Repository: spark Updated Branches: refs/heads/master b142157dc -> ec6f971dc
[SPARK-23161][PYSPARK][ML] Add missing APIs to Python GBTClassifier ## What changes were proposed in this pull request? Add featureSubsetStrategy in GBTClassifier and GBTRegressor. Also make GBTClassificationModel inherit from JavaClassificationModel instead of prediction model so it will have numClasses. ## How was this patch tested? Add tests in doctest Author: Huaxin Gao <huax...@us.ibm.com> Closes #21413 from huaxingao/spark-23161. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/ec6f971d Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/ec6f971d Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/ec6f971d Branch: refs/heads/master Commit: ec6f971dc57bcdc0ad65ac1987b6f0c1801157f4 Parents: b142157 Author: Huaxin Gao <huax...@us.ibm.com> Authored: Wed May 30 11:04:09 2018 -0700 Committer: Bryan Cutler <cutl...@gmail.com> Committed: Wed May 30 11:04:09 2018 -0700 ---------------------------------------------------------------------- python/pyspark/ml/classification.py | 35 +++++++++++++--- python/pyspark/ml/regression.py | 70 ++++++++++++++++++++------------ 2 files changed, 74 insertions(+), 31 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/ec6f971d/python/pyspark/ml/classification.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 424ecfd..1754c48 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -1131,6 +1131,13 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred def _create_model(self, java_model): return RandomForestClassificationModel(java_model) + @since("2.4.0") + def setFeatureSubsetStrategy(self, value): + """ + Sets the value of :py:attr:`featureSubsetStrategy`. + """ + return self._set(featureSubsetStrategy=value) + class RandomForestClassificationModel(TreeEnsembleModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable): @@ -1193,6 +1200,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol >>> si_model = stringIndexer.fit(df) >>> td = si_model.transform(df) >>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed", seed=42) + >>> gbt.getFeatureSubsetStrategy() + 'all' >>> model = gbt.fit(td) >>> model.featureImportances SparseVector(1, {0: 1.0}) @@ -1226,6 +1235,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol ... ["indexed", "features"]) >>> model.evaluateEachIteration(validation) [0.25..., 0.23..., 0.21..., 0.19..., 0.18...] + >>> model.numClasses + 2 .. versionadded:: 1.4.0 """ @@ -1244,19 +1255,22 @@ 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, seed=None, subsamplingRate=1.0): + maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, + featureSubsetStrategy="all"): """ __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, seed=None, subsamplingRate=1.0) + lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, \ + featureSubsetStrategy="all") """ 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, subsamplingRate=1.0) + lossType="logistic", maxIter=20, stepSize=0.1, subsamplingRate=1.0, + featureSubsetStrategy="all") kwargs = self._input_kwargs self.setParams(**kwargs) @@ -1265,12 +1279,14 @@ 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, seed=None, subsamplingRate=1.0): + lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, + featureSubsetStrategy="all"): """ 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, seed=None, subsamplingRate=1.0) + lossType="logistic", maxIter=20, stepSize=0.1, seed=None, subsamplingRate=1.0, \ + featureSubsetStrategy="all") Sets params for Gradient Boosted Tree Classification. """ kwargs = self._input_kwargs @@ -1293,8 +1309,15 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol """ return self.getOrDefault(self.lossType) + @since("2.4.0") + def setFeatureSubsetStrategy(self, value): + """ + Sets the value of :py:attr:`featureSubsetStrategy`. + """ + return self._set(featureSubsetStrategy=value) + -class GBTClassificationModel(TreeEnsembleModel, JavaPredictionModel, JavaMLWritable, +class GBTClassificationModel(TreeEnsembleModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable): """ Model fitted by GBTClassifier. http://git-wip-us.apache.org/repos/asf/spark/blob/ec6f971d/python/pyspark/ml/regression.