paperless-ngx/src/documents/classifier.py

import hashlib
import logging
import os
import pickle
import re

from django.conf import settings
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
from sklearn.utils.multiclass import type_of_target

from documents.models import Document, MatchingModel


class IncompatibleClassifierVersionError(Exception):
    pass


logger = logging.getLogger(__name__)


def preprocess_content(content):
    content = content.lower().strip()
    content = re.sub(r"\s+", " ", content)
    return content


class DocumentClassifier(object):

    FORMAT_VERSION = 6

    def __init__(self):
        # mtime of the model file on disk. used to prevent reloading when
        # nothing has changed.
        self.classifier_version = 0

        # hash of the training data. used to prevent re-training when the
        # training data has not changed.
        self.data_hash = None

        self.data_vectorizer = None
        self.tags_binarizer = None
        self.tags_classifier = None
        self.correspondent_classifier = None
        self.document_type_classifier = None

    def reload(self):
        if os.path.getmtime(settings.MODEL_FILE) > self.classifier_version:
            with open(settings.MODEL_FILE, "rb") as f:
                schema_version = pickle.load(f)

                if schema_version != self.FORMAT_VERSION:
                    raise IncompatibleClassifierVersionError(
                        "Cannor load classifier, incompatible versions.")
                else:
                    if self.classifier_version > 0:
                        # Don't be confused by this check. It's simply here
                        # so that we wont log anything on initial reload.
                        logger.info("Classifier updated on disk, "
                                    "reloading classifier models")
                    self.data_hash = pickle.load(f)
                    self.data_vectorizer = pickle.load(f)
                    self.tags_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.FORMAT_VERSION, f)
            pickle.dump(self.data_hash, f)
            pickle.dump(self.data_vectorizer, f)

            pickle.dump(self.tags_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__).debug("Gathering data from database...")
        m = hashlib.sha1()
        for doc in Document.objects.order_by('pk').exclude(tags__is_inbox_tag=True):  # NOQA: E501
            preprocessed_content = preprocess_content(doc.content)
            m.update(preprocessed_content.encode('utf-8'))
            data.append(preprocessed_content)

            y = -1
            dt = doc.document_type
            if dt and dt.matching_algorithm == MatchingModel.MATCH_AUTO:
                y = dt.pk
            m.update(y.to_bytes(4, 'little', signed=True))
            labels_document_type.append(y)

            y = -1
            cor = doc.correspondent
            if cor and cor.matching_algorithm == MatchingModel.MATCH_AUTO:
                y = cor.pk
            m.update(y.to_bytes(4, 'little', signed=True))
            labels_correspondent.append(y)

            tags = [tag.pk for tag in doc.tags.filter(
                matching_algorithm=MatchingModel.MATCH_AUTO
            )]
            m.update(bytearray(tags))
            labels_tags.append(tags)

        if not data:
            raise ValueError("No training data available.")

        new_data_hash = m.digest()

        if self.data_hash and new_data_hash == self.data_hash:
            return False

        labels_tags_unique = set([tag for tags in labels_tags for tag in tags])

        num_tags = len(labels_tags_unique)

        # substract 1 since -1 (null) is also part of the classes.

        # union with {-1} accounts for cases where all documents have
        # correspondents and types assigned, so -1 isnt part of labels_x, which
        # it usually is.
        num_correspondents = len(set(labels_correspondent) | {-1}) - 1
        num_document_types = len(set(labels_document_type) | {-1}) - 1

        logging.getLogger(__name__).debug(
            "{} documents, {} tag(s), {} correspondent(s), "
            "{} document type(s).".format(
                len(data),
                num_tags,
                num_correspondents,
                num_document_types
            )
        )

        # Step 2: vectorize data
        logging.getLogger(__name__).debug("Vectorizing data...")
        self.data_vectorizer = CountVectorizer(
            analyzer="word",
            ngram_range=(1, 2),
            min_df=0.01
        )
        data_vectorized = self.data_vectorizer.fit_transform(data)

        # Step 3: train the classifiers
        if num_tags > 0:
            logging.getLogger(__name__).debug("Training tags classifier...")

            if num_tags == 1:
                # Special case where only one tag has auto:
                # Fallback to binary classification.
                labels_tags = [label[0] if len(label) == 1 else -1
                               for label in labels_tags]
                self.tags_binarizer = LabelBinarizer()
                labels_tags_vectorized = self.tags_binarizer.fit_transform(
                    labels_tags).ravel()
            else:
                self.tags_binarizer = MultiLabelBinarizer()
                labels_tags_vectorized = self.tags_binarizer.fit_transform(
                    labels_tags)

            self.tags_classifier = MLPClassifier(tol=0.01)
            self.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
        else:
            self.tags_classifier = None
            logging.getLogger(__name__).debug(
                "There are no tags. Not training tags classifier."
            )

        if num_correspondents > 0:
            logging.getLogger(__name__).debug(
                "Training correspondent classifier..."
            )
            self.correspondent_classifier = MLPClassifier(tol=0.01)
            self.correspondent_classifier.fit(
                data_vectorized,
                labels_correspondent
            )
        else:
            self.correspondent_classifier = None
            logging.getLogger(__name__).debug(
                "There are no correspondents. Not training correspondent "
                "classifier."
            )

        if num_document_types > 0:
            logging.getLogger(__name__).debug(
                "Training document type classifier..."
            )
            self.document_type_classifier = MLPClassifier(tol=0.01)
            self.document_type_classifier.fit(
                data_vectorized,
                labels_document_type
            )
        else:
            self.document_type_classifier = None
            logging.getLogger(__name__).debug(
                "There are no document types. Not training document type "
                "classifier."
            )

        self.data_hash = new_data_hash

        return True

    def predict_correspondent(self, content):
        if self.correspondent_classifier:
            X = self.data_vectorizer.transform([preprocess_content(content)])
            correspondent_id = self.correspondent_classifier.predict(X)
            if correspondent_id != -1:
                return correspondent_id
            else:
                return None
        else:
            return None

    def predict_document_type(self, content):
        if self.document_type_classifier:
            X = self.data_vectorizer.transform([preprocess_content(content)])
            document_type_id = self.document_type_classifier.predict(X)
            if document_type_id != -1:
                return document_type_id
            else:
                return None
        else:
            return None

    def predict_tags(self, content):
        if self.tags_classifier:
            X = self.data_vectorizer.transform([preprocess_content(content)])
            y = self.tags_classifier.predict(X)
            tags_ids = self.tags_binarizer.inverse_transform(y)[0]
            if type_of_target(y).startswith('multilabel'):
                # the usual case when there are multiple tags.
                return list(tags_ids)
            elif type_of_target(y) == 'binary' and tags_ids != -1:
                # This is for when we have binary classification with only one
                # tag and the result is to assign this tag.
                return [tags_ids]
            else:
                # Usually binary as well with -1 as the result, but we're
                # going to catch everything else here as well.
                return []
        else:
            return []