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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/10/17 03:36:30 UTC
[GitHub] [flink-ml] lindong28 commented on a diff in pull request #163: [FLINK-29591] Add built-in UDFs to convert between arrays and vectors
lindong28 commented on code in PR #163:
URL: https://github.com/apache/flink-ml/pull/163#discussion_r996575556
##########
flink-ml-python/pyflink/ml/core/linalg.py:
##########
@@ -776,3 +777,15 @@ def _double_to_long_bits(value: float) -> int:
value = float("nan")
# pack double into 64 bits, then unpack as long int
return struct.unpack("Q", struct.pack("d", value))[0]
+
+
+parent_from_java_type = typeinfo._from_java_type
+
+
+def _from_java_type(j_type_info: JavaObject):
+ if "GenericType<org.apache.flink.ml.linalg.DenseVector>" == str(j_type_info):
+ return DenseVectorTypeInfo()
+ return parent_from_java_type(j_type_info)
+
+
+typeinfo._from_java_type = _from_java_type
Review Comment:
Would it be better to put the global module changes in `flink-ml-python/pyflink/ml/__init__.py`?
##########
flink-ml-python/pyflink/ml/lib/tests/test_functions.py:
##########
@@ -0,0 +1,114 @@
+################################################################################
+# 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.common import Types
+from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo, SparseVectorTypeInfo, \
+ VectorTypeInfo
+from pyflink.ml.lib.functions import vector_to_array, array_to_vector
+from pyflink.ml.tests.test_utils import PyFlinkMLTestCase
+from pyflink.table.expressions import col
+
+
+class FunctionsTest(PyFlinkMLTestCase):
+ def setUp(self):
+ super(FunctionsTest, self).setUp()
+
+ self.double_arrays = [
+ ([0.0, 0.0],),
+ ([0.0, 1.0],),
+ ]
+
+ self.float_arrays = [
+ ([float(0.0), float(0.0)],),
+ ([float(0.0), float(1.0)],),
+ ]
+
+ self.int_arrays = [
+ ([0, 0],),
+ ([0, 1],),
+ ]
+
+ self.dense_vectors = [
+ (Vectors.dense(0.0, 0.0),),
+ (Vectors.dense(0.0, 1.0),),
+ ]
+
+ self.sparse_vectors = [
+ (Vectors.sparse(2, [], []),),
+ (Vectors.sparse(2, [1], [1.0]),),
+ ]
+
+ self.mixed_vectors = [
+ (Vectors.dense(0.0, 0.0),),
+ (Vectors.sparse(2, [1], [1.0]),),
+ ]
+
+ def test_vector_to_array(self):
+ self._test_vector_to_array(self.dense_vectors, DenseVectorTypeInfo())
+ self._test_vector_to_array(self.sparse_vectors, SparseVectorTypeInfo())
+ self._test_vector_to_array(self.mixed_vectors, VectorTypeInfo())
+
+ def _test_vector_to_array(self, vectors, vector_type_info):
+ input_table = self.t_env.from_data_stream(
+ self.env.from_collection(vectors,
+ type_info=Types.ROW_NAMED(
+ ['vector'],
+ [vector_type_info])
+ ))
+
+ output_table = input_table.select(vector_to_array(col('vector')).alias('array'))
+
+ output_value = [x['array'] for x in self.t_env.to_data_stream(output_table)
+ .map(lambda r: r).execute_and_collect()]
+
+ self.assertEqual(len(output_value), len(self.double_arrays))
+
+ output_value.sort(key=lambda x: x[1])
+
+ for i in range(len(self.double_arrays)):
+ self.assertEqual(self.double_arrays[i][0], output_value[i])
+
+ def test_array_to_vector(self):
+ self._test_array_to_vector(self.double_arrays, Types.DOUBLE())
+ self._test_array_to_vector(self.float_arrays, Types.FLOAT())
+ self._test_array_to_vector(self.int_arrays, Types.INT())
+ self._test_array_to_vector(self.int_arrays, Types.LONG())
+
+ def _test_array_to_vector(self, arrays, array_element_type_info):
+ input_table = self.t_env.from_data_stream(
+ self.env.from_collection(
+ arrays,
+ type_info=Types.ROW_NAMED(
+ ['array'],
+ [Types.PRIMITIVE_ARRAY(array_element_type_info)]
+ )
+ )
+ )
+
+ output_table = input_table.select(array_to_vector(col('array')).alias('vector'))
+
+ field_names = output_table.get_schema().get_field_names()
+
+ output_value = [x[field_names.index('vector')] for x in
Review Comment:
output_value -> output_values
##########
docs/content/docs/operators/functions.md:
##########
@@ -0,0 +1,236 @@
+---
+title: "Functions"
+type: docs
+weight: 2
+aliases:
+- /operators/functions.html
+---
+<!--
+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.
