StreamingLogisticRegressionWithSGD¶
-
class
pyspark.mllib.classification.StreamingLogisticRegressionWithSGD(stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.0, convergenceTol=0.001)[source]¶ Train or predict a logistic regression model on streaming data. Training uses Stochastic Gradient Descent to update the model based on each new batch of incoming data from a DStream.
Each batch of data is assumed to be an RDD of LabeledPoints. The number of data points per batch can vary, but the number of features must be constant. An initial weight vector must be provided.
New in version 1.5.0.
- Parameters
- stepSizefloat, optional
Step size for each iteration of gradient descent. (default: 0.1)
- numIterationsint, optional
Number of iterations run for each batch of data. (default: 50)
- miniBatchFractionfloat, optional
Fraction of each batch of data to use for updates. (default: 1.0)
- regParamfloat, optional
L2 Regularization parameter. (default: 0.0)
- convergenceTolfloat, optional
Value used to determine when to terminate iterations. (default: 0.001)
Methods
Returns the latest model.
predictOn(dstream)Use the model to make predictions on batches of data from a DStream.
predictOnValues(dstream)Use the model to make predictions on the values of a DStream and carry over its keys.
setInitialWeights(initialWeights)Set the initial value of weights.
trainOn(dstream)Train the model on the incoming dstream.
Methods Documentation
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latestModel()¶ Returns the latest model.
New in version 1.5.0.
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predictOn(dstream)¶ Use the model to make predictions on batches of data from a DStream.
New in version 1.5.0.
- Returns
pyspark.streaming.DStreamDStream containing predictions.
-
predictOnValues(dstream)¶ Use the model to make predictions on the values of a DStream and carry over its keys.
New in version 1.5.0.
- Returns
pyspark.streaming.DStreamDStream containing predictions.