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Added parameters aplha_xu and tau_xu and ensambled model simulation

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master
Luca Moretti 4 years ago
parent 301ff69a8d
commit bb4745f430
12 changed files with 1627 additions and 9 deletions
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  1. 2
      main_package/classes/estimators/fam_score_calculator.py
  2. 9
      main_package/classes/estimators/structure_constraint_based_estimator.py
  3. 16
      main_package/classes/estimators/structure_estimator.py
  4. 11
      main_package/classes/estimators/structure_score_based_estimator.py
  5. 2
      main_package/classes/optimizers/hill_climbing_search.py
  6. 2
      main_package/classes/optimizers/tabu_search.py
  7. 198
      main_package/tests/results/results.csv
  8. 20
      main_package/tests/results/results16-32RAM.csv
  9. 1153
      main_package/tests/results/results_tabu.csv
  10. 84
      main_package/tests/simulators/test_ensambled_model.py
  11. 134
      main_package/tests/simulators/test_ensambled_model_all.py
  12. 3
      main_package/tests/simulators/test_simulation_score.py

2
main_package/classes/estimators/fam_score_calculator.py
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@ -207,7 +207,7 @@ class FamScoreCalculator:
def get_fam_score(self,
cims: np.array,
tau_xu: float=1,
tau_xu: float=0.1,
alpha_xu: float=1):
"""
calculate the FamScore value of the node identified by the label node_id

9
main_package/classes/estimators/structure_constraint_based_estimator.py
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@ -211,7 +211,7 @@ class StructureConstraintBasedEstimator(se.StructureEstimator):
tot_vars_count = tot_vars_count)
optimizer_obj.optimize_structure()
@timing_write
@timing
def ctpc_algorithm(self):
"""
Compute the CTPC algorithm.
@ -224,4 +224,11 @@ class StructureConstraintBasedEstimator(se.StructureEstimator):
total_vars_numb = self.sample_path.total_variables_count
[ctpc_algo(n, total_vars_numb) for n in self.nodes]
@timing
def estimate_structure(self):
self.ctpc_algorithm()
return set(self.complete_graph.edges)

16
main_package/classes/estimators/structure_estimator.py
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@ -10,6 +10,8 @@ from networkx.readwrite import json_graph
from abc import ABC
import abc
import utility.cache as ch
import structure_graph.conditional_intensity_matrix as condim
import structure_graph.network_graph as ng
@ -94,3 +96,17 @@ class StructureEstimator(ABC):
s0, s1 = matrix.strides
return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1)
@abc.abstractmethod
def estimate_structure(self) -> typing.List:
"""
Compute Optimization process for a structure_estimator
Parameters:
Returns:
the estimated structure for the node
"""
pass

11
main_package/classes/estimators/structure_score_based_estimator.py
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@ -41,11 +41,13 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
"""
def __init__(self, sample_path: sp.SamplePath):
def __init__(self, sample_path: sp.SamplePath, tau_xu:int=0.1, alpha_xu:int = 1):
super().__init__(sample_path)
self.tau_xu=tau_xu
self.alpha_xu=alpha_xu
@timing_write
@timing
def estimate_structure(self, max_parents:int = None, iterations_number:int= 40,
patience:int = None, tabu_length:int = None, tabu_rules_duration:int = None,
optimizer: str = 'hill',disable_multiprocessing:bool= False ):
@ -176,7 +178,8 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
return optimizer.optimize_structure()
def get_score_from_graph(self,graph: ng.NetworkGraph,node_id:str):
def get_score_from_graph(self,
graph: ng.NetworkGraph,node_id:str):
"""
Use the FamScore of a node in order to find the best parent nodes
Parameters:
@ -198,7 +201,7 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
'calculate the FamScore for the node'
fam_score_obj = fam_score.FamScoreCalculator()
score = fam_score_obj.get_fam_score(SoCims.actual_cims)
score = fam_score_obj.get_fam_score(SoCims.actual_cims,tau_xu = self.tau_xu,alpha_xu=self.alpha_xu)
#print(f" lo score per {node_id} risulta: {score} ")
return score

