Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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317 lines
13 KiB
317 lines
13 KiB
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import numpy as np
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import itertools
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import networkx as nx
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from networkx.readwrite import json_graph
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import json
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import typing
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from scipy.stats import f as f_dist
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from scipy.stats import chi2 as chi2_dist
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import sample_path as sp
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import structure as st
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import network_graph as ng
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import conditional_intensity_matrix as condim
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import parameters_estimator as pe
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import cache as ch
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class StructureEstimator:
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"""
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Has the task of estimating the network structure given the trajectories in samplepath.
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:sample_path: the sample_path object containing the trajectories and the real structure
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:exp_test_sign: the significance level for the exponential Hp test
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:chi_test_alfa: the significance level for the chi Hp test
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:nodes: the nodes labels
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:nodes_vals: the nodes cardinalities
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:nodes_indxs: the nodes indexes
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:complete_graph: the complete directed graph built using the nodes labels in nodes
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:cache: the cache object
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"""
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def __init__(self, sample_path: sp.SamplePath, exp_test_alfa: float, chi_test_alfa: float):
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self.sample_path = sample_path
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self.nodes = np.array(self.sample_path.structure.nodes_labels)
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self.nodes_vals = self.sample_path.structure.nodes_values
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self.nodes_indxs = self.sample_path.structure.nodes_indexes
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self.complete_graph = self.build_complete_graph(self.sample_path.structure.nodes_labels)
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self.exp_test_sign = exp_test_alfa
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self.chi_test_alfa = chi_test_alfa
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self.cache = ch.Cache()
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def build_complete_graph(self, node_ids: typing.List):
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"""
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Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
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Parameters:
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node_ids: the list of nodes labels
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Returns:
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a complete Digraph Object
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"""
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complete_graph = nx.DiGraph()
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complete_graph.add_nodes_from(node_ids)
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complete_graph.add_edges_from(itertools.permutations(node_ids, 2))
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return complete_graph
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def complete_test(self, test_parent: str, test_child: str, parent_set: typing.List, child_states_numb: int,
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tot_vars_count: int):
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"""
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Permorms a complete independence test on the directed graphs G1 = test_child U parent_set
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G2 = G1 U test_parent (added as an additional parent of the test_child).
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Generates all the necessary structures and datas to perform the tests.
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Parameters:
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test_parent: the node label of the test parent
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test_child: the node label of the child
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parent_set: the common parent set
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child_states_numb: the cardinality of the test_child
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tot_vars_count_ the total number of variables in the net
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Returns:
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True iff test_child and test_parent are independent given the sep_set parent_set
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False otherwise
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"""
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#print("Test Parent:", test_parent)
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#print("Sep Set", parent_set)
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p_set = parent_set[:]
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complete_info = parent_set[:]
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complete_info.append(test_child)
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parents = np.array(parent_set)
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parents = np.append(parents, test_parent)
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#print("PARENTS", parents)
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#parents.sort()
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sorted_parents = self.nodes[np.isin(self.nodes, parents)]
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#print("SORTED PARENTS", sorted_parents)
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cims_filter = sorted_parents != test_parent
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#print("PARENTS NO FROM MASK", cims_filter)
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#if not p_set:
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#print("EMPTY PSET TRYING TO FIND", test_child)
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#sofc1 = self.cache.find(test_child)
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#else:
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sofc1 = self.cache.find(set(p_set))
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if not sofc1:
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#print("CACHE MISSS SOFC1")
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bool_mask1 = np.isin(self.nodes,complete_info)
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#print("Bool mask 1", bool_mask1)
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l1 = list(self.nodes[bool_mask1])
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#print("L1", l1)
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indxs1 = self.nodes_indxs[bool_mask1]
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#print("INDXS 1", indxs1)
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vals1 = self.nodes_vals[bool_mask1]
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eds1 = list(itertools.product(parent_set,test_child))
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s1 = st.Structure(l1, indxs1, vals1, eds1, tot_vars_count)
