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removed ensable model

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master
Luca Moretti 4 years ago
parent bb4745f430
commit c1b073119f
6 changed files with 395 additions and 226 deletions
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  1. 4
      main_package/classes/estimators/fam_score_calculator.py
  2. 9
      main_package/classes/structure_graph/sample_path.py
  3. 8
      main_package/tests/optimizers/test_tabu_search.py
  4. 382
      main_package/tests/results/results.csv
  5. 84
      main_package/tests/simulators/test_ensambled_model.py
  6. 134
      main_package/tests/simulators/test_ensambled_model_all.py

4
main_package/classes/estimators/fam_score_calculator.py
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@ -21,7 +21,7 @@ import structure_graph.conditional_intensity_matrix as cim_class
'''
TODO: Parlare dell'idea di ciclare sulle cim senza filtrare
'''
@ -110,7 +110,7 @@ class FamScoreCalculator:
+ \
np.sum([self.single_internal_cim_xxu_marginal_likelihood_theta(
cim.state_transition_matrix[index,index_x_first],
alpha_xu * cim.state_transition_matrix[index,index_x_first] / cim.state_transition_matrix[index, index])
alpha_xxu)
for index_x_first in values])

9
main_package/classes/structure_graph/sample_path.py
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@ -6,6 +6,8 @@ import utility.json_importer as imp
import structure_graph.structure as st
import structure_graph.trajectory as tr
import pandas as pd
class SamplePath(asam.AbstractSamplePath):
@ -41,8 +43,13 @@ class SamplePath(asam.AbstractSamplePath):
void
"""
self.importer.import_data()
#TODO: VALUTARE PARAMETRO PER DATA AUGMENTATION
trajects_samples = pd.concat([self.importer.concatenated_samples,
self.importer.concatenated_samples])
self._trajectories = \
tr.Trajectory(self.importer.build_list_of_samples_array(self.importer.concatenated_samples),
tr.Trajectory(self.importer.build_list_of_samples_array(trajects_samples),
len(self.importer.sorter) + 1)
#self.trajectories.append(trajectory)
self.importer.clear_concatenated_frame()

8
main_package/tests/optimizers/test_tabu_search.py
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@ -23,8 +23,8 @@ class TestTabuSearch(unittest.TestCase):
@classmethod
def setUpClass(cls):
#cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_ternary_data_15.json",
'samples', 'dyn.str', 'variables', 'Time', 'Name', 0 )
cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_ternary_data_20.json",
'samples', 'dyn.str', 'variables', 'Time', 'Name', 2 )
cls.s1 = sp.SamplePath(cls.importer)
cls.s1.build_trajectories()
cls.s1.build_structure()
@ -40,8 +40,8 @@ class TestTabuSearch(unittest.TestCase):
max_parents = None,
iterations_number = 100,
patience = None,
tabu_length = 20,
tabu_rules_duration = 20,
tabu_length = 15,
tabu_rules_duration = 15,
optimizer = 'tabu'
)

382
main_package/tests/results/results.csv
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@ -1346,4 +1346,384 @@ Time,Type,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
2,2,15,0.4,2,2,1.0,1.0,1.0
2,2,20,0.4,2,0,0.961,1.0,0.925
2,2,20,0.4,2,1,0.87,1.0,0.769
2,2,20,0.4,2,2,0.963,1.0,0.929
2,2,20,0.4,2,2,0.963,1.0,0.929
2,2,3,0.1,3,0,1.0,1.0,1.0
2,2,3,0.1,3,1,1.0,1.0,1.0
2,2,3,0.1,3,2,1.0,1.0,1.0
2,2,3,0.1,3,3,0.8,0.667,1.0
2,2,3,0.1,3,4,1.0,1.0,1.0
2,2,3,0.1,3,5,1.0,1.0,1.0
2,2,3,0.1,3,6,0.667,0.5,1.0
2,2,3,0.1,3,7,0.8,0.667,1.0
2,2,3,0.1,3,8,0.8,0.667,1.0
2,2,3,0.1,3,9,1.0,1.0,1.0
2,2,4,0.1,3,0,0.857,0.75,1.0
2,2,4,0.1,3,1,0.667,0.5,1.0
2,2,4,0.1,3,2,0.857,0.75,1.0
2,2,4,0.1,3,3,1.0,1.0,1.0
2,2,4,0.1,3,4,1.0,1.0,1.0
2,2,4,0.1,3,5,0.75,0.6,1.0
2,2,4,0.1,3,6,1.0,1.0,1.0
2,2,4,0.1,3,7,1.0,1.0,1.0
2,2,4,0.1,3,8,0.8,0.667,1.0
2,2,4,0.1,3,9,1.0,1.0,1.0
2,2,5,0.1,3,0,0.889,0.8,1.0
2,2,5,0.1,3,1,1.0,1.0,1.0
2,2,5,0.1,3,2,1.0,1.0,1.0
2,2,5,0.1,3,3,0.889,0.8,1.0
2,2,5,0.1,3,4,1.0,1.0,1.0
2,2,5,0.1,3,5,0.769,0.625,1.0
2,2,5,0.1,3,6,0.727,0.571,1.0
2,2,5,0.1,3,7,0.889,0.8,1.0
