Fetch all node properties by default with from_gds

changelog.md

@@ -3,6 +3,8 @@
 
 ## Breaking changes
 
+* The `from_gds` methods now fetches all node properties of a given GDS projection by default, instead of none.
+
 
 ## New features
 

docs/source/integration.rst

@@ -110,8 +110,10 @@ and will be used to determine the sizes of the nodes in the visualization.
 
 The ``additional_node_properties`` parameter is also optional, and should be a list of additional node properties of the
 projection that you want to include in the visualization.
-For example, these properties could be used to color the nodes, or give captions to them in the visualization, or simply
-included in the nodes' `Node.properties` maps without directly impacting the visualization.
+The default is `None`, which means that all properties of the nodes in the projection will be included.
+Apart from being visible through on-hover tooltips, these properties could be used to color the nodes, or give captions
+to them in the visualization, or simply included in the nodes' `Node.properties` maps without directly impacting the
+visualization.
 
 The last optional property, ``node_radius_min_max``, can be used (and is used by default) to scale the node sizes for
 the visualization.

python-wrapper/src/neo4j_viz/gds.py

@@ -63,7 +63,8 @@ def from_gds(
     size_property : str, optional
         Property to use for node size, by default None.
     additional_node_properties : list[str], optional
-        Additional properties to include in the visualization node, by default None. They can be used later for modifying the node appearance.
+        Additional properties to include in the visualization node, by default None which means that all node
+        properties will be fetched.
     node_radius_min_max : tuple[float, float], optional
         Minimum and maximum node radius, by default (3, 60).
         To avoid tiny or huge nodes in the visualization, the node sizes are scaled to fit in the given range.
@@ -75,7 +76,9 @@ def from_gds(
     if size_property is not None and size_property not in actual_node_properties:
         raise ValueError(f"There is no node property '{size_property}' in graph '{G.name()}'")
 
-    if additional_node_properties is not None:
+    if additional_node_properties is None:
+        additional_node_properties = actual_node_properties
+    else:
         for prop in additional_node_properties:
             if prop not in actual_node_properties:
                 raise ValueError(f"There is no node property '{prop}' in graph '{G.name()}'")

python-wrapper/tests/test_gds.py

@@ -61,6 +61,58 @@ def test_from_gds_integration(gds: Any) -> None:
         ]
 
 
+@pytest.mark.requires_neo4j_and_gds
+def test_from_gds_integration_all_properties(gds: Any) -> None:
+    from neo4j_viz.gds import from_gds
+
+    nodes = pd.DataFrame(
+        {
+            "nodeId": [0, 1, 2],
+            "labels": [["A"], ["C"], ["A", "B"]],
+            "score": [1337, 42, 3.14],
+            "component": [1, 4, 2],
+            "size": [0.1, 0.2, 0.3],
+        }
+    )
+    rels = pd.DataFrame(
+        {
+            "sourceNodeId": [0, 1, 2],
+            "targetNodeId": [1, 2, 0],
+            "cost": [1.0, 2.0, 3.0],
+            "weight": [0.5, 1.5, 2.5],
+            "relationshipType": ["REL", "REL2", "REL"],
+        }
+    )
+
+    with gds.graph.construct("flo", nodes, rels) as G:
+        VG = from_gds(
+            gds,
+            G,
+            node_radius_min_max=None,
+        )
+
+        assert len(VG.nodes) == 3
+        assert sorted(VG.nodes, key=lambda x: x.id) == [
+            Node(id=0, size=0.1, properties=dict(labels=["A"], component=float(1), score=1337.0)),
+            Node(id=1, size=0.2, properties=dict(labels=["C"], component=float(4), score=42.0)),
+            Node(id=2, size=0.3, properties=dict(labels=["A", "B"], component=float(2), score=3.14)),
+        ]
+
+        assert len(VG.relationships) == 3
+        vg_rels = sorted(
+            [
+                (e.source, e.target, e.properties["relationshipType"], e.properties["cost"], e.properties["weight"])
+                for e in VG.relationships
+            ],
+            key=lambda x: x[0],
+        )
+        assert vg_rels == [
+            (0, 1, "REL", 1.0, 0.5),
+            (1, 2, "REL2", 2.0, 1.5),
+            (2, 0, "REL", 3.0, 2.5),
+        ]
+
+
 def test_from_gds_mocked(mocker: MockerFixture) -> None:
     from graphdatascience import Graph, GraphDataScience