WORC has defaults for all settings so it can be run out of the box to test the examples. However, you may want to alter the fastr configuration to your system settings, e.g. to locate your input and output folders and how much you want to parallelize the execution.
Fastr will search for a config file named config.py in the $FASTRHOME directory
(which defaults to ~/.fastr/ if it is not set). So if $FASTRHOME is set the ~/.fastr/
will be ignored. Additionally, .py files from the $FASTRHOME/config.d folder will be parsed
as well. You will see that upon installation, WORC has already put a WORC_config.py file in the
config.d folder.
% Note: Above was originally from quick start
As WORC and the default tools used are mostly Python based, we've chosen
to put our configuration in a configparser object. This has several
advantages:
- The object can be treated as a python dictionary and thus is easily adjusted.
- Second, each tool can be set to parse only specific parts of the configuration, enabling us to supply one file to all tools instead of needing many parameter files.
The default configuration is generated through the :py:meth:`WORC.defaultconfig() <WORC.defaultconfig()>` function. You can then change things as you would in a dictionary and then append it to the configs source:
>>> network = WORC.WORC('somename')
>>> config = network.defaultconfig()
>>> config['Classification']['classifier'] = 'RF'
>>> network.configs.append(config)When executing the :py:meth:`WORC.set() <WORC.set()>` command, the config objects are saved as
.ini files in the WORC.fastr_tempdir folder and added to the
:py:meth:`WORC.fastrconfigs() <WORC.fastrconfigs()>` source.
Below are some details on several of the fields in the configuration. Note that for many of the fields, we currently only provide one default value. However, when adding your own tools, these fields can be adjusted to your specific settings.
WORC performs Combined Algorithm Selection and Hyperparameter (CASH) optimization. The configuration determines how the optimization is performed and which hyperparameters and models will be included. Repeating specific models/parameters in the config will make them more likely to be used, e.g.
>>> config['Classification']['classifiers'] = 'SVM, SVM, LR'means that the SVM is 2x more likely to be tested in the model selection than LR.
Note
All fields in the config must either be supplied as strings. A list can be created by using commas for separation, e.g. :py:meth:`Network.create_source <'value1, value2, ... ')>`.
The config object can be indexed as config[key][subkey] = value. The various keys, subkeys, and the values
(description, defaults and options) can be found below.
Details on each section of the config can be found below.
These fields contain general settings for when using WORC. For more info on the Joblib settings, which are used in the Joblib Parallel function, see here. When you run WORC on a cluster with nodes supporting only a single core to be used per node, e.g. the BIGR cluster, use only 1 core and threading as a backend.
Description:
Defaults and Options:
When using the PREDICT toolbox for classification, you have to set the label used for classification.
This part is really important, as it should match your label file. Suppose your patientclass.txt file you supplied as source for labels looks like this:
| Patient | Label1 | Label2 |
|---|---|---|
| patient1 | 1 | 0 |
| patient2 | 2 | 1 |
| patient3 | 1 | 5 |
You can supply a single label or multiple labels split by commas, for each of which an estimator will be fit. For example, suppose you simply want to use Label1 for classification, then set:
config['Labels']['label_names'] = 'Label1'If you want to first train a classifier on Label1 and then Label2,
set: config[Labels][label_names] = Label1, Label2
Description:
Defaults and Options:
The preprocessing node acts before the feature extraction on the image. Currently, only normalization is included. Additionally, scans with image type CT (see later in the tutorial) provided as DICOM are scaled to Hounsfield Units.
Description:
Defaults and Options:
These fields are only important if you specified using the segmentix tool in the general configuration.
Description:
Defaults and Options:
If using the PREDICT toolbox, you can specify some settings for the feature computation here. Also, you can select if the certain features are computed or not.
Description:
Defaults and Options:
If using the PyRadiomics toolbox, you can specify some settings for the feature computation here. For more information, see https://pyradiomics.readthedocs.io/en/latest/customization.htm.
Description:
Defaults and Options:
If using the ComBat toolbox, you can specify some settings for the feature harmonization here. For more information, see https://github.com/Jfortin1/ComBatHarmonization.
Description:
Defaults and Options:
Before the features are given to the classification function, and thus the hyperoptimization, these can be preprocessed as following.
Description:
Defaults and Options:
When using the PREDICT toolbox for classification, these settings are used for feature imputation.Note that these settings are actually used in the hyperparameter optimization. Hence you can provide multiple values per field, of which random samples will be drawn of which finally the best setting in combination with the other hyperparameters is selected.
Description:
Defaults and Options:
Determines which method is applied to scale each feature.
Description:
Defaults and Options:
When using the PREDICT toolbox for classification, these settings can be used for feature selection methods. Note that these settings are actually used in the hyperparameter optimization. Hence you can provide multiple values per field, of which random samples will be drawn of which finally the best setting in combination with the other hyperparameters is selected. Again, these should be formatted as string containing the actual values, e.g. value1, value2.
Description:
Defaults and Options:
If the PREDICT feature computation and classification tools are used, then you can do a gridsearch among the various feature groups for the optimal combination. If you do not want this, set all fields to a single value.
Previously, there was a single parameter for the texture features, selecting all, none or a single group. This is still supported, but not recommended, and looks as follows:
Description:
Defaults and Options:
Before performing the hyperoptimization, you can use various resampling techniques to resample (under-sampling, over-sampling, or both) the data. All methods are adopted from imbalanced learn.
Description:
Defaults and Options:
When using the PREDICT toolbox for classification, you can specify the following settings. Almost all of these are used in CASH. Most of the classifiers are implemented using sklearn; hence descriptions of the hyperparameters can also be found there.
Description:
Defaults and Options:
When using the PREDICT toolbox for classification and you specified using cross validation, specify the following settings.
Description:
Defaults and Options:
When using the PREDICT toolbox for classification, you have to supply your hyperparameter optimization procedure here.
Description:
Defaults and Options:
WORC supports ensembling of workflows. This is not a default approach in radiomics, hence the default is to not use it and select only the best performing workflow.
Description:
Defaults and Options:
In the evaluation of the performance, several adjustments can be made.
Description:
Defaults and Options:
Besides cross validation, WORC supports bootstrapping on the test set for performance evaluation.
Description:
Defaults and Options: