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