num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
num_side_features=num_side_features,
logging=True)
else:
logging.warning(u"运行日志：构建不含边信息的模型")
model = RecommenderGAE(placeholders,
input_dim=u_features.shape[1],
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
logging=True)
# Convert sparse placeholders to tuples to construct feed_dict
val_support = sparse_to_tuple(val_support)
val_support_t = sparse_to_tuple(val_support_t)
train_support = sparse_to_tuple(train_support)
train_support_t = sparse_to_tuple(train_support_t)
u_features = sparse_to_tuple(u_features)
v_features = sparse_to_tuple(v_features)
def run(user_features, movie_features, learning_rate=0.01, epochs=500, hidden=[500, 75], feat_hidden=64, accumulation='sum', dropout=0.7,
num_basis_functions=2, features=False, symmetric=True, testing=True):
"""accumulation can be sum or stack"""
# Set random seed
# seed = 123 # use only for unit testing
seed = int(time.time())
np.random.seed(seed)
tf.set_random_seed(seed)
tf.reset_default_graph()
# Settings
# ap = argparse.ArgumentParser()
# # ap.add_argument("-d", "--dataset", type=str, default="ml_100k",
# # choices=['ml_100k', 'ml_1m', 'ml_10m', 'douban', 'yahoo_music', 'flixster'],
# # help="Dataset string.")
# ap.add_argument("-lr", "--learning_rate", type=float, default=0.01,
# help="Learning rate")
# ap.add_argument("-e", "--epochs", type=int, default=2500,
# help="Number training epochs")
# ap.add_argument("-hi", "--hidden", type=int, nargs=2, default=[500, 75],
# help="Number hidden units in 1st and 2nd layer")
# ap.add_argument("-fhi", "--feat_hidden", type=int, default=64,
# help="Number hidden units in the dense layer for features")
# ap.add_argument("-ac", "--accumulation", type=str, default="sum", choices=['sum', 'stack'],
# help="Accumulation function: sum or stack.")
# ap.add_argument("-do", "--dropout", type=float, default=0.7,
# help="Dropout fraction")
# ap.add_argument("-nb", "--num_basis_functions", type=int, default=2,
# help="Number of basis functions for Mixture Model GCN.")
# ap.add_argument("-ds", "--data_seed", type=int, default=1234,
# help="""Seed used to shuffle data in data_utils, taken from cf-nade (1234, 2341, 3412, 4123, 1324).
# Only used for ml_1m and ml_10m datasets. """)
# ap.add_argument("-sdir", "--summaries_dir", type=str, default='logs/' + str(datetime.datetime.now()).replace(' ', '_'),
# help="Directory for saving tensorflow summaries.")
# # Boolean flags
# fp = ap.add_mutually_exclusive_group(required=False)
# fp.add_argument('-nsym', '--norm_symmetric', dest='norm_symmetric',
# help="Option to turn on symmetric global normalization", action='store_true')
# fp.add_argument('-nleft', '--norm_left', dest='norm_symmetric',
# help="Option to turn on left global normalization", action='store_false')
# ap.set_defaults(norm_symmetric=True)
# fp = ap.add_mutually_exclusive_group(required=False)
# fp.add_argument('-f', '--features', dest='features',
# help="Whether to use features (1) or not (0)", action='store_true')
# fp.add_argument('-no_f', '--no_features', dest='features',
# help="Whether to use features (1) or not (0)", action='store_false')
# ap.set_defaults(features=False)
# fp = ap.add_mutually_exclusive_group(required=False)
# fp.add_argument('-ws', '--write_summary', dest='write_summary',
# help="Option to turn on summary writing", action='store_true')
# fp.add_argument('-no_ws', '--no_write_summary', dest='write_summary',
# help="Option to turn off summary writing", action='store_false')
# ap.set_defaults(write_summary=False)
# fp = ap.add_mutually_exclusive_group(required=False)
# fp.add_argument('-t', '--testing', dest='testing',
# help="Option to turn on test set evaluation", action='store_true')
# fp.add_argument('-v', '--validation', dest='testing',
# help="Option to only use validation set evaluation", action='store_false')
# ap.set_defaults(testing=False)
# args = vars(ap.parse_args())
# print('Settings:')
# print(args, '\n')
# Define parameters
DATASET = 'ml_100k'
DATASEED = 1234
NB_EPOCH = epochs
DO = dropout
HIDDEN = hidden
FEATHIDDEN = feat_hidden
BASES = num_basis_functions
LR = learning_rate
WRITESUMMARY = False
SUMMARIESDIR = 'logs/' + str(datetime.datetime.now()).replace(' ', '_')
FEATURES = features
SYM = symmetric
TESTING = testing
ACCUM = accumulation
SELFCONNECTIONS = False
SPLITFROMFILE = True
VERBOSE = True
NUMCLASSES = 5
# Splitting dataset in training, validation and test set
print("Using official MovieLens dataset split u1.base/u1.test with 20% validation set size...")
