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