2
main_package/classes/optimizers/hill_climbing_search.py
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@ -34,6 +34,8 @@ class HillClimbing(Optimizer):
Compute Optimization process for a structure_estimator
Parameters:
node_id: the node label
structure_estimator: a structure estimator object with the information about the net
max_parents: maximum number of parents for each variable. If None, disabled
iterations_number: maximum number of optimization algorithm's iteration
patience: number of iteration without any improvement before to stop the search.If None, disabled

2
main_package/classes/optimizers/tabu_search.py
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@ -38,6 +38,8 @@ class TabuSearch(Optimizer):
Compute Optimization process for a structure_estimator
Parameters:
node_id: the node label
structure_estimator: a structure estimator object with the information about the net
max_parents: maximum number of parents for each variable. If None, disabled
iterations_number: maximum number of optimization algorithm's iteration
patience: number of iteration without any improvement before to stop the search.If None, disabled

198
main_package/tests/results/results.csv
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@ -1,4 +1,4 @@
Time,Constraint,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
Time,Type,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
89.185,1,15,0.1,3,0,0.88,0.786,1.0
96.358,1,15,0.1,3,1,0.939,0.885,1.0
83.178,1,15,0.1,3,2,0.954,0.913,1.0
@ -1151,3 +1151,199 @@ Time,Constraint,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
112.711,0,15,0.4,4,0,0.311,1.0,0.184
119.185,0,15,0.4,4,1,0.46,1.0,0.299
119.897,0,15,0.4,4,2,0.383,1.0,0.237
2,2,3,0.1,2,0,1.0,1.0,1.0
2,2,3,0.1,2,1,1.0,1.0,1.0
2,2,3,0.1,2,2,1.0,1.0,1.0
2,2,3,0.1,2,3,1.0,1.0,1.0
2,2,3,0.1,2,4,1.0,1.0,1.0
2,2,3,0.1,2,5,1.0,1.0,1.0
2,2,3,0.1,2,6,1.0,1.0,1.0
2,2,3,0.1,2,7,1.0,1.0,1.0
2,2,3,0.1,2,8,1.0,1.0,1.0
2,2,3,0.1,2,9,1.0,1.0,1.0
2,2,4,0.1,2,0,1.0,1.0,1.0
2,2,4,0.1,2,1,1.0,1.0,1.0
2,2,4,0.1,2,2,1.0,1.0,1.0
