mirror of
https://github.com/paperless-ngx/paperless-ngx.git
synced 2025-05-03 11:29:28 -05:00
664 lines
21 KiB
Python
664 lines
21 KiB
Python
import os
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import re
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from pathlib import Path
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from unittest import mock
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import pytest
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from django.conf import settings
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from django.test import TestCase
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from django.test import override_settings
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from documents.classifier import ClassifierModelCorruptError
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from documents.classifier import DocumentClassifier
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from documents.classifier import IncompatibleClassifierVersionError
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from documents.classifier import load_classifier
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from documents.models import Correspondent
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from documents.models import Document
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from documents.models import DocumentType
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from documents.models import MatchingModel
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from documents.models import StoragePath
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from documents.models import Tag
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from documents.tests.utils import DirectoriesMixin
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def dummy_preprocess(content: str):
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"""
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Simpler, faster pre-processing for testing purposes
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"""
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content = content.lower().strip()
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content = re.sub(r"\s+", " ", content)
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return content
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class TestClassifier(DirectoriesMixin, TestCase):
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def setUp(self):
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super().setUp()
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self.classifier = DocumentClassifier()
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self.classifier.preprocess_content = mock.MagicMock(
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side_effect=dummy_preprocess,
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)
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def generate_test_data(self):
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self.c1 = Correspondent.objects.create(
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name="c1",
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matching_algorithm=Correspondent.MATCH_AUTO,
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)
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self.c2 = Correspondent.objects.create(name="c2")
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self.c3 = Correspondent.objects.create(
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name="c3",
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matching_algorithm=Correspondent.MATCH_AUTO,
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)
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self.t1 = Tag.objects.create(
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name="t1",
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matching_algorithm=Tag.MATCH_AUTO,
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pk=12,
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)
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self.t2 = Tag.objects.create(
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name="t2",
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matching_algorithm=Tag.MATCH_ANY,
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pk=34,
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is_inbox_tag=True,
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)
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self.t3 = Tag.objects.create(
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name="t3",
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matching_algorithm=Tag.MATCH_AUTO,
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pk=45,
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)
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self.t4 = Tag.objects.create(
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name="t4",
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matching_algorithm=Tag.MATCH_ANY,
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pk=46,
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)
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self.dt = DocumentType.objects.create(
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name="dt",
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matching_algorithm=DocumentType.MATCH_AUTO,
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)
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self.dt2 = DocumentType.objects.create(
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name="dt2",
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matching_algorithm=DocumentType.MATCH_AUTO,
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)
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self.sp1 = StoragePath.objects.create(
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name="sp1",
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path="path1",
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matching_algorithm=DocumentType.MATCH_AUTO,
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)
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self.sp2 = StoragePath.objects.create(
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name="sp2",
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path="path2",
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matching_algorithm=DocumentType.MATCH_AUTO,
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)
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self.store_paths = [self.sp1, self.sp2]
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self.doc1 = Document.objects.create(
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title="doc1",
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content="this is a document from c1",
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correspondent=self.c1,
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checksum="A",
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document_type=self.dt,
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storage_path=self.sp1,
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)
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self.doc2 = Document.objects.create(
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title="doc1",
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content="this is another document, but from c2",
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correspondent=self.c2,
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checksum="B",
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)
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self.doc_inbox = Document.objects.create(
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title="doc235",
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content="aa",
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checksum="C",
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)
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self.doc1.tags.add(self.t1)
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self.doc2.tags.add(self.t1)
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self.doc2.tags.add(self.t3)
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self.doc_inbox.tags.add(self.t2)
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def generate_train_and_save(self):
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"""
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Generates the training data, trains and saves the updated pickle
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file. This ensures the test is using the same scikit learn version
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and eliminates a warning from the test suite
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"""
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self.generate_test_data()
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self.classifier.train()
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self.classifier.save()
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def test_no_training_data(self):
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"""
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GIVEN:
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- No documents exist to train
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WHEN:
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- Classifier training is requested
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THEN:
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- Exception is raised
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"""
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with self.assertRaisesMessage(ValueError, "No training data available."):
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self.classifier.train()
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def test_no_non_inbox_tags(self):
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"""
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GIVEN:
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- No documents without an inbox tag exist
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WHEN:
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- Classifier training is requested
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THEN:
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- Exception is raised
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"""
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t1 = Tag.objects.create(
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name="t1",
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matching_algorithm=Tag.MATCH_ANY,
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pk=34,