u_features = user_features
v_features = movie_features
_, _, adj_train, train_labels, train_u_indices, train_v_indices, \
val_labels, val_u_indices, val_v_indices, test_labels, \
test_u_indices, test_v_indices, class_values = load_official_trainvaltest_split('ml_100k', TESTING)
num_users, num_items = adj_train.shape
num_side_features = 0
# feature loading
if not FEATURES:
u_features = sp.identity(num_users, format='csr')
v_features = sp.identity(num_items, format='csr')
u_features, v_features = preprocess_user_item_features(u_features, v_features)
elif FEATURES and u_features is not None and v_features is not None:
# use features as side information and node_id's as node input features
print("Normalizing feature vectors...")
u_features_side = normalize_features(u_features)
v_features_side = normalize_features(v_features)
u_features_side, v_features_side = preprocess_user_item_features(u_features_side, v_features_side)
u_features_side = np.array(u_features_side.todense(), dtype=np.float32)
v_features_side = np.array(v_features_side.todense(), dtype=np.float32)
num_side_features = u_features_side.shape[1]
# node id's for node input features
id_csr_v = sp.identity(num_items, format='csr')
id_csr_u = sp.identity(num_users, format='csr')
u_features, v_features = preprocess_user_item_features(id_csr_u, id_csr_v)
else:
raise ValueError('Features flag is set to true but no features are loaded from dataset ' + DATASET)
# global normalization
support = []
support_t = []
adj_train_int = sp.csr_matrix(adj_train, dtype=np.int32)
for i in range(NUMCLASSES):
# build individual binary rating matrices (supports) for each rating
support_unnormalized = sp.csr_matrix(adj_train_int == i + 1, dtype=np.float32)
if support_unnormalized.nnz == 0 and DATASET != 'yahoo_music':
# yahoo music has dataset split with not all ratings types present in training set.
# this produces empty adjacency matrices for these ratings.
sys.exit('ERROR: normalized bipartite adjacency matrix has only zero entries!!!!!')
support_unnormalized_transpose = support_unnormalized.T
support.append(support_unnormalized)
support_t.append(support_unnormalized_transpose)
support = globally_normalize_bipartite_adjacency(support, symmetric=SYM)
support_t = globally_normalize_bipartite_adjacency(support_t, symmetric=SYM)
if SELFCONNECTIONS:
support.append(sp.identity(u_features.shape[0], format='csr'))
support_t.append(sp.identity(v_features.shape[0], format='csr'))
num_support = len(support)
support = sp.hstack(support, format='csr')
support_t = sp.hstack(support_t, format='csr')
if ACCUM == 'stack':
div = HIDDEN[0] // num_support
if HIDDEN[0] % num_support != 0:
print("""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d such that
it can be evenly split in %d splits.\n""" % (HIDDEN[0], num_support * div, num_support))
HIDDEN[0] = num_support * div
# Collect all user and item nodes for test set
test_u = list(set(test_u_indices))
test_v = list(set(test_v_indices))
test_u_dict = {n: i for i, n in enumerate(test_u)}
test_v_dict = {n: i for i, n in enumerate(test_v)}
test_u_indices = np.array([test_u_dict[o] for o in test_u_indices])
test_v_indices = np.array([test_v_dict[o] for o in test_v_indices])
test_support = support[np.array(test_u)]
test_support_t = support_t[np.array(test_v)]
# Collect all user and item nodes for validation set
val_u = list(set(val_u_indices))
val_v = list(set(val_v_indices))
val_u_dict = {n: i for i, n in enumerate(val_u)}
val_v_dict = {n: i for i, n in enumerate(val_v)}
val_u_indices = np.array([val_u_dict[o] for o in val_u_indices])
val_v_indices = np.array([val_v_dict[o] for o in val_v_indices])
val_support = support[np.array(val_u)]
val_support_t = support_t[np.array(val_v)]
# Collect all user and item nodes for train set
train_u = list(set(train_u_indices))
train_v = list(set(train_v_indices))
train_u_dict = {n: i for i, n in enumerate(train_u)}
train_v_dict = {n: i for i, n in enumerate(train_v)}
train_u_indices = np.array([train_u_dict[o] for o in train_u_indices])
