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[SPARK-20498][PYSPARK][ML] Expose getMaxDepth for ensemble tree model in PySpark #18120

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[SPARK-20498][PYSPARK][ML] Expose getMaxDepth for ensemble tree model in PySpark #18120

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96e797f
ENH: add maxDepth for resemble tree model
facaiy May 26, 2017
5081592
TST: add doctest
facaiy May 26, 2017
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4 changes: 4 additions & 0 deletions python/pyspark/ml/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -862,6 +862,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
>>> model_path = temp_path + "/rfc_model"
>>> model.save(model_path)
>>> model2 = RandomForestClassificationModel.load(model_path)
>>> model.getMaxDepth() == model2.getMaxDepth()
True
>>> model.featureImportances == model2.featureImportances
True

Expand Down Expand Up @@ -997,6 +999,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
>>> model_path = temp_path + "gbtc_model"
>>> model.save(model_path)
>>> model2 = GBTClassificationModel.load(model_path)
>>> model.getMaxDepth() == model2.getMaxDepth()
True
>>> model.featureImportances == model2.featureImportances
True
>>> model.treeWeights == model2.treeWeights
Expand Down
10 changes: 10 additions & 0 deletions python/pyspark/ml/regression.py
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Expand Up @@ -785,6 +785,12 @@ def getNumTrees(self):
"""Number of trees in ensemble."""
return self._call_java("getNumTrees")

@property
@since("2.3.0")
def getMaxDepth(self):
"""Maximum depth of the tree (>= 0)."""
return self._call_java("getMaxDepth")

@property
@since("1.5.0")
def treeWeights(self):
Expand Down Expand Up @@ -877,6 +883,8 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
>>> model_path = temp_path + "/rfr_model"
>>> model.save(model_path)
>>> model2 = RandomForestRegressionModel.load(model_path)
>>> model.getMaxDepth() == model2.getMaxDepth()
True
>>> model.featureImportances == model2.featureImportances
True

Expand Down Expand Up @@ -996,6 +1004,8 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
>>> model_path = temp_path + "gbtr_model"
>>> model.save(model_path)
>>> model2 = GBTRegressionModel.load(model_path)
>>> model.getMaxDepth() == model2.getMaxDepth()
True
>>> model.featureImportances == model2.featureImportances
True
>>> model.treeWeights == model2.treeWeights
Expand Down