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Revert "[SPARK-22797][PYSPARK] Bucketizer support multi-column"
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This reverts commit ab1b5d9.
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Nick Pentreath committed Jan 26, 2018
1 parent ca3613b commit f5911d4
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Showing 3 changed files with 25 additions and 99 deletions.
105 changes: 25 additions & 80 deletions python/pyspark/ml/feature.py
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Original file line number Diff line number Diff line change
Expand Up @@ -317,33 +317,26 @@ class BucketedRandomProjectionLSHModel(LSHModel, JavaMLReadable, JavaMLWritable)


@inherit_doc
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, an Exception will be thrown. 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"])
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"])
>>> bucketizer = Bucketizer(splits=[-float("inf"), 0.5, 1.4, float("inf")],
... inputCol="values1", outputCol="buckets")
>>> bucketed = bucketizer.setHandleInvalid("keep").transform(df.select("values1"))
>>> bucketed.show(truncate=False)
+-------+-------+
|values1|buckets|
+-------+-------+
|0.1 |0.0 |
|0.4 |0.0 |
|1.2 |1.0 |
|1.5 |2.0 |
|NaN |3.0 |
|NaN |3.0 |
+-------+-------+
...
... 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
>>> bucketizer.setParams(outputCol="b").transform(df).head().b
0.0
>>> bucketizerPath = temp_path + "/bucketizer"
Expand All @@ -354,22 +347,6 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasInputCols, HasOu
>>> 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
"""
Expand All @@ -386,30 +363,14 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasInputCols, HasOu

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). 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.",
"'error' (throw an error), or 'keep' (keep invalid values in a special " +
"additional bucket).",
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",
splitsArray=None, inputCols=None, outputCols=None):
def __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)
__init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error")
"""
super(Bucketizer, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Bucketizer", self.uid)
Expand All @@ -419,11 +380,9 @@ 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",
splitsArray=None, inputCols=None, outputCols=None):
def 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)
setParams(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error")
Sets params for this Bucketizer.
"""
kwargs = self._input_kwargs
Expand All @@ -443,20 +402,6 @@ 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):
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10 changes: 0 additions & 10 deletions python/pyspark/ml/param/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,16 +134,6 @@ 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):
"""
Expand Down
9 changes: 0 additions & 9 deletions python/pyspark/ml/tests.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,15 +238,6 @@ 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):

Expand Down

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