diff options
Diffstat (limited to 'python/pyspark/ml/classification.py')
| -rw-r--r-- | python/pyspark/ml/classification.py | 35 |
1 files changed, 20 insertions, 15 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 1411d3fd9c..4e645519c4 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -55,7 +55,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti ... TypeError: Method setParams forces keyword arguments. """ - _java_class = "org.apache.spark.ml.classification.LogisticRegression" + # a placeholder to make it appear in the generated doc elasticNetParam = \ Param(Params._dummy(), "elasticNetParam", @@ -75,6 +75,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti threshold=0.5, probabilityCol="probability") """ super(LogisticRegression, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.LogisticRegression", self.uid) #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty # is an L2 penalty. For alpha = 1, it is an L1 penalty. self.elasticNetParam = \ @@ -111,7 +113,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti """ Sets the value of :py:attr:`elasticNetParam`. """ - self.paramMap[self.elasticNetParam] = value + self._paramMap[self.elasticNetParam] = value return self def getElasticNetParam(self): @@ -124,7 +126,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti """ Sets the value of :py:attr:`fitIntercept`. """ - self.paramMap[self.fitIntercept] = value + self._paramMap[self.fitIntercept] = value return self def getFitIntercept(self): @@ -137,7 +139,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti """ Sets the value of :py:attr:`threshold`. """ - self.paramMap[self.threshold] = value + self._paramMap[self.threshold] = value return self def getThreshold(self): @@ -208,7 +210,6 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred 1.0 """ - _java_class = "org.apache.spark.ml.classification.DecisionTreeClassifier" # a placeholder to make it appear in the generated doc impurity = Param(Params._dummy(), "impurity", "Criterion used for information gain calculation (case-insensitive). " + @@ -224,6 +225,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini") """ super(DecisionTreeClassifier, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.DecisionTreeClassifier", self.uid) #: param for Criterion used for information gain calculation (case-insensitive). self.impurity = \ Param(self, "impurity", @@ -256,7 +259,7 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred """ Sets the value of :py:attr:`impurity`. """ - self.paramMap[self.impurity] = value + self._paramMap[self.impurity] = value return self def getImpurity(self): @@ -299,7 +302,6 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred 1.0 """ - _java_class = "org.apache.spark.ml.classification.RandomForestClassifier" # a placeholder to make it appear in the generated doc impurity = Param(Params._dummy(), "impurity", "Criterion used for information gain calculation (case-insensitive). " + @@ -325,6 +327,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred numTrees=20, featureSubsetStrategy="auto", seed=42) """ super(RandomForestClassifier, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.RandomForestClassifier", self.uid) #: param for Criterion used for information gain calculation (case-insensitive). self.impurity = \ Param(self, "impurity", @@ -370,7 +374,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred """ Sets the value of :py:attr:`impurity`. """ - self.paramMap[self.impurity] = value + self._paramMap[self.impurity] = value return self def getImpurity(self): @@ -383,7 +387,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred """ Sets the value of :py:attr:`subsamplingRate`. """ - self.paramMap[self.subsamplingRate] = value + self._paramMap[self.subsamplingRate] = value return self def getSubsamplingRate(self): @@ -396,7 +400,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred """ Sets the value of :py:attr:`numTrees`. """ - self.paramMap[self.numTrees] = value + self._paramMap[self.numTrees] = value return self def getNumTrees(self): @@ -409,7 +413,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred """ Sets the value of :py:attr:`featureSubsetStrategy`. """ - self.paramMap[self.featureSubsetStrategy] = value + self._paramMap[self.featureSubsetStrategy] = value return self def getFeatureSubsetStrategy(self): @@ -452,7 +456,6 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol 1.0 """ - _java_class = "org.apache.spark.ml.classification.GBTClassifier" # a placeholder to make it appear in the generated doc lossType = Param(Params._dummy(), "lossType", "Loss function which GBT tries to minimize (case-insensitive). " + @@ -476,6 +479,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol lossType="logistic", maxIter=20, stepSize=0.1) """ super(GBTClassifier, self).__init__() + self._java_obj = self._new_java_obj( + "org.apache.spark.ml.classification.GBTClassifier", self.uid) #: param for Loss function which GBT tries to minimize (case-insensitive). self.lossType = Param(self, "lossType", "Loss function which GBT tries to minimize (case-insensitive). " + @@ -517,7 +522,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol """ Sets the value of :py:attr:`lossType`. """ - self.paramMap[self.lossType] = value + self._paramMap[self.lossType] = value return self def getLossType(self): @@ -530,7 +535,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol """ Sets the value of :py:attr:`subsamplingRate`. """ - self.paramMap[self.subsamplingRate] = value + self._paramMap[self.subsamplingRate] = value return self def getSubsamplingRate(self): @@ -543,7 +548,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol """ Sets the value of :py:attr:`stepSize`. """ - self.paramMap[self.stepSize] = value + self._paramMap[self.stepSize] = value return self def getStepSize(self): |