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import os
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import time as tm
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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 amalgamated_cims 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.scalar_indexes_converter = self.net_graph.scalar_indexing_structure
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self.columns_filtering_structure = self.net_graph.filtering_structure
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self.transition_scalar_index_converter = self.net_graph.transition_scalar_indexing_structure
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self.transition_filtering = self.net_graph.transition_filtering
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self.amalgamated_cims_struct = None
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def init_amalgamated_cims_struct(self):
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self.amalgamated_cims_struct = acims.AmalgamatedCims(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 parameters_estimation(self):
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print("Starting computing")
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t0 = tm.time()
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for trajectory in self.sample_path.trajectories:
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#tr_length = trajectory.size()
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self.parameters_estimation_single_trajectory(trajectory.get_trajectory())
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#print("Finished Trajectory number", indx)
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t1 = tm.time() - t0
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print("Elapsed Time ", t1)
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def parameters_estimation_single_trajectory(self, trajectory):
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row_length = trajectory.shape[1]
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for indx, row in enumerate(trajectory[:-1]):
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self.compute_sufficient_statistics_for_trajectory(trajectory.times, trajectory.actual_trajectory, trajectory.transitions, row_length)
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def compute_sufficient_statistics_for_trajectory(self, times, traj_values, traj_transitions, row_length):
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#time = self.compute_time_delta(current_row, next_row)
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#time = current_row[0]
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print(times)
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print(traj_values)
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print(traj_transitions)
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for row in traj_transitions:
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time = times[0]
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for indx in range(0, row_length):
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if row[indx] == 1:
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which_node = indx
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transition = [which_node, (traj_values[indx - 1], traj_values[indx])]
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which_matrix = self.which_matrix_to_update(row, which_node)
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which_element = transition[1]
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self.amalgamated_cims_struct.update_state_transition_for_matrix(which_node, which_matrix, which_element)
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which_element = transition[1][0]
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix,
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which_element,
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time)
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else:
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which_node = indx
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which_matrix = self.which_matrix_to_update(row, which_node)
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which_element = row[indx]
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(
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which_node, which_matrix, which_element, time)
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def which_matrix_to_update(self, current_row, node_indx):
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#print(type(self.fancy_indexing_structure[node_indx]))
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return tuple(current_row.take(self.fancy_indexing_structure[node_indx]))
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#return tuple(ParametersEstimator.taker(current_row, self.fancy_indexing_structure[node_indx]))
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def parameters_estimation_for_variable_multiple_parents(self, node_indx, times, transitions ,variable_values, parents_values):
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#print(times)
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#print(variable_values)
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#print(parents_values)
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#print("Starting computing")
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#t0 = tm.time()
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for indx, row in enumerate(variable_values):
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time = times[indx]
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which_matrix = tuple(parents_values[indx]) # questo è un vettore
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current_state = variable_values[indx]
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if transitions[indx] == 1:
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prev_state = variable_values[indx - 1]
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transition = [node_indx, (prev_state, current_state)]
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#which_node = transition[0]
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which_element = transition[1]
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self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix, which_element)
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#which_element = current_state
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
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current_state,
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time)
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def parameters_estimation_for_variable_single_parent(self, node_indx, times, transitions, variable_values,
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parents_values):
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for indx, row in enumerate(variable_values):
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time = times[indx]
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which_matrix = parents_values[indx] # Avendo un solo parent questo è uno scalare
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current_state = variable_values[indx]
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#which_matrix = ParametersEstimator.taker(parents_values, indx)
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# print(which_matrix.dtype)
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if transitions[indx] == 1:
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prev_state = variable_values[indx - 1]
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transition = [node_indx, (prev_state, current_state)]
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which_element = transition[1]
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self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix,
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which_element)
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which_element = current_state
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
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which_element,time)
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def parameters_estimation_for_variable_no_parent(self, node_indx, times, transitions,variable_values):
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for indx, row in enumerate(variable_values):
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time = times[indx]
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which_matrix = 0
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current_state = variable_values[indx]
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"""if transitions[indx] == 1:
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prev_state = variable_values[indx - 1]
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#current_state = variable_values[indx]
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transition = [node_indx, (prev_state, current_state)]
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which_element = transition[1]
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self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix,
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which_element)"""
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which_element = current_state
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
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which_element,
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time)
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def parameters_estimation_for_variable_no_parent_in_place(self, node_indx, times, transitions, variable_values):
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state_trans_matrix = np.zeros(shape=(3,3), dtype=np.int)
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state_res_time_array = np.zeros(shape=(3), dtype=np.float)
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for indx, row in enumerate(variable_values):
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time = times[indx]
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#which_matrix = 0
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current_state = variable_values[indx]
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if transitions[indx] == 1:
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prev_state = variable_values[indx - 1]
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#current_state = variable_values[indx]
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transition = [node_indx, (prev_state, current_state)]
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which_element = transition[1]
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#self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix,
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#which_element)
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state_trans_matrix[which_element] += 1
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which_element = current_state
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#self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
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#which_element,
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#time)
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state_res_time_array[which_element] += time
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def parameters_estimation_for_variable_single_parent_in_place(self, node_indx, times, transitions, variable_values,
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parents_values,values_tuple):
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state_res_time_dim = values_tuple[1:]
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state_trans_matricies = np.zeros(shape=27, dtype=np.int)
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state_res_time_array = np.zeros(shape=9, dtype=np.float)
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state_transition_indx = np.array(values_tuple, dtype=np.int)
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for indx, row in enumerate(variable_values):
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time = times[indx]
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#which_matrix = np.ravel_multi_index(parents_values[indx], ) # Avendo un solo parent questo è uno scalare
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#current_state = variable_values[indx]
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#which_matrix = ParametersEstimator.taker(parents_values, indx)
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state_transition_indx[0] = parents_values[indx]
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state_transition_indx[1] = variable_values[indx]
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# print(which_matrix.dtype)
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if transitions[indx] == 1:
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state_transition_indx[2] = variable_values[indx - 1]
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#transition = [node_indx, (prev_state, current_state)]
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#which_element = transition[1]
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#self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix,
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#which_element)
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scalar_indx = np.ravel_multi_index(state_transition_indx, values_tuple)
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print("State Transition", scalar_indx)
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state_trans_matricies[scalar_indx] += 1
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scalar_indx = np.ravel_multi_index(state_transition_indx[:-1], state_res_time_dim)
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print("Res Time",scalar_indx)
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state_res_time_array[scalar_indx] += time
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#which_element = current_state
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#self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
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#which_element,time)
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#t1 = tm.time() - t0
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#print("Elapsed Time ", t1)
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def compute_parameters(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(),
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self.columns_filtering_structure[node_indx],
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self.scalar_indexes_converter[node_indx],
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set_of_cims.state_residence_times)
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self.compute_state_transitions_for_a_node(node_indx,
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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],
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set_of_cims.transition_matrices)
