def train_next(self,
train_dataset,
test_dataset,
batch_size,
out_path,
params={},
**kargs):
# copy existing materials to new path
if out_path != self.save_path:
train.copy(self.save_path, out_path)
save_model_path = os.path.join(out_path, 'model.ckpt')
# setting some parameters
n_epoch = params.get('n_epoch', 10)
freq_eval = params.get('freq_eval', 20)
metric = params.get('metric', 'f1')
#
# build batches
#
train_batch, test_batch = create_batch(train_dataset,
batch_size), create_batch(
test_dataset, batch_size)
print('-- start training')
train_count = 0
eval_count = 0
best_test = -np.inf
decay_lr_every = 500
lr_decay = 0.9
init_lr = self.model.lr
#
# training
#
for epoch in range(n_epoch):
print("\n[E]poch %i, lr %.4f" % (epoch + 1, init_lr))
bar = Bar('training', max=len(train_batch), suffix='')
tabl = PrettyTable([
'status', 'train_epoch', 'train_batch', 'test_avg_score',
'test_avg_loss'
])
loss_total, acc_total = [], []
for i, batch_id in enumerate(
np.random.permutation(len(train_batch))):
train_count += 1
batch = train_batch[i]
loss, acc = self.model.batch_run(batch=batch,
i=train_count,
mode='train',
metric=metric)
loss_total.append(loss)
acc_total.append(acc)
bar.bar_prefix = " | batch %i |" % (batch_id + 1)
bar.bar_suffix = " | cur_loss: %.3f | cur_acc: %.3f | best_test_acc: %.3f" % (
loss, acc, best_test)
bar.next()
if train_count % decay_lr_every == 0:
init_lr = init_lr * lr_decay
if train_count % freq_eval == 0:
test_loss_lst, test_acc_lst = [], []
for t_i, t_batch_id in enumerate(range(len(test_batch))):
t_batch = test_batch[t_batch_id]
eval_count += 1
loss, acc = self.model.batch_run(batch=t_batch,
i=eval_count,
mode='eval',
metric=metric,
lr=init_lr)
test_loss_lst.append(loss)
test_acc_lst.append(acc)
test_loss = np.array(test_loss_lst).mean()
test_acc = np.array(test_acc_lst).mean()
status = '++++'
if test_acc > best_test:
best_test = test_acc
status = 'best'
self.model.save(save_model_path)
tabl.add_row([
status, epoch + 1, batch_id + 1,
'%.3f' % test_acc,
'%.3f' % test_loss
])
print "\nmean_train_loss: %.3f, mean_train_acc: %.3f" % (
np.mean(loss_total), np.mean(acc_total))
print(tabl.get_string(title="Local All Test Accuracies"))
def train(config, params, train_dataset, test_dataset, add_training_log):
#
# build batches
#
train_batch, test_batch = create_batch(train_dataset,
params['batch_size']), create_batch(
test_dataset,
params['batch_size'])
#
# save params
#
cPickle.dump(params, open(config['save_params_path'], 'w'))
#
# build tensorflow model
#
@timeit
def build_tf_model():
print('-- build model')
model = config['model'](**params)
model.build_model()
# build custom tensorboard
print('-- build tensorboard')
# tensorboard = CustomTensorboard(dir_summary=config['save_summary_path'], model_graph=model.sess.graph,
# metric=config['metric'])
tensorboard = None
return model, tensorboard
model, tensorboard = build_tf_model()
#
# training parameters
#
print('-- start training')
train_count = 0
eval_count = 0
best_test = 0.0
decay_lr_every = 500
lr_decay = 0.9
init_lr = model.lr
#
# training
#
for epoch in range(params['nepochs']):
print("\n[E]poch %i, lr %.4f" % (epoch + 1, init_lr))
bar = Bar('training', max=len(train_batch), suffix='')
tabl = PrettyTable([
'status', 'train_epoch', 'train_batch', 'test_avg_score',
'test_avg_loss'
])
loss_total, acc_total = [], []
for i, batch_id in enumerate(np.random.permutation(len(train_batch))):
train_count += 1
batch = train_batch[i]
loss, acc = model.batch_run(batch=batch,
i=train_count,
mode='train',
metric=config['metric'])
loss_total.append(loss)
acc_total.append(acc)
bar.bar_prefix = " | batch %i |" % (batch_id + 1)
bar.bar_suffix = " | cur_loss: %.3f | cur_acc: %.3f | best_test_acc: %.3f" % (
loss, acc, best_test)
bar.next()
if train_count % decay_lr_every == 0: init_lr = init_lr * lr_decay
if train_count % params['freq_eval'] == 0:
test_loss_lst, test_acc_lst = [], []
for t_i, t_batch_id in enumerate(range(len(test_batch))):
t_batch = test_batch[t_batch_id]
eval_count += 1
loss, acc = model.batch_run(batch=t_batch,
i=eval_count,
mode='eval',
metric=config['metric'],
lr=init_lr)
test_loss_lst.append(loss)
test_acc_lst.append(acc)
test_loss = np.array(test_loss_lst).mean()
test_acc = np.array(test_acc_lst).mean()
status = '++++'
if test_acc > best_test:
best_test = test_acc
status = 'best'
model.save(config['save_model_path'])
tabl.add_row([
status, epoch + 1, batch_id + 1,
'%.3f' % test_acc,
'%.3f' % test_loss
])
print "\nmean_train_loss: %.3f, mean_train_acc: %.3f" % (
np.mean(loss_total), np.mean(acc_total))
print(tabl.get_string(title="Local All Test Accuracies"))
return best_test, config['saved_path']
def main(test_cold_user_path, test_warm_path, train_path, u_content_path,
v_content_path, saved_model, batch_size, n_epoch, learning_rate,
n_items_per_user, dropout, topk, latent_dim, content_dim,
lightfm_path, **kargs):
test_cold_user_matrix = pd.read_csv(test_cold_user_path,
sep=',',
names=['profile', 'item', 'rating'])
test_warm_matrix = pd.read_csv(test_warm_path,
sep=',',
names=['profile', 'item', 'rating'])
train_matrix = pd.read_csv(train_path,
sep=',',
names=['profile', 'item', 'rating'])
lightfm = cPickle.load(open(lightfm_path, 'r'))
u_pref_matrix = lightfm.user_embeddings.astype('float32')
v_pref_matrix = lightfm.item_embeddings.astype('float32')
u_bias_matrix = lightfm.user_biases.reshape((-1, 1)).astype('float32')
v_bias_matrix = lightfm.item_biases.reshape((-1, 1)).astype('float32')
u_content_matrix = np.fromfile(file=u_content_path,
dtype=np.float32).reshape(
n_users, content_dim)
v_content_matrix = np.fromfile(file=v_content_path,
dtype=np.float32).reshape(
n_items, content_dim)
# adding zero for dropout purpose
u_pref_zero_matrix = np.vstack(
[u_pref_matrix, np.zeros_like(u_pref_matrix[0, :])])
v_pref_zero_matrix = np.vstack(
[v_pref_matrix, np.zeros_like(v_pref_matrix[0, :])])
u_bias_zero_matrix = np.vstack(
[u_bias_matrix, np.zeros_like(u_bias_matrix[0, :])])
v_bias_zero_matrix = np.vstack(
[v_bias_matrix, np.zeros_like(v_bias_matrix[0, :])])
zero_user_id = u_pref_zero_matrix.shape[0] - 1
zero_item_id = v_pref_zero_matrix.shape[0] - 1
