def main(args):
# process input file
input_file = util.ensure_local_file(args['train_file'])
user_map, item_map, tr_sparse, test_sparse = model.create_test_and_train_sets(
args, input_file, args['data_type'])
# train model
output_row, output_col = model.train_model(args, tr_sparse)
# save trained model to job directory
if args['data_type'] == 'user_ratings':
model.save_model_json(args, user_map, item_map, output_row, output_col)
user_items_w = model.get_user_items_w(input_file)
model.save_user_items_w(args, user_items_w)
else:
model.save_model(args, user_map, item_map, output_row, output_col)
# log results
train_rmse = wals.get_rmse(output_row, output_col, tr_sparse)
test_rmse = wals.get_rmse(output_row, output_col, test_sparse)
if args['hypertune']:
# write test_rmse metric for hyperparam tuning
util.write_hptuning_metric(args, test_rmse)
tf.logging.info('train RMSE = %.2f' % train_rmse)
tf.logging.info('test RMSE = %.2f' % test_rmse)
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
# input files
input_file = util.ensure_local_file(args.train_file)
user_map, item_map, tr_sparse, test_sparse = model.create_test_and_train_sets(
input_file)
# train model
output_row, output_col = model.train_model(args, tr_sparse)
# save trained model to job directory
model.save_model(args, user_map, item_map, output_row, output_col)
# log results
test_rmse = wals.get_rmse(output_row, output_col, test_sparse)
util.write_hptuning_metric(args, test_rmse)
def main(args):
# process input file
input_file = util.ensure_local_file(args['train_files'][0])
user_map, item_map, tr_sparse, test_sparse = model.create_test_and_train_sets(
args, input_file, args['data_type'])
# train model
output_row, output_col = model.train_model(args, tr_sparse)
# save trained model to job directory
model.save_model(args, user_map, item_map, output_row, output_col)
# log results
train_rmse = wals.get_rmse(output_row, output_col, tr_sparse)
test_rmse = wals.get_rmse(output_row, output_col, test_sparse)
if args['hypertune']:
# write test_rmse metric for hyperparam tuning
util.write_hptuning_metric(args, test_rmse)
tf.logging.info('train RMSE = %.2f' % train_rmse)
tf.logging.info('test RMSE = %.2f' % test_rmse)