learner adequacy check refactor

Author: JakaKokosar

Orange/base.py

@@ -3,12 +3,12 @@
 from collections.abc import Iterable
 import re
 import warnings
-from typing import Callable, Dict
+from typing import Callable, Dict, Optional
 
 import numpy as np
 import scipy
 
-from Orange.data import Table, Storage, Instance, Value
+from Orange.data import Table, Storage, Instance, Value, Domain
 from Orange.data.filter import HasClass
 from Orange.data.table import DomainTransformationError
 from Orange.data.util import one_hot
@@ -86,6 +86,9 @@ class Learner(ReprableWithPreprocessors):
     #: A sequence of data preprocessors to apply on data prior to
     #: fitting the model
     preprocessors = ()
+
+    # Note: Do not use this class attribute.
+    #       It remains here for compatibility reasons.
     learner_adequacy_err_msg = ''
 
     def __init__(self, preprocessors=None):
@@ -106,8 +109,9 @@ def fit_storage(self, data):
         return self.fit(X, Y, W)
 
     def __call__(self, data, progress_callback=None):
-        if not self.check_learner_adequacy(data.domain):
-            raise ValueError(self.learner_adequacy_err_msg)
+        incompatibility_reason = self.incompatibility_reason(data.domain)
+        if incompatibility_reason is not None:
+            raise ValueError(incompatibility_reason)
 
         origdomain = data.domain
 
@@ -174,8 +178,16 @@ def active_preprocessors(self):
             yield from type(self).preprocessors
 
     def check_learner_adequacy(self, _):
+        warnings.warn(
+            "check_learner_adequacy is deprecated and will be removed "
+            "in upcoming releases. Please use incompatibility_reason "
+            "to check if learner can fit data.",
+            OrangeDeprecationWarning)
         return True
 
+    def incompatibility_reason(self, domain: Domain) -> Optional[str]:
+        return None
+
     @property
     def name(self):
         """Return a short name derived from Learner type name"""

Orange/classification/base_classification.py

@@ -6,15 +6,13 @@
 
 class LearnerClassification(Learner):
 
-    def check_learner_adequacy(self, domain):
-        is_adequate = True
+    def incompatibility_reason(self, domain):
+        err_msg = None
         if len(domain.class_vars) > 1:
-            is_adequate = False
-            self.learner_adequacy_err_msg = "Too many target variables."
+            err_msg = "Too many target variables."
         elif not domain.has_discrete_class:
-            is_adequate = False
-            self.learner_adequacy_err_msg = "Categorical class variable expected."
-        return is_adequate
+            err_msg = "Categorical class variable expected."
+        return err_msg
 
 
 class ModelClassification(Model):

Orange/preprocess/impute.py

@@ -224,7 +224,8 @@ def __call__(self, data, variable):
         variable = data.domain[variable]
         domain = domain_with_class_var(data.domain, variable)
 
-        if self.learner.check_learner_adequacy(domain):
+        incompatibility_reason = self.learner.incompatibility_reason(data.domain)
+        if incompatibility_reason is None:
             data = data.transform(domain)
             model = self.learner(data)
             assert model.domain.class_var == variable
@@ -239,7 +240,7 @@ def copy(self):
 
     def supports_variable(self, variable):
         domain = Orange.data.Domain([], class_vars=variable)
-        return self.learner.check_learner_adequacy(domain)
+        return self.learner.incompatibility_reason(domain) is None
 
 
 def domain_with_class_var(domain, class_var):

Orange/regression/base_regression.py

@@ -6,15 +6,13 @@
 
 class LearnerRegression(Learner):
 
-    def check_learner_adequacy(self, domain):
-        is_adequate = True
+    def incompatibility_reason(self, domain):
+        err_msg = None
         if len(domain.class_vars) > 1:
-            is_adequate = False
-            self.learner_adequacy_err_msg = "Too many target variables."
+            err_msg = "Too many target variables."
         elif not domain.has_continuous_class:
-            is_adequate = False
-            self.learner_adequacy_err_msg = "Numeric class variable expected."
-        return is_adequate
+            err_msg = "Numeric class variable expected."
+        return err_msg
 
 
 class ModelRegression(Model):

Orange/widgets/utils/owlearnerwidget.py

@@ -246,8 +246,9 @@ def check_data(self):
         self.Error.sparse_not_supported.clear()
         if self.data is not None and self.learner is not None:
             self.Error.data_error.clear()
-            if not self.learner.check_learner_adequacy(self.data.domain):
-                self.Error.data_error(self.learner.learner_adequacy_err_msg)
+            incompatibility_reason = self.learner.incompatibility_reason(self.data.domain)
+            if incompatibility_reason is not None:
+                self.Error.data_error(incompatibility_reason)
             elif not len(self.data):
                 self.Error.data_error("Dataset is empty.")
             elif len(ut.unique(self.data.Y)) < 2: