def build_iceberg_lattice(filename, lattice, threshold):
irreducable = []
for i, (intent, extent) in enumerate(lattice):
coverage = list(intent)
if (len(intent) < threshold):
continue
is_irreducable = True
for j, (intent1, extent1) in enumerate(lattice):
if (j == i or len(intent1) < threshold
or len(intent) <= len(intent1)):
continue
is_subset = True
for obj in intent1:
if (not (obj in intent)):
is_subset = False
break
if is_subset:
for obj in intent1:
if obj in coverage:
coverage.remove(obj)
if (len(coverage) == 0):
is_irreducable = False
break
if is_irreducable:
irreducable.append((intent, extent))
#print intent, extent
#print '\n'
df = Definition()
for intent, extent in irreducable:
obj_name = ';'.join(intent)
df.add_object(obj_name, list(extent))
conc = Context(*df)
conc.tofile(filename='iceberg.' + filename, frmat='csv')
print res
return res
if __name__ == '__main__':
animaux = ["Bat","Eagle","Monkey","Parrot fish","Penguin","Shark","Lantern fish"]
proprietes = ["breathes in water","can fly","has beak","has hands","has skeleton","has wings","lives in water","is viviparous","produces light"]
matrix = [
(False, True, False, False, True, True, False, True, False), # Bat
(False, True, True, False, True, True, False, False, False), # Eagle
(False, False, False, True, True, False, False, True, False), # Monkey
(True, False, True, False, True, False, True, False, False), # Parrot Fish
(False, False, True, False, True, True, True, False, False), # Penguin
(True, False, False, False, True, False, True, False, False), # Shark
(True, False, False, False, True, False, True, False, True)] # Lantern Fish
exportContext(animaux,proprietes,matrix)
c = Context(animaux, proprietes, matrix) # doctest: +ELLIPSIS
'''clients = ['Anne','Basile','Carole']
articles = ['fromage','vin','lait','lessive']
matrix = [
(True, False, True, False), #A
(True, True, False, True), #B
(True,False,True,True)] #C
c = Context(clients, articles, matrix)'''
#print c
#c.lattice.graphviz(view=True)
#for intent, extent in c.lattice:
#print intent, extent
c.tofile('animaux.txt',frmat='cxt',encoding='utf-8')
writeConcepts(c.lattice)
def buildLattice(pattern = True, inputFiles = "dec", inputAttributes = "arts"):
if pattern == True:
name = "WithPS"
else:
name = "WithoutPS"
print inputFiles, inputAttributes, name
#Le contexte a construire
matrixAttribute = []
#
listFiles = []
#Liste des fichiers lus pour construire le contexte
if(inputFiles == "dec"):
listAllFiles = fg.getAllDecisions()
elif(inputFiles == "avis"):
listAllFiles = fg.getAllAvis()
elif(inputFiles == "all"):
listAllFiles = fg.getAllFiles()
else:
print "choix non reconnu. Choix possibles : 'dec' 'avis' 'all'"
listAllFiles = fg.getAllDecisions()
#Nombre de fichiers lus
lengthAllFiles = len(listAllFiles)
#L'ensemble des attributs du contexte
setOfAttributes = set()
#L'ensemble des attributs modifiés du contexte
setFormated = set()
#L'expression régulière des attributs possibles des différents textes
if (inputAttributes == "arts"):
expre = expreAttribute()
elif(inputAttributes == "artsdocs"):
expre = expreAttribute()+'|'+regex.exprReguliereDecision()
else:
print "choix non reconnu. Choix possibles : 'arts' 'docs' 'artsdocs'"
expre = expreAttribute()
#Compteur de fichiers lus
i = 0
#Lecture des fichiers pour lister les attributs
for dfile in listAllFiles:
f = open(dfile,'r')
#Enlever les sauts de lignes dûs au copier/coller du pdf
data = ' '.join([line.rstrip() for line in f])
#Pour chaque expression trouvée dans le texte
for m in re.finditer(expre, data):
#Expression réguliere