2,2,4,0.1,2,3,1.0,1.0,1.0
2,2,4,0.1,2,4,1.0,1.0,1.0
2,2,4,0.1,2,5,1.0,1.0,1.0
2,2,4,0.1,2,6,1.0,1.0,1.0
2,2,4,0.1,2,7,1.0,1.0,1.0
2,2,4,0.1,2,8,1.0,1.0,1.0
2,2,4,0.1,2,9,1.0,1.0,1.0
2,2,5,0.1,2,0,1.0,1.0,1.0
2,2,5,0.1,2,1,1.0,1.0,1.0
2,2,5,0.1,2,2,1.0,1.0,1.0
2,2,5,0.1,2,3,1.0,1.0,1.0
2,2,5,0.1,2,4,1.0,1.0,1.0
2,2,5,0.1,2,5,1.0,1.0,1.0
2,2,5,0.1,2,6,1.0,1.0,1.0
2,2,5,0.1,2,7,1.0,1.0,1.0
2,2,5,0.1,2,8,1.0,1.0,1.0
2,2,5,0.1,2,9,1.0,1.0,1.0
2,2,6,0.1,2,0,0.889,1.0,0.8
2,2,6,0.1,2,1,1.0,1.0,1.0
2,2,6,0.1,2,2,1.0,1.0,1.0
2,2,6,0.1,2,3,1.0,1.0,1.0
2,2,6,0.1,2,4,1.0,1.0,1.0
2,2,6,0.1,2,5,1.0,1.0,1.0
2,2,6,0.1,2,6,1.0,1.0,1.0
2,2,6,0.1,2,7,1.0,1.0,1.0
2,2,6,0.1,2,8,1.0,1.0,1.0
2,2,6,0.1,2,9,1.0,1.0,1.0
2,2,10,0.1,2,0,1.0,1.0,1.0
2,2,10,0.1,2,1,1.0,1.0,1.0
2,2,10,0.1,2,2,1.0,1.0,1.0
2,2,15,0.1,2,0,1.0,1.0,1.0
2,2,15,0.1,2,1,1.0,1.0,1.0
2,2,15,0.1,2,2,1.0,1.0,1.0
2,2,20,0.1,2,0,1.0,1.0,1.0
2,2,20,0.1,2,1,1.0,1.0,1.0
2,2,20,0.1,2,2,1.0,1.0,1.0
2,2,3,0.2,2,0,1.0,1.0,1.0
2,2,3,0.2,2,1,1.0,1.0,1.0
2,2,3,0.2,2,2,1.0,1.0,1.0
2,2,3,0.2,2,3,1.0,1.0,1.0
2,2,3,0.2,2,4,1.0,1.0,1.0
2,2,3,0.2,2,5,1.0,1.0,1.0
2,2,3,0.2,2,6,1.0,1.0,1.0
2,2,3,0.2,2,7,1.0,1.0,1.0
2,2,3,0.2,2,8,1.0,1.0,1.0
2,2,3,0.2,2,9,1.0,1.0,1.0
2,2,4,0.2,2,0,1.0,1.0,1.0
2,2,4,0.2,2,1,1.0,1.0,1.0
2,2,4,0.2,2,2,1.0,1.0,1.0
2,2,4,0.2,2,3,1.0,1.0,1.0
2,2,4,0.2,2,4,1.0,1.0,1.0
2,2,4,0.2,2,5,1.0,1.0,1.0
2,2,4,0.2,2,6,1.0,1.0,1.0
2,2,4,0.2,2,7,1.0,1.0,1.0
2,2,4,0.2,2,8,1.0,1.0,1.0
2,2,4,0.2,2,9,1.0,1.0,1.0
2,2,5,0.2,2,0,1.0,1.0,1.0
2,2,5,0.2,2,1,1.0,1.0,1.0
2,2,5,0.2,2,2,1.0,1.0,1.0
2,2,5,0.2,2,3,1.0,1.0,1.0
2,2,5,0.2,2,4,1.0,1.0,1.0
2,2,5,0.2,2,5,1.0,1.0,1.0
2,2,5,0.2,2,6,1.0,1.0,1.0
2,2,5,0.2,2,7,1.0,1.0,1.0
2,2,5,0.2,2,8,1.0,1.0,1.0
2,2,5,0.2,2,9,1.0,1.0,1.0
2,2,6,0.2,2,0,1.0,1.0,1.0
2,2,6,0.2,2,1,1.0,1.0,1.0
2,2,6,0.2,2,2,1.0,1.0,1.0
2,2,6,0.2,2,3,1.0,1.0,1.0
2,2,6,0.2,2,4,1.0,1.0,1.0
2,2,6,0.2,2,5,1.0,1.0,1.0
2,2,6,0.2,2,6,1.0,1.0,1.0
2,2,6,0.2,2,7,1.0,1.0,1.0
2,2,6,0.2,2,8,1.0,1.0,1.0
2,2,6,0.2,2,9,1.0,1.0,1.0
2,2,10,0.2,2,0,0.971,0.944,1.0