train_v_indices = np.array([train_v_dict[o] for o in train_v_indices])
train_support = support[np.array(train_u)]
train_support_t = support_t[np.array(train_v)]
# features as side info
if FEATURES:
test_u_features_side = u_features_side[np.array(test_u)]
test_v_features_side = v_features_side[np.array(test_v)]
val_u_features_side = u_features_side[np.array(val_u)]
val_v_features_side = v_features_side[np.array(val_v)]
train_u_features_side = u_features_side[np.array(train_u)]
train_v_features_side = v_features_side[np.array(train_v)]
else:
test_u_features_side = None
test_v_features_side = None
val_u_features_side = None
val_v_features_side = None
train_u_features_side = None
train_v_features_side = None
placeholders = {
'u_features': tf.sparse_placeholder(tf.float32, shape=np.array(u_features.shape, dtype=np.int64)),
'v_features': tf.sparse_placeholder(tf.float32, shape=np.array(v_features.shape, dtype=np.int64)),
'u_features_nonzero': tf.placeholder(tf.int32, shape=()),
'v_features_nonzero': tf.placeholder(tf.int32, shape=()),
'labels': tf.placeholder(tf.int32, shape=(None,)),
'u_features_side': tf.placeholder(tf.float32, shape=(None, num_side_features)),
'v_features_side': tf.placeholder(tf.float32, shape=(None, num_side_features)),
'user_indices': tf.placeholder(tf.int32, shape=(None,)),
'item_indices': tf.placeholder(tf.int32, shape=(None,)),
'class_values': tf.placeholder(tf.float32, shape=class_values.shape),
'dropout': tf.placeholder_with_default(0., shape=()),
'weight_decay': tf.placeholder_with_default(0., shape=()),
'support': tf.sparse_placeholder(tf.float32, shape=(None, None)),
'support_t': tf.sparse_placeholder(tf.float32, shape=(None, None)),
}
# create model
if FEATURES:
model = RecommenderSideInfoGAE(placeholders,
input_dim=u_features.shape[1],
feat_hidden_dim=FEATHIDDEN,
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
num_side_features=num_side_features,
logging=True)
else:
model = RecommenderGAE(placeholders,
input_dim=u_features.shape[1],
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
logging=True)
# Convert sparse placeholders to tuples to construct feed_dict
test_support = sparse_to_tuple(test_support)
test_support_t = sparse_to_tuple(test_support_t)
val_support = sparse_to_tuple(val_support)
val_support_t = sparse_to_tuple(val_support_t)
train_support = sparse_to_tuple(train_support)
train_support_t = sparse_to_tuple(train_support_t)
u_features = sparse_to_tuple(u_features)
v_features = sparse_to_tuple(v_features)
assert u_features[2][1] == v_features[2][1], 'Number of features of users and items must be the same!'
num_features = u_features[2][1]
u_features_nonzero = u_features[1].shape[0]
v_features_nonzero = v_features[1].shape[0]
# Feed_dicts for validation and test set stay constant over different update steps
train_feed_dict = construct_feed_dict(placeholders, u_features, v_features, u_features_nonzero,
v_features_nonzero, train_support, train_support_t,
train_labels, train_u_indices, train_v_indices, class_values, DO,
train_u_features_side, train_v_features_side)
# No dropout for validation and test runs
val_feed_dict = construct_feed_dict(placeholders, u_features, v_features, u_features_nonzero,
v_features_nonzero, val_support, val_support_t,
val_labels, val_u_indices, val_v_indices, class_values, 0.,
val_u_features_side, val_v_features_side)
test_feed_dict = construct_feed_dict(placeholders, u_features, v_features, u_features_nonzero,
v_features_nonzero, test_support, test_support_t,
test_labels, test_u_indices, test_v_indices, class_values, 0.,
test_u_features_side, test_v_features_side)
# Collect all variables to be logged into summary
merged_summary = tf.summary.merge_all()
#sess = tf.Session()
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
if WRITESUMMARY:
train_summary_writer = tf.summary.FileWriter(SUMMARIESDIR + '/train', sess.graph)
val_summary_writer = tf.summary.FileWriter(SUMMARIESDIR + '/val')
else:
train_summary_writer = None
val_summary_writer = None
best_val_score = np.inf
best_val_loss = np.inf
best_epoch = 0
wait = 0
print('Training...')
