From 80a8582f815195dac5074ff9ba8fc5da437d97d3 Mon Sep 17 00:00:00 2001 From: MStarmans91 Date: Thu, 1 Jul 2021 14:15:48 +0200 Subject: [PATCH] Prepare for 3.4.4 release. --- WORC/doc/_build/doctrees/environment.pickle | Bin 2877958 -> 2878561 bytes .../_build/doctrees/static/changelog.doctree | Bin 147770 -> 148285 bytes .../doctrees/static/quick_start.doctree | Bin 43175 -> 43171 bytes .../WORC/featureprocessing/Relief.html | 4 ++-- .../WORC/featureprocessing/SelectGroups.html | 4 ++-- .../featureprocessing/SelectIndividuals.html | 2 +- .../StatisticalTestThreshold.html | 2 +- .../featureprocessing/VarianceThreshold.html | 2 +- .../html/_sources/static/quick_start.rst.txt | 2 +- WORC/doc/_build/html/searchindex.js | 2 +- WORC/doc/_build/html/static/changelog.html | 2 ++ WORC/doc/_build/html/static/quick_start.html | 2 +- WORC/doc/static/quick_start.rst | 2 +- WORC/facade/simpleworc.py | 3 +++ .../WORCTutorialSimple_travis_regression.py | 12 ++++++++---- 15 files changed, 24 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     #!/usr/bin/env python
 
-# Copyright 2016-2019 Biomedical Imaging Group Rotterdam, Departments of
+# Copyright 2016-2021 Biomedical Imaging Group Rotterdam, Departments of
 # Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -181,7 +181,7 @@ 
    Source code for WORC.featureprocessing.Relief
    # limitations under the License.
 
 from sklearn.base import BaseEstimator
-from sklearn.feature_selection.base import SelectorMixin
+from sklearn.feature_selection import SelectorMixin
 import numpy as np
 import sklearn.neighbors as nn
 # from skrebate import ReliefF
diff --git a/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectGroups.html b/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectGroups.html
index f2499833..ec4ebc55 100644
--- a/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectGroups.html
+++ b/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectGroups.html
@@ -165,7 +165,7 @@
   
    Source code for WORC.featureprocessing.SelectGroups
     #!/usr/bin/env python
 
-# Copyright 2016-2020 Biomedical Imaging Group Rotterdam, Departments of
+# Copyright 2016-2021 Biomedical Imaging Group Rotterdam, Departments of
 # Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -181,7 +181,7 @@ 
    Source code for WORC.featureprocessing.SelectGroups
    # limitations under the License.
 
 from sklearn.base import BaseEstimator
-from sklearn.feature_selection.base import SelectorMixin
+from sklearn.feature_selection import SelectorMixin
 import numpy as np
 
 
diff --git a/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectIndividuals.html b/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectIndividuals.html
index bb5fc11f..a349a0d0 100644
--- a/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectIndividuals.html
+++ b/WORC/doc/_build/html/_modules/WORC/featureprocessing/SelectIndividuals.html
@@ -181,7 +181,7 @@ 
    Source code for WORC.featureprocessing.SelectIndividuals
    # limitations under the License.
 
 from sklearn.base import BaseEstimator
-from sklearn.feature_selection.base import SelectorMixin
+from sklearn.feature_selection import SelectorMixin
 import numpy as np
 
 
diff --git a/WORC/doc/_build/html/_modules/WORC/featureprocessing/StatisticalTestThreshold.html b/WORC/doc/_build/html/_modules/WORC/featureprocessing/StatisticalTestThreshold.html
index ec23bae8..3d9c32d5 100644
--- a/WORC/doc/_build/html/_modules/WORC/featureprocessing/StatisticalTestThreshold.html
+++ b/WORC/doc/_build/html/_modules/WORC/featureprocessing/StatisticalTestThreshold.html
@@ -181,7 +181,7 @@ 
    Source code for WORC.featureprocessing.StatisticalTestThreshold
    # limitations under the License.
 
 from sklearn.base import BaseEstimator
-from sklearn.feature_selection.base import SelectorMixin
+from sklearn.feature_selection import SelectorMixin
 import numpy as np
 from scipy.stats import ttest_ind, ranksums, mannwhitneyu
 
diff --git a/WORC/doc/_build/html/_modules/WORC/featureprocessing/VarianceThreshold.html b/WORC/doc/_build/html/_modules/WORC/featureprocessing/VarianceThreshold.html
index f0a25fc7..557e4e21 100644
--- a/WORC/doc/_build/html/_modules/WORC/featureprocessing/VarianceThreshold.html
+++ b/WORC/doc/_build/html/_modules/WORC/featureprocessing/VarianceThreshold.html
@@ -182,7 +182,7 @@ 
    Source code for WORC.featureprocessing.VarianceThreshold
    from sklearn.feature_selection import VarianceThreshold
 from sklearn.base import BaseEstimator
-from sklearn.feature_selection.base import SelectorMixin
+from sklearn.feature_selection import SelectorMixin
 import numpy as np
 import WORC.addexceptions as ae
 
