Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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159 lines
6.5 KiB
159 lines
6.5 KiB
import os
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from line_profiler import LineProfiler
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import numba as nb
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import numpy as np
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import network_graph as ng
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import sample_path as sp
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import sets_of_cims_container as acims
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class ParametersEstimator:
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def __init__(self, sample_path, net_graph):
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self.sample_path = sample_path
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self.net_graph = net_graph
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self.sets_of_cims_struct = None
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def init_amalgamated_cims_struct(self):
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self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.get_states_number_of_all_nodes_sorted(),
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self.net_graph.get_nodes(),
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self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes())
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def compute_parameters(self):
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#print(self.net_graph.get_nodes())
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#print(self.amalgamated_cims_struct.sets_of_cims)
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#enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims))
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for indx, aggr in enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims)):
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#print(self.net_graph.time_filtering[indx])
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#print(self.net_graph.time_scalar_indexing_strucure[indx])
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self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
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self.sample_path.trajectories.trajectory,
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self.net_graph.time_filtering[indx],
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self.net_graph.time_scalar_indexing_strucure[indx],
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aggr[1].state_residence_times)
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#print(self.net_graph.transition_filtering[indx])
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#print(self.net_graph.transition_scalar_indexing_structure[indx])
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self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]),
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self.sample_path.trajectories.complete_trajectory,
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self.net_graph.transition_filtering[indx],
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self.net_graph.transition_scalar_indexing_structure[indx],
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aggr[1].transition_matrices)
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aggr[1].build_cims(aggr[1].state_residence_times, aggr[1].transition_matrices)
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def compute_state_res_time_for_node(self, node_indx, times, trajectory, cols_filter, scalar_indexes_struct, T):
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#print(times.size)
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#print(trajectory)
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#print(cols_filter)
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#print(scalar_indexes_struct)
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#print(T)
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T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
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.astype(np.int), \
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times,
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minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
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#print("Done This NODE", T)
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def compute_state_residence_time_for_all_nodes(self):
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for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
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self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories[0].get_times(),
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self.sample_path.trajectories[0].get_trajectory(), self.columns_filtering_structure[node_indx],
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self.scalar_indexes_converter[node_indx], set_of_cims.state_residence_times)
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def compute_state_transitions_for_a_node(self, node_indx, trajectory, cols_filter, scalar_indexing, M):
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#print(node_indx)
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#print(trajectory)
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#print(cols_filter)
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#print(scalar_indexing)
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#print(M)
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diag_indices = np.array([x * M.shape[1] + x % M.shape[1] for x in range(M.shape[0] * M.shape[1])],
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dtype=np.int64)
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trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0]
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#print(trj_tmp)
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#print("Summing", np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int))
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#print(M.shape[1])
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#print(M.shape[2])
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M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int),
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minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2])
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M_raveled = M.ravel()
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M_raveled[diag_indices] = 0
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#print(M_raveled)
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M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
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#print(M_raveled)
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#print(M)
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def compute_state_transitions_for_all_nodes(self):
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for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
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self.compute_state_transitions_for_a_node(node_indx, self.sample_path.trajectories[0].get_complete_trajectory(),
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self.transition_filtering[node_indx],
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self.transition_scalar_index_converter[node_indx], set_of_cims.transition_matrices)
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# Simple Test #
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"""os.getcwd()
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os.chdir('..')
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path = os.getcwd() + '/data'
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s1 = sp.SamplePath(path)
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s1.build_trajectories()
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s1.build_structure()
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g1 = ng.NetworkGraph(s1.structure)
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g1.init_graph()
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pe = ParametersEstimator(s1, g1)
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pe.init_amalgamated_cims_struct()
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lp = LineProfiler()
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[[2999.2966 2749.2298 3301.5975]
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[3797.1737 3187.8345 2939.2009]
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[3432.224 3062.5402 4530.9028]]
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[[ 827.6058 838.1515 686.1365]
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[1426.384 2225.2093 1999.8528]
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[ 745.3068 733.8129 746.2347]
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[ 520.8113 690.9502 853.4022]
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[1590.8609 1853.0021 1554.1874]
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[ 637.5576 643.8822 654.9506]
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[ 718.7632 742.2117 998.5844]
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[1811.984 1598.0304 2547.988 ]
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[ 770.8503 598.9588 984.3304]]
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lp_wrapper = lp(pe.compute_state_residence_time_for_all_nodes)
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lp_wrapper()
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lp.print_stats()
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#pe.compute_state_residence_time_for_all_nodes()
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print(pe.amalgamated_cims_struct.sets_of_cims[0].state_residence_times)
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[[[14472, 3552, 10920],
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[12230, 25307, 13077],
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[ 9707, 14408, 24115]],
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[[22918, 6426, 16492],
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[10608, 16072, 5464],
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[10746, 11213, 21959]],
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[[23305, 6816, 16489],
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[ 3792, 19190, 15398],
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[13718, 18243, 31961]]])
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Raveled [14472 3552 10920 12230 25307 13077 9707 14408 24115 22918 6426 16492
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10608 16072 5464 10746 11213 21959 23305 6816 16489 3792 19190 15398
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13718 18243 31961]
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lp_wrapper = lp(pe.compute_parameters)
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lp_wrapper()
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#for variable in pe.amalgamated_cims_struct.sets_of_cims:
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#for cond in variable.get_cims():
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#print(cond.cim)
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print(pe.amalgamated_cims_struct.get_cims_of_node(1,[2]))
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lp.print_stats()"""
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