2,2,5,0.1,3,8,1.0,1.0,1.0
2,2,5,0.1,3,9,1.0,1.0,1.0
2,2,6,0.1,3,0,0.923,0.857,1.0
2,2,6,0.1,3,1,1.0,1.0,1.0
2,2,6,0.1,3,2,1.0,1.0,1.0
2,2,6,0.1,3,3,0.857,0.75,1.0
2,2,6,0.1,3,4,1.0,1.0,1.0
2,2,6,0.1,3,5,1.0,1.0,1.0
2,2,6,0.1,3,6,0.8,0.667,1.0
2,2,6,0.1,3,7,0.909,0.833,1.0
2,2,6,0.1,3,8,1.0,1.0,1.0
2,2,6,0.1,3,9,0.889,0.8,1.0
2,2,10,0.1,3,0,0.941,0.889,1.0
2,2,10,0.1,3,1,1.0,1.0,1.0
2,2,10,0.1,3,2,0.909,0.833,1.0
2,2,15,0.1,3,0,0.88,0.786,1.0
2,2,15,0.1,3,1,0.939,0.885,1.0
2,2,15,0.1,3,2,0.955,0.913,1.0
2,2,20,0.1,3,0,0.967,0.936,1.0
2,2,20,0.1,3,1,0.98,0.961,1.0
2,2,20,0.1,3,2,0.945,0.896,1.0
2,2,3,0.2,3,0,1.0,1.0,1.0
2,2,3,0.2,3,1,1.0,1.0,1.0
2,2,3,0.2,3,2,0.8,0.667,1.0
2,2,3,0.2,3,3,1.0,1.0,1.0
2,2,3,0.2,3,4,1.0,1.0,1.0
2,2,3,0.2,3,5,1.0,1.0,1.0
2,2,3,0.2,3,6,1.0,1.0,1.0
2,2,3,0.2,3,7,1.0,1.0,1.0
2,2,3,0.2,3,8,1.0,1.0,1.0
2,2,3,0.2,3,9,0.857,0.75,1.0
2,2,4,0.2,3,0,0.857,0.75,1.0
2,2,4,0.2,3,1,1.0,1.0,1.0
2,2,4,0.2,3,2,0.889,0.8,1.0
2,2,4,0.2,3,3,1.0,1.0,1.0
2,2,4,0.2,3,4,0.889,0.8,1.0
2,2,4,0.2,3,5,1.0,1.0,1.0
2,2,4,0.2,3,6,1.0,1.0,1.0
2,2,4,0.2,3,7,1.0,1.0,1.0
2,2,4,0.2,3,8,0.857,0.75,1.0
2,2,4,0.2,3,9,1.0,1.0,1.0
2,2,5,0.2,3,0,1.0,1.0,1.0
2,2,5,0.2,3,1,1.0,1.0,1.0
2,2,5,0.2,3,2,0.909,0.833,1.0
2,2,5,0.2,3,3,0.941,0.889,1.0
2,2,5,0.2,3,4,1.0,1.0,1.0
2,2,5,0.2,3,5,1.0,1.0,1.0
2,2,5,0.2,3,6,1.0,1.0,1.0
2,2,5,0.2,3,7,0.75,0.6,1.0
2,2,5,0.2,3,8,0.8,0.667,1.0
2,2,5,0.2,3,9,0.909,0.833,1.0
2,2,6,0.2,3,0,1.0,1.0,1.0
2,2,6,0.2,3,1,0.923,0.857,1.0
2,2,6,0.2,3,2,1.0,1.0,1.0
2,2,6,0.2,3,3,1.0,1.0,1.0
2,2,6,0.2,3,4,1.0,1.0,1.0
2,2,6,0.2,3,5,0.727,0.571,1.0
2,2,6,0.2,3,6,1.0,1.0,1.0
2,2,6,0.2,3,7,1.0,1.0,1.0
2,2,6,0.2,3,8,0.933,0.875,1.0
2,2,6,0.2,3,9,1.0,1.0,1.0
2,2,10,0.2,3,0,0.979,0.958,1.0
2,2,10,0.2,3,1,1.0,1.0,1.0
2,2,10,0.2,3,2,0.971,0.944,1.0
2,2,15,0.2,3,0,1.0,1.0,1.0
2,2,15,0.2,3,1,0.94,0.975,0.907
2,2,15,0.2,3,2,1.0,1.0,1.0
2,2,20,0.2,3,0,0.992,0.984,1.0
2,2,20,0.2,3,1,0.993,1.0,0.986
2,2,20,0.2,3,2,0.969,1.0,0.939
2,2,3,0.3,3,0,1.0,1.0,1.0
2,2,3,0.3,3,1,1.0,1.0,1.0
2,2,3,0.3,3,2,1.0,1.0,1.0
2,2,3,0.3,3,3,1.0,1.0,1.0
2,2,3,0.3,3,4,1.0,1.0,1.0
2,2,3,0.3,3,5,0.857,0.75,1.0
2,2,3,0.3,3,6,1.0,1.0,1.0
2,2,3,0.3,3,7,1.0,1.0,1.0
2,2,3,0.3,3,8,1.0,1.0,1.0
2,2,3,0.3,3,9,1.0,1.0,1.0
2,2,4,0.3,3,0,1.0,1.0,1.0
2,2,4,0.3,3,1,1.0,1.0,1.0
2,2,4,0.3,3,2,1.0,1.0,1.0
2,2,4,0.3,3,3,1.0,1.0,1.0
2,2,4,0.3,3,4,0.909,0.833,1.0
2,2,4,0.3,3,5,1.0,1.0,1.0
2,2,4,0.3,3,6,0.909,0.833,1.0
2,2,4,0.3,3,7,1.0,1.0,1.0
2,2,4,0.3,3,8,1.0,1.0,1.0
2,2,4,0.3,3,9,1.0,1.0,1.0
2,2,5,0.3,3,0,0.933,0.875,1.0
2,2,5,0.3,3,1,1.0,1.0,1.0
2,2,5,0.3,3,2,1.0,1.0,1.0
2,2,5,0.3,3,3,0.889,0.8,1.0
2,2,5,0.3,3,4,1.0,1.0,1.0
2,2,5,0.3,3,5,0.941,0.889,1.0
2,2,5,0.3,3,6,1.0,1.0,1.0
2,2,5,0.3,3,7,0.923,0.857,1.0
2,2,5,0.3,3,8,1.0,1.0,1.0
2,2,5,0.3,3,9,0.889,0.8,1.0
2,2,6,0.3,3,0,1.0,1.0,1.0
2,2,6,0.3,3,1,0.952,0.909,1.0
2,2,6,0.3,3,2,1.0,1.0,1.0
2,2,6,0.3,3,3,0.933,0.875,1.0
2,2,6,0.3,3,4,1.0,1.0,1.0
2,2,6,0.3,3,5,0.952,0.909,1.0
2,2,6,0.3,3,6,0.96,0.923,1.0
2,2,6,0.3,3,7,0.957,0.917,1.0
2,2,6,0.3,3,8,1.0,1.0,1.0
2,2,6,0.3,3,9,0.8,0.667,1.0
2,2,10,0.3,3,0,0.952,0.938,0.968
2,2,10,0.3,3,1,0.969,0.939,1.0
2,2,10,0.3,3,2,0.966,0.933,1.0
2,2,15,0.3,3,0,1.0,1.0,1.0
2,2,15,0.3,3,1,0.959,1.0,0.922
2,2,15,0.3,3,2,0.885,1.0,0.794
2,2,20,0.3,3,0,0.92,1.0,0.851
2,2,20,0.3,3,1,0.758,0.986,0.615
2,2,20,0.3,3,2,0.777,0.988,0.64
2,2,3,0.4,3,0,1.0,1.0,1.0
2,2,3,0.4,3,1,0.8,0.667,1.0
2,2,3,0.4,3,2,1.0,1.0,1.0
2,2,3,0.4,3,3,0.889,0.8,1.0
2,2,3,0.4,3,4,1.0,1.0,1.0
2,2,3,0.4,3,5,0.8,0.667,1.0
2,2,3,0.4,3,6,1.0,1.0,1.0
2,2,3,0.4,3,7,1.0,1.0,1.0
2,2,3,0.4,3,8,1.0,1.0,1.0
2,2,3,0.4,3,9,1.0,1.0,1.0
2,2,4,0.4,3,0,1.0,1.0,1.0
2,2,4,0.4,3,1,0.933,0.875,1.0
2,2,4,0.4,3,2,1.0,1.0,1.0
2,2,4,0.4,3,3,1.0,1.0,1.0
2,2,4,0.4,3,4,0.923,0.857,1.0
2,2,4,0.4,3,5,1.0,1.0,1.0
2,2,4,0.4,3,6,0.889,0.8,1.0
2,2,4,0.4,3,7,0.889,0.8,1.0
2,2,4,0.4,3,8,1.0,1.0,1.0
2,2,4,0.4,3,9,0.8,0.667,1.0
2,2,5,0.4,3,0,0.909,0.833,1.0
2,2,5,0.4,3,1,1.0,1.0,1.0
2,2,5,0.4,3,2,0.941,0.889,1.0
2,2,5,0.4,3,3,0.923,0.857,1.0
2,2,5,0.4,3,4,0.923,0.857,1.0
