paradizelost/paperless-ngx
1
0
Fork 0
mirror of https://github.com/paperless-ngx/paperless-ngx.git synced 2025-11-03 03:16:10 -06:00
Code Issues Packages Projects Releases Wiki Activity

implemented automatic classification field functionality

Browse Source
This commit is contained in:
Jonas Winkler
2018-09-05 14:31:02 +02:00
parent 582e9c5cb4
commit bbba57dd4d
2 changed files with 7 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
src/documents/classifier.py
View File

@@ -43,7 +43,7 @@ class DocumentClassifier(object):
def reload(self):
if self.classifier_version is None or os.path.getmtime(settings.MODEL_FILE) > self.classifier_version:
print("reloading classifier")
logging.getLogger(__name__).info("Reloading classifier models")
with open(settings.MODEL_FILE, "rb") as f:
self.data_vectorizer = pickle.load(f)
self.tags_binarizer = pickle.load(f)
@@ -77,11 +77,14 @@ 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_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_type.append(doc.document_type.name if doc.document_type is not None and doc.document_type.automatic_classification else "-")
labels_correspondent.append(doc.correspondent.name if doc.correspondent is not None and doc.correspondent.automatic_classification else "-")
tags = [tag.name 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) {}, {} type(s) {}.".format(len(data), len(labels_tags_unique), labels_tags_unique, len(set(labels_correspondent)), set(labels_correspondent), len(set(labels_type)), set(labels_type)))
# Step 2: vectorize data
logging.getLogger(__name__).info("Vectorizing data...")
self.data_vectorizer = CountVectorizer(analyzer='char', ngram_range=(2, 6), min_df=0.1)

1
src/documents/management/commands/document_create_classifier.py
View File

@@ -22,7 +22,6 @@ class Command(Renderable, BaseCommand):
clf.train()
logging.getLogger(__name__).info("Saving models to " + settings.MODEL_FILE + "...")
clf.save_classifier()