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feat: specify extras
instead of features
in to_tabular_dataset
#685
feat: specify extras
instead of features
in to_tabular_dataset
#685
Conversation
…ature nor target
Use the constructor instead
It was an internal method that was only used for tests. Moreover, this conversion makes little sense. We should instead be able to go back to a time series from a time series dataset.
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## main #685 +/- ##
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Coverage 100.00% 100.00%
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Lines 4816 4787 -29
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## [0.22.0](v0.21.0...v0.22.0) (2024-05-01) ### Features * `is_fitted` is now always a property ([#662](#662)) ([b1db881](b1db881)), closes [#586](#586) * add `Column.missing_value_count` ([#682](#682)) ([f084916](f084916)), closes [#642](#642) * Add `InputConversion` & `OutputConversion` for nn interface ([#625](#625)) ([fd723f7](fd723f7)), closes [#621](#621) * Add hash,eq and sizeof in ForwardLayer ([#634](#634)) ([72f7fde](72f7fde)), closes [#633](#633) * allow using tables that already contain target for prediction ([#687](#687)) ([e9f1cfb](e9f1cfb)), closes [#636](#636) * callback `Row.sort_columns` takes four parameters instead of two tuples ([#683](#683)) ([9c3e3de](9c3e3de)), closes [#584](#584) * rename `group_rows_by` in `Table` to `group_rows` ([#661](#661)) ([c1644b7](c1644b7)), closes [#611](#611) * rename `number_of_column` in `Row` to `number_of_columns` ([#660](#660)) ([0a08296](0a08296)), closes [#646](#646) * rework `TaggedTable` ([#680](#680)) ([db2b613](db2b613)), closes [#647](#647) * show missing value count/ratio in summarized statistics ([#684](#684)) ([74b8a35](74b8a35)), closes [#619](#619) * specify `extras` instead of `features` in `to_tabular_dataset` ([#685](#685)) ([841657f](841657f)), closes [#623](#623) ### Bug Fixes * actually use `kernel` of support vector machines for training ([#681](#681)) ([09c5082](09c5082)), closes [#602](#602) ### Performance Improvements * Faster plot_histograms and more reliable plots ([#659](#659)) ([b5f0a12](b5f0a12))
🎉 This PR is included in version 0.22.0 🎉 The release is available on:
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Closes #623
Summary of Changes
When creating a tabular dataset, users can now optionally specify extra columns, i.e. columns that are neither target nor feature. The feature columns are implicitly all columns that are neither target nor extra.
Previously, users had to specify the features instead and the extras were implicit. However, the list of features is usually much longer than the list of extras, making the previous approach cumbersome.