2,2,5,0.4,3,5,0.941,0.889,1.0
2,2,5,0.4,3,6,0.889,0.8,1.0
2,2,5,0.4,3,7,1.0,1.0,1.0
2,2,5,0.4,3,8,1.0,1.0,1.0
2,2,5,0.4,3,9,0.952,0.909,1.0
2,2,6,0.4,3,0,1.0,1.0,1.0
2,2,6,0.4,3,1,0.929,0.867,1.0
2,2,6,0.4,3,2,0.966,0.933,1.0
2,2,6,0.4,3,3,0.857,0.75,1.0
2,2,6,0.4,3,4,0.968,0.938,1.0
2,2,6,0.4,3,5,1.0,1.0,1.0
2,2,6,0.4,3,6,0.947,0.9,1.0
2,2,6,0.4,3,7,0.963,0.929,1.0
2,2,6,0.4,3,8,0.966,0.933,1.0
2,2,6,0.4,3,9,0.947,0.9,1.0
2,2,10,0.4,3,0,1.0,1.0,1.0
2,2,10,0.4,3,1,0.932,0.971,0.895
2,2,10,0.4,3,2,1.0,1.0,1.0
2,2,15,0.4,3,0,0.863,0.985,0.767
2,2,15,0.4,3,1,0.78,0.982,0.647
2,2,15,0.4,3,2,0.958,1.0,0.92
2,2,20,0.4,3,0,0.5,0.98,0.336
2,2,20,0.4,3,1,0.635,1.0,0.465
2,2,20,0.4,3,2,0.436,0.936,0.284
2,2,3,0.1,4,0,1.0,1.0,1.0
2,2,3,0.1,4,1,0.667,0.5,1.0
2,2,3,0.1,4,2,0.8,0.667,1.0
2,2,3,0.1,4,3,0.8,0.667,1.0
2,2,3,0.1,4,4,0.8,0.667,1.0
2,2,3,0.1,4,5,1.0,1.0,1.0
2,2,3,0.1,4,6,0.8,0.667,1.0
2,2,3,0.1,4,7,1.0,1.0,1.0
2,2,3,0.1,4,8,0.75,0.6,1.0
2,2,3,0.1,4,9,0.667,0.5,1.0
2,2,4,0.1,4,0,0.889,0.8,1.0
2,2,4,0.1,4,1,0.571,0.4,1.0
2,2,4,0.1,4,2,1.0,1.0,1.0
2,2,4,0.1,4,3,0.667,0.5,1.0
2,2,4,0.1,4,4,0.667,0.5,1.0
2,2,4,0.1,4,5,0.571,0.4,1.0
2,2,4,0.1,4,6,0.889,0.8,1.0
2,2,4,0.1,4,7,0.571,0.4,1.0
2,2,4,0.1,4,8,0.8,0.667,1.0
2,2,4,0.1,4,9,0.833,0.714,1.0
2,2,5,0.1,4,0,0.857,0.75,1.0
2,2,5,0.1,4,1,0.778,0.636,1.0
2,2,5,0.1,4,2,0.727,0.571,1.0
2,2,5,0.1,4,3,0.727,0.571,1.0
2,2,5,0.1,4,4,0.8,0.667,1.0
2,2,5,0.1,4,5,0.75,0.6,1.0
2,2,5,0.1,4,6,0.667,0.5,1.0
2,2,5,0.1,4,7,0.8,0.667,1.0
2,2,5,0.1,4,8,0.889,0.8,1.0
2,2,5,0.1,4,9,0.857,0.75,1.0
2,2,6,0.1,4,0,0.8,0.667,1.0
2,2,6,0.1,4,1,0.667,0.5,1.0
2,2,6,0.1,4,2,0.727,0.571,1.0
2,2,6,0.1,4,3,0.615,0.444,1.0
2,2,6,0.1,4,4,0.8,0.667,1.0
2,2,6,0.1,4,5,0.8,0.667,1.0
2,2,6,0.1,4,6,0.714,0.556,1.0
2,2,6,0.1,4,7,0.824,0.7,1.0
2,2,6,0.1,4,8,0.909,0.833,1.0
2,2,6,0.1,4,9,0.75,0.6,1.0
2,2,10,0.1,4,0,0.688,0.524,1.0
2,2,10,0.1,4,1,0.821,0.696,1.0
2,2,10,0.1,4,2,0.774,0.632,1.0
2,2,15,0.1,4,0,0.833,0.714,1.0
2,2,15,0.1,4,1,0.717,0.559,1.0
2,2,15,0.1,4,2,0.787,0.649,1.0
2,2,3,0.2,4,0,0.857,0.75,1.0
2,2,3,0.2,4,1,1.0,1.0,1.0
2,2,3,0.2,4,2,0.8,0.667,1.0
2,2,3,0.2,4,3,0.8,0.667,1.0
2,2,3,0.2,4,4,0.75,0.6,1.0
2,2,3,0.2,4,5,0.667,0.5,1.0
2,2,3,0.2,4,6,0.857,0.75,1.0
2,2,3,0.2,4,7,1.0,1.0,1.0
2,2,3,0.2,4,8,0.8,0.667,1.0
2,2,3,0.2,4,9,0.857,0.75,1.0
2,2,4,0.2,4,0,1.0,1.0,1.0
2,2,4,0.2,4,1,0.857,0.75,1.0
2,2,4,0.2,4,2,0.857,0.75,1.0
2,2,4,0.2,4,3,0.75,0.6,1.0
2,2,4,0.2,4,4,0.824,0.7,1.0
2,2,4,0.2,4,5,1.0,1.0,1.0
2,2,4,0.2,4,6,0.857,0.75,1.0
2,2,4,0.2,4,7,1.0,1.0,1.0
2,2,4,0.2,4,8,1.0,1.0,1.0
2,2,4,0.2,4,9,1.0,1.0,1.0
2,2,5,0.2,4,0,1.0,1.0,1.0
2,2,5,0.2,4,1,0.875,0.778,1.0
2,2,5,0.2,4,2,0.769,0.625,1.0
2,2,5,0.2,4,3,0.769,0.625,1.0
2,2,5,0.2,4,4,0.923,0.857,1.0
2,2,5,0.2,4,5,0.933,0.875,1.0
2,2,5,0.2,4,6,1.0,1.0,1.0
2,2,5,0.2,4,7,0.545,0.375,1.0
2,2,5,0.2,4,8,0.857,0.75,1.0
2,2,5,0.2,4,9,0.909,0.833,1.0
2,2,6,0.2,4,0,0.9,0.818,1.0
2,2,6,0.2,4,1,0.933,0.875,1.0
2,2,6,0.2,4,2,0.778,0.636,1.0
2,2,6,0.2,4,3,0.778,0.636,1.0
2,2,6,0.2,4,4,0.8,0.667,1.0
2,2,6,0.2,4,5,0.833,0.714,1.0
2,2,6,0.2,4,6,0.696,0.533,1.0
2,2,6,0.2,4,7,0.706,0.545,1.0
2,2,6,0.2,4,8,0.7,0.538,1.0
2,2,6,0.2,4,9,1.0,1.0,1.0
2,2,10,0.2,4,0,0.837,0.72,1.0
2,2,10,0.2,4,1,0.898,0.815,1.0
2,2,10,0.2,4,2,0.833,0.714,1.0
2,2,15,0.2,4,0,0.932,0.872,1.0
2,2,15,0.2,4,1,0.921,0.891,0.953
2,2,15,0.2,4,2,0.932,0.873,1.0
2,2,3,0.3,4,0,0.8,0.667,1.0
2,2,3,0.3,4,1,0.667,0.5,1.0
2,2,3,0.3,4,2,1.0,1.0,1.0
2,2,3,0.3,4,3,1.0,1.0,1.0
2,2,3,0.3,4,4,1.0,1.0,1.0
2,2,3,0.3,4,5,0.8,0.667,1.0
2,2,3,0.3,4,6,0.667,0.5,1.0
2,2,3,0.3,4,7,1.0,1.0,1.0
2,2,3,0.3,4,8,0.857,0.75,1.0
2,2,3,0.3,4,9,0.8,0.667,1.0
2,2,4,0.3,4,0,0.889,0.8,1.0
2,2,4,0.3,4,1,0.833,0.714,1.0
2,2,4,0.3,4,2,0.923,0.857,1.0
2,2,4,0.3,4,3,0.8,0.667,1.0
2,2,4,0.3,4,4,0.909,0.833,1.0
2,2,4,0.3,4,5,0.8,0.667,1.0
2,2,4,0.3,4,6,0.833,0.714,1.0
2,2,4,0.3,4,7,0.889,0.8,1.0
2,2,4,0.3,4,8,0.889,0.8,1.0
2,2,4,0.3,4,9,0.933,0.875,1.0
2,2,5,0.3,4,0,0.923,0.857,1.0
2,2,5,0.3,4,1,0.737,0.583,1.0
2,2,5,0.3,4,2,0.917,0.846,1.0
