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Merge pull request #1 from philipMartini/parameters_estimation_by_variable

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Parameters estimation by variable
parallel_struct_est
philipMartini 4 years ago committed by GitHub
parent 2425f491df 71c7197e7c
commit 6617ddaa4f
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9 changed files with 414 additions and 171 deletions
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  1. 11
      main_package/classes/amalgamated_cims.py
  2. 29
      main_package/classes/conditional_intensity_matrix.py
  3. 69
      main_package/classes/json_importer.py
  4. 121
      main_package/classes/network_graph.py
  5. 186
      main_package/classes/parameters_estimator.py
  6. 30
      main_package/classes/sample_path.py
  7. 87
      main_package/classes/set_of_cims.py
  8. 23
      main_package/classes/structure.py
  9. 27
      main_package/classes/trajectory.py

11
main_package/classes/amalgamated_cims.py
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@ -14,14 +14,19 @@ class AmalgamatedCims:
#self.states_per_variable = states_number #self.states_per_variable = states_number
def init_cims_structure(self, keys, states_number_per_node, list_of_parents_states_number): def init_cims_structure(self, keys, states_number_per_node, list_of_parents_states_number):
print(keys) #print(keys)
print(list_of_parents_states_number) #print(list_of_parents_states_number)
for indx, key in enumerate(keys): for indx, key in enumerate(keys):
self.sets_of_cims.append(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_indx): def get_set_of_cims(self, node_indx):
return self.sets_of_cims[node_indx] return self.sets_of_cims[node_indx]
def get_cims_of_node(self, node_indx, cim_indx):
return self.sets_of_cims[node_indx].get_cim(cim_indx)
def get_vars_order(self, node): def get_vars_order(self, node):
return self.actual_cims[node][1] return self.actual_cims[node][1]

29
main_package/classes/conditional_intensity_matrix.py
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@ -3,27 +3,26 @@ import numpy as np
class ConditionalIntensityMatrix: class ConditionalIntensityMatrix:
def __init__(self, dimension): def __init__(self, dimension, state_residence_times, state_transition_matrix):
self.state_residence_times = np.zeros(shape=dimension) self.state_residence_times = state_residence_times
self.state_transition_matrix = np.zeros(shape=(dimension, dimension), dtype=int) self.state_transition_matrix = state_transition_matrix
self.cim = np.zeros(shape=(dimension, dimension), dtype=float) #self.cim = np.zeros(shape=(dimension, dimension), dtype=float)
self.cim = self.state_transition_matrix.astype(np.float)
def update_state_transition_count(self, element_indx): def update_state_transition_count(self, element_indx):
#print(element_indx) #print(element_indx)
self.state_transition_matrix[element_indx[0]][element_indx[1]] = \ #self.state_transition_matrix[element_indx[0]][element_indx[1]] += 1
self.state_transition_matrix[element_indx[0]][element_indx[1]] + 1 self.state_transition_matrix[element_indx] += 1
def update_state_residence_time_for_state(self, state, time): def update_state_residence_time_for_state(self, state, time):
#print("Time updating In state", state, time) #print("Time updating In state", state, time)
#print(state) self.state_residence_times[state] += time
self.state_residence_times[state] = self.state_residence_times[state] + time
def compute_cim_coefficients(self): def compute_cim_coefficients(self):
for i, row in enumerate(self.state_transition_matrix): np.fill_diagonal(self.cim, self.cim.diagonal() * -1)
row_sum = 0.0 self.cim = ((self.cim.T + 1) / (self.state_residence_times + 1)).T
for j, elem in enumerate(row):
rate_coefficient = elem / self.state_residence_times[i] def __repr__(self):
self.cim[i][j] = rate_coefficient return 'CIM:\n' + str(self.cim)
row_sum = row_sum + rate_coefficient
self.cim[i][i] = -1 * row_sum

69
main_package/classes/json_importer.py
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@ -3,6 +3,7 @@ import glob
import pandas as pd import pandas as pd
import json import json
from abstract_importer import AbstractImporter from abstract_importer import AbstractImporter
