if label not in self.dic:
print("Erreur : strategie %s non trouve" % (label, ))
return SoccerAction()
return self.dic[label].compute_strategy(state, id_team, id_player)
if __name__ == "__main__":
prefix = "./test"
if len(sys.argv) > 1:
prefix = sys.argv[1]
## constitution de la base d'entrainement et des labels
train, labels = build_apprentissage(prefix + ".exp", gen_features)
## apprentissage de l'arbre
tree = apprendre_arbre(train, labels)
## sauvegarde de l'arbre
cPickle.dump(tree, file(prefix + ".pkl", "w"))
## exporter l'arbre en .dot
with file(prefix + ".dot", "w") as fn:
export_graphviz(tree,
fn,
class_names=tree.classes_,
feature_names=getattr(gen_features, "names", None),
filled=True,
rounded=True)
## puis utiliser ou dot -Tpdf -o tree.pdf tree.dot pour convertir
## ou aller sur http://www.webgraphviz.com/ et copier le fichier .dot
## puis pour utiliser :
##### tree = cPickle.load(file("./tree.pkl"))
##### dic = {"Random":RandomStrategy(),"Fonceur":FonceurStrategy(),"Defense":DefenseStrategy()}
##### treeStrat = DTreeStrategy(tree,dic,gen_features)
self.gen_feat= gen_feat
def compute_strategy(self, state, id_team, id_player):
label = self.tree.predict([self.gen_feat(state,id_team,id_player)])[0]
if label not in self.dic:
print("Erreur : strategie %s non trouve" %(label,))
return SoccerAction()
return self.dic[label].compute_strategy(state,id_team,id_player)
if __name__=="__main__":
prefix = "./test"
if len(sys.argv)>1:
prefix = sys.argv[1]
## constitution de la base d'entrainement et des labels
train,labels = build_apprentissage(prefix+".exp",gen_features)
## apprentissage de l'arbre
tree = apprendre_arbre(train,labels)
## sauvegarde de l'arbre
cPickle.dump(tree,file(prefix+".pkl","w"))
## exporter l'arbre en .dot
with file(prefix+".dot","w") as fn:
export_graphviz(tree,fn,class_names = tree.classes_,feature_names=getattr(gen_features,"names",None),
filled = True,rounded=True)
## puis utiliser ou dot -Tpdf -o tree.pdf tree.dot pour convertir
## ou aller sur http://www.webgraphviz.com/ et copier le fichier .dot
## puis pour utiliser :
##### tree = cPickle.load(file("./tree.pkl"))
##### dic = {"Random":RandomStrategy(),"Fonceur":FonceurStrategy(),"Defense":DefenseStrategy()}
##### treeStrat = DTreeStrategy(tree,dic,gen_features)
def apprendre_arbre(train,labels,depth=7,min_samples_split=40):
tree= DecisionTreeClassifier(max_depth=depth,min_samples_split=min_samples_split)
tree.fit(train,labels)
return tree
def affiche_arbre(tree):
long = 10
sep1="|"+"-"*(long-1)
sepl="|"+" "*(long-1)
sepr=" "*long
def aux(node,sep):
if tree.tree_.children_left[node]<0:
ls ="(%s)" % (", ".join( "%s: %d" %(tree.classes_[i],int(x)) for i,x in enumerate(tree.tree_.value[node].flat)))
return sep+sep1+"%s\n" % (ls,)
return (sep+sep1+"X%d<=%0.2f\n"+"%s"+sep+sep1+"X%d>%0.2f\n"+"%s" )% (tree.tree_.feature[node],tree.tree_.threshold[node],aux(tree.tree_.children_left[node],sep+sepl),tree.tree_.feature[node],tree.tree_.threshold[node],aux(tree.tree_.children_right[node],sep+sepr))
return aux(0,"")
## constitution de la base d'entrainement et des labels
train,labels = build_apprentissage("./tree.exp",gen_features)
## apprentissage de l'arbre
tree = apprendre_arbre(train,labels)
## sauvegarde de l'arbre
cPickle.dump(tree,file("tree.pkl","w"))
## exporter l'arbre en .dot
with file("tree.dot","w") as fn:
export_graphviz(tree,fn,class_names= tree.classes_,feature_names=getattr(gen_features,"names",None))
## puis utiliser ou dot -Tpdf -o tree.pdf tree.dot pour convertir
## ou aller sur http://www.webgraphviz.com/ et copier le fichier .dot
