diff options
| -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 |