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Refactor StructureEstimator save_results and adjacency_matrix methods

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Filippo Martini 4 years ago
parent 6ba5762d02
commit 028e6f6b22
13 changed files with 140 additions and 99 deletions
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  1. 3
      .idea/.gitignore
  2. 8
      .idea/PyCTBN.iml
  3. 6
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  8. BIN
      CTBN_project_dominio.pdf
  9. 18
      PyCTBN/PyCTBN/estimators/structure_estimator.py
  10. 1
      PyCTBN/PyCTBN/structure_graph/sample_path.py
  11. 1
      PyCTBN/PyCTBN/utility/json_importer.py
  12. 19
      PyCTBN/tests/estimators/test_structure_estimator.py
  13. 41
      basic_main.py

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18
PyCTBN/PyCTBN/estimators/structure_estimator.py
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@ -90,17 +90,14 @@ class StructureEstimator(object):
list_without_test_parent.remove(parent_label) list_without_test_parent.remove(parent_label)
return map(list, itertools.combinations(list_without_test_parent, size)) return map(list, itertools.combinations(list_without_test_parent, size))
def save_results(self) -> None: def save_results(self, file_path: str) -> None:
"""Save the estimated Structure to a .json file in the path where the data are loaded from. """Save the estimated Structure to a .json file in file_path.
The file is named as the input dataset but the `results_` word is appended to the results file.
:param file_path: the path including the file name with .json extension
:type file_path: string
""" """
res = json_graph.node_link_data(self._complete_graph) res = json_graph.node_link_data(self._complete_graph)
name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] with open(file_path, 'w') as f:
name = name.split('.', 1)[0]
name += '_' + str(self._sample_path._importer.dataset_id())
name += '.json'
file_name = 'results_' + name
with open(file_name, 'w') as f:
json.dump(res, f) json.dump(res, f)
@ -120,14 +117,13 @@ class StructureEstimator(object):
""" """
pass pass
def adjacency_matrix(self) -> np.ndarray: def adjacency_matrix(self) -> np.ndarray:
"""Converts the estimated structure ``_complete_graph`` to a boolean adjacency matrix representation. """Converts the estimated structure ``_complete_graph`` to a boolean adjacency matrix representation.
:return: The adjacency matrix of the graph ``_complete_graph`` :return: The adjacency matrix of the graph ``_complete_graph``
:rtype: numpy.ndArray :rtype: numpy.ndArray
""" """
return nx.adj_matrix(self._complete_graph).toarray().astype(bool) return nx.adj_matrix(self._complete_graph, self._nodes).toarray().astype(bool)
def spurious_edges(self) -> typing.List: def spurious_edges(self) -> typing.List:
"""Return the spurious edges present in the estimated structure, if a prior net structure is present in """Return the spurious edges present in the estimated structure, if a prior net structure is present in

1
PyCTBN/PyCTBN/structure_graph/sample_path.py
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@ -10,6 +10,7 @@ from ..utility.abstract_importer import AbstractImporter
MESSAGE_HAS_TO_CONTAIN_EXCEPTION = 'The importer object has to contain the all processed data!' MESSAGE_HAS_TO_CONTAIN_EXCEPTION = 'The importer object has to contain the all processed data!'
class SamplePath(object): class SamplePath(object):
"""Aggregates all the informations about the trajectories, the real structure of the sampled net and variables """Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
cardinalites. Has the task of creating the objects ``Trajectory`` and ``Structure`` that will cardinalites. Has the task of creating the objects ``Trajectory`` and ``Structure`` that will

1
PyCTBN/PyCTBN/utility/json_importer.py
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@ -131,7 +131,6 @@ class JsonImporter(AbstractImporter):
:rtype: List :rtype: List
""" """
dataframe = pd.DataFrame dataframe = pd.DataFrame
#print(raw_data)
smps = raw_data[indx][trajectories_key] smps = raw_data[indx][trajectories_key]
df_samples_list = [dataframe(sample) for sample in smps] df_samples_list = [dataframe(sample) for sample in smps]
return df_samples_list return df_samples_list