train_loss_values = []
train_rmse_values = []
val_loss_values = []
val_rmse_values = []
list_embeddings = []
for epoch in range(NB_EPOCH):
t = time.time()
# Run single weight update
# outs = sess.run([model.opt_op, model.loss, model.rmse], feed_dict=train_feed_dict)
# with exponential moving averages
outs = sess.run([model.training_op, model.loss, model.rmse], feed_dict=train_feed_dict)
#print(len(model.embeddings))
train_avg_loss = outs[1]
train_rmse = outs[2]
val_avg_loss, val_rmse = sess.run([model.loss, model.rmse], feed_dict=val_feed_dict)
train_loss_values.append(train_avg_loss)
train_rmse_values.append(train_rmse)
val_loss_values.append(val_avg_loss)
val_rmse_values.append(val_rmse)
if VERBOSE:
print("[*] Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(train_avg_loss),
"train_rmse=", "{:.5f}".format(train_rmse),
"val_loss=", "{:.5f}".format(val_avg_loss),
"val_rmse=", "{:.5f}".format(val_rmse),
"\t\ttime=", "{:.5f}".format(time.time() - t))
if epoch==NB_EPOCH - 1:
embedding_users = model.embeddings[0].eval(feed_dict=train_feed_dict)
embedding_movies = model.embeddings[1].eval(feed_dict=train_feed_dict)
if val_rmse < best_val_score:
best_val_score = val_rmse
best_epoch = epoch
if epoch % 20 == 0 and WRITESUMMARY:
# Train set summary
summary = sess.run(merged_summary, feed_dict=train_feed_dict)
train_summary_writer.add_summary(summary, epoch)
train_summary_writer.flush()
# Validation set summary
summary = sess.run(merged_summary, feed_dict=val_feed_dict)
val_summary_writer.add_summary(summary, epoch)
val_summary_writer.flush()
if epoch % 100 == 0 and epoch > 1000 and not TESTING and False:
saver = tf.train.Saver()
save_path = saver.save(sess, "tmp/%s_seed%d.ckpt" % (model.name, DATASEED), global_step=model.global_step)
# load polyak averages
variables_to_restore = model.variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, save_path)
val_avg_loss, val_rmse = sess.run([model.loss, model.rmse], feed_dict=val_feed_dict)
print('polyak val loss = ', val_avg_loss)
print('polyak val rmse = ', val_rmse)
# Load back normal variables
saver = tf.train.Saver()
saver.restore(sess, save_path)
# store model including exponential moving averages
saver = tf.train.Saver()
save_path = saver.save(sess, "tmp/%s.ckpt" % model.name, global_step=model.global_step)
if VERBOSE:
print("\nOptimization Finished!")
print('best validation score =', best_val_score, 'at iteration', best_epoch+1)
if TESTING:
test_avg_loss, test_rmse = sess.run([model.loss, model.rmse], feed_dict=test_feed_dict)
print('test loss = ', test_avg_loss)
print('test rmse = ', test_rmse)
# restore with polyak averages of parameters
variables_to_restore = model.variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, save_path)
test_avg_loss, test_rmse = sess.run([model.loss, model.rmse], feed_dict=test_feed_dict)
print('polyak test loss = ', test_avg_loss)
print('polyak test rmse = ', test_rmse)
else:
# restore with polyak averages of parameters
variables_to_restore = model.variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, save_path)
val_avg_loss, val_rmse = sess.run([model.loss, model.rmse], feed_dict=val_feed_dict)
print('polyak val loss = ', val_avg_loss)
print('polyak val rmse = ', val_rmse)
print('global seed = ', seed)
sess.close()
return embedding_users, embedding_movies, train_loss_values, train_rmse_values, val_loss_values, val_rmse_values
learning_rate=LR,
num_side_features=num_side_features,
regularization=REG,
use_class_weights=USE_CLASS_WEIGHTS,
class_weights=CLASS_WEIGHTS,
logging=True)
else:
print("Using RecommenderGAE...")
model = RecommenderGAE(placeholders,
input_dim=u_features.shape[1],
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
activation_function=ACTIVATION,
regularization=REG,
use_class_weights=USE_CLASS_WEIGHTS,
class_weights=CLASS_WEIGHTS,
logging=True)
# Convert sparse placeholders to tuples to construct feed_dict
test_support = sparse_to_tuple(test_support)
test_support_t = sparse_to_tuple(test_support_t)
val_support = sparse_to_tuple(val_support)
val_support_t = sparse_to_tuple(val_support_t)
def run(DATASET='douban',
DATASEED=1234,
random_seed=123,
NB_EPOCH=200,
DO=0,
HIDDEN=[100, 75],
FEATHIDDEN=64,
LR=0.01,
decay_rate=1.25,
consecutive_threshold=5,
FEATURES=False,
SYM=True,
TESTING=False,
ACCUM='stackRGGCN',
NUM_LAYERS=1,
GCMC_INDICES=False):
np.random.seed(random_seed)
tf.set_random_seed(random_seed)
SELFCONNECTIONS = False
SPLITFROMFILE = True
VERBOSE = False
BASES = 2
WRITESUMMARY = False
SUMMARIESDIR = 'logs/'
if DATASET == 'ml_1m' or DATASET == 'ml_100k' or DATASET == 'douban':
NUMCLASSES = 5
elif DATASET == 'ml_10m':
NUMCLASSES = 10
print(
'\n WARNING: this might run out of RAM, consider using train_minibatch.py for dataset %s'
% DATASET)
print(
'If you want to proceed with this option anyway, uncomment this.\n'
)
sys.exit(1)
elif DATASET == 'flixster':
NUMCLASSES = 10
elif DATASET == 'yahoo_music':
NUMCLASSES = 71
if ACCUM == 'sum':
print(
'\n WARNING: combining DATASET=%s with ACCUM=%s can cause memory issues due to large number of classes.'
