def baselineKNN(train, test, user_bool):
"""
Run KNN Baseline model from Surprise library.
@param train: the training set in the Surprise format.
@param test: the test set in the Surprise format.
@param user_bool: if True, runs the user based KNN baseline. Otherwise, runs item based KNN baseline.
@return: the predictions in a numpy array.
"""
algo = spr.KNNBaseline(name='pearson_baseline', user_based=user_bool)
algo.fit(train)
predictions = algo.test(test)
return get_predictions(predictions)
rating_scale=(0, 5))
trainset = Dataset.load_from_file(train_path, reader=train_reader)
trainset = trainset.build_full_trainset()
if args.model == 'NormalPredictor':
model = surprise.NormalPredictor()
elif args.model == 'BaselineOnly':
model = surprise.BaselineOnly()
elif args.model == 'KNNBasic':
model = surprise.KNNBasic()
elif args.model == 'KNNWithMeans':
model = surprise.KNNWithMeans()
elif args.model == 'KNNWithZScore':
model = surprise.KNNWithZScore()
elif args.model == 'KNNBaseline':
model = surprise.KNNBaseline()
elif args.model == 'SVD':
model = surprise.SVD()
elif args.model == 'SVDpp':
model = surprise.SVDpp(verbose=True)
elif args.model == 'NMF':
model = surprise.NMF()
elif args.model == 'SlopeOne':
model = surprise.SlopeOne()
elif args.model == 'CoClustering':
model = surprise.CoClustering()
# cross_validate(model, trainset, cv=5, verbose=True)
model.fit(trainset)
lines = []
