From b49b6ea7fb5ed71707c2c8b8add5d9aa98bdf58c Mon Sep 17 00:00:00 2001
From: araison <adrien.raison@xlim.fr>
Date: Fri, 9 Dec 2022 14:42:34 +0100
Subject: [PATCH] Revert "New fixes + reformat"

This reverts commit 5d2cacfa05a9a55580aa12dd294ebbcad0ce31ad.
---
 {tool/visualizer => utils}/__init__.py |   0
 utils/stat.py                          | 125 +++++++++++++++++++++++++
 visualizer/__init__.py                 |   0
 3 files changed, 125 insertions(+)
 rename {tool/visualizer => utils}/__init__.py (100%)
 create mode 100644 utils/stat.py
 create mode 100644 visualizer/__init__.py

diff --git a/tool/visualizer/__init__.py b/utils/__init__.py
similarity index 100%
rename from tool/visualizer/__init__.py
rename to utils/__init__.py
diff --git a/utils/stat.py b/utils/stat.py
new file mode 100644
index 0000000..0016a6b
--- /dev/null
+++ b/utils/stat.py
@@ -0,0 +1,125 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import logging
+import multiprocessing as mp
+import os
+import threading
+import time
+import types
+from inspect import getmembers, isfunction, signature
+
+import networkx as nx
+from torch_geometric.data import Data
+from torch_geometric.utils import to_networkx
+
+with open("mapping_nx.txt", "r") as file:
+    BLACK_LIST = [line.rstrip() for line in file]
+
+
+class GraphStat(object):
+    def __init__(self):
+        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)
+        MANUALLY_ADDED = [
+            "algebraic_connectivity",
+            "adjacency_spectrum",
+            "degree",
+            "density",
+            "laplacian_spectrum",
+            "normalized_laplacian_spectrum",
+            "number_of_selfloops",
+            "number_of_edges",
+            "number_of_nodes",
+        ]
+        MANUALLY_ADDED_LIST = [
+            item for item in getmembers(nx, isfunction) if item[0] in MANUALLY_ADDED
+        ]
+        functions_list = functions_list + MANUALLY_ADDED_LIST
+        maps = {}
+        for func in functions_list:
+            name, f = func
+            if (
+                name in BLACK_LIST
+                or name == "recursive_simple_cycles"
+                or "triad" in name
+                or "weisfeiler" in name
+                or "dfs" in name
+                or "trophic" in name
+                or "recursive" in name
+                or "scipy" in name
+                or "numpy" in name
+                or "sigma" in name
+                or "omega" in name
+                or "all_" in name
+            ):
+                continue
+            else:
+                maps[name] = f
+        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) if hasattr(x, name) else None
+            for name in names
+        }
+        return maps
+
+    def __call__(self, data):
+        data_ = data.__copy__()
+        stats = {}
+        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():
+                if f is None:
+                    stats[name] = None
+                    continue
+                else:
+                    try:
+                        t0 = time.time()
+                        val = f(_data_)
+                        t1 = time.time()
+                        delta = t1 - t0
+                    except Exception as e:
+                        print(name, e)
+                        with open(f"{name}.txt", "w") as f:
+                            f.write(str(e))
+                        # print(name, round(delta, 4))
+                        # if callable(val) and k == "torch_geometric":
+                        # val = val()
+                        # if isinstance(val, types.GeneratorType):
+                        # val = list(val)
+                    # stats[name] = val
+        return stats
+
+
+from torch_geometric.datasets import KarateClub, Planetoid
+
+d = Planetoid(root="/tmp/", name="Cora")
+# d = KarateClub()
+a = d[0]
+st = GraphStat()
+stat = st(a)
+for k, v in stat.items():
+    print("---------")
+    print("Name:", k)
+    print("Type:", type(v))
+    print("Val:", v)
+    print("---------")
diff --git a/visualizer/__init__.py b/visualizer/__init__.py
new file mode 100644
index 0000000..e69de29