)
print(
'Consider using "--accum stack" as an option for this dataset.'
)
print(
'If you want to proceed with this option anyway, uncomment this.\n'
)
sys.exit(1)
# Splitting dataset in training, validation and test set
if DATASET == 'ml_1m' or DATASET == 'ml_10m':
if FEATURES:
datasplit_path = 'data/' + DATASET + '/withfeatures_split_seed' + str(
DATASEED) + '.pickle'
else:
datasplit_path = 'data/' + DATASET + '/split_seed' + str(
DATASEED) + '.pickle'
elif FEATURES:
datasplit_path = 'data/' + DATASET + '/withfeatures.pickle'
else:
datasplit_path = 'data/' + DATASET + '/nofeatures.pickle'
if DATASET == 'flixster' or DATASET == 'douban' or DATASET == 'yahoo_music':
u_features, v_features, adj_train, train_labels, train_u_indices, train_v_indices, \
val_labels, val_u_indices, val_v_indices, test_labels, \
test_u_indices, test_v_indices, class_values = load_data_monti(DATASET, TESTING)
elif DATASET == 'ml_100k':
print(
"Using official MovieLens dataset split u1.base/u1.test with 20% validation set size..."
)
u_features, v_features, adj_train, train_labels, train_u_indices, train_v_indices, \
val_labels, val_u_indices, val_v_indices, test_labels, \
test_u_indices, test_v_indices, class_values = load_official_trainvaltest_split(DATASET, TESTING)
else:
print("Using random dataset split ...")
u_features, v_features, adj_train, train_labels, train_u_indices, train_v_indices, \
val_labels, val_u_indices, val_v_indices, test_labels, \
test_u_indices, test_v_indices, class_values = create_trainvaltest_split(DATASET, DATASEED, TESTING,
datasplit_path, SPLITFROMFILE,
VERBOSE)
num_users, num_items = adj_train.shape
num_side_features = 0
# feature loading
if not FEATURES:
u_features = sp.identity(
num_users, format='csr') # features is just one-hot vector!
v_features = sp.identity(num_items, format='csr')
u_features, v_features = preprocess_user_item_features(
u_features, v_features)
elif FEATURES and u_features is not None and v_features is not None:
# use features as side information and node_id's as node input features
print("Normalizing feature vectors...")
u_features_side = normalize_features(u_features)
v_features_side = normalize_features(v_features)
u_features_side, v_features_side = preprocess_user_item_features(
u_features_side, v_features_side)
u_features_side = np.array(u_features_side.todense(), dtype=np.float32)
v_features_side = np.array(v_features_side.todense(), dtype=np.float32)
num_side_features = u_features_side.shape[1]
# node id's for node input features
id_csr_v = sp.identity(num_items, format='csr')
id_csr_u = sp.identity(num_users, format='csr')
u_features, v_features = preprocess_user_item_features(
id_csr_u, id_csr_v)
else:
raise ValueError(
'Features flag is set to true but no features are loaded from dataset '
+ DATASET)
# print("User features shape: " + str(u_features.shape))
# print("Item features shape: " + str(v_features.shape))
# print("adj_train shape: " + str(adj_train.shape))
# global normalization
support = []
support_t = []
adj_train_int = sp.csr_matrix(adj_train, dtype=np.int32)
for i in range(NUMCLASSES):
# build individual binary rating matrices (supports) for each rating
support_unnormalized = sp.csr_matrix(adj_train_int == i + 1,
dtype=np.float32)
if support_unnormalized.nnz == 0 and DATASET != 'yahoo_music':
# yahoo music has dataset split with not all ratings types present in training set.
# this produces empty adjacency matrices for these ratings.
sys.exit(
'ERROR: normalized bipartite adjacency matrix has only zero entries!!!!!'
