unimib-thesis-mad
/
PyCTBN
Archived
1
0
Fork 0
Code Releases Activity

Updated TODOs

Browse Source
master
Luca Moretti 4 years ago
parent 4490f0bf1a
commit 5a57501164
2 changed files with 9 additions and 7 deletions
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Show Stats Download Patch File Download Diff File
  1. 1
      .gitignore
  2. 13
      main_package/classes/structure_score_based_estimator.py

1
.gitignore vendored
Unescape View File

@ -3,3 +3,4 @@ __pycache__
**/__pycache__ **/__pycache__
**/data **/data
**/results_data **/results_data
**/.scannerwork

13
main_package/classes/structure_score_based_estimator.py
Unescape View File

@ -20,14 +20,14 @@ import fam_score_calculator as fam_score
from decorators import timing from decorators import timing
#from numba import njit
import multiprocessing import multiprocessing
from multiprocessing import Pool from multiprocessing import Pool
''' '''
#TODO: Insert maximum number of parents
#TODO: Insert maximum number of iteration or other exit criterions
#TODO: Create a parent class StructureEstimator and Two Subclasses (Score-Based and Constraint-Based) #TODO: Create a parent class StructureEstimator and Two Subclasses (Score-Based and Constraint-Based)
#TODO: Evaluate if it could be better to change list_edges to set for improve the performance #TODO: Evaluate if it could be better to change list_edges to set for improve the performance
''' '''
@ -131,6 +131,7 @@ class StructureScoreBasedEstimator:
print(true_edges) print(true_edges)
print(list_edges) print(list_edges)
def estimate_parents(self,node_id:str, max_parents:int = None, iterations_number:int= 40, patience:int = 10 ): def estimate_parents(self,node_id:str, max_parents:int = None, iterations_number:int= 40, patience:int = 10 ):
""" """
Use the FamScore of a node in order to find the best parent nodes Use the FamScore of a node in order to find the best parent nodes
@ -147,7 +148,7 @@ class StructureScoreBasedEstimator:
graph = ng.NetworkGraph(self.sample_path.structure) graph = ng.NetworkGraph(self.sample_path.structure)
other_nodes = [node for node in self.sample_path.structure.nodes_labels if node != node_id] other_nodes = [node for node in self.sample_path.structure.nodes_labels if node != node_id]
actual_best_score = self.get_score_from_structure(graph,node_id) actual_best_score = self.get_score_from_graph(graph,node_id)
patince_count = 0 patince_count = 0
for i in range(iterations_number): for i in range(iterations_number):
@ -170,7 +171,7 @@ class StructureScoreBasedEstimator:
graph.add_edges([current_edge]) graph.add_edges([current_edge])
added = True added = True
current_score = self.get_score_from_structure(graph,node_id) current_score = self.get_score_from_graph(graph,node_id)
if current_score > actual_best_score: if current_score > actual_best_score:
'update current best score' 'update current best score'
@ -195,14 +196,14 @@ class StructureScoreBasedEstimator:
return graph.edges return graph.edges
def get_score_from_structure(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 Use the FamScore of a node in order to find the best parent nodes
Parameters: Parameters:
node_id: current node's id node_id: current node's id
graph: current graph to be computed graph: current graph to be computed
Returns: Returns:
The FamSCore for this structure The FamSCore for this graph structure
""" """
'inizialize the graph for a single node' 'inizialize the graph for a single node'