660/truncate feature values #661
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tamarl08
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Looks good @damien2012eng ! just added a couple of docstring nitpicks. |
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desilinguist
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@damien2012eng looks pretty good but it's still missing a critical piece. Do you remember how we check that the value of standardize_features specified for rsmexplain and in the original rsmtool experiment match and, if not, we raise a warning? We need to do this same for truncate_outliers as well.
Instead of duplicating code, may be factor out that code as a function in rsmexplain.py and use it for both fields?
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Use a simple `for` loop to verify and overwrite the values of `standardize_features` and `truncate_outliers` for rsmexplain.
Add new tests to check that `truncate_outliers` values are correctly overwritten when necessary.
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@damien2012eng don't forget to fix the file @tamarl08 pointed out. Otherwise this is ready to merge. |
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I verified the ASAP dataset using RSMTool toolset. The column mechanics has outlier values. Here is an example result (Response 181) for truncating and w/o truncating:
Truncating outlier: 3.213 (Original data) and 3.248 (preprocessed data)
W/O Truncating outlier: 3.213 (for both original data and preprocessed data)
I also verified the outliers range for the Mechanics: [3.248, 5.648]. Because the value 3.213 is lower than the lower bound, it is truncated to be the value of the lower bound.
Closes #660