def test_(self, testset, epoch=0):
assert (testset.num_features == self.num_features)
auc_ = 0.5
if self.evaluate_op:
teX, teY = testset.full_batch()
predicts, auc_, cost_ = self.session.run(
[self.predict_op, self.evaluate_op, self.cost],
feed_dict=melt.gen_feed_dict(self.trainer,
self.algo,
teX,
teY,
test_mode=True))
else:
# teX, teY = testset.full_batch()
# predicts, cost_ = self.session.run([self.predict_op, self.cost], feed_dict = melt.gen_feed_dict(self.trainer, self.algo, teX, teY, test_mode = True))
num_test_instances = testset.num_instances()
predicts = []
cost_ = 0
while True:
teX, teY = testset.next_batch(batch_size)
if teX is None:
break
predicts_, now_cost = self.session.run(
[self.predict_op, self.cost],
feed_dict=melt.gen_feed_dict(self.trainer,
self.algo,
teX,
teY,
test_mode=True))
predicts.extend(predicts_)
cost_ += now_cost
teY = testset.labels
predicts = np.array(predicts)
#print np.array(zip(teY, predicts))
#print len(teY), len(predicts)
auc_ = auc(teY, predicts)
print epoch, ' auc:', auc_, 'cost:', cost_ / len(teY)
need_stop = False
if FLAGS.auto_stop and (auc_ - self.auc) < FLAGS.min_improve:
need_stop = True
self.auc = auc_
return need_stop
def test_(self, testset, epoch=0):
assert (testset.num_features == self.num_features)
auc_ = 0.0
if self.evaluate_op:
teX, teY = testset.full_batch()
predicts, auc_, cost_ = self.session.run(
[self.predict_op, self.evaluate_op, self.cost],
feed_dict=melt.gen_feed_dict(self.trainer,
self.algo,
teX,
teY,
test_mode=True))
else:
# teX, teY = testset.full_batch()
# predicts, cost_ = self.session.run([self.predict_op, self.cost], feed_dict = melt.gen_feed_dict(self.trainer, self.algo, teX, teY, test_mode = True))
num_test_instances = testset.num_instances()
predicts = []
cost_ = 0
while True:
teX, teY = testset.next_batch(batch_size)
if teX is None:
break
predicts_, now_cost = self.session.run(
[self.predict_op, self.cost],
feed_dict=melt.gen_feed_dict(self.trainer,
self.algo,
teX,
teY,
test_mode=True))
predicts.extend(predicts_)
cost_ += now_cost
teY = testset.labels
predicts = np.array(predicts)
#print teY
#print predicts
print np.array(zip(teY, predicts))
#print len(teY), len(predicts)
auc_ = auc(teY, predicts)
#auc_ = 0.5
#predicts, auc_, cost_, weight = self.session.run([predict_op, evaluate_op, cost, self.weight], feed_dict = trainer.gen_feed_dict(teX, teY))
print epoch, ' auc:', auc_, 'cost:', cost_ / len(teY)
def calibrate_(self, dataset):
num_instances = dataset.num_instances()
#@TODO better iteration now will lost last group data
for start, end in zip(range(0, num_instances, batch_size),
range(batch_size, num_instances, batch_size)):
trX, trY = dataset.mini_batch(start, end)
predicts = self.session.run(self.predict_op,
feed_dict=melt.gen_feed_dict(
self.trainer, self.algo, trX, trY))
self.calibrate(trY, predicts)
self.calibrator.FinishTraining()
def predict(self, feature_vecs):
if type(feature_vecs) != list:
feature_vecs = [feature_vecs]
spf = melt.sparse_vectors2sparse_features(feature_vecs)
predicts = self.session.run([self.predict_op],
feed_dict=melt.gen_feed_dict(
self.trainer,
self.algo,
spf,
test_mode=True))
return predicts
def train_(self, trainset):
num_train_instances = trainset.num_instances()
#@TODO this minibatch will lost the last instances..
# for start, end in zip(range(0, num_train_instances, batch_size), range(batch_size, num_train_instances, batch_size)):
# trX, trY = trainset.mini_batch(start, end)
# self.session.run(self.train_op, feed_dict = melt.gen_feed_dict(self.trainer, self.algo, trX, trY))
while True:
trX, trY = trainset.next_batch(batch_size)
if trX is None:
break
self.session.run(self.train_op,
feed_dict=melt.gen_feed_dict(
self.trainer, self.algo, trX, trY))
def predict(self, feature_vecs):
if type(feature_vecs) != list:
feature_vecs = [feature_vecs]
trX = None
if self.trainer.type == 'sparse':
trX = melt.sparse_vectors2sparse_features(feature_vecs)
else: #dense
trX = melt.dense_vectors2features(feature_vecs,
self.trainer.index_only)
predicts = self.session.run([self.predict_op],
feed_dict=melt.gen_feed_dict(
self.trainer,
self.algo,
trX,
test_mode=True))
return predicts
def train_(self, trainset, testset=None, epoch=0):
num_train_instances = trainset.num_instances()
round = 0
start_time = time.time()
while True:
trX, trY = trainset.next_batch(batch_size)
if trX is None:
break
feed_dict = melt.gen_feed_dict(self.trainer, self.algo, trX, trY)
_, cost_, accuracy_ = self.session.run(
[self.train_op, self.cost, self.accuracy], feed_dict=feed_dict)
#py_x = self.session.run(self.py_x, feed_dict = feed_dict)
#print py_x
if round % 100 == 0:
end_time = time.time()
duration = end_time - start_time
start_time = end_time
print 'epoch:', epoch, 'round:', round, 'train precision@1:', accuracy_, 'cost:', cost_, 'duration:', duration
if round % 1000 == 0:
self.test_(testset, epoch, round)
round += 1
def test_(self, testset, epoch=0, round=0):
#assert(testset.num_features == self.num_features)
#num_test_instances = testset.num_instances()
predicts = []
cost_ = 0.