from line_profiler import LineProfiler
class JsonImporter(AbstractImporter): class JsonImporter(AbstractImporter):
@ -23,25 +24,30 @@ class JsonImporter(AbstractImporter):
def __init__(self, files_path): def __init__(self, files_path):
self.df_samples_list = [] self.df_samples_list = []
self.concatenated_samples = None self._df_structure = pd.DataFrame()
self.df_structure = pd.DataFrame() self._df_variables = pd.DataFrame()
self.df_variables = pd.DataFrame() self._concatenated_samples = None
super(JsonImporter, self).__init__(files_path) super(JsonImporter, self).__init__(files_path)
def import_data(self): def import_data(self):
raw_data = self.read_json_file() raw_data = self.read_json_file()
self.import_trajectories(raw_data) self.import_trajectories(raw_data)
self.compute_row_delta_in_all_samples_frames('Time')
self.import_structure(raw_data) self.import_structure(raw_data)
self.import_variables(raw_data) self.import_variables(raw_data)
#Le variabili DEVONO essere ordinate come le Colonne del dataset
assert list(self._df_variables['Name']) == \
(list(self._concatenated_samples.columns.values[1:len(self.variables['Name']) + 1]))
def import_trajectories(self, raw_data): def import_trajectories(self, raw_data):
self.normalize_trajectories(raw_data, 0, 'samples') self.normalize_trajectories(raw_data, 0, 'samples')
def import_structure(self, raw_data): def import_structure(self, raw_data):
self.df_structure = self.one_level_normalizing(raw_data, 0, 'dyn.str') self._df_structure = self.one_level_normalizing(raw_data, 0, 'dyn.str')
def import_variables(self, raw_data): def import_variables(self, raw_data):
self.df_variables = self.one_level_normalizing(raw_data, 0, 'variables') self._df_variables = self.one_level_normalizing(raw_data, 0, 'variables')
def read_json_file(self): def read_json_file(self):
""" """
@ -88,7 +94,27 @@ class JsonImporter(AbstractImporter):
void void
""" """
for sample_indx, sample in enumerate(raw_data[indx][trajectories_key]): 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])) self.df_samples_list.append(pd.DataFrame(sample))
def compute_row_delta_sigle_samples_frame(self, sample_frame, time_header_label, columns_header, shifted_cols_header):
sample_frame[time_header_label] = sample_frame[time_header_label].diff().shift(-1)
shifted_cols = sample_frame[columns_header[1:]].shift(-1)
shifted_cols.columns = shifted_cols_header
sample_frame = sample_frame.assign(**shifted_cols)
sample_frame.drop(sample_frame.tail(1).index, inplace=True)
return sample_frame
def compute_row_delta_in_all_samples_frames(self, time_header_label):
columns_header = list(self.df_samples_list[0].columns.values)
shifted_cols_header = [s + "S" for s in columns_header[1:]]
for indx, sample in enumerate(self.df_samples_list):
self.df_samples_list[indx] = self.compute_row_delta_sigle_samples_frame(sample,
time_header_label, columns_header, shifted_cols_header)
#print(self.df_samples_list[indx])
self._concatenated_samples = pd.concat(self.df_samples_list)
#print("Concatenated", self._concatenated_samples)
for indx in range(len(self.df_samples_list)): # Le singole traj non servono più
self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
def compute_row_delta_sigle_samples_frame(self, sample_frame): def compute_row_delta_sigle_samples_frame(self, sample_frame):
columns_header = list(sample_frame.columns.values) columns_header = list(sample_frame.columns.values)
@ -132,17 +158,34 @@ class JsonImporter(AbstractImporter):
Returns: Returns:
void void
""" """
for indx in range(len(self.df_samples_list)): self._concatenated_samples = self._concatenated_samples.iloc[0:0]
self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
self.concatenated_samples = self.concatenated_samples.iloc[0:0] @property
def concatenated_samples(self):
return self._concatenated_samples
@property
def variables(self):
return self._df_variables
@property
def structure(self):
return self._df_structure
"""ij = JsonImporter("../data") """ij = JsonImporter("../data")
#raw_data = ij.read_json_file()
lp = LineProfiler()
lp_wrapper = lp(ij.import_data)
lp_wrapper()
lp.print_stats()
ij.import_data() ij.import_data()
#print(ij.df_samples_list[7]) #print(ij.df_samples_list[7])
