def has_no_cycle(graph: 'Graph'):
nx_graph, _ = graph_structure_as_nx_graph(graph)
cycled = list(simple_cycles(nx_graph))
if len(cycled) > 0:
raise ValueError(f'{ERROR_PREFIX} Graph has cycles')
return True
def get_nodes_degrees(self):
""" Nodes degree as the number of edges the node has:
k = k(in) + k(out)"""
graph, _ = graph_structure_as_nx_graph(self._graph)
index_degree_pairs = graph.degree
node_degrees = [node_degree[1] for node_degree in index_degree_pairs]
return node_degrees
def has_no_isolated_components(graph: 'Graph'):
nx_graph, _ = graph_structure_as_nx_graph(graph)
ud_nx_graph = nx.Graph()
ud_nx_graph.add_nodes_from(nx_graph)
ud_nx_graph.add_edges_from(nx_graph.edges)
if not nx.is_connected(ud_nx_graph):
raise ValueError(f'{ERROR_PREFIX} Graph has isolated components')
return True
def has_no_isolated_nodes(graph: 'Graph'):
nx_graph, _ = graph_structure_as_nx_graph(graph)
isolated = list(isolates(nx_graph))
if len(isolated) > 0 and graph.length != 1:
raise ValueError(f'{ERROR_PREFIX} Graph has isolated nodes')
return True
def _has_no_duplicates(graph):
_, labels = graph_structure_as_nx_graph(graph)
list_of_nodes = [str(node) for node in labels.values()]
if len(list_of_nodes) != len(set(list_of_nodes)):
raise ValueError('Custom graph has duplicates')
return True
def evaluate(self, data: pd.DataFrame):
nodes = data.columns.to_list()
_, labels = graph_structure_as_nx_graph(self)
return len(nodes)
def custom_metric(custom_model: CustomModel):
_, labels = graph_structure_as_nx_graph(custom_model)
return [-len(labels) + custom_model.evaluate()]