_edges
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def test_get_existing_edges_with_relationship_types(self):
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"""Test getting existing edges with relationship type filtering."""
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# Mock graph store
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self.mock_graph_store.get_edges.return_value = [
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{"source": "A", "target": "B", "type": "RELATED"},
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{"source": "B", "target": "C", "type": "RELATED"},
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{"source": "C", "target": "D", "type": "DIFFERENT"}
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]
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edges = self.predictor._get_existing_edges(self.mock_graph_store, ["RELATED"])
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# Should only include RELATED edges
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assert ("A", "B") in edges
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assert ("B", "A") in edges
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assert ("B", "C") in edges
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assert ("C", "B") in edges
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assert ("C", "D") not in edges
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assert ("D", "C") not in edges
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def test_get_all_nodes_networkx(self):
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"""Test getting all nodes from NetworkX graph."""
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nodes = self.predictor._get_all_nodes(self.simple_graph)
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assert set(nodes) == set(self.simple_graph.nodes())
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def test_get_all_nodes_mock_graph(self):
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"""Test getting all nodes from mock graph store."""
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self.mock_graph_store.get_all_nodes.return_value = ["A", "B", "C"]
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nodes = self.predictor._get_all_nodes(self.mock_graph_store)
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assert nodes == ["A", "B", "C"]
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def test_node_exists_networkx(self):
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"""Test node existence check with NetworkX graph."""
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assert self.predictor._node_exists(self.simple_graph, "A")
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assert not self.predictor._node_exists(self.simple_graph, "X")
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def test_node_exists_mock_graph(self):
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"""Test node existence check with mock graph store."""
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self.mock_graph_store.has_node.return_value = True
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assert self.predictor._node_exists(self.mock_graph_store, "A")
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self.mock_graph_store.has_node.return_value = False
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assert not self.predictor._node_exists(self.mock_graph_store, "X")
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def test_edge_exists_networkx(self):
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"""Test edge existence check with NetworkX graph."""
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assert self.predictor._edge_exists(self.simple_graph, "A", "B")
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assert not self.predictor._edge_exists(self.simple_graph, "A", "D")
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def test_edge_exists_mock_graph(self):
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"""Test edge existence check with mock graph store."""
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self.mock_graph_store.has_edge.return_value = True
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assert self.predictor._edge_exists(self.mock_graph_store, "A", "B")
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self.mock_graph_store.has_edge.return_value = False
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assert not self.predictor._edge_exists(self.mock_graph_store, "A", "D")
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def test_get_node_degree_networkx(self):
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"""Test getting node degree from NetworkX graph."""
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degree = self.predictor._get_node_degree(self.simple_graph, "A")
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assert degree == 2 # A is connected to B and E
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def test_get_node_degree_mock_graph(self):
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"""Test getting node degree from mock graph store."""
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self.mock_graph_store.get_node_degree.return_value = 3
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degree = self.predictor._get_node_degree(self.mock_graph_store, "A")
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assert degree == 3
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def test_get_node_degree_fallback(self):
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"""Test getting node degree with fallback method."""
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self.mock_graph_store.get_node_degree = None
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self.mock_graph_store.get_neighbors.return_value = None # disable get_neighbors
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self.mock_graph_store.neighbors.return_value = ["B", "C", "D"]
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degree = self.predictor._get_node_degree(self.mock_graph_store, "A")
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assert degree == 3
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def test_get_node_neighbors_networkx(self):
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"""Test getting node neighbors from NetworkX graph."""
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neighbors = self.predictor._get_node_neighbors(self.simple_graph, "A")
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assert set(neighbors) == {"B", "E"}
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def test_get_node_neighbors_mock_graph(self):
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"""Test getting node neighbors from mock graph store."""
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self.mock_graph_store.get_neighbors.return_value = ["B", "C"]
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neighbors = self.predictor._get_node_neighbors(self.mock_graph_store, "A")
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assert neighbors == ["B", "C"]
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def test_get_node_neighbors_filtered(self):
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"""Test getting node neighbors with relationship type filtering."""
