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Returns to using hashing against primary keys, at least for fields. Improves testing coverage

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Trenton Holmes 2023-02-26 21:01:29 -08:00 committed by Trenton H
parent c958a7c593
commit 6b939f7567
2 changed files with 88 additions and 34 deletions
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59
src/documents/classifier.py
View File

@ -5,6 +5,7 @@ import re
import shutil
import warnings
from datetime import datetime
from hashlib import sha256
from typing import Iterator
from typing import List
from typing import Optional
@ -51,7 +52,7 @@ def load_classifier() -> Optional["DocumentClassifier"]:
except OSError:
logger.exception("IO error while loading document classification model")
classifier = None
except Exception:
except Exception: # pragma: nocover
logger.exception("Unknown error while loading document classification model")
classifier = None
@ -62,13 +63,14 @@ class DocumentClassifier:
# v7 - Updated scikit-learn package version
# v8 - Added storage path classifier
# v9 - Changed from hash to time for training data check
# v9 - Changed from hashing to time/ids for re-train check
FORMAT_VERSION = 9
def __init__(self):
# last time training data was calculated. used to prevent re-training when the
# training data has not changed.
self.last_data_change: Optional[datetime] = None
# last time a document changed and therefore training might be required
self.last_doc_change_time: Optional[datetime] = None
# Hash of primary keys of AUTO matching values last used in training
self.last_auto_type_hash: Optional[bytes] = None
self.data_vectorizer = None
self.tags_binarizer = None
@ -92,7 +94,9 @@ class DocumentClassifier:
)
else:
try:
self.last_data_change = pickle.load(f)
self.last_doc_change_time = pickle.load(f)
self.last_auto_type_hash = pickle.load(f)
self.data_vectorizer = pickle.load(f)
self.tags_binarizer = pickle.load(f)
@ -122,7 +126,9 @@ class DocumentClassifier:
with open(target_file_temp, "wb") as f:
pickle.dump(self.FORMAT_VERSION, f)
pickle.dump(self.last_data_change, f)
pickle.dump(self.last_doc_change_time, f)
pickle.dump(self.last_auto_type_hash, f)
pickle.dump(self.data_vectorizer, f)
pickle.dump(self.tags_binarizer, f)
@ -139,20 +145,14 @@ class DocumentClassifier:
def train(self):
# Get non-inbox documents
docs_queryset = Document.objects.exclude(tags__is_inbox_tag=True)
docs_queryset = Document.objects.exclude(
tags__is_inbox_tag=True,
)
# No documents exit to train against
if docs_queryset.count() == 0:
raise ValueError("No training data available.")
# No documents have changed since classifier was trained
latest_doc_change = docs_queryset.latest("modified").modified
if (
self.last_data_change is not None
and self.last_data_change >= latest_doc_change
):
return False
labels_tags = []
labels_correspondent = []
labels_document_type = []
@ -160,18 +160,21 @@ class DocumentClassifier:
# Step 1: Extract and preprocess training data from the database.
logger.debug("Gathering data from database...")
hasher = sha256()
for doc in docs_queryset:
y = -1
dt = doc.document_type
if dt and dt.matching_algorithm == MatchingModel.MATCH_AUTO:
y = dt.pk
hasher.update(y.to_bytes(4, "little", signed=True))
labels_document_type.append(y)
y = -1
cor = doc.correspondent
if cor and cor.matching_algorithm == MatchingModel.MATCH_AUTO:
y = cor.pk
hasher.update(y.to_bytes(4, "little", signed=True))
labels_correspondent.append(y)
tags = sorted(
@ -180,18 +183,31 @@ class DocumentClassifier:
matching_algorithm=MatchingModel.MATCH_AUTO,
)
)
for tag in tags:
hasher.update(tag.to_bytes(4, "little", signed=True))
labels_tags.append(tags)
y = -1
sd = doc.storage_path
if sd and sd.matching_algorithm == MatchingModel.MATCH_AUTO:
y = sd.pk
sp = doc.storage_path
if sp and sp.matching_algorithm == MatchingModel.MATCH_AUTO:
y = sp.pk
hasher.update(y.to_bytes(4, "little", signed=True))
labels_storage_path.append(y)
labels_tags_unique = {tag for tags in labels_tags for tag in tags}
num_tags = len(labels_tags_unique)
# Check if retraining is actually required.
# A document has been updated since the classifier was trained
# New auto tags, types, correspondent, storage paths exist
latest_doc_change = docs_queryset.latest("modified").modified
if (
self.last_doc_change_time is not None
and self.last_doc_change_time >= latest_doc_change
) and self.last_auto_type_hash == hasher.digest():
return False
# substract 1 since -1 (null) is also part of the classes.
# union with {-1} accounts for cases where all documents have
@ -301,11 +317,12 @@ class DocumentClassifier:
"There are no storage paths. Not training storage path classifier.",
)
self.last_data_change = latest_doc_change
self.last_doc_change_time = latest_doc_change
self.last_auto_type_hash = hasher.digest()
return True
def preprocess_content(self, content: str) -> str:
def preprocess_content(self, content: str) -> str: # pragma: nocover
"""
Process to contents of a document, distilling it down into
words which are meaningful to the content

