diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 1d5d3762ed8e9f73c50f8579a04375e276132608..fd0b9556c7d5444ca9af7b7cda6a2f22656dc8db 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -271,6 +271,7 @@ class PythonMLLibAPI extends Serializable {
.setNumIterations(numIterations)
.setRegParam(regParam)
.setStepSize(stepSize)
+ .setMiniBatchFraction(miniBatchFraction)
if (regType == "l2") {
lrAlg.optimizer.setUpdater(new SquaredL2Updater)
} else if (regType == "l1") {
@@ -341,16 +342,27 @@ class PythonMLLibAPI extends Serializable {
stepSize: Double,
regParam: Double,
miniBatchFraction: Double,
- initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
+ initialWeightsBA: Array[Byte],
+ regType: String,
+ intercept: Boolean): java.util.List[java.lang.Object] = {
+ val SVMAlg = new SVMWithSGD()
+ SVMAlg.setIntercept(intercept)
+ SVMAlg.optimizer
+ .setNumIterations(numIterations)
+ .setRegParam(regParam)
+ .setStepSize(stepSize)
+ .setMiniBatchFraction(miniBatchFraction)
+ if (regType == "l2") {
+ SVMAlg.optimizer.setUpdater(new SquaredL2Updater)
+ } else if (regType == "l1") {
+ SVMAlg.optimizer.setUpdater(new L1Updater)
+ } else if (regType != "none") {
+ throw new java.lang.IllegalArgumentException("Invalid value for 'regType' parameter."
+ + " Can only be initialized using the following string values: [l1, l2, none].")
+ }
trainRegressionModel(
(data, initialWeights) =>
- SVMWithSGD.train(
- data,
- numIterations,
- stepSize,
- regParam,
- miniBatchFraction,
- initialWeights),
+ SVMAlg.run(data, initialWeights),
dataBytesJRDD,
initialWeightsBA)
}
@@ -363,15 +375,28 @@ class PythonMLLibAPI extends Serializable {
numIterations: Int,
stepSize: Double,
miniBatchFraction: Double,
- initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
+ initialWeightsBA: Array[Byte],
+ regParam: Double,
+ regType: String,
+ intercept: Boolean): java.util.List[java.lang.Object] = {
+ val LogRegAlg = new LogisticRegressionWithSGD()
+ LogRegAlg.setIntercept(intercept)
+ LogRegAlg.optimizer
+ .setNumIterations(numIterations)
+ .setRegParam(regParam)
+ .setStepSize(stepSize)
+ .setMiniBatchFraction(miniBatchFraction)
+ if (regType == "l2") {
+ LogRegAlg.optimizer.setUpdater(new SquaredL2Updater)
+ } else if (regType == "l1") {
+ LogRegAlg.optimizer.setUpdater(new L1Updater)
+ } else if (regType != "none") {
+ throw new java.lang.IllegalArgumentException("Invalid value for 'regType' parameter."
+ + " Can only be initialized using the following string values: [l1, l2, none].")
+ }
trainRegressionModel(
(data, initialWeights) =>
- LogisticRegressionWithSGD.train(
- data,
- numIterations,
- stepSize,
- miniBatchFraction,
- initialWeights),
+ LogRegAlg.run(data, initialWeights),
dataBytesJRDD,
initialWeightsBA)
}
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 2bbb9c3fca315f20ee50c70eb83c23f777f58386..5ec1a8084d2690de84ca92575433cef35ba40e5b 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -73,11 +73,36 @@ class LogisticRegressionModel(LinearModel):
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:
+ regType = "none"
train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
- d._jrdd, iterations, step, miniBatchFraction, i)
+ d._jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
initialWeights)
@@ -115,11 +140,35 @@ class SVMModel(LinearModel):
class SVMWithSGD(object):
@classmethod
def train(cls, data, iterations=100, step=1.0, regParam=1.0,
- miniBatchFraction=1.0, initialWeights=None):
- """Train a support vector machine on the given data."""
+ miniBatchFraction=1.0, initialWeights=None, regType=None, intercept=False):
+ """
+ Train a support vector machine 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 regParam: The regularizer parameter (default: 1.0).
+ @param miniBatchFraction: Fraction of data to be used for each SGD
+ iteration.
+ @param initialWeights: The initial weights (default: None).
+ @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:
+ regType = "none"
train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
- d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+ d._jrdd, iterations, step, regParam, miniBatchFraction, i, regType, intercept)
return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)