print(ij.df_structure) print(ij.df_structure)
print(ij.df_variables) print(ij.df_variables)
#print((ij.build_list_of_samples_array(0)[1].size)) print(ij.concatenated_samples)"""
#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())"""

121
main_package/classes/network_graph.py
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@ -19,10 +19,22 @@ class NetworkGraph():
def __init__(self, graph_struct): def __init__(self, graph_struct):
self.graph_struct = graph_struct self.graph_struct = graph_struct
self.graph = nx.DiGraph() self.graph = nx.DiGraph()
self._nodes_indexes = self.graph_struct.list_of_nodes_indexes()
self._nodes_labels = self.graph_struct.list_of_nodes_labels()
self._fancy_indexing = None
self._time_scalar_indexing_structure = []
self._transition_scalar_indexing_structure = []
self._time_filtering = []
self._transition_filtering = []
def init_graph(self): def init_graph(self):
self.add_nodes(self.graph_struct.list_of_nodes()) self.add_nodes(self.graph_struct.list_of_nodes_labels())
self.add_edges(self.graph_struct.list_of_edges()) self.add_edges(self.graph_struct.list_of_edges())
self._fancy_indexing = self.build_fancy_indexing_structure(0)
self.build_time_scalar_indexing_structure()
self.build_time_columns_filtering_structure()
self.build_transition_scalar_indexing_structure()
self.build_transition_columns_filtering_structure()
def add_nodes(self, list_of_nodes): def add_nodes(self, list_of_nodes):
for indx, id in enumerate(list_of_nodes): for indx, id in enumerate(list_of_nodes):
@ -36,34 +48,33 @@ class NetworkGraph():
ordered_set = {} ordered_set = {}
parents = self.get_parents_by_id(node) parents = self.get_parents_by_id(node)
for n in parents: for n in parents:
indx = self.graph_struct.get_node_indx(n) indx = self._nodes_labels.index(n)
ordered_set[n] = indx ordered_set[n] = indx
{k: v for k, v in sorted(ordered_set.items(), key=lambda item: item[1])} {k: v for k, v in sorted(ordered_set.items(), key=lambda item: item[1])}
return list(ordered_set.keys()) return list(ordered_set.keys())
def get_ord_set_of_par_of_all_nodes(self): def get_ord_set_of_par_of_all_nodes(self):
result = [] result = []
for node in self.get_nodes(): for node in self._nodes_labels:
result.append(self.get_ordered_by_indx_set_of_parents(node)) result.append(self.get_ordered_by_indx_set_of_parents(node))
return result return result
def get_ordered_by_indx_parents_values(self, node): def get_ordered_by_indx_parents_values(self, node):
parents_values = [] parents_values = []
parents = self.get_parents_by_id(node) parents = self.get_ordered_by_indx_set_of_parents(node)
parents.sort() #Assumo che la structure rifletta l'ordine delle colonne del dataset
for n in parents: for n in parents:
parents_values.append(self.graph_struct.get_states_number(n)) parents_values.append(self.graph_struct.get_states_number(n))
return parents_values return parents_values
def get_ordered_by_indx_parents_values_for_all_nodes(self): def get_ordered_by_indx_parents_values_for_all_nodes(self):
result = [] result = []
for node in self.get_nodes(): #TODO bisogna essere sicuri che l'ordine sia coerente con quello del dataset serve un metodo get_nodes_sort_by_indx for node in self._nodes_labels:
result.append(self.get_ordered_by_indx_parents_values(node)) result.append(self.get_ordered_by_indx_parents_values(node))
return result return result
def get_states_number_of_all_nodes_sorted(self): def get_states_number_of_all_nodes_sorted(self):
states_number_list = [] states_number_list = []
for node in self.get_nodes(): #TODO SERVE UN get_nodes_ordered!!!!!! for node in self._nodes_labels:
states_number_list.append(self.get_states_number(node)) states_number_list.append(self.get_states_number(node))
return states_number_list return states_number_list
@ -74,9 +85,55 @@ class NetworkGraph():
indexes_for_a_node = [] indexes_for_a_node = []
for j, node in enumerate(list_of_parents): for j, node in enumerate(list_of_parents):
indexes_for_a_node.append(self.get_node_indx(node) + start_indx) indexes_for_a_node.append(self.get_node_indx(node) + start_indx)
index_structure.append(indexes_for_a_node) index_structure.append(np.array(indexes_for_a_node, dtype=np.int))
return index_structure return index_structure
