def _dump_example_ids(self, dumper, start_index, batch_num, batch_size):
self.assertEqual(start_index, dumper.get_next_index())
self.assertEqual(dumper.get_next_index(), start_index)
index = start_index
for i in range(batch_num):
example_id_batch = dj_pb.LiteExampleIds(
partition_id=0,
begin_index=index
)
for j in range(batch_size):
example_id_batch.example_id.append('{}'.format(index).encode())
example_id_batch.event_time.append(150000000+index)
self.end_index = index
index += 1
packed_example_id_batch = dj_pb.PackedLiteExampleIds(
partition_id=0,
begin_index=index-batch_size,
example_id_num=batch_size,
sered_lite_example_ids=example_id_batch.SerializeToString()
)
dumper.add_example_id_batch(packed_example_id_batch)
self.assertEqual(dumper.get_next_index(), index)
dumper.finish_sync_example_id()
self.assertTrue(dumper.need_dump())
with dumper.make_example_id_dumper() as eid:
eid()
def make_packed_lite_example_ids(self):
return dj_pb.PackedLiteExampleIds(
partition_id=self._partition_id,
begin_index=self._begin_index,
example_id_num=len(self._example_ids),
sered_lite_example_ids=dj_pb.LiteExampleIds(
partition_id=self._partition_id,
begin_index=self._begin_index,
example_id=self._example_ids,
event_time=self._event_times).SerializeToString())
def generate_example_id(self, dumper, start_index, item_count):
self.total_example_id_count += item_count
for req_index in range(start_index // 512,
self.total_example_id_count // 512):
cands = list(range(req_index * 512, (req_index + 1) * 512))
start_index = cands[0]
for i in range(len(cands)):
if random.randint(1, 4) > 1:
continue
a = random.randint(i - 64, i + 64)
b = random.randint(i - 64, i + 64)
if a < 0:
a = 0
if a >= len(cands):
a = len(cands) - 1
if b < 0:
b = 0
if b >= len(cands):
b = len(cands) - 1
if (abs(cands[a] - i - start_index) <= 64
and abs(cands[b] - i - start_index) <= 64):
cands[a], cands[b] = cands[b], cands[a]
example_id_list = []
event_time_list = []
for example_idx in cands:
example_id_list.append('{}'.format(example_idx).encode())
event_time_list.append(150000000 + example_idx)
tf_example_id = tf.train.Feature(bytes_list=tf.train.BytesList(
value=example_id_list))
tf_event_time = tf.train.Feature(int64_list=tf.train.Int64List(
value=event_time_list))
example_id_batch = dj_pb.LiteExampleIds(
partition_id=0,
begin_index=req_index * 512,
features=tf.train.Features(feature={
'example_id': tf_example_id,
'event_time': tf_event_time
}))
packed_example_id_batch = dj_pb.PackedLiteExampleIds(
partition_id=0,
begin_index=req_index * 512,
example_id_num=len(cands),
sered_lite_example_ids=example_id_batch.SerializeToString())
dumper.add_example_id_batch(packed_example_id_batch)
self.assertEqual(dumper.get_next_index(), (req_index + 1) * 512)
self.assertTrue(dumper.need_dump())
with dumper.make_example_id_dumper() as eid:
eid()
def generate_example_id(self, dumper, start_index, item_count):
self.total_example_id_count += item_count
for req_index in range(start_index // 512,
self.total_example_id_count // 512):
example_id_batch = dj_pb.LiteExampleIds(partition_id=0,
begin_index=req_index *
512)
cands = list(range(req_index * 512, (req_index + 1) * 512))
start_index = cands[0]
for i in range(len(cands)):
if random.randint(1, 4) > 1:
continue
a = random.randint(i - 64, i + 64)
b = random.randint(i - 64, i + 64)
if a < 0:
a = 0
if a >= len(cands):
a = len(cands) - 1
if b < 0:
b = 0
if b >= len(cands):
b = len(cands) - 1
if (abs(cands[a] - i - start_index) <= 64
and abs(cands[b] - i - start_index) <= 64):
cands[a], cands[b] = cands[b], cands[a]
for example_idx in cands:
example_id = '{}'.format(example_idx).encode()
example_id_batch.example_id.append(example_id)
example_id_batch.event_time.append(150000000 + example_idx)
if self.version == Version.V2:
click_id = '%s_%s' % (example_id.decode(),
example_id.decode())
example_id_batch.click_id.append(click_id.encode())
example_id_batch.id_type.append('IMEI'.encode())
example_id_batch.event_time_deep.append(150000000 +
example_idx + 1)
example_id_batch.type.append(b'1')
packed_example_id_batch = dj_pb.PackedLiteExampleIds(
partition_id=0,
begin_index=req_index * 512,
example_id_num=len(cands),
sered_lite_example_ids=example_id_batch.SerializeToString())
dumper.add_example_id_batch(packed_example_id_batch)
self.assertEqual(dumper.get_next_index(), (req_index + 1) * 512)
self.assertTrue(dumper.need_dump())
with dumper.make_example_id_dumper() as eid:
eid()
def make_packed_lite_example_ids(self):
serde_lite_examples = dj_pb.LiteExampleIds(
partition_id=self._partition_id,
begin_index=self._begin_index,
example_id=self._example_ids,
event_time=self._event_times,
)
if len(self._id_types) > 0:
