def get_most_popular(self):
MostPopular(
train_file=self.train_file,
test_file=self.test_file,
output_file=self.output_file,
rank_length=self.rank_length,
sep=self.sep,
output_sep=self.output_sep).compute(verbose_evaluation=False)
def generate_recommendation(self):
self.ranking = []
for n, train_file in enumerate(self.gb_train_files):
if self.recommender == 'UserKNN':
rec = UserKNN(train_file=train_file,
similarity_metric=self.similarity_metric,
as_binary=True,
as_similar_first=False)
rec.compute(verbose=False, verbose_evaluation=False)
self.ranking += rec.ranking
elif self.recommender == 'ItemKNN':
rec = ItemKNN(train_file=train_file,
test_file=self.test_file,
similarity_metric=self.similarity_metric,
as_binary=True)
rec.compute(verbose=False, verbose_evaluation=False)
self.ranking += rec.ranking
elif self.recommender == 'MostPopular':
rec = MostPopular(train_file=train_file,
test_file=self.test_file,
as_binary=True)
rec.compute(verbose=False, verbose_evaluation=False)
self.ranking += rec.ranking
elif self.recommender == 'BPRMF':
rec = BprMF(train_file=train_file,
test_file=self.test_file,
batch_size=4)
rec.compute(verbose=False, verbose_evaluation=False)
self.ranking += rec.ranking
else:
raise ValueError(
'Error: Recommender not implemented or not exist!')
self.ranking = sorted(self.ranking, key=lambda x: (x[0], -x[2]))
"""
Running Most Popular Recommender [Item Recommendation]
- Cross Validation
- Simple
"""
from caserec.recommenders.item_recommendation.most_popular import MostPopular
from caserec.utils.cross_validation import CrossValidation
db = '../../../datasets/ml-1m/ratings.csv'
folds_path = '../../../datasets/ml-1m/'
tr = '../../../datasets/ml-1m/folds/0/train.dat'
te = '../../../datasets/ml-1m/folds/0/test.dat'
# Cross Validation
recommender = MostPopular(as_binary=True)
CrossValidation(input_file=db,
recommender=recommender,
dir_folds=folds_path,
header=1,
k_folds=5).compute()
# Simple
MostPopular(tr, te, as_binary=True).compute()
from caserec.recommenders.item_recommendation.itemknn import ItemKNN
from caserec.recommenders.item_recommendation.most_popular import MostPopular
from caserec.utils.cross_validation import CrossValidation
db = 'u.data'
folds_path = ''
metrics = ('PREC', 'RECALL', 'NDCG', 'MAP')
recommender = ItemKNN()
CrossValidation(input_file=db,
recommender=recommender,
dir_folds=folds_path,
header=1,
k_folds=5).compute(metrics=metrics)
recommender = MostPopular()
CrossValidation(input_file=db,
recommender=recommender,
dir_folds=folds_path,
header=1,
k_folds=5).compute(metrics=metrics)