add tests for MetricDistanceFilter

Author: lfischerr

tests/test_metric_distance.py

@@ -0,0 +1,91 @@
+import numpy as np
+import networkx as nx
+import scipy.sparse as sp
+from graphconstructor import Graph
+from graphconstructor.operators import MetricDistanceFilter
+
+def _csr(data, rows, cols, n):
+    return sp.csr_matrix(
+        (np.asarray(data, float), (np.asarray(rows, int), np.asarray(cols, int))),
+        shape=(n, n),
+    )
+
+def simple_undirected_graph():
+    A = _csr(
+        data=[0.5, 0.5, 0.3, 0.3, 0.8, 0.8],
+        rows=[0, 1, 0, 2, 1, 2],
+        cols=[1, 0, 2, 0, 2, 1],
+        n=3,
+    )
+
+    return Graph.from_csr(A, directed=False, weighted=True, mode="similarity")
+
+def test_basic_undirected_filtering():
+    G0 = simple_undirected_graph()
+
+    out = MetricDistanceFilter(distortion=False, verbose=False).apply(G0)
+
+    assert isinstance(out, Graph)
+    assert out.directed == False
+    assert out.weighted == True
+
+    original_edges = G0.to_networkx().number_of_edges()
+    result_edges = out.to_networkx().number_of_edges()
+    assert result_edges <= original_edges
+    
+def test_undirected_filtering_distortion():
+    G0 = simple_undirected_graph()
+
+    out = MetricDistanceFilter(distortion=True, verbose=False).apply(G0)
+
+    assert isinstance(out, tuple)
+    assert len(out) == 2
+
+    filtered_graph, svals = out
+    assert isinstance(filtered_graph, Graph)
+    assert isinstance(svals, dict)
+    
+    if svals:
+        key = next(iter(svals.keys()))
+        assert isinstance(key, tuple)
+        assert len(key) == 2
+
+def test_directed_graph_not_implemented():
+    G0 = simple_undirected_graph()
+    out = MetricDistanceFilter().apply(G0)
+    assert out is None
+
+def test_edge_removal_logic():
+    G0 = simple_undirected_graph()
+    out = MetricDistanceFilter().apply(G0)
+
+    original_nx = G0.to_networkx()
+    out_nx = G0.to_networkx()
+
+    assert out_nx.number_of_edges() <= original_nx.number_of_edges()
+
+    if nx.is_connected(original_nx):
+        assert nx.is_connected(out_nx)
+
+def test_isolated_nodes():
+    A = _csr(
+        data=[0.5, 0.5],
+        rows=[0, 1],
+        cols=[1, 0],
+        n=3,
+    )
+    G0 = Graph.from_csr(A, directed=False, weighted=True, mode="distance")
+    out = MetricDistanceFilter().apply(G0)
+
+    assert out.to_networkx().number_of_nodes() == 3
+    assert 2 in out.to_networkx().nodes()
+
+def test_empty_graph():
+    A = _csr(data=[], rows=[], cols=[], n=3)
+    G0 = Graph.from_csr(A, directed=False, weighted=True, mode="distance")
+
+    out = MetricDistanceFilter().apply(G0)
+
+    assert out.to_networkx().number_of_edges() == 0
+    assert out.to_networkx().number_of_nodes() == 3
+