serde_lite_examples.id_type.extend(self._id_types)
serde_lite_examples.event_time_deep.extend(self._event_time_deeps)
serde_lite_examples.type.extend(self._types)
serde_lite_examples.click_id.extend(self._click_ids)
return dj_pb.PackedLiteExampleIds(
partition_id=self._partition_id,
begin_index=self._begin_index,
example_id_num=len(self._example_ids),
sered_lite_example_ids=serde_lite_examples.SerializeToString())
def make_packed_lite_example_ids(self):
features = {}
for name, value_list in self._feature_buf.items():
if len(value_list) > 0:
if isinstance(value_list[0], int):
features[name] = tf.train.Feature(
int64_list=tf.train.Int64List(value=value_list))
else:
features[name] = tf.train.Feature(
bytes_list=tf.train.BytesList(value=value_list))
tf_features = tf.train.Features(feature=features)
serde_lite_examples = dj_pb.LiteExampleIds(
partition_id=self._partition_id,
begin_index=self._begin_index,
features=tf_features)
return dj_pb.PackedLiteExampleIds(
partition_id=self._partition_id,
begin_index=self._begin_index,
example_id_num=self.__len__(),
sered_lite_example_ids=serde_lite_examples.SerializeToString())
def _dump_example_ids(self, dumper, start_index, batch_num, batch_size):
self.assertEqual(start_index, dumper.get_next_index())
self.assertEqual(dumper.get_next_index(), start_index)
index = start_index
for i in range(batch_num):
example_id_list = []
event_time_list = []
prev_index = index
for j in range(batch_size):
example_id_list.append('{}'.format(index).encode())
event_time_list.append(150000000 + index)
self.end_index = index
index += 1
tf_example_id = tf.train.Feature(bytes_list=tf.train.BytesList(
value=example_id_list))
tf_event_time = tf.train.Feature(int64_list=tf.train.Int64List(
value=event_time_list))
example_id_batch = dj_pb.LiteExampleIds(
partition_id=0,
begin_index=prev_index,
features=tf.train.Features(feature={
'example_id': tf_example_id,
'event_time': tf_event_time
}))
packed_example_id_batch = dj_pb.PackedLiteExampleIds(
partition_id=0,
begin_index=index - batch_size,
example_id_num=batch_size,
sered_lite_example_ids=example_id_batch.SerializeToString())
dumper.add_example_id_batch(packed_example_id_batch)
self.assertEqual(dumper.get_next_index(), index)
dumper.finish_sync_example_id()
self.assertTrue(dumper.need_dump())
with dumper.make_example_id_dumper() as eid:
eid()
def generate_example_id(self, dumper, start_index, item_count):
self.total_example_id_count += item_count
for req_index in range(start_index // 512,
self.total_example_id_count // 512):
cands = list(range(req_index * 512, (req_index + 1) * 512))
start_index = cands[0]
for i in range(len(cands)):
if random.randint(1, 4) > 1:
continue
a = random.randint(i - 64, i + 64)
b = random.randint(i - 64, i + 64)
if a < 0:
a = 0
if a >= len(cands):
a = len(cands) - 1
if b < 0:
b = 0
if b >= len(cands):
b = len(cands) - 1
if (abs(cands[a] - i - start_index) <= 64
and abs(cands[b] - i - start_index) <= 64):
cands[a], cands[b] = cands[b], cands[a]
features = {
'example_id': [],
'event_time': [],
'click_id': [],
'id_type': [],
'event_time_deep': [],
'type': [],
}
for example_idx in cands:
example_id = '{}'.format(example_idx).encode()
features['example_id'].append(example_id)
features['event_time'].append((150000000 + example_idx))
if self.version == Version.V2:
click_id = '%s_%s' % (example_id.decode(),
example_id.decode())
features['click_id'].append(click_id.encode())
features['id_type'].append('IMEI'.encode())
features['event_time_deep'].append(150000000 +
example_idx + 1)
features['type'].append(b'1')
feature_list = tf.train.Features(
feature={
'example_id':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=features['example_id'])),
'event_time':
tf.train.Feature(int64_list=tf.train.Int64List(
value=features['event_time'])),
'click_id':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=features['click_id'])),
'id_type':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=features['id_type'])),
'type':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=features['type'])),
'event_time_deep':
tf.train.Feature(int64_list=tf.train.Int64List(
value=features['event_time_deep']))
})
example_id_batch = dj_pb.LiteExampleIds(partition_id=0,
begin_index=req_index *
512,
features=feature_list)
packed_example_id_batch = dj_pb.PackedLiteExampleIds(
partition_id=0,
begin_index=req_index * 512,
example_id_num=len(cands),
sered_lite_example_ids=example_id_batch.SerializeToString())
dumper.add_example_id_batch(packed_example_id_batch)
self.assertEqual(dumper.get_next_index(), (req_index + 1) * 512)
self.assertTrue(dumper.need_dump())
with dumper.make_example_id_dumper() as eid:
eid()