tests for the classifier and fixes for edge cases with minimal data.

src/documents/classifier.py

@@ -6,7 +6,8 @@ import re
 
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.neural_network import MLPClassifier
-from sklearn.preprocessing import MultiLabelBinarizer
+from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
+from sklearn.utils.multiclass import type_of_target
 
 from documents.models import Document, MatchingModel
 from paperless import settings
@@ -27,7 +28,7 @@ def preprocess_content(content):
 
 class DocumentClassifier(object):
 
-    FORMAT_VERSION = 5
+    FORMAT_VERSION = 6
 
     def __init__(self):
         # mtime of the model file on disk. used to prevent reloading when
@@ -54,6 +55,8 @@ class DocumentClassifier(object):
                         "Cannor load classifier, incompatible versions.")
                 else:
                     if self.classifier_version > 0:
+                        # Don't be confused by this check. It's simply here
+                        # so that we wont log anything on initial reload.
                         logger.info("Classifier updated on disk, "
                                     "reloading classifier models")
                     self.data_hash = pickle.load(f)
@@ -122,9 +125,14 @@ class DocumentClassifier(object):
         labels_tags_unique = set([tag for tags in labels_tags for tag in tags])
 
         num_tags = len(labels_tags_unique)
+
         # substract 1 since -1 (null) is also part of the classes.
-        num_correspondents = len(set(labels_correspondent)) - 1
+
-        num_document_types = len(set(labels_document_type)) - 1
+        # union with {-1} accounts for cases where all documents have
+        # correspondents and types assigned, so -1 isnt part of labels_x, which
+        # it usually is.
+        num_correspondents = len(set(labels_correspondent) | {-1}) - 1
+        num_document_types = len(set(labels_document_type) | {-1}) - 1
 
         logging.getLogger(__name__).debug(
             "{} documents, {} tag(s), {} correspondent(s), "
@@ -145,12 +153,23 @@ class DocumentClassifier(object):
         )
         data_vectorized = self.data_vectorizer.fit_transform(data)
 
-        self.tags_binarizer = MultiLabelBinarizer()
-        labels_tags_vectorized = self.tags_binarizer.fit_transform(labels_tags)
-
         # Step 3: train the classifiers
         if num_tags > 0:
             logging.getLogger(__name__).debug("Training tags classifier...")
+
+            if num_tags == 1:
+                # Special case where only one tag has auto:
+                # Fallback to binary classification.
+                labels_tags = [label[0] if len(label) == 1 else -1
+                               for label in labels_tags]
+                self.tags_binarizer = LabelBinarizer()
+                labels_tags_vectorized = self.tags_binarizer.fit_transform(
+                    labels_tags).ravel()
+            else:
+                self.tags_binarizer = MultiLabelBinarizer()
+                labels_tags_vectorized = self.tags_binarizer.fit_transform(
+                    labels_tags)
+
             self.tags_classifier = MLPClassifier(tol=0.01)
             self.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
         else:
@@ -222,6 +241,16 @@ class DocumentClassifier(object):
             X = self.data_vectorizer.transform([preprocess_content(content)])
             y = self.tags_classifier.predict(X)
             tags_ids = self.tags_binarizer.inverse_transform(y)[0]
-            return tags_ids
+            if type_of_target(y).startswith('multilabel'):
+                # the usual case when there are multiple tags.
+                return list(tags_ids)
+            elif type_of_target(y) == 'binary' and tags_ids != -1:
+                # This is for when we have binary classification with only one
+                # tag and the result is to assign this tag.
+                return [tags_ids]
+            else:
+                # Usually binary as well with -1 as the result, but we're
+                # going to catch everything else here as well.
+                return []
         else:
             return []

src/documents/tests/test_classifier.py

@@ -1,8 +1,10 @@
 import tempfile
+from time import sleep
+from unittest import mock
 
 from django.test import TestCase, override_settings
 
-from documents.classifier import DocumentClassifier
+from documents.classifier import DocumentClassifier, IncompatibleClassifierVersionError
 from documents.models import Correspondent, Document, Tag, DocumentType
 
