Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/6849#discussion_r33152107
--- Diff: python/pyspark/mllib/classification.py ---
@@ -580,6 +583,102 @@ def train(cls, data, lambda_=1.0):
return NaiveBayesModel(labels.toArray(), pi.toArray(),
numpy.array(theta))
+class StreamingLinearAlgorithm(object):
+ """
+ Base class that has to be inherited by any StreamingLinearAlgorithm.
+
+ Prevents reimplementation of methods predictOn and predictOnValues.
+ """
+ def __init__(self, model):
+ self._model = model
+
+ def latestModel(self):
+ """
+ Returns the latest model.
+ """
+ return self._model
+
+ def _validate(self, dstream):
+ if not isinstance(dstream, DStream):
+ raise TypeError(
+ "dstream should be a DStream object, got %s" %
type(dstream))
+ if not self._model:
+ raise ValueError(
+ "Model must be intialized using setInitialWeights")
+
+ def predictOn(self, dstream):
+ """
+ Make predictions on a dstream.
+
+ Returns a transformed dstream object.
+ """
+ self._validate(dstream)
+ return dstream.map(lambda x: self._model.predict(x))
+
+ def predictOnValues(self, dstream):
+ """
+ Make predictions on a keyed dstream.
+
+ Returns a transformed dstream object.
+ """
+ self._validate(dstream)
+ return dstream.mapValues(lambda x: self._model.predict(x))
+
+
+@inherit_doc
+class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
+ """
+ Run LogisticRegression with SGD on a stream of data.
+
+ The weights obtained at the end of training a stream are used as
initial
+ weights for the next stream.
+ :param stepSize: Step size for each iteration of gradient descent.
+ :param numIterations: Number of iterations run for each batch of data.
+ :param miniBatchFraction: Fraction of data on which SGD is run for each
+ iteration.
+ :param regParam: L2 Regularization parameter.
+ """
+ def __init__(self, stepSize=0.1, numIterations=50,
--- End diff --
Is it not 79? Or is the convention in PySpark 100?
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