becketqin commented on a change in pull request #11344: [FLINK-16250][python][ml] Add interfaces for PipelineStage and Pipeline URL: https://github.com/apache/flink/pull/11344#discussion_r390733535
########## File path: flink-python/pyflink/ml/tests/test_pipeline_it_case.py ########## @@ -0,0 +1,171 @@ +################################################################################ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +################################################################################ + +from pyflink.table.types import DataTypes +from pyflink.testing.test_case_utils import MLTestCase + +from pyflink.ml.api import JavaTransformer, Transformer, Estimator, Model, \ + MLEnvironmentFactory, Pipeline +from pyflink.ml.api.param import WithParams, ParamInfo, TypeConverters +from pyflink.ml.lib.param.colname import HasSelectedCols,\ + HasPredictionCol, HasOutputCol +from pyflink import keyword +from pyflink.testing import source_sink_utils +from pyflink.java_gateway import get_gateway + + +class HasVectorCol(WithParams): + """ + Trait for parameter vectorColName. + """ + vector_col = ParamInfo( + "vectorCol", + "Name of a vector column", + is_optional=False, + type_converter=TypeConverters.to_string) + + def set_vector_col(self, v: str) -> 'HasVectorCol': + return super().set(self.vector_col, v) + + def get_vector_col(self) -> str: + return super().get(self.vector_col) + + +class WrapperTransformer(JavaTransformer, HasSelectedCols): + """ + A Transformer wrappers Java Transformer. + """ + @keyword + def __init__(self, *, selected_cols=None): + _j_obj = get_gateway().jvm.org.apache.flink.ml.pipeline.\ + UserDefinedPipelineStages.SelectColumnTransformer() + super().__init__(_j_obj) + kwargs = self._input_kwargs + self._set(**kwargs) + + +class PythonAddTransformer(Transformer, HasSelectedCols, HasOutputCol): + """ + A Transformer which is implemented with Python. Output a column + contains the sum of all columns. + """ + @keyword + def __init__(self, *, selected_cols=None, output_col=None): + super().__init__() + kwargs = self._input_kwargs + self._set(**kwargs) + + def transform(self, table_env, table): + input_columns = self.get_selected_cols() + expr = "+".join(input_columns) + expr = expr + " as " + self.get_output_col() + return table.add_columns(expr) + + +class PythonEstimator(Estimator, HasVectorCol, HasPredictionCol): + + def __init__(self): + super().__init__() + + def fit(self, table_env, table): + return PythonModel( + table_env, + table.select("max(features) as max_sum"), + self.get_prediction_col()) + + +class PythonModel(Model): + + def __init__(self, table_env, model_data_table, output_col_name): + self._model_data_table = model_data_table + self._output_col_name = output_col_name + self.max_sum = 0 + self.load_model(table_env) + + def load_model(self, table_env): + """ + Train the model to get the max_sum value which is used to predicate data. + """ + table_sink = source_sink_utils.TestRetractSink(["max_sum"], [DataTypes.BIGINT()]) + table_env.register_table_sink("Model_Results", table_sink) + self._model_data_table.insert_into("Model_Results") + table_env.execute("load model") + actual = source_sink_utils.results() + self.max_sum = actual.apply(0) + + def transform(self, table_env, table): + """ + Use max_sum to predicate input. Return turn if input value is bigger than max_sum + """ + return table\ + .add_columns("features > {} as {}".format(self.max_sum, self._output_col_name))\ + .select("{}".format(self._output_col_name)) + + +class PythonPipelineTest(MLTestCase): + + def test_java_transformer(self): + t_env = MLEnvironmentFactory().get_default().get_stream_table_environment() + + table_sink = source_sink_utils.TestAppendSink( + ['a', 'b'], [DataTypes.BIGINT(), DataTypes.BIGINT()]) + t_env.register_table_sink("TransformerResults", table_sink) + + source_table = t_env.from_elements([(1, 2, 3, 4), (4, 3, 2, 1)], ['a', 'b', 'c', 'd']) + transformer = WrapperTransformer(selected_cols=["a", "b"]) + transformer\ + .transform(t_env, source_table)\ + .insert_into("TransformerResults") + + # execute + t_env.execute('JavaPipelineITCase') + actual = source_sink_utils.results() + self.assert_equals(actual, ["1,2", "4,3"]) + + def test_pipeline(self): + t_env = MLEnvironmentFactory().get_default().get_stream_table_environment() + train_table = t_env.from_elements( + [(1, 2), (1, 4), (1, 0), (10, 2), (10, 4), (10, 0)], ['a', 'b']) + serving_table = t_env.from_elements([(0, 0), (12, 3)], ['a', 'b']) + + table_sink = source_sink_utils.TestAppendSink( + ['predicate_result'], Review comment: typo -> predictResult ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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