 
@@ -15,10 +17,12 @@ class TestClassifier(TestCase):
     def generate_test_data(self):
         self.c1 = Correspondent.objects.create(name="c1", matching_algorithm=Correspondent.MATCH_AUTO)
         self.c2 = Correspondent.objects.create(name="c2")
+        self.c3 = Correspondent.objects.create(name="c3", matching_algorithm=Correspondent.MATCH_AUTO)
         self.t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
         self.t2 = Tag.objects.create(name="t2", matching_algorithm=Tag.MATCH_ANY, pk=34, is_inbox_tag=True)
         self.t3 = Tag.objects.create(name="t3", matching_algorithm=Tag.MATCH_AUTO, pk=45)
         self.dt = DocumentType.objects.create(name="dt", matching_algorithm=DocumentType.MATCH_AUTO)
+        self.dt2 = DocumentType.objects.create(name="dt2", matching_algorithm=DocumentType.MATCH_AUTO)
 
         self.doc1 = Document.objects.create(title="doc1", content="this is a document from c1", correspondent=self.c1, checksum="A", document_type=self.dt)
         self.doc2 = Document.objects.create(title="doc1", content="this is another document, but from c2", correspondent=self.c2, checksum="B")
@@ -59,8 +63,8 @@ class TestClassifier(TestCase):
         self.classifier.train()
         self.assertEqual(self.classifier.predict_correspondent(self.doc1.content), self.c1.pk)
         self.assertEqual(self.classifier.predict_correspondent(self.doc2.content), None)
-        self.assertTupleEqual(self.classifier.predict_tags(self.doc1.content), (self.t1.pk,))
+        self.assertListEqual(self.classifier.predict_tags(self.doc1.content), [self.t1.pk])
-        self.assertTupleEqual(self.classifier.predict_tags(self.doc2.content), (self.t1.pk, self.t3.pk))
+        self.assertListEqual(self.classifier.predict_tags(self.doc2.content), [self.t1.pk, self.t3.pk])
         self.assertEqual(self.classifier.predict_document_type(self.doc1.content), self.dt.pk)
         self.assertEqual(self.classifier.predict_document_type(self.doc2.content), None)
 
@@ -71,6 +75,42 @@ class TestClassifier(TestCase):
         self.assertTrue(self.classifier.train())
         self.assertFalse(self.classifier.train())
 
+    def testVersionIncreased(self):
+
+        self.generate_test_data()
+        self.assertTrue(self.classifier.train())
+        self.assertFalse(self.classifier.train())
+
+        classifier2 = DocumentClassifier()
+
+        current_ver = DocumentClassifier.FORMAT_VERSION
+        with mock.patch("documents.classifier.DocumentClassifier.FORMAT_VERSION", current_ver+1):
+            # assure that we won't load old classifiers.
+            self.assertRaises(IncompatibleClassifierVersionError, self.classifier.reload)
+
+            self.classifier.save_classifier()
+
+            # assure that we can load the classifier after saving it.
+            classifier2.reload()
+
+    def testReload(self):
+
+        self.generate_test_data()
+        self.assertTrue(self.classifier.train())
+        self.classifier.save_classifier()
+
+        classifier2 = DocumentClassifier()
+        classifier2.reload()
+        v1 = classifier2.classifier_version
+
+        # change the classifier after some time.
+        sleep(1)
+        self.classifier.save_classifier()
+
+        classifier2.reload()
+        v2 = classifier2.classifier_version
+        self.assertNotEqual(v1, v2)
+
     @override_settings(DATA_DIR=tempfile.mkdtemp())
     def testSaveClassifier(self):
 