# create model for storing necessary materials
if not os.path.isdir(saved_model): os.makedirs(saved_model)
save_paths = {
'model_class': os.path.join(saved_model, 'model.pkl'),
'model_deep': os.path.join(saved_model, 'model.ckpt')
}
# augment training datas
train_matrix = data_augment(R=train_matrix,
n_items_per_user=n_items_per_user,
batch_size=batch_size,
u_pref=u_pref_matrix,
v_pref=v_pref_matrix,
u_bias=u_bias_matrix,
v_bias=v_bias_matrix)
# create train and eval batchs
train_batchs = create_train_batchs(R=train_matrix, batch_size=batch_size)
test_cold_user_batchs = create_eval_batchs(R=test_cold_user_matrix,
batch_size=batch_size,
is_cold_user=True)
test_warm_batchs = create_eval_batchs(R=test_warm_matrix,
batch_size=batch_size)
# model
model = NN(latent_dim=latent_dim,
user_feature_dim=content_dim,
item_feature_dim=content_dim,
out_dim=200,
default_lr=learning_rate,
k=topk,
do_batch_norm=True)
cPickle.dump(model, open(save_paths['model_class'], 'w'))
model.set_np_matrix(np_u_pref_zero=u_pref_zero_matrix,
np_v_pref_zero=v_pref_zero_matrix,
np_u_cont=u_content_matrix,
np_v_cont=v_content_matrix,
np_u_bias_zero=u_bias_zero_matrix,
np_v_bias_zero=v_bias_zero_matrix)
model.build()
# train
batch_len = len(train_batchs)
n_step = 0
freq_eval = 40
best_cu_score, best_ci_score, best_w_score = -np.inf, -np.inf, -np.inf
decay_lr_every = 100
lr_decay = 0.95
for epoch_i in range(n_epoch):
print("\n[E]poch %i, lr %.4f, dropout %.2f" %
(epoch_i + 1, learning_rate, dropout))
bar = Bar('training', max=batch_len, suffix='')
tabl = PrettyTable([
'status', 'train_epoch', 'train_batch', 'cold_user_acc',
'cold_item_acc', 'warm_acc', 'total'
])
batch_ids = np.random.permutation(batch_len)
train_loss_total = []
for batch_i, batch_id in enumerate(batch_ids):
n_step += 1
batch = train_batchs[batch_id]
# dropout or not
if dropout != 0:
batch.dropout_user(dropout_prob=dropout, zero_id=zero_user_id)
#batch.dropout_item(dropout_prob = dropout, zero_id = zero_item_id)
train_loss, _ = model.run(datas=batch,
mode=model.train_signal,
lr=learning_rate)
train_loss_total.append(train_loss)
bar.bar_prefix = " | batch %i |" % (batch_i + 1)
bar.bar_suffix = " | cur_loss: %.4f | " % train_loss
bar.next()
if n_step % decay_lr_every == 0:
learning_rate = lr_decay * learning_rate
if n_step % freq_eval == 0:
cu_acc_mean = score_eval_batch(datas=test_cold_user_batchs,
model=model)
ci_acc_mean = 1.0 #score_eval_batch_map(datas=test_cold_item_batchs, model=model, topks=topks)
w_acc_mean = score_eval_batch(datas=test_warm_batchs,
model=model)
status = '++++'
if (cu_acc_mean + ci_acc_mean + w_acc_mean) >= (
best_cu_score + best_ci_score + best_w_score):
best_w_score = w_acc_mean
best_ci_score = ci_acc_mean
best_cu_score = cu_acc_mean
status = 'best'
# save model
model.save(save_paths['model_deep'])
tabl.add_row([
status, epoch_i + 1, batch_i + 1,
"%.3f" % cu_acc_mean,
"%.3f" % ci_acc_mean,
"%.3f" % w_acc_mean,
"%.4f" % (cu_acc_mean + w_acc_mean + ci_acc_mean)
])
print "\nmean_loss: %.3f" % np.mean(train_loss_total)
print(tabl.get_string(title="Local All Test Accuracies"))