attributFormated = m.group(0)
#Lissage de l'expression :
#Enlever les accents
attributFormated = regex.removeAccent(attributFormated)
#Corriger les erreurs potentielles
attributFormated = correctSyntaxe(attributFormated)
attributFormated = regex.supprNumero(attributFormated)
setOfAttributes.add(attributFormated)
i = i + 0.5
if i%100==0:
print str(int(i))+' fichiers lus sur '+str(lengthAllFiles)
#Modification des attributs pour éviter les doublons
setOfAttributes = list(setOfAttributes)
for item in setOfAttributes:
setFormated.add(regex.formatArticle(item))
if pattern == True:
developAttributes = buildAttributes(setFormated)
setFormated = list(developAttributes)
else:
setFormated = list(setFormated)
#Nombre d'attributs dans le contexte
lenset = len(setFormated)
print str(lenset)
writeAttributes(setFormated,name)
#Construction du contexte
for dfile in listAllFiles:
f = open(dfile, 'r')
data = ' '.join([line.rstrip() for line in f])
#Lister les documents pour la construction du contexte
listFiles.append(regex.nomDocument(dfile))
#Construction d'une ligne du contexte
nuplet = (False,)*lenset
listuple = list(nuplet)
#Pour chaque expression
for m in re.finditer(expre, data):
attributFormated = m.group(0)
#Formater l'expression régulière
attributFormated = regex.removeAccent(attributFormated)
attributFormated = correctSyntaxe(attributFormated)
attributFormated = regex.supprNumero(attributFormated)
attributFormated = regex.formatArticle(attributFormated)
#Si pattern, on découpe chaque attribut
if pattern == True:
listAtt = developAttribute(attributFormated)
for item in listAtt:
#Trouver l'indice de l'attribut
index = setFormated.index(item)
#Mettre à jour la valeur
listuple[index] = True
#Sinon on cherche juste les attributs
else:
index = setFormated.index(attributFormated)
listuple[index] = True
i = i + 0.5
if i%100==0:
print str(int(i))+' fichiers lus sur '+str(lengthAllFiles)
nuplet = tuple(listuple)
#Ajoute le nouvel objet au contexte
matrixAttribute.append(nuplet)
print str(int(i))+' fichiers lus sur '+str(lengthAllFiles)
#Sauvegarde les attributs dans un txt
#sauvegarde le contexte dans un json
exportContext(listFiles,setFormated,matrixAttribute,name)
c = Context(listFiles,setFormated,matrixAttribute)
print "construction de la lattice. Cela peut prendre quelques instants"
c.lattice.graphviz(view=True)
#Sauvegarde le contexte dans un txt
writeConcepts(c.lattice,name)
c.tofile('latticeEtContext/saveLatticeWithPS.txt',frmat='cxt',encoding='utf-8')
# Creating and save context for implication rules
X_train_one_hot['Class'] = y_train
X_train_Class_split = pd.concat([
X_train_one_hot,
pd.get_dummies(X_train_one_hot['Class'], prefix='Class')
],
axis=1)
X_train_Class_split = X_train_Class_split.drop(["Class"],
axis=1).drop_duplicates()
objects = X_train_Class_split.index.values
objects = [str(oi) for oi in objects]
properties = X_train_Class_split.columns.values
bools = list(
X_train_Class_split.astype(bool).itertuples(index=False, name=None))
cxt = Context(objects, properties, bools)
cxt.tofile('diabetes_context.cxt', frmat='cxt', encoding='utf-8')
## Create concepts lattices for each class
c = {}
l = {}
no_of_classes = 2
X_train_one_hot['Class'] = y_train
X_train_one_hot = X_train_one_hot.drop_duplicates()
for i in range(0, no_of_classes):
X_temp = X_train_one_hot.copy(deep=True)
X_temp = X_temp[X_temp['Class'] == i].drop(["Class"], axis=1)
objects = X_temp.index.values
objects = [str(oi) for oi in objects]
properties = X_temp.columns.values
bools = list(X_temp.astype(bool).itertuples(index=False, name=None))
