[SPARK-33556][ML] Add array_to_vector function for dataframe column

### What changes were proposed in this pull request?

Add array_to_vector function for dataframe column

### Why are the changes needed?
Utility function for array to vector conversion.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
scala unit test & doctest.

Closes apache#30498 from WeichenXu123/array_to_vec.

Lead-authored-by: Weichen Xu <weichen.xu@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>

mllib/src/main/scala/org/apache/spark/ml/functions.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.ml
 
 import org.apache.spark.annotation.Since
-import org.apache.spark.ml.linalg.{SparseVector, Vector}
+import org.apache.spark.ml.linalg.{SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.linalg.{Vector => OldVector}
 import org.apache.spark.sql.Column
 import org.apache.spark.sql.functions.udf
@@ -72,6 +72,20 @@ object functions {
     }
   }
 
+  private val arrayToVectorUdf = udf { array: Seq[Double] =>
+    Vectors.dense(array.toArray)
+  }
+
+  /**
+   * Converts a column of array of numeric type into a column of dense vectors in MLlib.
+   * @param v: the column of array<NumericType&gt type
+   * @return a column of type `org.apache.spark.ml.linalg.Vector`
+   * @since 3.1.0
+   */
+  def array_to_vector(v: Column): Column = {
+    arrayToVectorUdf(v)
+  }
+
   private[ml] def checkNonNegativeWeight = udf {
     value: Double =>
       require(value >= 0, s"illegal weight value: $value. weight must be >= 0.0.")

mllib/src/test/scala/org/apache/spark/ml/FunctionsSuite.scala

@@ -18,8 +18,8 @@
 package org.apache.spark.ml
 
 import org.apache.spark.SparkException
-import org.apache.spark.ml.functions.vector_to_array
-import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.functions.{array_to_vector, vector_to_array}
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.MLTest
 import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
 import org.apache.spark.sql.functions.col
@@ -87,4 +87,18 @@ class FunctionsSuite extends MLTest {
     assert(thrown2.getMessage.contains(
       s"Unsupported dtype: float16. Valid values: float64, float32."))
   }
+
+  test("test array_to_vector") {
+    val df1 = Seq(Tuple1(Array(0.5, 1.5))).toDF("c1")
+    val resultVec = df1.select(array_to_vector(col("c1"))).collect()(0)(0).asInstanceOf[Vector]
+    assert(resultVec === Vectors.dense(Array(0.5, 1.5)))
+
+    val df2 = Seq(Tuple1(Array(1.5f, 2.5f))).toDF("c1")
+    val resultVec2 = df2.select(array_to_vector(col("c1"))).collect()(0)(0).asInstanceOf[Vector]
+    assert(resultVec2 === Vectors.dense(Array(1.5, 2.5)))
+
+    val df3 = Seq(Tuple1(Array(1, 2))).toDF("c1")
+    val resultVec3 = df3.select(array_to_vector(col("c1"))).collect()(0)(0).asInstanceOf[Vector]
+    assert(resultVec3 === Vectors.dense(Array(1.0, 2.0)))
+  }
 }

python/docs/source/reference/pyspark.ml.rst

@@ -196,6 +196,7 @@ ML Functions
 .. autosummary::
     :toctree: api/
 
+    array_to_vector
     vector_to_array
 
 

python/pyspark/ml/functions.py

@@ -69,6 +69,40 @@ def vector_to_array(col, dtype="float64"):
         sc._jvm.org.apache.spark.ml.functions.vector_to_array(_to_java_column(col), dtype))
 
 
+def array_to_vector(col):
+    """
+    Converts a column of array of numeric type into a column of dense vectors in MLlib
+
+    .. versionadded:: 3.1.0
+
+    Parameters
+    ----------
+    col : :py:class:`pyspark.sql.Column` or str
+        Input column
+
+    Returns
+    -------
+    :py:class:`pyspark.sql.Column`
+        The converted column of MLlib dense vectors.
+
+    Examples
+    --------
+    >>> from pyspark.ml.functions import array_to_vector
+    >>> df1 = spark.createDataFrame([([1.5, 2.5],),], schema='v1 array<double>')
+    >>> df1.select(array_to_vector('v1').alias('vec1')).collect()
+    [Row(vec1=DenseVector([1.5, 2.5]))]
+    >>> df2 = spark.createDataFrame([([1.5, 3.5],),], schema='v1 array<float>')
+    >>> df2.select(array_to_vector('v1').alias('vec1')).collect()
+    [Row(vec1=DenseVector([1.5, 3.5]))]
+    >>> df3 = spark.createDataFrame([([1, 3],),], schema='v1 array<int>')
+    >>> df3.select(array_to_vector('v1').alias('vec1')).collect()
+    [Row(vec1=DenseVector([1.0, 3.0]))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(
+        sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
+
+
 def _test():
     import doctest
     from pyspark.sql import SparkSession

python/pyspark/ml/functions.pyi

@@ -20,3 +20,5 @@ from pyspark import SparkContext as SparkContext, since as since  # noqa: F401
 from pyspark.sql.column import Column as Column
 
 def vector_to_array(col: Column) -> Column: ...
+
+def array_to_vector(col: Column) -> Column: ...