From 23299683c070e2a20210c764d5a8c488d178f982 Mon Sep 17 00:00:00 2001 From: MStarmans91 Date: Tue, 5 Apr 2022 10:19:20 +0200 Subject: [PATCH] Prepare for 3.6.0 release. --- CHANGELOG | 2 +- README.rst | 2 +- .../autogen/WORC.classification.doctree | Bin 665753 -> 760170 bytes .../doctrees/autogen/WORC.config.doctree | Bin 16026 -> 17322 bytes .../doctrees/autogen/WORC.detectors.doctree | Bin 46371 -> 46355 bytes WORC/doc/_build/doctrees/autogen/WORC.doctree | Bin 100820 -> 103479 bytes .../doctrees/autogen/WORC.facade.doctree | Bin 115307 -> 119589 bytes .../doctrees/autogen/WORC.plotting.doctree | Bin 147919 -> 148487 bytes .../doctrees/autogen/WORC.tools.doctree | Bin 83548 -> 95437 bytes WORC/doc/_build/doctrees/environment.pickle | Bin 2902053 -> 3003419 bytes .../_build/doctrees/static/changelog.doctree | Bin 156277 -> 159680 bytes .../doctrees/static/configuration.doctree | Bin 370385 -> 395823 bytes .../doctrees/static/quick_start.doctree | Bin 43171 -> 43621 bytes WORC/doc/_build/html/.buildinfo | 2 +- .../_modules/WORC/IOparser/config_WORC.html | 6 +- .../WORC/IOparser/config_io_classifier.html | 54 +- .../WORC/IOparser/config_preprocessing.html | 4 +- .../WORC/IOparser/config_segmentix.html | 4 +- .../html/_modules/WORC/IOparser/file_io.html | 4 +- WORC/doc/_build/html/_modules/WORC/WORC.html | 291 ++++- .../html/_modules/WORC/addexceptions.html | 4 +- .../WORC/classification/AdvancedSampler.html | 4 +- .../WORC/classification/ObjectSampler.html | 4 +- .../WORC/classification/RankedSVM.html | 4 +- .../WORC/classification/SearchCV.html | 1090 +++++++++++++---- .../classification/construct_classifier.html | 65 +- .../classification/createfixedsplits.html | 4 +- .../WORC/classification/crossval.html | 65 +- .../WORC/classification/estimators.html | 4 +- .../WORC/classification/fitandscore.html | 371 ++++-- .../_modules/WORC/classification/metrics.html | 4 +- .../parameter_optimization.html | 87 +- .../WORC/classification/trainclassifier.html | 23 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WORC/doc/_build/html/static/introduction.html | 4 +- WORC/doc/_build/html/static/quick_start.html | 9 +- WORC/doc/_build/html/static/user_manual.html | 4 +- WORC/doc/autogen/WORC.classification.rst | 9 + WORC/doc/autogen/WORC.config.rst | 4 +- WORC/doc/autogen/WORC.rst | 1 + WORC/doc/autogen/WORC.tools.rst | 9 + .../WORC.config_Classification_defopts.rst | 88 +- .../config/WORC.config_Ensemble_defopts.rst | 6 +- .../WORC.config_Ensemble_description.rst | 6 +- .../config/WORC.config_General_defopts.rst | 2 +- .../WORC.config_ImageFeatures_description.rst | 2 +- .../WORC.config_PyRadiomics_description.rst | 52 +- .../config/WORC.config_SMAC_defopts.rst | 10 + .../config/WORC.config_SMAC_description.rst | 10 + requirements.txt | 2 +- 107 files changed, 3142 insertions(+), 1421 deletions(-) create mode 100644 WORC/doc/autogen/config/WORC.config_SMAC_defopts.rst create mode 100644 WORC/doc/autogen/config/WORC.config_SMAC_description.rst diff --git a/CHANGELOG b/CHANGELOG index f400a65b..bc2a3531 100644 --- a/CHANGELOG +++ b/CHANGELOG @@ -6,7 +6,7 @@ All notable changes to this project will be documented in this file. The format is based on `Keep a Changelog `_ and this project adheres to `Semantic Versioning `_ -3.6.0 - 2022-03-31 +3.6.0 - 2022-04-05 ------------------ Added diff --git a/README.rst b/README.rst index e80b8a70..a7e5a9d4 100644 --- a/README.rst +++ b/README.rst @@ -1,4 +1,4 @@ -WORC v3.5.0 +WORC v3.6.0 =========== Workflow for Optimal Radiomics Classification diff --git a/WORC/doc/_build/doctrees/autogen/WORC.classification.doctree b/WORC/doc/_build/doctrees/autogen/WORC.classification.doctree index 09d10e6b1113d32183fe3406e184c9b99f38850d..d2f87b8dc3a0fcd2bb2b6cfd65e04f22fd05727f 100644 GIT binary patch delta 115501 zcmeEvcYIVu`>$uVWET=hB%vgfO+raR2m}IwBtSxk&_a_?vyctRlHL$3fCWX7Vt_$J ziUk!b>T9et6;!$vDFW8_wE=d074K8d*>g^I<4=FT_ufD5y?#EpIWte2d7gRBoSE;L zbJy{>FP=(TvQ`OJlz)UM8y;3l>$g9wj8{4&x;&*-o{~Amp4qkT;-Y+abx~RA?5eqC zm7e}%%Sz?38%5vxZjUID!kVXCaYg-_8$_lO**I67vM;iJ%j3#rhwSjQ(k!$6pNjXa zEex8uwlHEvqhxi*H%2HgN%vDqioB;@S*g5-f47iJVwHBv+xT|}xjN2TEdTkO(n>i- 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diff --git a/WORC/doc/_build/html/_sources/autogen/WORC.classification.rst.txt b/WORC/doc/_build/html/_sources/autogen/WORC.classification.rst.txt index 1f358e9b..49bc90f1 100644 --- a/WORC/doc/_build/html/_sources/autogen/WORC.classification.rst.txt +++ b/WORC/doc/_build/html/_sources/autogen/WORC.classification.rst.txt @@ -100,6 +100,15 @@ classification Package :show-inheritance: :special-members: +:mod:`smac` Module +------------------ + +.. automodule:: WORC.classification.smac + :members: + :undoc-members: + :show-inheritance: + :special-members: + :mod:`trainclassifier` Module ----------------------------- diff --git a/WORC/doc/_build/html/_sources/autogen/WORC.config.rst.txt b/WORC/doc/_build/html/_sources/autogen/WORC.config.rst.txt index c6c0f742..e6fb47dd 100644 --- a/WORC/doc/_build/html/_sources/autogen/WORC.config.rst.txt +++ b/WORC/doc/_build/html/_sources/autogen/WORC.config.rst.txt @@ -10,6 +10,7 @@ Evaluation :ref:`Evaluation ` FeatPreProcess :ref:`FeatPreProcess ` Featsel :ref:`Featsel ` FeatureScaling :ref:`FeatureScaling ` +Fingerprinting :ref:`Fingerprinting ` General :ref:`General ` HyperOptimization :ref:`HyperOptimization ` ImageFeatures :ref:`ImageFeatures ` @@ -19,6 +20,7 @@ OneHotEncoding :ref:`OneHotEncoding ` Preprocessing :ref:`Preprocessing ` PyRadiomics :ref:`PyRadiomics ` Resampling :ref:`Resampling ` +SMAC :ref:`SMAC ` Segmentix :ref:`Segmentix ` SelectFeatGroup :ref:`SelectFeatGroup ` ================= =================================================== \ No newline at end of file diff --git a/WORC/doc/_build/html/_sources/autogen/WORC.rst.txt b/WORC/doc/_build/html/_sources/autogen/WORC.rst.txt index 6872a9c8..02387248 100644 --- a/WORC/doc/_build/html/_sources/autogen/WORC.rst.txt +++ b/WORC/doc/_build/html/_sources/autogen/WORC.rst.txt @@ -47,5 +47,6 @@ Subpackages WORC.statistics WORC.tests WORC.tools + WORC.tutorial WORC.validators diff --git a/WORC/doc/_build/html/_sources/autogen/WORC.tools.rst.txt b/WORC/doc/_build/html/_sources/autogen/WORC.tools.rst.txt index f9059a22..786d8f28 100644 --- a/WORC/doc/_build/html/_sources/autogen/WORC.tools.rst.txt +++ b/WORC/doc/_build/html/_sources/autogen/WORC.tools.rst.txt @@ -55,3 +55,12 @@ tools Package :show-inheritance: :special-members: +:mod:`fingerprinting` Module +---------------------------- + +.. automodule:: WORC.tools.fingerprinting + :members: + :undoc-members: + :show-inheritance: + :special-members: + diff --git a/WORC/doc/_build/html/_sources/static/configuration.rst.txt b/WORC/doc/_build/html/_sources/static/configuration.rst.txt index 261b5fc6..0ad52d55 100644 --- a/WORC/doc/_build/html/_sources/static/configuration.rst.txt +++ b/WORC/doc/_build/html/_sources/static/configuration.rst.txt @@ -403,6 +403,23 @@ Specify the hyperparameter optimization procedure here. .. include:: ../autogen/config/WORC.config_HyperOptimization_defopts.rst +.. _config-SMAC: + +SMAC +~~~~ +WORC enables the use of the SMAC algorithm for the hyperparameter optimization. +SMAC uses the same parameter options as the default random search, except for +resampling which is currently not compatible with SMAC. + +**Description:** + +.. include:: ../autogen/config/WORC.config_SMAC_description.rst + +**Defaults and Options:** + +.. include:: ../autogen/config/WORC.config_SMAC_defopts.rst + + .. _config-Ensemble: Ensemble diff --git a/WORC/doc/_build/html/_sources/static/quick_start.rst.txt b/WORC/doc/_build/html/_sources/static/quick_start.rst.txt index a1cca080..1289e11b 100644 --- a/WORC/doc/_build/html/_sources/static/quick_start.rst.txt +++ b/WORC/doc/_build/html/_sources/static/quick_start.rst.txt @@ -183,6 +183,11 @@ After defining the inputs, the following code can be used to run your first expe experiment.labels_from_this_file(label_file) experiment.predict_labels(label_name) + # Set the types of images WORC has to process. Used in fingerprinting + # Valid quantitative types are ['CT', 'PET', 'Thermography', 'ADC'] + # Valid qualitative types are ['MRI', 'DWI', 'US'] + experiment.set_image_types(['CT']) + # Use the standard workflow for your specific modus if modus == 'binary_classification': experiment.binary_classification(coarse=coarse) diff --git a/WORC/doc/_build/html/_static/documentation_options.js b/WORC/doc/_build/html/_static/documentation_options.js index 1d0981c2..9ddc46e0 100644 --- a/WORC/doc/_build/html/_static/documentation_options.js +++ b/WORC/doc/_build/html/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '3.5.0', + VERSION: '3.6.0', LANGUAGE: 'None', COLLAPSE_INDEX: false, FILE_SUFFIX: '.html', diff --git a/WORC/doc/_build/html/autogen/WORC.IOparser.html b/WORC/doc/_build/html/autogen/WORC.IOparser.html index 120cb57b..c5c4a3c8 100644 --- a/WORC/doc/_build/html/autogen/WORC.IOparser.html +++ b/WORC/doc/_build/html/autogen/WORC.IOparser.html @@ -8,7 +8,7 @@ - IOparser Package — WORC 3.5.0 documentation + IOparser Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
- 3.5.0 + 3.6.0
diff --git a/WORC/doc/_build/html/autogen/WORC.classification.html b/WORC/doc/_build/html/autogen/WORC.classification.html index 92008c45..68d92d4e 100644 --- a/WORC/doc/_build/html/autogen/WORC.classification.html +++ b/WORC/doc/_build/html/autogen/WORC.classification.html @@ -8,7 +8,7 @@ - classification Package — WORC 3.5.0 documentation + classification Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
- 3.5.0 + 3.6.0
@@ -119,6 +119,7 @@
  • metrics Module
  • parameter_optimization Module
  • regressors Module
  • +
  • smac Module
  • trainclassifier Module
  • @@ -540,7 +541,7 @@

    SearchCV ModuleΒΆ

    -class WORC.classification.SearchCV.BaseSearchCV(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False)[source]ΒΆ
    +class WORC.classification.SearchCV.BaseSearchCV(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False, ensemble_validation_score=None)[source]ΒΆ

    Bases: sklearn.base.BaseEstimator, sklearn.base.MetaEstimatorMixin

    Base class for hyper parameter search with cross-validation.

    @@ -550,7 +551,7 @@

    -__init__(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False)[source]ΒΆ
    +__init__(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False, ensemble_validation_score=None)[source]ΒΆ

    Initialize SearchCV Object.

    @@ -561,30 +562,34 @@

    -create_ensemble(X_train, Y_train, verbose=None, initialize=True, scoring=None, method=50, overfit_scaler=False)[source]ΒΆ
    -

    Create ensemble of multiple workflows.

    -

    Create an (optimal) ensemble of a combination of hyperparameter settings +create_ensemble(X_train, Y_train, verbose=None, initialize=False, scoring=None, method='top_N', size=50, overfit_scaler=False)[source]ΒΆ +

    Create an (optimal) ensemble of a combination of hyperparameter settings and the associated groupsels, PCAs, estimators etc.

    -

    Based on Caruana et al. 2004, but a little different:

    -
      -
    1. Recreate the training/validation splits for a n-fold cross validation.

    2. -
    3. -
      For each fold:
        -
      1. Start with an empty ensemble

      2. -
      3. Create starting ensemble by adding N individually best performing -models on the validation set. N is tuned on the validation set.

      4. -
      5. Add model that improves ensemble performance on validation set the most, with replacement.

      6. -
      7. Repeat (c) untill performance does not increase

      8. -
      +
      +
      # The following ensemble methods are supported:

      # Single: +# only use the single best classifier. Performance is computed +# using the same predict function as during the optimization +# top_N: +# make an ensemble of the best N individual classifiers, where N is +# given as an input. If N==1, then only the single best classifier is +# used, but it is evaluated using predict_proba. +# FitNumber: +# make an ensemble of the best N individual classifiers, choosing N +# that gives the highest performance +# ForwardSelection: +# add the model that optimizes the total ensemble performance, +# then repeat with replacement until there is no more improvement +# in performance +# Caruana: +# for a fixed number of iterations, add the model that optimizes +# the total ensemble performance, then choose the ensemble size +# which gave the best performance +# Bagging: +# same as Caruana method, but the final ensemble is a weighted average +# of a number of ensembles that each use only a subset of the available +# models

      -
    4. -
    -

    The performance metric is the same as for the original hyperparameter -search, i.e. probably the F1-score for classification and r2-score -for regression. However, we recommend using the SAR score, as this is -more universal.

    -

    Method: top50 or Caruana

    @@ -660,7 +665,7 @@

    -process_fit(n_splits, parameters_all, test_sample_counts, test_score_dicts, train_score_dicts, fit_time, score_time, cv_iter, X, y, fitted_workflows=None)[source]ΒΆ
    +process_fit(n_splits, parameters_all, test_sample_counts, test_score_dicts, train_score_dicts, fit_time, score_time, cv_iter, X, y, fitted_workflows=None, use_smac=False)[source]ΒΆ

    Process a fit.

    Process the outcomes of a SearchCV fit and find the best settings over all cross validations from all hyperparameters tested

    @@ -722,7 +727,7 @@

    -class WORC.classification.SearchCV.BaseSearchCVJoblib(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False)[source]ΒΆ
    +class WORC.classification.SearchCV.BaseSearchCVJoblib(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False, ensemble_validation_score=None)[source]ΒΆ

    Bases: WORC.classification.SearchCV.BaseSearchCV

    Base class for hyper parameter search with cross-validation.

    @@ -737,9 +742,26 @@

    +
    +
    +class WORC.classification.SearchCV.BaseSearchCVSMAC(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False, ensemble_validation_score=None)[source]ΒΆ
    +

    Bases: WORC.classification.SearchCV.BaseSearchCV

    +

    Base class for Bayesian hyper parameter search with cross-validation.

    +
    +
    +__abstractmethods__ = frozenset({'__init__'})ΒΆ
    +
    + +
    +
    +__module__ = 'WORC.classification.SearchCV'ΒΆ
    +
    + +
    +
    -class WORC.classification.SearchCV.BaseSearchCVfastr(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False)[source]ΒΆ
    +class WORC.classification.SearchCV.BaseSearchCVfastr(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_workflows=False, ensemble_validation_score=None)[source]ΒΆ

    Bases: WORC.classification.SearchCV.BaseSearchCV

    Base class for hyper parameter search with cross-validation.

    @@ -1393,6 +1415,236 @@

    +
    +
    +class WORC.classification.SearchCV.GuidedSearchCVSMAC(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, fastr_plugin=None, maxlen=100, ranking_score='test_score', features=None, labels=None, smac_result_file=None)[source]ΒΆ
    +

    Bases: WORC.classification.SearchCV.BaseSearchCVSMAC

    +

    Guided search on hyperparameters.

    +

    GuidedSearchCV implements a β€œfit” and a β€œscore” method. +It also implements β€œpredict”, β€œpredict_proba”, β€œdecision_function”, +β€œtransform” and β€œinverse_transform” if they are implemented in the +estimator used.

    +

    The parameters of the estimator used to apply these methods are optimized +by cross-validated search over parameter settings.

    +

    The optimization is performed using the Sequential Model-based Algorithm +Configuration (SMAC) method. A probabilistic model of the objective function +is constructed and updated with each function evaluation.

    +

    If all parameters are presented as a list, +sampling without replacement is performed. If at least one parameter +is given as a distribution, sampling with replacement is used. +It is highly recommended to use continuous distributions for continuous +parameters.

    +
    +
    param_distributionsdict

    Dictionary with parameter names (string) as keys and details of their +domains as values. From this dictionary the complete search space +will later be constructed.

    +
    +
    n_iterint, default=10

    Number of function evaluations allowed in each optimization sequence +of SMAC.

    +
    +
    scoringstring, callable or None, default=None

    A string (see model evaluation documentation) or +a scorer callable object / function with signature +scorer(estimator, X, y). +If None, the score method of the estimator is used.

    +
    +
    fit_paramsdict, optional

    Parameters to pass to the fit method.

    +
    +
    n_jobsint, default=1

    Number of jobs to run in parallel.

    +
    +
    pre_dispatchint, or string, optional

    Controls the number of jobs that get dispatched during parallel +execution. Reducing this number can be useful to avoid an +explosion of memory consumption when more jobs get dispatched +than CPUs can process. This parameter can be:

    +
    +
      +
    • None, in which case all the jobs are immediately +created and spawned. Use this for lightweight and +fast-running jobs, to avoid delays due to on-demand +spawning of the jobs

    • +
    • An int, giving the exact number of total jobs that are +spawned

    • +
    • A string, giving an expression as a function of n_jobs, +as in β€˜2*n_jobs’

    • +
    +
    +
    +
    iidboolean, default=True

    If True, the data is assumed to be identically distributed across +the folds, and the loss minimized is the total loss per sample, +and not the mean loss across the folds.

    +
    +
    cvint, cross-validation generator or an iterable, optional

    Determines the cross-validation splitting strategy. +Possible inputs for cv are:

    +
    +
      +
    • None, to use the default 3-fold cross validation,

    • +
    • integer, to specify the number of folds in a (Stratified)KFold,

    • +
    • An object to be used as a cross-validation generator.

    • +
    • An iterable yielding train, test splits.

    • +
    +
    +

    For integer/None inputs, if the estimator is a classifier and y is +either binary or multiclass, StratifiedKFold is used. In all +other cases, KFold is used.

    +

    Refer User Guide for the various +cross-validation strategies that can be used here.

    +
    +
    refitboolean, default=True

    Refit the best estimator with the entire dataset. +If β€œFalse”, it is impossible to make predictions using +this RandomizedSearchCV instance after fitting.

    +
    +
    verboseinteger

    Controls the verbosity: the higher, the more messages.

    +
    +
    random_stateint or RandomState

    Pseudo random number generator state used for random uniform sampling +from lists of possible values instead of scipy.stats distributions.

    +
    +
    error_scoreβ€˜raise’ (default) or numeric

    Value to assign to the score if an error occurs in estimator fitting. +If set to β€˜raise’, the error is raised. If a numeric value is given, +FitFailedWarning is raised. This parameter does not affect the refit +step, which will always raise the error.

    +
    +
    return_train_scoreboolean, default=True

    If 'False', the cv_results_ attribute will not include training +scores.

    +
    +
    +
    +
    cv_results_dict of numpy (masked) ndarrays

    A dict with keys as column headers and values as columns, that can be +imported into a pandas DataFrame.

    +

    For instance the below given table

    + +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    param_kernel

    param_gamma

    split0_test_score

    …

    rank_test_score

    β€˜rbf’

    0.1

    0.8

    …

    2

    β€˜rbf’

    0.2

    0.9

    …

    1

    β€˜rbf’

    0.3

    0.7

    …

    1

    +

    will be represented by a cv_results_ dict of:

    +
    {
    +'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
    +                              mask = False),
    +'param_gamma'  : masked_array(data = [0.1 0.2 0.3], mask = False),
    +'split0_test_score'  : [0.8, 0.9, 0.7],
    +'split1_test_score'  : [0.82, 0.5, 0.7],
    +'mean_test_score'    : [0.81, 0.7, 0.7],
    +'std_test_score'     : [0.02, 0.2, 0.],
    +'rank_test_score'    : [3, 1, 1],
    +'split0_train_score' : [0.8, 0.9, 0.7],
    +'split1_train_score' : [0.82, 0.5, 0.7],
    +'mean_train_score'   : [0.81, 0.7, 0.7],
    +'std_train_score'    : [0.03, 0.03, 0.04],
    +'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
    +'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
    +'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
    +'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
    +'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
    +}
    +
    +
    +

    NOTE that the key 'params' is used to store a list of parameter +settings dict for all the parameter candidates.

    +

    The mean_fit_time, std_fit_time, mean_score_time and +std_score_time are all in seconds.

    +
    +
    best_estimator_estimator

    Estimator that was chosen by the search, i.e. estimator +which gave highest score (or smallest loss if specified) +on the left out data. Not available if refit=False.

    +
    +
    best_score_float

    Score of best_estimator on the left out data.

    +
    +
    best_params_dict

    Parameter setting that gave the best results on the hold out data.

    +
    +
    best_index_int

    The index (of the cv_results_ arrays) which corresponds to the best +candidate parameter setting.

    +

    The dict at search.cv_results_['params'][search.best_index_] gives +the parameter setting for the best model, that gives the highest +mean score (search.best_score_).

    +
    +
    scorer_function

    Scorer function used on the held out data to choose the best +parameters for the model.

    +
    +
    n_splits_int

    The number of cross-validation splits (folds/iterations).

    +
    +
    +

    The parameters selected are those that maximize the score of the held-out +data, according to the scoring parameter.

    +

    If n_jobs was set to a value higher than one, the data is copied for each +parameter setting(and not n_jobs times). This is done for efficiency +reasons if individual jobs take very little time, but may raise errors if +the dataset is large and not enough memory is available. A workaround in +this case is to set pre_dispatch. Then, the memory is copied only +pre_dispatch many times. A reasonable value for pre_dispatch is 2 * +n_jobs.

    +
    +
    GridSearchCV:

    Does exhaustive search over a grid of parameters.

    +
    +
    ParameterSampler:

    A generator over parameter settings, constructed from +param_distributions.

    +
    +
    +
    +
    +__abstractmethods__ = frozenset({})ΒΆ
    +
    + +
    +
    +__init__(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, fastr_plugin=None, maxlen=100, ranking_score='test_score', features=None, labels=None, smac_result_file=None)[source]ΒΆ
    +

    Initialize SearchCV Object.

    +
    + +
    +
    +__module__ = 'WORC.classification.SearchCV'ΒΆ
    +
    + +
    +
    +fit(X, y=None, groups=None)[source]ΒΆ
    +

    Run fit on the estimator with randomly drawn parameters.

    +
    +
    Xarray-like, shape = [n_samples, n_features]

    Training vector, where n_samples in the number of samples and +n_features is the number of features.

    +
    +
    yarray-like, shape = [n_samples] or [n_samples, n_output], optional

    Target relative to X for classification or regression; +None for unsupervised learning.

    +
    +
    groupsarray-like, with shape (n_samples,), optional

    Group labels for the samples used while splitting the dataset into +train/test set.

    +
    +
    +
    + +
    +
    class WORC.classification.SearchCV.RandomizedSearchCVJoblib(param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, ranking_score='test_score')[source]ΒΆ
    @@ -1493,7 +1745,7 @@

    -
    cv_results_dict of numpy (masked) ndarrays

    A dict with keys as column headers and values as columns, that can be +

    cv_results_dict of numpy (masked) ndarrays

    A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame.

    For instance the below given table

    @@ -1560,24 +1812,24 @@

    The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds.

    -
    best_estimator_estimator

    Estimator that was chosen by the search, i.e. estimator +

    best_estimator_estimator

    Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

    -
    best_score_float

    Score of best_estimator on the left out data.

    +
    best_score_float

    Score of best_estimator on the left out data.

    -
    best_params_dict

    Parameter setting that gave the best results on the hold out data.

    +
    best_params_dict

    Parameter setting that gave the best results on the hold out data.

    -
    best_index_int

    The index (of the cv_results_ arrays) which corresponds to the best +

    best_index_int

    The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.

    The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

    -
    scorer_function

    Scorer function used on the held out data to choose the best +

    scorer_function

    Scorer function used on the held out data to choose the best parameters for the model.

    -
    n_splits_int

    The number of cross-validation splits (folds/iterations).

    +
    n_splits_int

    The number of cross-validation splits (folds/iterations).

    The parameters selected are those that maximize the score of the held-out @@ -1731,7 +1983,7 @@

    -
    cv_results_dict of numpy (masked) ndarrays

    A dict with keys as column headers and values as columns, that can be +

    cv_results_dict of numpy (masked) ndarrays

    A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame.

    For instance the below given table

    @@ -1798,24 +2050,24 @@

    The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds.

    -
    best_estimator_estimator

    Estimator that was chosen by the search, i.e. estimator +

    best_estimator_estimator

    Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

    -
    best_score_float

    Score of best_estimator on the left out data.

    +
    best_score_float

    Score of best_estimator on the left out data.

    -
    best_params_dict

    Parameter setting that gave the best results on the hold out data.

    +
    best_params_dict

    Parameter setting that gave the best results on the hold out data.

    -
    best_index_int

    The index (of the cv_results_ arrays) which corresponds to the best +

    best_index_int

    The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.

    The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

    -
    scorer_function

    Scorer function used on the held out data to choose the best +

    scorer_function

    Scorer function used on the held out data to choose the best parameters for the model.

    -
    n_splits_int

    The number of cross-validation splits (folds/iterations).

    +
    n_splits_int

    The number of cross-validation splits (folds/iterations).

    The parameters selected are those that maximize the score of the held-out @@ -1950,14 +2202,14 @@

    crossval ModuleΒΆ

    -WORC.classification.crossval.LOO_cross_validation(image_features, feature_labels, classes, patient_ids, param_grid, config, modus, test_size, start=0, save_data=None, tempsave=False, tempfolder=None, fixedsplits=None, fixed_seed=False, use_fastr=None, fastr_plugin=None)[source]ΒΆ
    +WORC.classification.crossval.LOO_cross_validation(image_features, feature_labels, classes, patient_ids, param_grid, config, modus, test_size, start=0, save_data=None, tempsave=False, tempfolder=None, fixedsplits=None, fixed_seed=False, use_fastr=None, fastr_plugin=None, use_SMAC=False, smac_result_file=None)[source]ΒΆ

    Cross-validation in which each sample is once used as the test set.

