import logging import os import pickle from sklearn.neural_network import MLPClassifier 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() content = content.strip() content = content.replace("\n", " ") content = content.replace("\r", " ") while content.find(" ") > -1: content = content.replace(" ", " ") return content class DocumentClassifier(object): classifier_version = None data_vectorizer = None tags_binarizer = None correspondent_binarizer = None document_type_binarizer = None tags_classifier = None correspondent_classifier = None document_type_classifier = None @staticmethod def load_classifier(): clf = DocumentClassifier() clf.reload() return clf def reload(self): if self.classifier_version is None or 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) self.tags_binarizer = pickle.load(f) self.correspondent_binarizer = pickle.load(f) self.document_type_binarizer = pickle.load(f) self.tags_classifier = pickle.load(f) self.correspondent_classifier = pickle.load(f) self.document_type_classifier = pickle.load(f) self.classifier_version = os.path.getmtime(settings.MODEL_FILE) def save_classifier(self): with open(settings.MODEL_FILE, "wb") as f: pickle.dump(self.data_vectorizer, f) pickle.dump(self.tags_binarizer, f) pickle.dump(self.correspondent_binarizer, f) pickle.dump(self.document_type_binarizer, f) pickle.dump(self.tags_classifier, f) pickle.dump(self.correspondent_classifier, f) pickle.dump(self.document_type_classifier, f) def train(self): data = list() labels_tags = list() labels_correspondent = list() labels_document_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_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)] 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)))) # Step 2: vectorize data logging.getLogger(__name__).info("Vectorizing data...") 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) self.document_type_binarizer = LabelBinarizer() 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: logging.getLogger(__name__).info("Training tags classifier...") self.tags_classifier = MLPClassifier(verbose=True) 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 = MLPClassifier(verbose=True) 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.document_type_binarizer.classes_) > 0: 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) else: self.document_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_document_type=False, classify_tags=False, replace_tags=False): X = self.data_vectorizer.transform([preprocess_content(document.content)]) update_fields=() 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] 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)