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| author | Yanbo Liang <ybliang8@gmail.com> | 2016-07-19 12:31:04 +0100 |
|---|---|---|
| committer | Sean Owen <sowen@cloudera.com> | 2016-07-19 12:31:04 +0100 |
| commit | 670891496a82538a5e2bf981a4044fb6f4cbb062 (patch) | |
| tree | a91eb7ad9962b2eb5f2de12e3522ec45aab951d1 /python | |
| parent | 5d92326be76cb15edc6e18e94a373e197f696803 (diff) | |
| download | spark-670891496a82538a5e2bf981a4044fb6f4cbb062.tar.gz spark-670891496a82538a5e2bf981a4044fb6f4cbb062.tar.bz2 spark-670891496a82538a5e2bf981a4044fb6f4cbb062.zip | |
[SPARK-16494][ML] Upgrade breeze version to 0.12
## What changes were proposed in this pull request?
breeze 0.12 has been released for more than half a year, and it brings lots of new features, performance improvement and bug fixes.
One of the biggest features is ```LBFGS-B``` which is an implementation of ```LBFGS``` with box constraints and much faster for some special case.
We would like to implement Huber loss function for ```LinearRegression``` ([SPARK-3181](https://issues.apache.org/jira/browse/SPARK-3181)) and it requires ```LBFGS-B``` as the optimization solver. So we should bump up the dependent breeze version to 0.12.
For more features, improvements and bug fixes of breeze 0.12, you can refer the following link:
https://groups.google.com/forum/#!topic/scala-breeze/nEeRi_DcY5c
## How was this patch tested?
No new tests, should pass the existing ones.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #14150 from yanboliang/spark-16494.
Diffstat (limited to 'python')
| -rw-r--r-- | python/pyspark/ml/classification.py | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 3c4af90aca..613bc8cb3e 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -1299,7 +1299,7 @@ class OneVsRest(Estimator, OneVsRestParams, MLReadable, MLWritable): >>> [x.coefficients for x in model.models] [DenseVector([3.3925, 1.8785]), DenseVector([-4.3016, -6.3163]), DenseVector([-4.5855, 6.1785])] >>> [x.intercept for x in model.models] - [-3.6474708290602034, 2.5507881951814495, -1.1016513228162115] + [-3.64747..., 2.55078..., -1.10165...] >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 0.0))]).toDF() >>> model.transform(test0).head().prediction 1.0 |