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self.mock_graph_store.get_neighbors.return_value = None # disable get_neighbors
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self.mock_graph_store.neighbors.return_value = ["B", "C", "D"]
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self.mock_graph_store.get_edge_data.side_effect = lambda u, v: {
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("A", "B"): {"type": "RELATED"},
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("A", "C"): {"type": "DIFFERENT"},
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("A", "D"): {"type": "RELATED"}
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}[(u, v)]
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neighbors = self.predictor._get_node_neighbors(
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self.mock_graph_store, "A", ["RELATED"]
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)
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assert set(neighbors) == {"B", "D"}
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class TestLinkPredictorMethods:
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"""Test different link prediction methods."""
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def setup_method(self):
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"""Set up test fixtures."""
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self.predictor = LinkPredictor()
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# Create a more complex graph for testing
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self.graph = nx.Graph()
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self.graph.add_edges_from([
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("A", "B"), ("B", "C"), ("C", "D"), ("D", "E"),
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("A", "F"), ("F", "G"), ("G", "H"), ("H", "E"),
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("B", "F"), ("C", "G"), ("D", "H")
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])
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def test_preferential_attachment_scores(self):
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"""Test preferential attachment scoring on complex graph."""
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# Central nodes should have higher scores
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score_b_d = self.predictor._preferential_attachment(self.graph, "B", "D")
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score_a_e = self.predictor._preferential_attachment(self.graph, "A", "E")
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# B and D are more connected than A and E
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assert score_b_d > score_a_e
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def test_common_neighbors_scores(self):
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"""Test common neighbors scoring on complex graph."""
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# A and G have F as common neighbor
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score_a_g = self.predictor._common_neighbors(self.graph, "A", "G")
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assert score_a_g == 1.0
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# A and E have no direct common neighbors
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score_a_e = self.predictor._common_neighbors(self.graph, "A", "E")
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assert score_a_e == 0.0
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def test_jaccard_coefficient_scores(self):
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"""Test Jaccard coefficient scoring on complex graph."""
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# A and G: intersection={F}, union depends on their neighborhoods
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score_a_g = self.predictor._jaccard_coefficient(self.graph, "A", "G")
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assert 0.0 <= score_a_g <= 1.0
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# A and E: no common neighbors
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score_a_e = self.predictor._jaccard_coefficient(self.graph, "A", "E")
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assert score_a_e == 0.0
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def test_adamic_adar_scores(self):
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"""Test Adamic-Adar index scoring on complex graph."""
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# A and G have F as common neighbor
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score_a_g = self.predictor._adamic_adar_index(self.graph, "A", "G")
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assert score_a_g > 0.0
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# A and E have no common neighbors
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score_a_e = self.predictor._adamic_adar_index(self.graph, "A", "E")
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assert score_a_e == 0.0
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def test_resource_allocation_scores(self):
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"""Test resource allocation index scoring on complex graph."""
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# A and G have F as common neighbor
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score_a_g = self.predictor._resource_allocation_index(self.graph, "A", "G")
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assert score_a_g > 0.0
|
|
|
|
# A and E have no common neighbors
|
|
score_a_e = self.predictor._resource_allocation_index(self.graph, "A", "E")
|
|
assert score_a_e == 0.0
|
|
|
|
def test_predict_links_different_methods(self):
|
|
"""Test link prediction with different methods."""
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(self.graph, method=method, top_k=5)
|
|
|
|
assert isinstance(links, list)
|
|
assert len(links) <= 5
|
|
|
|
for link in links:
|
|
assert len(link) == 3
|
|
assert link[2] >= 0 # Scores should be non-negative
|
|
|
|
def test_score_link_different_methods(self):
|
|
"""Test scoring links with different methods."""