    Mostly based on the default sklearn object.

    -WORC.classification.crossval.crossval(config, label_data, image_features, param_grid=None, use_fastr=False, fastr_plugin=None, tempsave=False, fixedsplits=None, ensemble={'Use': False}, outputfolder=None, modus='singlelabel')[source]ΒΆ
    +WORC.classification.crossval.crossval(config, label_data, image_features, param_grid=None, use_fastr=False, fastr_plugin=None, tempsave=False, fixedsplits=None, ensemble={'Use': False}, outputfolder=None, modus='singlelabel', use_SMAC=False, smac_result_file=None)[source]ΒΆ

    Constructs multiple individual classifiers based on the label settings.

    config: dict, mandatory

    Dictionary with config settings. See the Github Wiki for the @@ -2053,7 +2305,7 @@

    -WORC.classification.crossval.random_split_cross_validation(image_features, feature_labels, classes, patient_ids, n_iterations, param_grid, config, modus, test_size, start=0, save_data=None, tempsave=False, tempfolder=None, fixedsplits=None, fixed_seed=False, use_fastr=None, fastr_plugin=None, do_test_RS_Ensemble=False)[source]ΒΆ
    +WORC.classification.crossval.random_split_cross_validation(image_features, feature_labels, classes, patient_ids, n_iterations, param_grid, config, modus, test_size, start=0, save_data=None, tempsave=False, tempfolder=None, fixedsplits=None, fixed_seed=False, use_fastr=None, fastr_plugin=None, do_test_RS_Ensemble=False, use_SMAC=False, smac_result_file=None)[source]ΒΆ

    Cross-validation in which data is randomly split in each iteration.

    Due to options of doing single-label and multi-label classification, stratified splitting, and regression, we use a manual loop instead @@ -2088,7 +2340,7 @@

    -WORC.classification.fitandscore.fit_and_score(X, y, scoring, train, test, parameters, fit_params=None, return_train_score=True, return_n_test_samples=True, return_times=True, return_parameters=False, return_estimator=False, error_score='raise', verbose=True, return_all=True, refit_workflows=False)[source]ΒΆ
    +WORC.classification.fitandscore.fit_and_score(X, y, scoring, train, test, parameters, fit_params=None, return_train_score=True, return_n_test_samples=True, return_times=True, return_parameters=False, return_estimator=False, error_score='raise', verbose=False, return_all=True, refit_workflows=False, use_smac=False)[source]ΒΆ

    Fit an estimator to a dataset and score the performance.

    The following methods can currently be applied as preprocessing before fitting, in @@ -2308,6 +2560,43 @@

    parameter_optimization ModuleΒΆ

    +
    +
    +WORC.classification.parameter_optimization.guided_search_parameters(features, labels, N_iter, test_size, parameters, scoring_method, n_splits=5, n_jobspercore=200, use_fastr=False, n_cores=1, fastr_plugin=None, memory='2G', maxlen=100, ranking_score='test_score', random_seed=None, refit_workflows=False, smac_result_file=None)[source]ΒΆ
    +

    Train a classifier and simultaneously optimizes hyperparameters using a +Bayesian optimization approach.

    +
    +
    Arguments:

    features: numpy array containing the training features. +labels: list containing the object labels to be trained on. +N_iter: integer listing the number of iterations to be used in the

    +
    +

    hyperparameter optimization.

    +
    +
    +
    test_size: float listing the test size percentage used in the cross

    validation.

    +
    +
    +

    classifier: sklearn classifier to be tested +param_grid: dictionary containing all possible hyperparameters and their

    +
    +

    values or distrubitions.

    +
    +
    +
    scoring_method: string defining scoring method used in optimization,

    e.g. f1_weighted for a SVM.

    +
    +
    n_jobsperscore: integer listing the number of jobs that are ran on a

    single core when using the fastr randomized search.

    +
    +
    use_fastr: Boolean determining of either fastr or joblib should be used

    for the opimization.

    +
    +
    fastr_plugin: determines which plugin is used for fastr executions.

    When None, uses the default plugin from the fastr config.

    +
    +
    +
    +
    Returns:

    guided_search: object containing the results

    +
    +
    +
    +
    WORC.classification.parameter_optimization.random_search_parameters(features, labels, N_iter, test_size, param_grid, scoring_method, n_splits=5, n_jobspercore=200, use_fastr=False, n_cores=1, fastr_plugin=None, memory='2G', maxlen=100, ranking_score='test_score', random_seed=None, refit_workflows=False)[source]ΒΆ
    @@ -2348,6 +2637,27 @@

    regressors ModuleΒΆ

    +
    +
    +

    smac ModuleΒΆ

    +
    +
    +WORC.classification.smac.build_smac_config(parameters)[source]ΒΆ
    +

    Reads a parameter dictionary and constructs a SMAC configuration object +from it, to be used as input for the Bayesian optimization

    +
    +
    +
    parameters: dict, mandatory

    Contains the required config settings

    +
    +
    +
    +
    +
    Returns:

    ConfigurationSpace object that defines the search space of the hyperparameter +optimization

    +
    +
    +
    +

    trainclassifier ModuleΒΆ

    @@ -2359,7 +2669,7 @@

    -WORC.classification.trainclassifier.trainclassifier(feat_train, patientinfo_train, config, output_hdf, feat_test=None, patientinfo_test=None, fixedsplits=None, verbose=True)[source]ΒΆ
    +WORC.classification.trainclassifier.trainclassifier(feat_train, patientinfo_train, config, output_hdf, feat_test=None, patientinfo_test=None, fixedsplits=None, output_smac=None, verbose=True)[source]ΒΆ

    Train a classifier using machine learning from features.

    By default, if no split in training and test is supplied, a cross validation diff --git a/WORC/doc/_build/html/autogen/WORC.config.html b/WORC/doc/_build/html/autogen/WORC.config.html index 6ceb0649..f0c9a3d9 100644 --- a/WORC/doc/_build/html/autogen/WORC.config.html +++ b/WORC/doc/_build/html/autogen/WORC.config.html @@ -8,7 +8,7 @@ - <no title> — WORC 3.5.0 documentation + <no title> — WORC 3.6.0 documentation @@ -62,7 +62,7 @@

    - 3.5.0 + 3.6.0
    @@ -202,33 +202,39 @@

    - + + + + - + - + - + - + - + - + - + - + + + + diff --git a/WORC/doc/_build/html/autogen/WORC.detectors.html b/WORC/doc/_build/html/autogen/WORC.detectors.html index a8cf4925..3aab24ce 100644 --- a/WORC/doc/_build/html/autogen/WORC.detectors.html +++ b/WORC/doc/_build/html/autogen/WORC.detectors.html @@ -8,7 +8,7 @@ - detectors Package — WORC 3.5.0 documentation + detectors Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    @@ -246,22 +246,6 @@

    detectors Package -
    -class WORC.detectors.detectors.CartesiusClusterDetector[source]ΒΆ
    -

    Bases: WORC.detectors.detectors.AbstractDetector

    -
    -
    -__abstractmethods__ = frozenset({})ΒΆ
    -
    - -
    -
    -__module__ = 'WORC.detectors.detectors'ΒΆ
    -
    - -
    -
    class WORC.detectors.detectors.CsvDetector(csv_file_path)[source]ΒΆ
    @@ -338,6 +322,22 @@

    detectors Package +
    +class WORC.detectors.detectors.SnelliusClusterDetector[source]ΒΆ
    +

    Bases: WORC.detectors.detectors.AbstractDetector

    +
    +
    +__abstractmethods__ = frozenset({})ΒΆ
    +
    + +
    +
    +__module__ = 'WORC.detectors.detectors'ΒΆ
    +
    + +

    +
    class WORC.detectors.detectors.WORCDirectoryDetector[source]ΒΆ
    diff --git a/WORC/doc/_build/html/autogen/WORC.exampledata.html b/WORC/doc/_build/html/autogen/WORC.exampledata.html index e8f1602e..3d555247 100644 --- a/WORC/doc/_build/html/autogen/WORC.exampledata.html +++ b/WORC/doc/_build/html/autogen/WORC.exampledata.html @@ -8,7 +8,7 @@ - exampledata Package — WORC 3.5.0 documentation + exampledata Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    diff --git a/WORC/doc/_build/html/autogen/WORC.facade.html b/WORC/doc/_build/html/autogen/WORC.facade.html index 984195ba..685eac7c 100644 --- a/WORC/doc/_build/html/autogen/WORC.facade.html +++ b/WORC/doc/_build/html/autogen/WORC.facade.html @@ -8,7 +8,7 @@ - facade Package — WORC 3.5.0 documentation + facade Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    @@ -252,7 +252,7 @@

    Please also see the WORCTutorial Github.

    -__dict__ = mappingproxy({'__module__': 'WORC.facade.simpleworc', '__doc__': 'Facade around the main WORC object for simple interaction.\n\n Please also see the `WORCTutorial Github <https://github.com/MStarmans91/WORCTutorial/>`_.\n ', '__init__': <function SimpleWORC.__init__>, 'set_fixed_splits': <function SimpleWORC.set_fixed_splits>, 'features_from_this_directory': <function SimpleWORC.features_from_this_directory>, 'images_from_this_directory': <function SimpleWORC.images_from_this_directory>, 'segmentations_from_this_directory': <function SimpleWORC.segmentations_from_this_directory>, 'masks_from_this_directory': <function SimpleWORC.masks_from_this_directory>, 'labels_from_this_file': <function SimpleWORC.labels_from_this_file>, 'semantics_from_this_file': <function SimpleWORC.semantics_from_this_file>, 'predict_labels': <function SimpleWORC.predict_labels>, '_set_and_validate_estimators': <function SimpleWORC._set_and_validate_estimators>, 'count_num_subjects': <function SimpleWORC.count_num_subjects>, '_validate': <function SimpleWORC._validate>, 'binary_classification': <function SimpleWORC.binary_classification>, 'multiclass_classification': <function SimpleWORC.multiclass_classification>, 'regression': <function SimpleWORC.regression>, 'survival': <function SimpleWORC.survival>, 'add_config_overrides': <function SimpleWORC.add_config_overrides>, 'add_evaluation': <function SimpleWORC.add_evaluation>, 'set_tmpdir': <function SimpleWORC.set_tmpdir>, 'set_multicore_execution': <function SimpleWORC.set_multicore_execution>, 'features_from_radiomix_xlsx': <function SimpleWORC.features_from_radiomix_xlsx>, 'execute': <function SimpleWORC.execute>, '__dict__': <attribute '__dict__' of 'SimpleWORC' objects>, '__weakref__': <attribute '__weakref__' of 'SimpleWORC' objects>})ΒΆ
    +__dict__ = mappingproxy({'__module__': 'WORC.facade.simpleworc', '__doc__': 'Facade around the main WORC object for simple interaction.\n\n Please also see the `WORCTutorial Github <https://github.com/MStarmans91/WORCTutorial/>`_.\n ', '__init__': <function SimpleWORC.__init__>, 'set_fixed_splits': <function SimpleWORC.set_fixed_splits>, 'set_image_types': <function SimpleWORC.set_image_types>, 'features_from_this_directory': <function SimpleWORC.features_from_this_directory>, 'images_from_this_directory': <function SimpleWORC.images_from_this_directory>, 'segmentations_from_this_directory': <function SimpleWORC.segmentations_from_this_directory>, 'masks_from_this_directory': <function SimpleWORC.masks_from_this_directory>, 'labels_from_this_file': <function SimpleWORC.labels_from_this_file>, 'semantics_from_this_file': <function SimpleWORC.semantics_from_this_file>, 'predict_labels': <function SimpleWORC.predict_labels>, '_set_and_validate_estimators': <function SimpleWORC._set_and_validate_estimators>, 'count_num_subjects': <function SimpleWORC.count_num_subjects>, '_validate': <function SimpleWORC._validate>, 'binary_classification': <function SimpleWORC.binary_classification>, 'multiclass_classification': <function SimpleWORC.multiclass_classification>, 'regression': <function SimpleWORC.regression>, 'survival': <function SimpleWORC.survival>, 'add_config_overrides': <function SimpleWORC.add_config_overrides>, 'add_evaluation': <function SimpleWORC.add_evaluation>, 'set_tmpdir': <function SimpleWORC.set_tmpdir>, 'set_multicore_execution': <function SimpleWORC.set_multicore_execution>, 'features_from_radiomix_xlsx': <function SimpleWORC.features_from_radiomix_xlsx>, 'execute': <function SimpleWORC.execute>, '__dict__': <attribute '__dict__' of 'SimpleWORC' objects>, '__weakref__': <attribute '__weakref__' of 'SimpleWORC' objects>})ΒΆ
    @@ -526,7 +526,22 @@

    set_fixed_splits(fixed_splits_csv)[source]ΒΆ
    -
    +

    Provide CSV with fixed splits for cross-validation.

    +
    +
    fixed_splits_csv: string

    Path of CSV file

    +
    +
    +

    + +
    +
    +set_image_types(image_types)[source]ΒΆ
    +

    Set the type of images to be processed.

    +
    +
    image_types: list of strings

    Names of types of images that are provided to WORC.

    +
    +
    +
    @@ -536,7 +551,7 @@

    only a single core will be used and jobs are executed in series. When multicore mode is enabled, jobs are parallellized over all available cores, which majorly speeds up the computation.

