def model(rank, itr, lmbda, alpha):
""" Invoke script responsible for training data.
For more details refer to 'https://spark.apache.org/docs/2.1.0/mllib-collaborative-filtering.html'
"""
from listenbrainz_spark.recommendations import train_models
with app.app_context():
_ = train_models.main(ranks=rank, lambdas=lmbda, iterations=itr, alpha=alpha)
def model():
""" Invoke script responsible for training data.
"""
from listenbrainz_spark.recommendations import train_models
with app.app_context():
train_models.main()
import sys
import logging
from listenbrainz_spark.stats import user
from listenbrainz_spark.recommendations import create_dataframes
from listenbrainz_spark.recommendations import train_models
from listenbrainz_spark.recommendations import recommend
if __name__ == '__main__':
if len(sys.argv) != 2:
print("Usage: manage.py <module_name>")
sys.exit(-1)
# The root logger always defaults to WARNING level
# The level is changed from WARNING to INFO
logging.getLogger().setLevel(logging.INFO)
module_name = sys.argv[1]
if module_name == 'create_dataframes':
create_dataframes.main()
elif module_name == 'train_models':
train_models.main()
elif module_name == 'recommend':
recommend.main()
elif module_name == 'user':
user.main()