1
0
Fork 0

Add Merged Trajectories in a single frame

parallel_struct_est
philpMartin 4 years ago
parent b9188c9275
commit b48fd3e67f
  1. 8
      main_package/classes/amalgamated_cims.py
  2. 28
      main_package/classes/json_importer.py
  3. 44
      main_package/classes/parameters_estimator.py

@ -9,7 +9,7 @@ class AmalgamatedCims:
"""
# list_of_vars_orders contiene tutte le liste con i parent ordinati secondo il valore indx
def __init__(self, states_number_per_node, list_of_keys, list_of_parents_states_number):
self.sets_of_cims = {}
self.sets_of_cims = []
self.init_cims_structure(list_of_keys, states_number_per_node, list_of_parents_states_number)
#self.states_per_variable = states_number
@ -17,10 +17,10 @@ class AmalgamatedCims:
print(keys)
print(list_of_parents_states_number)
for indx, key in enumerate(keys):
self.sets_of_cims[key] = socim.SetOfCims(key, list_of_parents_states_number[indx], states_number_per_node[indx])
self.sets_of_cims.append(socim.SetOfCims(key, list_of_parents_states_number[indx], states_number_per_node[indx]))
def get_set_of_cims(self, node_id):
return self.sets_of_cims[node_id]
def get_set_of_cims(self, node_indx):
return self.sets_of_cims[node_indx]
def get_vars_order(self, node):
return self.actual_cims[node][1]

@ -23,6 +23,7 @@ class JsonImporter(AbstractImporter):
def __init__(self, files_path):
self.df_samples_list = []
self.concatenated_samples = None
self.df_structure = pd.DataFrame()
self.df_variables = pd.DataFrame()
super(JsonImporter, self).__init__(files_path)
@ -89,6 +90,26 @@ class JsonImporter(AbstractImporter):
for sample_indx, sample in enumerate(raw_data[indx][trajectories_key]):
self.df_samples_list.append(pd.json_normalize(raw_data[indx][trajectories_key][sample_indx]))
def compute_row_delta_sigle_samples_frame(self, sample_frame):
columns_header = list(sample_frame.columns.values)
#print(columns_header)
for col_name in columns_header:
if col_name == 'Time':
sample_frame[col_name + 'Delta'] = sample_frame[col_name].diff()
else:
sample_frame[col_name + 'Delta'] = (sample_frame[col_name].diff().bfill() != 0).astype(int)
#sample_frame['Delta'] = sample_frame['Time'].diff()
#print(sample_frame)
def compute_row_delta_in_all_samples_frames(self):
for sample in self.df_samples_list:
self.compute_row_delta_sigle_samples_frame(sample)
self.concatenated_samples = pd.concat(self.df_samples_list)
self.concatenated_samples['Time'] = self.concatenated_samples['TimeDelta']
del self.concatenated_samples['TimeDelta']
self.concatenated_samples['Time'] = self.concatenated_samples['Time'].fillna(0)
def build_list_of_samples_array(self, data_frame):
"""
Costruisce una lista contenente le colonne presenti nel dataframe data_frame convertendole in numpy_array
@ -115,9 +136,12 @@ class JsonImporter(AbstractImporter):
self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
"""ij = JsonImporter("../data")
ij = JsonImporter("../data")
ij.import_data()
#print(ij.df_samples_list[7])
print(ij.df_structure)
print(ij.df_variables)
print((ij.build_list_of_samples_array(0)[1].size))"""
#print((ij.build_list_of_samples_array(0)[1].size))
#ij.compute_row_delta_sigle_samples_frame(ij.df_samples_list[0])
ij.compute_row_delta_in_all_samples_frames()
print(ij.concatenated_samples.to_numpy())

@ -1,5 +1,6 @@
import os
import time as tm
from line_profiler import LineProfiler
import network_graph as ng
import sample_path as sp
@ -20,22 +21,22 @@ class ParametersEstimator:
self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes())
def parameters_estimation(self):
print("Starting computing")
t0 = tm.time()
#print("Starting computing")
#t0 = tm.time()
for indx, trajectory in enumerate(self.sample_path.trajectories):
self.parameters_estimation_single_trajectory(trajectory.get_trajectory())
#print("Finished Trajectory number", indx)
t1 = tm.time() - t0
print("Elapsed Time ", t1)
#t1 = tm.time() - t0
#print("Elapsed Time ", t1)
def parameters_estimation_single_trajectory(self, trajectory):
#print(type(trajectory[0][0]))
t0 = tm.time()
for indx, row in enumerate(trajectory):
if trajectory[indx][1] == -1:
break
if trajectory[indx + 1][1] != -1:
transition = self.find_transition(trajectory[indx], trajectory[indx + 1])
which_node = self.net_graph.get_node_by_index(transition[0])
which_node = transition[0]
# print(which_node)
which_matrix = self.which_matrix_to_update(row, transition[0])
which_element = transition[1]
@ -43,17 +44,22 @@ class ParametersEstimator:
#changed_node = which_node
time = self.compute_time_delta(trajectory[indx], trajectory[indx + 1])
which_element = transition[1][0]
self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix, which_element,
time)
for node_indx, node in enumerate(self.net_graph.get_nodes()):
#if node != changed_node:
for node_indx in range(0, 3):
if node_indx != transition[0]:
# print(node)
which_node = node
which_node = node_indx
which_matrix = self.which_matrix_to_update(row, node_indx)
which_element = row[node_indx + 1]
# print("State res time element " + str(which_element) + node)
# print("State res time matrix indx" + str(which_matrix))
self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix, which_element, time)
self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix,
which_element, time)
t1 = tm.time() - t0
print("Elapsed Time ", t1)
def find_transition(self, current_row, next_row):
for indx in range(1, len(current_row)):
@ -85,14 +91,18 @@ g1.init_graph()
pe = ParametersEstimator(s1, g1)
pe.init_amalgamated_cims_struct()
print(pe.amalgamated_cims_struct.get_set_of_cims('X').get_cims_number())
print(pe.amalgamated_cims_struct.get_set_of_cims('Y').get_cims_number())
print(pe.amalgamated_cims_struct.get_set_of_cims('Z').get_cims_number())
print(pe.amalgamated_cims_struct.get_set_of_cims(0).get_cims_number())
print(pe.amalgamated_cims_struct.get_set_of_cims(1).get_cims_number())
print(pe.amalgamated_cims_struct.get_set_of_cims(2).get_cims_number())
#pe.parameters_estimation_single_trajectory(pe.sample_path.trajectories[0].get_trajectory())
pe.parameters_estimation()
for matrix in pe.amalgamated_cims_struct.get_set_of_cims('Y').actual_cims:
lp = LineProfiler()
lp_wrapper = lp(pe.parameters_estimation_single_trajectory)
lp_wrapper(pe.sample_path.trajectories[0].get_trajectory())
lp.print_stats()
#pe.parameters_estimation()
"""for matrix in pe.amalgamated_cims_struct.get_set_of_cims(1).actual_cims:
print(matrix.state_residence_times)
print(matrix.state_transition_matrix)
matrix.compute_cim_coefficients()
print(matrix.cim)
print(matrix.cim)"""