feat: Improve Error Handling of classifiers and regressors#355
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alex-senger merged 15 commits intomainfrom Jun 16, 2023
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…de feedback if the data cannot be used. feat: Added `NonNumericColumnError` and `DatasetMissesDataError` feat: Added possibility to write help messages in `NonNumericColumnError` and `MissingValuesColumnError` Co-authored-by: alex-senger <91055000+alex-senger@users.noreply.github.com>
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Co-authored-by: alex-senger <91055000+alex-senger@users.noreply.github.com>
jxnior01
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Jun 12, 2023
… to test `LearningError` and `PredictionError` refactor: Removed option in `_util_sklearn.predict` to set feature_names or target_name to None as if at least one of them is None the method will always raise an Exception Co-authored-by: alex-senger <91055000+alex-senger@users.noreply.github.com>
…s' of https://github.com/Safe-DS/Stdlib into 153-improve-error-handling-of-classifiers-and-regressors
Co-authored-by: Junior Atemebang <129027012+jxnior01@users.noreply.github.com>
…s or target_name to None to satisfy the linter again Co-authored-by: alex-senger <91055000+alex-senger@users.noreply.github.com>
…rOnFitAndPredict` to satisfy the linter Co-authored-by: alex-senger <91055000+alex-senger@users.noreply.github.com>
…alueErrorOnFitAndPredict` to satisfy the linter Co-authored-by: alex-senger <91055000+alex-senger@users.noreply.github.com>
jxnior01
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Jun 30, 2023
## [0.14.0](v0.13.0...v0.14.0) (2023-06-30) ### Features * 290 properties for width-height of image ([#359](#359)) ([d9ebdc1](d9ebdc1)), closes [#290](#290) * Add `find_edges` method to `Image` class ([#383](#383)) ([d14b6ce](d14b6ce)), closes [#288](#288) * Add `StandardScaler` transformer ([#316](#316)) ([57b0572](57b0572)), closes [#142](#142) * Add docstrings to the getter methods for hyperparameters in Regression and Classification models ([#371](#371)) ([9073f04](9073f04)), closes [#313](#313) * Added `Table.group_by` to group a table by a given key ([#343](#343)) ([afb98be](afb98be)), closes [#160](#160) * Added and improved errors and warnings in the table transformers ([#372](#372)) ([544e307](544e307)), closes [#152](#152) * added crop() method in image and tests ([#365](#365)) ([eba8163](eba8163)) * added invert_colors method ([#367](#367)) ([1e4d110](1e4d110)) * adjust brightness and contrast of image ([#368](#368)) ([1752feb](1752feb)), closes [#289](#289) [#291](#291) * blur Image method ([#363](#363)) ([c642176](c642176)) * check that methods of table can handle an empty table ([#314](#314)) ([686c2e7](686c2e7)), closes [#123](#123) * convert image to grayscale ([#366](#366)) ([1312fe7](1312fe7)), closes [#287](#287) * enhance `replace_column` to accept a list of new columns ([#312](#312)) ([d50c5b5](d50c5b5)), closes [#301](#301) * Explicitly throw `UnknownColumnNameError` in `TaggedTable._from_table` ([#334](#334)) ([498999f](498999f)), closes [#333](#333) * flip images / eq method for image ([#360](#360)) ([54f4ae1](54f4ae1)), closes [#280](#280) * improve `table.summary`. Catch `ValueError` thrown by `column.stability` ([#390](#390)) ([dbbe0e3](dbbe0e3)), closes [#320](#320) * improve error handling of `column.stability` when given a column that contains only None ([#388](#388)) ([1da2499](1da2499)), closes [#319](#319) * Improve Error Handling of classifiers and regressors ([#355](#355)) ([66f5f64](66f5f64)), closes [#153](#153) * Resize image ([#354](#354)) ([3a971ca](3a971ca)), closes [#283](#283) * rotate_left and rotate_right added to Image ([#361](#361)) ([c877530](c877530)), closes [#281](#281) * set kernel of support vector machine ([#350](#350)) ([1326f40](1326f40)), closes [#172](#172) * sharpen image ([#364](#364)) ([3444700](3444700)), closes [#286](#286) ### Bug Fixes * Keeping no columns with Table.keep_only_columns results in an empty Table with a row count above 0 ([#386](#386)) ([15dab06](15dab06)), closes [#318](#318) * remove default value of `positive_class` parameter of classifier metrics ([#382](#382)) ([58fc09e](58fc09e)) * remove default value of `radius` parameter of `blur` ([#378](#378)) ([7f07f29](7f07f29))
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Closes #153 .
Summary of Changes
The Error Handling of classifiers and regressors was not ideal. Now every classifier and regressor validates the data, especially when given non-numerical or missing values in the table.