Source code for pyspark.ml.functions

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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()