def predict_model_cold_users(self):
res = []
for user in self.rg.testColdUserSet_u.keys():
for item in self.rg.testColdUserSet_u[user].keys():
rating = self.rg.testColdUserSet_u[user][item]
pred = self.predict(user, item)
# denormalize
pred = denormalize(pred, self.config.min_val, self.config.max_val)
pred = self.checkRatingBoundary(pred)
res.append([user, item, rating, pred])
rmse = Metric.RMSE(res)
return rmse
def valid_model(self):
res = []
for ind, entry in enumerate(self.rg.validSet()):
user, item, rating = entry
# predict
prediction = self.predict(user, item)
# denormalize
prediction = denormalize(prediction, self.config.min_val, self.config.max_val)
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
# self.dao.testData[ind].append(pred)
res.append([user, item, rating, pred])
rmse = Metric.RMSE(res)
mae = Metric.MAE(res)
self.iter_rmse.append(rmse) # for plot
self.iter_mae.append(mae)
return rmse, mae
def predict_model(self):
res = []
for ind, entry in enumerate(self.rg.testSet()):
user, item, rating = entry
rating_length = len(self.rg.trainSet_u[user]) # remove cold start users for test
if rating_length <= self.config.coldUserRating:
continue
prediction = self.predict(user, item)
# denormalize
prediction = denormalize(prediction, self.config.min_val, self.config.max_val)
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
res.append([user, item, rating, pred])
rmse = Metric.RMSE(res)
mae = Metric.MAE(res)
self.iter_rmse.append(rmse) # for plot
self.iter_mae.append(mae)
return rmse, mae