[FLINK-30122] Flink ML KMeans getting model data throws TypeError

• Type: Bug
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Affects Version/s: ml-2.1.0
• Fix Version/s: ml-2.2.0
• Component/s: Library / Machine Learning
• Assignee: Yunfeng Zhou
• Reporter: Yunfeng Zhou
• Created: 22/Nov/22 01:25
• Updated: 10/Jan/23 04:11
• Resolved: 10/Jan/23 04:11

Description

When the following test case is added to flink-ml-python/pyflink/ml/lib/clustering/tests/test_kmeans.py，

def test_get_model_data(self):
kmeans = KMeans().set_max_iter(2).set_k(2)
model = kmeans.fit(self.data_table)
model_data = model.get_model_data()[0]
expected_field_names = ['centroids', 'weights']
self.assertEqual(expected_field_names, model_data.get_schema().get_field_names())self.t_env.to_data_stream(model_data).execute_and_collect().next()

The following exception would be thrown.

data = 0, field_type = DenseVectorTypeInfo
def pickled_bytes_to_python_converter(data, field_type):
if isinstance(field_type, RowTypeInfo):
row_kind = RowKind(int.from_bytes(data[0], 'little'))
data = zip(list(data[1:]), field_type.get_field_types())
fields = []
for d, d_type in data:
fields.append(pickled_bytes_to_python_converter(d, d_type))
row = Row.of_kind(row_kind, *fields)
return row
else:
> data = pickle.loads(data)
E TypeError: a bytes-like object is required, not 'int'