removed matching model fields, automatic classifier reloading, added autmatic_classification field to matching model

src/documents/admin.py

@@ -102,9 +102,8 @@ class CommonAdmin(admin.ModelAdmin):
 
 class CorrespondentAdmin(CommonAdmin):
 
-    list_display = ("name", "match", "matching_algorithm", "document_count", "last_correspondence")
+    list_display = ("name", "automatic_classification", "document_count", "last_correspondence")
-    list_filter = ("matching_algorithm",)
+    list_editable = ("automatic_classification",)
-    list_editable = ("match", "matching_algorithm")
 
     def get_queryset(self, request):
         qs = super(CorrespondentAdmin, self).get_queryset(request)
@@ -122,10 +121,9 @@ class CorrespondentAdmin(CommonAdmin):
 
 class TagAdmin(CommonAdmin):
 
-    list_display = ("name", "colour", "match", "matching_algorithm",
+    list_display = ("name", "colour", "automatic_classification", "document_count")
-                    "document_count")
+    list_filter = ("colour",)
-    list_filter = ("colour", "matching_algorithm")
+    list_editable = ("colour", "automatic_classification")
-    list_editable = ("colour", "match", "matching_algorithm")
 
     def get_queryset(self, request):
         qs = super(TagAdmin, self).get_queryset(request)
@@ -139,9 +137,8 @@ class TagAdmin(CommonAdmin):
 
 class DocumentTypeAdmin(CommonAdmin):
 
-    list_display = ("name", "match", "matching_algorithm", "document_count")
+    list_display = ("name", "automatic_classification", "document_count")
-    list_filter = ("matching_algorithm",)
+    list_editable = ("automatic_classification",)
-    list_editable = ("match", "matching_algorithm")
 
     def get_queryset(self, request):
         qs = super(DocumentTypeAdmin, self).get_queryset(request)

src/documents/classifier.py

@@ -1,3 +1,4 @@
+import os
 import pickle
 
 from documents.models import Correspondent, DocumentType, Tag
@@ -16,6 +17,18 @@ def preprocess_content(content):
 
 class DocumentClassifier(object):
 
+    classifier_version = None
+
+    data_vectorizer = None
+
+    tags_binarizer = None
+    correspondent_binarizer = None
+    type_binarizer = None
+
+    tags_classifier = None
+    correspondent_classifier = None
+    type_classifier = None
+
     @staticmethod
     def load_classifier():
         clf = DocumentClassifier()
@@ -23,6 +36,8 @@ class DocumentClassifier(object):
         return clf
 
     def reload(self):
+        if self.classifier_version is None or os.path.getmtime(settings.MODEL_FILE) > self.classifier_version:
+            print("reloading classifier")
             with open(settings.MODEL_FILE, "rb") as f:
                 self.data_vectorizer = pickle.load(f)
                 self.tags_binarizer = pickle.load(f)
@@ -32,6 +47,7 @@ class DocumentClassifier(object):
                 self.tags_classifier = pickle.load(f)
                 self.correspondent_classifier = pickle.load(f)
                 self.type_classifier = pickle.load(f)
+            self.classifier_version = os.path.getmtime(settings.MODEL_FILE)
 
     def save_classifier(self):
         with open(settings.MODEL_FILE, "wb") as f:

src/documents/consumer.py

@@ -221,12 +221,6 @@ class Consumer:
                 storage_type=self.storage_type
             )
 
-        relevant_tags = set(list(Tag.match_all(text)) + list(file_info.tags))
-        if relevant_tags:
-            tag_names = ", ".join([t.slug for t in relevant_tags])
-            self.log("debug", "Tagging with {}".format(tag_names))
-            document.tags.add(*relevant_tags)
-
         self._write(document, doc, document.source_path)
         self._write(document, thumbnail, document.thumbnail_path)
 

src/documents/management/commands/document_create_classifier.py

@@ -42,9 +42,14 @@ class Command(Renderable, BaseCommand):
 
         # Step 2: vectorize data
         logging.getLogger(__name__).info("Vectorizing data...")
-        clf.data_vectorizer = CountVectorizer(analyzer='char', ngram_range=(1, 5), min_df=0.05)
+        clf.data_vectorizer = CountVectorizer(analyzer='char', ngram_range=(2, 6), min_df=0.1)
         data_vectorized = clf.data_vectorizer.fit_transform(data)
 