2,2,5,0.3,4,3,0.933,0.875,1.0
2,2,5,0.3,4,4,0.9,0.818,1.0
2,2,5,0.3,4,5,0.923,0.857,1.0
2,2,5,0.3,4,6,0.8,0.667,1.0
2,2,5,0.3,4,7,0.833,0.714,1.0
2,2,5,0.3,4,8,0.769,0.625,1.0
2,2,5,0.3,4,9,0.889,0.8,1.0
2,2,6,0.3,4,0,0.833,0.714,1.0
2,2,6,0.3,4,1,0.87,0.769,1.0
2,2,6,0.3,4,2,0.88,0.786,1.0
2,2,6,0.3,4,3,0.917,0.846,1.0
2,2,6,0.3,4,4,0.737,0.583,1.0
2,2,6,0.3,4,5,0.897,0.812,1.0
2,2,6,0.3,4,6,0.714,0.556,1.0
2,2,6,0.3,4,7,0.947,0.9,1.0
2,2,6,0.3,4,8,0.889,0.8,1.0
2,2,6,0.3,4,9,0.818,0.692,1.0
2,2,10,0.3,4,0,0.943,0.893,1.0
2,2,10,0.3,4,1,0.848,0.8,0.903
2,2,10,0.3,4,2,0.852,0.742,1.0
2,2,15,0.3,4,0,0.8,0.667,1.0
2,2,15,0.3,4,1,0.708,0.548,1.0
2,2,15,0.3,4,2,0.833,0.714,1.0
2,2,3,0.4,4,0,0.889,0.8,1.0
2,2,3,0.4,4,1,0.857,0.75,1.0
2,2,3,0.4,4,2,0.75,0.6,1.0
2,2,3,0.4,4,3,0.857,0.75,1.0
2,2,3,0.4,4,4,0.857,0.75,1.0
2,2,3,0.4,4,5,1.0,1.0,1.0
2,2,3,0.4,4,6,0.8,0.667,1.0
2,2,3,0.4,4,7,0.8,0.667,1.0
2,2,3,0.4,4,8,0.889,0.8,1.0
2,2,3,0.4,4,9,1.0,1.0,1.0
2,2,4,0.4,4,0,0.923,0.857,1.0
2,2,4,0.4,4,1,0.933,0.875,1.0
2,2,4,0.4,4,2,1.0,1.0,1.0
2,2,4,0.4,4,3,0.75,0.6,1.0
2,2,4,0.4,4,4,1.0,1.0,1.0
2,2,4,0.4,4,5,0.857,0.75,1.0
2,2,4,0.4,4,6,0.889,0.8,1.0
2,2,4,0.4,4,7,0.889,0.8,1.0
2,2,4,0.4,4,8,0.667,0.5,1.0
2,2,4,0.4,4,9,0.833,0.714,1.0
2,2,5,0.4,4,0,0.778,0.636,1.0
2,2,5,0.4,4,1,0.842,0.727,1.0
2,2,5,0.4,4,2,0.909,0.833,1.0
2,2,5,0.4,4,3,0.857,0.75,1.0
2,2,5,0.4,4,4,0.966,0.933,1.0
2,2,5,0.4,4,5,0.778,0.636,1.0
2,2,5,0.4,4,6,0.8,0.667,1.0
2,2,5,0.4,4,7,0.714,0.556,1.0
2,2,5,0.4,4,8,0.8,0.667,1.0
2,2,5,0.4,4,9,0.737,0.583,1.0
2,2,6,0.4,4,0,0.815,0.688,1.0
2,2,6,0.4,4,1,0.889,0.8,1.0
2,2,6,0.4,4,2,0.929,0.867,1.0
2,2,6,0.4,4,3,0.875,0.778,1.0
2,2,6,0.4,4,4,0.706,0.545,1.0
2,2,6,0.4,4,5,0.88,0.786,1.0
2,2,6,0.4,4,6,0.889,0.8,1.0
2,2,6,0.4,4,7,0.815,0.688,1.0
2,2,6,0.4,4,8,0.938,0.882,1.0
2,2,6,0.4,4,9,0.857,0.75,1.0
2,2,10,0.4,4,0,0.972,0.946,1.0
2,2,10,0.4,4,1,0.927,0.95,0.905
2,2,10,0.4,4,2,0.892,0.967,0.829
2,2,15,0.4,4,0,0.691,0.923,0.552
2,2,15,0.4,4,1,0.815,0.948,0.714
2,2,15,0.4,4,2,0.794,0.945,0.684
1 Time Type Variables Density_Network Cardinality Index F1 Precision Recall
1346 2 2 15 0.4 2 2 1.0 1.0 1.0
1347 2 2 20 0.4 2 0 0.961 1.0 0.925
1348 2 2 20 0.4 2 1 0.87 1.0 0.769
1349 2 2 20 0.4 2 2 0.963 1.0 0.929
1350 2 2 3 0.1 3 0 1.0 1.0 1.0
1351 2 2 3 0.1 3 1 1.0 1.0 1.0
1352 2 2 3 0.1 3 2 1.0 1.0 1.0
1353 2 2 3 0.1 3 3 0.8 0.667 1.0
1354 2 2 3 0.1 3 4 1.0 1.0 1.0
1355 2 2 3 0.1 3 5 1.0 1.0 1.0
1356 2 2 3 0.1 3 6 0.667 0.5 1.0
1357 2 2 3 0.1 3 7 0.8 0.667 1.0
1358 2 2 3 0.1 3 8 0.8 0.667 1.0
1359 2 2 3 0.1 3 9 1.0 1.0 1.0
1360 2 2 4 0.1 3 0 0.857 0.75 1.0
1361 2 2 4 0.1 3 1 0.667 0.5 1.0
1362 2 2 4 0.1 3 2 0.857 0.75 1.0
1363 2 2 4 0.1 3 3 1.0 1.0 1.0
1364 2 2 4 0.1 3 4 1.0 1.0 1.0
1365 2 2 4 0.1 3 5 0.75 0.6 1.0
1366 2 2 4 0.1 3 6 1.0 1.0 1.0
1367 2 2 4 0.1 3 7 1.0 1.0 1.0
1368 2 2 4 0.1 3 8 0.8 0.667 1.0
1369 2 2 4 0.1 3 9 1.0 1.0 1.0
1370 2 2 5 0.1 3 0 0.889 0.8 1.0
1371 2 2 5 0.1 3 1 1.0 1.0 1.0
1372 2 2 5 0.1 3 2 1.0 1.0 1.0
1373 2 2 5 0.1 3 3 0.889 0.8 1.0
1374 2 2 5 0.1 3 4 1.0 1.0 1.0
1375 2 2 5 0.1 3 5 0.769 0.625 1.0
1376 2 2 5 0.1 3 6 0.727 0.571 1.0
1377 2 2 5 0.1 3 7 0.889 0.8 1.0
1378 2 2 5 0.1 3 8 1.0 1.0 1.0
1379 2 2 5 0.1 3 9 1.0 1.0 1.0
1380 2 2 6 0.1 3 0 0.923 0.857 1.0
1381 2 2 6 0.1 3 1 1.0 1.0 1.0
1382 2 2 6 0.1 3 2 1.0 1.0 1.0
1383 2 2 6 0.1 3 3 0.857 0.75 1.0
1384 2 2 6 0.1 3 4 1.0 1.0 1.0
1385 2 2 6 0.1 3 5 1.0 1.0 1.0
1386 2 2 6 0.1 3 6 0.8 0.667 1.0
1387 2 2 6 0.1 3 7 0.909 0.833 1.0
1388 2 2 6 0.1 3 8 1.0 1.0 1.0
1389 2 2 6 0.1 3 9 0.889 0.8 1.0
1390 2 2 10 0.1 3 0 0.941 0.889 1.0
1391 2 2 10 0.1 3 1 1.0 1.0 1.0
1392 2 2 10 0.1 3 2 0.909 0.833 1.0
1393 2 2 15 0.1 3 0 0.88 0.786 1.0
1394 2 2 15 0.1 3 1 0.939 0.885 1.0
1395 2 2 15 0.1 3 2 0.955 0.913 1.0
1396 2 2 20 0.1 3 0 0.967 0.936 1.0
1397 2 2 20 0.1 3 1 0.98 0.961 1.0
1398 2 2 20 0.1 3 2 0.945 0.896 1.0
1399 2 2 3 0.2 3 0 1.0 1.0 1.0
1400 2 2 3 0.2 3 1 1.0 1.0 1.0
1401 2 2 3 0.2 3 2 0.8 0.667 1.0
1402 2 2 3 0.2 3 3 1.0 1.0 1.0
1403 2 2 3 0.2 3 4 1.0 1.0 1.0