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g1 = ng.NetworkGraph(s1)
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g1.fast_init(test_child)
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p1 = pe.ParametersEstimator(self.sample_path, g1)
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p1.fast_init(test_child)
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sofc1 = p1.compute_parameters_for_node(test_child)
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#if not p_set:
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#self.cache.put(test_child, sofc1)
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#else:
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self.cache.put(set(p_set), sofc1)
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sofc2 = None
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#p_set.append(test_parent)
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p_set.insert(0, test_parent)
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if p_set:
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#print("FULL PSET TRYING TO FIND", p_set)
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#p_set.append(test_parent)
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#print("PSET ", p_set)
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#set_p_set = set(p_set)
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sofc2 = self.cache.find(set(p_set))
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#if sofc2:
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#print("Sofc2 in CACHE ", sofc2.actual_cims)
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#print(self.cache.list_of_sets_of_indxs)
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if not sofc2:
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#print("Cache MISSS SOFC2")
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complete_info.append(test_parent)
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bool_mask2 = np.isin(self.nodes, complete_info)
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#print("BOOL MASK 2",bool_mask2)
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l2 = list(self.nodes[bool_mask2])
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#print("L2", l2)
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indxs2 = self.nodes_indxs[bool_mask2]
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#print("INDXS 2", indxs2)
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vals2 = self.nodes_vals[bool_mask2]
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eds2 = list(itertools.product(p_set, test_child))
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s2 = st.Structure(l2, indxs2, vals2, eds2, tot_vars_count)
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g2 = ng.NetworkGraph(s2)
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g2.fast_init(test_child)
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p2 = pe.ParametersEstimator(self.sample_path, g2)
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p2.fast_init(test_child)
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sofc2 = p2.compute_parameters_for_node(test_child)
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self.cache.put(set(p_set), sofc2)
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for cim1, p_comb in zip(sofc1.actual_cims, sofc1.p_combs):
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#print("GETTING THIS P COMB", p_comb)
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#if len(parent_set) > 1:
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cond_cims = sofc2.filter_cims_with_mask(cims_filter, p_comb)
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#else:
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#cond_cims = sofc2.actual_cims
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#print("COnd Cims", cond_cims)
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for cim2 in cond_cims:
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#cim2 = sofc2.actual_cims[j]
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#print(indx)
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#print("Run Test", i, j)
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if not self.independence_test(child_states_numb, cim1, cim2):
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return False
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return True
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def independence_test(self, child_states_numb: int, cim1: condim.ConditionalIntensityMatrix,
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cim2: condim.ConditionalIntensityMatrix):
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"""
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Compute the actual independence test using two cims.
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It is performed first the exponential test and if the null hypothesis is not rejected,
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it is permormed also the chi_test.
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Parameters:
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child_states_numb: the cardinality of the test child
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cim1: a cim belonging to the graph without test parent
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cim2: a cim belonging to the graph with test parent
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Returns:
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True iff both tests do NOT reject the null hypothesis of indipendence
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False otherwise
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"""
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M1 = cim1.state_transition_matrix
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M2 = cim2.state_transition_matrix
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r1s = M1.diagonal()
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r2s = M2.diagonal()
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C1 = cim1.cim
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C2 = cim2.cim
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F_stats = C2.diagonal() / C1.diagonal()
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exp_alfa = self.exp_test_sign
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for val in range(0, child_states_numb):
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if F_stats[val] < f_dist.ppf(exp_alfa / 2, r1s[val], r2s[val]) or \
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F_stats[val] > f_dist.ppf(1 - exp_alfa / 2, r1s[val], r2s[val]):
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#print("CONDITIONALLY DEPENDENT EXP")
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return False
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#M1_no_diag = self.remove_diagonal_elements(cim1.state_transition_matrix)
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#M2_no_diag = self.remove_diagonal_elements(cim2.state_transition_matrix)
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M1_no_diag = M1[~np.eye(M1.shape[0], dtype=bool)].reshape(M1.shape[0], -1)
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M2_no_diag = M2[~np.eye(M2.shape[0], dtype=bool)].reshape(
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M2.shape[0], -1)
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chi_2_quantile = chi2_dist.ppf(1 - self.chi_test_alfa, child_states_numb - 1)
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"""
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Ks = np.sqrt(cim1.state_transition_matrix.diagonal() / cim2.state_transition_matrix.diagonal())
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Ls = np.reciprocal(Ks)
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chi_stats = np.sum((np.power((M2_no_diag.T * Ks).T - (M1_no_diag.T * Ls).T, 2) \
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/ (M1_no_diag + M2_no_diag)), axis=1)"""
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Ks = np.sqrt(r1s / r2s)
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Ls = np.sqrt(r2s / r1s)
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for val in range(0, child_states_numb):
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#K = math.sqrt(cim1.state_transition_matrix[val][val] / cim2.state_transition_matrix[val][val])
