[SPARK-17941][ML][TEST] Logistic regression tests should use sample weights. #15488
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sethah
commented
Oct 14, 2016
| coefficients = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0)) | ||
| coefficients | ||
| Use the following R code to load the data and train the model using glmnet package. | ||
| library("glmnet") |
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I pasted every R code snippet into an R shell, so we can be reasonably certain of its correctness
sethah
commented
Oct 14, 2016
| label = as.factor(data$V1) | ||
| features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) | ||
| coefficientsStd = coef(glmnet(features, label, family="multinomial", alpha = 1, | ||
| lambda = 0.05, standardization=T)) |
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standardization is an invalid argument. I changed these to be correct standardize
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Test build #66966 has finished for PR 15488 at commit
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LGTM. |
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Merged into master. Thanks. |
robert3005
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Nov 1, 2016
…eights. ## What changes were proposed in this pull request? The sample weight testing for logistic regressions is not robust. Logistic regression suite already has many test cases comparing results to R glmnet. Since both libraries support sample weights, we should use sample weights in the test to increase coverage for sample weighting. This patch doesn't really add any code and makes the testing more complete. Also fixed some errors with the R code that was referenced in the test suit. Changed `standardization=T` to `standardize=T` since the former is invalid. ## How was this patch tested? Existing unit tests are modified. No non-test code is touched. Author: sethah <seth.hendrickson16@gmail.com> Closes apache#15488 from sethah/logreg_weight_tests.
uzadude
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Jan 27, 2017
…eights. ## What changes were proposed in this pull request? The sample weight testing for logistic regressions is not robust. Logistic regression suite already has many test cases comparing results to R glmnet. Since both libraries support sample weights, we should use sample weights in the test to increase coverage for sample weighting. This patch doesn't really add any code and makes the testing more complete. Also fixed some errors with the R code that was referenced in the test suit. Changed `standardization=T` to `standardize=T` since the former is invalid. ## How was this patch tested? Existing unit tests are modified. No non-test code is touched. Author: sethah <seth.hendrickson16@gmail.com> Closes apache#15488 from sethah/logreg_weight_tests.
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What changes were proposed in this pull request?
The sample weight testing for logistic regressions is not robust. Logistic regression suite already has many test cases comparing results to R glmnet. Since both libraries support sample weights, we should use sample weights in the test to increase coverage for sample weighting. This patch doesn't really add any code and makes the testing more complete.
Also fixed some errors with the R code that was referenced in the test suit. Changed
standardization=Ttostandardize=Tsince the former is invalid.How was this patch tested?
Existing unit tests are modified. No non-test code is touched.