Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
298 lines
12 KiB
298 lines
12 KiB
|
|
import numpy as np
|
|
import itertools
|
|
import networkx as nx
|
|
import typing
|
|
from scipy.stats import f as f_dist
|
|
from scipy.stats import chi2 as chi2_dist
|
|
|
|
import sample_path as sp
|
|
import structure as st
|
|
import network_graph as ng
|
|
import conditional_intensity_matrix as condim
|
|
import parameters_estimator as pe
|
|
import cache as ch
|
|
|
|
|
|
class StructureEstimator:
|
|
"""
|
|
Has the task of estimating the network structure given the trajectories in samplepath.
|
|
|
|
:sample_path: the sample_path object containing the trajectories and the real structure
|
|
:exp_test_sign: the significance level for the exponential Hp test
|
|
:chi_test_alfa: the significance level for the chi Hp test
|
|
|
|
:nodes: the nodes labels
|
|
:nodes_vals: the nodes cardinalities
|
|
:nodes_indxs: the nodes indexes
|
|
:complete_graph: the complete directed graph built using the nodes labels in nodes
|
|
:cache: the cache object
|
|
"""
|
|
|
|
def __init__(self, sample_path: sp.SamplePath, exp_test_alfa: float, chi_test_alfa: float):
|
|
self.sample_path = sample_path
|
|
self.nodes = np.array(self.sample_path.structure.nodes_labels)
|
|
self.nodes_vals = self.sample_path.structure.nodes_values
|
|
self.nodes_indxs = self.sample_path.structure.nodes_indexes
|
|
self.complete_graph = self.build_complete_graph(self.sample_path.structure.nodes_labels)
|
|
self.exp_test_sign = exp_test_alfa
|
|
self.chi_test_alfa = chi_test_alfa
|
|
self.cache = ch.Cache()
|
|
|
|
def build_complete_graph(self, node_ids: typing.List):
|
|
"""
|
|
Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
|
|
|
|
Parameters:
|
|
node_ids: the list of nodes labels
|
|
Returns:
|
|
a complete Digraph Object
|
|
"""
|
|
complete_graph = nx.DiGraph()
|
|
complete_graph.add_nodes_from(node_ids)
|
|
complete_graph.add_edges_from(itertools.permutations(node_ids, 2))
|
|
return complete_graph
|
|
|
|
def complete_test(self, test_parent: str, test_child: str, parent_set: typing.List, child_states_numb: int,
|
|
tot_vars_count: int):
|
|
"""
|
|
Permorms a complete independence test on the directed graphs G1 = test_child U parent_set
|
|
G2 = G1 U test_parent (added as an additional parent of the test_child).
|
|
Generates all the necessary structures and datas to perform the tests.
|
|
|
|
Parameters:
|
|
test_parent: the node label of the test parent
|
|
test_child: the node label of the child
|
|
parent_set: the common parent set
|
|
child_states_numb: the cardinality of the test_child
|
|
tot_vars_count_ the total number of variables in the net
|
|
Returns:
|
|
True iff test_child and test_parent are independent given the sep_set parent_set
|
|
False otherwise
|
|
"""
|
|
#print("Test Parent:", test_parent)
|
|
#print("Sep Set", parent_set)
|
|
p_set = parent_set[:]
|
|
complete_info = parent_set[:]
|
|
complete_info.append(test_child)
|
|
|
|
parents = np.array(parent_set)
|
|
parents = np.append(parents, test_parent)
|
|
#print("PARENTS", parents)
|
|
#parents.sort()
|
|
sorted_parents = self.nodes[np.isin(self.nodes, parents)]
|
|
#print("SORTED PARENTS", sorted_parents)
|
|
cims_filter = sorted_parents != test_parent
|
|
#print("PARENTS NO FROM MASK", cims_filter)
|
|
#if not p_set:
|
|
#print("EMPTY PSET TRYING TO FIND", test_child)
|
|
#sofc1 = self.cache.find(test_child)
|
|
#else:
|
|
sofc1 = self.cache.find(set(p_set))
|
|
|
|
if not sofc1:
|
|
#print("CACHE MISSS SOFC1")
|
|
bool_mask1 = np.isin(self.nodes,complete_info)
|
|
#print("Bool mask 1", bool_mask1)
|
|
l1 = list(self.nodes[bool_mask1])
