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new acd086f [SPARK-19591][ML][PYSPARK][FOLLOWUP] Add sample weights to
decision trees
acd086f is described below
commit acd086f207bbd3e6d3654eb8b06900793a781f27
Author: zhengruifeng <ruife...@foxmail.com>
AuthorDate: Wed Feb 27 21:11:30 2019 -0600
[SPARK-19591][ML][PYSPARK][FOLLOWUP] Add sample weights to decision trees
## What changes were proposed in this pull request?
Add sample weights to decision trees
## How was this patch tested?
updated testsuites
Closes #23818 from zhengruifeng/py_tree_support_sample_weight.
Authored-by: zhengruifeng <ruife...@foxmail.com>
Signed-off-by: Sean Owen <sean.o...@databricks.com>
---
.../ml/classification/DecisionTreeClassifier.scala | 1 -
.../ml/regression/DecisionTreeRegressor.scala | 1 -
python/pyspark/ml/classification.py | 28 +++++++++++++++-------
python/pyspark/ml/regression.py | 24 +++++++++++++------
4 files changed, 37 insertions(+), 17 deletions(-)
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 200ac00..cbb7e4f 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -25,7 +25,6 @@ import org.apache.spark.annotation.Since
import org.apache.spark.ml.feature.{Instance, LabeledPoint}
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.param.shared.HasWeightCol
import org.apache.spark.ml.tree._
import org.apache.spark.ml.tree.{DecisionTreeModel, Node, TreeClassifierParams}
import org.apache.spark.ml.tree.DecisionTreeModelReadWrite._
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 5254791..f4f4e56 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -26,7 +26,6 @@ import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.feature.{Instance, LabeledPoint}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.param.shared.HasWeightCol
import org.apache.spark.ml.tree._
import org.apache.spark.ml.tree.DecisionTreeModelReadWrite._
import org.apache.spark.ml.tree.impl.RandomForest
diff --git a/python/pyspark/ml/classification.py
b/python/pyspark/ml/classification.py
index 134b9e0..131756b 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -889,10 +889,10 @@ class TreeClassifierParams(object):
@inherit_doc
-class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol,
- HasProbabilityCol, HasRawPredictionCol,
DecisionTreeParams,
- TreeClassifierParams, HasCheckpointInterval,
HasSeed, JavaMLWritable,
- JavaMLReadable):
+class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasWeightCol,
+ HasPredictionCol, HasProbabilityCol,
HasRawPredictionCol,
+ DecisionTreeParams, TreeClassifierParams,
HasCheckpointInterval,
+ HasSeed, JavaMLWritable, JavaMLReadable):
"""
`Decision tree <http://en.wikipedia.org/wiki/Decision_tree_learning>`_
learning algorithm for classification.
@@ -944,6 +944,18 @@ class DecisionTreeClassifier(JavaEstimator,
HasFeaturesCol, HasLabelCol, HasPred
>>> model.featureImportances == model2.featureImportances
True
+ >>> df3 = spark.createDataFrame([
+ ... (1.0, 0.2, Vectors.dense(1.0)),
+ ... (1.0, 0.8, Vectors.dense(1.0)),
+ ... (0.0, 1.0, Vectors.sparse(1, [], []))], ["label", "weight",
"features"])
+ >>> si3 = StringIndexer(inputCol="label", outputCol="indexed")
+ >>> si_model3 = si3.fit(df3)
+ >>> td3 = si_model3.transform(df3)
+ >>> dt3 = DecisionTreeClassifier(maxDepth=2, weightCol="weight",
labelCol="indexed")
+ >>> model3 = dt3.fit(td3)
+ >>> print(model3.toDebugString)
+ DecisionTreeClassificationModel (uid=...) of depth 1 with 3 nodes...
+
.. versionadded:: 1.4.0
"""
@@ -952,13 +964,13 @@ class DecisionTreeClassifier(JavaEstimator,
HasFeaturesCol, HasLabelCol, HasPred
probabilityCol="probability",
rawPredictionCol="rawPrediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="gini",
- seed=None):
+ seed=None, weightCol=None):
"""
__init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
probabilityCol="probability",
rawPredictionCol="rawPrediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="gini", \
- seed=None)
+ seed=None, weightCol=None)
"""
super(DecisionTreeClassifier, self).__init__()
self._java_obj = self._new_java_obj(
@@ -975,13 +987,13 @@ class DecisionTreeClassifier(JavaEstimator,
HasFeaturesCol, HasLabelCol, HasPred
probabilityCol="probability",
rawPredictionCol="rawPrediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
- impurity="gini", seed=None):
+ impurity="gini", seed=None, weightCol=None):
"""
setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
probabilityCol="probability",
rawPredictionCol="rawPrediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False,
checkpointInterval=10, impurity="gini", \
- seed=None)
+ seed=None, weightCol=None)
Sets params for the DecisionTreeClassifier.
"""
kwargs = self._input_kwargs
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 7841de9..927cc77 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -748,9 +748,10 @@ class GBTRegressorParams(GBTParams, TreeRegressorParams):
@inherit_doc
-class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol,
- DecisionTreeParams, TreeRegressorParams,
HasCheckpointInterval,
- HasSeed, JavaMLWritable, JavaMLReadable,
HasVarianceCol):
+class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasWeightCol,
+ HasPredictionCol, DecisionTreeParams,
TreeRegressorParams,
+ HasCheckpointInterval, HasSeed, JavaMLWritable,
JavaMLReadable,
+ HasVarianceCol):
"""
`Decision tree <http://en.wikipedia.org/wiki/Decision_tree_learning>`_
learning algorithm for regression.
@@ -791,6 +792,15 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol,
HasLabelCol, HasPredi
>>> model.transform(test1).head().variance
0.0
+ >>> df3 = spark.createDataFrame([
+ ... (1.0, 0.2, Vectors.dense(1.0)),
+ ... (1.0, 0.8, Vectors.dense(1.0)),
+ ... (0.0, 1.0, Vectors.sparse(1, [], []))], ["label", "weight",
"features"])
+ >>> dt3 = DecisionTreeRegressor(maxDepth=2, weightCol="weight",
varianceCol="variance")
+ >>> model3 = dt3.fit(df3)
+ >>> print(model3.toDebugString)
+ DecisionTreeRegressionModel (uid=...) of depth 1 with 3 nodes...
+
.. versionadded:: 1.4.0
"""
@@ -798,12 +808,12 @@ class DecisionTreeRegressor(JavaEstimator,
HasFeaturesCol, HasLabelCol, HasPredi
def __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="variance",
- seed=None, varianceCol=None):
+ seed=None, varianceCol=None, weightCol=None):
"""
__init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
\
- impurity="variance", seed=None, varianceCol=None)
+ impurity="variance", seed=None, varianceCol=None,
weightCol=None)
"""
super(DecisionTreeRegressor, self).__init__()
self._java_obj = self._new_java_obj(
@@ -819,12 +829,12 @@ class DecisionTreeRegressor(JavaEstimator,
HasFeaturesCol, HasLabelCol, HasPredi
def setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
- impurity="variance", seed=None, varianceCol=None):
+ impurity="variance", seed=None, varianceCol=None,
weightCol=None):
"""
setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False,
checkpointInterval=10, \
- impurity="variance", seed=None, varianceCol=None)
+ impurity="variance", seed=None, varianceCol=None,
weightCol=None)
Sets params for the DecisionTreeRegressor.
"""
kwargs = self._input_kwargs
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