accuracy_ = 0.
ground = round
round = 0
while True:
teX, teY = testset.next_batch(batch_size * 10)
if teX is None:
break
feed_dict = melt.gen_feed_dict(self.trainer,
self.algo,
teX,
teY,
test_mode=True)
now_cost, now_accuracy = self.session.run(
[self.cost, self.accuracy], feed_dict=feed_dict)
cost_ += now_cost
accuracy_ += now_accuracy
round += 1
cost_ / round
accuracy_ /= round
print 'epoch:', epoch, 'round:', ground, 'test precision@1:', accuracy_, 'cost:', cost_
need_stop = False
#if FLAGS.auto_stop and (accuracy_ - self.avg_accuracy) < FLAGS.min_improve:
# need_stop = True
self.avg_accuracy = accuracy_
# if FLAGS.use_summary:
# teX, teY = testset.full_batch()
# summary_str = self.session.run(merged_summary_op, feed_dict = melt.gen_feed_dict(self.trainer, self.algo, teX, teY, test_mode = True))
# summary_writer.add_summary(summary_str, epoch)
return need_stop
def train(self, trainset_file, testset_file, method, num_epochs,
learning_rate, model_path):
print 'batch_size:', batch_size, ' learning_rate:', learning_rate, ' num_epochs:', num_epochs
print 'method:', method
trainset = melt.load_dataset(trainset_file)
print "finish loading train set ", trainset_file
self.num_features = trainset.num_features
print 'num_features: ', self.num_features
print 'trainSet size: ', trainset.num_instances()
testset = melt.load_dataset(testset_file)
print "finish loading test set ", testset_file
assert (trainset.num_features == testset.num_features)
print 'testSet size: ', testset.num_instances()
algo = self.gen_algo(method)
trainer = melt.gen_binary_classification_trainer(trainset)
self.algo = algo
self.trainer = trainer
print 'trainer_type:', trainer.type
print 'trainer_index_only:', trainer.index_only
cost, train_op, predict_op, evaluate_op = self.foward(
algo, trainer, learning_rate)
#self.foward(algo, trainer, learning_rate)
config = None
if not FLAGS.show_device:
config = tf.ConfigProto()
else:
config = tf.ConfigProto(log_device_placement=True)
config.gpu_options.allocator_type = 'BFC'
self.session = tf.Session(config=config)
init = tf.initialize_all_variables()
self.session.run(init)
summary_writer = None
if FLAGS.use_summary:
tf.scalar_summary("cross_entropy", self.cost)
if FLAGS.use_auc_op:
tf.scalar_summary("auc", evaluate_op)
merged_summary_op = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter(FLAGS.summary_path,
self.session.graph_def)
#os.system('rm -rf ' + FLAGS.model)
os.system('mkdir -p ' + FLAGS.model)
self.save_info(model_path)
for epoch in range(num_epochs):
if epoch > 0 and FLAGS.shuffle:
trainset = melt.load_dataset(trainset_file)
self.train_(trainset)
need_stop = self.test_(testset, epoch=epoch)
if need_stop:
print 'need stop as improve is smaller then %f' % FLAGS.min_improve
break
#print weight
#@FIXME
if epoch % FLAGS.save_epochs == 0 and not trainer.index_only:
self.save_model(model_path, epoch)
self.save_model(model_path)
if FLAGS.calibrate:
dataset = trainset
if not FLAGS.calibrate_trainset:
dataset = testset
self.calibrate_(dataset) #@TODO may be test set is right?
CalibratorFactory.Save(self.calibrator,
model_path + '/calibrator.bin')
#self.calibrator.Save(model_path + '/calibrator.bin')
self.calibrator.SaveText(model_path + '/calibrator.txt')
if FLAGS.use_summary:
teX, teY = testset.full_batch()
summary_str = self.session.run(merged_summary_op,
feed_dict=melt.gen_feed_dict(
self.trainer,
self.algo,
teX,
teY,
test_mode=True))
summary_writer.add_summary(summary_str, epoch)