1404 2 2 3 0.2 3 5 1.0 1.0 1.0
1405 2 2 3 0.2 3 6 1.0 1.0 1.0
1406 2 2 3 0.2 3 7 1.0 1.0 1.0
1407 2 2 3 0.2 3 8 1.0 1.0 1.0
1408 2 2 3 0.2 3 9 0.857 0.75 1.0
1409 2 2 4 0.2 3 0 0.857 0.75 1.0
1410 2 2 4 0.2 3 1 1.0 1.0 1.0
1411 2 2 4 0.2 3 2 0.889 0.8 1.0
1412 2 2 4 0.2 3 3 1.0 1.0 1.0
1413 2 2 4 0.2 3 4 0.889 0.8 1.0
1414 2 2 4 0.2 3 5 1.0 1.0 1.0
1415 2 2 4 0.2 3 6 1.0 1.0 1.0
1416 2 2 4 0.2 3 7 1.0 1.0 1.0
1417 2 2 4 0.2 3 8 0.857 0.75 1.0
1418 2 2 4 0.2 3 9 1.0 1.0 1.0
1419 2 2 5 0.2 3 0 1.0 1.0 1.0
1420 2 2 5 0.2 3 1 1.0 1.0 1.0
1421 2 2 5 0.2 3 2 0.909 0.833 1.0
1422 2 2 5 0.2 3 3 0.941 0.889 1.0
1423 2 2 5 0.2 3 4 1.0 1.0 1.0
1424 2 2 5 0.2 3 5 1.0 1.0 1.0
1425 2 2 5 0.2 3 6 1.0 1.0 1.0
1426 2 2 5 0.2 3 7 0.75 0.6 1.0
1427 2 2 5 0.2 3 8 0.8 0.667 1.0
1428 2 2 5 0.2 3 9 0.909 0.833 1.0
1429 2 2 6 0.2 3 0 1.0 1.0 1.0
1430 2 2 6 0.2 3 1 0.923 0.857 1.0
1431 2 2 6 0.2 3 2 1.0 1.0 1.0
1432 2 2 6 0.2 3 3 1.0 1.0 1.0
1433 2 2 6 0.2 3 4 1.0 1.0 1.0
1434 2 2 6 0.2 3 5 0.727 0.571 1.0
1435 2 2 6 0.2 3 6 1.0 1.0 1.0
1436 2 2 6 0.2 3 7 1.0 1.0 1.0
1437 2 2 6 0.2 3 8 0.933 0.875 1.0
1438 2 2 6 0.2 3 9 1.0 1.0 1.0
1439 2 2 10 0.2 3 0 0.979 0.958 1.0
1440 2 2 10 0.2 3 1 1.0 1.0 1.0
1441 2 2 10 0.2 3 2 0.971 0.944 1.0
1442 2 2 15 0.2 3 0 1.0 1.0 1.0
1443 2 2 15 0.2 3 1 0.94 0.975 0.907
1444 2 2 15 0.2 3 2 1.0 1.0 1.0
1445 2 2 20 0.2 3 0 0.992 0.984 1.0
1446 2 2 20 0.2 3 1 0.993 1.0 0.986
1447 2 2 20 0.2 3 2 0.969 1.0 0.939
1448 2 2 3 0.3 3 0 1.0 1.0 1.0
1449 2 2 3 0.3 3 1 1.0 1.0 1.0
1450 2 2 3 0.3 3 2 1.0 1.0 1.0
1451 2 2 3 0.3 3 3 1.0 1.0 1.0
1452 2 2 3 0.3 3 4 1.0 1.0 1.0
1453 2 2 3 0.3 3 5 0.857 0.75 1.0
1454 2 2 3 0.3 3 6 1.0 1.0 1.0
1455 2 2 3 0.3 3 7 1.0 1.0 1.0
1456 2 2 3 0.3 3 8 1.0 1.0 1.0
1457 2 2 3 0.3 3 9 1.0 1.0 1.0
1458 2 2 4 0.3 3 0 1.0 1.0 1.0
1459 2 2 4 0.3 3 1 1.0 1.0 1.0
1460 2 2 4 0.3 3 2 1.0 1.0 1.0
1461 2 2 4 0.3 3 3 1.0 1.0 1.0
1462 2 2 4 0.3 3 4 0.909 0.833 1.0
1463 2 2 4 0.3 3 5 1.0 1.0 1.0
1464 2 2 4 0.3 3 6 0.909 0.833 1.0
1465 2 2 4 0.3 3 7 1.0 1.0 1.0
1466 2 2 4 0.3 3 8 1.0 1.0 1.0
1467 2 2 4 0.3 3 9 1.0 1.0 1.0
1468 2 2 5 0.3 3 0 0.933 0.875 1.0
1469 2 2 5 0.3 3 1 1.0 1.0 1.0
1470 2 2 5 0.3 3 2 1.0 1.0 1.0
1471 2 2 5 0.3 3 3 0.889 0.8 1.0
1472 2 2 5 0.3 3 4 1.0 1.0 1.0
1473 2 2 5 0.3 3 5 0.941 0.889 1.0
1474 2 2 5 0.3 3 6 1.0 1.0 1.0
1475 2 2 5 0.3 3 7 0.923 0.857 1.0
1476 2 2 5 0.3 3 8 1.0 1.0 1.0
1477 2 2 5 0.3 3 9 0.889 0.8 1.0
1478 2 2 6 0.3 3 0 1.0 1.0 1.0
1479 2 2 6 0.3 3 1 0.952 0.909 1.0
1480 2 2 6 0.3 3 2 1.0 1.0 1.0
1481 2 2 6 0.3 3 3 0.933 0.875 1.0
1482 2 2 6 0.3 3 4 1.0 1.0 1.0
1483 2 2 6 0.3 3 5 0.952 0.909 1.0
1484 2 2 6 0.3 3 6 0.96 0.923 1.0
1485 2 2 6 0.3 3 7 0.957 0.917 1.0
1486 2 2 6 0.3 3 8 1.0 1.0 1.0
1487 2 2 6 0.3 3 9 0.8 0.667 1.0
1488 2 2 10 0.3 3 0 0.952 0.938 0.968
1489 2 2 10 0.3 3 1 0.969 0.939 1.0
1490 2 2 10 0.3 3 2 0.966 0.933 1.0
1491 2 2 15 0.3 3 0 1.0 1.0 1.0
1492 2 2 15 0.3 3 1 0.959 1.0 0.922
1493 2 2 15 0.3 3 2 0.885 1.0 0.794
1494 2 2 20 0.3 3 0 0.92 1.0 0.851
1495 2 2 20 0.3 3 1 0.758 0.986 0.615
1496 2 2 20 0.3 3 2 0.777 0.988 0.64
1497 2 2 3 0.4 3 0 1.0 1.0 1.0
1498 2 2 3 0.4 3 1 0.8 0.667 1.0
1499 2 2 3 0.4 3 2 1.0 1.0 1.0
1500 2 2 3 0.4 3 3 0.889 0.8 1.0
1501 2 2 3 0.4 3 4 1.0 1.0 1.0
1502 2 2 3 0.4 3 5 0.8 0.667 1.0
1503 2 2 3 0.4 3 6 1.0 1.0 1.0
1504 2 2 3 0.4 3 7 1.0 1.0 1.0
1505 2 2 3 0.4 3 8 1.0 1.0 1.0
1506 2 2 3 0.4 3 9 1.0 1.0 1.0
1507 2 2 4 0.4 3 0 1.0 1.0 1.0
1508 2 2 4 0.4 3 1 0.933 0.875 1.0
1509 2 2 4 0.4 3 2 1.0 1.0 1.0
1510 2 2 4 0.4 3 3 1.0 1.0 1.0
1511 2 2 4 0.4 3 4 0.923 0.857 1.0
1512 2 2 4 0.4 3 5 1.0 1.0 1.0
1513 2 2 4 0.4 3 6 0.889 0.8 1.0
1514 2 2 4 0.4 3 7 0.889 0.8 1.0
1515 2 2 4 0.4 3 8 1.0 1.0 1.0
1516 2 2 4 0.4 3 9 0.8 0.667 1.0
1517 2 2 5 0.4 3 0 0.909 0.833 1.0
1518 2 2 5 0.4 3 1 1.0 1.0 1.0
1519 2 2 5 0.4 3 2 0.941 0.889 1.0
1520 2 2 5 0.4 3 3 0.923 0.857 1.0
1521 2 2 5 0.4 3 4 0.923 0.857 1.0
1522 2 2 5 0.4 3 5 0.941 0.889 1.0
1523 2 2 5 0.4 3 6 0.889 0.8 1.0
1524 2 2 5 0.4 3 7 1.0 1.0 1.0
1525 2 2 5 0.4 3 8 1.0 1.0 1.0
1526 2 2 5 0.4 3 9 0.952 0.909 1.0