)
support_unnormalized_transpose = support_unnormalized.T
support.append(support_unnormalized)
support_t.append(support_unnormalized_transpose)
support = globally_normalize_bipartite_adjacency(support, symmetric=SYM)
support_t = globally_normalize_bipartite_adjacency(support_t,
symmetric=SYM)
if SELFCONNECTIONS:
support.append(sp.identity(u_features.shape[0], format='csr'))
support_t.append(sp.identity(v_features.shape[0], format='csr'))
num_support = len(support)
support = sp.hstack(support, format='csr')
support_t = sp.hstack(support_t, format='csr')
# support and support_t become 3000x15000 (for douban with 3000 users/items and 5 ratings)
# support is n_users x (n_items*n_ratings). support_t is n_items x (n_users*ratings)
# NOTE: support is sparse matrix so the shape may not be as large as expected (?)
# When is num_support ever not == num_rating_classes?
# print('support shape: ' + str(support.shape))
# print('support_t shape: ' + str(support_t.shape))
if ACCUM == 'stack' or ACCUM == 'stackRGGCN':
div = HIDDEN[0] // num_support
if HIDDEN[0] % num_support != 0:
print(
"""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d such that
it can be evenly split in %d splits.\n""" %
(HIDDEN[0], num_support * div, num_support))
HIDDEN[0] = num_support * div
##################################################################################################################
""" support contains only training set ratings. index into support using user/item indices to create test set support. """
test_support = val_support = train_support = support
test_support_t = val_support_t = train_support_t = support_t
if GCMC_INDICES:
# Collect all user and item nodes for test set
test_u = list(set(test_u_indices))
test_v = list(set(test_v_indices))
test_support = support[np.array(test_u)]
test_support_t = support_t[np.array(test_v)]
# Collect all user and item nodes for validation set
val_u = list(set(val_u_indices))
val_v = list(set(val_v_indices))
val_support = support[np.array(val_u)]
val_support_t = support_t[np.array(val_v)]
# Collect all user and item nodes for train set
train_u = list(set(train_u_indices))
train_v = list(set(train_v_indices))
train_support = support[np.array(train_u)]
train_support_t = support_t[np.array(train_v)]
test_u_dict = {n: i for i, n in enumerate(test_u)}
test_v_dict = {n: i for i, n in enumerate(test_v)}
test_u_indices = np.array([test_u_dict[o] for o in test_u_indices])
test_v_indices = np.array([test_v_dict[o] for o in test_v_indices])
val_u_dict = {n: i for i, n in enumerate(val_u)}
val_v_dict = {n: i for i, n in enumerate(val_v)}
val_u_indices = np.array([val_u_dict[o] for o in val_u_indices])
val_v_indices = np.array([val_v_dict[o] for o in val_v_indices])
train_u_dict = {n: i for i, n in enumerate(train_u)}
train_v_dict = {n: i for i, n in enumerate(train_v)}
print('max train_u_indices: {}'.format(max(train_u_indices)))
train_u_indices = np.array(
[train_u_dict[o] for o in train_u_indices]
) ### HERE IS WHERE indices get changed to suit the new indexing into smaller set of users
train_v_indices = np.array([train_v_dict[o] for o in train_v_indices])
print('max train_u_indices after: {}'.format(max(train_u_indices)))
# print('train_support_shape: {}'.format(train_support.shape)) # if GCMC_INDICES, THIS IS NO LONGER (n_users, n_items*n_rating_types). but < n_users
##################################################################################################################
# features as side info
if FEATURES:
test_u_features_side = u_features_side[np.array(test_u)]
test_v_features_side = v_features_side[np.array(test_v)]
val_u_features_side = u_features_side[np.array(val_u)]
val_v_features_side = v_features_side[np.array(val_v)]
train_u_features_side = u_features_side[np.array(train_u)]
train_v_features_side = v_features_side[np.array(train_v)]
else:
test_u_features_side = None
test_v_features_side = None
val_u_features_side = None
val_v_features_side = None
train_u_features_side = None
train_v_features_side = None
placeholders = {
'u_features':
tf.sparse_placeholder(tf.float32,
shape=np.array(u_features.shape,
dtype=np.int64)),
'v_features':
tf.sparse_placeholder(tf.float32,
shape=np.array(v_features.shape,
dtype=np.int64)),
'u_features_nonzero':
tf.placeholder(tf.int32, shape=()),
'v_features_nonzero':
tf.placeholder(tf.int32, shape=()),
'labels':
tf.placeholder(tf.int32, shape=(None, )),
'u_features_side':
tf.placeholder(tf.float32, shape=(None, num_side_features)),
'v_features_side':