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 a3a17661..a1cca080 100644
--- a/WORC/doc/_build/html/_sources/static/quick_start.rst.txt
+++ b/WORC/doc/_build/html/_sources/static/quick_start.rst.txt
@@ -181,7 +181,7 @@ After defining the inputs, the following code can be used to run your first expe
     experiment.segmentations_from_this_directory(imagedatadir,
                                         segmentation_file_name=segmentation_file_name)
     experiment.labels_from_this_file(label_file)
-    experiment.predict_labels([label_name])
+    experiment.predict_labels(label_name)
 
     # Use the standard workflow for your specific modus
     if modus == 'binary_classification':
diff --git a/WORC/doc/_build/html/searchindex.js b/WORC/doc/_build/html/searchindex.js
index 8bccab4f..370cf0d1 100644
--- a/WORC/doc/_build/html/searchindex.js
+++ b/WORC/doc/_build/html/searchindex.js
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Package","IOparser Package","classification Package","<no title>","detectors Package","exampledata 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>","WORC: Workflow for Optimal Radiomics Classification","Additional 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\ 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 85602d02..d275ad08 100644
--- a/WORC/doc/_build/html/static/changelog.html
+++ b/WORC/doc/_build/html/static/changelog.html
@@ -325,6 +325,8 @@ 
    3.4.4 - 2021-07-01ΒΆ
    
 
      
 
    • Bug where most recent added estimators were not valid in SimpleWORC.
      • 
+
    • SelectorMixin is now imported directly from sklearn.feature_selection,
+as sklearn.feature_selection.base is deprecated and will be removed.
      • 
 
    
 
    
 
    
diff --git a/WORC/doc/_build/html/static/quick_start.html b/WORC/doc/_build/html/static/quick_start.html
index 703f427c..ad9d0430 100644
--- a/WORC/doc/_build/html/static/quick_start.html
+++ b/WORC/doc/_build/html/static/quick_start.html
@@ -345,7 +345,7 @@ 
    The actual experimentexperiment.segmentations_from_this_directory(imagedatadir,
                                     segmentation_file_name=segmentation_file_name)
 experiment.labels_from_this_file(label_file)
-experiment.predict_labels([label_name])
+experiment.predict_labels(label_name)
 
 # Use the standard workflow for your specific modus
 if modus == 'binary_classification':
diff --git a/WORC/doc/static/quick_start.rst b/WORC/doc/static/quick_start.rst
index a3a17661..a1cca080 100644
--- a/WORC/doc/static/quick_start.rst
+++ b/WORC/doc/static/quick_start.rst
@@ -181,7 +181,7 @@ After defining the inputs, the following code can be used to run your first expe
     experiment.segmentations_from_this_directory(imagedatadir,
                                         segmentation_file_name=segmentation_file_name)
     experiment.labels_from_this_file(label_file)
-    experiment.predict_labels([label_name])
+    experiment.predict_labels(label_name)
 
     # Use the standard workflow for your specific modus
     if modus == 'binary_classification':
diff --git a/WORC/facade/simpleworc.py b/WORC/facade/simpleworc.py
index 8d78582a..241c9191 100644
--- a/WORC/facade/simpleworc.py
+++ b/WORC/facade/simpleworc.py
@@ -366,6 +366,9 @@ def predict_labels(self, label_names: list):
             raise TypeError(f'label_names is of type {type(label_names)} while list is expected')
 
         for label in label_names:
+            if not isinstance(label, str):
+                raise TypeError(f'label {label} is of type {type(label)} while str is expected')
+
             if len(label.strip()) == 0:
                 raise ValueError('Invalid label, length = 0')
 
diff --git a/WORC/tests/WORCTutorialSimple_travis_regression.py b/WORC/tests/WORCTutorialSimple_travis_regression.py
index 4f8dbf1d..b97375af 100644
--- a/WORC/tests/WORCTutorialSimple_travis_regression.py
+++ b/WORC/tests/WORCTutorialSimple_travis_regression.py
@@ -80,13 +80,17 @@ def main():
     label_file = os.path.join(data_path, 'Examplefiles', 'pinfo_HN.csv')
 
     # Name of the label you want to predict
-    if modus == 'classification':
+    if modus == 'binary_classification':
         # Classification: predict a binary (0 or 1) label
-        label_name = 'imaginary_label_1'
+        label_name = ['imaginary_label_1']
 
     elif modus == 'regression':
         # Regression: predict a continuous label
-        label_name = 'Age'
+        label_name = ['Age']
+
+    elif modus == 'multiclass_classification':
+        # Multiclass classification: predict several mutually exclusive binaru labels together
+        label_name = ['imaginary_label_1', 'complement_label_1']
 
     # Determine whether we want to do a coarse quick experiment, or a full lengthy
     # one. Again, change this accordingly if you use your own data.
@@ -126,7 +130,7 @@ def main():
 
     # Labels
     experiment.labels_from_this_file(label_file)
-    experiment.predict_labels([label_name])
+    experiment.predict_labels(label_name)
 
     # Use the standard workflow for binary classification
     if modus == 'classification':