1527 2 2 6 0.4 3 0 1.0 1.0 1.0
1528 2 2 6 0.4 3 1 0.929 0.867 1.0
1529 2 2 6 0.4 3 2 0.966 0.933 1.0
1530 2 2 6 0.4 3 3 0.857 0.75 1.0
1531 2 2 6 0.4 3 4 0.968 0.938 1.0
1532 2 2 6 0.4 3 5 1.0 1.0 1.0
1533 2 2 6 0.4 3 6 0.947 0.9 1.0
1534 2 2 6 0.4 3 7 0.963 0.929 1.0
1535 2 2 6 0.4 3 8 0.966 0.933 1.0
1536 2 2 6 0.4 3 9 0.947 0.9 1.0
1537 2 2 10 0.4 3 0 1.0 1.0 1.0
1538 2 2 10 0.4 3 1 0.932 0.971 0.895
1539 2 2 10 0.4 3 2 1.0 1.0 1.0
1540 2 2 15 0.4 3 0 0.863 0.985 0.767
1541 2 2 15 0.4 3 1 0.78 0.982 0.647
1542 2 2 15 0.4 3 2 0.958 1.0 0.92
1543 2 2 20 0.4 3 0 0.5 0.98 0.336
1544 2 2 20 0.4 3 1 0.635 1.0 0.465
1545 2 2 20 0.4 3 2 0.436 0.936 0.284
1546 2 2 3 0.1 4 0 1.0 1.0 1.0
1547 2 2 3 0.1 4 1 0.667 0.5 1.0
1548 2 2 3 0.1 4 2 0.8 0.667 1.0
1549 2 2 3 0.1 4 3 0.8 0.667 1.0
1550 2 2 3 0.1 4 4 0.8 0.667 1.0
1551 2 2 3 0.1 4 5 1.0 1.0 1.0
1552 2 2 3 0.1 4 6 0.8 0.667 1.0
1553 2 2 3 0.1 4 7 1.0 1.0 1.0
1554 2 2 3 0.1 4 8 0.75 0.6 1.0
1555 2 2 3 0.1 4 9 0.667 0.5 1.0
1556 2 2 4 0.1 4 0 0.889 0.8 1.0
1557 2 2 4 0.1 4 1 0.571 0.4 1.0
1558 2 2 4 0.1 4 2 1.0 1.0 1.0
1559 2 2 4 0.1 4 3 0.667 0.5 1.0
1560 2 2 4 0.1 4 4 0.667 0.5 1.0
1561 2 2 4 0.1 4 5 0.571 0.4 1.0
1562 2 2 4 0.1 4 6 0.889 0.8 1.0
1563 2 2 4 0.1 4 7 0.571 0.4 1.0
1564 2 2 4 0.1 4 8 0.8 0.667 1.0
1565 2 2 4 0.1 4 9 0.833 0.714 1.0
1566 2 2 5 0.1 4 0 0.857 0.75 1.0
1567 2 2 5 0.1 4 1 0.778 0.636 1.0
1568 2 2 5 0.1 4 2 0.727 0.571 1.0
1569 2 2 5 0.1 4 3 0.727 0.571 1.0
1570 2 2 5 0.1 4 4 0.8 0.667 1.0
1571 2 2 5 0.1 4 5 0.75 0.6 1.0
1572 2 2 5 0.1 4 6 0.667 0.5 1.0
1573 2 2 5 0.1 4 7 0.8 0.667 1.0
1574 2 2 5 0.1 4 8 0.889 0.8 1.0
1575 2 2 5 0.1 4 9 0.857 0.75 1.0
1576 2 2 6 0.1 4 0 0.8 0.667 1.0
1577 2 2 6 0.1 4 1 0.667 0.5 1.0
1578 2 2 6 0.1 4 2 0.727 0.571 1.0
1579 2 2 6 0.1 4 3 0.615 0.444 1.0
1580 2 2 6 0.1 4 4 0.8 0.667 1.0
1581 2 2 6 0.1 4 5 0.8 0.667 1.0
1582 2 2 6 0.1 4 6 0.714 0.556 1.0
1583 2 2 6 0.1 4 7 0.824 0.7 1.0
1584 2 2 6 0.1 4 8 0.909 0.833 1.0
1585 2 2 6 0.1 4 9 0.75 0.6 1.0
1586 2 2 10 0.1 4 0 0.688 0.524 1.0
1587 2 2 10 0.1 4 1 0.821 0.696 1.0
1588 2 2 10 0.1 4 2 0.774 0.632 1.0
1589 2 2 15 0.1 4 0 0.833 0.714 1.0
1590 2 2 15 0.1 4 1 0.717 0.559 1.0
1591 2 2 15 0.1 4 2 0.787 0.649 1.0
1592 2 2 3 0.2 4 0 0.857 0.75 1.0
1593 2 2 3 0.2 4 1 1.0 1.0 1.0
1594 2 2 3 0.2 4 2 0.8 0.667 1.0
1595 2 2 3 0.2 4 3 0.8 0.667 1.0
1596 2 2 3 0.2 4 4 0.75 0.6 1.0
1597 2 2 3 0.2 4 5 0.667 0.5 1.0
1598 2 2 3 0.2 4 6 0.857 0.75 1.0
1599 2 2 3 0.2 4 7 1.0 1.0 1.0
1600 2 2 3 0.2 4 8 0.8 0.667 1.0
1601 2 2 3 0.2 4 9 0.857 0.75 1.0
1602 2 2 4 0.2 4 0 1.0 1.0 1.0
1603 2 2 4 0.2 4 1 0.857 0.75 1.0
1604 2 2 4 0.2 4 2 0.857 0.75 1.0
1605 2 2 4 0.2 4 3 0.75 0.6 1.0
1606 2 2 4 0.2 4 4 0.824 0.7 1.0
1607 2 2 4 0.2 4 5 1.0 1.0 1.0
1608 2 2 4 0.2 4 6 0.857 0.75 1.0
1609 2 2 4 0.2 4 7 1.0 1.0 1.0
1610 2 2 4 0.2 4 8 1.0 1.0 1.0
1611 2 2 4 0.2 4 9 1.0 1.0 1.0
1612 2 2 5 0.2 4 0 1.0 1.0 1.0
1613 2 2 5 0.2 4 1 0.875 0.778 1.0
1614 2 2 5 0.2 4 2 0.769 0.625 1.0
1615 2 2 5 0.2 4 3 0.769 0.625 1.0
1616 2 2 5 0.2 4 4 0.923 0.857 1.0
1617 2 2 5 0.2 4 5 0.933 0.875 1.0
1618 2 2 5 0.2 4 6 1.0 1.0 1.0
1619 2 2 5 0.2 4 7 0.545 0.375 1.0
1620 2 2 5 0.2 4 8 0.857 0.75 1.0
1621 2 2 5 0.2 4 9 0.909 0.833 1.0
1622 2 2 6 0.2 4 0 0.9 0.818 1.0
1623 2 2 6 0.2 4 1 0.933 0.875 1.0
1624 2 2 6 0.2 4 2 0.778 0.636 1.0
1625 2 2 6 0.2 4 3 0.778 0.636 1.0
1626 2 2 6 0.2 4 4 0.8 0.667 1.0
1627 2 2 6 0.2 4 5 0.833 0.714 1.0
1628 2 2 6 0.2 4 6 0.696 0.533 1.0
1629 2 2 6 0.2 4 7 0.706 0.545 1.0
1630 2 2 6 0.2 4 8 0.7 0.538 1.0
1631 2 2 6 0.2 4 9 1.0 1.0 1.0
1632 2 2 10 0.2 4 0 0.837 0.72 1.0
1633 2 2 10 0.2 4 1 0.898 0.815 1.0
1634 2 2 10 0.2 4 2 0.833 0.714 1.0
1635 2 2 15 0.2 4 0 0.932 0.872 1.0
1636 2 2 15 0.2 4 1 0.921 0.891 0.953
1637 2 2 15 0.2 4 2 0.932 0.873 1.0
1638 2 2 3 0.3 4 0 0.8 0.667 1.0
1639 2 2 3 0.3 4 1 0.667 0.5 1.0
1640 2 2 3 0.3 4 2 1.0 1.0 1.0
1641 2 2 3 0.3 4 3 1.0 1.0 1.0
1642 2 2 3 0.3 4 4 1.0 1.0 1.0
1643 2 2 3 0.3 4 5 0.8 0.667 1.0
1644 2 2 3 0.3 4 6 0.667 0.5 1.0
