def setup_features(self):
self.ncount = self.features["dimensions"][0]
self.feature_number = self.features["dimensions"][1]
self.class_number = max(self.target) + 1
self.target = torch.LongTensor(self.target)
self.propagation_matrix = create_propagator_matrix(
self.graph, self.args.alpha, self.args.model)
def setup_features(self):
"""
Creating a feature matrix, target vector and propagation matrix.
"""
self.ncount = self.features["dimensions"][0]
self.feature_number = self.features["dimensions"][1]
self.class_number = torch.max(self.target).item() + 1
self.propagation_matrix = create_propagator_matrix(self.graph)
def setup_propagator(self):
"""
Defining propagation matrix (Personalized Pagrerank or adjacency).
"""
self.propagator = create_propagator_matrix(self.graph, self.args.alpha,
self.args.model)
if self.args.model == "exact":
self.propagator = self.propagator.to(self.device)
else:
self.edge_indices = self.propagator["indices"].to(self.device)
self.edge_weights = self.propagator["values"].to(self.device)
def setup_propagator(self):
"""
Propagation matrix creation
"""
self.propagator = create_propagator_matrix(self.graph, self.alpha,
self.model)
if self.model == "exact":
self.propagator = self.propagator.to(self.device)
else:
self.edge_indices = self.propagator["indices"].to(self.device)
self.edge_weights = self.propagator["values"].to(self.device)
