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author | Davies Liu <davies.liu@gmail.com> | 2015-07-13 11:30:36 -0700 |
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committer | Davies Liu <davies.liu@gmail.com> | 2015-07-13 11:30:36 -0700 |
commit | 79c35826e626805c5df776b3d22ed37829d239b6 (patch) | |
tree | 5141a2c0bfd8c166f74e093a989d0fa1fae6fd47 /python/pyspark/ml/param | |
parent | 5ca26fb64de99fa414dc59ce4cf29a0171894793 (diff) | |
download | spark-79c35826e626805c5df776b3d22ed37829d239b6.tar.gz spark-79c35826e626805c5df776b3d22ed37829d239b6.tar.bz2 spark-79c35826e626805c5df776b3d22ed37829d239b6.zip |
Revert "[SPARK-8706] [PYSPARK] [PROJECT INFRA] Add pylint checks to PySpark"
This reverts commit 9b62e9375f032548d386aec7468e3d0f7c6da7b2.
Diffstat (limited to 'python/pyspark/ml/param')
-rw-r--r-- | python/pyspark/ml/param/shared.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 5951247263..bc088e4c29 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -444,7 +444,7 @@ class DecisionTreeParams(Params): minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.") maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.") cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.") - + def __init__(self): super(DecisionTreeParams, self).__init__() @@ -460,7 +460,7 @@ class DecisionTreeParams(Params): self.maxMemoryInMB = Param(self, "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.") #: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. self.cacheNodeIds = Param(self, "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.") - + def setMaxDepth(self, value): """ Sets the value of :py:attr:`maxDepth`. |