Repository: spark Updated Branches: refs/heads/master cf2821ef5 -> ce6f3f163
[SPARK-10194] [MLLIB] [PYSPARK] SGD algorithms need convergenceTol parameter in Python [SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382) added a ```convergenceTol``` parameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed). Author: Yanbo Liang <[email protected]> Closes #8457 from yanboliang/spark-10194. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/ce6f3f16 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/ce6f3f16 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/ce6f3f16 Branch: refs/heads/master Commit: ce6f3f163bc667cb5da9ab4331c8bad10cc0d701 Parents: cf2821e Author: Yanbo Liang <[email protected]> Authored: Mon Sep 14 12:08:52 2015 -0700 Committer: Xiangrui Meng <[email protected]> Committed: Mon Sep 14 12:08:52 2015 -0700 ---------------------------------------------------------------------- .../spark/mllib/api/python/PythonMLLibAPI.scala | 20 +++++++++--- python/pyspark/mllib/classification.py | 17 ++++++++--- python/pyspark/mllib/regression.py | 32 +++++++++++++------- 3 files changed, 48 insertions(+), 21 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/ce6f3f16/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---------------------------------------------------------------------- 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 f585aac..69ce7f5 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 @@ -132,7 +132,8 @@ private[python] class PythonMLLibAPI extends Serializable { regParam: Double, regType: String, intercept: Boolean, - validateData: Boolean): JList[Object] = { + validateData: Boolean, + convergenceTol: Double): JList[Object] = { val lrAlg = new LinearRegressionWithSGD() lrAlg.setIntercept(intercept) .setValidateData(validateData) @@ -141,6 +142,7 @@ private[python] class PythonMLLibAPI extends Serializable { .setRegParam(regParam) .setStepSize(stepSize) .setMiniBatchFraction(miniBatchFraction) + .setConvergenceTol(convergenceTol) lrAlg.optimizer.setUpdater(getUpdaterFromString(regType)) trainRegressionModel( lrAlg, @@ -159,7 +161,8 @@ private[python] class PythonMLLibAPI extends Serializable { miniBatchFraction: Double, initialWeights: Vector, intercept: Boolean, - validateData: Boolean): JList[Object] = { + validateData: Boolean, + convergenceTol: Double): JList[Object] = { val lassoAlg = new LassoWithSGD() lassoAlg.setIntercept(intercept) .setValidateData(validateData) @@ -168,6 +171,7 @@ private[python] class PythonMLLibAPI extends Serializable { .setRegParam(regParam) .setStepSize(stepSize) .setMiniBatchFraction(miniBatchFraction) + .setConvergenceTol(convergenceTol) trainRegressionModel( lassoAlg, data, @@ -185,7 +189,8 @@ private[python] class PythonMLLibAPI extends Serializable { miniBatchFraction: Double, initialWeights: Vector, intercept: Boolean, - validateData: Boolean): JList[Object] = { + validateData: Boolean, + convergenceTol: Double): JList[Object] = { val ridgeAlg = new RidgeRegressionWithSGD() ridgeAlg.setIntercept(intercept) .setValidateData(validateData) @@ -194,6 +199,7 @@ private[python] class PythonMLLibAPI extends Serializable { .setRegParam(regParam) .setStepSize(stepSize) .setMiniBatchFraction(miniBatchFraction) + .setConvergenceTol(convergenceTol) trainRegressionModel( ridgeAlg, data, @@ -212,7 +218,8 @@ private[python] class PythonMLLibAPI extends Serializable { initialWeights: Vector, regType: String, intercept: Boolean, - validateData: Boolean): JList[Object] = { + validateData: Boolean, + convergenceTol: Double): JList[Object] = { val SVMAlg = new SVMWithSGD() SVMAlg.setIntercept(intercept) .setValidateData(validateData) @@ -221,6 +228,7 @@ private[python] class PythonMLLibAPI extends Serializable { .setRegParam(regParam) .setStepSize(stepSize) .setMiniBatchFraction(miniBatchFraction) + .setConvergenceTol(convergenceTol) SVMAlg.optimizer.setUpdater(getUpdaterFromString(regType)) trainRegressionModel( SVMAlg, @@ -240,7 +248,8 @@ private[python] class PythonMLLibAPI extends Serializable { regParam: Double, regType: String, intercept: Boolean, - validateData: Boolean): JList[Object] = { + validateData: Boolean, + convergenceTol: Double): JList[Object] = { val LogRegAlg = new LogisticRegressionWithSGD() LogRegAlg.setIntercept(intercept) .setValidateData(validateData) @@ -249,6 +258,7 @@ private[python] class PythonMLLibAPI extends Serializable { .setRegParam(regParam) .setStepSize(stepSize) .setMiniBatchFraction(miniBatchFraction) + .setConvergenceTol(convergenceTol) LogRegAlg.optimizer.setUpdater(getUpdaterFromString(regType)) trainRegressionModel( LogRegAlg, http://git-wip-us.apache.org/repos/asf/spark/blob/ce6f3f16/python/pyspark/mllib/classification.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index 8f27c44..cb4ee83 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -241,7 +241,7 @@ class LogisticRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None, regParam=0.01, regType="l2", intercept=False, - validateData=True): + validateData=True, convergenceTol=0.001): """ Train a logistic regression model on the given data. @@ -274,11 +274,13 @@ class LogisticRegressionWithSGD(object): :param validateData: Boolean parameter which indicates if the algorithm should validate data before training. (default: True) + :param convergenceTol: A condition which decides iteration termination. + (default: 0.001) """ def train(rdd, i): return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), regType, - bool(intercept), bool(validateData)) + bool(intercept), bool(validateData), float(convergenceTol)) return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) @@ -439,7 +441,7 @@ class SVMWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None, regType="l2", - intercept=False, validateData=True): + intercept=False, validateData=True, convergenceTol=0.001): """ Train a support vector machine on the given data. @@ -472,11 +474,13 @@ class SVMWithSGD(object): :param validateData: Boolean parameter which indicates if the algorithm should validate data before training. (default: True) + :param convergenceTol: A condition which decides iteration termination. + (default: 0.001) """ def train(rdd, i): return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, regType, - bool(intercept), bool(validateData)) + bool(intercept), bool(validateData), float(convergenceTol)) return _regression_train_wrapper(train, SVMModel, data, initialWeights) @@ -600,12 +604,15 @@ class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm): :param miniBatchFraction: Fraction of data on which SGD is run for each iteration. :param regParam: L2 Regularization parameter. + :param convergenceTol: A condition which decides iteration termination. """ - def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01): + def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01, + convergenceTol=0.001): self.stepSize = stepSize self.numIterations = numIterations self.regParam = regParam self.miniBatchFraction = miniBatchFraction + self.convergenceTol = convergenceTol self._model = None super(StreamingLogisticRegressionWithSGD, self).__init__( model=self._model) http://git-wip-us.apache.org/repos/asf/spark/blob/ce6f3f16/python/pyspark/mllib/regression.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 41946e3..256b753 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -28,7 +28,8 @@ __all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'LinearRegressionWithSGD', 'RidgeRegressionModel', 'RidgeRegressionWithSGD', 'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel', - 'IsotonicRegression'] + 'IsotonicRegression', 'StreamingLinearAlgorithm', + 'StreamingLinearRegressionWithSGD'] class LabeledPoint(object): @@ -202,7 +203,7 @@ class LinearRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None, regParam=0.0, regType=None, intercept=False, - validateData=True): + validateData=True, convergenceTol=0.001): """ Train a linear regression model using Stochastic Gradient Descent (SGD). @@ -244,11 +245,14 @@ class LinearRegressionWithSGD(object): :param validateData: Boolean parameter which indicates if the algorithm should validate data before training. (default: True) + :param convergenceTol: A condition which decides iteration termination. + (default: 0.001) """ def train(rdd, i): return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), - regType, bool(intercept), bool(validateData)) + regType, bool(intercept), bool(validateData), + float(convergenceTol)) return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights) @@ -330,7 +334,7 @@ class LassoWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None, intercept=False, - validateData=True): + validateData=True, convergenceTol=0.001): """ Train a regression model with L1-regularization using Stochastic Gradient Descent. @@ -362,11 +366,13 @@ class LassoWithSGD(object): :param validateData: Boolean parameter which indicates if the algorithm should validate data before training. (default: True) + :param convergenceTol: A condition which decides iteration termination. + (default: 0.001) """ def train(rdd, i): return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, bool(intercept), - bool(validateData)) + bool(validateData), float(convergenceTol)) return _regression_train_wrapper(train, LassoModel, data, initialWeights) @@ -449,7 +455,7 @@ class RidgeRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None, intercept=False, - validateData=True): + validateData=True, convergenceTol=0.001): """ Train a regression model with L2-regularization using Stochastic Gradient Descent. @@ -481,11 +487,13 @@ class RidgeRegressionWithSGD(object): :param validateData: Boolean parameter which indicates if the algorithm should validate data before training. (default: True) + :param convergenceTol: A condition which decides iteration termination. + (default: 0.001) """ def train(rdd, i): return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, bool(intercept), - bool(validateData)) + bool(validateData), float(convergenceTol)) return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights) @@ -636,15 +644,17 @@ class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm): After training on a batch of data, the weights obtained at the end of training are used as initial weights for the next batch. - :param: stepSize Step size for each iteration of gradient descent. - :param: numIterations Total number of iterations run. - :param: miniBatchFraction Fraction of data on which SGD is run for each + :param stepSize: Step size for each iteration of gradient descent. + :param numIterations: Total number of iterations run. + :param miniBatchFraction: Fraction of data on which SGD is run for each iteration. + :param convergenceTol: A condition which decides iteration termination. """ - def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0): + def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, convergenceTol=0.001): self.stepSize = stepSize self.numIterations = numIterations self.miniBatchFraction = miniBatchFraction + self.convergenceTol = convergenceTol self._model = None super(StreamingLinearRegressionWithSGD, self).__init__( model=self._model) --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
