[SPARK-7388][SPARK-7383] wrapper for VectorAssembler in Python

mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala

@@ -30,7 +30,7 @@ import org.apache.spark.sql.types._
 
 /**
  * :: AlphaComponent ::
- * A feature transformer than merge multiple columns into a vector column.
+ * A feature transformer that merges multiple columns into a vector column.
  */
 @AlphaComponent
 class VectorAssembler extends Transformer with HasInputCols with HasOutputCol {

mllib/src/main/scala/org/apache/spark/ml/param/params.scala

@@ -22,6 +22,7 @@ import java.util.NoSuchElementException
 
 import scala.annotation.varargs
 import scala.collection.mutable
+import scala.reflect.ClassTag
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.util.Identifiable
@@ -218,6 +219,22 @@ class BooleanParam(parent: Params, name: String, doc: String) // No need for isV
   override def w(value: Boolean): ParamPair[Boolean] = super.w(value)
 }
 
+/** Specialized version of [[Param[Array[T]]]] for Java. */
+class ArrayParam[T : ClassTag](
+    parent: Params,
+    name: String,
+    doc: String,
+    isValid: Array[T] => Boolean)
+  extends Param[Array[T]](parent, name, doc, isValid) {
+
+  def this(parent: Params, name: String, doc: String) =
+    this(parent, name, doc, ParamValidators.alwaysTrue)
+
+  override def w(value: Array[T]): ParamPair[Array[T]] = super.w(value)
+
+  private[param] def wCast(value: Seq[T]): ParamPair[Array[T]] = w(value.toArray)
+}
+
 /**
  * A param amd its value.
  */
@@ -311,7 +328,11 @@ trait Params extends Identifiable with Serializable {
    */
   protected final def set[T](param: Param[T], value: T): this.type = {
     shouldOwn(param)
-    paramMap.put(param.asInstanceOf[Param[Any]], value)
+    if (param.isInstanceOf[ArrayParam[_]] && value.isInstanceOf[Seq[_]]) {
+      paramMap.put(param.asInstanceOf[ArrayParam[Any]].wCast(value.asInstanceOf[Seq[Any]]))
+    } else {
+      paramMap.put(param.w(value))
+    }
     this
   }
 

mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala

@@ -83,6 +83,7 @@ private[shared] object SharedParamsCodeGen {
         case _ if c == classOf[Float] => "FloatParam"
         case _ if c == classOf[Double] => "DoubleParam"
         case _ if c == classOf[Boolean] => "BooleanParam"
+        case _ if c.isArray => s"ArrayParam[${getTypeString(c.getComponentType)}]"
         case _ => s"Param[${getTypeString(c)}]"
       }
     }

mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala

@@ -178,7 +178,7 @@ private[ml] trait HasInputCols extends Params {
    * Param for input column names.
    * @group param
    */
-  final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names")
+  final val inputCols: ArrayParam[String] = new ArrayParam[String](this, "inputCols", "input column names")
 
   /** @group getParam */
   final def getInputCols: Array[String] = $(inputCols)

python/pyspark/ml/feature.py

@@ -16,12 +16,12 @@
 #
 
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures
+from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasNumFeatures
 from pyspark.ml.util import keyword_only
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.mllib.common import inherit_doc
 
-__all__ = ['Tokenizer', 'HashingTF']
+__all__ = ['Tokenizer', 'HashingTF', 'VectorAssembler']
 
 
 @inherit_doc
@@ -112,6 +112,45 @@ def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None):
         return self._set(**kwargs)
 
 
+@inherit_doc
+class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol):
+    """
+    A feature transformer that merges multiple columns into a vector column.
+
+    >>> from pyspark.sql import Row
+    >>> df = sc.parallelize([Row(a=1, b=0, c=3)]).toDF()
+    >>> vecAssembler = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features")
+    >>> vecAssembler.transform(df).head().features
+    SparseVector(3, {0: 1.0, 2: 3.0})
+    >>> vecAssembler.setParams(outputCol="freqs").transform(df).head().freqs
+    SparseVector(3, {0: 1.0, 2: 3.0})
+    >>> params = {vecAssembler.inputCols: ["b", "a"], vecAssembler.outputCol: "vector"}
+    >>> vecAssembler.transform(df, params).head().vector
+    SparseVector(2, {1: 1.0})
+    """
+
+    _java_class = "org.apache.spark.ml.feature.VectorAssembler"
+
+    @keyword_only
+    def __init__(self, inputCols=None, outputCol=None):
+        """
+        __init__(self, inputCols=None, outputCol=None)
+        """
+        super(VectorAssembler, self).__init__()
+        self._setDefault()
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCols=None, outputCol=None):
+        """
+        setParams(self, inputCols=None, outputCol=None)
+        Sets params for this VectorAssembler.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

python/pyspark/ml/param/_shared_params_code_gen.py

@@ -95,6 +95,7 @@ def get$Name(self):
         ("predictionCol", "prediction column name", "'prediction'"),
         ("rawPredictionCol", "raw prediction column name", "'rawPrediction'"),
         ("inputCol", "input column name", None),
+        ("inputCols", "input column names", None),
         ("outputCol", "output column name", None),
         ("numFeatures", "number of features", None)]
     code = []

python/pyspark/ml/param/shared.py

@@ -223,6 +223,35 @@ def getInputCol(self):
         return self.getOrDefault(self.inputCol)
 
 
+class HasInputCols(Params):
+    """
+    Mixin for param inputCols: input column names.
+    """
+
+    # a placeholder to make it appear in the generated doc
+    inputCols = Param(Params._dummy(), "inputCols", "input column names")
+
+    def __init__(self):
+        super(HasInputCols, self).__init__()
+        #: param for input column names
+        self.inputCols = Param(self, "inputCols", "input column names")
+        if None is not None:
+            self._setDefault(inputCols=None)
+
+    def setInputCols(self, value):
+        """
+        Sets the value of :py:attr:`inputCols`.
+        """
+        self.paramMap[self.inputCols] = value
+        return self
+
+    def getInputCols(self):
+        """
+        Gets the value of inputCols or its default value.
+        """
+        return self.getOrDefault(self.inputCols)
+
+
 class HasOutputCol(Params):
     """
     Mixin for param outputCol: output column name.

python/pyspark/ml/wrapper.py

@@ -67,7 +67,10 @@ def _transfer_params_to_java(self, params, java_obj):
         paramMap = self.extractParamMap(params)
         for param in self.params:
             if param in paramMap:
-                java_obj.set(param.name, paramMap[param])
+                value = paramMap[param]
+                if isinstance(value, list):
+                    value = _jvm().PythonUtils.toSeq(value)
+                java_obj.set(param.name, value)
 
     def _empty_java_param_map(self):
         """
@@ -126,10 +129,8 @@ class JavaTransformer(Transformer, JavaWrapper):
 
     def transform(self, dataset, params={}):
         java_obj = self._java_obj()
-        self._transfer_params_to_java({}, java_obj)
-        java_param_map = self._create_java_param_map(params, java_obj)
-        return DataFrame(java_obj.transform(dataset._jdf, java_param_map),
-                         dataset.sql_ctx)
+        self._transfer_params_to_java(params, java_obj)
+        return DataFrame(java_obj.transform(dataset._jdf), dataset.sql_ctx)
 
 
 @inherit_doc