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index dd0b62f..dba0e57 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -602,6 +602,14 @@ class TreeEnsembleParams(DecisionTreeParams): "used for learning each decision tree, in range (0, 1].", typeConverter=TypeConverters.toFloat) + supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"] + + featureSubsetStrategy = \ + Param(Params._dummy(), "featureSubsetStrategy", + "The number of features to consider for splits at each tree node. Supported " + + "options: " + ", ".join(supportedFeatureSubsetStrategies) + ", (0.0-1.0], [1-n].", + typeConverter=TypeConverters.toString) + def __init__(self): super(TreeEnsembleParams, self).__init__() @@ -619,6 +627,22 @@ class TreeEnsembleParams(DecisionTreeParams): """ return self.getOrDefault(self.subsamplingRate) + @since("1.4.0") + def setFeatureSubsetStrategy(self, value): + """ + Sets the value of :py:attr:`featureSubsetStrategy`. + + .. note:: Deprecated in 2.4.0 and will be removed in 3.0.0. + """ + return self._set(featureSubsetStrategy=value) + + @since("1.4.0") + def getFeatureSubsetStrategy(self): + """ + Gets the value of featureSubsetStrategy or its default value. + """ + return self.getOrDefault(self.featureSubsetStrategy) + class TreeRegressorParams(Params): """ @@ -654,14 +678,8 @@ class RandomForestParams(TreeEnsembleParams): Private class to track supported random forest parameters. """ - supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"] numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1).", typeConverter=TypeConverters.toInt) - featureSubsetStrategy = \ - Param(Params._dummy(), "featureSubsetStrategy", - "The number of features to consider for splits at each tree node. Supported " + - "options: " + ", ".join(supportedFeatureSubsetStrategies) + ", (0.0-1.0], [1-n].", - typeConverter=TypeConverters.toString) def __init__(self): super(RandomForestParams, self).__init__() @@ -680,20 +698,6 @@ class RandomForestParams(TreeEnsembleParams): """ return self.getOrDefault(self.numTrees) - @since("1.4.0") - def setFeatureSubsetStrategy(self, value): - """ - Sets the value of :py:attr:`featureSubsetStrategy`. - """ - return self._set(featureSubsetStrategy=value) - - @since("1.4.0") - def getFeatureSubsetStrategy(self): - """ - Gets the value of featureSubsetStrategy or its default value. - """ - return self.getOrDefault(self.featureSubsetStrategy) - class GBTParams(TreeEnsembleParams): """ @@ -981,6 +985,13 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi def _create_model(self, java_model): return RandomForestRegressionModel(java_model) + @since("2.4.0") + def setFeatureSubsetStrategy(self, value): + """ + Sets the value of :py:attr:`featureSubsetStrategy`. + """ + return self._set(featureSubsetStrategy=value) + class RandomForestRegressionModel(TreeEnsembleModel, JavaPredictionModel, JavaMLWritable, JavaMLReadable): @@ -1029,6 +1040,8 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, >>> gbt = GBTRegressor(maxIter=5, maxDepth=2, seed=42) >>> print(gbt.getImpurity()) variance + >>> print(gbt.getFeatureSubsetStrategy()) + all >>> model = gbt.fit(df) >>> model.featureImportances SparseVector(1, {0: 1.0}) @@ -1079,20 +1092,20 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, 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, seed=None, - impurity="variance"): + impurity="variance", featureSubsetStrategy="all"): """ __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, seed=None, \ - impurity="variance") + impurity="variance", featureSubsetStrategy="all") """ 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, - impurity="variance") + impurity="variance", featureSubsetStrategy="all") kwargs = self._input_kwargs self.setParams(**kwargs) @@ -1102,13 +1115,13 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, 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, seed=None, - impuriy="variance"): + impuriy="variance", featureSubsetStrategy="all"): """ 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, seed=None, \ - impurity="variance") + impurity="variance", featureSubsetStrategy="all") Sets params for Gradient Boosted Tree Regression. """ kwargs = self._input_kwargs @@ -1131,6 +1144,13 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, """ return self.getOrDefault(self.lossType) + @since("2.4.0") + def setFeatureSubsetStrategy(self, value): + """ + Sets the value of :py:attr:`featureSubsetStrategy`. + """ + return self._set(featureSubsetStrategy=value) + class GBTRegressionModel(TreeEnsembleModel, JavaPredictionModel, JavaMLWritable, JavaMLReadable): """ --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