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set_of_cims.build_cims(set_of_cims.state_residence_times, set_of_cims.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)
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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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#print(pe.amalgamated_cims_struct.get_set_of_cims(0).get_cims_number())
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#print(pe.amalgamated_cims_struct.get_set_of_cims(1).get_cims_number())
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#print(pe.amalgamated_cims_struct.get_set_of_cims(2).get_cims_number())
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#print(np.shape(s1.trajectories[0].transitions)[0])
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#print(pe.columns_filtering_structure)
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#print(pe.scalar_indexes_converter)
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#print(pe.amalgamated_cims_struct.sets_of_cims[1].state_residence_times)
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#print(pe.amalgamated_cims_struct.sets_of_cims[2].state_residence_times)
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#print(pe.amalgamated_cims_struct.sets_of_cims[2].transition_matrices)
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#print(pe.amalgamated_cims_struct.sets_of_cims[1].transition_matrices)
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#print(pe.amalgamated_cims_struct.sets_of_cims[0].transition_matrices)
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#pe.compute_state_transitions_for_all_nodes()
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lp = LineProfiler()
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"""pe.compute_state_residence_time_for_all_nodes()
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#pe.parameters_estimation_for_variable(0, pe.sample_path.trajectories[0].get_trajectory()[:, 0],
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# pe.sample_path.trajectories[0].get_trajectory()[:, 1], [])
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#pe.parameters_estimation_single_trajectory(pe.sample_path.trajectories[0].get_trajectory())
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#pe.parameters_estimation()
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#lp.add_function(pe.compute_sufficient_statistics_for_row) # add additional function to profile
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#lp_wrapper = lp(pe.parameters_estimation_single_trajectory)
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#lp_wrapper = lp(pe.parameters_estimation)
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#lp_wrapper(pe.sample_path.trajectories[0].get_trajectory())
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#lp.print_stats()
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#lp_wrapper = lp(pe.parameters_estimation_for_variable)
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#lp_wrapper(2, pe.sample_path.trajectories[0].get_times(),
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#pe.sample_path.trajectories[0].get_trajectory()[:, 2],
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#pe.sample_path.trajectories[0].get_trajectory()[:, [0,1]])
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lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent)
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lp_wrapper(1, pe.sample_path.trajectories[0].get_times(),
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pe.sample_path.trajectories[0].get_trajectory()[:, 1],
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pe.sample_path.trajectories[0].get_trajectory()[:, 2])
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lp.print_stats()
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#print( pe.sample_path.trajectories[0].get_trajectory()[:, [1,2]])
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for matrix in pe.amalgamated_cims_struct.get_set_of_cims(1).actual_cims:
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print(matrix.state_residence_times)
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print(matrix.state_transition_matrix)
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matrix.compute_cim_coefficients()
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print(matrix.cim)"""
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"""lp_wrapper = lp(pe.parameters_estimation_for_variable_no_parent_in_place)
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lp_wrapper(0, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 0],
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pe.sample_path.trajectories[0].get_trajectory()[:, 0] )
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lp.print_stats()
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lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent)
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lp_wrapper(1, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 1],
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pe.sample_path.trajectories[0].get_trajectory()[:,1], pe.sample_path.trajectories[0].get_trajectory()[:,2] )
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lp.print_stats()
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lp_wrapper = lp(pe.parameters_estimation_for_variable_multiple_parents)
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lp_wrapper(2, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 2],
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pe.sample_path.trajectories[0].get_trajectory()[:,2], pe.sample_path.trajectories[0].get_trajectory()[:, [0,1]] )
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lp.print_stats()"""
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"""lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent_in_place)
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lp_wrapper(1, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 1],
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pe.sample_path.trajectories[0].get_trajectory()[:,1], pe.sample_path.trajectories[0].get_trajectory()[:,2], (3,3,3) )
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lp.print_stats()"""
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"""lp_wrapper = lp(pe.compute_sufficient_statistics_for_trajectory)
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lp_wrapper(pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].actual_trajectory,
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pe.sample_path.trajectories[0].transitions, 3)
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lp.print_stats()
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lp_wrapper = lp(pe.compute_state_res_time_for_node)
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lp_wrapper(0, pe.sample_path.trajectories[0].get_times(),
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pe.sample_path.trajectories[0].actual_trajectory, [0], [3], np.zeros([3,3], dtype=np.float))
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lp.print_stats()
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#pe.compute_state_res_time_for_node(0, pe.sample_path.trajectories[0].get_times(),
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#pe.sample_path.trajectories[0].actual_trajectory, [0], [3], np.zeros([3,3], dtype=np.float))"""
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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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|
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|
lp.print_stats()
|
