Add component_labels and n_components properties for disconnected graphs. Bump to version 2.0.0

graphtools/base.py

@@ -723,6 +723,81 @@ def kernel(self):
         """Synonym for K"""
         return self.K
 
+    @property
+    def n_connected_components(self):
+        """Number of connected components in the graph (cached)
+
+        A connected component is a maximal set of nodes where there is a path
+        between every pair of nodes in the set. This property uses scipy's
+        connected_components function on the kernel matrix to count components.
+
+        Returns
+        -------
+        n_components : int
+            Number of connected components in the graph
+
+        Examples
+        --------
+        >>> G = graphtools.Graph(data)
+        >>> print(G.n_connected_components)
+        1
+        """
+        try:
+            return self._n_connected_components
+        except AttributeError:
+            from scipy.sparse.csgraph import connected_components
+
+            self._n_connected_components, self._component_labels = connected_components(
+                csgraph=self.kernel, directed=False, return_labels=True
+            )
+            return self._n_connected_components
+
+    @property
+    def component_labels(self):
+        """Component label for each node (cached)
+
+        Returns the connected component index for each node in the graph.
+        Nodes with the same label belong to the same connected component.
+
+        Returns
+        -------
+        labels : np.ndarray, shape=[n_samples]
+            Component index for each node (0 to n_connected_components - 1)
+
+        Examples
+        --------
+        >>> G = graphtools.Graph(data)
+        >>> labels = G.component_labels
+        >>> # Find nodes in component 0
+        >>> component_0_nodes = np.where(labels == 0)[0]
+        """
+        try:
+            return self._component_labels
+        except AttributeError:
+            # Trigger computation via n_connected_components
+            _ = self.n_connected_components
+            return self._component_labels
+
+    @property
+    def is_connected(self):
+        """Check if the graph is connected (cached)
+
+        A graph is connected if there is a path between every pair of nodes,
+        i.e., if it has exactly one connected component.
+
+        Returns
+        -------
+        connected : bool
+            True if graph has exactly 1 connected component, False otherwise
+
+        Examples
+        --------
+        >>> G = graphtools.Graph(data)
+        >>> if not G.is_connected:
+        ...     print(f"Warning: Graph has {G.n_connected_components} components")
+        """
+        return self.n_connected_components == 1
+
     @property
     def weighted(self):
         return self.decay is not None

graphtools/version.py

@@ -1 +1 @@
-__version__ = "1.5.3"
+__version__ = "2.0.0"

