[SPARK-22797][PySpark] Bucketizer support multi-column

python/pyspark/ml/feature.py

@@ -317,26 +317,34 @@ class BucketedRandomProjectionLSHModel(LSHModel, JavaMLReadable, JavaMLWritable)
 
 
 @inherit_doc
-class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasHandleInvalid,
-                 JavaMLReadable, JavaMLWritable):
-    """
-    Maps a column of continuous features to a column of feature buckets.
-
-    >>> values = [(0.1,), (0.4,), (1.2,), (1.5,), (float("nan"),), (float("nan"),)]
-    >>> df = spark.createDataFrame(values, ["values"])
+class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasInputCols, HasOutputCols,
+                 HasHandleInvalid, JavaMLReadable, JavaMLWritable):
+    """
+    Maps a column of continuous features to a column of feature buckets. Since 2.3.0,
+    :py:class:`Bucketizer` can map multiple columns at once by setting the :py:attr:`inputCols`
+    parameter. Note that when both the :py:attr:`inputCol` and :py:attr:`inputCols` parameters
+    are set, a log warning will be printed and only :py:attr:`inputCol` will take effect, while
+    :py:attr:`inputCols` will be ignored. The :py:attr:`splits` parameter is only used for single
+    column usage, and :py:attr:`splitsArray` is for multiple columns.
+
+    >>> values = [(0.1, 0.0), (0.4, 1.0), (1.2, 1.3), (1.5, float("nan")),
+    ...     (float("nan"), 1.0), (float("nan"), 0.0)]
+    >>> df = spark.createDataFrame(values, ["values1", "values2"])
     >>> bucketizer = Bucketizer(splits=[-float("inf"), 0.5, 1.4, float("inf")],
-    ...     inputCol="values", outputCol="buckets")
-    >>> bucketed = bucketizer.setHandleInvalid("keep").transform(df).collect()
-    >>> len(bucketed)
-    6
-    >>> bucketed[0].buckets
-    0.0
-    >>> bucketed[1].buckets
-    0.0
-    >>> bucketed[2].buckets
-    1.0
-    >>> bucketed[3].buckets
-    2.0
+    ...     inputCol="values1", outputCol="buckets")
+    >>> bucketed = bucketizer.setHandleInvalid("keep").transform(df)
+    >>> bucketed.show(truncate=False)
+    +-------+-------+-------+
+    |values1|values2|buckets|
+    +-------+-------+-------+
+    |0.1    |0.0    |0.0    |
+    |0.4    |1.0    |0.0    |
+    |1.2    |1.3    |1.0    |
+    |1.5    |NaN    |2.0    |
+    |NaN    |1.0    |3.0    |
+    |NaN    |0.0    |3.0    |
+    +-------+-------+-------+
+    ...
     >>> bucketizer.setParams(outputCol="b").transform(df).head().b
     0.0
     >>> bucketizerPath = temp_path + "/bucketizer"
@@ -347,6 +355,22 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasHandleInvalid,
     >>> bucketed = bucketizer.setHandleInvalid("skip").transform(df).collect()
     >>> len(bucketed)
     4
+    >>> bucketizer2 = Bucketizer(splitsArray=
+    ...     [[-float("inf"), 0.5, 1.4, float("inf")], [-float("inf"), 0.5, float("inf")]],
+    ...     inputCols=["values1", "values2"], outputCols=["buckets1", "buckets2"])
+    >>> bucketed2 = bucketizer2.setHandleInvalid("keep").transform(df)
+    >>> bucketed2.show(truncate=False)
+    +-------+-------+--------+--------+
+    |values1|values2|buckets1|buckets2|
+    +-------+-------+--------+--------+
+    |0.1    |0.0    |0.0     |0.0     |
+    |0.4    |1.0    |0.0     |1.0     |
+    |1.2    |1.3    |1.0     |1.0     |
+    |1.5    |NaN    |2.0     |2.0     |
+    |NaN    |1.0    |3.0     |1.0     |
+    |NaN    |0.0    |3.0     |0.0     |
+    +-------+-------+--------+--------+
+    ...
 
