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Fixed a few things

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This commit is contained in:
Jonas Winkler 2018-09-05 12:43:11 +02:00
parent 70bd05450a
commit 82bc0e3368
4 changed files with 99 additions and 71 deletions
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26
src/documents/actions.py
View File

@ -4,6 +4,7 @@ from django.contrib.admin.utils import model_ngettext
from django.core.exceptions import PermissionDenied
from django.template.response import TemplateResponse
from documents.classifier import DocumentClassifier
from documents.models import Tag, Correspondent, DocumentType
@ -223,3 +224,28 @@ def remove_document_type_from_selected(modeladmin, request, queryset):
remove_document_type_from_selected.short_description = "Remove document type from selected documents"
def run_document_classifier_on_selected(modeladmin, request, queryset):
if not modeladmin.has_change_permission(request):
raise PermissionDenied
try:
clf = DocumentClassifier.load_classifier()
except FileNotFoundError:
modeladmin.message_user(request, "Classifier model file not found.", messages.ERROR)
return None
n = queryset.count()
if n:
for obj in queryset:
clf.classify_document(obj, classify_correspondent=True, classify_tags=True, classify_type=True, replace_tags=True)
modeladmin.log_change(request, obj, str(obj))
modeladmin.message_user(request, "Successfully applied tags, correspondent and type to %(count)d %(items)s." % {
"count": n, "items": model_ngettext(modeladmin.opts, n)
}, messages.SUCCESS)
return None
run_document_classifier_on_selected.short_description = "Run document classifier on selected"

5
src/documents/admin.py
View File

@ -13,7 +13,8 @@ from django.utils.safestring import mark_safe
from django.db import models
from documents.actions import add_tag_to_selected, remove_tag_from_selected, set_correspondent_on_selected, \
remove_correspondent_from_selected, set_document_type_on_selected, remove_document_type_from_selected
remove_correspondent_from_selected, set_document_type_on_selected, remove_document_type_from_selected, \
run_document_classifier_on_selected
from .models import Correspondent, Tag, Document, Log, DocumentType
@ -165,7 +166,7 @@ class DocumentAdmin(CommonAdmin):
ordering = ["-created", "correspondent"]
actions = [add_tag_to_selected, remove_tag_from_selected, set_correspondent_on_selected, remove_correspondent_from_selected, set_document_type_on_selected, remove_document_type_from_selected]
actions = [add_tag_to_selected, remove_tag_from_selected, set_correspondent_on_selected, remove_correspondent_from_selected, set_document_type_on_selected, remove_document_type_from_selected, run_document_classifier_on_selected]
date_hierarchy = 'created'

72
src/documents/classifier.py
View File

@ -1,9 +1,15 @@
import logging
import os
import pickle
from documents.models import Correspondent, DocumentType, Tag
from documents.models import Correspondent, DocumentType, Tag, Document
from paperless import settings
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
def preprocess_content(content):
content = content.lower()
@ -61,29 +67,85 @@ class DocumentClassifier(object):
pickle.dump(self.correspondent_classifier, f)
pickle.dump(self.type_classifier, f)
def classify_document(self, document, classify_correspondent=False, classify_type=False, classify_tags=False):
def train(self):
data = list()
labels_tags = list()
labels_correspondent = list()
labels_type = list()
# Step 1: Extract and preprocess training data from the database.
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_type.append(doc.document_type.name if doc.document_type is not None else "-")
labels_correspondent.append(doc.correspondent.name if doc.correspondent is not None else "-")
tags = [tag.name for tag in doc.tags.all()]
labels_tags.append(tags)
# Step 2: vectorize data
logging.getLogger(__name__).info("Vectorizing data...")
self.data_vectorizer = CountVectorizer(analyzer='char', ngram_range=(2, 6), 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)
self.type_binarizer = LabelBinarizer()
labels_type_vectorized = self.type_binarizer.fit_transform(labels_type)
# Step 3: train the classifiers
if len(self.tags_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training tags classifier...")
self.tags_classifier = OneVsRestClassifier(MultinomialNB())
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.")
if len(self.correspondent_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training correspondent classifier...")
self.correspondent_classifier = OneVsRestClassifier(MultinomialNB())
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.")
if len(self.type_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training document type classifier...")
self.type_classifier = OneVsRestClassifier(MultinomialNB())
self.type_classifier.fit(data_vectorized, labels_type_vectorized)
else:
self.type_classifier = None
logging.getLogger(__name__).info("There are no document types. Not training document type classifier.")
def classify_document(self, document, classify_correspondent=False, classify_type=False, classify_tags=False, replace_tags=False):
X = self.data_vectorizer.transform([preprocess_content(document.content)])
update_fields=()
if classify_correspondent:
if classify_correspondent and self.correspondent_classifier is not None:
y_correspondent = self.correspondent_classifier.predict(X)
correspondent = self.correspondent_binarizer.inverse_transform(y_correspondent)[0]
print("Detected correspondent:", correspondent)
document.correspondent = Correspondent.objects.filter(name=correspondent).first()
update_fields = update_fields + ("correspondent",)
if classify_type:
if classify_type and self.type_classifier is not None:
y_type = self.type_classifier.predict(X)
type = self.type_binarizer.inverse_transform(y_type)[0]
print("Detected document type:", type)
document.document_type = DocumentType.objects.filter(name=type).first()
update_fields = update_fields + ("document_type",)
if classify_tags:
if classify_tags and self.tags_classifier is not None:
y_tags = self.tags_classifier.predict(X)
tags = self.tags_binarizer.inverse_transform(y_tags)[0]
print("Detected tags:", tags)
if replace_tags:
document.tags.clear()
document.tags.add(*[Tag.objects.filter(name=t).first() for t in tags])
document.save(update_fields=update_fields)

67
src/documents/management/commands/document_create_classifier.py
View File

@ -3,13 +3,7 @@ import os.path
import pickle
from django.core.management.base import BaseCommand
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder
from documents.classifier import preprocess_content, DocumentClassifier
from documents.models import Document
from documents.classifier import DocumentClassifier
from paperless import settings
from ...mixins import Renderable
@ -26,64 +20,9 @@ class Command(Renderable, BaseCommand):
def handle(self, *args, **options):
clf = DocumentClassifier()
data = list()
labels_tags = list()
labels_correspondent = list()
labels_type = list()
clf.train()
# Step 1: Extract and preprocess training data from the database.
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_type.append(doc.document_type.name if doc.document_type is not None else "-")
labels_correspondent.append(doc.correspondent.name if doc.correspondent is not None else "-")
tags = [tag.name for tag in doc.tags.all()]
labels_tags.append(tags)
# Step 2: vectorize data
logging.getLogger(__name__).info("Vectorizing data...")
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)
clf.correspondent_binarizer = LabelEncoder()
labels_correspondent_vectorized = clf.correspondent_binarizer.fit_transform(labels_correspondent)
clf.type_binarizer = LabelEncoder()
labels_type_vectorized = clf.type_binarizer.fit_transform(labels_type)
# Step 3: train the classifiers
if len(clf.tags_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training tags classifier")
clf.tags_classifier = OneVsRestClassifier(MultinomialNB())
clf.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
else:
clf.tags_classifier = None
logging.getLogger(__name__).info("There are no tags. Not training tags classifier.")
if len(clf.correspondent_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training correspondent classifier")
clf.correspondent_classifier = MultinomialNB()
clf.correspondent_classifier.fit(data_vectorized, labels_correspondent_vectorized)
else:
clf.correspondent_classifier = None
logging.getLogger(__name__).info("There are no correspondents. Not training correspondent classifier.")
if len(clf.type_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training document type classifier")
clf.type_classifier = MultinomialNB()
clf.type_classifier.fit(data_vectorized, labels_type_vectorized)
else:
clf.type_classifier = None
logging.getLogger(__name__).info("There are no document types. Not training document type classifier.")
logging.getLogger(__name__).info("Saving models to " + settings.MODEL_FILE + "...")
clf.save_classifier()
clf.save_classifier()