88542bc3d9
### What changes were proposed in this pull request?
PySpark UDF to convert MLlib vectors to dense arrays.
Example:
```
from pyspark.ml.functions import vector_to_array
df.select(vector_to_array(col("features"))
```
### Why are the changes needed?
If a PySpark user wants to convert MLlib sparse/dense vectors in a DataFrame into dense arrays, an efficient approach is to do that in JVM. However, it requires PySpark user to write Scala code and register it as a UDF. Often this is infeasible for a pure python project.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT.
Closes #26910 from WeichenXu123/vector_to_array.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from pyspark import since, SparkContext
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from pyspark.sql.column import Column, _to_java_column
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@since(3.0)
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def vector_to_array(col):
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"""
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Converts a column of MLlib sparse/dense vectors into a column of dense arrays.
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>>> from pyspark.ml.linalg import Vectors
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>>> from pyspark.ml.functions import vector_to_array
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>>> from pyspark.mllib.linalg import Vectors as OldVectors
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>>> df = spark.createDataFrame([
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... (Vectors.dense(1.0, 2.0, 3.0), OldVectors.dense(10.0, 20.0, 30.0)),
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... (Vectors.sparse(3, [(0, 2.0), (2, 3.0)]),
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... OldVectors.sparse(3, [(0, 20.0), (2, 30.0)]))],
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... ["vec", "oldVec"])
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>>> df.select(vector_to_array("vec").alias("vec"),
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... vector_to_array("oldVec").alias("oldVec")).collect()
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[Row(vec=[1.0, 2.0, 3.0], oldVec=[10.0, 20.0, 30.0]),
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Row(vec=[2.0, 0.0, 3.0], oldVec=[20.0, 0.0, 30.0])]
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"""
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sc = SparkContext._active_spark_context
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return Column(
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sc._jvm.org.apache.spark.ml.functions.vector_to_array(_to_java_column(col)))
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def _test():
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import doctest
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from pyspark.sql import SparkSession
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import pyspark.ml.functions
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import sys
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globs = pyspark.ml.functions.__dict__.copy()
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spark = SparkSession.builder \
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.master("local[2]") \
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.appName("ml.functions tests") \
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.getOrCreate()
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sc = spark.sparkContext
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globs['sc'] = sc
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globs['spark'] = spark
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(failure_count, test_count) = doctest.testmod(
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pyspark.ml.functions, globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
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spark.stop()
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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