diff options
| -rw-r--r-- | python/pyspark/ml/classification.py | 14 | ||||
| -rw-r--r-- | python/pyspark/ml/param/_shared_params_code_gen.py | 4 | ||||
| -rw-r--r-- | python/pyspark/ml/param/shared.py | 24 | ||||
| -rw-r--r-- | python/pyspark/ml/regression.py | 11 |
4 files changed, 42 insertions, 11 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 33ada27454..d1522d78fa 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -64,7 +64,7 @@ class JavaClassificationModel(JavaPredictionModel): class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol, HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds, - HasWeightCol, JavaMLWritable, JavaMLReadable): + HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable): """ Logistic regression. Currently, this class only supports binary classification. @@ -121,12 +121,14 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, threshold=0.5, thresholds=None, probabilityCol="probability", - rawPredictionCol="rawPrediction", standardization=True, weightCol=None): + rawPredictionCol="rawPrediction", standardization=True, weightCol=None, + aggregationDepth=2): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ threshold=0.5, thresholds=None, probabilityCol="probability", \ - rawPredictionCol="rawPrediction", standardization=True, weightCol=None) + rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \ + aggregationDepth=2) If the threshold and thresholds Params are both set, they must be equivalent. """ super(LogisticRegression, self).__init__() @@ -142,12 +144,14 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, threshold=0.5, thresholds=None, probabilityCol="probability", - rawPredictionCol="rawPrediction", standardization=True, weightCol=None): + rawPredictionCol="rawPrediction", standardization=True, weightCol=None, + aggregationDepth=2): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ threshold=0.5, thresholds=None, probabilityCol="probability", \ - rawPredictionCol="rawPrediction", standardization=True, weightCol=None) + rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \ + aggregationDepth=2) Sets params for logistic regression. If the threshold and thresholds Params are both set, they must be equivalent. """ diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py index c32dcc467d..4f4328bcad 100644 --- a/python/pyspark/ml/param/_shared_params_code_gen.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -147,7 +147,9 @@ if __name__ == "__main__": ("solver", "the solver algorithm for optimization. If this is not set or empty, " + "default value is 'auto'.", "'auto'", "TypeConverters.toString"), ("varianceCol", "column name for the biased sample variance of prediction.", - None, "TypeConverters.toString")] + None, "TypeConverters.toString"), + ("aggregationDepth", "suggested depth for treeAggregate (>= 2).", "2", + "TypeConverters.toInt")] code = [] for name, doc, defaultValueStr, typeConverter in shared: diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index c5ccf81540..24af07afc7 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -560,6 +560,30 @@ class HasVarianceCol(Params): return self.getOrDefault(self.varianceCol) +class HasAggregationDepth(Params): + """ + Mixin for param aggregationDepth: suggested depth for treeAggregate (>= 2). + """ + + aggregationDepth = Param(Params._dummy(), "aggregationDepth", "suggested depth for treeAggregate (>= 2).", typeConverter=TypeConverters.toInt) + + def __init__(self): + super(HasAggregationDepth, self).__init__() + self._setDefault(aggregationDepth=2) + + def setAggregationDepth(self, value): + """ + Sets the value of :py:attr:`aggregationDepth`. + """ + return self._set(aggregationDepth=value) + + def getAggregationDepth(self): + """ + Gets the value of aggregationDepth or its default value. + """ + return self.getOrDefault(self.aggregationDepth) + + class DecisionTreeParams(Params): """ Mixin for Decision Tree parameters. diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 56312f672f..19afc723bb 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -39,7 +39,8 @@ __all__ = ['AFTSurvivalRegression', 'AFTSurvivalRegressionModel', @inherit_doc class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept, - HasStandardization, HasSolver, HasWeightCol, JavaMLWritable, JavaMLReadable): + HasStandardization, HasSolver, HasWeightCol, HasAggregationDepth, + JavaMLWritable, JavaMLReadable): """ Linear regression. @@ -97,11 +98,11 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, - standardization=True, solver="auto", weightCol=None): + standardization=True, solver="auto", weightCol=None, aggregationDepth=2): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ - standardization=True, solver="auto", weightCol=None) + standardization=True, solver="auto", weightCol=None, aggregationDepth=2) """ super(LinearRegression, self).__init__() self._java_obj = self._new_java_obj( @@ -114,11 +115,11 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction @since("1.4.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, - standardization=True, solver="auto", weightCol=None): + standardization=True, solver="auto", weightCol=None, aggregationDepth=2): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ - standardization=True, solver="auto", weightCol=None) + standardization=True, solver="auto", weightCol=None, aggregationDepth=2) Sets params for linear regression. """ kwargs = self.setParams._input_kwargs |