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author | Yanbo Liang <ybliang8@gmail.com> | 2016-02-29 00:55:51 -0800 |
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committer | DB Tsai <dbt@netflix.com> | 2016-02-29 00:55:51 -0800 |
commit | d81a71357e24160244b6eeff028b0d9a4863becf (patch) | |
tree | 0d5f6bdde7ce4edbe45883a908d80ab292845eb6 /python/pyspark | |
parent | dd3b5455c61bddce96a94c2ce8f5d76ed4948ea1 (diff) | |
download | spark-d81a71357e24160244b6eeff028b0d9a4863becf.tar.gz spark-d81a71357e24160244b6eeff028b0d9a4863becf.tar.bz2 spark-d81a71357e24160244b6eeff028b0d9a4863becf.zip |
[SPARK-13545][MLLIB][PYSPARK] Make MLlib LogisticRegressionWithLBFGS's default parameters consistent in Scala and Python
## What changes were proposed in this pull request?
* The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.)
* BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route.
* Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly.
cc mengxr dbtsai
## How was this patch tested?
No new tests, it should pass all current tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #11424 from yanboliang/spark-13545.
Diffstat (limited to 'python/pyspark')
-rw-r--r-- | python/pyspark/mllib/classification.py | 8 |
1 files changed, 5 insertions, 3 deletions
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index b4d54ef61b..53a0df27ca 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -326,7 +326,7 @@ class LogisticRegressionWithLBFGS(object): """ @classmethod @since('1.2.0') - def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2", + def train(cls, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2", intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2): """ Train a logistic regression model on the given data. @@ -341,7 +341,7 @@ class LogisticRegressionWithLBFGS(object): (default: None) :param regParam: The regularizer parameter. - (default: 0.01) + (default: 0.0) :param regType: The type of regularizer used for training our model. Allowed values: @@ -356,7 +356,9 @@ class LogisticRegressionWithLBFGS(object): (default: False) :param corrections: The number of corrections used in the LBFGS update. - (default: 10) + If a known updater is used for binary classification, + it calls the ml implementation and this parameter will + have no effect. (default: 10) :param tolerance: The convergence tolerance of iterations for L-BFGS. (default: 1e-6) |