def build_time_scalar_indexing_structure_for_a_node(self, node_id, parents_id):
#print(parents_id)
T_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
#print(T_vector)
T_vector = np.append(T_vector, [self.graph_struct.variables_frame.iloc[x, 1] for x in parents_id])
#print(T_vector)
T_vector = T_vector.cumprod().astype(np.int)
return T_vector
#print(T_vector)
def build_time_scalar_indexing_structure(self):
parents_indexes_list = self._fancy_indexing
for node_indx, p_indxs in enumerate(parents_indexes_list):
if p_indxs.size == 0:
self._time_scalar_indexing_structure.append(np.array([self.get_states_number_by_indx(node_indx)], dtype=np.int))
else:
self._time_scalar_indexing_structure.append(
self.build_time_scalar_indexing_structure_for_a_node(node_indx, p_indxs))
def build_transition_scalar_indexing_structure_for_a_node(self, node_id, parents_id):
M_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1],
self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
M_vector = np.append(M_vector, [self.graph_struct.variables_frame.iloc[x, 1] for x in parents_id])
M_vector = M_vector.cumprod().astype(np.int)
return M_vector
def build_transition_scalar_indexing_structure(self):
parents_indexes_list = self._fancy_indexing
for node_indx, p_indxs in enumerate(parents_indexes_list):
self._transition_scalar_indexing_structure.append(
self.build_transition_scalar_indexing_structure_for_a_node(node_indx, p_indxs))
def build_time_columns_filtering_structure(self):
parents_indexes_list = self._fancy_indexing
for node_indx, p_indxs in enumerate(parents_indexes_list):
if p_indxs.size == 0:
self._time_filtering.append(np.append(p_indxs, np.array([node_indx], dtype=np.int)))
else:
self._time_filtering.append(np.append(np.array([node_indx], dtype=np.int), p_indxs))
def build_transition_columns_filtering_structure(self):
parents_indexes_list = self._fancy_indexing
nodes_number = len(parents_indexes_list)
for node_indx, p_indxs in enumerate(parents_indexes_list):
self._transition_filtering.append(np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int))
def get_nodes(self): def get_nodes(self):
return list(self.graph.nodes) return list(self.graph.nodes)
@ -89,12 +146,30 @@ class NetworkGraph():
def get_states_number(self, node_id): def get_states_number(self, node_id):
return self.graph_struct.get_states_number(node_id) return self.graph_struct.get_states_number(node_id)
def get_states_number_by_indx(self, node_indx):
return self.graph_struct.get_states_number_by_indx(node_indx)
def get_node_by_index(self, node_indx): def get_node_by_index(self, node_indx):
return self.graph_struct.get_node_id(node_indx) return self.graph_struct.get_node_id(node_indx)
def get_node_indx(self, node_id): def get_node_indx(self, node_id):
return nx.get_node_attributes(self.graph, 'indx')[node_id] return nx.get_node_attributes(self.graph, 'indx')[node_id]
@property
def time_scalar_indexing_strucure(self):
return self._time_scalar_indexing_structure
@property
def time_filtering(self):
return self._time_filtering
@property
def transition_scalar_indexing_structure(self):
return self._transition_scalar_indexing_structure
@property
def transition_filtering(self):
return self._transition_filtering
@ -110,16 +185,22 @@ s1.build_structure()
g1 = NetworkGraph(s1.structure) g1 = NetworkGraph(s1.structure)
g1.init_graph() g1.init_graph()
print(g1.graph.number_of_nodes()) print(g1.transition_scalar_indexing_structure)
print(g1.graph.number_of_edges()) print(g1.transition_filtering)
print(g1.time_scalar_indexing_strucure)
print(nx.get_node_attributes(g1.graph, 'indx')['X']) print(g1.time_filering)
for node in g1.get_parents_by_id('Z'):
# print(g1.get_node_by_index(node))
print(node) #print(g1.build_fancy_indexing_structure(0))
print(g1.get_ordered_by_indx_parents_values_for_all_nodes()) #print(g1.get_states_number_of_all_nodes_sorted())
print(g1.build_fancy_indexing_structure()) g1.build_scalar_indexing_structure()
print(g1.get_states_number_of_all_nodes_sorted())""" print(g1.scalar_indexing_structure)
print(g1.build_columns_filtering_structure())
g1.build_transition_scalar_indexing_structure()
print(g1.transition_scalar_indexing_structure)
g1.build_transition_columns_filtering_structure()
print(g1.transition_filtering)
