import tensorflow as tf
class RandomModel(object):
def manual_eval_ops(self, device='/cpu:0'):
""" This is the baseline random model, this takes all the targets,
randomly assign values to it and then report the result.
:param device:
:return:
"""
with tf.name_scope("namual_evaluation"):
with tf.device('/cpu:0'):
# head rel pair to evaluate
ph_head_rel = tf.placeholder(tf.string, [1, 2], name='ph_head_rel')
# tail targets to evaluate
ph_eval_targets = tf.placeholder(tf.string, [1, None], name='ph_eval_targets')
# indices of true tail targets in ph_eval_targets. Mask these when calculating filtered mean rank
ph_true_target_idx = tf.placeholder(tf.int32, [None], name='ph_true_target_idx')
# indices of true targets in the evaluation set, we will return the ranks of these targets
ph_test_target_idx = tf.placeholder(tf.int32, [None], name='ph_test_target_idx')
# We put random numbers into the pred_scores_queue
pred_scores_queue = tf.FIFOQueue(1000000, dtypes=tf.float32, shapes=[[1]], name='pred_scorse_queue')