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is_inbox_tag=True,
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)
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doc1 = Document.objects.create(
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title="doc1",
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content="this is a document from c1",
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checksum="A",
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)
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doc1.tags.add(t1)
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with self.assertRaisesMessage(ValueError, "No training data available."):
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self.classifier.train()
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def testEmpty(self):
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"""
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GIVEN:
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- A document exists
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- No tags/not enough data to predict
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WHEN:
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- Classifier prediction is requested
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THEN:
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- Classifier returns no predictions
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"""
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Document.objects.create(title="WOW", checksum="3457", content="ASD")
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self.classifier.train()
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self.assertIsNone(self.classifier.document_type_classifier)
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self.assertIsNone(self.classifier.tags_classifier)
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self.assertIsNone(self.classifier.correspondent_classifier)
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self.assertListEqual(self.classifier.predict_tags(""), [])
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self.assertIsNone(self.classifier.predict_document_type(""))
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self.assertIsNone(self.classifier.predict_correspondent(""))
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def testTrain(self):
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"""
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GIVEN:
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- Test data
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WHEN:
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- Classifier is trained
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THEN:
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- Classifier uses correct values for correspondent learning
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- Classifier uses correct values for tags learning
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"""
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self.generate_test_data()
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self.classifier.train()
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self.assertListEqual(
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list(self.classifier.correspondent_classifier.classes_),
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[-1, self.c1.pk],
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)
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self.assertListEqual(
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list(self.classifier.tags_binarizer.classes_),
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[self.t1.pk, self.t3.pk],
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)
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def testPredict(self):
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"""
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GIVEN:
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- Classifier trained against test data
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WHEN:
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- Prediction requested for correspondent, tags, type
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THEN:
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- Expected predictions based on training set
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"""
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self.generate_test_data()
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self.classifier.train()
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self.assertEqual(
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self.classifier.predict_correspondent(self.doc1.content),
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self.c1.pk,
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)
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self.assertEqual(self.classifier.predict_correspondent(self.doc2.content), None)
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self.assertListEqual(
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self.classifier.predict_tags(self.doc1.content),
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[self.t1.pk],
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)
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self.assertListEqual(
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self.classifier.predict_tags(self.doc2.content),
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[self.t1.pk, self.t3.pk],
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)
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self.assertEqual(
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self.classifier.predict_document_type(self.doc1.content),
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self.dt.pk,
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)
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self.assertEqual(self.classifier.predict_document_type(self.doc2.content), None)
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def test_no_retrain_if_no_change(self):
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"""
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GIVEN:
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- Classifier trained with current data
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WHEN:
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- Classifier training is requested again
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THEN:
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- Classifier does not redo training
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"""
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self.generate_test_data()
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self.assertTrue(self.classifier.train())
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self.assertFalse(self.classifier.train())
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def test_retrain_if_change(self):
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"""
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GIVEN:
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- Classifier trained with current data
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WHEN:
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- Classifier training is requested again
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- Documents have changed
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THEN:
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- Classifier does not redo training
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"""
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self.generate_test_data()
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self.assertTrue(self.classifier.train())
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self.doc1.correspondent = self.c2
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self.doc1.save()
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self.assertTrue(self.classifier.train())
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def test_retrain_if_auto_match_set_changed(self):
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"""
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GIVEN:
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- Classifier trained with current data
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WHEN:
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- Classifier training is requested again
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- Some new AUTO match object exists
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THEN:
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- Classifier does redo training
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"""
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self.generate_test_data()
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# Add the ANY type
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self.doc1.tags.add(self.t4)
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self.assertTrue(self.classifier.train())
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# Change the matching type
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self.t4.matching_algorithm = MatchingModel.MATCH_AUTO
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self.t4.save()
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self.assertTrue(self.classifier.train())
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def testVersionIncreased(self):
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"""
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GIVEN:
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- Existing classifier model saved at a version
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WHEN:
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- Attempt to load classifier file from newer version
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THEN:
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- Exception is raised
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"""
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self.generate_train_and_save()
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classifier2 = DocumentClassifier()
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current_ver = DocumentClassifier.FORMAT_VERSION
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with mock.patch(
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"documents.classifier.DocumentClassifier.FORMAT_VERSION",
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current_ver + 1,
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):
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# assure that we won't load old classifiers.