1645 2 2 3 0.3 4 7 1.0 1.0 1.0
1646 2 2 3 0.3 4 8 0.857 0.75 1.0
1647 2 2 3 0.3 4 9 0.8 0.667 1.0
1648 2 2 4 0.3 4 0 0.889 0.8 1.0
1649 2 2 4 0.3 4 1 0.833 0.714 1.0
1650 2 2 4 0.3 4 2 0.923 0.857 1.0
1651 2 2 4 0.3 4 3 0.8 0.667 1.0
1652 2 2 4 0.3 4 4 0.909 0.833 1.0
1653 2 2 4 0.3 4 5 0.8 0.667 1.0
1654 2 2 4 0.3 4 6 0.833 0.714 1.0
1655 2 2 4 0.3 4 7 0.889 0.8 1.0
1656 2 2 4 0.3 4 8 0.889 0.8 1.0
1657 2 2 4 0.3 4 9 0.933 0.875 1.0
1658 2 2 5 0.3 4 0 0.923 0.857 1.0
1659 2 2 5 0.3 4 1 0.737 0.583 1.0
1660 2 2 5 0.3 4 2 0.917 0.846 1.0
1661 2 2 5 0.3 4 3 0.933 0.875 1.0
1662 2 2 5 0.3 4 4 0.9 0.818 1.0
1663 2 2 5 0.3 4 5 0.923 0.857 1.0
1664 2 2 5 0.3 4 6 0.8 0.667 1.0
1665 2 2 5 0.3 4 7 0.833 0.714 1.0
1666 2 2 5 0.3 4 8 0.769 0.625 1.0
1667 2 2 5 0.3 4 9 0.889 0.8 1.0
1668 2 2 6 0.3 4 0 0.833 0.714 1.0
1669 2 2 6 0.3 4 1 0.87 0.769 1.0
1670 2 2 6 0.3 4 2 0.88 0.786 1.0
1671 2 2 6 0.3 4 3 0.917 0.846 1.0
1672 2 2 6 0.3 4 4 0.737 0.583 1.0
1673 2 2 6 0.3 4 5 0.897 0.812 1.0
1674 2 2 6 0.3 4 6 0.714 0.556 1.0
1675 2 2 6 0.3 4 7 0.947 0.9 1.0
1676 2 2 6 0.3 4 8 0.889 0.8 1.0
1677 2 2 6 0.3 4 9 0.818 0.692 1.0
1678 2 2 10 0.3 4 0 0.943 0.893 1.0
1679 2 2 10 0.3 4 1 0.848 0.8 0.903
1680 2 2 10 0.3 4 2 0.852 0.742 1.0
1681 2 2 15 0.3 4 0 0.8 0.667 1.0
1682 2 2 15 0.3 4 1 0.708 0.548 1.0
1683 2 2 15 0.3 4 2 0.833 0.714 1.0
1684 2 2 3 0.4 4 0 0.889 0.8 1.0
1685 2 2 3 0.4 4 1 0.857 0.75 1.0
1686 2 2 3 0.4 4 2 0.75 0.6 1.0
1687 2 2 3 0.4 4 3 0.857 0.75 1.0
1688 2 2 3 0.4 4 4 0.857 0.75 1.0
1689 2 2 3 0.4 4 5 1.0 1.0 1.0
1690 2 2 3 0.4 4 6 0.8 0.667 1.0
1691 2 2 3 0.4 4 7 0.8 0.667 1.0
1692 2 2 3 0.4 4 8 0.889 0.8 1.0
1693 2 2 3 0.4 4 9 1.0 1.0 1.0
1694 2 2 4 0.4 4 0 0.923 0.857 1.0
1695 2 2 4 0.4 4 1 0.933 0.875 1.0
1696 2 2 4 0.4 4 2 1.0 1.0 1.0
1697 2 2 4 0.4 4 3 0.75 0.6 1.0
1698 2 2 4 0.4 4 4 1.0 1.0 1.0
1699 2 2 4 0.4 4 5 0.857 0.75 1.0
1700 2 2 4 0.4 4 6 0.889 0.8 1.0
1701 2 2 4 0.4 4 7 0.889 0.8 1.0
1702 2 2 4 0.4 4 8 0.667 0.5 1.0
1703 2 2 4 0.4 4 9 0.833 0.714 1.0
1704 2 2 5 0.4 4 0 0.778 0.636 1.0
1705 2 2 5 0.4 4 1 0.842 0.727 1.0
1706 2 2 5 0.4 4 2 0.909 0.833 1.0
1707 2 2 5 0.4 4 3 0.857 0.75 1.0
1708 2 2 5 0.4 4 4 0.966 0.933 1.0
1709 2 2 5 0.4 4 5 0.778 0.636 1.0
1710 2 2 5 0.4 4 6 0.8 0.667 1.0
1711 2 2 5 0.4 4 7 0.714 0.556 1.0
1712 2 2 5 0.4 4 8 0.8 0.667 1.0
1713 2 2 5 0.4 4 9 0.737 0.583 1.0
1714 2 2 6 0.4 4 0 0.815 0.688 1.0
1715 2 2 6 0.4 4 1 0.889 0.8 1.0
1716 2 2 6 0.4 4 2 0.929 0.867 1.0
1717 2 2 6 0.4 4 3 0.875 0.778 1.0
1718 2 2 6 0.4 4 4 0.706 0.545 1.0
1719 2 2 6 0.4 4 5 0.88 0.786 1.0
1720 2 2 6 0.4 4 6 0.889 0.8 1.0
1721 2 2 6 0.4 4 7 0.815 0.688 1.0
1722 2 2 6 0.4 4 8 0.938 0.882 1.0
1723 2 2 6 0.4 4 9 0.857 0.75 1.0
1724 2 2 10 0.4 4 0 0.972 0.946 1.0
1725 2 2 10 0.4 4 1 0.927 0.95 0.905
1726 2 2 10 0.4 4 2 0.892 0.967 0.829
1727 2 2 15 0.4 4 0 0.691 0.923 0.552
1728 2 2 15 0.4 4 1 0.815 0.948 0.714
1729 2 2 15 0.4 4 2 0.794 0.945 0.684

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main_package/tests/simulators/test_ensambled_model.py
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import sys
sys.path.append("../../classes/")
import glob
import math
import os
import unittest
import networkx as nx
import numpy as np
import psutil
from line_profiler import LineProfiler
import copy
import utility.cache as ch
import structure_graph.sample_path as sp
import estimators.structure_score_based_estimator as se
import estimators.structure_constraint_based_estimator as se_
import utility.json_importer as ji
class TestHybridMethod(unittest.TestCase):
@classmethod
def setUpClass(cls):
#cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_binary_data_04_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
cls.s1 = sp.SamplePath(cls.importer)
cls.s1.build_trajectories()
cls.s1.build_structure()
def test_structure(self):
true_edges = copy.deepcopy(self.s1.structure.edges)
true_edges = set(map(tuple, true_edges))
s2= copy.deepcopy(self.s1)
se1 = se.StructureScoreBasedEstimator(self.s1,1,1)
edges_score = se1.estimate_structure(
