GA-152 | Generalize Algorithm Dispatching

_nx_arangodb/__init__.py

@@ -11,6 +11,8 @@
 $ python _nx_arangodb/__init__.py
 """
 
+import networkx as nx
+
 from _nx_arangodb._version import __version__
 
 # This is normally handled by packaging.version.Version, but instead of adding
@@ -28,14 +30,7 @@
     # "description": "TODO",
     "functions": {
         # BEGIN: functions
-        "betweenness_centrality",
-        "is_partition",
-        "louvain_communities",
-        "louvain_partitions",
-        "modularity",
-        "pagerank",
         "shortest_path",
-        "to_scipy_sparse_array",
         # END: functions
     },
     "additional_docs": {
@@ -45,27 +40,9 @@
     },
     "additional_parameters": {
         # BEGIN: additional_parameters
-        "is_partition": {
-            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
-        },
-        "louvain_communities": {
-            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
-        },
-        "louvain_partitions": {
-            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
-        },
-        "modularity": {
-            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
-        },
-        "pagerank": {
-            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
-        },
         "shortest_path": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
-        "to_scipy_sparse_array": {
-            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
-        },
         # END: additional_parameters
     },
 }
@@ -96,6 +73,17 @@ def get_info():
 
     for key in info_keys:
         del d[key]
+
+    d["default_config"] = {
+        "host": None,
+        "username": None,
+        "password": None,
+        "db_name": None,
+        "load_parallelism": None,
+        "load_batch_size": None,
+        "pull_graph": True,
+    }
+
     return d
 
 

nx_arangodb/algorithms/__init__.py

@@ -1,5 +1,2 @@
-from . import centrality, community, link_analysis, shortest_paths
-from .centrality import *
-from .community import *
-from .link_analysis import *
+from . import shortest_paths
 from .shortest_paths import *

nx_arangodb/algorithms/shortest_paths/generic.py

@@ -30,10 +30,10 @@ def shortest_path(
         )
 
     if target is None or source is None:
-        raise ShortestPathError("Both source and target must be specified for now")
+        raise NotImplementedError("Both source and target must be specified for now")
 
     if method != "dijkstra":
-        raise ShortestPathError("Only dijkstra method is supported")
+        raise NotImplementedError("Only dijkstra method is supported")
 
     query = """
         FOR vertex IN ANY SHORTEST_PATH @source TO @target GRAPH @graph

nx_arangodb/classes/dict.py

@@ -529,13 +529,19 @@ def items(self, data: str | None = None, default: Any | None = None) -> Any:
     def __fetch_all(self):
         self.clear()
 
-        node_dict, _, _, _, _ = get_arangodb_graph(
+        (
+            node_dict,
+            *_,
+        ) = get_arangodb_graph(
             self.graph,
             load_node_dict=True,
             load_adj_dict=False,
+            load_coo=False,
+            load_all_vertex_attributes=True,
+            load_all_edge_attributes=False,  # not used
             is_directed=False,  # not used
             is_multigraph=False,  # not used
-            load_coo=False,
+            symmetrize_edges_if_directed=False,  # not used
         )
 
         for node_id, node_data in node_dict.items():
@@ -1174,13 +1180,20 @@ def items(self, data: str | None = None, default: Any | None = None) -> Any:
     def __fetch_all(self) -> None:
         self.clear()
 
-        _, adj_dict, _, _, _ = get_arangodb_graph(
+        (
+            _,
+            adj_dict,
+            *_,
+        ) = get_arangodb_graph(
             self.graph,
             load_node_dict=False,
             load_adj_dict=True,
+            load_coo=False,
+            load_all_vertex_attributes=False,  # not used
+            load_all_edge_attributes=True,
             is_directed=False,  # TODO: Abstract based on Graph type
             is_multigraph=False,  # TODO: Abstract based on Graph type
-            load_coo=False,
+            symmetrize_edges_if_directed=False,  # TODO: Abstract based on Graph type
         )
 
         for src_node_id, inner_dict in adj_dict.items():