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
score = self.predictor.score_link(self.graph, "A", "E", method=method)
|
|
assert isinstance(score, (int, float))
|
|
assert score >= 0
|
|
|
|
|
|
class TestLinkPredictorEdgeCases:
|
|
"""Edge case tests for LinkPredictor."""
|
|
|
|
def setup_method(self):
|
|
"""Set up test fixtures."""
|
|
self.predictor = LinkPredictor()
|
|
|
|
def test_empty_graph(self):
|
|
"""Test link prediction on empty graph."""
|
|
empty_graph = nx.Graph()
|
|
|
|
links = self.predictor.predict_links(empty_graph, top_k=5)
|
|
assert links == []
|
|
|
|
def test_single_node_graph(self):
|
|
"""Test link prediction on single node graph."""
|
|
single_node_graph = nx.Graph()
|
|
single_node_graph.add_node("A")
|
|
|
|
links = self.predictor.predict_links(single_node_graph, top_k=5)
|
|
assert links == []
|
|
|
|
def test_complete_graph(self):
|
|
"""Test link prediction on complete graph."""
|
|
complete_graph = nx.complete_graph(3)
|
|
complete_graph = nx.relabel_nodes(complete_graph, {i: str(i) for i in range(3)})
|
|
|
|
links = self.predictor.predict_links(complete_graph, top_k=5, exclude_existing=True)
|
|
assert links == [] # No missing links in complete graph
|
|
|
|
def test_disconnected_components(self):
|
|
"""Test link prediction on disconnected graph."""
|
|
disconnected_graph = nx.Graph()
|
|
disconnected_graph.add_edges_from([("A", "B"), ("B", "C")]) # Component 1
|
|
disconnected_graph.add_edges_from([("X", "Y"), ("Y", "Z")]) # Component 2
|
|
|
|
# Should predict links between components
|
|
links = self.predictor.predict_links(disconnected_graph, top_k=10)
|
|
|
|
# Should include cross-component predictions
|
|
cross_component_links = [
|
|
link for link in links
|
|
if (link[0] in ["A", "B", "C"] and link[1] in ["X", "Y", "Z"]) or
|
|
(link[1] in ["A", "B", "C"] and link[0] in ["X", "Y", "Z"])
|
|
]
|
|
assert len(cross_component_links) > 0
|
|
|
|
def test_star_graph(self):
|
|
"""Test link prediction on star graph."""
|
|
star_graph = nx.star_graph(5) # Central node 0, leaves 1-5
|
|
star_graph = nx.relabel_nodes(star_graph, {i: str(i) for i in range(6)})
|
|
|
|
# Preferential attachment should favor connections to center
|
|
center_links = self.predictor.predict_links(
|
|
star_graph,
|
|
method="preferential_attachment",
|
|
top_k=10
|
|
)
|
|
|
|
# Should predict leaf-leaf connections (center already connected to all)
|
|
leaf_links = [
|
|
link for link in center_links
|
|
if link[0] != "0" and link[1] != "0" # Both are leaves
|
|
]
|
|
assert len(leaf_links) > 0
|
|
|
|
def test_high_degree_node_bias(self):
|
|
"""Test preferential attachment bias towards high-degree nodes."""
|
|
# Create graph with one high-degree node
|
|
biased_graph = nx.Graph()
|
|
biased_graph.add_node("hub")
|
|
for i in range(10):
|
|
biased_graph.add_node(f"leaf_{i}")
|
|
biased_graph.add_edge("hub", f"leaf_{i}")
|
|
|
|
# Preferential attachment should favor hub-hub connections (if any)
|
|
# But since hub is already connected to all, should predict leaf-leaf
|
|
links = self.predictor.predict_links(
|
|
biased_graph,
|
|
method="preferential_attachment",
|
|
top_k=5
|
|
)
|
|
|
|
# All predicted links should be between leaves
|
|
for node1, node2, score in links:
|
|
assert node1.startswith("leaf_")
|
|
assert node2.startswith("leaf_")
|
|
assert node1 != node2
|
|
|
|
|
|
class TestLinkPredictorEdgeCases:
|
|
"""Edge case tests for LinkPredictor."""
|
|
|
|
def setup_method(self):
|
|
"""Set up test fixtures."""
|
|
self.predictor = LinkPredictor()
|
|
|
|
def test_empty_graph_all_methods(self):
|
|
"""Test all link prediction methods on empty graph."""
|
|
empty_graph = nx.Graph()
|
|
|
|
# All methods should return empty results
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(empty_graph, method=method, top_k=10)
|
|
assert links == []
|
|
|
|
def test_single_node_graph_all_methods(self):
|
|
"""Test all link prediction methods on single node graph."""
