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models.py
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models.py
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import tensorflow as tf
from layers import *
from metrics import *
flags = tf.app.flags
FLAGS = flags.FLAGS
class Model(object):
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging'}
for kwarg in kwargs.keys():
assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
name = kwargs.get('name')
if not name:
name = self.__class__.__name__.lower()
self.name = name
logging = kwargs.get('logging', False)
self.logging = logging
self.vars = {}
self.placeholders = {}
self.layers = []
self.activations = []
self.inputs = None
self.outputs = None
self.loss = 0
self.accuracy = 0
self.mrr = 0
self.hrat1 = 0
self.hrat5 = 0
self.hrat10 = 0
self.hrat20 = 0
self.ndcg1 = 0
self.ndcg5 = 0
self.ndcg10 = 0
self.ndcg20 = 0
self.test = None
self.alphas = None
self.optimizer = None
self.opt_op = None
def _build(self):
raise NotImplementedError
def build(self):
""" Wrapper for _build() """
with tf.variable_scope(self.name):
self._build()
# Build sequential layer model
self.activations.append(self.inputs)
for i in range(len(self.layers)):
hidden = self.layers[i](self.activations[-1])
if i == 3:
self.test = hidden
self.activations.append(hidden)
self.outputs = self.activations[-1]
self.alphas = self.layers[3].alphas
# Store model variables for easy access
# variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name)
# self.vars = {var.name: var for var in variables}
# Build metrics
self._loss()
self._hrat()
self._ndcg()
self._accuracy()
self._mrr()
self.opt_op = self.optimizer.minimize(self.loss)
def predict(self):
pass
def _loss(self):
raise NotImplementedError
def _accuracy(self):
raise NotImplementedError
def _hrat(self):
raise NotImplementedError
def _mrr(self):
raise NotImplementedError
def _ndcg(self):
raise NotImplementedError
def save(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = saver.save(sess, "tmp/%s.ckpt" % self.name)
print("Model saved in file: %s" % save_path)
def load(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = "tmp/%s.ckpt" % self.name
saver.restore(sess, save_path)
print("Model restored from file: %s" % save_path)
class GCN(Model):
def __init__(self, placeholders, input_dim, num_support, **kwargs):
super(GCN, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
self.output_dim = FLAGS.output_dim
self.placeholders = placeholders
self.rating = placeholders['rating']
self.negative = placeholders['negative']
self.length = int(self.rating.shape[0])
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# Weight decay loss
for i in range(4):
for var in self.layers[i].vars.values():
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# RMSE
self.loss += rmse_loss(rating=self.rating,rate=self.outputs, length=self.length)
def _accuracy(self):
self.accuracy = auc(self.outputs, self.negative, length=self.length)
def _ndcg(self):
self.ndcg1 = ndcg(self.outputs, self.negative, length=self.length, k=1)
self.ndcg5 = ndcg(self.outputs, self.negative, length=self.length, k=5)
self.ndcg10 = ndcg(self.outputs, self.negative, length=self.length, k=10)
self.ndcg20 = ndcg(self.outputs, self.negative, length=self.length, k=20)
def _hrat(self):
self.hrat1 = hr(self.outputs, self.negative, length=self.length, k=1)
self.hrat5 = hr(self.outputs, self.negative, length=self.length, k=5)
self.hrat10 = hr(self.outputs, self.negative, length=self.length, k=10)
self.hrat20 = hr(self.outputs, self.negative, length=self.length, k=20)
def _mrr(self):
self.mrr = mrr(self.outputs, self.negative, length=int(self.rating.shape[0]))
def _build(self):
self.layers.append(GraphConvolution(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
sparse_inputs=True,
logging=self.logging,
name='first'))
self.layers.append(GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
logging=self.logging))
self.layers.append(GraphConvolution(input_dim=FLAGS.hidden2,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=False,
logging=self.logging))
self.layers.append(SimpleAttLayer(attention_size=32,
time_major=True))
self.layers.append(RateLayer(placeholders=self.placeholders,
user_dim=int(self.rating.shape[0]),
item_dim=int(self.rating.shape[1])))