Worc

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Fixed
~~~~~
- Bugfixes in some error messages.
- If a classifier cannot give a score, use the prediction instead.
- Bugfix in bootstrapping.
- Bugfix: use f1-weighted score in SimpleWORC binary classification.

Changed
~~~~~~~
- There are no longer ''patient_features'' in PREDICT: these are extracted
from the DICOM tags and are thus now called ''dicom_features.
- As bootstrapping is now more efficient, increase default to 1000 iterations.

Added
~~~~~~~
- Option to transpose all images and segmentations to a default orientation.
Currently only supports axial.
- Support for PREDICT DICOM features.
- Memory of single optimization job to general config.
- Catch when imputation completely removes a feature.
- Clipping as preprocessing option
- Function to show which hyperparameters were used in the best workflows.

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Fixed
~~~~~
- In the RobustStandardScaler, if less than two values for a feature are left,
use the original set inset of the ``robust'' reduced set.

Changed
~~~~~~~
- By default, semantic features are skipped in scaling, as the robust scaler
cannot deal well with categorical variables.
- Wrapped scalers in single WORC scaler object to allow above for all scalers.

Added
~~~~~~~
- Leave-One-Out (LOO) cross-validation.
- Option to skip features in scaling.
- Bias correction in preprocessing.
- Option to check whether Nifti spacing seems incorrect and correct
with DICOM metadata.
- ElasticNet as classifier through LogisticRegression penalty.

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Fixed
~~~~~
- Bug in fit and score when using scaling: was incorrectly parsed as string
and always set to None.
- Catch exception in ADASYN sampling.
- Typo in configuration documentation.

Added
~~~~~~~
- New type of scaler (robust z-scoring)
- Resampling of image and mask in preprocessing (preprocessing and segmentix
nodes)

Changed
~~~~~~~
- Newly added scaler is now also the default to use, instead of the search
over the older included scalers.
- Evaluation of estimator is now separate from training it.

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Changed
~~~~~~~
- Updated to using tikzplotlib for conversion of figures to LaTeX instead
of deprecated matplotlib2tikz.
- Output of evaluation pipeline now in separate subfolder.
- KNNImputer now also in sklearn, missingpy deprecated, so switched to
sklearn KNNImputer.

Fixed
~~~~~
- Bug in fixandscore when using resampling.

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Added
~~~~~~~
- Graphviz vizualization of network is now nicely grouped.
- Properly integrated ObjectSampler: various resampling options now available.
- Verbose option to fit and score tool
- Validator for PyRadiomics output.
- FAQ version to documentation

Changed
~~~~~~~
- Upgraded to new versions of sklearn (0.23.1) and imbalanced learn (0.7.0)
- Some defaults, based on computation time.
- Do not skip workflow if feature selection selects zero features,
but disable the feature selection.
- Do not skip workflow if resampling is unsuccesfull,
but disable the resampling.
- Default scaling is now not only Z-score, but also MinMax and Robust
- Renamed plot SVM function and all functions using it, as now
we use all kinds of estimators.
- L1 penalty does not work with new standard LR solver. Removed L1 penalty.

Fixed
~~~~~
- Bug when using both elastix and segmentix.
- Bug when using elastix in train-test workflow.
- IMPORTANT: Previously, all methods except the machine learning where fit on
both the training and validation set together in fitandscore. This led
to overfitting on the validation set. Now, these are properly split.
- Bugfix in Evaluate standalone for decompositon tool.
- Applied imputation in decomposition if NaNs are detected.
- In the facade ConfigBuilder, an error is raised when incorrect
overrides are given.
- Bugfix in statistical feature test plotting.
- Bugfix in Evaluate when using ComBat
- Bugfix in feature converter of PyRadiomics when using 2D images.
- Catch Graphviz error.
- Bug in ICC.

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Added
~~~~~~~
- In classify node, when using temporary saves, start from where
the process previously stopped instead of from the beginning.
- Imputation to ComBat.

Changed
~~~~~~~
- Imputation is now the first step in the workflows. More logical as
scaler and variance threshold can crash on missing values.
- In config, preprocessing fields are now actually called preprocessing and
not normalize.

Fixed
~~~~~
- Preflightcheck now also compatible with BasicWORC.
- Bugfix in ComBat when not using mod variable and skipping patients.
- Bug in PyRadiomics feature converter: can now handle 2D images.
- ReliefSampleSize parameter is now a uniform distribution, which it should be.
- Gabor features now actually used in model instead of only computing them.