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self.assertRaises(IncompatibleClassifierVersionError, classifier2.load)
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self.classifier.save()
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# assure that we can load the classifier after saving it.
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classifier2.load()
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def testSaveClassifier(self):
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self.generate_train_and_save()
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new_classifier = DocumentClassifier()
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new_classifier.load()
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new_classifier.preprocess_content = mock.MagicMock(side_effect=dummy_preprocess)
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self.assertFalse(new_classifier.train())
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def test_load_and_classify(self):
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self.generate_train_and_save()
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new_classifier = DocumentClassifier()
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new_classifier.load()
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new_classifier.preprocess_content = mock.MagicMock(side_effect=dummy_preprocess)
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self.assertCountEqual(new_classifier.predict_tags(self.doc2.content), [45, 12])
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@mock.patch("documents.classifier.pickle.load")
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def test_load_corrupt_file(self, patched_pickle_load: mock.MagicMock):
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"""
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GIVEN:
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- Corrupted classifier pickle file
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WHEN:
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- An attempt is made to load the classifier
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THEN:
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- The ClassifierModelCorruptError is raised
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"""
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self.generate_train_and_save()
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# First load is the schema version,allow it
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patched_pickle_load.side_effect = [DocumentClassifier.FORMAT_VERSION, OSError()]
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with self.assertRaises(ClassifierModelCorruptError):
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self.classifier.load()
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patched_pickle_load.assert_called()
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patched_pickle_load.reset_mock()
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patched_pickle_load.side_effect = [
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DocumentClassifier.FORMAT_VERSION,
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ClassifierModelCorruptError(),
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]
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self.assertIsNone(load_classifier())
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patched_pickle_load.assert_called()
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def test_load_new_scikit_learn_version(self):
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"""
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GIVEN:
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- classifier pickle file created with a different scikit-learn version
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WHEN:
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- An attempt is made to load the classifier
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THEN:
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- The classifier reports the warning was captured and processed
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"""
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# TODO: This wasn't testing the warning anymore, as the schema changed
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# but as it was implemented, it would require installing an old version
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# rebuilding the file and committing that. Not developer friendly
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# Need to rethink how to pass the load through to a file with a single
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# old model?
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def test_one_correspondent_predict(self):
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c1 = Correspondent.objects.create(
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name="c1",
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matching_algorithm=Correspondent.MATCH_AUTO,
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)
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doc1 = Document.objects.create(
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title="doc1",
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content="this is a document from c1",
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correspondent=c1,
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checksum="A",
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)
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self.classifier.train()
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self.assertEqual(self.classifier.predict_correspondent(doc1.content), c1.pk)
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def test_one_correspondent_predict_manydocs(self):
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c1 = Correspondent.objects.create(
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name="c1",
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matching_algorithm=Correspondent.MATCH_AUTO,
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)
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doc1 = Document.objects.create(
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title="doc1",
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content="this is a document from c1",
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correspondent=c1,
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checksum="A",
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)
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doc2 = Document.objects.create(
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title="doc2",
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content="this is a document from noone",
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checksum="B",
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)
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self.classifier.train()
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self.assertEqual(self.classifier.predict_correspondent(doc1.content), c1.pk)
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self.assertIsNone(self.classifier.predict_correspondent(doc2.content))
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def test_one_type_predict(self):
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dt = DocumentType.objects.create(
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name="dt",
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matching_algorithm=DocumentType.MATCH_AUTO,
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)
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doc1 = Document.objects.create(
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title="doc1",
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content="this is a document from c1",
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checksum="A",
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document_type=dt,
