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():
user_map, item_map, tr_sparse, test_sparse = make_tran_and_test(
sys.argv[1])
output_row, output_col = train_model(tr_sparse)
train_rmse = wals.get_rmse(output_row, output_col, tr_sparse)
test_rmse = wals.get_rmse(output_row, output_col, test_sparse)
print(train_rmse)
print(test_rmse)
def process(args):
if args.format == "adjlist":
G = graph.load_adjacencylist(args.input, undirected=args.undirected)
elif args.format == "edgelist":
G = graph.load_edgelist(args.input, undirected=args.undirected)
elif args.format == "mat":
G = graph.load_matfile(args.input,
variable_name=args.matfile_variable_name,
undirected=args.undirected)
else:
raise Exception(
"Unknown file format: '%s'. Valid formats: 'adjlist', 'edgelist', 'mat'"
% args.format)
print("Number of nodes: {}".format(len(G.nodes())))
nxG = nx.Graph(G)
Gmat = nx.adjacency_matrix(nxG).tocoo()
num_iters = args.num_iters
tf.logging.info('Train Start: {:%Y-%m-%d %H:%M:%S}'.format(
datetime.datetime.now()))
# generate model
input_tensor, row_factor, col_factor, model = wals.wals_model(
Gmat, args.dim // 2, args.reg, args.unobs)
# factorize matrix
session = wals.simple_train(model, input_tensor, num_iters)
tf.logging.info('Train Finish: {:%Y-%m-%d %H:%M:%S}'.format(
datetime.datetime.now()))
# evaluate output factor matrices
output_row = row_factor.eval(session=session)
output_col = col_factor.eval(session=session)
# close the training session now that we've evaluated the output
session.close()
embedding = np.concatenate((output_row, output_col), axis=1)
print(embedding)
# save trained model to job directory
np.savetxt(args.output, embedding)
# log results
train_rmse = wals.get_rmse(output_row, output_col, Gmat)
log_info = 'train RMSE = %.2f' % train_rmse
tf.logging.info(log_info)
print(log_info)
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)
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)