     .. versionadded:: 1.4.0
     """
@@ -363,14 +387,30 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasHandleInvalid,
 
     handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. " +
                           "Options are 'skip' (filter out rows with invalid values), " +
-                          "'error' (throw an error), or 'keep' (keep invalid values in a special " +
-                          "additional bucket).",
+                          "'error' (throw an error), or 'keep' (keep invalid values in a " +
+                          "special additional bucket). Note that in the multiple column " +
+                          "case, the invalid handling is applied to all columns. That said " +
+                          "for 'error' it will throw an error if any invalids are found in " +
+                          "any column, for 'skip' it will skip rows with any invalids in " +
+                          "any columns, etc.",
                           typeConverter=TypeConverters.toString)
 
+    splitsArray = Param(Params._dummy(), "splitsArray", "The array of split points for mapping " +
+                        "continuous features into buckets for multiple columns. For each input " +
+                        "column, with n+1 splits, there are n buckets. A bucket defined by " +
+                        "splits x,y holds values in the range [x,y) except the last bucket, " +
+                        "which also includes y. The splits should be of length >= 3 and " +
+                        "strictly increasing. Values at -inf, inf must be explicitly provided " +
+                        "to cover all Double values; otherwise, values outside the splits " +
+                        "specified will be treated as errors.",
+                        typeConverter=TypeConverters.toListListFloat)
+
     @keyword_only
-    def __init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error"):
+    def __init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error",
+                 splitsArray=None, inputCols=None, outputCols=None):
         """
-        __init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error")
+        __init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error", \
+                 splitsArray=None, inputCols=None, outputCols=None)
         """
         super(Bucketizer, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Bucketizer", self.uid)
@@ -380,9 +420,11 @@ def __init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="er
 
     @keyword_only
     @since("1.4.0")
-    def setParams(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error"):
+    def setParams(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error",
+                  splitsArray=None, inputCols=None, outputCols=None):
         """
-        setParams(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error")
+        setParams(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error", \
+                  splitsArray=None, inputCols=None, outputCols=None)
         Sets params for this Bucketizer.
         """
         kwargs = self._input_kwargs
@@ -402,6 +444,20 @@ def getSplits(self):
         """
         return self.getOrDefault(self.splits)
 
+    @since("2.3.0")
+    def setSplitsArray(self, value):
+        """
+        Sets the value of :py:attr:`splitsArray`.
+        """
+        return self._set(splitsArray=value)
+
+    @since("2.3.0")
+    def getSplitsArray(self):
+        """
+        Gets the array of split points or its default value.
+        """
+        return self.getOrDefault(self.splitsArray)
+
 
 @inherit_doc
 class CountVectorizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):

python/pyspark/ml/param/__init__.py

@@ -134,6 +134,16 @@ def toListFloat(value):
                 return [float(v) for v in value]
         raise TypeError("Could not convert %s to list of floats" % value)
 
+    @staticmethod
+    def toListListFloat(value):
+        """
+        Convert a value to list of list of floats, if possible.
+        """
+        if TypeConverters._can_convert_to_list(value):
+            value = TypeConverters.toList(value)
+            return [TypeConverters.toListFloat(v) for v in value]
+        raise TypeError("Could not convert %s to list of list of floats" % value)
+
     @staticmethod
     def toListInt(value):
         """

python/pyspark/ml/tests.py

@@ -238,6 +238,15 @@ def test_bool(self):
         self.assertRaises(TypeError, lambda: LogisticRegression(fitIntercept=1))
         self.assertRaises(TypeError, lambda: LogisticRegression(fitIntercept="false"))
 
+    def test_list_list_float(self):
+        b = Bucketizer(splitsArray=[[-0.1, 0.5, 3], [-5, 1.5]])
+        self.assertEqual(b.getSplitsArray(), [[-0.1, 0.5, 3.0], [-5.0, 1.5]])
+        self.assertTrue(all([type(v) == list for v in b.getSplitsArray()]))
+        self.assertTrue(all([type(v) == float for v in b.getSplitsArray()[0]]))
+        self.assertTrue(all([type(v) == float for v in b.getSplitsArray()[1]]))
+        self.assertRaises(TypeError, lambda: Bucketizer(splitsArray=["a", 1.0]))
+        self.assertRaises(TypeError, lambda: Bucketizer(splitsArray=[[-5, 1.5], ["a", 1.0]]))
+
 
 class PipelineTests(PySparkTestCase):