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author | sethah <seth.hendrickson16@gmail.com> | 2016-03-24 19:14:24 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2016-03-24 19:14:24 -0700 |
commit | 585097716c1979ea538ef733cf33225ef7be06f5 (patch) | |
tree | 9512613eae0625697ac1a4ce3744e560720c1ca7 | |
parent | fdd460f5f47e4023d81d5a3d918bd4a16ecbb580 (diff) | |
download | spark-585097716c1979ea538ef733cf33225ef7be06f5.tar.gz spark-585097716c1979ea538ef733cf33225ef7be06f5.tar.bz2 spark-585097716c1979ea538ef733cf33225ef7be06f5.zip |
[SPARK-14107][PYSPARK][ML] Add seed as named argument to GBTs in pyspark
## What changes were proposed in this pull request?
GBTs in pyspark previously had seed parameters, but they could not be passed as keyword arguments through the class constructor. This patch adds seed as a keyword argument and also sets default value.
## How was this patch tested?
Doc tests were updated to pass a random seed through the GBTClassifier and GBTRegressor constructors.
Author: sethah <seth.hendrickson16@gmail.com>
Closes #11944 from sethah/SPARK-14107.
-rw-r--r-- | python/pyspark/ml/classification.py | 12 | ||||
-rw-r--r-- | python/pyspark/ml/regression.py | 13 |
2 files changed, 13 insertions, 12 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index fdeccf822c..850d775db0 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -520,7 +520,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed") >>> si_model = stringIndexer.fit(df) >>> td = si_model.transform(df) - >>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed") + >>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed", seed=42) >>> model = gbt.fit(td) >>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1]) True @@ -543,19 +543,19 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="logistic", - maxIter=20, stepSize=0.1): + maxIter=20, stepSize=0.1, seed=None): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ - lossType="logistic", maxIter=20, stepSize=0.1) + lossType="logistic", maxIter=20, stepSize=0.1, seed=None) """ super(GBTClassifier, self).__init__() self._java_obj = self._new_java_obj( "org.apache.spark.ml.classification.GBTClassifier", self.uid) self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, - lossType="logistic", maxIter=20, stepSize=0.1) + lossType="logistic", maxIter=20, stepSize=0.1, seed=None) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @@ -564,12 +564,12 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, - lossType="logistic", maxIter=20, stepSize=0.1): + lossType="logistic", maxIter=20, stepSize=0.1, seed=None): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ - lossType="logistic", maxIter=20, stepSize=0.1) + lossType="logistic", maxIter=20, stepSize=0.1, seed=None) Sets params for Gradient Boosted Tree Classification. """ kwargs = self.setParams._input_kwargs diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 898260879d..59d4fe3cf4 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -641,7 +641,7 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, >>> df = sqlContext.createDataFrame([ ... (1.0, Vectors.dense(1.0)), ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) - >>> gbt = GBTRegressor(maxIter=5, maxDepth=2) + >>> gbt = GBTRegressor(maxIter=5, maxDepth=2, seed=42) >>> model = gbt.fit(df) >>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1]) True @@ -664,18 +664,19 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, - checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1): + checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \ - checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1) + checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None) """ super(GBTRegressor, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", self.uid) self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, - checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1) + checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, + seed=None) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @@ -684,12 +685,12 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, - checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1): + checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \ - checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1) + checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None) Sets params for Gradient Boosted Tree Regression. """ kwargs = self.setParams._input_kwargs |