markitect-main/tests/unit/analysis/test_graph.py

"""Tests for markitect.analysis.graph."""

import pytest

nx = pytest.importorskip("networkx", reason="networkx not installed")

from markitect.prompts.dependencies.models import DependencyGraph, EdgeType
from markitect.analysis.graph import (
    to_networkx,
    connected_components,
    betweenness_centrality,
    detect_communities,
    modularity_score,
    degree_distribution,
    cohesion_coupling,
)


# ── Helpers ──────────────────────────────────────────────────────────


def _linear_graph():
    """A -> B -> C -> D (simple chain)."""
    g = DependencyGraph()
    g.add_edge("A", "B")
    g.add_edge("B", "C")
    g.add_edge("C", "D")
    return g


def _two_clusters():
    """Two dense clusters connected by a single bridge edge.

    Cluster 1: A -- B -- C (fully connected)
    Cluster 2: X -- Y -- Z (fully connected)
    Bridge: C -> X
    """
    g = DependencyGraph()
    # Cluster 1
    g.add_edge("A", "B")
    g.add_edge("B", "A")
    g.add_edge("B", "C")
    g.add_edge("C", "B")
    g.add_edge("A", "C")
    g.add_edge("C", "A")
    # Cluster 2
    g.add_edge("X", "Y")
    g.add_edge("Y", "X")
    g.add_edge("Y", "Z")
    g.add_edge("Z", "Y")
    g.add_edge("X", "Z")
    g.add_edge("Z", "X")
    # Bridge
    g.add_edge("C", "X")
    return g


def _disconnected_graph():
    """Two separate components: {A, B} and {X, Y}."""
    g = DependencyGraph()
    g.add_edge("A", "B")
    g.add_edge("X", "Y")
    return g


def _empty_graph():
    """Graph with no nodes or edges."""
    return DependencyGraph()


def _isolated_nodes():
    """Graph with nodes but no edges."""
    g = DependencyGraph()
    # add_edge creates both nodes, so we use two separate edges
    # and then extract a subgraph with isolated nodes
    g.add_edge("A", "B")
    return g.get_subgraph({"A", "B", "C"})


# ── to_networkx ─────────────────────────────────────────────────────


class TestToNetworkx:
    def test_preserves_nodes(self):
        g = _linear_graph()
        G = to_networkx(g)
        assert set(G.nodes) == {"A", "B", "C", "D"}

    def test_preserves_edges(self):
        g = _linear_graph()
        G = to_networkx(g)
        assert G.has_edge("A", "B")
        assert G.has_edge("B", "C")
        assert not G.has_edge("D", "A")

    def test_preserves_edge_type(self):
        g = DependencyGraph()
        g.add_edge("A", "B", EdgeType.GENERATES)
        G = to_networkx(g)
        assert G.edges["A", "B"]["edge_type"] == "generates"

    def test_empty_graph(self):
        G = to_networkx(_empty_graph())
        assert len(G.nodes) == 0
        assert len(G.edges) == 0


# ── Connected components ────────────────────────────────────────────


class TestConnectedComponents:
    def test_single_component(self):
        comps = connected_components(_linear_graph())
        assert len(comps) == 1
        assert comps[0] == {"A", "B", "C", "D"}

    def test_two_components(self):
        comps = connected_components(_disconnected_graph())
        assert len(comps) == 2
        node_sets = [frozenset(c) for c in comps]
        assert frozenset({"A", "B"}) in node_sets
        assert frozenset({"X", "Y"}) in node_sets

    def test_sorted_largest_first(self):
        g = DependencyGraph()
        g.add_edge("A", "B")
        g.add_edge("B", "C")
        g.add_edge("X", "Y")
        comps = connected_components(g)
        assert len(comps[0]) >= len(comps[1])

    def test_empty_graph(self):
        assert connected_components(_empty_graph()) == []


