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Code style changes

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This commit is contained in:
Jonas Winkler 2018-09-26 10:51:42 +02:00
parent 5b9f38d398
commit 7257cece30
5 changed files with 155 additions and 75 deletions
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4
src/documents/actions.py
View File

@ -165,8 +165,9 @@ def remove_document_type_from_selected(modeladmin, request, queryset):
def run_document_classifier_on_selected(modeladmin, request, queryset):
clf = DocumentClassifier()
try:
clf = DocumentClassifier.load_classifier()
clf.reload()
return simple_action(
modeladmin=modeladmin,
request=request,
@ -201,4 +202,3 @@ remove_document_type_from_selected.short_description = \
"Remove document type from selected documents"
run_document_classifier_on_selected.short_description = \
"Run document classifier on selected"

213
src/documents/classifier.py
View File

@ -2,14 +2,13 @@ import logging
import os
import pickle
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
from documents.models import Correspondent, DocumentType, Tag, Document
from paperless import settings
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
def preprocess_content(content):
content = content.lower()
@ -23,26 +22,21 @@ def preprocess_content(content):
class DocumentClassifier(object):
classifier_version = None
def __init__(self):
self.classifier_version = 0
data_vectorizer = None
self.data_vectorizer = None
tags_binarizer = None
correspondent_binarizer = None
document_type_binarizer = None
self.tags_binarizer = None
self.correspondent_binarizer = None
self.document_type_binarizer = None
tags_classifier = None
correspondent_classifier = None
document_type_classifier = None
@staticmethod
def load_classifier():
clf = DocumentClassifier()
clf.reload()
return clf
self.tags_classifier = None
self.correspondent_classifier = None
self.document_type_classifier = None
def reload(self):
if self.classifier_version is None or os.path.getmtime(settings.MODEL_FILE) > self.classifier_version:
if os.path.getmtime(settings.MODEL_FILE) > self.classifier_version:
logging.getLogger(__name__).info("Reloading classifier models")
with open(settings.MODEL_FILE, "rb") as f:
self.data_vectorizer = pickle.load(f)
@ -77,27 +71,54 @@ class DocumentClassifier(object):
logging.getLogger(__name__).info("Gathering data from database...")
for doc in Document.objects.exclude(tags__is_inbox_tag=True):
data.append(preprocess_content(doc.content))
labels_document_type.append(doc.document_type.id if doc.document_type is not None and doc.document_type.automatic_classification else -1)
labels_correspondent.append(doc.correspondent.id if doc.correspondent is not None and doc.correspondent.automatic_classification else -1)
tags = [tag.id for tag in doc.tags.filter(automatic_classification=True)]
y = -1
if doc.document_type:
if doc.document_type.automatic_classification:
y = doc.document_type.id
labels_document_type.append(y)
y = -1
if doc.correspondent:
if doc.correspondent.automatic_classification:
y = doc.correspondent.id
labels_correspondent.append(y)
tags = [tag.id for tag in doc.tags.filter(
automatic_classification=True
)]
labels_tags.append(tags)
labels_tags_unique = set([tag for tags in labels_tags for tag in tags])
logging.getLogger(__name__).info("{} documents, {} tag(s), {} correspondent(s), {} document type(s).".format(len(data), len(labels_tags_unique), len(set(labels_correspondent)), len(set(labels_document_type))))
logging.getLogger(__name__).info(
"{} documents, {} tag(s), {} correspondent(s), "
"{} document type(s).".format(
len(data),
len(labels_tags_unique),
len(set(labels_correspondent)),
len(set(labels_document_type))
)
)
# Step 2: vectorize data
logging.getLogger(__name__).info("Vectorizing data...")
self.data_vectorizer = CountVectorizer(analyzer="char", ngram_range=(3, 5), min_df=0.1)
self.data_vectorizer = CountVectorizer(
analyzer="char",
ngram_range=(3, 5),
min_df=0.1
)
data_vectorized = self.data_vectorizer.fit_transform(data)
self.tags_binarizer = MultiLabelBinarizer()
labels_tags_vectorized = self.tags_binarizer.fit_transform(labels_tags)
self.correspondent_binarizer = LabelBinarizer()
labels_correspondent_vectorized = self.correspondent_binarizer.fit_transform(labels_correspondent)
labels_correspondent_vectorized = \
self.correspondent_binarizer.fit_transform(labels_correspondent)
self.document_type_binarizer = LabelBinarizer()
labels_document_type_vectorized = self.document_type_binarizer.fit_transform(labels_document_type)
labels_document_type_vectorized = \
self.document_type_binarizer.fit_transform(labels_document_type)
# Step 3: train the classifiers
if len(self.tags_binarizer.classes_) > 0:
@ -106,62 +127,114 @@ class DocumentClassifier(object):
self.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
else:
self.tags_classifier = None
logging.getLogger(__name__).info("There are no tags. Not training tags classifier.")
logging.getLogger(__name__).info(
"There are no tags. Not training tags classifier."
)
if len(self.correspondent_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training correspondent classifier...")
logging.getLogger(__name__).info(
"Training correspondent classifier..."
)
self.correspondent_classifier = MLPClassifier(verbose=True)
self.correspondent_classifier.fit(data_vectorized, labels_correspondent_vectorized)
self.correspondent_classifier.fit(
data_vectorized,
labels_correspondent_vectorized