+-->
+
+## Functions
+
+Flink ML provides users with some built-in functions for data transformations.
+This page gives a brief overview of them.
+
+### vectorToArray
+
+This function converts vectors into double arrays.
Review Comment:
Would it be useful to mention sparse/dense like below? Same for similar comments in this PR.
This function converts a column of Flink ML sparse/dense vectors into a column of dense arrays.
##########
docs/content/docs/operators/functions.md:
##########
@@ -0,0 +1,236 @@
+---
+title: "Functions"
+type: docs
+weight: 2
+aliases:
+- /operators/functions.html
+---
+<!--
+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.
+-->
+
+## Functions
+
+Flink ML provides users with some built-in functions for data transformations.
+This page gives a brief overview of them.
+
+### vectorToArray
+
+This function converts vectors into double arrays.
+
+{{< tabs vectorToArray_examples >}}
+
+{{< tab "Java">}}
+```java
+import org.apache.flink.ml.linalg.Vector;
+import org.apache.flink.ml.linalg.Vectors;
+import org.apache.flink.ml.linalg.typeinfo.VectorTypeInfo;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.table.api.Table;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+import org.apache.flink.types.Row;
+import org.apache.flink.util.CloseableIterator;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.apache.flink.ml.Functions.vectorToArray;
+import static org.apache.flink.table.api.Expressions.$;
+
+/** Simple program that converts vectors to double arrays. */
+public class VectorToArrayExample {
+ public static void main(String[] args) {
+ StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+ StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
+
+ // Generates input vector data.
+ List<Vector> vectors =
+ Arrays.asList(
+ Vectors.dense(0.0, 0.0),
+ Vectors.sparse(2, new int[] {1}, new double[] {1.0}));
+ Table inputTable =
+ tEnv.fromDataStream(env.fromCollection(vectors, VectorTypeInfo.INSTANCE))
+ .as("vector");
+
+ // Converts each vector to a double array.
+ Table outputTable = inputTable.select($("vector"), vectorToArray($("vector")).as("array"));
+
+ // Extracts and displays the results.
+ for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
+ Row row = it.next();
+ Vector vector = row.getFieldAs("vector");
+ Double[] doubleArray = row.getFieldAs("array");
+ System.out.printf(
+ "Input vector: %s\tOutput double array: %s\n",
+ vector, Arrays.toString(doubleArray));
+ }
+ }
+}
+```
+{{< /tab>}}
+
+{{< tab "Python">}}
+```python
+# Simple program that converts vectors to double arrays.
+
+from pyflink.common import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import StreamTableEnvironment
+
+from pyflink.ml.core.linalg import Vectors, VectorTypeInfo
+
+from pyflink.ml.lib.functions import vector_to_array
+from pyflink.table.expressions import col
+
+# create a new StreamExecutionEnvironment
+env = StreamExecutionEnvironment.get_execution_environment()
+
+# create a StreamTableEnvironment
+t_env = StreamTableEnvironment.create(env)
+
+# generate input vector data
+vectors = [
+ (Vectors.dense(0.0, 0.0),),
+ (Vectors.sparse(2, [1], [1.0]),),
+]
+input_table = t_env.from_data_stream(
+ env.from_collection(
+ vectors,
+ type_info=Types.ROW_NAMED(
+ ['vector'],
+ [VectorTypeInfo()])
+ ))
+
+# convert each vector to a double array
+output_table = input_table.select(vector_to_array(col('vector')).alias('array'))
+
+# extract and display the results
+output_value = [x for x in
+ t_env.to_data_stream(output_table).map(lambda r: r).execute_and_collect()]
+
+output_value.sort(key=lambda x: x[0])
+
+field_names = output_table.get_schema().get_field_names()
+for i in range(len(output_value)):
+ vector = vectors[i][0]
+ double_array = output_value[i][field_names.index("array")]
+ print("Input vector: %s \t output double array: %s" % (vector, double_array))
+
+```
+{{< /tab>}}
+
+{{< /tabs>}}
+
+### arrayToVector
+
+This function converts numerical arrays into vectors.
Review Comment:
Would it be useful to mention the specific python class of the output value like below? Same for related comments in this PR.
This function converts a column of arrays of numeric type into a column of pyflink.ml.core.linalg.DenseVector instances.
##########
flink-ml-python/pyflink/ml/lib/tests/test_functions.py:
##########
@@ -0,0 +1,114 @@
+################################################################################
+# 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.common import Types
+from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo, SparseVectorTypeInfo, \
+ VectorTypeInfo
+from pyflink.ml.lib.functions import vector_to_array, array_to_vector
+from pyflink.ml.tests.test_utils import PyFlinkMLTestCase
+from pyflink.table.expressions import col
+
+
+class FunctionsTest(PyFlinkMLTestCase):
+ def setUp(self):
+ super(FunctionsTest, self).setUp()
+
+ self.double_arrays = [
+ ([0.0, 0.0],),
+ ([0.0, 1.0],),
+ ]
+
+ self.float_arrays = [
+ ([float(0.0), float(0.0)],),
+ ([float(0.0), float(1.0)],),
+ ]
+
+ self.int_arrays = [
+ ([0, 0],),
+ ([0, 1],),
+ ]
+
+ self.dense_vectors = [
+ (Vectors.dense(0.0, 0.0),),
+ (Vectors.dense(0.0, 1.0),),
+ ]
+
+ self.sparse_vectors = [
+ (Vectors.sparse(2, [], []),),
+ (Vectors.sparse(2, [1], [1.0]),),
+ ]
+
+ self.mixed_vectors = [
+ (Vectors.dense(0.0, 0.0),),
+ (Vectors.sparse(2, [1], [1.0]),),
+ ]
+
+ def test_vector_to_array(self):
+ self._test_vector_to_array(self.dense_vectors, DenseVectorTypeInfo())
+ self._test_vector_to_array(self.sparse_vectors, SparseVectorTypeInfo())
+ self._test_vector_to_array(self.mixed_vectors, VectorTypeInfo())
+
+ def _test_vector_to_array(self, vectors, vector_type_info):
+ input_table = self.t_env.from_data_stream(
+ self.env.from_collection(vectors,
+ type_info=Types.ROW_NAMED(
+ ['vector'],
+ [vector_type_info])
+ ))
+
+ output_table = input_table.select(vector_to_array(col('vector')).alias('array'))
+
+ output_value = [x['array'] for x in self.t_env.to_data_stream(output_table)
Review Comment:
output_value -> output_values
##########
docs/content/docs/operators/functions.md:
##########
@@ -0,0 +1,236 @@
+---
+title: "Functions"
+type: docs
+weight: 2
+aliases:
+- /operators/functions.html
+---
+<!--
+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.
+-->
+
+## Functions
+
+Flink ML provides users with some built-in functions for data transformations.
Review Comment:
Would it be useful to specifically mention that these are table functions?
Same for related comments in this PR.
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