# ── Betweenness centrality ──────────────────────────────────────────


class TestBetweennessCentrality:
    def test_linear_chain_middle_node_highest(self):
        g = _linear_graph()
        bc = betweenness_centrality(g)
        # B and C are on all shortest paths between endpoints
        assert bc["B"] > bc["A"]
        assert bc["C"] > bc["D"]

    def test_values_in_range(self):
        bc = betweenness_centrality(_two_clusters())
        for v in bc.values():
            assert 0.0 <= v <= 1.0

    def test_empty_graph(self):
        assert betweenness_centrality(_empty_graph()) == {}


# ── Community detection ─────────────────────────────────────────────


class TestDetectCommunities:
    def test_two_clusters_detected(self):
        comms = detect_communities(_two_clusters(), seed=42)
        # Should detect at least 2 communities
        assert len(comms) >= 2
        # Each node in exactly one community
        all_nodes = set()
        for c in comms:
            all_nodes.update(c)
        assert all_nodes == {"A", "B", "C", "X", "Y", "Z"}

    def test_deterministic_with_seed(self):
        g = _two_clusters()
        c1 = detect_communities(g, seed=42)
        c2 = detect_communities(g, seed=42)
        assert c1 == c2

    def test_empty_graph(self):
        assert detect_communities(_empty_graph()) == []

    def test_sorted_largest_first(self):
        comms = detect_communities(_two_clusters(), seed=42)
        sizes = [len(c) for c in comms]
        assert sizes == sorted(sizes, reverse=True)


# ── Modularity score ────────────────────────────────────────────────


class TestModularityScore:
    def test_no_edges_returns_zero(self):
        assert modularity_score(_empty_graph()) == 0.0

    def test_two_clusters_positive(self):
        g = _two_clusters()
        comms = [{"A", "B", "C"}, {"X", "Y", "Z"}]
        score = modularity_score(g, communities=comms)
        assert score > 0.0

    def test_single_community_near_zero(self):
        g = _two_clusters()
        all_nodes = {"A", "B", "C", "X", "Y", "Z"}
        score = modularity_score(g, communities=[all_nodes])
        assert score == pytest.approx(0.0, abs=1e-10)


# ── Degree distribution ─────────────────────────────────────────────


class TestDegreeDistribution:
    def test_linear_chain(self):
        dd = degree_distribution(_linear_graph())
        # A: out=1 in=0; B: out=1 in=1; D: out=0 in=1
        assert dd["A"]["out_degree"] == 1
        assert dd["A"]["in_degree"] == 0
        assert dd["B"]["in_degree"] == 1
        assert dd["B"]["out_degree"] == 1
        assert dd["D"]["in_degree"] == 1
        assert dd["D"]["out_degree"] == 0

    def test_total_degree(self):
        dd = degree_distribution(_linear_graph())
        for node, degrees in dd.items():
            assert degrees["total_degree"] == degrees["in_degree"] + degrees["out_degree"]

    def test_empty_graph(self):
        assert degree_distribution(_empty_graph()) == {}


# ── Cohesion / coupling ─────────────────────────────────────────────


class TestCohesionCoupling:
    def test_two_clusters_with_bridge(self):
        g = _two_clusters()
        comms = [{"A", "B", "C"}, {"X", "Y", "Z"}]
        cc = cohesion_coupling(g, communities=comms)
        # 12 intra-cluster edges + 1 bridge = 13 total
        assert cc["intra_edges"] == 12
        assert cc["inter_edges"] == 1
        assert cc["total_edges"] == 13
        assert cc["cohesion"] == pytest.approx(12 / 13)
        assert cc["coupling"] == pytest.approx(1 / 13)
        assert cc["communities"] == 2

    def test_no_edges(self):
        cc = cohesion_coupling(_empty_graph())
        assert cc["cohesion"] == 0.0
        assert cc["coupling"] == 0.0
        assert cc["total_edges"] == 0

    def test_ratios_sum_to_one(self):
        g = _two_clusters()
        comms = [{"A", "B", "C"}, {"X", "Y", "Z"}]
        cc = cohesion_coupling(g, communities=comms)
        assert cc["cohesion"] + cc["coupling"] == pytest.approx(1.0)