)
else:
self.correspondent_classifier = None
logging.getLogger(__name__).info("There are no correspondents. Not training correspondent classifier.")
logging.getLogger(__name__).info(
"There are no correspondents. Not training correspondent "
"classifier."
)
if len(self.document_type_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training document type classifier...")
logging.getLogger(__name__).info(
"Training document type classifier..."
)
self.document_type_classifier = MLPClassifier(verbose=True)
self.document_type_classifier.fit(data_vectorized, labels_document_type_vectorized)
self.document_type_classifier.fit(
data_vectorized,
labels_document_type_vectorized
)
else:
self.document_type_classifier = None
logging.getLogger(__name__).info("There are no document types. Not training document type classifier.")
logging.getLogger(__name__).info(
"There are no document types. Not training document type "
"classifier."
)
def classify_document(self, document, classify_correspondent=False, classify_document_type=False, classify_tags=False, replace_tags=False):
X = self.data_vectorizer.transform([preprocess_content(document.content)])
def classify_document(
self, document, classify_correspondent=False,
classify_document_type=False, classify_tags=False,
replace_tags=False):
update_fields = ()
X = self.data_vectorizer.transform(
[preprocess_content(document.content)]
)
if classify_correspondent and self.correspondent_classifier is not None:
y_correspondent = self.correspondent_classifier.predict(X)
correspondent_id = self.correspondent_binarizer.inverse_transform(y_correspondent)[0]
if classify_correspondent and self.correspondent_classifier:
self._classify_correspondent(X, document)
if classify_document_type and self.document_type_classifier:
self._classify_document_type(X, document)
if classify_tags and self.tags_classifier:
self._classify_tags(X, document, replace_tags)
document.save(update_fields=("correspondent", "document_type"))
def _classify_correspondent(self, X, document):
y = self.correspondent_classifier.predict(X)
correspondent_id = self.correspondent_binarizer.inverse_transform(y)[0]
try:
correspondent = None
if correspondent_id != -1:
correspondent = Correspondent.objects.get(id=correspondent_id)
logging.getLogger(__name__).info(
"Detected correspondent: {}".format(correspondent.name)
)
else:
logging.getLogger(__name__).info("Detected correspondent: -")
document.correspondent = correspondent
except Correspondent.DoesNotExist:
logging.getLogger(__name__).warning(
"Detected correspondent with id {} does not exist "
"anymore! Did you delete it?".format(correspondent_id)
)
def _classify_document_type(self, X, document):
y = self.document_type_classifier.predict(X)
document_type_id = self.document_type_binarizer.inverse_transform(y)[0]
try:
document_type = None
if document_type_id != -1:
document_type = DocumentType.objects.get(id=document_type_id)
logging.getLogger(__name__).info(
"Detected document type: {}".format(document_type.name)
)
else:
logging.getLogger(__name__).info("Detected document type: -")
document.document_type = document_type
except DocumentType.DoesNotExist:
logging.getLogger(__name__).warning(
"Detected document type with id {} does not exist "
"anymore! Did you delete it?".format(document_type_id)
)
def _classify_tags(self, X, document, replace_tags):
y = self.tags_classifier.predict(X)
tags_ids = self.tags_binarizer.inverse_transform(y)[0]
if replace_tags:
document.tags.clear()
for tag_id in tags_ids:
try:
correspondent = Correspondent.objects.get(id=correspondent_id) if correspondent_id != -1 else None
logging.getLogger(__name__).info("Detected correspondent: {}".format(correspondent.name if correspondent else "-"))
document.correspondent = correspondent
update_fields = update_fields + ("correspondent",)
except Correspondent.DoesNotExist:
logging.getLogger(__name__).warning("Detected correspondent with id {} does not exist anymore! Did you delete it?".format(correspondent_id))
if classify_document_type and self.document_type_classifier is not None:
y_type = self.document_type_classifier.predict(X)
type_id = self.document_type_binarizer.inverse_transform(y_type)[0]
try:
document_type = DocumentType.objects.get(id=type_id) if type_id != -1 else None
logging.getLogger(__name__).info("Detected document type: {}".format(document_type.name if document_type else "-"))
document.document_type = document_type
update_fields = update_fields + ("document_type",)
except DocumentType.DoesNotExist:
logging.getLogger(__name__).warning("Detected document type with id {} does not exist anymore! Did you delete it?".format(type_id))
if classify_tags and self.tags_classifier is not None:
y_tags = self.tags_classifier.predict(X)
tags_ids = self.tags_binarizer.inverse_transform(y_tags)[0]
if replace_tags:
document.tags.clear()
for tag_id in tags_ids:
try:
tag = Tag.objects.get(id=tag_id)
document.tags.add(tag)
logging.getLogger(__name__).info("Detected tag: {}".format(tag.name))
except Tag.DoesNotExist:
logging.getLogger(__name__).warning("Detected tag with id {} does not exist anymore! Did you delete it?".format(tag_id))
document.save(update_fields=update_fields)
tag = Tag.objects.get(id=tag_id)
logging.getLogger(__name__).info(
"Detected tag: {}".format(tag.name)
)
document.tags.add(tag)
except Tag.DoesNotExist:
logging.getLogger(__name__).warning(
"Detected tag with id {} does not exist anymore! Did "
"you delete it?".format(tag_id)
)

