handling qunfold versioning, and more bug fix

CHANGE_LOG.txt

@@ -15,6 +15,7 @@ CLEAN TODO-FILE
         my_acc.fit(the_data, fit_classifier=False)
         in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction
         indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing.
+    - This change imposes a versioning constrain with qunfold, which now must be >= 0.1.6
 - EMQ has been modified, so that the representation function "classify" now only provides posterior
     probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function.
     - A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow

TODO.txt

@@ -1,3 +1,5 @@
+Update READMEs, wiki, & examples for new fit-predict interface
+
 Add the fix suggested by Alexander:
 
 For a more general application, I would maybe first stablish a per-class threshold value of plausible prevalence

quapy/method/composable.py

@@ -1,13 +1,21 @@
 """This module allows the composition of quantification methods from loss functions and feature transformations. This functionality is realized through an integration of the qunfold package: https://github.com/mirkobunse/qunfold."""
 
-_import_error_message = """qunfold, the back-end of quapy.method.composable, is not properly installed.
+__install_istructions = """
-
 To fix this error, call:
 
     pip install --upgrade pip setuptools wheel
     pip install "jax[cpu]"
-    pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
+    pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.5"
 """
+__import_error_message = (
+        "qunfold, the back-end of quapy.method.composable, is not properly installed." + __install_istructions
+)
+__old_version_message = (
+    "The version of qunfold you have installed is not compatible with current quapy's version, "
+    "which requires qunfold>=0.1.5. " + __install_istructions
+)
+
+from packaging.version import Version
 
 try:
     import qunfold
@@ -51,7 +59,19 @@ try:
         "GaussianRFFKernelTransformer",
     ]
 except ImportError as e:
-    raise ImportError(_import_error_message) from e
+    raise ImportError(__import_error_message) from e
+
+
+def check_compatible_qunfold_version():
+    try:
+        version_str = qunfold.__version__
+    except AttributeError:
+        # versions of qunfold <= 0.1.4 did not declare __version__ in the __init__.py but only in the setup.py
+        version_str = "0.1.4"
+
+    compatible = Version(version_str) >= Version("0.1.5")
+    return compatible
+
 
 def ComposableQuantifier(loss, transformer, **kwargs):
     """A generic quantification / unfolding method that solves a linear system of equations.
@@ -99,4 +119,7 @@ def ComposableQuantifier(loss, transformer, **kwargs):
             >>>     ClassTransformer(CVClassifier(LogisticRegression(), 10))
             >>> )
         """
+    if not check_compatible_qunfold_version():
+        raise ImportError(__old_version_message)
+
     return QuaPyWrapper(qunfold.GenericMethod(loss, transformer, **kwargs))

quapy/tests/test_methods.py

@@ -17,6 +17,7 @@ from quapy.method.composable import (
     ClassTransformer,
     HistogramTransformer,
     CVClassifier,
+    check_compatible_qunfold_version
 )
 COMPOSABLE_METHODS = [
     ComposableQuantifier( # ACC
@@ -111,13 +112,18 @@ class TestMethods(unittest.TestCase):
         self.assertTrue(check_prevalence_vector(estim_prevalences))
 
     def test_composable(self):
-        for dataset in TestMethods.datasets:
+        from packaging.version import Version
-            for q in COMPOSABLE_METHODS:
+        if check_compatible_qunfold_version():
-                print('testing', q)
+            for dataset in TestMethods.datasets:
-                q.fit(*dataset.training.Xy)
+                for q in COMPOSABLE_METHODS:
-                estim_prevalences = q.predict(dataset.test.X)
+                    print('testing', q)
-                print(estim_prevalences)
+                    q.fit(*dataset.training.Xy)
-                self.assertTrue(check_prevalence_vector(estim_prevalences))
+                    estim_prevalences = q.predict(dataset.test.X)
+                    print(estim_prevalences)
+                    self.assertTrue(check_prevalence_vector(estim_prevalences))
+        else:
+            from quapy.method.composable import __old_version_message
+            print(__old_version_message)
 
 
 if __name__ == '__main__':