tf.placeholder(tf.float32, shape=(None, num_side_features)),
'user_indices':
tf.placeholder(tf.int32, shape=(None, )),
'item_indices':
tf.placeholder(tf.int32, shape=(None, )),
'class_values':
tf.placeholder(tf.float32, shape=class_values.shape),
'dropout':
tf.placeholder_with_default(0., shape=()),
'weight_decay':
tf.placeholder_with_default(0., shape=()),
'support':
tf.sparse_placeholder(tf.float32, shape=(None, None)),
'support_t':
tf.sparse_placeholder(tf.float32, shape=(None, None)),
}
##################################################################################################################
E_start, E_end = get_edges_matrices(adj_train)
# E_start = sp.hstack(E_start, format='csr') # confirm if vstack is correct and not hstack
# E_end = sp.hstack(E_end, format='csr')
# placeholders['E_start'] = tf.sparse_placeholder(tf.float32, shape=(None, None, None))
# placeholders['E_end'] = tf.sparse_placeholder(tf.float32, shape=(None, None, None))
placeholders['E_start_list'] = []
placeholders['E_end_list'] = []
for i in range(num_support):
placeholders['E_start_list'].append(
tf.sparse_placeholder(tf.float32, shape=(None, None)))
placeholders['E_end_list'].append(
tf.sparse_placeholder(tf.float32, shape=(None, None)))
# print('shape of E_end for first rating type: {}'.format(E_end[0].toarray().shape))
##################################################################################################################
# create model
if FEATURES:
model = RecommenderSideInfoGAE(placeholders,
input_dim=u_features.shape[1],
feat_hidden_dim=FEATHIDDEN,
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
num_side_features=num_side_features,
logging=True)
else:
model = RecommenderGAE(placeholders,
input_dim=u_features.shape[1],
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
num_layers=NUM_LAYERS,
logging=True)
# Convert sparse placeholders to tuples to construct feed_dict. sparse placeholders expect tuple of (indices, values, shape)
test_support = sparse_to_tuple(test_support)
test_support_t = sparse_to_tuple(test_support_t)
val_support = sparse_to_tuple(val_support)
val_support_t = sparse_to_tuple(val_support_t)
train_support = sparse_to_tuple(train_support)
train_support_t = sparse_to_tuple(train_support_t)
u_features = sparse_to_tuple(u_features)
v_features = sparse_to_tuple(v_features)
assert u_features[2][1] == v_features[2][
1], 'Number of features of users and items must be the same!'
num_features = u_features[2][1]
u_features_nonzero = u_features[1].shape[0]
v_features_nonzero = v_features[1].shape[0]
# setting E_start to be the same for train, val, and test. E_start already only contains train edges (from preprocessing script)
train_E_start = []
train_E_end = []
# print('LENGTH OF E_START: {}'.format(len(E_start)))
# print('NUM_SUPPORT: {}'.format(num_support))
for i in range(num_support):
train_E_start.append(sparse_to_tuple(E_start[i]))
train_E_end.append(sparse_to_tuple(E_end[i]))
val_E_start = test_E_start = train_E_start
val_E_end = test_E_end = train_E_end
# Feed_dicts for validation and test set stay constant over different update steps
train_feed_dict = construct_feed_dict(
placeholders, u_features, v_features, u_features_nonzero,
v_features_nonzero, train_support, train_support_t, train_labels,
train_u_indices, train_v_indices, class_values, DO,
train_u_features_side, train_v_features_side, train_E_start,
train_E_end)
# No dropout for validation and test runs. DO = dropout. input for val and test is same u_features and v_features.
val_feed_dict = construct_feed_dict(
placeholders, u_features, v_features, u_features_nonzero,
v_features_nonzero, val_support, val_support_t, val_labels,
val_u_indices, val_v_indices, class_values, 0., val_u_features_side,
val_v_features_side, val_E_start, val_E_end)
test_feed_dict = construct_feed_dict(
placeholders, u_features, v_features, u_features_nonzero,
v_features_nonzero, test_support, test_support_t, test_labels,
test_u_indices, test_v_indices, class_values, 0., test_u_features_side,
test_v_features_side, test_E_start, test_E_end)
# Collect all variables to be logged into summary
merged_summary = tf.summary.merge_all()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
if WRITESUMMARY:
train_summary_writer = tf.summary.FileWriter(SUMMARIESDIR + '/train',
sess.graph)
val_summary_writer = tf.summary.FileWriter(SUMMARIESDIR + '/val')
else:
train_summary_writer = None
val_summary_writer = None
best_val_score = np.inf
best_val_loss = np.inf
best_epoch = 0
wait = 0
print('Training...')