    -

    Note: SimpleWORC has an automatic detector for the BIGR and Cartesius cluster. Hence, +

    Note: SimpleWORC has an automatic detector for the BIGR and Snellius cluster. Hence, on those clusters, do not use the multicore execution, as this will overwrite the changes applied by the detectors.

    diff --git a/WORC/doc/_build/html/autogen/WORC.featureprocessing.html b/WORC/doc/_build/html/autogen/WORC.featureprocessing.html index 71ebd779..a64483c6 100644 --- a/WORC/doc/_build/html/autogen/WORC.featureprocessing.html +++ b/WORC/doc/_build/html/autogen/WORC.featureprocessing.html @@ -8,7 +8,7 @@ - featureprocessing Package — WORC 3.5.0 documentation + featureprocessing Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    diff --git a/WORC/doc/_build/html/autogen/WORC.html b/WORC/doc/_build/html/autogen/WORC.html index cd76b6a0..0b86420a 100644 --- a/WORC/doc/_build/html/autogen/WORC.html +++ b/WORC/doc/_build/html/autogen/WORC.html @@ -8,7 +8,7 @@ - WORC Package — WORC 3.5.0 documentation + WORC Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    @@ -294,7 +294,7 @@

    -__dict__ = mappingproxy({'__module__': 'WORC.WORC', '__doc__': 'Workflow for Optimal Radiomics Classification.\n\n A Workflow for Optimal Radiomics Classification (WORC) object that\n serves as a pipeline spawner and manager for optimizating radiomics\n studies. Depending on the attributes set, the object will spawn an\n appropriate pipeline and manage it.\n\n Note that many attributes are lists and can therefore contain multiple\n instances. For example, when providing two sequences per patient,\n the "images" list contains two items. The type of items in the lists\n is described below.\n\n All objects that serve as source for your network, i.e. refer to\n actual files to be used, should be formatted as fastr sources suited for\n one of the fastr plugings, see also\n http://fastr.readthedocs.io/en/stable/fastr.reference.html#ioplugin-reference\n The objects should be lists of these fastr sources or dictionaries with the\n sample ID\'s, e.g.\n\n images_train = [{\'Patient001\': vfs://input/CT001.nii.gz,\n \'Patient002\': vfs://input/CT002.nii.gz},\n {\'Patient001\': vfs://input/MR001.nii.gz,\n \'Patient002\': vfs://input/MR002.nii.gz}]\n\n Attributes\n ------------------\n name: String, default \'WORC\'\n name of the network.\n\n configs: list, required\n Configuration parameters, either ConfigParser objects\n created through the defaultconfig function or paths of config .ini\n files. (list, required)\n\n labels: list, required\n Paths to files containing patient labels (.txt files).\n\n network: automatically generated\n The FASTR network generated through the "build" function.\n\n images: list, optional\n Paths refering to the images used for Radiomics computation. Images\n should be of the ITK Image type.\n\n segmentations: list, optional\n Paths refering to the segmentations used for Radiomics computation.\n Segmentations should be of the ITK Image type.\n\n semantics: semantic features per image type (list, optional)\n\n masks: state which pixels of images are valid (list, optional)\n\n features: input Radiomics features for classification (list, optional)\n\n metadata: DICOM headers belonging to images (list, optional)\n\n Elastix_Para: parameter files for Elastix (list, optional)\n\n fastr_plugin: plugin to use for FASTR execution\n\n fastr_tempdir: temporary directory to use for FASTR execution\n\n additions: additional inputs for your network (dict, optional)\n\n source_data: data to use as sources for FASTR (dict)\n\n sink_data: data to use as sinks for FASTR (dict)\n\n CopyMetadata: Boolean, default True\n when using elastix, copy metadata from image to segmentation or not\n\n ', '__init__': <function WORC.__init__>, 'defaultconfig': <function WORC.defaultconfig>, 'add_tools': <function WORC.add_tools>, 'build': <function WORC.build>, 'build_training': <function WORC.build_training>, 'add_ComBat': <function WORC.add_ComBat>, 'add_preprocessing': <function WORC.add_preprocessing>, 'add_feature_calculator': <function WORC.add_feature_calculator>, 'add_elastix_sourcesandsinks': <function WORC.add_elastix_sourcesandsinks>, 'add_elastix': <function WORC.add_elastix>, 'add_segmentix': <function WORC.add_segmentix>, 'set': <function WORC.set>, 'execute': <function WORC.execute>, 'add_evaluation': <function WORC.add_evaluation>, 'save_config': <function WORC.save_config>, '__dict__': <attribute '__dict__' of 'WORC' objects>, '__weakref__': <attribute '__weakref__' of 'WORC' objects>})ΒΆ
    +__dict__ = mappingproxy({'__module__': 'WORC.WORC', '__doc__': 'Workflow for Optimal Radiomics Classification.\n\n A Workflow for Optimal Radiomics Classification (WORC) object that\n serves as a pipeline spawner and manager for optimizating radiomics\n studies. Depending on the attributes set, the object will spawn an\n appropriate pipeline and manage it.\n\n Note that many attributes are lists and can therefore contain multiple\n instances. For example, when providing two sequences per patient,\n the "images" list contains two items. The type of items in the lists\n is described below.\n\n All objects that serve as source for your network, i.e. refer to\n actual files to be used, should be formatted as fastr sources suited for\n one of the fastr plugings, see also\n http://fastr.readthedocs.io/en/stable/fastr.reference.html#ioplugin-reference\n The objects should be lists of these fastr sources or dictionaries with the\n sample ID\'s, e.g.\n\n images_train = [{\'Patient001\': vfs://input/CT001.nii.gz,\n \'Patient002\': vfs://input/CT002.nii.gz},\n {\'Patient001\': vfs://input/MR001.nii.gz,\n \'Patient002\': vfs://input/MR002.nii.gz}]\n\n Attributes\n ------------------\n name: String, default \'WORC\'\n name of the network.\n\n configs: list, required\n Configuration parameters, either ConfigParser objects\n created through the defaultconfig function or paths of config .ini\n files. (list, required)\n\n labels: list, required\n Paths to files containing patient labels (.txt files).\n\n network: automatically generated\n The FASTR network generated through the "build" function.\n\n images: list, optional\n Paths refering to the images used for Radiomics computation. Images\n should be of the ITK Image type.\n\n segmentations: list, optional\n Paths refering to the segmentations used for Radiomics computation.\n Segmentations should be of the ITK Image type.\n\n semantics: semantic features per image type (list, optional)\n\n masks: state which pixels of images are valid (list, optional)\n\n features: input Radiomics features for classification (list, optional)\n\n metadata: DICOM headers belonging to images (list, optional)\n\n Elastix_Para: parameter files for Elastix (list, optional)\n\n fastr_plugin: plugin to use for FASTR execution\n\n fastr_tempdir: temporary directory to use for FASTR execution\n\n additions: additional inputs for your network (dict, optional)\n\n source_data: data to use as sources for FASTR (dict)\n\n sink_data: data to use as sinks for FASTR (dict)\n\n CopyMetadata: Boolean, default True\n when using elastix, copy metadata from image to segmentation or not\n\n ', '__init__': <function WORC.__init__>, 'defaultconfig': <function WORC.defaultconfig>, 'add_tools': <function WORC.add_tools>, 'build': <function WORC.build>, 'build_training': <function WORC.build_training>, 'add_fingerprinter': <function WORC.add_fingerprinter>, 'add_ComBat': <function WORC.add_ComBat>, 'add_preprocessing': <function WORC.add_preprocessing>, 'add_feature_calculator': <function WORC.add_feature_calculator>, 'add_elastix_sourcesandsinks': <function WORC.add_elastix_sourcesandsinks>, 'add_elastix': <function WORC.add_elastix>, 'add_segmentix': <function WORC.add_segmentix>, 'set': <function WORC.set>, 'execute': <function WORC.execute>, 'add_evaluation': <function WORC.add_evaluation>, 'save_config': <function WORC.save_config>, '__dict__': <attribute '__dict__' of 'WORC' objects>, '__weakref__': <attribute '__weakref__' of 'WORC' objects>})ΒΆ
    @@ -355,6 +355,14 @@

    Add a feature calculation node to the network.

    +
    +
    +add_fingerprinter(id, type, config_source)[source]ΒΆ
    +

    Add WORC Fingerprinter to the network.

    +

    Note: applied per imaging sequence, or per feature file if no +images are present.

    +
    +
    add_preprocessing(preprocess_node, label, nmod)[source]ΒΆ
    @@ -567,6 +575,7 @@

    Subpackagesmetrics Module
  • parameter_optimization Module
  • regressors Module
  • +
  • smac Module
  • trainclassifier Module
  • @@ -683,6 +692,7 @@

    SubpackagesSlicer Module
  • Transformix Module
  • createfixedsplits Module
  • +
  • fingerprinting Module
  • validators Package
      diff --git a/WORC/doc/_build/html/autogen/WORC.plotting.html b/WORC/doc/_build/html/autogen/WORC.plotting.html index dd0ca31a..dfd60b80 100644 --- a/WORC/doc/_build/html/autogen/WORC.plotting.html +++ b/WORC/doc/_build/html/autogen/WORC.plotting.html @@ -8,7 +8,7 @@ - plotting Package — WORC 3.5.0 documentation + plotting Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
      - 3.5.0 + 3.6.0
      @@ -283,7 +283,7 @@

      -WORC.plotting.plot_ROC.plot_ROC(prediction, pinfo, ensemble=1, label_type=None, ROC_png=None, ROC_tex=None, ROC_csv=None, PRC_png=None, PRC_tex=None, PRC_csv=None)[source]ΒΆ
      +WORC.plotting.plot_ROC.plot_ROC(prediction, pinfo, ensemble_method='top_N', ensemble_size=1, label_type=None, ROC_png=None, ROC_tex=None, ROC_csv=None, PRC_png=None, PRC_tex=None, PRC_csv=None)[source]ΒΆ
      @@ -427,7 +427,7 @@

      -WORC.plotting.plot_estimator_performance.fit_thresholds(thresholds, estimator, label_type, X_train, Y_train, ensemble, ensemble_scoring)[source]ΒΆ
      +WORC.plotting.plot_estimator_performance.fit_thresholds(thresholds, estimator, label_type, X_train, Y_train, ensemble_method, ensemble_size, ensemble_scoring)[source]ΒΆ
      @@ -437,7 +437,7 @@

      -WORC.plotting.plot_estimator_performance.plot_estimator_performance(prediction, label_data, label_type, crossval_type=None, alpha=0.95, ensemble=None, verbose=True, ensemble_scoring=None, output=None, modus=None, thresholds=None, survival=False, shuffle_estimators=False, bootstrap=None, bootstrap_N=None, overfit_scaler=None, save_memory=True, refit_ensemble=False)[source]ΒΆ
      +WORC.plotting.plot_estimator_performance.plot_estimator_performance(prediction, label_data, label_type, crossval_type=None, alpha=0.95, ensemble_method='top_N', ensemble_size=100, verbose=True, ensemble_scoring=None, output=None, modus=None, thresholds=None, survival=False, shuffle_estimators=False, bootstrap=None, bootstrap_N=None, overfit_scaler=None, save_memory=True, refit_ensemble=False)[source]ΒΆ

      Plot the output of a single estimator, e.g. a SVM.

      prediction: pandas dataframe or string, mandatory

      output of trainclassifier function, either a pandas dataframe @@ -452,11 +452,10 @@

      alpha: float, default 0.95

      Significance of confidence intervals.

      -
      ensemble: False, integer or β€˜Caruana’

      Determine whether an ensemble will be created. If so, -either provide an integer to determine how many of the -top performing classifiers should be in the ensemble, or use -the string β€œCaruana” to use smart ensembling based on -Caruana et al. 2004.

      +
      ensemble_method: string, default β€˜top_N’

      Determine which method to use for creating the ensemble. +Choices: top_N or Caruana

      +
      +
      ensemble_size: int, default 50

      Determine the size of the ensemble. Only relevant for top_N

      verbose: boolean, default True

      Plot intermedate messages.

      @@ -533,13 +532,13 @@

      -WORC.plotting.plot_images.plot_im_and_overlay(image, mask=None, figsize=(3, 3), alpha=0.4, color='cyan')[source]ΒΆ
      +WORC.plotting.plot_images.plot_im_and_overlay(image, mask=None, figsize=(3, 3), alpha=0.4, color='cyan', colormap='gray', colorbar=False)[source]ΒΆ

      Plot an image in a matplotlib figure and overlay with a mask.

      -WORC.plotting.plot_images.slicer(image, mask=None, output_name=None, output_name_zoom=None, thresholds=[-240, 160], zoomfactor=4, dpi=500, normalize=False, expand=False, boundary=False, square=False, flip=True, rot90=0, alpha=0.4, axis='axial', index=None, color='cyan', radius=2)[source]ΒΆ
      +WORC.plotting.plot_images.slicer(image, mask=None, output_name=None, output_name_zoom=None, thresholds=[-5, 5], zoomfactor=4, dpi=500, normalize=True, expand=False, boundary=False, square=False, flip=True, rot90=0, alpha=0.4, axis='axial', index=None, color='cyan', radius=2, colormap='gray')[source]ΒΆ

      Plot slice of image where mask is largest, with mask as overlay.

      image and mask should both be arrays

      @@ -555,11 +554,6 @@

      plot_ranked_scores ModuleΒΆ

      -
      -
      -WORC.plotting.plot_ranked_scores.example()[source]ΒΆ
      -
      -
      WORC.plotting.plot_ranked_scores.flatten_object(input)[source]ΒΆ
      @@ -578,17 +572,17 @@

      -WORC.plotting.plot_ranked_scores.plot_ranked_percentages(estimator, pinfo, label_type=None, ensemble=50, output_csv=None)[source]ΒΆ
      +WORC.plotting.plot_ranked_scores.plot_ranked_percentages(estimator, pinfo, label_type=None, ensemble_method='top_N', ensemble_size=100, output_csv=None)[source]ΒΆ
      -WORC.plotting.plot_ranked_scores.plot_ranked_posteriors(estimator, pinfo, label_type=None, ensemble=50, output_csv=None)[source]ΒΆ
      +WORC.plotting.plot_ranked_scores.plot_ranked_posteriors(estimator, pinfo, label_type=None, ensemble_method='top_N', ensemble_size=100, output_csv=None)[source]ΒΆ
      -WORC.plotting.plot_ranked_scores.plot_ranked_scores(estimator, pinfo, label_type, scores='percentages', images=[], segmentations=[], ensemble=50, output_csv=None, output_zip=None, output_itk=None)[source]ΒΆ
      +WORC.plotting.plot_ranked_scores.plot_ranked_scores(estimator, pinfo, label_type, scores='percentages', images=[], segmentations=[], ensemble_method='top_N', ensemble_size=100, output_csv=None, output_zip=None, output_itk=None)[source]ΒΆ

      Rank the patients according to their average score. The score can either be the average posterior or the percentage of times the patient was classified correctly in the cross validations. Additionally, @@ -611,9 +605,9 @@

      segmentations: list, optional

      List containing the filepaths to the ITKImage segmentation files of the patients.