3
src/documents/management/commands/document_retagger.py
View File

@ -54,8 +54,9 @@ class Command(Renderable, BaseCommand):
documents = queryset.distinct()
logging.getLogger(__name__).info("Loading classifier")
clf = DocumentClassifier()
try:
clf = DocumentClassifier.load_classifier()
clf.reload()
except FileNotFoundError:
logging.getLogger(__name__).fatal("Cannot classify documents, "
"classifier model file was not "

8
src/documents/views.py
View File

@ -20,7 +20,13 @@ from rest_framework.viewsets import (
ReadOnlyModelViewSet
)
from .filters import CorrespondentFilterSet, DocumentFilterSet, TagFilterSet, DocumentTypeFilterSet
from .filters import (
CorrespondentFilterSet,
DocumentFilterSet,
TagFilterSet,
DocumentTypeFilterSet
)
from .forms import UploadForm
from .models import Correspondent, Document, Log, Tag, DocumentType
from .serialisers import (

2
src/paperless/settings.py
View File

@ -58,7 +58,7 @@ if _allowed_hosts:
ALLOWED_HOSTS = _allowed_hosts.split(",")
FORCE_SCRIPT_NAME = os.getenv("PAPERLESS_FORCE_SCRIPT_NAME")
# Application definition
INSTALLED_APPS = [