2,2,10,0.2,2,1,1.0,1.0,1.0
2,2,10,0.2,2,2,1.0,1.0,1.0
2,2,15,0.2,2,0,1.0,1.0,1.0
2,2,15,0.2,2,1,1.0,1.0,1.0
2,2,15,0.2,2,2,1.0,1.0,1.0
2,2,20,0.2,2,0,1.0,1.0,1.0
2,2,20,0.2,2,1,1.0,1.0,1.0
2,2,20,0.2,2,2,1.0,1.0,1.0
2,2,3,0.3,2,0,1.0,1.0,1.0
2,2,3,0.3,2,1,1.0,1.0,1.0
2,2,3,0.3,2,2,1.0,1.0,1.0
2,2,3,0.3,2,3,1.0,1.0,1.0
2,2,3,0.3,2,4,1.0,1.0,1.0
2,2,3,0.3,2,5,1.0,1.0,1.0
2,2,3,0.3,2,6,1.0,1.0,1.0
2,2,3,0.3,2,7,1.0,1.0,1.0
2,2,3,0.3,2,8,1.0,1.0,1.0
2,2,3,0.3,2,9,1.0,1.0,1.0
2,2,4,0.3,2,0,1.0,1.0,1.0
2,2,4,0.3,2,1,1.0,1.0,1.0
2,2,4,0.3,2,2,1.0,1.0,1.0
2,2,4,0.3,2,3,1.0,1.0,1.0
2,2,4,0.3,2,4,1.0,1.0,1.0
2,2,4,0.3,2,5,1.0,1.0,1.0
2,2,4,0.3,2,6,1.0,1.0,1.0
2,2,4,0.3,2,7,1.0,1.0,1.0
2,2,4,0.3,2,8,1.0,1.0,1.0
2,2,4,0.3,2,9,1.0,1.0,1.0
2,2,5,0.3,2,0,1.0,1.0,1.0
2,2,5,0.3,2,1,1.0,1.0,1.0
2,2,5,0.3,2,2,1.0,1.0,1.0
2,2,5,0.3,2,3,1.0,1.0,1.0
2,2,5,0.3,2,4,1.0,1.0,1.0
2,2,5,0.3,2,5,1.0,1.0,1.0
2,2,5,0.3,2,6,1.0,1.0,1.0
2,2,5,0.3,2,7,1.0,1.0,1.0
2,2,5,0.3,2,8,1.0,1.0,1.0
2,2,5,0.3,2,9,1.0,1.0,1.0
2,2,6,0.3,2,0,1.0,1.0,1.0
2,2,6,0.3,2,1,1.0,1.0,1.0
2,2,6,0.3,2,2,1.0,1.0,1.0
2,2,6,0.3,2,3,1.0,1.0,1.0
2,2,6,0.3,2,4,0.941,1.0,0.889
2,2,6,0.3,2,5,1.0,1.0,1.0
2,2,6,0.3,2,6,1.0,1.0,1.0
2,2,6,0.3,2,7,1.0,1.0,1.0
2,2,6,0.3,2,8,1.0,1.0,1.0
2,2,6,0.3,2,9,1.0,1.0,1.0
2,2,10,0.3,2,0,1.0,1.0,1.0
2,2,10,0.3,2,1,1.0,1.0,1.0
2,2,10,0.3,2,2,1.0,1.0,1.0
2,2,15,0.3,2,0,1.0,1.0,1.0
2,2,15,0.3,2,1,1.0,1.0,1.0
2,2,15,0.3,2,2,1.0,1.0,1.0
2,2,20,0.3,2,0,1.0,1.0,1.0
2,2,20,0.3,2,1,1.0,1.0,1.0
2,2,20,0.3,2,2,1.0,1.0,1.0
2,2,3,0.4,2,0,1.0,1.0,1.0
2,2,3,0.4,2,1,1.0,1.0,1.0
2,2,3,0.4,2,2,1.0,1.0,1.0
2,2,3,0.4,2,3,1.0,1.0,1.0
2,2,3,0.4,2,4,1.0,1.0,1.0
2,2,3,0.4,2,5,1.0,1.0,1.0
2,2,3,0.4,2,6,1.0,1.0,1.0
2,2,3,0.4,2,7,1.0,1.0,1.0
2,2,3,0.4,2,8,1.0,1.0,1.0
2,2,3,0.4,2,9,1.0,1.0,1.0
2,2,4,0.4,2,0,1.0,1.0,1.0
2,2,4,0.4,2,1,1.0,1.0,1.0
2,2,4,0.4,2,2,1.0,1.0,1.0
2,2,4,0.4,2,3,1.0,1.0,1.0
2,2,4,0.4,2,4,1.0,1.0,1.0
2,2,4,0.4,2,5,0.923,0.857,1.0
2,2,4,0.4,2,6,1.0,1.0,1.0
2,2,4,0.4,2,7,1.0,1.0,1.0
2,2,4,0.4,2,8,1.0,1.0,1.0
2,2,4,0.4,2,9,1.0,1.0,1.0
