Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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211 lines
9.3 KiB
211 lines
9.3 KiB
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import networkx as nx
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import numpy as np
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class NetworkGraph():
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"""
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Rappresenta il grafo che contiene i nodi e gli archi presenti nell'oggetto Structure graph_struct.
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Ogni nodo contine la label node_id, al nodo è anche associato un id numerico progressivo indx che rappresenta la posizione
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dei sui valori nella colonna indx della traj
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:graph_struct: l'oggetto Structure da cui estrarre i dati per costruire il grafo graph
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:graph: il grafo
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"""
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def __init__(self, graph_struct):
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self.graph_struct = graph_struct
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self.graph = nx.DiGraph()
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self._nodes_indexes = self.graph_struct.list_of_nodes_indexes()
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self._nodes_labels = self.graph_struct.list_of_nodes_labels()
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self.aggregated_info_about_nodes_parents = None
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self._fancy_indexing = None
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self._time_scalar_indexing_structure = None
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self._transition_scalar_indexing_structure = None
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self._time_filtering = None
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self._transition_filtering = None
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def init_graph(self):
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self.add_nodes(self.graph_struct.list_of_nodes_labels())
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self.add_edges(self.graph_struct.list_of_edges())
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self.aggregated_info_about_nodes_parents = self.get_ord_set_of_par_of_all_nodes()
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self._fancy_indexing = self.build_fancy_indexing_structure(0)
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self.build_scalar_indexing_structures()
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#self.build_time_scalar_indexing_structure()
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self.build_time_columns_filtering_structure()
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#self.build_transition_scalar_indexing_structure()
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self.build_transition_columns_filtering_structure()
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def add_nodes(self, list_of_nodes):
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#self.graph.add_nodes_from(list_of_nodes)
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nodes_indxs = self.graph_struct.list_of_nodes_indexes()
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nodes_vals = self.graph_struct.nodes_values()
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pos = 0
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for id, node_indx, node_val in zip(list_of_nodes, nodes_indxs, nodes_vals):
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self.graph.add_node(id, indx=node_indx, val=node_val, pos_indx=pos)
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pos += 1
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#set_node_attr(self.graph, {id:node_indx}, 'indx')
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def add_edges(self, list_of_edges):
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self.graph.add_edges_from(list_of_edges)
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def get_ordered_by_indx_set_of_parents(self, node):
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parents = self.get_parents_by_id(node)
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nodes = self.get_nodes()
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sorted_parents = [x for _, x in sorted(zip(nodes, parents))]
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#p_indxes= []
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#p_values = []
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get_node_indx = self.get_node_indx
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p_indxes = [get_node_indx(node) for node in sorted_parents]
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p_values = [self.get_states_number(node) for node in sorted_parents]
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return (sorted_parents, p_indxes, p_values)
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def get_ord_set_of_par_of_all_nodes(self):
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#result = []
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#for node in self._nodes_labels:
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#result.append(self.get_ordered_by_indx_set_of_parents(node))
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get_ordered_by_indx_set_of_parents = self.get_ordered_by_indx_set_of_parents
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result = [get_ordered_by_indx_set_of_parents(node) for node in self._nodes_labels]
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return result
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"""def get_ordered_by_indx_parents_values(self, node):
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parents_values = []
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parents = self.get_ordered_by_indx_set_of_parents(node)
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for n in parents:
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parents_values.append(self.graph_struct.get_states_number(n))
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return parents_values"""
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def get_ordered_by_indx_parents_values_for_all_nodes(self):
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"""result = []
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for node in self._nodes_labels:
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result.append(self.get_ordered_by_indx_parents_values(node))
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return result"""
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pars_values = [i[2] for i in self.aggregated_info_about_nodes_parents]
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return pars_values
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def get_states_number_of_all_nodes_sorted(self):
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#states_number_list = []
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#for node in self._nodes_labels:
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#states_number_list.append(self.get_states_number(node))
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get_states_number = self.get_states_number
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states_number_list = [get_states_number(node) for node in self._nodes_labels]
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return states_number_list
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def build_fancy_indexing_structure(self, start_indx):
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if start_indx > 0:
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pass
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else:
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fancy_indx = [i[1] for i in self.aggregated_info_about_nodes_parents]
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return fancy_indx
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def build_time_scalar_indexing_structure_for_a_node(self, node_id, parents_indxs):
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T_vector = np.array([self.get_states_number(node_id)])
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T_vector = np.append(T_vector, parents_indxs)