19
PyCTBN/tests/estimators/test_structure_estimator.py
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@ -24,6 +24,7 @@ class TestStructureEstimator(unittest.TestCase):
cls.s1 = SamplePath(cls.importer) cls.s1 = SamplePath(cls.importer)
cls.s1.build_trajectories() cls.s1.build_trajectories()
cls.s1.build_structure() cls.s1.build_structure()
cls.real_net_structure = nx.DiGraph(cls.s1.structure.edges)
def test_init(self): def test_init(self):
exp_alfa = 0.1 exp_alfa = 0.1
@ -50,7 +51,6 @@ class TestStructureEstimator(unittest.TestCase):
no_self_loops.remove(node) no_self_loops.remove(node)
for n2 in no_self_loops: for n2 in no_self_loops:
self.assertIn((node, n2), cg.edges) self.assertIn((node, n2), cg.edges)
#se1.save_plot_estimated_structure_graph()
def test_build_removable_edges_matrix(self): def test_build_removable_edges_matrix(self):
exp_alfa = 0.1 exp_alfa = 0.1
@ -84,18 +84,12 @@ class TestStructureEstimator(unittest.TestCase):
print("Execution Time: ", exec_time) print("Execution Time: ", exec_time)
for ed in self.s1.structure.edges: for ed in self.s1.structure.edges:
self.assertIn(tuple(ed), se1._complete_graph.edges) self.assertIn(tuple(ed), se1._complete_graph.edges)
#print("Spurious Edges:", se1.spurious_edges())
#se1.save_plot_estimated_structure_graph()
def test_save_results(self): def test_save_results(self):
se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1) se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
se1.ctpc_algorithm() se1.ctpc_algorithm()
se1.save_results() file_name = './PyCTBN/tests/estimators/test_save.json'
name = self.s1._importer.file_path.rsplit('/', 1)[-1] se1.save_results(file_name)
name = name.split('.', 1)[0]
name += '_' + str(self.s1._importer.dataset_id())
name += '.json'
file_name = 'results_' + name
with open(file_name) as f: with open(file_name) as f:
js_graph = json.load(f) js_graph = json.load(f)
result_graph = nx.json_graph.node_link_graph(js_graph) result_graph = nx.json_graph.node_link_graph(js_graph)
@ -105,13 +99,16 @@ class TestStructureEstimator(unittest.TestCase):
def test_adjacency_matrix(self): def test_adjacency_matrix(self):
se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1) se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
se1.ctpc_algorithm() se1.ctpc_algorithm()
adj_matrix = nx.adj_matrix(se1._complete_graph).toarray().astype(bool) adj_matrix = nx.adj_matrix(self.real_net_structure, self.s1.structure.nodes_labels).toarray().astype(bool)
self.assertTrue(np.array_equal(adj_matrix, se1.adjacency_matrix())) self.assertTrue(np.array_equal(adj_matrix, se1.adjacency_matrix()))
def test_save_plot_estimated_graph(self): def test_save_plot_estimated_graph(self):
se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1) se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
edges = se1.estimate_structure(disable_multiprocessing=True) edges = se1.estimate_structure(disable_multiprocessing=True)
se1.save_plot_estimated_structure_graph('./networks_and_trajectories_ternary_data_3.png') file_name = './PyCTBN/tests/estimators/test_plot.png'
se1.save_plot_estimated_structure_graph(file_name)
os.remove(file_name)
if __name__ == '__main__': if __name__ == '__main__':

41
basic_main.py
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import glob
import os
import sys
sys.path.append("./PyCTBN/")
import structure_graph.network_graph as ng
import structure_graph.sample_path as sp
import structure_graph.set_of_cims as sofc
import estimators.parameters_estimator as pe
import utility.json_importer as ji
def main():
read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
#import data
importer = ji.JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
#Create a SamplePath Obj
s1 = sp.SamplePath(importer)
#Build The trajectries and the structural infos
s1.build_trajectories()
s1.build_structure()
#From The Structure Object build the Graph
g = ng.NetworkGraph(s1.structure)
#Select a node you want to estimate the parameters
node = g.nodes[1]
#Init the graph specifically for THIS node
g.fast_init(node)
#Use SamplePath and Grpah to create a ParametersEstimator Object
p1 = pe.ParametersEstimator(s1, g)
#Init the peEst specifically for THIS node
p1.fast_init(node)
#Compute the parameters
sofc1 = p1.compute_parameters_for_node(node)
#The est CIMS are inside the resultant SetOfCIms Obj
print(sofc1.actual_cims)
if __name__ == "__main__":
main()