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"""SEAL-CI model."""
import random
from tqdm import trange
from utils import hierarchical_graph_reader, GraphDatasetGenerator
from sklearn import metrics
import pandas as pd
import numpy as np
class SEALCITrainer(object):
"""
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective Cautious Iteration model.
"""
def __init__(self, args):
"""
Creating dataset, doing dataset split, creating target and node index vectors.
:param args: Arguments object.
"""
self.args = args
self.macro_graph = hierarchical_graph_reader(self.args.hierarchical_graph) # 大图
self.dataset_generator = GraphDatasetGenerator(self.args.graphs, self.args.feature_which)
self._setup_macro_graph() # 大图的边 边给加好了self.macro_graph_edges
#self._create_split() # 区分了带标签的和不带标签的 self.labled_indices, self.unlabeld_indices
#self._create_labeled_target() # self.labeled_mask, self.labeled_target
self._create_node_indices() # node_indices
def _setup_macro_graph(self):
"""
Creating an edge list for the hierarchical graph.
"""
self.macro_graph_edges = [[edge[0], edge[1]] for edge in self.macro_graph.edges()]
self.macro_graph_edges = self.macro_graph_edges + [[edge[1], edge[0]] for edge in self.macro_graph.edges()]
self.macro_graph_edges = np.array(self.macro_graph_edges).T
def _create_node_indices(self):
"""
Creating an index of nodes.
"""
self.node_indices = [index for index in range(self.macro_graph.number_of_nodes())]
self.node_indices = np.array(self.node_indices)
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