|
|
single_node_graph = nx.Graph()
|
|
single_node_graph.add_node("A")
|
|
|
|
# All methods should return empty results (no possible links)
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(single_node_graph, method=method, top_k=10)
|
|
assert links == []
|
|
|
|
def test_complete_graph_all_methods(self):
|
|
"""Test all link prediction methods on complete graph."""
|
|
complete_graph = nx.complete_graph(3)
|
|
complete_graph = nx.relabel_nodes(complete_graph, {i: str(i) for i in range(3)})
|
|
|
|
# With exclude_existing=True, should return empty results
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(
|
|
complete_graph, method=method, top_k=10, exclude_existing=True
|
|
)
|
|
assert links == []
|
|
|
|
# With exclude_existing=False, should return existing edges with scores
|
|
links = self.predictor.predict_links(
|
|
complete_graph, method=method, top_k=10, exclude_existing=False
|
|
)
|
|
# Should have some results, but scores might be 0 for some methods
|
|
assert isinstance(links, list)
|
|
|
|
def test_disconnected_graph_all_methods(self):
|
|
"""Test all link prediction methods on disconnected graph."""
|
|
disconnected_graph = nx.Graph()
|
|
disconnected_graph.add_edges_from([("A", "B"), ("B", "C")]) # Component 1
|
|
disconnected_graph.add_edges_from([("X", "Y"), ("Y", "Z")]) # Component 2
|
|
|
|
# Should predict cross-component links
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(disconnected_graph, method=method, top_k=10)
|
|
|
|
# Should find cross-component predictions
|
|
cross_component_links = [
|
|
link for link in links
|
|
if (link[0] in ["A", "B", "C"] and link[1] in ["X", "Y", "Z"]) or
|
|
(link[1] in ["A", "B", "C"] and link[0] in ["X", "Y", "Z"])
|
|
]
|
|
|
|
if method == "preferential_attachment":
|
|
# Preferential attachment should find cross-component links
|
|
assert len(cross_component_links) > 0
|
|
else:
|
|
# Common neighbors based methods should have 0 score for cross-component
|
|
assert all(score == 0.0 for _, _, score in cross_component_links)
|
|
|
|
def test_star_graph_all_methods(self):
|
|
"""Test all link prediction methods on star graph."""
|
|
star_graph = nx.star_graph(5) # Central node 0, leaves 1-5
|
|
star_graph = nx.relabel_nodes(star_graph, {i: str(i) for i in range(6)})
|
|
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(star_graph, method=method, top_k=10)
|
|
|
|
# Should predict leaf-leaf connections (center already connected to all)
|
|
leaf_links = [
|
|
link for link in links
|
|
if link[0] != "0" and link[1] != "0" # Both are leaves
|
|
]
|
|
|
|
if method == "preferential_attachment":
|
|
# Should predict leaf-leaf connections
|
|
assert len(leaf_links) > 0
|
|
else:
|
|
# Common neighbors based methods should predict leaf-leaf with center as common neighbor
|
|
assert len(leaf_links) > 0
|
|
|
|
def test_graph_with_self_loops(self):
|
|
"""Test link prediction with self-loops."""
|
|
loop_graph = nx.Graph()
|
|
loop_graph.add_edges_from([("A", "B"), ("B", "C")])
|
|
loop_graph.add_edge("A", "A") # Self-loop
|
|
loop_graph.add_edge("B", "B") # Self-loop
|
|
|
|
# Should handle self-loops gracefully
|
|
links = self.predictor.predict_links(loop_graph, method="preferential_attachment", top_k=10)
|
|
|
|
# Should not predict self-loops
|
|
self_loops = [link for link in links if link[0] == link[1]]
|
|
assert len(self_loops) == 0
|
|
|
|
def test_graph_with_isolated_nodes(self):
|
|
"""Test link prediction with isolated nodes."""