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#L = 1 / K
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Chi = np.sum(np.power(Ks[val] * M2_no_diag[val] - Ls[val] *M1_no_diag[val], 2) /
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(M1_no_diag[val] + M2_no_diag[val]))
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#print("Chi Stats", Chi)
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#print("Chi Quantile", chi_2_quantile)
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if Chi > chi_2_quantile:
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#if np.any(chi_stats > chi_2_quantile):
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#print("CONDITIONALLY DEPENDENT CHI")
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return False
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#print("Chi test", Chi)
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return True
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def one_iteration_of_CTPC_algorithm(self, var_id: str, tot_vars_count: int):
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"""
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Performs an iteration of the CTPC algorithm using the node var_id as test_child.
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Parameters:
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var_id: the node label of the test child
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tot_vars_count: the number of nodes in the net
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Returns:
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void
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"""
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print("##################TESTING VAR################", var_id)
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u = list(self.complete_graph.predecessors(var_id))
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#tests_parents_numb = len(u)
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#complete_frame = self.complete_graph_frame
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#test_frame = complete_frame.loc[complete_frame['To'].isin([var_id])]
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child_states_numb = self.sample_path.structure.get_states_number(var_id)
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b = 0
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while b < len(u):
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#for parent_id in u:
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parent_indx = 0
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while parent_indx < len(u):
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#print("Parent_indx",parent_indx)
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#print("LEN U", len(u))
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removed = False
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#if not list(self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])):
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#break
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S = self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])
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#print("U Set", u)
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#print("S", S)
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test_parent = u[parent_indx]
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#print("Test Parent", test_parent)
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for parents_set in S:
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#print("Parent Set", parents_set)
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#print("Test Parent", test_parent)
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if self.complete_test(test_parent, var_id, parents_set, child_states_numb, tot_vars_count):
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#print("Removing EDGE:", test_parent, var_id)
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self.complete_graph.remove_edge(test_parent, var_id)
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u.remove(test_parent)
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removed = True
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break
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#else:
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#parent_indx += 1
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if not removed:
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parent_indx += 1
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b += 1
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self.cache.clear()
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def generate_possible_sub_sets_of_size(self, u: typing.List, size: int, parent_label: str):
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"""
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Creates a list containing all possible subsets of the list u of size size,
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that do not contains a the node identified by parent_label.
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Parameters:
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u: the list of nodes
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size: the size of the subsets
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parent_label: the nodes to exclude in the subsets generation
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Returns:
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a Map Object containing a list of lists
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"""
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list_without_test_parent = u[:]
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list_without_test_parent.remove(parent_label)
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return map(list, itertools.combinations(list_without_test_parent, size))
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def ctpc_algorithm(self):
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"""
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Compute the CTPC algorithm.
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Parameters:
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void
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Returns:
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void
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"""
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ctpc_algo = self.one_iteration_of_CTPC_algorithm
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total_vars_numb = self.sample_path.total_variables_count
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[ctpc_algo(n, total_vars_numb) for n in self.nodes]
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def save_results(self):
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"""
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Save the estimated Structure to a .json file
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Parameters:
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void
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Returns:
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void
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"""
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res = json_graph.node_link_data(self.complete_graph)
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name = self.sample_path.importer.file_path.rsplit('/',1)[-1]
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#print(name)
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name = 'results_' + name
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with open(name, 'w') as f:
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json.dump(res, f)
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def remove_diagonal_elements(self, matrix):
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m = matrix.shape[0]
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strided = np.lib.stride_tricks.as_strided
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s0, s1 = matrix.strides
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return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1)
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