|
|
#print("L1", l1)
|
|
indxs1 = self.nodes_indxs[bool_mask1]
|
|
#print("INDXS 1", indxs1)
|
|
vals1 = self.nodes_vals[bool_mask1]
|
|
eds1 = list(itertools.product(parent_set,test_child))
|
|
s1 = st.Structure(l1, indxs1, vals1, eds1, tot_vars_count)
|
|
g1 = ng.NetworkGraph(s1)
|
|
g1.fast_init(test_child)
|
|
p1 = pe.ParametersEstimator(self.sample_path, g1)
|
|
p1.fast_init(test_child)
|
|
sofc1 = p1.compute_parameters_for_node(test_child)
|
|
#if not p_set:
|
|
#self.cache.put(test_child, sofc1)
|
|
#else:
|
|
self.cache.put(set(p_set), sofc1)
|
|
sofc2 = None
|
|
#p_set.append(test_parent)
|
|
p_set.insert(0, test_parent)
|
|
if p_set:
|
|
#print("FULL PSET TRYING TO FIND", p_set)
|
|
#p_set.append(test_parent)
|
|
#print("PSET ", p_set)
|
|
#set_p_set = set(p_set)
|
|
sofc2 = self.cache.find(set(p_set))
|
|
#if sofc2:
|
|
#print("Sofc2 in CACHE ", sofc2.actual_cims)
|
|
#print(self.cache.list_of_sets_of_indxs)
|
|
if not sofc2:
|
|
#print("Cache MISSS SOFC2")
|
|
complete_info.append(test_parent)
|
|
bool_mask2 = np.isin(self.nodes, complete_info)
|
|
#print("BOOL MASK 2",bool_mask2)
|
|
l2 = list(self.nodes[bool_mask2])
|
|
#print("L2", l2)
|
|
indxs2 = self.nodes_indxs[bool_mask2]
|
|
#print("INDXS 2", indxs2)
|
|
vals2 = self.nodes_vals[bool_mask2]
|
|
eds2 = list(itertools.product(p_set, test_child))
|
|
s2 = st.Structure(l2, indxs2, vals2, eds2, tot_vars_count)
|
|
g2 = ng.NetworkGraph(s2)
|
|
g2.fast_init(test_child)
|
|
p2 = pe.ParametersEstimator(self.sample_path, g2)
|
|
p2.fast_init(test_child)
|
|
sofc2 = p2.compute_parameters_for_node(test_child)
|
|
self.cache.put(set(p_set), sofc2)
|
|
for cim1, p_comb in zip(sofc1.actual_cims, sofc1.p_combs):
|
|
#print("GETTING THIS P COMB", p_comb)
|
|
#if len(parent_set) > 1:
|
|
cond_cims = sofc2.filter_cims_with_mask(cims_filter, p_comb)
|
|
#else:
|
|
#cond_cims = sofc2.actual_cims
|
|
#print("COnd Cims", cond_cims)
|
|
for cim2 in cond_cims:
|
|
#cim2 = sofc2.actual_cims[j]
|
|
#print(indx)
|
|
#print("Run Test", i, j)
|
|
if not self.independence_test(child_states_numb, cim1, cim2):
|
|
return False
|
|
return True
|
|
|
|
def independence_test(self, child_states_numb: int, cim1: condim.ConditionalIntensityMatrix,
|
|
cim2: condim.ConditionalIntensityMatrix):
|
|
"""
|
|
Compute the actual independence test using two cims.
|
|
It is performed first the exponential test and if the null hypothesis is not rejected,
|
|
it is permormed also the chi_test.
|
|
|
|
Parameters:
|
|
child_states_numb: the cardinality of the test child
|
|
cim1: a cim belonging to the graph without test parent
|
|
cim2: a cim belonging to the graph with test parent
|
|
|
|
Returns:
|
|
True iff both tests do NOT reject the null hypothesis of indipendence
|
|
False otherwise
|
|
"""
|
|
M1 = cim1.state_transition_matrix
|
|
M2 = cim2.state_transition_matrix
|
|
r1s = M1.diagonal()
|
|
r2s = M2.diagonal()
|
|
C1 = cim1.cim
|
|
C2 = cim2.cim
|
|
F_stats = C2.diagonal() / C1.diagonal()
|
|
exp_alfa = self.exp_test_sign
|
|
for val in range(0, child_states_numb):
|
|
if F_stats[val] < f_dist.ppf(exp_alfa / 2, r1s[val], r2s[val]) or \
|
|
F_stats[val] > f_dist.ppf(1 - exp_alfa / 2, r1s[val], r2s[val]):
|
|
#print("CONDITIONALLY DEPENDENT EXP")
|
|
return False
|
|
#M1_no_diag = self.remove_diagonal_elements(cim1.state_transition_matrix)
|
|
#M2_no_diag = self.remove_diagonal_elements(cim2.state_transition_matrix)
|
|
M1_no_diag = M1[~np.eye(M1.shape[0], dtype=bool)].reshape(M1.shape[0], -1)
|
|
M2_no_diag = M2[~np.eye(M2.shape[0], dtype=bool)].reshape(
|
|
M2.shape[0], -1)
|
|
chi_2_quantile = chi2_dist.ppf(1 - self.chi_test_alfa, child_states_numb - 1)
