diff options
Diffstat (limited to 'python/pyspark/ml/classification.py')
| -rw-r--r-- | python/pyspark/ml/classification.py | 14 |
1 files changed, 9 insertions, 5 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. """ |