Parallel implementations of Logistic Regression.
How to run logistic regression on stratosphere
- Ensemble + SGD (mahout) Training
Parameters: [numPartitions] [inputPathTrain] [inputPathTest] [outputPath] [numFeatures] [runValidation (0 or 1)]
example: bin/stratosphere run -j logreg-pact-0.0.1-SNAPSHOT-jar-with-dependencies.jar -c de.tuberlin.dima.ml.pact.logreg.ensemble.EnsembleJob -a 1 file:///Users/qml_moon/Documents/TUB/DIMA/code/lr file:///Users/qml_moon/Documents/TUB/DIMA/code/lr file:///Users/qml_moon/Documents/TUB/DIMA/code/lr-out 3 0
- Iterative Batch Gradient descent training
Parameters: [numSubTasks] [inputPathTrain] [inputPathTest] [outputPath] [numIteration] [runValidation (0 or 1)] [learningRate] [positiveClass] [numFeatuer]
example: bin/stratosphere run -j logreg-pact-0.0.1-SNAPSHOT-jar-with-dependencies.jar -c de.tuberlin.dima.ml.pact.logreg.batchgd.BatchGDPlanAssembler -a 1 file:///Users/qml_moon/Documents/TUB/DIMA/code/lr file:///Users/qml_moon/Documents/TUB/DIMA/code/lr file:///Users/qml_moon/Documents/TUB/DIMA/code/lr-out 10 0 0.05 1 3
- Forward Feature Selection using SFO (Single Feature optimization)
Parameters: [numSubStasks] [inputPathTrain] [inputPathTest] [isMultiLabel (true/false)] [positiveClass] [outputPath] [numFeatures] [newton tolerance] [newton max iterations] [regularization] [iterations] [addFeaturePerIteration] [Optional: baseModel (base64 encoded)]
example: bin/stratosphere run -j logreg-pact-0.0.1-SNAPSHOT-jar-with-dependencies.jar -c de.tuberlin.dima.ml.pact.logreg.sfo.SFOPlanAssembler -a 1 file:///Users/qml_moon/Documents/TUB/DIMA/code/lr file:///Users/qml_moon/Documents/TUB/DIMA/code/lr false 1 file:///Users/qml_moon/Documents/TUB/DIMA/code/lr-out 3 0.000001 5 0 2 1
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