|
|
isolated_graph = nx.Graph()
|
|
isolated_graph.add_edges_from([("A", "B")])
|
|
isolated_graph.add_nodes_from(["X", "Y", "Z"]) # Isolated nodes
|
|
|
|
# Should handle isolated nodes
|
|
links = self.predictor.predict_links(isolated_graph, method="preferential_attachment", top_k=10)
|
|
|
|
# Should predict connections involving isolated nodes
|
|
isolated_links = [
|
|
link for link in links
|
|
if link[0] in ["X", "Y", "Z"] or link[1] in ["X", "Y", "Z"]
|
|
]
|
|
|
|
# Preferential attachment should predict connections to isolated nodes (degree 0)
|
|
# But degree 0 * degree anything = 0, so they might not appear in top results
|
|
assert isinstance(isolated_links, list)
|
|
|
|
def test_very_large_graph_performance(self):
|
|
"""Test link prediction performance on large graph."""
|
|
# Create a larger sparse graph
|
|
large_graph = nx.erdos_renyi_graph(100, 0.1) # 100 nodes, moderate density
|
|
large_graph = nx.relabel_nodes(large_graph, {i: str(i) for i in range(100)})
|
|
|
|
# Test that methods complete without error
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(large_graph, method=method, top_k=20)
|
|
assert isinstance(links, list)
|
|
assert len(links) <= 20
|
|
|
|
# All links should be valid
|
|
for node1, node2, score in links:
|
|
assert node1 in large_graph.nodes()
|
|
assert node2 in large_graph.nodes()
|
|
assert node1 != node2
|
|
assert isinstance(score, (int, float))
|
|
|
|
def test_predict_top_links_edge_cases(self):
|
|
"""Test predict_top_links with edge cases."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([("A", "B"), ("B", "C"), ("A", "C")])
|
|
|
|
# Test with top_k=0
|
|
top_links = self.predictor.predict_top_links(graph, "A", top_k=0)
|
|
assert top_links == []
|
|
|
|
# Test with negative top_k
|
|
top_links = self.predictor.predict_top_links(graph, "A", top_k=-1)
|
|
assert top_links == []
|
|
|
|
# Test with top_k larger than possible connections
|
|
top_links = self.predictor.predict_top_links(graph, "A", top_k=100)
|
|
# Should not return more links than possible
|
|
max_possible = len(graph.nodes()) - 1 # Can't connect to self
|
|
assert len(top_links) <= max_possible
|
|
|
|
# Test with non-existent source node
|
|
with pytest.raises(ValueError, match="Node X not found"):
|
|
self.predictor.predict_top_links(graph, "X", top_k=5)
|
|
|
|
def test_batch_score_links_edge_cases(self):
|
|
"""Test batch_score_links with edge cases."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([("A", "B"), ("B", "C")])
|
|
|
|
# Empty node pairs
|
|
scores = self.predictor.batch_score_links(graph, [])
|
|
assert scores == []
|
|
|
|
# Single node pair
|
|
node_pairs = [("A", "B")]
|
|
scores = self.predictor.batch_score_links(graph, node_pairs)
|
|
assert len(scores) == 1
|
|
assert scores[0][0] == "A"
|
|
assert scores[0][1] == "B"
|
|
assert scores[0][2] == 0.0 # Existing edge should have score 0
|
|
|
|
# Mixed valid and invalid pairs
|
|
mixed_pairs = [("A", "C"), ("X", "B"), ("A", "Y"), ("B", "C")]
|
|
scores = self.predictor.batch_score_links(graph, mixed_pairs)
|
|
assert len(scores) == 4
|
|
|
|
# Valid pairs should have positive scores, invalid pairs should have 0
|
|
for node1, node2, score in scores:
|
|
if node1 in graph.nodes() and node2 in graph.nodes():
|
|
if graph.has_edge(node1, node2):
|
|
assert score == 0.0 # Existing edge
|
|
else:
|
|
assert score >= 0.0 # Valid prediction
|
|
else:
|
|
assert score == 0.0 # Invalid nodes
|
|
|
|
def test_score_link_edge_cases(self):
|
|
"""Test score_link with edge cases."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([("A", "B"), ("B", "C")])
|
|
|
|
# Test with existing edge
|
|
score = self.predictor.score_link(graph, "A", "B")
|
|
assert score == 0.0 # Existing edges should have score 0
|
|
|
|
# Test with non-existent edge
|
|
score = self.predictor.score_link(graph, "A", "C")
|
|
assert score > 0.0 # Should predict positive score
|
|
|
|
# Test with non-existent nodes
|
|
with pytest.raises(ValueError, match="Node X not found"):
|
|
self.predictor.score_link(graph, "X", "A")
|
|
|
|
with pytest.raises(ValueError, match="Node X not found"):
|
|
self.predictor.score_link(graph, "A", "X")
|
|
|
|
# Test with same node (self-link)
|
|
score = self.predictor.score_link(graph, "A", "A")
|
|
assert score == 0.0 # Self-links should have score 0
|
|
|
|
def test_different_graph_types(self):
|
|
"""Test link prediction with different NetworkX graph types."""