      -
      ensemble: integer or string, optional

      Method to be used for ensembling. Either an integer for a fixed size -or β€˜Caruana’ for the Caruana method, see the SearchCV function for more -details.

      +
      ensemble_method: string, optional

      Method to be used for ensembling.

      +
      +
      ensemble_size: int, optional

      If top_N method is used, number of workflows to be included in ensemble.

      output_csv: filepath, optional

      If given, the scores will be written to this csv file.

      diff --git a/WORC/doc/_build/html/autogen/WORC.processing.html b/WORC/doc/_build/html/autogen/WORC.processing.html index 6f34f570..5cb03459 100644 --- a/WORC/doc/_build/html/autogen/WORC.processing.html +++ b/WORC/doc/_build/html/autogen/WORC.processing.html @@ -8,7 +8,7 @@ - processing Package — WORC 3.5.0 documentation + processing Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
      - 3.5.0 + 3.6.0
      diff --git a/WORC/doc/_build/html/autogen/WORC.resources.fastr_tests.html b/WORC/doc/_build/html/autogen/WORC.resources.fastr_tests.html index d1f9ee00..52572c85 100644 --- a/WORC/doc/_build/html/autogen/WORC.resources.fastr_tests.html +++ b/WORC/doc/_build/html/autogen/WORC.resources.fastr_tests.html @@ -8,7 +8,7 @@ - fastr_tests Package — WORC 3.5.0 documentation + fastr_tests Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
      - 3.5.0 + 3.6.0
      diff --git a/WORC/doc/_build/html/autogen/WORC.resources.fastr_tools.html b/WORC/doc/_build/html/autogen/WORC.resources.fastr_tools.html index e218762d..b1dae46d 100644 --- a/WORC/doc/_build/html/autogen/WORC.resources.fastr_tools.html +++ b/WORC/doc/_build/html/autogen/WORC.resources.fastr_tools.html @@ -8,7 +8,7 @@ - fastr_tools Package — WORC 3.5.0 documentation + fastr_tools Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
      - 3.5.0 + 3.6.0
      diff --git a/WORC/doc/_build/html/autogen/WORC.resources.html b/WORC/doc/_build/html/autogen/WORC.resources.html index ac656c76..8e8fb6b8 100644 --- a/WORC/doc/_build/html/autogen/WORC.resources.html +++ b/WORC/doc/_build/html/autogen/WORC.resources.html @@ -8,7 +8,7 @@ - resources Package — WORC 3.5.0 documentation + resources Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
      - 3.5.0 + 3.6.0
      diff --git a/WORC/doc/_build/html/autogen/WORC.tools.html b/WORC/doc/_build/html/autogen/WORC.tools.html index ea7350b0..b1e931f1 100644 --- a/WORC/doc/_build/html/autogen/WORC.tools.html +++ b/WORC/doc/_build/html/autogen/WORC.tools.html @@ -8,7 +8,7 @@ - tools Package — WORC 3.5.0 documentation + tools Package — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
      - 3.5.0 + 3.6.0
      @@ -126,6 +126,7 @@
    • Slicer Module
    • Transformix Module
    • createfixedsplits Module
    • +
    • fingerprinting Module
  • validators Package
  • @@ -263,7 +264,7 @@

    tools Package

    Evaluate ModuleΒΆ

    -class WORC.tools.Evaluate.Evaluate(label_type, modus='binary_classification', ensemble=50, scores='percentages', parent=None, features=None, fastr_plugin='LinearExecution', name='Example')[source]ΒΆ
    +class WORC.tools.Evaluate.Evaluate(label_type, modus='binary_classification', scores='percentages', ensemble_method='top_N', ensemble_size=100, parent=None, features=None, fastr_plugin='LinearExecution', name='Example')[source]ΒΆ

    Bases: object

    Build a network that evaluates the performance of an estimator.

    @@ -273,7 +274,7 @@

    tools Package
    -__init__(label_type, modus='binary_classification', ensemble=50, scores='percentages', parent=None, features=None, fastr_plugin='LinearExecution', name='Example')[source]ΒΆ
    +__init__(label_type, modus='binary_classification', scores='percentages', ensemble_method='top_N', ensemble_size=100, parent=None, features=None, fastr_plugin='LinearExecution', name='Example')[source]ΒΆ

    Initialize object.

    network: fastr network, default None

    If you input a network, the evaluate network is added @@ -472,6 +473,48 @@

    tools Package +

    fingerprinting ModuleΒΆ

    +
    +
    +class WORC.tools.fingerprinting.Fingerprinter[source]ΒΆ
    +

    Bases: object

    +

    Fingerprinting object for WORC configuration.

    +
    +
    +__dict__ = mappingproxy({'__module__': 'WORC.tools.fingerprinting', '__doc__': 'Fingerprinting object for WORC configuration.', '__init__': <function Fingerprinter.__init__>, 'execute': <function Fingerprinter.execute>, '__dict__': <attribute '__dict__' of 'Fingerprinter' objects>, '__weakref__': <attribute '__weakref__' of 'Fingerprinter' objects>})ΒΆ
    +
    + +
    +
    +__init__()[source]ΒΆ
    +

    Initialize object.

    +
    + +
    +
    +__module__ = 'WORC.tools.fingerprinting'ΒΆ
    +
    + +
    +
    +__weakref__ΒΆ
    +

    list of weak references to the object (if defined)

    +
    + +
    +
    +execute()[source]ΒΆ
    +

    Determine fingerprint of dataset.

    +
    +
    worcobject: WORC object

    WORC object to fingerprint

    +
    +
    +
    + +
    + diff --git a/WORC/doc/_build/html/genindex.html b/WORC/doc/_build/html/genindex.html index d91a03fb..9c527668 100644 --- a/WORC/doc/_build/html/genindex.html +++ b/WORC/doc/_build/html/genindex.html @@ -9,7 +9,7 @@ - Index — WORC 3.5.0 documentation + Index — WORC 3.6.0 documentation @@ -63,7 +63,7 @@
    - 3.5.0 + 3.6.0
    @@ -196,6 +196,8 @@

    _

  • __iter__() (WORC.classification.AdvancedSampler.AdvancedSampler method) @@ -426,6 +436,8 @@

    _

  • (WORC.classification.SearchCV.BaseSearchCV attribute)
  • (WORC.classification.SearchCV.BaseSearchCVJoblib attribute) +
  • +
  • (WORC.classification.SearchCV.BaseSearchCVSMAC attribute)
  • (WORC.classification.SearchCV.BaseSearchCVfastr attribute)
  • @@ -434,6 +446,8 @@

    _

  • (WORC.classification.SearchCV.GridSearchCVJoblib attribute)
  • (WORC.classification.SearchCV.GridSearchCVfastr attribute) +
  • +
  • (WORC.classification.SearchCV.GuidedSearchCVSMAC attribute)
  • (WORC.classification.SearchCV.RandomizedSearchCVJoblib attribute)
  • @@ -442,8 +456,6 @@

    _

  • (WORC.detectors.detectors.AbstractDetector attribute)
  • (WORC.detectors.detectors.BigrClusterDetector attribute) -
  • -
  • (WORC.detectors.detectors.CartesiusClusterDetector attribute)
  • (WORC.detectors.detectors.CsvDetector attribute)
  • @@ -452,6 +464,8 @@

    _

  • (WORC.detectors.detectors.HostnameDetector attribute)
  • (WORC.detectors.detectors.LinuxDetector attribute) +
  • +
  • (WORC.detectors.detectors.SnelliusClusterDetector attribute)
  • (WORC.detectors.detectors.WORCDirectoryDetector attribute)
  • @@ -510,6 +524,8 @@

    _

  • (WORC.tools.Slicer.Slicer attribute)
  • (WORC.tools.Transformix.Transformix attribute) +
  • +
  • (WORC.tools.fingerprinting.Fingerprinter attribute)
  • (WORC.validators.preflightcheck.AbstractValidator attribute)
  • @@ -586,6 +602,8 @@

    _

  • (WORC.tools.Slicer.Slicer attribute)
  • (WORC.tools.Transformix.Transformix attribute) +
  • +
  • (WORC.tools.fingerprinting.Fingerprinter attribute)
  • (WORC.validators.preflightcheck.AbstractValidator attribute)
  • @@ -619,6 +637,8 @@

    A

    + + + + + + - + + + + - + - + - + - + - + - + - + - + + + + @@ -381,6 +388,9 @@

    Contents

    + + +

    FeatureScaling

    FeatureScaling

    General

    Fingerprinting

    Fingerprinting

    General

    General

    HyperOptimization

    HyperOptimization

    HyperOptimization

    ImageFeatures

    ImageFeatures

    ImageFeatures

    Imputation

    Imputation

    Imputation

    Labels

    Labels

    Labels

    OneHotEncoding

    OneHotEncoding

    OneHotEncoding

    Preprocessing

    Preprocessing

    Preprocessing

    PyRadiomics

    PyRadiomics

    PyRadiomics

    Resampling

    Resampling

    Resampling

    SMAC

    SMAC

    Segmentix

    Segmentix

  • set_fixed_splits() (WORC.facade.simpleworc.SimpleWORC method) +
  • +
  • set_image_types() (WORC.facade.simpleworc.SimpleWORC method)
  • set_multicore_execution() (WORC.facade.simpleworc.SimpleWORC method)
  • @@ -1319,6 +1351,8 @@

    S

  • Slicer (class in WORC.tools.Slicer)
  • slicer() (in module WORC.plotting.plot_images) +
  • +
  • SnelliusClusterDetector (class in WORC.detectors.detectors)
  • source_data() (in module WORC.resources.fastr_tests.CalcFeatures_test) @@ -1453,6 +1487,8 @@

    W

  • WORC.classification.regressors (module)
  • WORC.classification.SearchCV (module) +
  • +
  • WORC.classification.smac (module)
  • WORC.classification.trainclassifier (module)
  • @@ -1591,6 +1627,8 @@

    W

  • WORC.tools.Elastix (module)
  • WORC.tools.Evaluate (module) +
  • +
  • WORC.tools.fingerprinting (module)
  • WORC.tools.Inference (module)
  • diff --git a/WORC/doc/_build/html/index.html b/WORC/doc/_build/html/index.html index d014a85f..24807176 100644 --- a/WORC/doc/_build/html/index.html +++ b/WORC/doc/_build/html/index.html @@ -8,7 +8,7 @@ - WORC: Workflow for Optimal Radiomics Classification — WORC 3.5.0 documentation + WORC: Workflow for Optimal Radiomics Classification — WORC 3.6.0 documentation @@ -63,7 +63,7 @@
    - 3.5.0 + 3.6.0
    @@ -279,6 +279,7 @@

    WORC DocumentationClassification
  • CrossValidation
  • HyperOptimization
  • +
  • SMAC
  • Ensemble
  • Evaluation
  • Bootstrap
  • @@ -319,6 +320,7 @@

    WORC DocumentationFeature scaling
  • Image Registration
  • ComBat
  • +
  • Bayesian optimization with SMAC instead of random search
  • Multilabel classification and regression
  • References
  • @@ -350,160 +352,165 @@

    WORC DocumentationResource File Formats
  • Changelog
      -
    • 3.5.0 - 2021-08-18
        -
      • Fixed
      • -
      • Changed
      • +
      • 3.6.0 - 2022-04-05
      • -
      • 3.4.5 - 2021-07-09
          +
        • 3.5.0 - 2021-08-18
        • -
        • 3.4.4 - 2021-07-01
            +
          • 3.4.5 - 2021-07-09
          • -
          • 3.4.3 - 2021-06-02
              -
            • Fixed
            • +
            • 3.4.4 - 2021-07-01
            • -
            • 3.4.2 - 2021-05-27
                +
              • 3.4.3 - 2021-06-02
              • -
              • 3.4.1 - 2021-05-18
                  +
                • 3.4.2 - 2021-05-27
                • -
                • 3.4.0 - 2021-02-02
                    -
                  • Fixed
                  • -
                  • Changed
                  • -
                  • Added
                  • +
                  • 3.4.1 - 2021-05-18
                  • -
                  • 3.3.5 - 2020-10-21
                      -
                    • Fixed
                    • -
                    • Changed
                    • -
                    • Added
                    • +
                    • 3.4.0 - 2021-02-02
                    • -
                    • 3.3.4 - 2020-10-06
                        -
                      • Fixed
                      • -
                      • Changed
                      • -
                      • Added
                      • +
                      • 3.3.5 - 2020-10-21
                      • -
                      • 3.3.3 - 2020-09-11
                          -
                        • Fixed
                        • -
                        • Changed
                        • -
                        • Added
                        • +
                        • 3.3.4 - 2020-10-06
                        • -
                        • 3.3.2 - 2020-08-19
                            -
                          • Fixed
                          • +
                          • 3.3.3 - 2020-09-11
                          • -
                          • 3.3.1 - 2020-07-31
                              -
                            • Changed
                            • -
                            • Fixed
                            • +
                            • 3.3.2 - 2020-08-19
                            • -
                            • 3.3.0 - 2020-07-28
                                -
                              • Added
                              • -
                              • Changed
                              • -
                              • Fixed
                              • +
                              • 3.3.1 - 2020-07-31
                              • -
                              • 3.2.2 - 2020-07-14
                                  -
                                • Added
                                • -
                                • Changed
                                • -
                                • Fixed
                                • +
                                • 3.3.0 - 2020-07-28
                                • -
                                • 3.2.1 - 2020-07-02
                                    -
                                  • Added
                                  • -
                                  • Changed
                                  • -
                                  • Fixed
                                  • +
                                  • 3.2.2 - 2020-07-14
                                  • -
                                  • 3.2.0 - 2020-06-26
                                      -
                                    • Added
                                    • -
                                    • Changed
                                    • -
                                    • Fixed
                                    • +
                                    • 3.2.1 - 2020-07-02
                                    • -
                                    • 3.1.4 - 2020-05-26
                                        -
                                      • Added
                                      • -
                                      • Changed
                                      • -
                                      • Fixed
                                      • +
                                      • 3.2.0 - 2020-06-26
                                      • -
                                      • 3.1.3 - 2020-01-24
                                          -
                                        • Added
                                        • -
                                        • Changed
                                        • -
                                        • Fixed
                                        • +
                                        • 3.1.4 - 2020-05-26
                                        • -
                                        • 3.1.2 - 2019-12-09
                                            -
                                          • Added
                                          • -
                                          • Changed
                                          • -
                                          • Fixed
                                          • +
                                          • 3.1.3 - 2020-01-24
                                          • -
                                          • 3.1.1 - 2019-11-28
                                              -
                                            • Added
                                            • -
                                            • Changed
                                            • -
                                            • Fixed
                                            • +
                                            • 3.1.2 - 2019-12-09
                                            • -
                                            • 3.1.0 - 2019-10-16
                                                -
                                              • Added
                                              • -
                                              • Changed
                                              • -
                                              • Fixed
                                              • +
                                              • 3.1.1 - 2019-11-28
                                              • -
                                              • 3.0.0 - 2019-05-08
                                                  -
                                                • Added
                                                • -
                                                • Changed
                                                • -
                                                • Fixed
                                                • +
                                                • 3.1.0 - 2019-10-16
                                                • -
                                                • 2.1.3 - 2019-04-08
                                                    +
                                                  • 3.0.0 - 2019-05-08
                                                  • -
                                                  • 2.1.2 - 2019-04-02
                                                      -
                                                    • Added
                                                    • +
                                                    • 2.1.3 - 2019-04-08
                                                    • -
                                                    • 2.1.1 - 2019-02-15
                                                        -
                                                      • Added
                                                      • -
                                                      • Changed
                                                      • -
                                                      • Fixed
                                                      • +
                                                      • 2.1.2 - 2019-04-02 +
                                                      • +
                                                      • 2.1.1 - 2019-02-15
                                                      • -
                                                      • 2.1.0 - 2018-08-09
                                                      • validators Package
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                                                          - 3.5.0 + 3.6.0
                                                          @@ -245,6 +245,11 @@

                                                          Python Module Index

  •     WORC.classification.SearchCV
        + WORC.classification.smac +
        @@ -585,6 +590,11 @@

    Python Module Index

        WORC.tools.Evaluate
        + WORC.tools.fingerprinting +
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    - 3.5.0 + 3.6.0
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Package","IOparser Package","classification Package","<no title>","detectors Package","exampledata Package","export Package","facade Package","helpers Package","fastrconfig Package","featureprocessing Package","plotting Package","processing Package","resources Package","fastr_tests Package","fastr_tools Package","statistics Package","tests Package","tools Package","validators Package","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","<no title>","WORC: Workflow for Optimal Radiomics Classification","Additional functionality","Changelog","Configuration","Data Mining Methods","Developer documentation","FAQ","Radiomics Features","Resource File Formats","Introduction","Quick start guide","User Manual"],titleterms:{"0rc1":66,"boolean":70,"class":12,"default":70,"export":6,"function":65,"import":74,Added:66,Adding:69,The:[74,75],actual:74,addexcept:[0,70],addit:65,advancedsampl:2,alter:70,analysi:[68,74],arrai:70,attribut:75,basicworc:7,bayesian:65,begin:70,bigr:70,bin:71,binari:71,bootstrap:67,calcfeatures_test:14,can:70,chang:66,changelog:66,choic:71,classif:[2,64,65,67,68],classifi:[],cluster:70,column:70,combat:[10,65,67],command:75,compon:68,comput:70,compute_ci:11,config_io_classifi:1,config_io_combat:1,config_io_pyradiom:1,config_preprocess:1,config_segmentix:1,config_worc:1,configbuild:8,configur:67,construct:75,construct_classifi:2,content:67,convert:70,crash:70,create_example_data:5,createfixedsplit:[2,18],creation:67,crossval:2,crossvalid:67,data:[68,69,75],datadownload:5,debug:75,decomposit:10,definit:75,delet:70,delong:16,depend:71,detector:4,develop:[64,69],dicom:71,differ:[70,71],dimension:68,document:[64,69],elastix:18,elastix_para:75,elastix_test:14,ensembl:67,entri:70,error:70,estim:[],evalu:[18,67,75],exampl:[69,75],exampledata:5,except:8,execut:[70,75],experi:[70,74],extract:71,extractnlargestblobsn:12,facad:7,fals:70,faq:70,fastr:73,fastr_test:14,fastr_tool:15,fastrconfig:9,feat_out_0:70,featpreprocess:67,featsel:67,featur:[65,68,69,70,71,75],featureconvert:10,featureprocess:10,featuresc:67,file:[70,72,75],file_io:1,filter:71,fingerprint:18,first:70,fitandscor:2,fix:[66,71],format:[70,72],found:70,from:68,full:71,function_bas:70,gabor:71,gaussian:71,gener:67,given:70,glcm:71,gldm:71,glrlm:71,glszm:71,grai:71,groupwis:68,guid:74,hdf5:70,helper:[8,12],histogram:71,hyper_params_export:6,hyperoptim:[67,69],icc:[],iccthreshold:10,imag:[65,71,75],imagefeatur:67,imput:[10,67,68],indexerror:70,indic:[64,70],infer:18,input:[74,75],instal:[70,74],instead:[65,70],integ:70,interact:[67,75],introduct:[67,73],iopars:1,job:70,keep:70,label:[67,70,75],label_process:12,labels_from_this_fil:70,laplacian:71,lbp:71,learn:68,length:71,level:71,lib:70,like:70,line:70,linstretch:11,local:71,log:71,look:70,machin:68,manual:75,mask:75,matrix:71,metadata:75,method:[68,69],metric:2,mine:68,model:68,modul:[0,1,2,4,5,6,7,8,9,10,11,12,14,16,17,18,19,64,70],modular:73,modulenotfounderror:70,multilabel:65,must:70,name:70,need:70,neighborhood:71,network:75,ngtdm:71,numpi:70,obj:70,object:75,objectsampl:2,occur:71,onehotencod:[67,68],onehotencoderwrapp:10,ones:70,optim:[64,65,73],orient:71,other:[70,71],own:70,packag:[0,1,2,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,70,74],paramet:71,parameter_optim:2,patient:70,pattern:71,pca:68,phase:71,plot:11,plot_barchart:11,plot_boxplot_featur:11,plot_boxplot_perform:11,plot_error:11,plot_estimator_perform:11,plot_hyperparamet:11,plot_imag:11,plot_pvalues_featur:11,plot_ranked_scor:11,plot_roc:11,plotminmaxrespons:11,preflightcheck:19,preprocess:[12,65,67],preprocessor:10,princip:68,process:[8,12,69],pyradiom:67,queue:70,quick:74,radiom:[64,71,73],random:65,rankedsvm:[],reduct:68,refer:[64,65],registr:65,regress:[65,68],regressor:2,relief:[10,68],resampl:[67,68],resourc:[13,72],result:74,roi:71,run:[71,74],scale:[65,68],scaler:10,scatterplot:[],search:65,searchcv:2,segment:75,segmentix:[12,67],segmentix_test:14,select:[68,70,71],selectfeatgroup:67,selectgroup:10,selectindividu:10,semant:[71,75],shape:71,simpleworc:[7,70],site:70,size:71,slicer:18,smac:[2,65,67],some:70,sourc:75,standard:[70,73],start:74,statist:[16,68],statisticaltestfeatur:10,statisticaltestthreshold:10,submit:70,subpackag:[0,7,13],surviv:68,tabl:64,tell:70,terminolog:73,test:[17,68,69],test_combat:17,test_help:17,test_iccthreshold:17,test_plot_error:17,test_valid:17,textur:71,tip:74,tone:71,tool:18,toolbox:69,trainclassifi:2,transformix:18,trick:74,tutori:74,type:70,univari:68,unreleas:[],use:70,used:70,user:[64,75],valid:19,varianc:68,variancethreshold:10,vessel:71,wavelet:71,welcom:64,where:70,width:71,worc:[0,64,70,75],worc_config:9,worckeyerror:70,worctutorialsimple_travis_multiclass:[],worctutorialsimple_travis_regress:[],worctutorialsimple_unittest_multiclass:17,worctutorialsimple_unittest_regress:17,worcvalueerror:70,work:70,workflow:64,would:70,your:[70,75],zone:71}}) \ No newline at end of file diff --git a/WORC/doc/_build/html/static/changelog.html b/WORC/doc/_build/html/static/changelog.html index 3c8da0a3..755fddc6 100644 --- a/WORC/doc/_build/html/static/changelog.html +++ b/WORC/doc/_build/html/static/changelog.html @@ -8,7 +8,7 @@ - Changelog — WORC 3.5.0 documentation + Changelog — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    @@ -99,160 +99,165 @@
  • Developer documentation
  • Resource File Formats
  • Changelog
  • FeatureScaling

    FeatureScaling

    General

    Fingerprinting

    Fingerprinting

    General

    General

    HyperOptimization

    HyperOptimization

    HyperOptimization

    ImageFeatures

    ImageFeatures

    ImageFeatures

    Imputation

    Imputation

    Imputation

    Labels

    Labels

    Labels

    OneHotEncoding

    OneHotEncoding

    OneHotEncoding

    Preprocessing

    Preprocessing

    Preprocessing

    PyRadiomics

    PyRadiomics

    PyRadiomics

    Resampling

    Resampling

    Resampling

    SMAC

    SMAC

    Segmentix

    Segmentix

    ComBat

    Whether to use ComBat feature harmonization on your FULL dataset, i.e. not in a train-test setting. See <https://github.com/Jfortin1/ComBatHarmonization for more information./>`_ .

    Fingerprint

    Whether to use Fingerprinting or not.

    Defaults and Options:

    @@ -430,7 +440,7 @@

    Contents

    tempsave

    -

    False

    +

    True

    True, False

    AssumeSameImageAndMaskMetadata

    @@ -441,6 +451,10 @@

    Contents

    Fingerprint

    +

    True

    +

    True, False

    + @@ -704,7 +718,7 @@

    Contents

    mask

    -

    If a mask is supplied, should the mask be subtracted from the contour or multiplied.

    +

    If None, masks will not be used by segmentix. If a mask is supplied, should the mask be subtracted from the contour or multiplied.

    segtype

    If Ring, then a ring around the segmentation will be used as contour. If Dilate, the segmentation will be dilated per 2-D axial slice with a disc.

    @@ -729,9 +743,9 @@

    Contents - - + +

    Subkey

    @@ -741,8 +755,8 @@

    Contents

    mask

    -

    subtract

    -

    subtract, multiply

    +

    None

    +

    None, subtract, multiply

    segtype

    None

    @@ -780,8 +794,8 @@

    Contents - - + +

    Subkey

    @@ -832,51 +846,54 @@

    Contents

    image_type

    -

    Modality of images supplied. Determines how the image is loaded.

    +

    Modality of images supplied. Determines how the image is loaded. Mandatory to supply by user. Should be one of the valid quantitative modalities [β€˜CT’, β€˜PET’, β€˜Thermography’, β€˜ADC’, β€˜MG’] or qualitative modalities [β€˜MRI’, β€˜MR’, β€˜DWI’, β€˜US’].

    -

    gabor_frequencies

    +

    extraction_mode

    +

    Determine how to extract the features: 2D if your masks and/or images have only one 2D slice, 3D for tru 3D images, 2.5D for 3D images but in a slice-by-slice stacked 2D manner. The latter is recommended when the slice thickness is much larger (>2) than the pixel spacing.

    + +

    gabor_frequencies

    Frequencies of Gabor filters used: can be a single float or a list.

    -

    gabor_angles

    +

    gabor_angles

    Angles of Gabor filters in degrees: can be a single integer or a list.

    -

    GLCM_angles

    +

    GLCM_angles

    Angles used in GLCM computation in radians: can be a single float or a list.

    -

    GLCM_levels

    +

    GLCM_levels

    Number of grayscale levels used in discretization before GLCM computation.

    -

    GLCM_distances

    +

    GLCM_distances

    Distance(s) used in GLCM computation in pixels: can be a single integer or a list.

    -

    LBP_radius

    +

    LBP_radius

    Radii used for LBP computation: can be a single integer or a list.

    -

    LBP_npoints

    +

    LBP_npoints

    Number(s) of points used in LBP computation: can be a single integer or a list.

    -

    phase_minwavelength

    +

    phase_minwavelength

    Minimal wavelength in pixels used for phase features.

    -

    phase_nscale

    +

    phase_nscale

    Number of scales used in phase feature computation.

    -

    log_sigma

    +

    log_sigma

    Standard deviation(s) in pixels used in log feature computation: can be a single integer or a list.

    -

    vessel_scale_range

    +

    vessel_scale_range

    Scale in pixels used for Frangi vessel filter. Given as a minimum and a maximum.

    -

    vessel_scale_step

    +

    vessel_scale_step

    Step size used to go from minimum to maximum scale on Frangi vessel filter.