@@ -83,3 +123,112 @@ class TestClassifier(TestCase):
         new_classifier = DocumentClassifier()
         new_classifier.reload()
         self.assertFalse(new_classifier.train())
+
+    def test_one_correspondent_predict(self):
+        c1 = Correspondent.objects.create(name="c1", matching_algorithm=Correspondent.MATCH_AUTO)
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1", correspondent=c1, checksum="A")
+
+        self.classifier.train()
+        self.assertEqual(self.classifier.predict_correspondent(doc1.content), c1.pk)
+
+    def test_one_correspondent_predict_manydocs(self):
+        c1 = Correspondent.objects.create(name="c1", matching_algorithm=Correspondent.MATCH_AUTO)
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1", correspondent=c1, checksum="A")
+        doc2 = Document.objects.create(title="doc2", content="this is a document from noone", checksum="B")
+
+        self.classifier.train()
+        self.assertEqual(self.classifier.predict_correspondent(doc1.content), c1.pk)
+        self.assertIsNone(self.classifier.predict_correspondent(doc2.content))
+
+    def test_one_type_predict(self):
+        dt = DocumentType.objects.create(name="dt", matching_algorithm=DocumentType.MATCH_AUTO)
+
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1",
+                                            checksum="A", document_type=dt)
+
+        self.classifier.train()
+        self.assertEqual(self.classifier.predict_document_type(doc1.content), dt.pk)
+
+    def test_one_type_predict_manydocs(self):
+        dt = DocumentType.objects.create(name="dt", matching_algorithm=DocumentType.MATCH_AUTO)
+
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1",
+                                            checksum="A", document_type=dt)
+
+        doc2 = Document.objects.create(title="doc1", content="this is a document from c2",
+                                            checksum="B")
+
+        self.classifier.train()
+        self.assertEqual(self.classifier.predict_document_type(doc1.content), dt.pk)
+        self.assertIsNone(self.classifier.predict_document_type(doc2.content))
+
+    def test_one_tag_predict(self):
+        t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
+
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
+
+        doc1.tags.add(t1)
+        self.classifier.train()
+        self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
+
+    def test_one_tag_predict_unassigned(self):
+        t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
+
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
+
+        self.classifier.train()
+        self.assertListEqual(self.classifier.predict_tags(doc1.content), [])
+
+    def test_two_tags_predict_singledoc(self):
+        t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
+        t2 = Tag.objects.create(name="t2", matching_algorithm=Tag.MATCH_AUTO, pk=121)
+
+        doc4 = Document.objects.create(title="doc1", content="this is a document from c4", checksum="D")
+
+        doc4.tags.add(t1)
+        doc4.tags.add(t2)
+        self.classifier.train()
+        self.assertListEqual(self.classifier.predict_tags(doc4.content), [t1.pk, t2.pk])
+
+    def test_two_tags_predict(self):
+        t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
+        t2 = Tag.objects.create(name="t2", matching_algorithm=Tag.MATCH_AUTO, pk=121)
+
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
+        doc2 = Document.objects.create(title="doc1", content="this is a document from c2", checksum="B")
+        doc3 = Document.objects.create(title="doc1", content="this is a document from c3", checksum="C")
+        doc4 = Document.objects.create(title="doc1", content="this is a document from c4", checksum="D")
+
+        doc1.tags.add(t1)
+        doc2.tags.add(t2)
+
+        doc4.tags.add(t1)
+        doc4.tags.add(t2)
+        self.classifier.train()
+        self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
+        self.assertListEqual(self.classifier.predict_tags(doc2.content), [t2.pk])
+        self.assertListEqual(self.classifier.predict_tags(doc3.content), [])
+        self.assertListEqual(self.classifier.predict_tags(doc4.content), [t1.pk, t2.pk])
+
+    def test_one_tag_predict_multi(self):
+        t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
+
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
+        doc2 = Document.objects.create(title="doc2", content="this is a document from c2", checksum="B")
+
+        doc1.tags.add(t1)
+        doc2.tags.add(t1)
+        self.classifier.train()
+        self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
+        self.assertListEqual(self.classifier.predict_tags(doc2.content), [t1.pk])
+
+    def test_one_tag_predict_multi_2(self):
+        t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
+
+        doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
+        doc2 = Document.objects.create(title="doc2", content="this is a document from c2", checksum="B")
+
+        doc1.tags.add(t1)
+        self.classifier.train()
+        self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
+        self.assertListEqual(self.classifier.predict_tags(doc2.content), [])