|
|
# Test with DiGraph
|
|
directed_graph = nx.DiGraph()
|
|
directed_graph.add_edges_from([("A", "B"), ("B", "C")])
|
|
|
|
links = self.predictor.predict_links(directed_graph, method="preferential_attachment", top_k=10)
|
|
assert isinstance(links, list)
|
|
|
|
# Test with MultiGraph
|
|
multigraph = nx.MultiGraph()
|
|
multigraph.add_edge("A", "B")
|
|
multigraph.add_edge("A", "B") # Multiple edges
|
|
|
|
links = self.predictor.predict_links(multigraph, method="preferential_attachment", top_k=10)
|
|
assert isinstance(links, list)
|
|
|
|
# Test with MultiDiGraph
|
|
multi_digraph = nx.MultiDiGraph()
|
|
multi_digraph.add_edge("A", "B")
|
|
multi_digraph.add_edge("B", "A")
|
|
|
|
links = self.predictor.predict_links(multi_digraph, method="preferential_attachment", top_k=10)
|
|
assert isinstance(links, list)
|
|
|
|
def test_extreme_degree_nodes(self):
|
|
"""Test link prediction with nodes of extreme degrees."""
|
|
# Create graph with one high-degree node and many low-degree nodes
|
|
extreme_graph = nx.Graph()
|
|
hub = "hub"
|
|
leaves = [f"leaf_{i}" for i in range(20)]
|
|
|
|
# Connect hub to all leaves
|
|
for leaf in leaves:
|
|
extreme_graph.add_edge(hub, leaf)
|
|
|
|
# Add some connections between leaves
|
|
for i in range(0, 18, 2):
|
|
extreme_graph.add_edge(leaves[i], leaves[i + 1])
|
|
|
|
# Test preferential attachment (should favor hub-hub connections, but hub is already connected to all)
|
|
links = self.predictor.predict_links(extreme_graph, method="preferential_attachment", top_k=10)
|
|
|
|
# Should predict leaf-leaf connections
|
|
leaf_links = [
|
|
link for link in links
|
|
if link[0].startswith("leaf_") and link[1].startswith("leaf_")
|
|
]
|
|
assert len(leaf_links) > 0
|
|
|
|
# Test common neighbors (should predict leaf-leaf with hub as common neighbor)
|
|
links = self.predictor.predict_links(extreme_graph, method="common_neighbors", top_k=10)
|
|
|
|
leaf_links = [
|
|
link for link in links
|
|
if link[0].startswith("leaf_") and link[1].startswith("leaf_")
|
|
]
|
|
assert len(leaf_links) > 0
|
|
|
|
def test_graph_with_node_attributes(self):
|
|
"""Test link prediction with graphs that have node attributes."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([("A", "B"), ("B", "C")])
|
|
|
|
# Add node attributes
|
|
graph.nodes["A"]["type"] = "person"
|
|
graph.nodes["B"]["type"] = "person"
|
|
graph.nodes["C"]["type"] = "organization"
|
|
|
|
# Should ignore node attributes for basic link prediction
|
|
links = self.predictor.predict_links(graph, method="preferential_attachment", top_k=10)
|
|
assert isinstance(links, list)
|
|
|
|
# Should work the same regardless of attributes
|
|
links_with_attrs = self.predictor.predict_links(graph, method="common_neighbors", top_k=10)
|
|
assert isinstance(links_with_attrs, list)
|
|
|
|
def test_graph_with_edge_attributes(self):
|
|
"""Test link prediction with graphs that have edge attributes."""
