def itemContent(self, attributes='', splitAttribute='', newUsers=None, newObservationData=None, eval=False, similarItem=None):
newItems = self._items.copy()
if attributes:
newItems = self._items[attributes]
if splitAttribute:
newItems = self._splitAttribute(newItems, splitAttribute)
icr = gl.item_content_recommender.create(newItems, 'item_id', self._ratings, 'user_id', target='rating')
recs = icr.recommend(users=newUsers, new_observation_data=newObservationData, k=10).join(self._items, on='item_id').sort('rank')
print 'Recomendacoes por conteudo com base completa'
print(recs)
if similarItem:
similarity = icr.get_similar_items(similarItem).join(self._items, on={'similar': 'item_id'}).sort('rank')
print 'Recomendacoes por item'
print(similarity)
if eval:
icrTrain = gl.item_content_recommender.create(newItems, 'item_id', self._train, 'user_id', target='rating')
evalPrecisionRecall = icrTrain.evaluate_precision_recall(self._test)
evalRMSE = icrTrain.evaluate_rmse(self._test, target='rating')
eval = icrTrain.evaluate(self._test)
print 'Avaliacao com grupos train e test'
print(eval)
print 'Valores de Precision e Recal'
print(evalPrecisionRecall)
print 'RMSE'
print(evalRMSE)
if newUsers:
route = Route(recs['latitude'], recs['longitude'], recs['name'])
route.map(newUsers[0])
def itemSimilarity(self,
similarityType='jaccard',
newUsers=None,
newObservationData=None,
eval=False,
similarItem=None):
print(self._ratings.dtype())
isr = gl.item_similarity_recommender.create(
self._ratings, target='rating', similarity_type=similarityType)
recs = isr.recommend(users=newUsers,
new_observation_data=newObservationData).join(
self._items, on='item_id').sort('rank')
print(recs)
if eval:
isrTrain = gl.item_similarity_recommender.create(
self._train, target='rating', similarity_type=similarityType)
evalPrecisionRecall = isrTrain.evaluate_precision_recall(
self._test)
evalRMSE = isrTrain.evaluate_rmse(self._test, target='rating')
eval = isrTrain.evaluate(self._test)
print(eval)
if similarItem:
similarity = isr.get_similar_items(similarItem).join(
self._items, on={
'similar': 'item_id'
}).sort('rank')
print(similarity)
if newUsers and newObservationData:
route = Route(recs['latitude'], recs['longitude'])
route.map()
def itemSimilarity(self, similarityType='jaccard', newUsers=None, newObservationData=None, eval=False, similarItem=None):
# print(self._ratings.dtype())
ratings_without_recommend = self._ratings.filter_by(newUsers, 'user_id', exclude=True)
isr = gl.recommender.ranking_factorization_recommender.create(ratings_without_recommend, target='rating')
recs = isr.recommend(users=newUsers, new_observation_data=newObservationData).join(self._items, on='item_id').sort('rank')
print 'Recomendacoes por similaridade com base completa'
print(recs)
if eval:
print 'Recomendacoes EVAAL'
# Executa o treinamento e teste com os grupos previamente criados
isrTrain = gl.recommender.ranking_factorization_recommender.create(self._train, target='rating')
# Retorna o precision e o recall
evalPrecisionRecall = isrTrain.evaluate_precision_recall(self._test)
# Avaliação do erro Root Mean Square Error
evalRMSE = isrTrain.evaluate_rmse(self._test, target='rating')
eval = isrTrain.evaluate(self._test)
print 'Avaliacao com grupos train e test'
print(eval)
print 'Valores de Precision e Recal'
print(evalPrecisionRecall)
print 'RMSE'
print(evalRMSE)
#import pdb; pdb.set_trace()
# Recomendação feita baseada em item
if similarItem:
similarity = isr.get_similar_items(similarItem).join(self._items, on={'similar': 'item_id'}).sort('rank')
print 'Recomendacoes por item'
print(similarity)
# Escreve as rotas para serem utilizadas no web
if newUsers and newObservationData:
route = Route(recs['latitude'], recs['longitude'], recs['name'])
route.map()
def itemContent(self,
attributes='',
splitAttribute='',
newUsers=None,
newObservationData=None,
eval=False,
similarItem=None):
newItems = self._items.copy()
if attributes:
newItems = self._items[attributes]
if splitAttribute:
newItems = self._splitAttribute(newItems, splitAttribute)
icr = gl.item_content_recommender.create(newItems,
'item_id',
self._ratings,
'user_id',
target='rating')
recs = icr.recommend(users=newUsers,
new_observation_data=newObservationData).join(
self._items, on='item_id').sort('rank')
print(recs)
if similarItem:
similarity = icr.get_similar_items(similarItem).join(
self._items, on={
'similar': 'item_id'
}).sort('rank')
print(similarity)
if eval:
icrTrain = gl.item_content_recommender.create(newItems,
'item_id',
self._train,
'user_id',
target='rating')
evalPrecisionRecall = icrTrain.evaluate_precision_recall(
self._test)
evalRMSE = icrTrain.evaluate_rmse(self._test, target='rating')
eval = icrTrain.evaluate(self._test)
print(eval)
if newUsers and newObservationData:
route = Route(recs['latitude'], recs['longitude'])
route.map()