Github user miccagiann commented on a diff in the pull request: https://github.com/apache/spark/pull/1775#discussion_r15795902 --- Diff: python/pyspark/mllib/classification.py --- @@ -73,11 +73,36 @@ def predict(self, x): class LogisticRegressionWithSGD(object): @classmethod - def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None): - """Train a logistic regression model on the given data.""" + def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, + initialWeights=None, regParam=1.0, regType=None, intercept=False): + """ + Train a logistic regression model on the given data. + + @param data: The training data. + @param iterations: The number of iterations (default: 100). + @param step: The step parameter used in SGD + (default: 1.0). + @param miniBatchFraction: Fraction of data to be used for each SGD + iteration. + @param initialWeights: The initial weights (default: None). + @param regParam: The regularizer parameter (default: 1.0). + @param regType: The type of regularizer used for training + our model. + Allowed values: "l1" for using L1Updater, + "l2" for using + SquaredL2Updater, + "none" for no regularizer. + (default: "none") + @param intercept: Boolean parameter which indicates the use + or not of the augmented representation for + training data (i.e. whether bias features + are activated or not). + """ sc = data.context + if regType is None: --- End diff -- Xiangrui suggested to keep Scala code as simple as possible and only to throw the `IllegalArgumentException` from there. I tried with pattern matching and by creating enumerations however the result was complicated and I ended up adding more classes to the scala and to python code.
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