    -

    vessel_radius

    +

    vessel_radius

    Radius to determine boundary of between inner part and edge in Frangi vessel filter.

    -

    dicom_feature_tags

    +

    dicom_feature_tags

    DICOM tags to be extracted as features. See https://worc.readthedocs.io/en/latest/static/features.html.

    -

    dicom_feature_labels

    +

    dicom_feature_labels

    For each of the DICOM tag values extracted, name that should be assigned to the feature. See https://worc.readthedocs.io/en/latest/static/features.html.

    @@ -952,66 +969,70 @@

    Contents

    image_type

    -

    CT

    -

    CT

    + +

    String

    -

    gabor_frequencies

    +

    extraction_mode

    +

    2.5D

    +

    String: 2D, 2.5D or 3D

    + +

    gabor_frequencies

    0.05, 0.2, 0.5

    Float(s)

    -

    gabor_angles

    +

    gabor_angles

    0, 45, 90, 135

    Integer(s)

    -

    GLCM_angles

    +

    GLCM_angles

    0, 0.79, 1.57, 2.36

    Float(s)

    -

    GLCM_levels

    +

    GLCM_levels

    16

    Integer > 0

    -

    GLCM_distances

    +

    GLCM_distances

    1, 3

    Integer(s) > 0

    -

    LBP_radius

    +

    LBP_radius

    3, 8, 15

    Integer(s) > 0

    -

    LBP_npoints

    +

    LBP_npoints

    12, 24, 36

    Integer(s) > 0

    -

    phase_minwavelength

    +

    phase_minwavelength

    3

    Integer > 0

    -

    phase_nscale

    +

    phase_nscale

    5

    Integer > 0

    -

    log_sigma

    +

    log_sigma

    1, 5, 10

    Integer(s)

    -

    vessel_scale_range

    +

    vessel_scale_range

    1, 10

    Two integers: min and max.

    -

    vessel_scale_step

    +

    vessel_scale_step

    2

    Integer > 0

    -

    vessel_radius

    +

    vessel_radius

    5

    Integer > 0

    -

    dicom_feature_tags

    +

    dicom_feature_tags

    0010 1010, 0010 0040

    DICOM tag keys, e.g. 0010 0010, separated by comma’s

    -

    dicom_feature_labels

    +

    dicom_feature_labels

    age, sex

    List of strings

    @@ -1054,7 +1075,7 @@

    Contentshttps://pyradiomics.readthedocs.io/en/latest/customization.html/>`_ .

    binCount

    -

    We advice to use a fixed bin count instead of a fixed bin width, as on imaging modalities such as MRI, the scale of the values varies a lot, which is incompatible with a fixed bin width. See <https://pyradiomics.readthedocs.io/en/latest/customization.html/>`_ .

    +

    We advice to use a fixed bin count instead of a fixed bin width, as on imaging modalities such as MR, the scale of the values varies a lot, which is incompatible with a fixed bin width. See <https://pyradiomics.readthedocs.io/en/latest/customization.html/>`_ .

    binWidth

    See <https://pyradiomics.readthedocs.io/en/latest/customization.html/>`_ .

    @@ -1968,8 +1989,8 @@

    Contents - - + +

    Subkey

    @@ -2079,14 +2100,32 @@

    Contents

    XGB_colsample_bytree

    Col sample by tree in XGB.

    +

    LightGBM_num_leaves

    +

    Maximum tree leaves for base learners. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.

    + +

    LightGBM_max_depth

    +

    Maximum tree depth for base learners. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.

    + +

    LightGBM_min_child_samples

    +

    Minimum number of data needed in a child (leaf). See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.

    + +

    LightGBM_reg_alpha

    +

    L1 regularization term on weights. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.

    + +

    LightGBM_reg_lambda

    +

    L2 regularization term on weights. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.

    + +

    LightGBM_min_child_weight

    +

    Minimum sum of instance weight (hessian) needed in a child (leaf). See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.

    +

    Defaults and Options:

    ---+++ @@ -2231,6 +2270,30 @@

    Contents

    + + + + + + + + + + + + + + + + + + + + + + +

    Subkey

    LightGBM_num_leaves

    5, 95

    Two Integers: loc and scale

    LightGBM_max_depth

    3, 12

    Two Integers: loc and scale

    LightGBM_min_child_samples

    5, 45

    Two Integers: loc and scale

    LightGBM_reg_alpha

    0.01, 0.99

    Two Floats: loc and scale

    LightGBM_reg_lambda

    0.01, 0.99

    Two Floats: loc and scale

    LightGBM_min_child_weight

    -7, 4

    Two Integers: loc and scale

    @@ -2393,6 +2456,84 @@

    Contents +

    SMACΒΆ

    +

    WORC enables the use of the SMAC algorithm for the hyperparameter optimization. +SMAC uses the same parameter options as the default random search, except for +resampling which is currently not compatible with SMAC.

    +

    Description:

    + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + +

    Subkey

    Description

    use

    If True, use SMAC as the optimization strategy.

    n_smac_cores

    Number of independent, parallel SMAC instances to use.

    budget_type

    Type of budget to use for the SMAC optimization, either an evaluation limit or a time limit.

    budget

    Size of the budget, which depends on the type of budget. Number of evaluations for an evaluation limit, or wallclock seconds for a time limit.

    init_method

    Initialization method of SMAC. Supported are a random initialization or a sobol sequence.

    init_budget

    Number of evaluations used for the initialization. Always an evaluation limit, regardless of the budget type choice of the optimization.

    +

    Defaults and Options:

    + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    Subkey

    Default

    Options

    use

    False

    True, False

    n_smac_cores

    1

    Integer

    budget_type

    evals

    evals, time

    budget

    100

    Integer

    init_method

    random

    random, sobol

    init_budget

    20

    Integer

    +

    EnsembleΒΆ

    WORC supports ensembling of workflows. This is not a default approach in @@ -2410,10 +2551,13 @@

    Contents

    Use

    -

    Determine whether to use ensembling or not. Provide an integer to state how many estimators to include: 1 equals no ensembling.

    +

    Method

    +

    Choose which ensemble method to use. If you do not wish to use an ensemble, use Single or top_N with size 1.

    -

    Metric

    +

    Size

    +

    Number of estimators to use in the ensemble for the top_N method, or the number of bags for the Bagging method.

    + +

    Metric

    Metric used to determine ranking of estimators in ensemble. When using default, the metric that is used in the hyperoptimization is used.

    @@ -2421,9 +2565,9 @@

    Contents - - - + + +

    Subkey

    @@ -2432,11 +2576,15 @@

    Contents

    Use

    +

    Method

    +

    top_N

    +

    Single, top_N, FitNumber, ForwardSelection, Caruana, Bagging

    + +

    Size

    100

    Integer

    -

    Metric

    +

    Metric

    Default

    Default, generalization

    @@ -2526,7 +2674,7 @@

    Contents

    N_iterations

    -

    1000

    +

    10000

    Integer

    diff --git a/WORC/doc/_build/html/static/file_description.html b/WORC/doc/_build/html/static/file_description.html index 998f3b1c..e946d69e 100644 --- a/WORC/doc/_build/html/static/file_description.html +++ b/WORC/doc/_build/html/static/file_description.html @@ -8,7 +8,7 @@ - Resource File Formats — WORC 3.5.0 documentation + Resource File Formats — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    diff --git a/WORC/doc/_build/html/static/introduction.html b/WORC/doc/_build/html/static/introduction.html index 75cba34c..1c735afc 100644 --- a/WORC/doc/_build/html/static/introduction.html +++ b/WORC/doc/_build/html/static/introduction.html @@ -8,7 +8,7 @@ - Introduction — WORC 3.5.0 documentation + Introduction — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    diff --git a/WORC/doc/_build/html/static/quick_start.html b/WORC/doc/_build/html/static/quick_start.html index 1f29f213..08c3aafc 100644 --- a/WORC/doc/_build/html/static/quick_start.html +++ b/WORC/doc/_build/html/static/quick_start.html @@ -8,7 +8,7 @@ - Quick start guide — WORC 3.5.0 documentation + Quick start guide — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    @@ -347,6 +347,11 @@

