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PyCTBN
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Refactor caches list

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parallel_struct_est
Filippo Martini 4 years ago
parent a2f11b3666
commit a96551a915
1 changed files with 14 additions and 8 deletions
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  1. 22
      PyCTBN/PyCTBN/structure_estimator.py

22
PyCTBN/PyCTBN/structure_estimator.py
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@ -49,7 +49,7 @@ class StructureEstimator:
self._complete_graph = self.build_complete_graph(sample_path.structure.nodes_labels) self._complete_graph = self.build_complete_graph(sample_path.structure.nodes_labels)
self._exp_test_sign = exp_test_alfa self._exp_test_sign = exp_test_alfa
self._chi_test_alfa = chi_test_alfa self._chi_test_alfa = chi_test_alfa
self._caches = [Cache() for _ in range(len(self._nodes))] #self._caches = [Cache() for _ in range(len(self._nodes))]
self._result_graph = None self._result_graph = None
def build_complete_graph(self, node_ids: typing.List) -> nx.DiGraph: def build_complete_graph(self, node_ids: typing.List) -> nx.DiGraph:
@ -251,6 +251,7 @@ class StructureEstimator:
"""Compute the CTPC algorithm over the entire net. """Compute the CTPC algorithm over the entire net.
""" """
ctpc_algo = StructureEstimator.one_iteration_of_CTPC_algorithm ctpc_algo = StructureEstimator.one_iteration_of_CTPC_algorithm
#SHM INIT
shm_times = multiprocessing.shared_memory.SharedMemory(name='sh_times', create=True, shm_times = multiprocessing.shared_memory.SharedMemory(name='sh_times', create=True,
size=self._times.nbytes) size=self._times.nbytes)
shm_trajectories = multiprocessing.shared_memory.SharedMemory(name='sh_traj', create=True, shm_trajectories = multiprocessing.shared_memory.SharedMemory(name='sh_traj', create=True,
@ -262,21 +263,26 @@ class StructureEstimator:
trajectories_arr = np.ndarray(self._trajectories.shape, trajectories_arr = np.ndarray(self._trajectories.shape,
self._trajectories.dtype, shm_trajectories.buf) self._trajectories.dtype, shm_trajectories.buf)
trajectories_arr[:] = self._trajectories[:] trajectories_arr[:] = self._trajectories[:]
total_vars_numb_list = [self._tot_vars_number] * len(self._nodes)
nodes = np.copy(self._nodes)
nodes_vals = np.copy(self._nodes_vals)
nodes_numb = len(self._nodes)
caches = [Cache() for _ in range(nodes_numb)]
total_vars_numb_list = [self._tot_vars_number] * nodes_numb
parents_list = [list(self._complete_graph.predecessors(var_id)) for var_id in self._nodes] parents_list = [list(self._complete_graph.predecessors(var_id)) for var_id in self._nodes]
nodes_array_list = [self._nodes] * len(self._nodes) nodes_array_list = [self._nodes] * len(self._nodes)
nodes_indxs_array_list = [self._nodes_indxs] * len(self._nodes) nodes_indxs_array_list = [self._nodes_indxs] * nodes_numb
nodes_vals_array_list = [self._nodes_vals] * len(self._nodes) nodes_vals_array_list = [self._nodes_vals] * nodes_numb
tests_alfa_dims_list = [(self._exp_test_sign, self._chi_test_alfa)] * len(self._nodes) tests_alfa_dims_list = [(self._exp_test_sign, self._chi_test_alfa)] * nodes_numb
datas_dims_list = [[self._times.shape, self._trajectories.shape]] * len(self._nodes) datas_dims_list = [[self._times.shape, self._trajectories.shape]] * nodes_numb
if multi_processing: if multi_processing:
cpu_count = multiprocessing.cpu_count() cpu_count = multiprocessing.cpu_count()
else: else:
cpu_count = 1 cpu_count = 1
print("CPU COUNT", cpu_count) print("CPU COUNT", cpu_count)
with multiprocessing.Pool(processes=cpu_count) as pool: with multiprocessing.Pool(processes=cpu_count) as pool:
parent_sets = pool.starmap(ctpc_algo, zip(self._nodes, self._nodes_vals, parents_list, parent_sets = pool.starmap(ctpc_algo, zip(nodes, nodes_vals, parents_list,
self._caches, total_vars_numb_list, caches, total_vars_numb_list,
nodes_array_list, nodes_indxs_array_list, nodes_vals_array_list, nodes_array_list, nodes_indxs_array_list, nodes_vals_array_list,
tests_alfa_dims_list, datas_dims_list)) tests_alfa_dims_list, datas_dims_list))