2,2,5,0.4,2,0,1.0,1.0,1.0
2,2,5,0.4,2,1,1.0,1.0,1.0
2,2,5,0.4,2,2,1.0,1.0,1.0
2,2,5,0.4,2,3,1.0,1.0,1.0
2,2,5,0.4,2,4,1.0,1.0,1.0
2,2,5,0.4,2,5,1.0,1.0,1.0
2,2,5,0.4,2,6,1.0,1.0,1.0
2,2,5,0.4,2,7,1.0,1.0,1.0
2,2,5,0.4,2,8,1.0,1.0,1.0
2,2,5,0.4,2,9,1.0,1.0,1.0
2,2,6,0.4,2,0,1.0,1.0,1.0
2,2,6,0.4,2,1,1.0,1.0,1.0
2,2,6,0.4,2,2,1.0,1.0,1.0
2,2,6,0.4,2,3,1.0,1.0,1.0
2,2,6,0.4,2,4,1.0,1.0,1.0
2,2,6,0.4,2,5,1.0,1.0,1.0
2,2,6,0.4,2,6,1.0,1.0,1.0
2,2,6,0.4,2,7,1.0,1.0,1.0
2,2,6,0.4,2,8,1.0,1.0,1.0
2,2,6,0.4,2,9,1.0,1.0,1.0
2,2,10,0.4,2,0,1.0,1.0,1.0
2,2,10,0.4,2,1,1.0,1.0,1.0
2,2,10,0.4,2,2,1.0,1.0,1.0
2,2,15,0.4,2,0,1.0,1.0,1.0
2,2,15,0.4,2,1,1.0,1.0,1.0
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
1 Time Constraint Type Variables Density_Network Cardinality Index F1 Precision Recall
2 89.185 1 15 0.1 3 0 0.88 0.786 1.0
3 96.358 1 15 0.1 3 1 0.939 0.885 1.0
4 83.178 1 15 0.1 3 2 0.954 0.913 1.0
1151 112.711 0 15 0.4 4 0 0.311 1.0 0.184
1152 119.185 0 15 0.4 4 1 0.46 1.0 0.299
1153 119.897 0 15 0.4 4 2 0.383 1.0 0.237
1154 2 2 3 0.1 2 0 1.0 1.0 1.0
1155 2 2 3 0.1 2 1 1.0 1.0 1.0
1156 2 2 3 0.1 2 2 1.0 1.0 1.0
1157 2 2 3 0.1 2 3 1.0 1.0 1.0
1158 2 2 3 0.1 2 4 1.0 1.0 1.0
1159 2 2 3 0.1 2 5 1.0 1.0 1.0
1160 2 2 3 0.1 2 6 1.0 1.0 1.0
1161 2 2 3 0.1 2 7 1.0 1.0 1.0
1162 2 2 3 0.1 2 8 1.0 1.0 1.0
1163 2 2 3 0.1 2 9 1.0 1.0 1.0
1164 2 2 4 0.1 2 0 1.0 1.0 1.0
1165 2 2 4 0.1 2 1 1.0 1.0 1.0
1166 2 2 4 0.1 2 2 1.0 1.0 1.0
1167 2 2 4 0.1 2 3 1.0 1.0 1.0
1168 2 2 4 0.1 2 4 1.0 1.0 1.0
1169 2 2 4 0.1 2 5 1.0 1.0 1.0
1170 2 2 4 0.1 2 6 1.0 1.0 1.0
1171 2 2 4 0.1 2 7 1.0 1.0 1.0
1172 2 2 4 0.1 2 8 1.0 1.0 1.0
1173 2 2 4 0.1 2 9 1.0 1.0 1.0
1174 2 2 5 0.1 2 0 1.0 1.0 1.0
1175 2 2 5 0.1 2 1 1.0 1.0 1.0
1176 2 2 5 0.1 2 2 1.0 1.0 1.0
1177 2 2 5 0.1 2 3 1.0 1.0 1.0
1178 2 2 5 0.1 2 4 1.0 1.0 1.0
1179 2 2 5 0.1 2 5 1.0 1.0 1.0
1180 2 2 5 0.1 2 6 1.0 1.0 1.0
1181 2 2 5 0.1 2 7 1.0 1.0 1.0
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20
main_package/tests/results/results16-32RAM.csv
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@ -0,0 +1,20 @@
