Revert "Merge branch 'main' of https://github.com/NatLibFi/ekirjasto-…

…circulation"

This reverts commit 28178a0, reversing
changes made to 3e09192.

core/classifier/__init__.py

@@ -1,8 +1,3 @@
-"""
-A classifier module that classifies books and subjects into various categories. This module is called when importing
-collections to a library. It's called by the core/model/classification.py.
-"""
-
 # If the genre classification does not match the fiction classification, throw
 # away the genre classifications.
 #
@@ -42,15 +37,13 @@ class ClassifierConstants:
     BISAC = "BISAC"
     BIC = "BIC"
     TAG = "tag"  # Folksonomic tags.
-    DEMARQUE = "De Marque"
 
     # Appeal controlled vocabulary developed by NYPL
     NYPL_APPEAL = "NYPL Appeal"
 
     GRADE_LEVEL = "Grade level"  # "1-2", "Grade 4", "Kindergarten", etc.
     AGE_RANGE = "schema:typicalAgeRange"  # "0-2", etc.
     AXIS_360_AUDIENCE = "Axis 360 Audience"
-    DEMARQUE_AUDIENCE = "schema:Audience"
 
     # We know this says something about the audience but we're not sure what.
     # Could be any of the values from GRADE_LEVEL or AGE_RANGE, plus
@@ -1111,6 +1104,7 @@ def add(self, classification):
         self.seen_classifications.add(key)
         if self.debug:
             self.classifications.append(classification)
+
         # Make sure the Subject is ready to be used in calculations.
         if not classification.subject.checked:  # or self.debug
             classification.subject.assign_to_genre()
@@ -1229,32 +1223,6 @@ def add(self, classification):
                     # "Juvenile Fiction".
                     self.overdrive_juvenile_generic = classification
 
-            # E-kirjasto: Since De Marque classifications have target ages for children's and YA books, we want to weigh
-            # them more heavily by setting their weights to 1.0. This ensures that those books are classified accordingly.
-            if subject.type == "De Marque" and (
-                subject.audience == Classifier.AUDIENCE_CHILDREN
-                or subject.audience == Classifier.AUDIENCE_YOUNG_ADULT
-            ):
-                if subject.target_age:
-                    # Set the weight to 1.0 for any target age.
-                    self.audience_weights = Counter()
-                    self.audience_weights[subject.audience] += weight * 1.0
-                    scaled_weight = classification.weight_as_indicator_of_target_age
-                    target_min = subject.target_age.lower
-                    target_max = subject.target_age.upper
-                    if target_min is not None:
-                        self.target_age_lower_weights[target_min] = 1.0
-                    if target_max is not None:
-                        self.target_age_upper_weights[target_max] = 1.0
-            # E-kirjasto: Some De Marque adult books were incorrectly classified as children's books. Let's set the
-            # weight to 1.0 for any adult audience books.
-            if (
-                subject.type == "De Marque"
-                and subject.audience == Classifier.AUDIENCE_ADULT
-            ):
-                self.audience_weights = Counter()
-                self.audience_weights[subject.audience] += weight * 1.0
-
     def weigh_metadata(self):
         """Modify the weights according to the given Work's metadata.
 
@@ -1529,10 +1497,12 @@ def target_age(self, audience):
         if target_age_min is None:
             target_age_min = target_age_max
 
-        # Err on the side of setting the minimum age too high but first ensure we have values to compare.
-        if target_age_min and target_age_max and target_age_min > target_age_max:
+        if target_age_max is None:
             target_age_max = target_age_min
 
+        # Err on the side of setting the minimum age too high.
+        if target_age_min > target_age_max:
+            target_age_max = target_age_min
         return Classifier.range_tuple(target_age_min, target_age_max)
 
     def genres(self, fiction, cutoff=0.15):
@@ -1654,7 +1624,6 @@ def consolidate_genre_weights(cls, weights, subgenre_swallows_parent_at=0.03):
 from core.classifier.bic import BICClassifier
 from core.classifier.bisac import BISACClassifier
 from core.classifier.ddc import DeweyDecimalClassifier
-from core.classifier.demarque import DeMarqueClassifier
 from core.classifier.gutenberg import GutenbergBookshelfClassifier
 from core.classifier.keyword import (
     Eg,

core/model/classification.py

@@ -59,7 +59,6 @@ class Subject(Base):
     TAG: str = Classifier.TAG  # Folksonomic tags.
     FREEFORM_AUDIENCE: str = Classifier.FREEFORM_AUDIENCE
     NYPL_APPEAL = Classifier.NYPL_APPEAL
-    DEMARQUE = Classifier.DEMARQUE
 
     # Types with terms that are suitable for search.
     TYPES_FOR_SEARCH = [FAST, OVERDRIVE, BISAC, TAG]
@@ -93,7 +92,6 @@ class Subject(Base):
         "http://www.bisg.org/standards/bisac_subject/": BISAC,
         # Feedbooks uses a modified BISAC which we know how to handle.
         "http://www.feedbooks.com/categories": BISAC,
-        "http://schema.org/Audience": DEMARQUE,
     }
 
     uri_lookup = dict()