[array([3, 9]), array([ 3, 9, 27]), array([ 3, 9, 27, 81])]
[array([3, 0]), array([4, 1, 2]), array([5, 2, 0, 1])]"""

186
main_package/classes/parameters_estimator.py
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@ -1,8 +1,9 @@
import os import os
import time as tm
from line_profiler import LineProfiler from line_profiler import LineProfiler
import numpy as np
import numba as nb
import numpy as np
import network_graph as ng import network_graph as ng
import sample_path as sp import sample_path as sp
import amalgamated_cims as acims import amalgamated_cims as acims
@ -13,7 +14,6 @@ class ParametersEstimator:
def __init__(self, sample_path, net_graph): def __init__(self, sample_path, net_graph):
self.sample_path = sample_path self.sample_path = sample_path
self.net_graph = net_graph self.net_graph = net_graph
self.fancy_indexing_structure = self.net_graph.build_fancy_indexing_structure(1)
self.amalgamated_cims_struct = None self.amalgamated_cims_struct = None
def init_amalgamated_cims_struct(self): def init_amalgamated_cims_struct(self):
@ -21,67 +21,71 @@ class ParametersEstimator:
self.net_graph.get_nodes(), self.net_graph.get_nodes(),
self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes()) self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes())
def parameters_estimation(self):
#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)
def parameters_estimation_single_trajectory(self, trajectory):
tr_len = trajectory.shape[0]
row_length = trajectory.shape[1]
print(tr_len)
print(row_length)
t0 = tm.time()
for indx, row in enumerate(trajectory):
""" #if int(trajectory[indx][1]) == -1:
#break
if indx == tr_len - 2:
break
if trajectory[indx + 1][1] != -1:
transition = self.find_transition(trajectory[indx], trajectory[indx + 1], row_length)
which_node = transition[0]
# print(which_node)
which_matrix = self.which_matrix_to_update(row, transition[0])
which_element = transition[1]
self.amalgamated_cims_struct.update_state_transition_for_matrix(which_node, which_matrix, which_element)
#changed_node = which_node
if int(trajectory[indx][0]) == 0:
time = trajectory[indx + 1][0]
#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 in range(0, 3):
if node_indx != transition[0]:
# print(node)
which_node = node_indx
which_matrix = self.which_matrix_to_update(row, node_indx)
which_element = int(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)
t1 = tm.time() - t0
print("Elapsed Time ", t1)"""
def find_transition(self, current_row, next_row, row_length):
for indx in range(1, row_length):
if current_row[indx] != next_row[indx]:
return [indx - 1, (current_row[indx], next_row[indx])]
def compute_time_delta(self, current_row, next_row):
return next_row[0] - current_row[0]
def which_matrix_to_update(self, current_row, node_indx): # produce strutture {'X':1, 'Y':2} dove X e Y sono i parent di node_id
return current_row[self.fancy_indexing_structure[node_indx]]
def compute_parameters(self):
for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories.times,
self.sample_path.trajectories.trajectory,
self.net_graph.time_filtering[node_indx],
self.net_graph.time_scalar_indexing_strucure[node_indx],
set_of_cims.state_residence_times)
self.compute_state_transitions_for_a_node(node_indx,
self.sample_path.trajectories.complete_trajectory,
self.net_graph.transition_filtering[node_indx],
self.net_graph.transition_scalar_indexing_structure[node_indx],
set_of_cims.transition_matrices)
set_of_cims.build_cims(set_of_cims.state_residence_times, set_of_cims.transition_matrices)
def compute_state_res_time_for_node(self, node_indx, times, trajectory, cols_filter, scalar_indexes_struct, T):
#print(times.size)
#print(trajectory)
#print(cols_filter)
#print(scalar_indexes_struct)
#print(T)
T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
.astype(np.int), \
times,
minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
#print("Done This NODE", T)
def compute_state_residence_time_for_all_nodes(self):
for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories[0].get_times(),
self.sample_path.trajectories[0].get_trajectory(), self.columns_filtering_structure[node_indx],
self.scalar_indexes_converter[node_indx], set_of_cims.state_residence_times)
def compute_state_transitions_for_a_node(self, node_indx, trajectory, cols_filter, scalar_indexing, M):
#print(node_indx)
#print(trajectory)
#print(cols_filter)
#print(scalar_indexing)
#print(M)
diag_indices = np.array([x * M.shape[1] + x % M.shape[1] for x in range(M.shape[0] * M.shape[1])],
dtype=np.int64)
trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0]
#print(trj_tmp)
#print("Summing", np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int))
#print(M.shape[1])
#print(M.shape[2])
M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int),
minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2])
M_raveled = M.ravel()
M_raveled[diag_indices] = 0
#print(M_raveled)
M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
#print(M_raveled)
#print(M)
def compute_state_transitions_for_all_nodes(self):
for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
self.compute_state_transitions_for_a_node(node_indx, self.sample_path.trajectories[0].get_complete_trajectory(),
self.transition_filtering[node_indx],
self.transition_scalar_index_converter[node_indx], set_of_cims.transition_matrices)
@ -99,18 +103,50 @@ g1.init_graph()
pe = ParametersEstimator(s1, g1) pe = ParametersEstimator(s1, g1)
pe.init_amalgamated_cims_struct() pe.init_amalgamated_cims_struct()
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())
lp = LineProfiler() lp = LineProfiler()
lp_wrapper = lp(pe.parameters_estimation_single_trajectory)
lp_wrapper(pe.sample_path.trajectories.get_trajectory()) """[[2999.2966 2749.2298 3301.5975]
[3797.1737 3187.8345 2939.2009]
[3432.224 3062.5402 4530.9028]]
[[ 827.6058 838.1515 686.1365]
[1426.384 2225.2093 1999.8528]
[ 745.3068 733.8129 746.2347]
[ 520.8113 690.9502 853.4022]
[1590.8609 1853.0021 1554.1874]
[ 637.5576 643.8822 654.9506]
[ 718.7632 742.2117 998.5844]
[1811.984 1598.0304 2547.988 ]
[ 770.8503 598.9588 984.3304]]
lp_wrapper = lp(pe.compute_state_residence_time_for_all_nodes)
lp_wrapper()
lp.print_stats()
#pe.compute_state_residence_time_for_all_nodes()
print(pe.amalgamated_cims_struct.sets_of_cims[0].state_residence_times)
[[[14472, 3552, 10920],
[12230, 25307, 13077],
[ 9707, 14408, 24115]],
[[22918, 6426, 16492],
[10608, 16072, 5464],
[10746, 11213, 21959]],
[[23305, 6816, 16489],
[ 3792, 19190, 15398],
[13718, 18243, 31961]]])
Raveled [14472 3552 10920 12230 25307 13077 9707 14408 24115 22918 6426 16492
10608 16072 5464 10746 11213 21959 23305 6816 16489 3792 19190 15398
13718 18243 31961]"""
lp_wrapper = lp(pe.compute_parameters)
lp_wrapper()
#for variable in pe.amalgamated_cims_struct.sets_of_cims:
#for cond in variable.get_cims():
#print(cond.cim)
print(pe.amalgamated_cims_struct.get_cims_of_node(1,[2]))
lp.print_stats() 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)"""

30
main_package/classes/sample_path.py
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@ -19,25 +19,30 @@ class SamplePath:
""" """
def __init__(self, files_path): def __init__(self, files_path):
print()
self.importer = imp.JsonImporter(files_path) self.importer = imp.JsonImporter(files_path)
self.trajectories = None self._trajectories = None
self.structure = None self._structure = None
def build_trajectories(self): def build_trajectories(self):
self.importer.import_data() self.importer.import_data()
self.importer.compute_row_delta_in_all_samples_frames() self._trajectories = tr.Trajectory(self.importer.build_list_of_samples_array(self.importer.concatenated_samples))
#self.trajectories = self.importer.concatenated_samples.to_numpy() #self.trajectories.append(trajectory)
#for traj_data_frame in self.importer.df_samples_list:
self.trajectories = tr.Trajectory(self.importer.build_list_of_samples_array(self.importer.concatenated_samples))
#self.trajectories.append(trajectory)
self.importer.clear_data_frames() self.importer.clear_data_frames()
def build_structure(self): def build_structure(self):
self.structure = st.Structure(self.importer.df_structure, self.importer.df_variables) self._structure = st.Structure(self.importer.structure, self.importer.variables)
def get_number_trajectories(self): @property
return len(self.trajectories) def trajectories(self):
return self._trajectories
@property
def structure(self):
return self._structure
"""os.getcwd()
os.chdir('..')
path = os.getcwd() + '/data'
"""os.getcwd() """os.getcwd()
@ -47,4 +52,5 @@ path = os.getcwd() + '/data'
s1 = SamplePath(path) s1 = SamplePath(path)
s1.build_trajectories() s1.build_trajectories()
s1.build_structure() s1.build_structure()
print(s1.trajectories.get_trajectory())""" print(s1.trajectories[0].get_complete_trajectory())"""

87
main_package/classes/set_of_cims.py
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@ -1,4 +1,5 @@
import numpy as np import numpy as np
from numba import njit, int32
import conditional_intensity_matrix as cim import conditional_intensity_matrix as cim
@ -16,45 +17,91 @@ class SetOfCims:
self.node_id = node_id self.node_id = node_id
self.parents_states_number = parents_states_number self.parents_states_number = parents_states_number
self.node_states_number = node_states_number self.node_states_number = node_states_number
self.actual_cims = None self.actual_cims = []
self.state_residence_times = None
self.transition_matrices = None
self.build_actual_cims_structure() self.build_actual_cims_structure()
def build_actual_cims_structure(self): def build_actual_cims_structure(self):
cims_number = 1 #cims_number = 1
for state_number in self.parents_states_number: #for state_number in self.parents_states_number:
cims_number = cims_number * state_number #cims_number = cims_number * state_number
self.actual_cims = np.empty(cims_number, dtype=cim.ConditionalIntensityMatrix) if not self.parents_states_number:
for indx, matrix in enumerate(self.actual_cims): #self.actual_cims = np.empty(1, dtype=cim.ConditionalIntensityMatrix)
self.actual_cims[indx] = cim.ConditionalIntensityMatrix(self.node_states_number) #self.actual_cims[0] = cim.ConditionalIntensityMatrix(self.node_states_number)
self.state_residence_times = np.zeros((1, self.node_states_number), dtype=np.float)
self.transition_matrices = np.zeros((1,self.node_states_number, self.node_states_number), dtype=np.int)
else:
#self.actual_cims = np.empty(self.parents_states_number, dtype=cim.ConditionalIntensityMatrix)
#self.build_actual_cims(self.actual_cims)
#for indx, matrix in enumerate(self.actual_cims):
#self.actual_cims[indx] = cim.ConditionalIntensityMatrix(self.node_states_number)
self.state_residence_times = \
np.zeros((np.prod(self.parents_states_number), self.node_states_number), dtype=np.float)
self.transition_matrices = np.zeros([np.prod(self.parents_states_number), self.node_states_number,
self.node_states_number], dtype=np.int)
def update_state_transition(self, indexes, element_indx_tuple): def update_state_transition(self, indexes, element_indx_tuple):
matrix_indx = self.indexes_converter(indexes) #matrix_indx = self.indexes_converter(indexes)
self.actual_cims[matrix_indx].update_state_transition_count(element_indx_tuple) #print(indexes)
if not indexes:
self.actual_cims[0].update_state_transition_count(element_indx_tuple)
else:
self.actual_cims[indexes].update_state_transition_count(element_indx_tuple)
def update_state_residence_time(self, which_matrix, which_element, time): def update_state_residence_time(self, which_matrix, which_element, time):
matrix_indx = self.indexes_converter(which_matrix) #matrix_indx = self.indexes_converter(which_matrix)
self.actual_cims[matrix_indx].update_state_residence_time_for_state(which_element, time)
if not which_matrix:
self.actual_cims[0].update_state_residence_time_for_state(which_element, time)
else:
#print(type(which_matrix))
#print(self.actual_cims[(2,2)])
self.actual_cims[which_matrix].update_state_residence_time_for_state(which_element, time)
def build_actual_cims(self, cim_structure):
for indx in range(len(cim_structure)):
if cim_structure[indx] is None:
cim_structure[indx] = cim.ConditionalIntensityMatrix(self.node_states_number)
else:
self.build_actual_cims(cim_structure[indx])
def get_cims_number(self): def get_cims_number(self):
return len(self.actual_cims) return len(self.actual_cims)
def indexes_converter(self, indexes): # Si aspetta array del tipo [2,2] dove def indexes_converter(self, indexes): # Si aspetta array del tipo [2,2] dove
#print(type(indexes)) assert len(indexes) == len(self.parents_states_number)
if indexes.size == 0: vector_index = 0
return 0 if not indexes:
return vector_index
else: else:
vector_index = 0
for indx, value in enumerate(indexes): for indx, value in enumerate(indexes):
vector_index = vector_index*self.parents_states_number[indx] + indexes[indx] vector_index = vector_index*self.parents_states_number[indx] + indexes[indx]
return vector_index return vector_index
def build_cims(self, state_res_times, transition_matrices):
for state_res_time_vector, transition_matrix in zip(state_res_times, transition_matrices):
#print(state_res_time_vector, transition_matrix)
cim_to_add = cim.ConditionalIntensityMatrix(self.node_states_number,
state_res_time_vector, transition_matrix)
cim_to_add.compute_cim_coefficients()
#print(cim_to_add)
self.actual_cims.append(cim_to_add)
self.transition_matrices = None
self.state_residence_times = None
""" def get_cims(self):
sofc = SetOfCims('W', [], 2) return self.actual_cims
sofc.build_actual_cims_structure()
print(sofc.actual_cims)
print(sofc.indexes_converter([]))"""
def get_cim(self, index):
flat_index = self.indexes_converter(index)
return self.actual_cims[flat_index]
"""sofc = SetOfCims('Z', [3, 3], 3)
sofc.build_actual_cims_structure()
print(sofc.actual_cims)
print(sofc.actual_cims[0,0])
print(sofc.actual_cims[1,2])
#print(sofc.indexes_converter([]))"""

23
main_package/classes/structure.py
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@ -12,6 +12,9 @@ class Structure:
def __init__(self, structure, variables): def __init__(self, structure, variables):
self.structure_frame = structure self.structure_frame = structure
self.variables_frame = variables self.variables_frame = variables
#self._nodes_indexes = self.list_of_nodes_indexes()
self.name_label = variables.columns.values[0]
self.value_label = variables.columns.values[1]
def list_of_edges(self): def list_of_edges(self):
edges_list = [] edges_list = []
@ -20,14 +23,24 @@ class Structure:
edges_list.append(row_tuple) edges_list.append(row_tuple)
return edges_list return edges_list
def list_of_nodes(self): def list_of_nodes_labels(self):
return self.variables_frame['Name'].values.tolist() #TODO rimuovere dipendenza diretta dalla key 'Name' return self.variables_frame[self.name_label].values.tolist()
def list_of_nodes_indexes(self):
nodes_indexes = []
for indx in self.list_of_nodes_labels():
nodes_indexes.append(indx)
return nodes_indexes
def get_node_id(self, node_indx): def get_node_id(self, node_indx):
return self.variables_frame['Name'][node_indx] return self.variables_frame[self.name_label][node_indx]
def get_node_indx(self, node_id): def get_node_indx(self, node_id):
return list(self.variables_frame['Name']).index(node_id) return list(self.variables_frame[self.name_label]).index(node_id)
def get_states_number(self, node): def get_states_number(self, node):
return self.variables_frame['Value'][self.get_node_indx(node)] return self.variables_frame[self.value_label][self.get_node_indx(node)]
def get_states_number_by_indx(self, node_indx):
#print(self.value_label)
return self.variables_frame[self.value_label][node_indx]

27
main_package/classes/trajectory.py
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@ -1,8 +1,8 @@
import pandas as pd
import numpy as np import numpy as np
class Trajectory(): class Trajectory:
""" """
Rappresenta una traiettoria come un numpy_array contenente n-ple (indx, T_k,S_i,.....,Sj) Rappresenta una traiettoria come un numpy_array contenente n-ple (indx, T_k,S_i,.....,Sj)
Offre i metodi utili alla computazione sulla struttura stessa. Offre i metodi utili alla computazione sulla struttura stessa.
@ -15,11 +15,24 @@ class Trajectory():
""" """
def __init__(self, list_of_columns): def __init__(self, list_of_columns):
self.actual_trajectory = np.array(list_of_columns, dtype=object).T print(list_of_columns)
self._actual_trajectory = np.array(list_of_columns[1:], dtype=np.int).T
self._times = np.array(list_of_columns[0], dtype=np.float)
print(self._times)
@property
def trajectory(self):
return self._actual_trajectory[:, :4]
@property
def complete_trajectory(self):
return self._actual_trajectory
@property
def times(self):
return self._times
def get_trajectory(self): def size(self):
return self.actual_trajectory return self.actual_trajectory.shape[0]
def merge_columns(self, list_of_cols):
return np.vstack(list_of_cols).T