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# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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from pyspark import since, SparkContext
from pyspark.sql.column import Column, _to_java_column
[docs]@since("3.0.0")
def vector_to_array(col, dtype="float64"):
"""
Converts a column of MLlib sparse/dense vectors into a column of dense arrays.
:param col: A string of the column name or a Column
:param dtype: The data type of the output array. Valid values: "float64" or "float32".
:return: The converted column of dense arrays.
.. versionadded:: 3.0.0
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.functions import vector_to_array
>>> from pyspark.mllib.linalg import Vectors as OldVectors
>>> df = spark.createDataFrame([
... (Vectors.dense(1.0, 2.0, 3.0), OldVectors.dense(10.0, 20.0, 30.0)),
... (Vectors.sparse(3, [(0, 2.0), (2, 3.0)]),
... OldVectors.sparse(3, [(0, 20.0), (2, 30.0)]))],
... ["vec", "oldVec"])
>>> df1 = df.select(vector_to_array("vec").alias("vec"),
... vector_to_array("oldVec").alias("oldVec"))
>>> df1.collect()
[Row(vec=[1.0, 2.0, 3.0], oldVec=[10.0, 20.0, 30.0]),
Row(vec=[2.0, 0.0, 3.0], oldVec=[20.0, 0.0, 30.0])]
>>> df2 = df.select(vector_to_array("vec", "float32").alias("vec"),
... vector_to_array("oldVec", "float32").alias("oldVec"))
>>> df2.collect()
[Row(vec=[1.0, 2.0, 3.0], oldVec=[10.0, 20.0, 30.0]),
Row(vec=[2.0, 0.0, 3.0], oldVec=[20.0, 0.0, 30.0])]
>>> df1.schema.fields
[StructField(vec,ArrayType(DoubleType,false),false),
StructField(oldVec,ArrayType(DoubleType,false),false)]
>>> df2.schema.fields
[StructField(vec,ArrayType(FloatType,false),false),
StructField(oldVec,ArrayType(FloatType,false),false)]
"""
sc = SparkContext._active_spark_context
return Column(
sc._jvm.org.apache.spark.ml.functions.vector_to_array(_to_java_column(col), dtype))
def _test():
import doctest
from pyspark.sql import SparkSession
import pyspark.ml.functions
import sys
globs = pyspark.ml.functions.__dict__.copy()
spark = SparkSession.builder \
.master("local[2]") \
.appName("ml.functions tests") \
.getOrCreate()
sc = spark.sparkContext
globs['sc'] = sc
globs['spark'] = spark
(failure_count, test_count) = doctest.testmod(
pyspark.ml.functions, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()
