Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/17922#discussion_r126766060
--- Diff: python/pyspark/ml/classification.py ---
@@ -246,18 +246,55 @@ class LogisticRegression(JavaEstimator,
HasFeaturesCol, HasLabelCol, HasPredicti
"be used in the model. Supported options: auto,
binomial, multinomial",
typeConverter=TypeConverters.toString)
+ lowerBoundsOnCoefficients = Param(Params._dummy(),
"lowerBoundsOnCoefficients",
+ "The lower bounds on coefficients if
fitting under bound "
+ "constrained optimization. The bound
matrix must be "
+ "compatible with the shape "
+ "(1, number of features) for
binomial regression, or "
+ "(number of classes, number of
features) "
+ "for multinomial regression.",
+
typeConverter=TypeConverters.toMatrix)
+
+ upperBoundsOnCoefficients = Param(Params._dummy(),
"upperBoundsOnCoefficients",
+ "The upper bounds on coefficients if
fitting under bound "
+ "constrained optimization. The bound
matrix must be "
+ "compatible with the shape "
+ "(1, number of features) for
binomial regression, or "
+ "(number of classes, number of
features) "
+ "for multinomial regression.",
+
typeConverter=TypeConverters.toMatrix)
+
+ lowerBoundsOnIntercepts = Param(Params._dummy(),
"lowerBoundsOnIntercepts",
+ "The lower bounds on intercepts if
fitting under bound "
+ "constrained optimization. The bounds
vector size must be"
+ "equal with 1 for binomial regression,
or the number of"
+ "lasses for multinomial regression.",
+ typeConverter=TypeConverters.toVector)
+
+ upperBoundsOnIntercepts = Param(Params._dummy(),
"upperBoundsOnIntercepts",
+ "The upper bounds on intercepts if
fitting under bound "
+ "constrained optimization. The bound
vector size must be "
+ "equal with 1 for binomial regression,
or the number of "
+ "classes for multinomial regression.",
+ typeConverter=TypeConverters.toVector)
+
@keyword_only
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,
- aggregationDepth=2, family="auto"):
+ aggregationDepth=2, family="auto",
+ lowerBoundsOnCoefficients=None,
upperBoundsOnCoefficients=None,
--- End diff --
should fill up the previous line before starting another, here and below
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