def get_result_from_eset(self, eset):
# compose result dictionary
item_score = {}
ranking = []
for e in eset:
package = e.term.lstrip("XP")
item_score[package] = e.weight
ranking.append(package)
return recommender.RecommendationResult(item_score, ranking)
def run(self, rec, user, rec_size):
terms_name, debtags_name = self.load_terms_and_debtags()
pkgs, pkgs_score = self.get_pkgs_and_scores(rec, user)
pkgs_classifications = self.get_pkgs_classifications(pkgs, terms_name,
debtags_name)
item_score = self.get_item_score(pkgs_score, pkgs_classifications)
result = recommender.RecommendationResult(item_score, limit=rec_size)
return result
def run(self, rec, user, recommendation_size):
"""
Perform recommendation strategy.
"""
neighborhood = self.get_neighborhood(user, rec)
weights = data.tfidf_plus(rec.users_repository, neighborhood,
PkgExpandDecider(user.items()))
item_score = {}
ranking = []
for pkg in weights[:recommendation_size]:
package = pkg[0].lstrip("XP")
item_score[package] = pkg[1]
ranking.append(package)
result = recommender.RecommendationResult(item_score, ranking)
return result
def get_sugestion_from_profile(self, rec, user, profile,
recommendation_size):
query = xapian.Query(xapian.Query.OP_OR, profile)
enquire = xapian.Enquire(rec.items_repository)
enquire.set_weighting_scheme(rec.weight)
enquire.set_query(query)
# Retrieve matching packages
try:
mset = enquire.get_mset(0, recommendation_size, None,
PkgMatchDecider(user.installed_pkgs))
except xapian.DatabaseError as error:
logging.critical("Content-based strategy: " + error.get_msg())
# Compose result dictionary
item_score = {}
ranking = []
for m in mset:
item_score[m.document.get_data()] = m.weight
ranking.append(m.document.get_data())
result = recommender.RecommendationResult(item_score, ranking)
return result