From d2e29516f2064f93f3a9070c91fc7460706e0b0a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 18 Nov 2014 15:57:33 -0800
Subject: [PATCH] [SPARK-4306] [MLlib] Python API for
LogisticRegressionWithLBFGS
```
class LogisticRegressionWithLBFGS
| train(cls, data, iterations=100, initialWeights=None, corrections=10, tolerance=0.0001, regParam=0.01, intercept=False)
| Train a logistic regression model on the given data.
|
| :param data: The training data, an RDD of LabeledPoint.
| :param iterations: The number of iterations (default: 100).
| :param initialWeights: The initial weights (default: None).
| :param regParam: The regularizer parameter (default: 0.01).
| :param regType: The type of regularizer used for training
| our model.
| :Allowed values:
| - "l1" for using L1 regularization
| - "l2" for using L2 regularization
| - None for no regularization
| (default: "l2")
| :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).
| :param corrections: The number of corrections used in the LBFGS update (default: 10).
| :param tolerance: The convergence tolerance of iterations for L-BFGS (default: 1e-4).
|
| >>> data = [
| ... LabeledPoint(0.0, [0.0, 1.0]),
| ... LabeledPoint(1.0, [1.0, 0.0]),
| ... ]
| >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
| >>> lrm.predict([1.0, 0.0])
| 1
| >>> lrm.predict([0.0, 1.0])
| 0
| >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
| [1, 0]
```
Author: Davies Liu <davies@databricks.com>
Closes #3307 from davies/lbfgs and squashes the following commits:
34bd986 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into lbfgs
5a945a6 [Davies Liu] address comments
941061b [Davies Liu] Merge branch 'master' of github.com:apache/spark into lbfgs
03e5543 [Davies Liu] add it to docs
ed2f9a8 [Davies Liu] add regType
76cd1b6 [Davies Liu] reorder arguments
4429a74 [Davies Liu] Update classification.py
9252783 [Davies Liu] python api for LogisticRegressionWithLBFGS
---
.../mllib/api/python/PythonMLLibAPI.scala | 35 ++++++++++++
python/pyspark/mllib/classification.py | 57 +++++++++++++++++--
2 files changed, 88 insertions(+), 4 deletions(-)
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 c8476a5370..6f94b7f483 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
@@ -229,6 +229,41 @@ class PythonMLLibAPI extends Serializable {
initialWeights)
}
+ /**
+ * Java stub for Python mllib LogisticRegressionWithLBFGS.train()
+ */
+ def trainLogisticRegressionModelWithLBFGS(
+ data: JavaRDD[LabeledPoint],
+ numIterations: Int,
+ initialWeights: Vector,
+ regParam: Double,
+ regType: String,
+ intercept: Boolean,
+ corrections: Int,
+ tolerance: Double): JList[Object] = {
+ val LogRegAlg = new LogisticRegressionWithLBFGS()
+ LogRegAlg.setIntercept(intercept)
+ LogRegAlg.optimizer
+ .setNumIterations(numIterations)
+ .setRegParam(regParam)
+ .setNumCorrections(corrections)
+ .setConvergenceTol(tolerance)
+ if (regType == "l2") {
+ LogRegAlg.optimizer.setUpdater(new SquaredL2Updater)
+ } else if (regType == "l1") {
+ LogRegAlg.optimizer.setUpdater(new L1Updater)
+ } else if (regType == null) {
+ LogRegAlg.optimizer.setUpdater(new SimpleUpdater)
+ } else {
+ throw new java.lang.IllegalArgumentException("Invalid value for 'regType' parameter."
+ + " Can only be initialized using the following string values: ['l1', 'l2', None].")
+ }
+ trainRegressionModel(
+ LogRegAlg,
+ data,
+ initialWeights)
+ }
+
/**
* Java stub for NaiveBayes.train()
*/
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index ee0729b1eb..f14d0ed11c 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -26,8 +26,8 @@ from pyspark.mllib.linalg import SparseVector, _convert_to_vector
from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper
-__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'SVMModel',
- 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes']
+__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS',
+ 'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes']
class LinearBinaryClassificationModel(LinearModel):
@@ -151,7 +151,7 @@ class LogisticRegressionWithSGD(object):
(default: "l2")
- @param intercept: Boolean parameter which indicates the use
+ :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).
@@ -164,6 +164,55 @@ class LogisticRegressionWithSGD(object):
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
+class LogisticRegressionWithLBFGS(object):
+
+ @classmethod
+ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2",
+ intercept=False, corrections=10, tolerance=1e-4):
+ """
+ Train a logistic regression model on the given data.
+
+ :param data: The training data, an RDD of LabeledPoint.
+ :param iterations: The number of iterations (default: 100).
+ :param initialWeights: The initial weights (default: None).
+ :param regParam: The regularizer parameter (default: 0.01).
+ :param regType: The type of regularizer used for training
+ our model.
+
+ :Allowed values:
+ - "l1" for using L1 regularization
+ - "l2" for using L2 regularization
+ - None for no regularization
+
+ (default: "l2")
+
+ :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).
+ :param corrections: The number of corrections used in the LBFGS
+ update (default: 10).
+ :param tolerance: The convergence tolerance of iterations for
+ L-BFGS (default: 1e-4).
+
+ >>> data = [
+ ... LabeledPoint(0.0, [0.0, 1.0]),
+ ... LabeledPoint(1.0, [1.0, 0.0]),
+ ... ]
+ >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
+ >>> lrm.predict([1.0, 0.0])
+ 1
+ >>> lrm.predict([0.0, 1.0])
+ 0
+ """
+ def train(rdd, i):
+ return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
+ float(regParam), str(regType), bool(intercept), int(corrections),
+ float(tolerance))
+
+ return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
+
+
class SVMModel(LinearBinaryClassificationModel):
"""A support vector machine.
@@ -241,7 +290,7 @@ class SVMWithSGD(object):
(default: "l2")
- @param intercept: Boolean parameter which indicates the use
+ :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).
--
GitLab