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All_possi.py
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All_possi.py
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import os
import numpy as np
from itertools import chain, combinations
#Building tree of possibilities
class TreeNode:
"""
Class of the tree of possibilities :
Make the consensus between the labels of both methods
"""
def __init__(self,root):
self.root = root
self.leaves = []
def count_leaves(tree):
"""
Count the number of leaves in the tree
Entry : tree -> TreeNode
Return : nb_leaves -> int
"""
nb_leaves = 0
for i in range(len(tree.leaves)):
if tree.leaves[i].leaves==[]:
nb_leaves+=1
else:
nb_leaves+=count_leaves(tree.leaves[i])
return nb_leaves
def add_leaves(tree, nb_cpt, nb_methods, depth=1):
"""
Add leaves in the tree
Entries : tree -> TreeNode
nb_cpt -> int
nb_methods -> int
depth -> int
Return : nb_leaves -> int
"""
if depth==nb_methods:
for i in range(nb_cpt):
tree.leaves.append(TreeNode(i))
return tree
else:
for i in range(nb_cpt):
tree.leaves.append(TreeNode(i))
for j in range(nb_cpt):
tree.leaves[j] = add_leaves(tree.leaves[j], nb_cpt, nb_methods, depth=depth+1)
return tree
def create_tree(nb_cpt, nb_methods):
"""
Create the tree
Entries : nb_cpt -> int
nb_methods -> int
Return : All_trees -> TreeNode
"""
All_trees = []
for i in range(nb_cpt):
tree = TreeNode(i)
tree = add_leaves(tree, nb_cpt, nb_methods)
All_trees.append(tree)
return All_trees
# function to get all path from root to leaf
def get_Paths(tree):
"""
Get the paths from the root to the leaves
Entry : tree -> TreeNode
"""
# list to store path
path = []
get_PathsRec(tree, path, 0)
#Helper function to get path from root to leaf
def get_PathsRec(tree, path, pathLen):
"""
Get a path from the root to a leaf
Entries : tree -> TreeNode
path -> list
pathLen -> int
Return : path -> list
"""
#print(All_paths)
# if length of list is gre
if(len(path) > pathLen):
path[pathLen] = tree.root
else:
path.append(tree.root)
# increment pathLen by 1
pathLen = pathLen + 1
if tree.leaves==[]:
file_save = open("save_variable.txt", "a")
str_save = repr(path)
file_save.write(str_save + "\n")
file_save.close()
return path
else:
# try for each subtree
for subtree in tree.leaves:
path2 = get_PathsRec(subtree, path, pathLen)
return path2
def loadPaths(filename):
"""
Load the paths in a file
Entry : filename -> TreeNode
Return : Paths -> list of list
"""
Paths = []
lecture = np.loadtxt(filename, dtype=object)
for line in lecture:
path = []
for cpt in line:
node = ''
for letter in cpt:
try:
node+=str(int(letter))
except:
pass
path.append(int(node))
Paths.append(path)
return Paths
def all_subsets(ss):
"""
Take all scenarios (paths) in the tree and return the association between labels
Entry : ss -> list
Return : list
"""
return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))
def get_All_possiblesPaths(nb_cpt, np_methods):
"""
Return all possibles paths (scenarios) in the tree that can be associated
Entries : nb_cpt -> int
nb_methods -> int
Return : All_possi -> list
"""
tree = create_tree(nb_cpt,np_methods)
All_Paths = []
filename = "save_variable.txt"
for cpt_subtree in tree:
get_Paths(cpt_subtree)
Paths = loadPaths(filename)
os.remove(filename)
All_Paths.append(Paths)
All_possi = []
joined_list = []
for path in All_Paths:
joined_list += path
for subset in all_subsets(joined_list):
if len(subset)==nb_cpt:
if len(set(np.array(subset)[:,0]))==nb_cpt:
All_possi.append(subset)
return All_possi
def calculateSimilarity(scenario, All_Preds, nb_cpt, nb_methods):
"""
Return the similarity between two scenarios
Entries : scenario -> list
All_Preds -> list
nb_cpt -> int
nb_methods -> int
Return : All_possi -> list
"""
#calculate the similarity of 2 methods for a scenario given (labels association)
dico_count = {}
for cpt in range(len(All_Preds[0])):
current_scenario = []
for i in range(len(All_Preds)):
current_scenario.append(All_Preds[i][cpt])
for i in range(len(current_scenario)):
if current_scenario[i]==-1:
current_scenario[i] = 0
if tuple(current_scenario) in dico_count:
dico_count[tuple(current_scenario)]+=1
else:
dico_count[tuple(current_scenario)] = 0
good_cla = 0
bad_cla = 0
for element in scenario:
try:
good_cla+=dico_count[tuple(element)]
except:
pass
total = np.sum(list(dico_count.values()))
bad_cla = total-good_cla
similarity = good_cla/total
return similarity
def find_bestScenario(All_tests, nb_cpt):
"""
Return the best scenario (path) in the tree
Entries : All_tests -> list
nb_cpt -> int
Return : best -> list
"""
#find best scenario of labels association
best = All_tests[0]
similarity = 0
for scenario in All_tests:
good_association = True
current_similarity = scenario[1]
for i in range(len(scenario[0][0])):
test = set(np.array(scenario[0])[:,i])
if (len(test)<nb_cpt):
good_association = False
if (good_association==True) and (current_similarity>similarity):
best = scenario
similarity = current_similarity
return best