[jira] [Created] (FLINK-30122) Flink ML KMeans getting model data throws TypeError

["Yunfeng Zhou (Jira)" <ji...@apache.org>]

Yunfeng Zhou created FLINK-30122:
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             Summary: Flink ML KMeans getting model data throws TypeError
                 Key: FLINK-30122
                 URL: https://issues.apache.org/jira/browse/FLINK-30122
             Project: Flink
          Issue Type: Bug
          Components: Library / Machine Learning
    Affects Versions: ml-2.1.0
            Reporter: Yunfeng Zhou


When the following test case is added to flink-ml-python/pyflink/ml/lib/clustering/tests/test_kmeans.py，
{code:java}
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(){code}
The following exception would be thrown.
{code:java}
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'{code}



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