Adding some graph stats

utils/stat.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import logging
+import os
+import threading
+from inspect import getmembers, isfunction, signature
+
+#  import custom_graphgym  # noqa, register custom modules
+import networkx as nx
+import pandas as pd
+from docstring_parser import parse
+from torch_geometric.data import Data
+#  from torch_geometric.explain import Explanation
+from torch_geometric.utils import to_networkx
+
+GRAPH_STAT_TYPE = ["int", "float", "bool", "boolean", "dict", "dictionary"]
+
+
+class GraphStat(object):
+    def __init__(self):
+        self.stat = {}
+        self.maps = {
+            "networkx": self.available_map_networkx(),
+            "torch_geometric": self.available_map_torch_geometric(),
+        }
+
+    def available_map_networkx(self):
+        functions_list = getmembers(nx.algorithms, isfunction)
+        maps = {}
+        for func in functions_list:
+            name, f = func
+            if "all_" in name:
+                continue
+            docstring = parse(f.__doc__)
+            try:
+                #  rt = docstring.returns.type_name
+                #  if rt in GRAPH_STAT_TYPE:
+                maps[name] = f
+            except AttributeError:
+                continue
+        return maps
+
+    def available_map_torch_geometric(self):
+        names = [
+            "num_nodes",
+            "num_edges",
+            "has_self_loops",
+            "has_isolated_nodes",
+            #  "num_nodes_features",
+            "y",
+        ]
+        maps = {name:lambda x,name=name: x.__getattr__(name) for name in names}
+        return maps
+
+    def to_series(self, name, val):
+        self.stat.append(pd.Series(data={name: val}))
+
+    def __call__(self, data):
+        data_ = data.__copy__()
+        self.stat = []
+        process = []
+        for k, v in self.maps.items():
+            if k == "networkx":
+                _data_ = to_networkx(data)
+                _data_ = _data_.to_undirected()
+            elif k == "torch_geometric":
+                _data_ = data.__copy__()
+            for name, f in v.items():
+                try:
+                    proc = f(_data_)
+                    if callable(proc) and k == "torch_geometric":
+                        proc = proc()
+                    self.to_series(name, proc)
+                except:
+                    continue
+        return self.stat
+
+
+from torch_geometric.datasets import KarateClub
+
+d = KarateClub()
+a = d[0]
+st = GraphStat()
+stat = st(a)
+for item in stat:
+    if item.dtypes == 'int' or item.dtypes == 'float':
+        continue
+    else:
+        print(item)