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Posted to commits@spark.apache.org by me...@apache.org on 2016/02/25 22:29:14 UTC
spark git commit: [SPARK-13292] [ML] [PYTHON] QuantileDiscretizer
should take random seed in PySpark
Repository: spark
Updated Branches:
refs/heads/master 14e2700de -> 35316cb0b
[SPARK-13292] [ML] [PYTHON] QuantileDiscretizer should take random seed in PySpark
## What changes were proposed in this pull request?
QuantileDiscretizer in Python should also specify a random seed.
## How was this patch tested?
unit tests
Author: Yu ISHIKAWA <yu...@gmail.com>
Closes #11362 from yu-iskw/SPARK-13292 and squashes the following commits:
02ffa76 [Yu ISHIKAWA] [SPARK-13292][ML][PYTHON] QuantileDiscretizer should take random seed in PySpark
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/35316cb0
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/35316cb0
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/35316cb0
Branch: refs/heads/master
Commit: 35316cb0b744bef9bcb390411ddc321167f953be
Parents: 14e2700
Author: Yu ISHIKAWA <yu...@gmail.com>
Authored: Thu Feb 25 13:29:10 2016 -0800
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Thu Feb 25 13:29:10 2016 -0800
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python/pyspark/ml/feature.py | 14 ++++++++------
1 file changed, 8 insertions(+), 6 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/35316cb0/python/pyspark/ml/feature.py
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diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 464c944..67bccfa 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -939,7 +939,7 @@ class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol):
@inherit_doc
-class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol):
+class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, HasSeed):
"""
.. note:: Experimental
@@ -951,7 +951,9 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol):
>>> df = sqlContext.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"])
>>> qds = QuantileDiscretizer(numBuckets=2,
- ... inputCol="values", outputCol="buckets")
+ ... inputCol="values", outputCol="buckets", seed=123)
+ >>> qds.getSeed()
+ 123
>>> bucketizer = qds.fit(df)
>>> splits = bucketizer.getSplits()
>>> splits[0]
@@ -971,9 +973,9 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol):
"categories) into which data points are grouped. Must be >= 2. Default 2.")
@keyword_only
- def __init__(self, numBuckets=2, inputCol=None, outputCol=None):
+ def __init__(self, numBuckets=2, inputCol=None, outputCol=None, seed=None):
"""
- __init__(self, numBuckets=2, inputCol=None, outputCol=None)
+ __init__(self, numBuckets=2, inputCol=None, outputCol=None, seed=None)
"""
super(QuantileDiscretizer, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.QuantileDiscretizer",
@@ -987,9 +989,9 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol):
@keyword_only
@since("2.0.0")
- def setParams(self, numBuckets=2, inputCol=None, outputCol=None):
+ def setParams(self, numBuckets=2, inputCol=None, outputCol=None, seed=None):
"""
- setParams(self, numBuckets=2, inputCol=None, outputCol=None)
+ setParams(self, numBuckets=2, inputCol=None, outputCol=None, seed=None)
Set the params for the QuantileDiscretizer
"""
kwargs = self.setParams._input_kwargs
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