class PredictionOutputsProcessor(BasePredictionOutputsProcessor):
def __init__(self):
if all(k in os.environ for k in (
ODPSConfig.PROJECT_NAME,
ODPSConfig.ACCESS_ID,
ODPSConfig.ACCESS_KEY,
ODPSConfig.ENDPOINT,
)):
self.odps_writer = ODPSWriter(
os.environ[ODPSConfig.PROJECT_NAME],
os.environ[ODPSConfig.ACCESS_ID],
os.environ[ODPSConfig.ACCESS_KEY],
os.environ[ODPSConfig.ENDPOINT],
"cifar10_prediction_outputs",
# TODO: Print out helpful error message if the columns and
# column_types do not match with the prediction outputs
columns=["f" + str(i) for i in range(10)],
column_types=["double" for _ in range(10)],
)
else:
self.odps_writer = None
def process(self, predictions, worker_id):
if self.odps_writer:
self.odps_writer.from_iterator(iter(predictions.numpy().tolist()),
worker_id)
else:
logger.info(predictions.numpy())
def test_write_from_iterator(self):
columns = ["num", "num2"]
column_types = ["bigint", "double"]
# If the table doesn't exist yet
writer = ODPSWriter(
self._project,
self._access_id,
self._access_key,
self._endpoint,
self._test_write_table,
columns,
column_types,
)
writer.from_iterator(iter([[1, 0.5], [2, 0.6]]), 2)
table = self._odps_client.get_table(self._test_write_table,
self._project)
self.assertEqual(table.schema.names, columns)
self.assertEqual(table.schema.types, column_types)
self.assertEqual(table.to_df().count(), 1)
# If the table already exists
writer = ODPSWriter(
self._project,
self._access_id,
self._access_key,
self._endpoint,
self._test_write_table,
)
writer.from_iterator(iter([[1, 0.5], [2, 0.6]]), 2)
table = self._odps_client.get_table(self._test_write_table,
self._project)
self.assertEqual(table.schema.names, columns)
self.assertEqual(table.schema.types, column_types)
self.assertEqual(table.to_df().count(), 2)
def __init__(self):
if is_odps_configured():
self.odps_writer = ODPSWriter(
os.environ[ODPSConfig.PROJECT_NAME],
os.environ[ODPSConfig.ACCESS_ID],
os.environ[ODPSConfig.ACCESS_KEY],
os.environ[ODPSConfig.ENDPOINT],
"cifar10_prediction_outputs",
# TODO: Print out helpful error message if the columns and
# column_types do not match with the prediction outputs
columns=["f" + str(i) for i in range(10)],
column_types=["double" for _ in range(10)],
)
else:
self.odps_writer = None