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)
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self.classifier.train()
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self.assertEqual(self.classifier.predict_document_type(doc1.content), dt.pk)
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def test_one_type_predict_manydocs(self):
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dt = DocumentType.objects.create(
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name="dt",
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matching_algorithm=DocumentType.MATCH_AUTO,
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)
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|
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doc1 = Document.objects.create(
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title="doc1",
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content="this is a document from c1",
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checksum="A",
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document_type=dt,
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)
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|
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doc2 = Document.objects.create(
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title="doc1",
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content="this is a document from c2",
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checksum="B",
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)
|
|
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self.classifier.train()
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self.assertEqual(self.classifier.predict_document_type(doc1.content), dt.pk)
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self.assertIsNone(self.classifier.predict_document_type(doc2.content))
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|
|
def test_one_path_predict(self):
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sp = StoragePath.objects.create(
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name="sp",
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matching_algorithm=StoragePath.MATCH_AUTO,
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)
|
|
|
|
doc1 = Document.objects.create(
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title="doc1",
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content="this is a document from c1",
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checksum="A",
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storage_path=sp,
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)
|
|
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self.classifier.train()
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self.assertEqual(self.classifier.predict_storage_path(doc1.content), sp.pk)
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|
|
|
def test_one_path_predict_manydocs(self):
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sp = StoragePath.objects.create(
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name="sp",
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matching_algorithm=StoragePath.MATCH_AUTO,
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)
|
|
|
|
doc1 = Document.objects.create(
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title="doc1",
|
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content="this is a document from c1",
|
|
checksum="A",
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|
storage_path=sp,
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)
|
|
|
|
doc2 = Document.objects.create(
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title="doc1",
|
|
content="this is a document from c2",
|
|
checksum="B",
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|
)
|
|
|
|
self.classifier.train()
|
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self.assertEqual(self.classifier.predict_storage_path(doc1.content), sp.pk)
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self.assertIsNone(self.classifier.predict_storage_path(doc2.content))
|
|
|
|
def test_one_tag_predict(self):
|
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t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
|
|
|
doc1 = Document.objects.create(
|
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title="doc1",
|
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content="this is a document from c1",
|
|
checksum="A",
|
|
)
|
|
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doc1.tags.add(t1)
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self.classifier.train()
|
|
self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
|
|
|
|
def test_one_tag_predict_unassigned(self):
|
|
Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
|
|
|
doc1 = Document.objects.create(
|
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title="doc1",
|
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content="this is a document from c1",
|
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checksum="A",
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)
|
|
|
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self.classifier.train()
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self.assertListEqual(self.classifier.predict_tags(doc1.content), [])
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|
|
|
def test_two_tags_predict_singledoc(self):
|
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t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
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t2 = Tag.objects.create(name="t2", matching_algorithm=Tag.MATCH_AUTO, pk=121)
|
|
|
|
doc4 = Document.objects.create(
|
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title="doc1",
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content="this is a document from c4",
|
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checksum="D",
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|
)
|
|
|
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doc4.tags.add(t1)
|
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doc4.tags.add(t2)
|
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self.classifier.train()
|
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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(
|
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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), [])
|
|
|
|
def test_load_classifier_not_exists(self):
|
|
self.assertFalse(os.path.exists(settings.MODEL_FILE))
|
|
self.assertIsNone(load_classifier())
|
|
|
|
@mock.patch("documents.classifier.DocumentClassifier.load")
|
|
def test_load_classifier(self, load):
|
|
Path(settings.MODEL_FILE).touch()
|
|
self.assertIsNotNone(load_classifier())
|
|
load.assert_called_once()
|
|
|
|
@override_settings(
|
|
CACHES={
|
|
"default": {"BACKEND": "django.core.cache.backends.locmem.LocMemCache"},
|
|
},
|
|
)
|
|
@override_settings(
|
|
MODEL_FILE=os.path.join(os.path.dirname(__file__), "data", "model.pickle"),
|
|
)
|
|
@pytest.mark.skip(
|
|
reason="Disabled caching due to high memory usage - need to investigate.",
|
|
)
|
|
def test_load_classifier_cached(self):
|
|
classifier = load_classifier()
|
|
self.assertIsNotNone(classifier)
|
|
|
|
with mock.patch("documents.classifier.DocumentClassifier.load") as load:
|
|
load_classifier()
|
|
load.assert_not_called()
|
|
|
|
@mock.patch("documents.classifier.DocumentClassifier.load")
|
|
def test_load_classifier_incompatible_version(self, load):
|
|
Path(settings.MODEL_FILE).touch()
|
|
self.assertTrue(os.path.exists(settings.MODEL_FILE))
|
|
|
|
load.side_effect = IncompatibleClassifierVersionError()
|
|
self.assertIsNone(load_classifier())
|
|
self.assertFalse(os.path.exists(settings.MODEL_FILE))
|
|
|
|
@mock.patch("documents.classifier.DocumentClassifier.load")
|
|
def test_load_classifier_os_error(self, load):
|
|
Path(settings.MODEL_FILE).touch()
|
|
self.assertTrue(os.path.exists(settings.MODEL_FILE))
|
|
|
|
load.side_effect = OSError()
|
|
self.assertIsNone(load_classifier())
|
|
self.assertTrue(os.path.exists(settings.MODEL_FILE))
|