    The actual experimentexperiment.labels_from_this_file(label_file) experiment.predict_labels(label_name) +# Set the types of images WORC has to process. Used in fingerprinting +# Valid quantitative types are ['CT', 'PET', 'Thermography', 'ADC'] +# Valid qualitative types are ['MRI', 'DWI', 'US'] +experiment.set_image_types(['CT']) + # Use the standard workflow for your specific modus if modus == 'binary_classification': experiment.binary_classification(coarse=coarse) diff --git a/WORC/doc/_build/html/static/user_manual.html b/WORC/doc/_build/html/static/user_manual.html index c51ef806..061444f2 100644 --- a/WORC/doc/_build/html/static/user_manual.html +++ b/WORC/doc/_build/html/static/user_manual.html @@ -8,7 +8,7 @@ - User Manual — WORC 3.5.0 documentation + User Manual — WORC 3.6.0 documentation @@ -64,7 +64,7 @@
    - 3.5.0 + 3.6.0
    diff --git a/WORC/doc/autogen/WORC.classification.rst b/WORC/doc/autogen/WORC.classification.rst index 1f358e9b..49bc90f1 100644 --- a/WORC/doc/autogen/WORC.classification.rst +++ b/WORC/doc/autogen/WORC.classification.rst @@ -100,6 +100,15 @@ classification Package :show-inheritance: :special-members: +:mod:`smac` Module +------------------ + +.. automodule:: WORC.classification.smac + :members: + :undoc-members: + :show-inheritance: + :special-members: + :mod:`trainclassifier` Module ----------------------------- diff --git a/WORC/doc/autogen/WORC.config.rst b/WORC/doc/autogen/WORC.config.rst index 5111b92f..e6fb47dd 100644 --- a/WORC/doc/autogen/WORC.config.rst +++ b/WORC/doc/autogen/WORC.config.rst @@ -20,7 +20,7 @@ OneHotEncoding :ref:`OneHotEncoding ` Preprocessing :ref:`Preprocessing ` PyRadiomics :ref:`PyRadiomics ` Resampling :ref:`Resampling ` +SMAC :ref:`SMAC ` Segmentix :ref:`Segmentix ` -SelectFeatGroup :ref:`SelectFeatGroup ` -SMAC :ref:`SMAC ` +SelectFeatGroup :ref:`SelectFeatGroup ` ================= =================================================== \ No newline at end of file diff --git a/WORC/doc/autogen/WORC.rst b/WORC/doc/autogen/WORC.rst index 6872a9c8..02387248 100644 --- a/WORC/doc/autogen/WORC.rst +++ b/WORC/doc/autogen/WORC.rst @@ -47,5 +47,6 @@ Subpackages WORC.statistics WORC.tests WORC.tools + WORC.tutorial WORC.validators diff --git a/WORC/doc/autogen/WORC.tools.rst b/WORC/doc/autogen/WORC.tools.rst index f9059a22..786d8f28 100644 --- a/WORC/doc/autogen/WORC.tools.rst +++ b/WORC/doc/autogen/WORC.tools.rst @@ -55,3 +55,12 @@ tools Package :show-inheritance: :special-members: +:mod:`fingerprinting` Module +---------------------------- + +.. automodule:: WORC.tools.fingerprinting + :members: + :undoc-members: + :show-inheritance: + :special-members: + diff --git a/WORC/doc/autogen/config/WORC.config_Classification_defopts.rst b/WORC/doc/autogen/config/WORC.config_Classification_defopts.rst index 4abf9921..8ddff69d 100644 --- a/WORC/doc/autogen/config/WORC.config_Classification_defopts.rst +++ b/WORC/doc/autogen/config/WORC.config_Classification_defopts.rst @@ -1,44 +1,44 @@ -========================== ======================================================================================== ================================================================================================================================================================================================================================================== -Subkey Default Options -========================== ======================================================================================== ================================================================================================================================================================================================================================================== -fastr True True, False -fastr_plugin LinearExecution Any `fastr execution plugin `_ . -classifiers SVM, RF, LR, LDA, QDA, GaussianNB, AdaBoostClassifier, XGBClassifier, LightGBMClassifier SVM , SVR, SGD, SGDR, RF, LDA, QDA, ComplementND, GaussianNB, AdaBoostClassifier, XGBClassifier, LR, RFR, Lasso, ElasticNet, LinR, Ridge, AdaBoostRegressor, XGBRegressor. All are estimators from `sklearn `_ -max_iter 100000 Integer -SVMKernel linear, poly, rbf poly, linear, rbf -SVMC 0, 6 Two Integers: loc and scale -SVMdegree 1, 6 Two Integers: loc and scale -SVMcoef0 0, 1 Two Integers: loc and scale -SVMgamma -5, 5 Two Integers: loc and scale -RFn_estimators 10, 90 Two Integers: loc and scale -RFmin_samples_split 2, 3 Two Integers: loc and scale -RFmax_depth 5, 5 Two Integers: loc and scale -LRpenalty l1, l2, elasticnet none, l2, l1 -LRC 0.01, 0.99 Two Floats: loc and scale -LR_solver lbfgs, saga Comma separated list of strings, for the options see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html -LR_l1_ratio 0, 1 Float between 0.0 and 1.0. -LDA_solver svd, lsqr, eigen svd, lsqr, eigen -LDA_shrinkage -5, 5 Two Integers: loc and scale -QDA_reg_param -5, 5 Two Integers: loc and scale -ElasticNet_alpha -5, 5 Two Integers: loc and scale -ElasticNet_l1_ratio 0, 1 Two Integers: loc and scale -SGD_alpha -5, 5 Two Integers: loc and scale -SGD_l1_ratio 0, 1 Two Integers: loc and scale -SGD_loss squared_loss, huber, epsilon_insensitive, squared_epsilon_insensitive hinge, squared_hinge, modified_huber -SGD_penalty none, l2, l1 none, l2, l1 -CNB_alpha 0, 1 Two Integers: loc and scale -AdaBoost_n_estimators 10, 90 Two Integers: loc and scale -AdaBoost_learning_rate 0.01, 0.99 Two Floats: loc and scale -XGB_boosting_rounds 10, 90 Two Integers: loc and scale -XGB_max_depth 3, 12 Two Integers: loc and scale -XGB_learning_rate 0.01, 0.99 Two Floats: loc and scale -XGB_gamma 0.01, 9.99 Two Floats: loc and scale -XGB_min_child_weight 1, 6 Two Integers: loc and scale -XGB_colsample_bytree 0.3, 0.7 Two Floats: loc and scale -LightGBM_num_leaves 5, 95 Two Integers: loc and scale -LightGBM_max_depth 3, 12 Two Integers: loc and scale -LightGBM_min_child_samples 5, 45 Two Integers: loc and scale -LightGBM_reg_alpha 0.01, 0.99 Two Floats: loc and scale -LightGBM_reg_lambda 0.01, 0.99 Two Floats: loc and scale -LightGBM_min_child_weight -7, 4 Two Integers: loc and scale -========================== ======================================================================================== ================================================================================================================================================================================================================================================== \ No newline at end of file +========================== ===================================================================== ================================================================================================================================================================================================================================================== +Subkey Default Options +========================== ===================================================================== ================================================================================================================================================================================================================================================== +fastr True True, False +fastr_plugin LinearExecution Any `fastr execution plugin `_ . +classifiers SVM, RF, LR, LDA, QDA, GaussianNB, AdaBoostClassifier, XGBClassifier SVM , SVR, SGD, SGDR, RF, LDA, QDA, ComplementND, GaussianNB, AdaBoostClassifier, XGBClassifier, LR, RFR, Lasso, ElasticNet, LinR, Ridge, AdaBoostRegressor, XGBRegressor. All are estimators from `sklearn `_ +max_iter 100000 Integer +SVMKernel linear, poly, rbf poly, linear, rbf +SVMC 0, 6 Two Integers: loc and scale +SVMdegree 1, 6 Two Integers: loc and scale +SVMcoef0 0, 1 Two Integers: loc and scale +SVMgamma -5, 5 Two Integers: loc and scale +RFn_estimators 10, 90 Two Integers: loc and scale +RFmin_samples_split 2, 3 Two Integers: loc and scale +RFmax_depth 5, 5 Two Integers: loc and scale +LRpenalty l1, l2, elasticnet none, l2, l1 +LRC 0.01, 0.99 Two Floats: loc and scale +LR_solver lbfgs, saga Comma separated list of strings, for the options see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html +LR_l1_ratio 0, 1 Float between 0.0 and 1.0. +LDA_solver svd, lsqr, eigen svd, lsqr, eigen +LDA_shrinkage -5, 5 Two Integers: loc and scale +QDA_reg_param -5, 5 Two Integers: loc and scale +ElasticNet_alpha -5, 5 Two Integers: loc and scale +ElasticNet_l1_ratio 0, 1 Two Integers: loc and scale +SGD_alpha -5, 5 Two Integers: loc and scale +SGD_l1_ratio 0, 1 Two Integers: loc and scale +SGD_loss squared_loss, huber, epsilon_insensitive, squared_epsilon_insensitive hinge, squared_hinge, modified_huber +SGD_penalty none, l2, l1 none, l2, l1 +CNB_alpha 0, 1 Two Integers: loc and scale +AdaBoost_n_estimators 10, 90 Two Integers: loc and scale +AdaBoost_learning_rate 0.01, 0.99 Two Floats: loc and scale +XGB_boosting_rounds 10, 90 Two Integers: loc and scale +XGB_max_depth 3, 12 Two Integers: loc and scale +XGB_learning_rate 0.01, 0.99 Two Floats: loc and scale +XGB_gamma 0.01, 9.99 Two Floats: loc and scale +XGB_min_child_weight 1, 6 Two Integers: loc and scale +XGB_colsample_bytree 0.3, 0.7 Two Floats: loc and scale +LightGBM_num_leaves 5, 95 Two Integers: loc and scale +LightGBM_max_depth 3, 12 Two Integers: loc and scale +LightGBM_min_child_samples 5, 45 Two Integers: loc and scale +LightGBM_reg_alpha 0.01, 0.99 Two Floats: loc and scale +LightGBM_reg_lambda 0.01, 0.99 Two Floats: loc and scale +LightGBM_min_child_weight -7, 4 Two Integers: loc and scale +========================== ===================================================================== ================================================================================================================================================================================================================================================== \ No newline at end of file diff --git a/WORC/doc/autogen/config/WORC.config_Ensemble_defopts.rst b/WORC/doc/autogen/config/WORC.config_Ensemble_defopts.rst index e9a0a782..063fba07 100644 --- a/WORC/doc/autogen/config/WORC.config_Ensemble_defopts.rst +++ b/WORC/doc/autogen/config/WORC.config_Ensemble_defopts.rst @@ -1,7 +1,7 @@ ====== ======= ============================================================ -Subkey Default Options +Subkey Default Options ====== ======= ============================================================ Method top_N Single, top_N, FitNumber, ForwardSelection, Caruana, Bagging -Size 100 Integer -Metric Default Default, generalization +Size 100 Integer +Metric Default Default, generalization ====== ======= ============================================================ \ No newline at end of file diff --git a/WORC/doc/autogen/config/WORC.config_Ensemble_description.rst b/WORC/doc/autogen/config/WORC.config_Ensemble_description.rst index f7492174..95135f7d 100644 --- a/WORC/doc/autogen/config/WORC.config_Ensemble_description.rst +++ b/WORC/doc/autogen/config/WORC.config_Ensemble_description.rst @@ -1,7 +1,7 @@ ====== ========================================================================================================================================= -Subkey Description +Subkey Description ====== ========================================================================================================================================= -Method Choose which ensemble method to use. If you do not wish to use an ensemble, use Single or top_N with size 1. -Size Number of estimators to use in the ensemble for the top_N method, or the number of bags for the Bagging method. +Method Choose which ensemble method to use. If you do not wish to use an ensemble, use Single or top_N with size 1. +Size Number of estimators to use in the ensemble for the top_N method, or the number of bags for the Bagging method. Metric Metric used to determine ranking of estimators in ensemble. When using default, the metric that is used in the hyperoptimization is used. ====== ========================================================================================================================================= \ No newline at end of file diff --git a/WORC/doc/autogen/config/WORC.config_General_defopts.rst b/WORC/doc/autogen/config/WORC.config_General_defopts.rst index 317e2e55..cb0f9951 100644 --- a/WORC/doc/autogen/config/WORC.config_General_defopts.rst +++ b/WORC/doc/autogen/config/WORC.config_General_defopts.rst @@ -9,7 +9,7 @@ RegistrationNode elastix4.8/Elastix:4.8 TransformationNode elastix4.8/Transformix:4.8 'elastix4.8/Transformix:4.8', your own tool reference Joblib_ncores 1 Integer > 0 Joblib_backend threading multiprocessing, threading -tempsave False True, False +tempsave True True, False AssumeSameImageAndMaskMetadata False True, False ComBat False True, False Fingerprint True True, False diff --git a/WORC/doc/autogen/config/WORC.config_ImageFeatures_description.rst b/WORC/doc/autogen/config/WORC.config_ImageFeatures_description.rst index 2328b117..c97d8895 100644 --- a/WORC/doc/autogen/config/WORC.config_ImageFeatures_description.rst +++ b/WORC/doc/autogen/config/WORC.config_ImageFeatures_description.rst @@ -15,7 +15,7 @@ coliage Determine whether coliage features are computed or not. vessel Determine whether vessel features are computed or not. log Determine whether LoG features are computed or not. phase Determine whether local phase features are computed or not. -image_type Modality of images supplied. Determines how the image is loaded. Mandatory to supply by user. Should be one of the valid quantitative modalities ['CT', 'PET', 'Thermography', 'ADC'] or qualitative modalities ['MRI', 'DWI', 'US']. +image_type Modality of images supplied. Determines how the image is loaded. Mandatory to supply by user. Should be one of the valid quantitative modalities ['CT', 'PET', 'Thermography', 'ADC', 'MG'] or qualitative modalities ['MRI', 'MR', 'DWI', 'US']. extraction_mode Determine how to extract the features: 2D if your masks and/or images have only one 2D slice, 3D for tru 3D images, 2.5D for 3D images but in a slice-by-slice stacked 2D manner. The latter is recommended when the slice thickness is much larger (>2) than the pixel spacing. gabor_frequencies Frequencies of Gabor filters used: can be a single float or a list. gabor_angles Angles of Gabor filters in degrees: can be a single integer or a list. diff --git a/WORC/doc/autogen/config/WORC.config_PyRadiomics_description.rst b/WORC/doc/autogen/config/WORC.config_PyRadiomics_description.rst index 41970f23..742c71f9 100644 --- a/WORC/doc/autogen/config/WORC.config_PyRadiomics_description.rst +++ b/WORC/doc/autogen/config/WORC.config_PyRadiomics_description.rst @@ -1,26 +1,26 @@ -===================== ===================================================================================================================================================================================================================================================================== -Subkey Description -===================== ===================================================================================================================================================================================================================================================================== -geometryTolerance See `_ . -normalize See `_ . -normalizeScale See `_ . -resampledPixelSpacing See `_ . -interpolator See `_ . -preCrop See `_ . -binCount We advice to use a fixed bin count instead of a fixed bin width, as on imaging modalities such as MRI, the scale of the values varies a lot, which is incompatible with a fixed bin width. See `_ . -binWidth See `_ . -force2D See `_ . -force2Ddimension See `_ . -voxelArrayShift See `_ . -Original Enable/Disable computation of original image features. -Wavelet Enable/Disable computation of wavelet image features. -LoG Enable/Disable computation of Laplacian of Gaussian (LoG) image features. -label "Intensity" of the pixels in the mask to be used for feature extraction. If using segmentix, use 1, as your mask will be boolean. Otherwise, select the integer(s) corresponding to the ROI in your mask. -extract_firstorder Determine whether first order features are computed or not. -extract_shape Determine whether shape features are computed or not. -texture_GLCM Determine whether GLCM features are computed or not. -texture_GLRLM Determine whether GLRLM features are computed or not. -texture_GLSZM Determine whether GLSZM features are computed or not. -texture_GLDM Determine whether GLDM features are computed or not. -texture_NGTDM Determine whether NGTDM features are computed or not. -===================== ===================================================================================================================================================================================================================================================================== \ No newline at end of file +===================== ==================================================================================================================================================================================================================================================================== +Subkey Description +===================== ==================================================================================================================================================================================================================================================================== +geometryTolerance See `_ . +normalize See `_ . +normalizeScale See `_ . +resampledPixelSpacing See `_ . +interpolator See `_ . +preCrop See `_ . +binCount We advice to use a fixed bin count instead of a fixed bin width, as on imaging modalities such as MR, the scale of the values varies a lot, which is incompatible with a fixed bin width. See `_ . +binWidth See `_ . +force2D See `_ . +force2Ddimension See `_ . +voxelArrayShift See `_ . +Original Enable/Disable computation of original image features. +Wavelet Enable/Disable computation of wavelet image features. +LoG Enable/Disable computation of Laplacian of Gaussian (LoG) image features. +label "Intensity" of the pixels in the mask to be used for feature extraction. If using segmentix, use 1, as your mask will be boolean. Otherwise, select the integer(s) corresponding to the ROI in your mask. +extract_firstorder Determine whether first order features are computed or not. +extract_shape Determine whether shape features are computed or not. +texture_GLCM Determine whether GLCM features are computed or not. +texture_GLRLM Determine whether GLRLM features are computed or not. +texture_GLSZM Determine whether GLSZM features are computed or not. +texture_GLDM Determine whether GLDM features are computed or not. +texture_NGTDM Determine whether NGTDM features are computed or not. +===================== ==================================================================================================================================================================================================================================================================== \ No newline at end of file diff --git a/WORC/doc/autogen/config/WORC.config_SMAC_defopts.rst b/WORC/doc/autogen/config/WORC.config_SMAC_defopts.rst new file mode 100644 index 00000000..f7f6f3a4 --- /dev/null +++ b/WORC/doc/autogen/config/WORC.config_SMAC_defopts.rst @@ -0,0 +1,10 @@ +============ ======= ============= +Subkey Default Options +============ ======= ============= +use False True, False +n_smac_cores 1 Integer +budget_type evals evals, time +budget 100 Integer +init_method random random, sobol +init_budget 20 Integer +============ ======= ============= \ No newline at end of file diff --git a/WORC/doc/autogen/config/WORC.config_SMAC_description.rst b/WORC/doc/autogen/config/WORC.config_SMAC_description.rst new file mode 100644 index 00000000..d7bfacb0 --- /dev/null +++ b/WORC/doc/autogen/config/WORC.config_SMAC_description.rst @@ -0,0 +1,10 @@ +============ ============================================================================================================================================== +Subkey Description +============ ============================================================================================================================================== +use If True, use SMAC as the optimization strategy. +n_smac_cores Number of independent, parallel SMAC instances to use. +budget_type Type of budget to use for the SMAC optimization, either an evaluation limit or a time limit. +budget Size of the budget, which depends on the type of budget. Number of evaluations for an evaluation limit, or wallclock seconds for a time limit. +init_method Initialization method of SMAC. Supported are a random initialization or a sobol sequence. +init_budget Number of evaluations used for the initialization. Always an evaluation limit, regardless of the budget type choice of the optimization. +============ ============================================================================================================================================== \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 230414da..0ffccc6a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,7 +11,7 @@ nose-parameterized>=0.5.0 nose>=1.3.7 numpy>=1.16.3 pandas>=0.24.2 -PREDICT>=3.1.14 +PREDICT>=3.1.15 pyOpenSSL>=16.2.0 pydicom>=1.2.2 python-dateutil<=2.8.0

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