Removing ignoring files
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araison
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@ -1,228 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import types
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from inspect import getmembers, isfunction, signature
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import networkx as nx
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import numpy as np
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import torch
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from torch_geometric.data import Data
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from torch_geometric.utils import to_networkx
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__maps__ = [
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"adamic_adar_index",
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"approximate_current_flow_betweenness_centrality",
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"average_clustering",
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"average_degree_connectivity",
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"average_neighbor_degree",
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"average_node_connectivity",
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"average_shortest_path_length",
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"betweenness_centrality",
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"bridges",
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"closeness_centrality",
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"clustering",
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"cn_soundarajan_hopcroft",
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"common_neighbor_centrality",
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"communicability",
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"communicability_betweenness_centrality",
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"communicability_exp",
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"connected_components",
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"constraint",
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"core_number",
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"current_flow_betweenness_centrality",
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"current_flow_closeness_centrality",
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"cycle_basis",
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"degree_assortativity_coefficient",
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"degree_centrality",
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"degree_mixing_matrix",
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"degree_pearson_correlation_coefficient",
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"diameter",
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"dispersion",
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"dominating_set",
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"eccentricity",
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"effective_size",
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"eigenvector_centrality",
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"estrada_index",
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"generalized_degree",
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"global_efficiency",
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"global_reaching_centrality",
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"graph_clique_number",
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"harmonic_centrality",
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"has_bridges",
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"has_eulerian_path",
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"hits",
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"information_centrality",
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"is_at_free",
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"is_biconnected",
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"is_bipartite",
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"is_chordal",
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"is_connected",
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"is_directed_acyclic_graph",
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"is_distance_regular",
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"is_eulerian",
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"is_forest",
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"is_graphical",
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"is_multigraphical",
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"is_planar",
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"is_pseudographical",
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"is_regular",
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"is_semieulerian",
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"is_strongly_regular",
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"is_tree",
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"is_valid_degree_sequence_erdos_gallai",
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"is_valid_degree_sequence_havel_hakimi",
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"jaccard_coefficient",
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"k_components",
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"k_core",
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"katz_centrality",
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"load_centrality",
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"minimum_cycle_basis",
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"minimum_edge_cut",
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"minimum_node_cut",
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"node_clique_number",
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"node_connectivity",
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"node_degree_xy",
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"number_connected_components",
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"number_of_cliques",
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"number_of_isolates",
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"pagerank",
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"periphery",
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"preferential_attachment",
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"reciprocity",
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"resource_allocation_index",
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"rich_club_coefficient",
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"square_clustering",
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"stoer_wagner",
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"subgraph_centrality",
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"subgraph_centrality_exp",
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"transitivity",
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"triangles",
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"voterank",
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"wiener_index",
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"algebraic_connectivity",
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"degree",
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"density",
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"normalized_laplacian_spectrum",
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"number_of_edges",
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"number_of_nodes",
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"number_of_selfloops",
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]
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class GraphStat(object):
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def __init__(self):
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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, 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 name in __maps__:
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maps[name] = f
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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 = {
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name: lambda x, name=name: x.__getattr__(name) if hasattr(x, name) else None
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for name in names
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}
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return maps
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def __call__(self, data):
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data_ = data.__copy__()
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datahash = hash(data.__repr__)
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stats = {}
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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, func in v.items():
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try:
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val = func(_data_)
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except:
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val = None
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if callable(val):
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val = val()
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if isinstance(val, types.GeneratorType):
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try:
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val = dict(val)
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except:
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val = None
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if name == "hits":
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val = val[0]
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if name == "k_components":
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for key, value in val.items():
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val[key] = list(val[key][0])
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stats[name] = self.convert(val)
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stats["hash"] = datahash
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return stats
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def convert(self, val, K=4):
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if type(val) == set:
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val = list(val)
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return val
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elif isinstance(val, torch.Tensor):
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val = val.cpu().numpy().tolist()
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return val
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elif isinstance(val, list):
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for ind in range(len(val)):
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item = val[ind]
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if isinstance(item, list):
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for subind in range(len(item)):
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subitem = item[subind]
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if type(subitem) == float:
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item[subind] = round(subitem, K)
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else:
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if type(item) == float:
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val[ind] = round(item, K)
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return val
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elif isinstance(val, dict):
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for k, v in val.items():
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if isinstance(v, dict):
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for k1, v1 in v.items():
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if type(v1) == float:
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v[k1] = round(v1, K)
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if type(v1) == set:
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v[k1] = list(v1)
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else:
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if type(v) == float:
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val[k] = round(v, K)
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return val
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elif isinstance(val, nx.classes.reportviews.DegreeView):
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val = np.array(val).tolist()
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return val
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elif isinstance(val, nx.Graph):
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val = nx.node_link_data(val)
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return val
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elif isinstance(val, np.ndarray):
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val = np.around(val, decimals=K)
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return val.tolist()
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else:
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return val
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@ -1,3 +0,0 @@
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class BaseExplaining(object):
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def __init__(self,model,explainer_name:wq
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@ -1 +0,0 @@
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from torch_geometric.nn.models.captum import CaptumModel
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