|
|
"""
|
|
Ks = np.sqrt(cim1.state_transition_matrix.diagonal() / cim2.state_transition_matrix.diagonal())
|
|
Ls = np.reciprocal(Ks)
|
|
chi_stats = np.sum((np.power((M2_no_diag.T * Ks).T - (M1_no_diag.T * Ls).T, 2) \
|
|
/ (M1_no_diag + M2_no_diag)), axis=1)"""
|
|
Ks = np.sqrt(r1s / r2s)
|
|
Ls = np.sqrt(r2s / r1s)
|
|
for val in range(0, child_states_numb):
|
|
#K = math.sqrt(cim1.state_transition_matrix[val][val] / cim2.state_transition_matrix[val][val])
|
|
#L = 1 / K
|
|
Chi = np.sum(np.power(Ks[val] * M2_no_diag[val] - Ls[val] *M1_no_diag[val], 2) /
|
|
(M1_no_diag[val] + M2_no_diag[val]))
|
|
|
|
#print("Chi Stats", Chi)
|
|
#print("Chi Quantile", chi_2_quantile)
|
|
if Chi > chi_2_quantile:
|
|
#if np.any(chi_stats > chi_2_quantile):
|
|
#print("CONDITIONALLY DEPENDENT CHI")
|
|
return False
|
|
#print("Chi test", Chi)
|
|
return True
|
|
|
|
def one_iteration_of_CTPC_algorithm(self, var_id: str, tot_vars_count: int):
|
|
"""
|
|
Performs an iteration of the CTPC algorithm using the node var_id as test_child.
|
|
|
|
Parameters:
|
|
var_id: the node label of the test child
|
|
tot_vars_count: the number of nodes in the net
|
|
Returns:
|
|
void
|
|
"""
|
|
print("##################TESTING VAR################", var_id)
|
|
u = list(self.complete_graph.predecessors(var_id))
|
|
#tests_parents_numb = len(u)
|
|
#complete_frame = self.complete_graph_frame
|
|
#test_frame = complete_frame.loc[complete_frame['To'].isin([var_id])]
|
|
child_states_numb = self.sample_path.structure.get_states_number(var_id)
|
|
b = 0
|
|
while b < len(u):
|
|
#for parent_id in u:
|
|
parent_indx = 0
|
|
while parent_indx < len(u):
|
|
#print("Parent_indx",parent_indx)
|
|
#print("LEN U", len(u))
|
|
|
|
removed = False
|
|
#if not list(self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])):
|
|
#break
|
|
S = self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])
|
|
#print("U Set", u)
|
|
#print("S", S)
|
|
test_parent = u[parent_indx]
|
|
#print("Test Parent", test_parent)
|
|
for parents_set in S:
|
|
#print("Parent Set", parents_set)
|
|
#print("Test Parent", test_parent)
|
|
if self.complete_test(test_parent, var_id, parents_set, child_states_numb, tot_vars_count):
|
|
#print("Removing EDGE:", test_parent, var_id)
|
|
self.complete_graph.remove_edge(test_parent, var_id)
|
|
u.remove(test_parent)
|
|
removed = True
|
|
break
|
|
#else:
|
|
#parent_indx += 1
|
|
if not removed:
|
|
parent_indx += 1
|
|
b += 1
|
|
self.cache.clear()
|
|
|
|
def generate_possible_sub_sets_of_size(self, u: typing.List, size: int, parent_label: str):
|
|
"""
|
|
Creates a list containing all possible subsets of the list u of size size,
|
|
that do not contains a the node identified by parent_label.
|
|
|
|
Parameters:
|
|
u: the list of nodes
|
|
size: the size of the subsets
|
|
parent_label: the nodes to exclude in the subsets generation
|
|
Returns:
|
|
a Map Object containing a list of lists
|
|
|
|
"""
|
|
list_without_test_parent = u[:]
|
|
list_without_test_parent.remove(parent_label)
|
|
return map(list, itertools.combinations(list_without_test_parent, size))
|
|
|
|
def ctpc_algorithm(self):
|
|
"""
|
|
Compute the CTPC algorithm.
|
|
Parameters:
|
|
void
|
|
Returns:
|
|
void
|
|
"""
|
|
ctpc_algo = self.one_iteration_of_CTPC_algorithm
|
|
total_vars_numb = self.sample_path.total_variables_count
|
|
[ctpc_algo(n, total_vars_numb) for n in self.nodes]
|
|
|
|
|
|
def remove_diagonal_elements(self, matrix):
|
|
m = matrix.shape[0]
|
|
strided = np.lib.stride_tricks.as_strided
|
|
s0, s1 = matrix.strides
|
|
return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1)
|
|
|
|
|