#### COUTNING PARAMS
total_parameters = 0
for variable in tf.trainable_variables():
# shape is an array of tf.Dimension
shape = variable.get_shape()
variable_parameters = 1
for dim in shape:
variable_parameters *= dim.value
total_parameters += variable_parameters
print('Total params: {}'.format(total_parameters))
# FOR A VARIABLE LEARNING RATE
assign_placeholder = tf.placeholder(tf.float32)
assign_op = model.learning_rate.assign(assign_placeholder)
old_loss = float('inf')
# print('Original learning rate is {}'.format(sess.run(model.optimizer._lr)))
train_rmses, val_rmses, train_losses, val_losses = [], [], [], []
for epoch in tqdm(range(NB_EPOCH)):
t = time.time()
# Run single weight update
# outs = sess.run([model.opt_op, model.loss, model.rmse], feed_dict=train_feed_dict)
# with exponential moving averages
outs = sess.run([model.training_op, model.loss, model.rmse],
feed_dict=train_feed_dict)
train_avg_loss = outs[1]
train_rmse = outs[2]
val_avg_loss, val_rmse = sess.run([model.loss, model.rmse],
feed_dict=val_feed_dict)
# if train_avg_loss > 0.999*old_loss:
# consecutive += 1
# if consecutive >= consecutive_threshold:
# LR /= decay_rate
# sess.run(assign_op, feed_dict={assign_placeholder: LR})
# print('New learning rate is {}'.format(sess.run(model.optimizer._lr)))
# consecutive = 0
# else:
# consecutive = 0
# old_loss = train_avg_loss
train_rmses.append(train_rmse)
val_rmses.append(val_rmse)
train_losses.append(train_avg_loss)
val_losses.append(val_avg_loss)
if VERBOSE:
print("[*] Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(train_avg_loss), "train_rmse=",
"{:.5f}".format(train_rmse), "val_loss=",
"{:.5f}".format(val_avg_loss), "val_rmse=",
"{:.5f}".format(val_rmse), "\t\ttime=",
"{:.5f}".format(time.time() - t))
if val_rmse < best_val_score:
best_val_score = val_rmse
best_epoch = epoch
if epoch % 20 == 0 and WRITESUMMARY:
# Train set summary
summary = sess.run(merged_summary, feed_dict=train_feed_dict)
train_summary_writer.add_summary(summary, epoch)
train_summary_writer.flush()
# Validation set summary
summary = sess.run(merged_summary, feed_dict=val_feed_dict)
val_summary_writer.add_summary(summary, epoch)
val_summary_writer.flush()
if epoch % 100 == 0 and epoch > 1000 and not TESTING and False:
saver = tf.train.Saver()
save_path = saver.save(sess,
"tmp/%s_seed%d.ckpt" %
(model.name, DATASEED),
global_step=model.global_step)
# load polyak averages
variables_to_restore = model.variable_averages.variables_to_restore(
)
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, save_path)
val_avg_loss, val_rmse = sess.run([model.loss, model.rmse],
feed_dict=val_feed_dict)
print('polyak val loss = ', val_avg_loss)
print('polyak val rmse = ', val_rmse)
# Load back normal variables
saver = tf.train.Saver()
saver.restore(sess, save_path)
# store model including exponential moving averages
saver = tf.train.Saver()
save_path = saver.save(sess,
"tmp/%s.ckpt" % model.name,
global_step=model.global_step)
if VERBOSE:
print("\nOptimization Finished!")
print('best validation score =', best_val_score, 'at iteration',
best_epoch)
if TESTING:
test_avg_loss, test_rmse = sess.run([model.loss, model.rmse],
feed_dict=test_feed_dict)
print('test loss = ', test_avg_loss)
print('test rmse = ', test_rmse)
# restore with polyak averages of parameters
variables_to_restore = model.variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, save_path)
test_avg_loss, test_rmse = sess.run([model.loss, model.rmse],
feed_dict=test_feed_dict)
print('polyak test loss = ', test_avg_loss)
print('polyak test rmse = ', test_rmse)
sess.close()
tf.reset_default_graph()
return train_rmses, val_rmses, train_losses, val_losses, test_rmse
else:
# restore with polyak averages of parameters
variables_to_restore = model.variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, save_path)
val_avg_loss, val_rmse = sess.run([model.loss, model.rmse],
feed_dict=val_feed_dict)
print('polyak val loss = ', val_avg_loss)
print('polyak val rmse = ', val_rmse)
sess.close()
tf.reset_default_graph()
return train_rmses, val_rmses, train_losses, val_losses, val_rmse