max_parents = None,
iterations_number = 100,
patience = 50,
tabu_length = 20,
tabu_rules_duration = 20,
optimizer = 'tabu'
)
se2 = se_.StructureConstraintBasedEstimator(s2, 0.1, 0.1)
edges_constraint = se2.estimate_structure()
set_list_edges = set.union(edges_constraint,edges_score)
n_added_fake_edges = len(set_list_edges.difference(true_edges))
n_missing_edges = len(true_edges.difference(set_list_edges))
n_true_positive = len(true_edges) - n_missing_edges
precision = n_true_positive / (n_true_positive + n_added_fake_edges)
recall = n_true_positive / (n_true_positive + n_missing_edges)
f1_measure = round(2* (precision*recall) / (precision+recall),3)
# print(f"n archi reali non trovati: {n_missing_edges}")
# print(f"n archi non reali aggiunti: {n_added_fake_edges}")
print(true_edges)
print(set_list_edges)
print(f"precision: {precision} ")
print(f"recall: {recall} ")
print(f"F1: {f1_measure} ")
self.assertEqual(set_list_edges, true_edges)
if __name__ == '__main__':
unittest.main()

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main_package/tests/simulators/test_ensambled_model_all.py
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import sys
sys.path.append("../../classes/")
import glob
import math
import os
import unittest
import networkx as nx
import numpy as np
import psutil
from line_profiler import LineProfiler
import copy
import utility.cache as ch
import structure_graph.sample_path as sp
import estimators.structure_score_based_estimator as se
import estimators.structure_constraint_based_estimator as se_
import utility.json_importer as ji
class TestTabuSearch(unittest.TestCase):
@classmethod
def setUpClass(cls):
pass
def test_constr(self):
list_vals= [3,4,5,6,10,15,20]
list_card=[[3,"ternary"],[4,"quaternary"]]
list_dens = [["0.1","_01"],["0.2","_02"], ["0.3",""], ["0.4","_04"] ]
for card in list_card:
for dens in list_dens:
list_vals= [3,4,5,6,10,15,20]
if card[0]==4:
list_vals.remove(20)
for var_n in list_vals:
patience = 20
var_number= var_n
if var_number > 11:
patience = 25
if var_number > 16:
patience = 35
cardinality = card[0]
cardinality_string = card[1]
density= dens[0]
density_string = dens[1]
constraint = 2
index = 0
num_networks=10
if var_number > 9:
num_networks=3
while index < num_networks:
#cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
self.importer = ji.JsonImporter(f"../../data/networks_and_trajectories_{cardinality_string}_data{density_string}_{var_number}.json",
'samples', 'dyn.str', 'variables', 'Time', 'Name', index )
self.s1 = sp.SamplePath(self.importer)
self.s1.build_trajectories()
self.s1.build_structure()
true_edges = copy.deepcopy(self.s1.structure.edges)
true_edges = set(map(tuple, true_edges))
s2= copy.deepcopy(self.s1)
se1 = se.StructureScoreBasedEstimator(self.s1,1,1)
edges_score = se1.estimate_structure(
max_parents = None,
iterations_number = 100,
patience = patience,
tabu_length = var_number,
tabu_rules_duration = var_number,
optimizer = 'tabu'
)
se2 = se_.StructureConstraintBasedEstimator(s2, 0.1, 0.1)
edges_constraint = se2.estimate_structure()
set_list_edges = set.union(edges_constraint,edges_score)
n_added_fake_edges = len(set_list_edges.difference(true_edges))
n_missing_edges = len(true_edges.difference(set_list_edges))
n_true_positive = len(true_edges) - n_missing_edges
precision = n_true_positive / (n_true_positive + n_added_fake_edges)
recall = n_true_positive / (n_true_positive + n_missing_edges)
f1_measure = round(2* (precision*recall) / (precision+recall),3)
# print(f"n archi reali non trovati: {n_missing_edges}")
# print(f"n archi non reali aggiunti: {n_added_fake_edges}")
print(true_edges)
print(set_list_edges)
print(f"precision: {precision} ")
print(f"recall: {recall} ")
with open("../results/results.csv", 'a+') as fi:
fi.write(f"\n2,{constraint},{var_number},{density},{cardinality},{index},{f1_measure},{round(precision,3)},{round(recall,3)}")
index += 1
self.assertEqual(set_list_edges, true_edges)
if __name__ == '__main__':
unittest.main()