mirror of
https://github.com/paperless-ngx/paperless-ngx.git
synced 2025-04-02 13:45:10 -05:00
Fixed a few things
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parent
70bd05450a
commit
82bc0e3368
@ -4,6 +4,7 @@ from django.contrib.admin.utils import model_ngettext
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from django.core.exceptions import PermissionDenied
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from django.template.response import TemplateResponse
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from documents.classifier import DocumentClassifier
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from documents.models import Tag, Correspondent, DocumentType
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@ -223,3 +224,28 @@ def remove_document_type_from_selected(modeladmin, request, queryset):
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remove_document_type_from_selected.short_description = "Remove document type from selected documents"
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def run_document_classifier_on_selected(modeladmin, request, queryset):
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if not modeladmin.has_change_permission(request):
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raise PermissionDenied
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try:
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clf = DocumentClassifier.load_classifier()
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except FileNotFoundError:
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modeladmin.message_user(request, "Classifier model file not found.", messages.ERROR)
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return None
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n = queryset.count()
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if n:
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for obj in queryset:
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clf.classify_document(obj, classify_correspondent=True, classify_tags=True, classify_type=True, replace_tags=True)
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modeladmin.log_change(request, obj, str(obj))
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modeladmin.message_user(request, "Successfully applied tags, correspondent and type to %(count)d %(items)s." % {
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"count": n, "items": model_ngettext(modeladmin.opts, n)
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}, messages.SUCCESS)
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return None
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run_document_classifier_on_selected.short_description = "Run document classifier on selected"
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@ -13,7 +13,8 @@ from django.utils.safestring import mark_safe
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from django.db import models
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from documents.actions import add_tag_to_selected, remove_tag_from_selected, set_correspondent_on_selected, \
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remove_correspondent_from_selected, set_document_type_on_selected, remove_document_type_from_selected
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remove_correspondent_from_selected, set_document_type_on_selected, remove_document_type_from_selected, \
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run_document_classifier_on_selected
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from .models import Correspondent, Tag, Document, Log, DocumentType
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@ -165,7 +166,7 @@ class DocumentAdmin(CommonAdmin):
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ordering = ["-created", "correspondent"]
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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]
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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]
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date_hierarchy = 'created'
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@ -1,9 +1,15 @@
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import logging
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import os
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import pickle
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from documents.models import Correspondent, DocumentType, Tag
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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.multiclass import OneVsRestClassifier
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from sklearn.naive_bayes import MultinomialNB
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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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@ -61,29 +67,85 @@ class DocumentClassifier(object):
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pickle.dump(self.correspondent_classifier, f)
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pickle.dump(self.type_classifier, f)
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def classify_document(self, document, classify_correspondent=False, classify_type=False, classify_tags=False):
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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_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_type.append(doc.document_type.name if doc.document_type is not None else "-")
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labels_correspondent.append(doc.correspondent.name if doc.correspondent is not None else "-")
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tags = [tag.name for tag in doc.tags.all()]
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labels_tags.append(tags)
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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=(2, 6), 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.type_binarizer = LabelBinarizer()
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labels_type_vectorized = self.type_binarizer.fit_transform(labels_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 = OneVsRestClassifier(MultinomialNB())
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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 = OneVsRestClassifier(MultinomialNB())
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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.type_binarizer.classes_) > 0:
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logging.getLogger(__name__).info("Training document type classifier...")
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self.type_classifier = OneVsRestClassifier(MultinomialNB())
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self.type_classifier.fit(data_vectorized, labels_type_vectorized)
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else:
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self.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_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:
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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 = self.correspondent_binarizer.inverse_transform(y_correspondent)[0]
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print("Detected correspondent:", correspondent)
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document.correspondent = Correspondent.objects.filter(name=correspondent).first()
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update_fields = update_fields + ("correspondent",)
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if classify_type:
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if classify_type and self.type_classifier is not None:
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y_type = self.type_classifier.predict(X)
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type = self.type_binarizer.inverse_transform(y_type)[0]
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print("Detected document type:", type)
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document.document_type = DocumentType.objects.filter(name=type).first()
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update_fields = update_fields + ("document_type",)
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if classify_tags:
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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 = self.tags_binarizer.inverse_transform(y_tags)[0]
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print("Detected tags:", tags)
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if replace_tags:
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document.tags.clear()
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document.tags.add(*[Tag.objects.filter(name=t).first() for t in tags])
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document.save(update_fields=update_fields)
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@ -3,13 +3,7 @@ import os.path
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import pickle
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from django.core.management.base import BaseCommand
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder
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from documents.classifier import preprocess_content, DocumentClassifier
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from documents.models import Document
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from documents.classifier import DocumentClassifier
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from paperless import settings
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from ...mixins import Renderable
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@ -26,64 +20,9 @@ class Command(Renderable, BaseCommand):
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def handle(self, *args, **options):
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clf = DocumentClassifier()
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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_type = list()
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clf.train()
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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_type.append(doc.document_type.name if doc.document_type is not None else "-")
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labels_correspondent.append(doc.correspondent.name if doc.correspondent is not None else "-")
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tags = [tag.name for tag in doc.tags.all()]
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labels_tags.append(tags)
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# Step 2: vectorize data
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logging.getLogger(__name__).info("Vectorizing data...")
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clf.data_vectorizer = CountVectorizer(analyzer='char', ngram_range=(2, 6), min_df=0.1)
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data_vectorized = clf.data_vectorizer.fit_transform(data)
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print(clf.data_vectorizer.vocabulary_)
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logging.getLogger(__name__).info("Shape of vectorized data: {}".format(data_vectorized.shape))
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clf.tags_binarizer = MultiLabelBinarizer()
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labels_tags_vectorized = clf.tags_binarizer.fit_transform(labels_tags)
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clf.correspondent_binarizer = LabelEncoder()
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labels_correspondent_vectorized = clf.correspondent_binarizer.fit_transform(labels_correspondent)
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clf.type_binarizer = LabelEncoder()
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labels_type_vectorized = clf.type_binarizer.fit_transform(labels_type)
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# Step 3: train the classifiers
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if len(clf.tags_binarizer.classes_) > 0:
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logging.getLogger(__name__).info("Training tags classifier")
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clf.tags_classifier = OneVsRestClassifier(MultinomialNB())
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clf.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
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else:
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clf.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(clf.correspondent_binarizer.classes_) > 0:
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logging.getLogger(__name__).info("Training correspondent classifier")
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clf.correspondent_classifier = MultinomialNB()
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clf.correspondent_classifier.fit(data_vectorized, labels_correspondent_vectorized)
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else:
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clf.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(clf.type_binarizer.classes_) > 0:
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logging.getLogger(__name__).info("Training document type classifier")
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clf.type_classifier = MultinomialNB()
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clf.type_classifier.fit(data_vectorized, labels_type_vectorized)
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else:
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clf.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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logging.getLogger(__name__).info("Saving models to " + settings.MODEL_FILE + "...")
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clf.save_classifier()
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clf.save_classifier()
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