6.255,0,3,0.2,2,0,1.0,1.0,1.0
7.305,0,4,0.2,2,0,1.0,1.0,1.0
13.017,0,5,0.2,2,0,1.0,1.0,1.0
18.742,0,6,0.2,2,0,1.0,1.0,1.0
47.785,0,10,0.2,2,0,1.0,1.0,1.0
161.731,0,15,0.2,2,0,1.0,1.0,1.0
284.029,0,20,0.2,2,0,0.989,0.978,1.0
7.5,0,3,0.2,3,0,1.0,1.0,1.0
11.541,0,4,0.2,3,0,1.0,1.0,1.0
19.318,0,5,0.2,3,0,1.0,1.0,1.0
29.8,0,6,0.2,3,0,1.0,1.0,1.0
106.663,0,10,0.2,3,0,1.0,1.0,1.0
265.436,0,15,0.2,3,0,0.876,1.0,0.78
511.009,0,20,0.2,3,0,0.95,1.0,0.905
9.866,0,3,0.2,4,0,1.0,1.0,1.0
16.127,0,4,0.2,4,0,1.0,1.0,1.0
25.694,0,5,0.2,4,0,1.0,1.0,1.0
44.268,0,6,0.2,4,0,1.0,1.0,1.0
146.875,0,10,0.2,4,0,1.0,1.0,1.0
403.063,0,15,0.2,4,0,0.935,1.0,0.878
1 6.255 0 3 0.2 2 0 1.0 1.0 1.0
2 7.305 0 4 0.2 2 0 1.0 1.0 1.0
3 13.017 0 5 0.2 2 0 1.0 1.0 1.0
4 18.742 0 6 0.2 2 0 1.0 1.0 1.0
5 47.785 0 10 0.2 2 0 1.0 1.0 1.0
6 161.731 0 15 0.2 2 0 1.0 1.0 1.0
7 284.029 0 20 0.2 2 0 0.989 0.978 1.0
8 7.5 0 3 0.2 3 0 1.0 1.0 1.0
9 11.541 0 4 0.2 3 0 1.0 1.0 1.0
10 19.318 0 5 0.2 3 0 1.0 1.0 1.0
11 29.8 0 6 0.2 3 0 1.0 1.0 1.0
12 106.663 0 10 0.2 3 0 1.0 1.0 1.0
13 265.436 0 15 0.2 3 0 0.876 1.0 0.78
14 511.009 0 20 0.2 3 0 0.95 1.0 0.905
15 9.866 0 3 0.2 4 0 1.0 1.0 1.0
16 16.127 0 4 0.2 4 0 1.0 1.0 1.0
17 25.694 0 5 0.2 4 0 1.0 1.0 1.0
18 44.268 0 6 0.2 4 0 1.0 1.0 1.0
19 146.875 0 10 0.2 4 0 1.0 1.0 1.0
20 403.063 0 15 0.2 4 0 0.935 1.0 0.878

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main_package/tests/results/results_tabu.csv
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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()

134
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()

3
main_package/tests/simulators/test_simulation_score.py
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@ -61,6 +61,7 @@ class TestTabuSearch(unittest.TestCase):
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",
@ -80,7 +81,7 @@ class TestTabuSearch(unittest.TestCase):
patience = patience,
tabu_length = var_number,
tabu_rules_duration = var_number,
optimizer = 'tabu'
optimizer = 'hill'
)