Shuo Xiang created SPARK-13029:
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Summary: Logistic regression returns inaccurate results when there
is a column with identical value, and fit_intercept=false
Key: SPARK-13029
URL: https://issues.apache.org/jira/browse/SPARK-13029
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 1.6.0, 1.5.2
Reporter: Shuo Xiang
This is a bug that appears while fitting a Logistic Regression model with
`.setStandardization(false)` and `setFitIntercept(false)`. If the data matrix
has one column with identical value, the resulting model is not correct.
Specifically, the special column will always get a weight of 0, due to the
special check inside the code. However, the correct solution, which is unique
for L2 logistic regression, usually has non-zero weight.
I use the [heart_scale
data](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) and
manually augmented the data matrix with a column of one. The resulting data is
run with reg=1.0, max_iter=1000, tol=1e-9 on the following tools:
- libsvm
- scikit-learn
- sparkml
(Notice libsvm and scikit-learn use a slightly different formulation, so their
regularizer is equivalently set to 1/270).
The first two will have an objective value 0.7275 and give a solution vector:
[0.03007516959304916, 0.09054186091216457, 0.09540306114820495,
0.02436266296315414, 0.01739437315700921, -0.0006404006623321454
0.06367837291956932, -0.0589096636263823, 0.1382458934368336,
0.06653302996539669, 0.07988499067852513, 0.1197789052423401,
0.1801661775839843, -0.01248615347419409].
Spark will produce an objective value 0.7278 and give a solution vector:
[0.029917351003921247,0.08993936770232434,0.09458507615360119,0.024920710363734895,0.018259589234194296,5.929247527202199E-4,0.06362198973221662,-0.059307008587031494,0.13886738997128056,0.0678246717525043,0.08062880450385658,0.12084979858539521,0.180460850026883,0.0]
I have a fix for it and passed the above test.
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