test/test_connectivity.py

@@ -0,0 +1,151 @@
+from __future__ import print_function
+
+from load_tests import build_graph
+from load_tests import data
+from load_tests import graphtools
+from load_tests import np
+
+import pytest
+
+
+def test_connected_graph():
+    """Test that a normal graph is connected"""
+    G = build_graph(data, n_pca=20, decay=10, knn=5)
+
+    # Check that the graph is connected
+    assert G.is_connected, "Expected graph to be connected"
+    assert G.n_connected_components == 1, "Expected exactly 1 connected component"
+
+    # Check component labels
+    labels = G.component_labels
+    assert labels.shape[0] == data.shape[0], "Component labels should match data size"
+    assert np.all(labels == 0), "All nodes should be in component 0"
+
+
+def test_disconnected_graph():
+    """Test a graph that is intentionally disconnected"""
+    # Create two separate clusters of data that won't connect
+    cluster1 = np.random.randn(50, 10)
+    cluster2 = np.random.randn(50, 10) + 100  # Far away from cluster1
+    disconnected_data = np.vstack([cluster1, cluster2])
+
+    # Build graph with small knn to ensure disconnection
+    G = build_graph(disconnected_data, n_pca=None, decay=10, knn=3, thresh=1e-4)
+
+    # Check that the graph is disconnected
+    assert not G.is_connected, "Expected graph to be disconnected"
+    assert G.n_connected_components >= 2, "Expected at least 2 connected components"
+
+    # Check component labels
+    labels = G.component_labels
+    assert labels.shape[0] == disconnected_data.shape[0], "Component labels should match data size"
+    assert len(np.unique(labels)) >= 2, "Should have at least 2 unique component labels"
+
+
+def test_component_labels_consistency():
+    """Test that component labels are consistent across calls"""
+    # Create disconnected data
+    cluster1 = np.random.randn(30, 5)
+    cluster2 = np.random.randn(30, 5) + 50
+    disconnected_data = np.vstack([cluster1, cluster2])
+
+    G = build_graph(disconnected_data, n_pca=None, decay=10, knn=2)
+
+    # Get labels multiple times - should be cached and identical
+    labels1 = G.component_labels
+    labels2 = G.component_labels
+    n_comp1 = G.n_connected_components
+    n_comp2 = G.n_connected_components
+
+    assert np.array_equal(labels1, labels2), "Component labels should be cached"
+    assert n_comp1 == n_comp2, "n_connected_components should be cached"
+
+
+def test_precomputed_graph_connectivity():
+    """Test connectivity with precomputed distance matrix"""
+    from scipy.spatial.distance import pdist, squareform
+
+    # Create small disconnected dataset
+    cluster1 = np.array([[0, 0], [0, 1], [1, 0]])
+    cluster2 = np.array([[100, 100], [100, 101], [101, 100]])
+    disconnected_data = np.vstack([cluster1, cluster2])
+
+    # Compute distance matrix
+    dist_matrix = squareform(pdist(disconnected_data))
+
+    # For precomputed graphs, n_pca must be None
+    G = build_graph(dist_matrix, n_pca=None, precomputed="distance", decay=10, knn=2)
+
+    # Should be disconnected
+    assert not G.is_connected, "Precomputed disconnected graph should be disconnected"
+    assert G.n_connected_components == 2, "Should have exactly 2 components"
+
+
+def test_landmark_graph_connectivity():
+    """Test connectivity with landmark graphs"""
+    G = build_graph(data, n_pca=20, decay=10, knn=5, n_landmark=100)
+
+    # Landmark graphs should still support connectivity checks
+    assert hasattr(G, 'is_connected'), "Landmark graph should have is_connected property"
+    assert hasattr(G, 'n_connected_components'), "Landmark graph should have n_connected_components"
+    assert hasattr(G, 'component_labels'), "Landmark graph should have component_labels"
+
+    # Check that properties work
+    is_conn = G.is_connected
+    n_comp = G.n_connected_components
+    labels = G.component_labels
+
+    assert isinstance(is_conn, (bool, np.bool_)), "is_connected should return boolean"
+    assert isinstance(n_comp, (int, np.integer)), "n_connected_components should return int"
+    assert labels.shape[0] == data.shape[0], "component_labels should match data size"
+
+
+def test_knn_graph_connectivity():
+    """Test connectivity with different knn values"""
+    # With high knn, should be connected
+    G_high_knn = build_graph(data, n_pca=20, knn=10, decay=10)
+    assert G_high_knn.is_connected, "Graph with high knn should be connected"
+
+    # Create data that might disconnect with very low knn
+    sparse_data = np.random.randn(100, 10) * 2
+    G_low_knn = build_graph(sparse_data, n_pca=None, knn=2, decay=10)
+
+    # Just check that the properties work (connectivity depends on data)
+    assert isinstance(G_low_knn.is_connected, (bool, np.bool_))
+    assert G_low_knn.n_connected_components >= 1
+
+
+def test_component_labels_range():
+    """Test that component labels are in the correct range"""
+    cluster1 = np.random.randn(20, 5)
+    cluster2 = np.random.randn(20, 5) + 100
+    cluster3 = np.random.randn(20, 5) - 100
+    disconnected_data = np.vstack([cluster1, cluster2, cluster3])
+
+    G = build_graph(disconnected_data, n_pca=None, decay=10, knn=2)
+
+    labels = G.component_labels
+    n_comp = G.n_connected_components
+
+    # Labels should be in range [0, n_components)
+    assert labels.min() >= 0, "Minimum label should be >= 0"
+    assert labels.max() < n_comp, "Maximum label should be < n_connected_components"
+    assert len(np.unique(labels)) == n_comp, "Number of unique labels should equal n_components"
+
+
+def test_connectivity_caching():
+    """Test that connectivity properties are properly cached"""
+    G = build_graph(data, n_pca=20, decay=10, knn=5)
+
+    # Access properties to trigger caching
+    _ = G.is_connected
+
+    # Check that internal cache exists
+    assert hasattr(G, '_n_connected_components'), "Should cache n_connected_components"
+    assert hasattr(G, '_component_labels'), "Should cache component_labels"
+
+    # Verify cached values are used
+    n_comp_cached = G._n_connected_components
+    n_comp_property = G.n_connected_components
+
+    assert n_comp_cached == n_comp_property, "Cached value should match property value"