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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2016/03/01 08:00:23 UTC
[jira] [Resolved] (SPARK-13029) Logistic regression returns
inaccurate results when there is a column with identical value, and
fit_intercept=false
[ https://issues.apache.org/jira/browse/SPARK-13029?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng resolved SPARK-13029.
-----------------------------------
Resolution: Won't Fix
As discussed on the PR page, we decided to keep the current behavior, which is the same as glmnet. I'll create a PR for the documentation.
> 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: 1.5.2, 1.6.0
> Reporter: Shuo Xiang
> Assignee: 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 (available in the PR). 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]
> Notice the last element of the weight vector is 0.
> A even simpler example is:
> {code:title=benchmark.py|borderStyle=solid}
> import numpy as np
> from sklearn.datasets import load_svmlight_file
> from sklearn.linear_model import LogisticRegression
> x_train = np.array([[1, 1], [0, 1]])
> y_train = np.array([1, 0])
> model = LogisticRegression(tol=1e-9, C=0.5, max_iter=1000, fit_intercept=False).fit(x_train, y_train)
> print model.coef_
> [[ 0.22478867 -0.02241016]]
> {code}
> The same data trained by the current solver also gives a different result, see the unit test in the PR.
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