|
|
graph = nx.Graph()
|
|
graph.add_edge("A", "B", weight=1.5, type="friendship")
|
|
graph.add_edge("B", "C", weight=2.0, type="colleague")
|
|
|
|
# Should ignore edge attributes for basic link prediction
|
|
links = self.predictor.predict_links(graph, method="preferential_attachment", top_k=10)
|
|
assert isinstance(links, list)
|
|
|
|
# Should work the same regardless of edge attributes
|
|
links_with_attrs = self.predictor.predict_links(graph, method="common_neighbors", top_k=10)
|
|
assert isinstance(links_with_attrs, list)
|
|
|
|
def test_numeric_node_names(self):
|
|
"""Test link prediction with numeric node names."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([(1, 2), (2, 3), (3, 4)])
|
|
|
|
# Should handle numeric node names
|
|
links = self.predictor.predict_links(graph, method="preferential_attachment", top_k=10)
|
|
assert isinstance(links, list)
|
|
|
|
# All node names should be numeric
|
|
for node1, node2, score in links:
|
|
assert isinstance(node1, (int, str))
|
|
assert isinstance(node2, (int, str))
|
|
|
|
def test_special_characters_in_node_names(self):
|
|
"""Test link prediction with special characters in node names."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([("node-1", "node_2"), ("node_2", "node.3"), ("node.3", "node@4")])
|
|
|
|
# Should handle special characters
|
|
links = self.predictor.predict_links(graph, method="preferential_attachment", top_k=10)
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assert isinstance(links, list)
|
|
|
|
# Special characters should be preserved
|
|
for node1, node2, score in links:
|
|
assert isinstance(node1, str)
|
|
assert isinstance(node2, str)
|
|
|
|
def test_very_small_top_k_values(self):
|
|
"""Test predict_links with very small top_k values."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([("A", "B"), ("B", "C"), ("C", "D"), ("D", "E")])
|
|
|
|
# Test with top_k=1
|
|
links = self.predictor.predict_links(graph, method="preferential_attachment", top_k=1)
|
|
assert len(links) <= 1
|
|
|
|
# Test with top_k=2
|
|
links = self.predictor.predict_links(graph, method="preferential_attachment", top_k=2)
|
|
assert len(links) <= 2
|
|
|
|
def test_all_methods_with_same_graph(self):
|
|
"""Test all link prediction methods on the same graph for comparison."""
|
|
graph = nx.Graph()
|
|
graph.add_edges_from([
|
|
("A", "B"), ("B", "C"), ("C", "D"), # Path
|
|
("A", "E"), ("E", "F"), ("F", "D") # Another path
|
|
])
|
|
|
|
methods = ["preferential_attachment", "common_neighbors", "jaccard_coefficient", "adamic_adar"]
|
|
results = {}
|
|
|
|
for method in methods:
|
|
links = self.predictor.predict_links(graph, method=method, top_k=5)
|
|
results[method] = links
|
|
|
|
# All should return valid results
|
|
assert isinstance(links, list)
|
|
for node1, node2, score in links:
|
|
assert node1 in graph.nodes()
|
|
assert node2 in graph.nodes()
|
|
assert node1 != node2
|
|
assert isinstance(score, (int, float))
|
|
|
|
# Different methods should give different results (scores, possibly rankings)
|
|
# This is a basic sanity check that methods are actually different
|
|
method_pairs = [(methods[i], methods[j]) for i in range(len(methods)) for j in range(i+1, len(methods))]
|
|
|
|
for method1, method2 in method_pairs:
|
|
# Convert to sets of node pairs for comparison (ignoring scores)
|
|
pairs1 = set((node1, node2) for node1, node2, _ in results[method1])
|
|
pairs2 = set((node1, node2) for node1, node2, _ in results[method2])
|
|
|
|
# Should not be identical (though they might overlap)
|
|
# This is a weak test, but ensures methods are doing something different
|
|
if pairs1 and pairs2:
|
|
# At least one method should return results
|
|
pass
|