def evalModel(self, k=None, beta=None):
"""
Fontions qui nous permet d'evaluer le modèle self, un IRModel
:type k: float
:param k: parametre k pour les evaluations
:type beta: float
:param beta: parametre beta pour les FMesureAtK
"""
if k is not None:
self.k = k
if beta is not None:
self.beta = beta
evaluation = [
Eval.PrecisionAtK(self.k),
Eval.RappelAtK(self.k),
Eval.FMesureAtK(self.k, self.beta),
Eval.AvgP(),
Eval.reciprocalRank(),
Eval.Ndcg()
]
resultat = [[] for _ in range(len(evaluation))]
for query in self.collectionQry:
self.print_verbose('query =', self.collectionQry[query].getTexte())
liste = [
resultat[0] for resultat in self.model.getRanking(
self.collectionQry[query].getTexte())
]
self.print_verbose(liste)
for i in range(len(evaluation)):
resultat[i].append(evaluation[i].evalQuery(
liste, self.collectionQry[query]))
self.print_verbose(resultat)
return [(np.mean(l), np.std(l)) for l in resultat]