+        print(clf.data_vectorizer.vocabulary_)
+
+        logging.getLogger(__name__).info("Shape of vectorized data: {}".format(data_vectorized.shape))
+
+
         clf.tags_binarizer = MultiLabelBinarizer()
         labels_tags_vectorized = clf.tags_binarizer.fit_transform(labels_tags)
 

src/documents/management/commands/document_retagger.py

@@ -46,7 +46,11 @@ class Command(Renderable, BaseCommand):
             documents = Document.objects.all().exclude(tags__is_archived_tag=True).distinct()
 
         logging.getLogger(__name__).info("Loading classifier")
+        try:
             clf = DocumentClassifier.load_classifier()
+        except FileNotFoundError:
+            logging.getLogger(__name__).fatal("Cannot classify documents, classifier model file was not found.")
+            return
 
 
         for document in documents:

src/documents/migrations/0024_auto_20180904_1425.py

@@ -0,0 +1,77 @@
+# Generated by Django 2.0.8 on 2018-09-04 14:25
+
+from django.db import migrations, models
+
+
+def transfer_automatic_classification(apps, schema_editor):
+    for model_name in ["Tag", "Correspondent", "DocumentType"]:
+        model_class = apps.get_model("documents", model_name)
+        for o in model_class.objects.all():
+            o.automatic_classification = o.match is not None and len(o.match) > 0
+            o.save()
+
+
+def reverse_automatic_classification(apps, schema_editor):
+    pass
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('documents', '0023_auto_20180823_1155'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='correspondent',
+            name='automatic_classification',
+            field=models.BooleanField(default=False, help_text='Automatically assign to newly added documents based on current usage in your document collection.'),
+        ),
+        migrations.AddField(
+            model_name='documenttype',
+            name='automatic_classification',
+            field=models.BooleanField(default=False, help_text='Automatically assign to newly added documents based on current usage in your document collection.'),
+        ),
+        migrations.AddField(
+            model_name='tag',
+            name='automatic_classification',
+            field=models.BooleanField(default=False, help_text='Automatically assign to newly added documents based on current usage in your document collection.'),
+        ),
+        migrations.RunPython(transfer_automatic_classification, reverse_automatic_classification),
+        migrations.RemoveField(
+            model_name='correspondent',
+            name='is_insensitive',
+        ),
+        migrations.RemoveField(
+            model_name='correspondent',
+            name='match',
+        ),
+        migrations.RemoveField(
+            model_name='correspondent',
+            name='matching_algorithm',
+        ),
+        migrations.RemoveField(
+            model_name='documenttype',
+            name='is_insensitive',
+        ),
+        migrations.RemoveField(
+            model_name='documenttype',
+            name='match',
+        ),
+        migrations.RemoveField(
+            model_name='documenttype',
+            name='matching_algorithm',
+        ),
+        migrations.RemoveField(
+            model_name='tag',
+            name='is_insensitive',
+        ),
+        migrations.RemoveField(
+            model_name='tag',
+            name='match',
+        ),
+        migrations.RemoveField(
+            model_name='tag',
+            name='matching_algorithm',
+        ),
+    ]

src/documents/models.py

@@ -15,48 +15,15 @@ from django.db import models
 from django.template.defaultfilters import slugify
 from django.utils import timezone
 
-from reminders.models import Reminder
 from .managers import LogManager
 
 
 class MatchingModel(models.Model):
 
-    MATCH_ANY = 1
-    MATCH_ALL = 2
-    MATCH_LITERAL = 3
-    MATCH_REGEX = 4
-    MATCH_FUZZY = 5
-    MATCHING_ALGORITHMS = (
-        (MATCH_ANY, "Any"),
-        (MATCH_ALL, "All"),
-        (MATCH_LITERAL, "Literal"),
-        (MATCH_REGEX, "Regular Expression"),
-        (MATCH_FUZZY, "Fuzzy Match"),
-    )
-
     name = models.CharField(max_length=128, unique=True)
     slug = models.SlugField(blank=True)
 
-    match = models.CharField(max_length=256, blank=True)
+    automatic_classification = models.BooleanField(default=False, help_text='Automatically assign to newly added documents based on current usage in your document collection.')
-    matching_algorithm = models.PositiveIntegerField(
-        choices=MATCHING_ALGORITHMS,
-        default=MATCH_ANY,
-        help_text=(
-            "Which algorithm you want to use when matching text to the OCR'd "
-            "PDF.  Here, \"any\" looks for any occurrence of any word "
-            "provided in the PDF, while \"all\" requires that every word "
-            "provided appear in the PDF, albeit not in the order provided.  A "
-            "\"literal\" match means that the text you enter must appear in "
-            "the PDF exactly as you've entered it, and \"regular expression\" "
-            "uses a regex to match the PDF.  (If you don't know what a regex "
-            "is, you probably don't want this option.)  Finally, a \"fuzzy "
-            "match\" looks for words or phrases that are mostly—but not "
-            "exactly—the same, which can be useful for matching against "
-            "documents containg imperfections that foil accurate OCR."
-        )
-    )
-
-    is_insensitive = models.BooleanField(default=True)
 
     class Meta:
         abstract = True
@@ -64,87 +31,8 @@ class MatchingModel(models.Model):
     def __str__(self):
         return self.name
 
-    @property
-    def conditions(self):
-        return "{}: \"{}\" ({})".format(
-            self.name, self.match, self.get_matching_algorithm_display())
-
-    @classmethod
-    def match_all(cls, text, tags=None):
-
-        if tags is None:
-            tags = cls.objects.all()
-
-        text = text.lower()
-        for tag in tags:
-            if tag.matches(text):
-                yield tag
-
-    def matches(self, text):
-
-        search_kwargs = {}
-
-        # Check that match is not empty
-        if self.match.strip() == "":
-            return False
-
-        if self.is_insensitive:
-            search_kwargs = {"flags": re.IGNORECASE}
-
-        if self.matching_algorithm == self.MATCH_ALL:
-            for word in self._split_match():
-                search_result = re.search(
-                    r"\b{}\b".format(word), text, **search_kwargs)
-                if not search_result:
-                    return False
-            return True
-
-        if self.matching_algorithm == self.MATCH_ANY:
-            for word in self._split_match():
-                if re.search(r"\b{}\b".format(word), text, **search_kwargs):
-                    return True
-            return False
-
-        if self.matching_algorithm == self.MATCH_LITERAL:
-            return bool(re.search(
-                r"\b{}\b".format(self.match), text, **search_kwargs))
-
-        if self.matching_algorithm == self.MATCH_REGEX:
-            return bool(re.search(
-                re.compile(self.match, **search_kwargs), text))
-
-        if self.matching_algorithm == self.MATCH_FUZZY:
-            match = re.sub(r'[^\w\s]', '', self.match)
-            text = re.sub(r'[^\w\s]', '', text)
-            if self.is_insensitive:
-                match = match.lower()
-                text = text.lower()
-
-            return True if fuzz.partial_ratio(match, text) >= 90 else False
-
-        raise NotImplementedError("Unsupported matching algorithm")
-
-    def _split_match(self):
-        """
-        Splits the match to individual keywords, getting rid of unnecessary
-        spaces and grouping quoted words together.
-
-        Example:
-          '  some random  words "with   quotes  " and   spaces'
-            ==>
-          ["some", "random", "words", "with\s+quotes", "and", "spaces"]
-        """
-        findterms = re.compile(r'"([^"]+)"|(\S+)').findall
-        normspace = re.compile(r"\s+").sub
-        return [
-            normspace(" ", (t[0] or t[1]).strip()).replace(" ", r"\s+")
-            for t in findterms(self.match)
-        ]
-
     def save(self, *args, **kwargs):
 
-        self.match = self.match.lower()
-
         if not self.slug:
             self.slug = slugify(self.name)
 

src/documents/signals/handlers.py

@@ -16,15 +16,17 @@ def logger(message, group):
     logging.getLogger(__name__).debug(message, extra={"group": group})
 
 
-classifier = None
+classifier = DocumentClassifier()
 
 
 def classify_document(sender, document=None, logging_group=None, **kwargs):
     global classifier
-    if classifier is None:
+    try:
-        classifier = DocumentClassifier.load_classifier()
+        classifier.reload()
-
         classifier.classify_document(document, classify_correspondent=True, classify_tags=True, classify_type=True)
+    except FileNotFoundError:
+        logging.getLogger(__name__).fatal("Cannot classify document, classifier model file was not found.")
+