def test_train_and_eval_with_document_interactions(self):
data_dir = tf.compat.v1.test.get_temp_dir()
data_file = os.path.join(data_dir, "elwc.tfrecord")
if tf.io.gfile.exists(data_file):
tf.io.gfile.remove(data_file)
with tf.io.TFRecordWriter(data_file) as writer:
for elwc in [ELWC] * 10:
writer.write(elwc.SerializeToString())
model_dir = os.path.join(data_dir, "model")
with flagsaver.flagsaver(train_path=data_file,
eval_path=data_file,
data_format="example_list_with_context",
model_dir=model_dir,
num_train_steps=10,
listwise_inference=True,
use_document_interactions=True,
group_size=1,
weights_feature_name="doc_weight"):
tf_ranking_tfrecord.train_and_eval()
if tf.io.gfile.exists(model_dir):
tf.io.gfile.rmtree(model_dir)
def test_train_and_eval(self, listwise_inference):
tmp_dir = self.create_tempdir()
data_file = os.path.join(tmp_dir, "elwc.tfrecord")
if tf.io.gfile.exists(data_file):
tf.io.gfile.remove(data_file)
with tf.io.TFRecordWriter(data_file) as writer:
for elwc in [ELWC] * 10:
writer.write(elwc.SerializeToString())
model_dir = os.path.join(tmp_dir, "model")
with flagsaver.flagsaver(train_path=data_file,
eval_path=data_file,
data_format="example_list_with_context",
model_dir=model_dir,
num_train_steps=10,
listwise_inference=listwise_inference,
group_size=1,
weights_feature_name="doc_weight"):
tf_ranking_tfrecord.train_and_eval()