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T_vector = T_vector.cumprod().astype(np.int)
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# print(T_vector)
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return T_vector
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def build_transition_scalar_indexing_structure_for_a_node(self, node_id, parents_indxs):
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#M_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1],
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#self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
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node_states_number = self.get_states_number(node_id)
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#get_states_number_by_indx = self.graph_struct.get_states_number_by_indx
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M_vector = np.array([node_states_number,
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node_states_number])
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#M_vector = np.append(M_vector, [get_states_number_by_indx(x) for x in parents_indxs])
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M_vector = np.append(M_vector, parents_indxs)
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M_vector = M_vector.cumprod().astype(np.int)
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return M_vector
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def build_time_columns_filtering_structure(self):
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#parents_indexes_list = self._fancy_indexing
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"""for node_indx, p_indxs in zip(self.graph_struct.list_of_nodes_indexes(), self._fancy_indexing):
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self._time_filtering.append(np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int))"""
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nodes_indxs = self.graph_struct.list_of_nodes_indexes()
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self._time_filtering = [np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
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for node_indx, p_indxs in zip(nodes_indxs, self._fancy_indexing)]
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def build_transition_columns_filtering_structure(self):
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#parents_indexes_list = self._fancy_indexing
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nodes_number = self.graph_struct.total_variables_number
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"""for node_indx, p_indxs in zip(self.graph_struct.list_of_nodes_indexes(), self._fancy_indexing):
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self._transition_filtering.append(np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int))"""
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nodes_indxs = self.graph_struct.list_of_nodes_indexes()
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self._transition_filtering = [np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
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for node_indx, p_indxs in zip(nodes_indxs,
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self._fancy_indexing)]
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def build_scalar_indexing_structures(self):
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parents_values_for_all_nodes = self.get_ordered_by_indx_parents_values_for_all_nodes()
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build_transition_scalar_indexing_structure_for_a_node = self.build_transition_scalar_indexing_structure_for_a_node
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build_time_scalar_indexing_structure_for_a_node = self.build_time_scalar_indexing_structure_for_a_node
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aggr = [(build_transition_scalar_indexing_structure_for_a_node(node_indx, p_indxs),
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build_time_scalar_indexing_structure_for_a_node(node_indx, p_indxs))
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for node_indx, p_indxs in
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zip(self._nodes_labels,
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parents_values_for_all_nodes)]
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self._transition_scalar_indexing_structure = [i[0] for i in aggr]
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self._time_scalar_indexing_structure = [i[1] for i in aggr]
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def get_nodes(self):
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return list(self.graph.nodes)
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def get_edges(self):
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return list(self.graph.edges)
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def get_nodes_sorted_by_indx(self):
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return self.graph_struct.list_of_nodes_labels()
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def get_parents_by_id(self, node_id):
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return list(self.graph.predecessors(node_id))
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def get_states_number(self, node_id):
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#return self.graph_struct.get_states_number(node_id)
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return self.graph.nodes[node_id]['val']
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def get_states_number_by_indx(self, node_indx):
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return self.graph_struct.get_states_number_by_indx(node_indx)
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def get_node_by_index(self, node_indx):
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return self.graph_struct.get_node_id(node_indx)
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def get_node_indx(self, node_id):
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return nx.get_node_attributes(self.graph, 'indx')[node_id]
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#return self.graph_struct.get_node_indx(node_id)
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def get_positional_node_indx(self, node_id):
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return self.graph.nodes[node_id]['pos_indx']
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@property
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def time_scalar_indexing_strucure(self):
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return self._time_scalar_indexing_structure
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@property
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def time_filtering(self):
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return self._time_filtering
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@property
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def transition_scalar_indexing_structure(self):
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return self._transition_scalar_indexing_structure
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@property
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def transition_filtering(self):
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return self._transition_filtering
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"""def remove_node(self, node_id):
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node_indx = self.get_node_indx(node_id)
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self.graph_struct.remove_node(node_id)
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self.graph.remove_node(node_id)
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del self._fancy_indexing[node_indx]
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del self._time_filtering[node_indx]
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del self._nodes_labels[node_indx]
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del self._transition_scalar_indexing_structure[node_indx]
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del self._transition_filtering[node_indx]
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del self._time_scalar_indexing_structure[node_indx]
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del self.aggregated_info_about_nodes_parents[node_indx]
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del self._nodes_indexes[node_indx]"""
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