class TestModel(object):
def __init__(self, args):
self.args = args
self.doc = Documents(num_total=args.num_documents,
num_feature=args.feature_size,
num_visible=args.feature_size_vis,
seed=100)
self.user = UserResponse(doc_feature_size=args.feature_size,
slate_size=args.slate_size,
seed=100)
self.evaluator = Evaluator(model_path=os.path.join(
os.path.dirname(os.path.realpath(__file__)), args.evaluator_path))
self.test_set = Dataset(self.doc, 'test')
self.model = self._model_init(args)
def _model_init(self, args):
raise NotImplementedError
def test_on_testset(self):
raise NotImplementedError
def test_on_user(self):
with self.model.model_graph.as_default() as g:
user_res_all, eval_pred_all = [], []
while True:
try:
x_data_id, x_data, y = self.test_set.read(1000)
rank_id = self.model.rank(x_data)
batch_id = np.tile(
np.arange(rank_id.shape[0]).reshape((-1, 1)),
(1, rank_id.shape[1]))
x_data_id_rank = x_data_id[(batch_id, rank_id)]
# user
slate = self.doc.get_feature_all(
x_data_id_rank.reshape((-1, )))
slate = slate.reshape(
(x_data_id_rank.shape[0], x_data_id_rank.shape[1], -1))
res = self.user.response(slate)
user_res_all.append(res)
# evaluator
slate_ = self.doc.get_feature_visible(
x_data_id_rank.reshape((-1, )))
slate_ = slate_.reshape(
(x_data_id_rank.shape[0], x_data_id_rank.shape[1], -1))
pred = self.evaluator.predict(slate_)
eval_pred_all.append(pred)
except Exception as e:
print(e)
break
user_res_all = np.concatenate(user_res_all, axis=0)
eval_pred_all = np.concatenate(eval_pred_all, axis=0)
print('True score:', np.mean(np.sum(user_res_all, axis=1)))
print('Eval score:', np.mean(np.sum(eval_pred_all, axis=1)))
def __init__(self, args):
self.args = args
self.doc = Documents(num_total=args.num_documents,
num_feature=args.feature_size,
num_visible=args.feature_size_vis,
seed=100)
self.user = UserResponse(doc_feature_size=args.feature_size,
slate_size=args.slate_size,
seed=100)
self.evaluator = Evaluator(model_path=os.path.join(
os.path.dirname(os.path.realpath(__file__)), args.evaluator_path))
self.test_set = Dataset(self.doc, 'test')
self.model = self._model_init(args)
default=datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
parser.add_argument('--evaluator_path',
type=str,
default='model_eval',
help='evaluator ckpt dir')
FLAGS, _ = parser.parse_known_args()
return FLAGS
if __name__ == "__main__":
args = parse_args()
print(args)
write_args(args, './model_params.txt')
# dataset
doc = Documents(num_total=args.num_documents,
num_feature=args.feature_size,
num_visible=args.feature_size_vis,
seed=100)
train_set = Dataset(doc, 'train')
val_set = Dataset(doc, 'validation')
model_path = os.path.join(args.checkpointDir, args.algo, args.timestamp)
model = MontCarloModel(args, model_path, args.algo)
evaluator = Evaluator(model_path=os.path.join(
os.path.dirname(os.path.realpath(__file__)), args.evaluator_path))
with model.model_graph.as_default() as g:
sess = tf.Session(graph=g)
model.set_sess(sess)
path1, path2 = os.path.join(model_path,
'train'), os.path.join(model_path, 'test')
if not os.path.isdir(path1):
os.makedirs(path1)