0.3.0
May, 2020
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Metafeature extraction with confidence intervals
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Pydoc fixes and package documentation/code consistency improvements
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Reformatted 'model-based' group metafeature extraction methods arguments to
a consistent format (all model-based metafeatures now receive a single
mandatory argument 'dt_model', and all other arguments are optional
arguments from precomputations.) Now it is much easier to use those
methods directly without the main class (mfe) filter, if desired. -
Now accepting user custom arguments in precomputation methods.
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Added 'extract_from_model' MFE method, making easy to extract model-based
metafeatures from a pre-fitted model without using the training data.
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Memory issues
- Now handling memory errors in precomputations, postcomputations and
metafeature extraction as a regular exception.
- Now handling memory errors in precomputations, postcomputations and
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Categorical attributes one-hot encoding option
- Added option to encode categorical attributes using one-hot encoding
instead of the current gray encoding.
- Added option to encode categorical attributes using one-hot encoding
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New nan-resilient summary functions
- All summary functions now can be calculated ignoring 'nan' values, using
its nan-resilient version.
- All summary functions now can be calculated ignoring 'nan' values, using
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Online documentation improvement