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PyCTBN
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Add setup file

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parallel_struct_est
philpMartin 4 years ago
parent 98ba2bef24
commit 813c2943bd
3 changed files with 40 additions and 35 deletions
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  1. 8
      main_package/classes/json_importer.py
  2. 55
      main_package/classes/parameters_estimator.py
  3. 12
      main_package/setup.py

8
main_package/classes/json_importer.py
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@ -94,7 +94,7 @@ class JsonImporter(AbstractImporter):
#self.sorter.sort()
#print("Sorter:", self.sorter)
self._df_variables[self.variables_key] = self._df_variables[self.variables_key].astype("category")
self._df_variables[self.variables_key] = self._df_variables[self.variables_key].cat.set_categories(self.sorter)
self._df_variables[self.variables_key] = self._df_variables[self.variables_key].cat.set_categories(sorter)
self._df_variables = self._df_variables.sort_values([self.variables_key])
self._df_variables.reset_index(inplace=True)
print("Var Frame", self._df_variables)
@ -186,14 +186,8 @@ class JsonImporter(AbstractImporter):
Returns:
void
"""
"""columns_header = list(self.df_samples_list[0].columns.values)
columns_header.remove('Time')
self.sorter = columns_header"""
shifted_cols_header = [s + "S" for s in self.sorter]
compute_row_delta = self.compute_row_delta_sigle_samples_frame
"""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, self.sorter, shifted_cols_header)"""
self.df_samples_list = [compute_row_delta(sample, time_header_label, self.sorter, shifted_cols_header)
for sample in self.df_samples_list]
self._concatenated_samples = pd.concat(self.df_samples_list)

55
main_package/classes/parameters_estimator.py
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@ -22,12 +22,6 @@ class ParametersEstimator:
self.sets_of_cims_struct = None
self.single_set_of_cims = None
def init_sets_cims_container(self):
self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.nodes,
self.net_graph.nodes_values,
self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes(),
self.net_graph.p_combs)
def fast_init(self, node_id: str):
"""
Initializes all the necessary structures for the parameters estimation.
@ -41,28 +35,6 @@ class ParametersEstimator:
node_states_number = self.net_graph.get_states_number(node_id)
self.single_set_of_cims = sofc.SetOfCims(node_id, p_vals, node_states_number, self.net_graph.p_combs)
def compute_parameters(self):
#print(self.net_graph.get_nodes())
#print(self.amalgamated_cims_struct.sets_of_cims)
#enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims))
for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)):
#print(self.net_graph.time_filtering[indx])
#print(self.net_graph.time_scalar_indexing_strucure[indx])
self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
self.sample_path.trajectories.trajectory,
self.net_graph.time_filtering[indx],
self.net_graph.time_scalar_indexing_strucure[indx],
aggr[1].state_residence_times)
#print(self.net_graph.transition_filtering[indx])
#print(self.net_graph.transition_scalar_indexing_structure[indx])
self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]),
self.sample_path.trajectories.complete_trajectory,
self.net_graph.transition_filtering[indx],
self.net_graph.transition_scalar_indexing_structure[indx],
aggr[1].transition_matrices)
aggr[1].build_cims(aggr[1].state_residence_times, aggr[1].transition_matrices)
def compute_parameters_for_node(self, node_id: str) -> sofc.SetOfCims:
"""
Compute the CIMS of the node identified by the label node_id
@ -132,6 +104,33 @@ class ParametersEstimator:
M_raveled[diag_indices] = 0
M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
def init_sets_cims_container(self):
self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.nodes,
self.net_graph.nodes_values,
self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes(),
self.net_graph.p_combs)
def compute_parameters(self):
#print(self.net_graph.get_nodes())
#print(self.amalgamated_cims_struct.sets_of_cims)
#enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims))
for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)):
#print(self.net_graph.time_filtering[indx])
#print(self.net_graph.time_scalar_indexing_strucure[indx])
self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
self.sample_path.trajectories.trajectory,
self.net_graph.time_filtering[indx],
self.net_graph.time_scalar_indexing_strucure[indx],
aggr[1].state_residence_times)
#print(self.net_graph.transition_filtering[indx])
#print(self.net_graph.transition_scalar_indexing_structure[indx])
self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]),
self.sample_path.trajectories.complete_trajectory,
self.net_graph.transition_filtering[indx],
self.net_graph.transition_scalar_indexing_structure[indx],
aggr[1].transition_matrices)
aggr[1].build_cims(aggr[1].state_residence_times, aggr[1].transition_matrices)

12
main_package/setup.py
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@ -0,0 +1,12 @@
from setuptools import setup, find_packages
setup(name='ctbn',
version='1.0',
url='https://github.com/philipMartini/CTBN_Project',
#license='MIT',
author='Filippo Martini',
author_email='f.martini@campus.unimib.it',
description='A Continuous Time Bayesian Network Library',
packages=find_packages(exclude=['tests', 'data']),
long_description=open('README.md').read(),
zip_safe=False)