63
src/documents/tests/test_classifier.py
View File

@ -14,6 +14,7 @@ from documents.classifier import load_classifier
from documents.models import Correspondent
from documents.models import Document
from documents.models import DocumentType
from documents.models import MatchingModel
from documents.models import StoragePath
from documents.models import Tag
from documents.tests.utils import DirectoriesMixin
@ -46,6 +47,7 @@ class TestClassifier(DirectoriesMixin, TestCase):
name="c3",
matching_algorithm=Correspondent.MATCH_AUTO,
)
self.t1 = Tag.objects.create(
name="t1",
matching_algorithm=Tag.MATCH_AUTO,
@ -62,6 +64,12 @@ class TestClassifier(DirectoriesMixin, TestCase):
matching_algorithm=Tag.MATCH_AUTO,
pk=45,
)
self.t4 = Tag.objects.create(
name="t4",
matching_algorithm=Tag.MATCH_ANY,
pk=46,
)
self.dt = DocumentType.objects.create(
name="dt",
matching_algorithm=DocumentType.MATCH_AUTO,
@ -70,6 +78,7 @@ class TestClassifier(DirectoriesMixin, TestCase):
name="dt2",
matching_algorithm=DocumentType.MATCH_AUTO,
)
self.sp1 = StoragePath.objects.create(
name="sp1",
path="path1",
@ -80,6 +89,7 @@ class TestClassifier(DirectoriesMixin, TestCase):
path="path2",
matching_algorithm=DocumentType.MATCH_AUTO,
)
self.store_paths = [self.sp1, self.sp2]
self.doc1 = Document.objects.create(
title="doc1",
@ -87,6 +97,7 @@ class TestClassifier(DirectoriesMixin, TestCase):
correspondent=self.c1,
checksum="A",
document_type=self.dt,
storage_path=self.sp1,
)
self.doc2 = Document.objects.create(
@ -107,8 +118,6 @@ class TestClassifier(DirectoriesMixin, TestCase):
self.doc2.tags.add(self.t3)
self.doc_inbox.tags.add(self.t2)
self.doc1.storage_path = self.sp1
def generate_train_and_save(self):
"""
Generates the training data, trains and saves the updated pickle
@ -267,6 +276,28 @@ class TestClassifier(DirectoriesMixin, TestCase):
self.assertTrue(self.classifier.train())
def test_retrain_if_auto_match_set_changed(self):
"""
GIVEN:
- Classifier trained with current data
WHEN:
- Classifier training is requested again
- Some new AUTO match object exists
THEN:
- Classifier does redo training
"""
self.generate_test_data()
# Add the ANY type
self.doc1.tags.add(self.t4)
self.assertTrue(self.classifier.train())
# Change the matching type
self.t4.matching_algorithm = MatchingModel.MATCH_AUTO
self.t4.save()
self.assertTrue(self.classifier.train())
def testVersionIncreased(self):
"""
GIVEN:
@ -314,7 +345,7 @@ class TestClassifier(DirectoriesMixin, TestCase):
self.assertCountEqual(new_classifier.predict_tags(self.doc2.content), [45, 12])
@mock.patch("documents.classifier.pickle.load")
def test_load_corrupt_file(self, patched_pickle_load):
def test_load_corrupt_file(self, patched_pickle_load: mock.MagicMock):
"""
GIVEN:
- Corrupted classifier pickle file
@ -330,14 +361,17 @@ class TestClassifier(DirectoriesMixin, TestCase):
with self.assertRaises(ClassifierModelCorruptError):
self.classifier.load()
patched_pickle_load.assert_called()
patched_pickle_load.reset_mock()
patched_pickle_load.side_effect = [
DocumentClassifier.FORMAT_VERSION,
ClassifierModelCorruptError(),
]
self.assertIsNone(load_classifier())
patched_pickle_load.assert_called()
@override_settings(
MODEL_FILE=os.path.join(
os.path.dirname(__file__),
"data",
"v1.0.2.model.pickle",
),
)
def test_load_new_scikit_learn_version(self):
"""
GIVEN:
@ -347,9 +381,12 @@ class TestClassifier(DirectoriesMixin, TestCase):
THEN:
- The classifier reports the warning was captured and processed
"""
with self.assertRaises(IncompatibleClassifierVersionError):
self.classifier.load()
# TODO: This wasn't testing the warning anymore, as the schema changed
# but as it was implemented, it would require installing an old version
# rebuilding the file and committing that. Not developer friendly
# Need to rethink how to pass the load through to a file with a single
# old model?
pass
def test_one_correspondent_predict(self):
c1 = Correspondent.objects.create(