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changed classifier
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models/.keep
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0
models/.keep
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@ -2,12 +2,13 @@ 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.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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@ -87,7 +88,7 @@ class DocumentClassifier(object):
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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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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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@ -102,7 +103,7 @@ class DocumentClassifier(object):
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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 = 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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@ -110,7 +111,7 @@ class DocumentClassifier(object):
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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 = 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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@ -118,7 +119,7 @@ class DocumentClassifier(object):
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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 = MLPClassifier(verbose=True)
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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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@ -18,7 +18,7 @@ class Command(Renderable, BaseCommand):
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with open("dataset_tags.txt", "w") as f:
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for doc in Document.objects.exclude(tags__is_inbox_tag=True):
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labels = []
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for tag in doc.tags.all():
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for tag in doc.tags.filter(automatic_classification=True):
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labels.append(tag.name)
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f.write(",".join(labels))
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f.write(";")
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@ -27,14 +27,14 @@ class Command(Renderable, BaseCommand):
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with open("dataset_types.txt", "w") as f:
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for doc in Document.objects.exclude(tags__is_inbox_tag=True):
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f.write(doc.document_type.name if doc.document_type is not None else "None")
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f.write(doc.document_type.name if doc.document_type is not None and doc.document_type.automatic_classification else "-")
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f.write(";")
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f.write(preprocess_content(doc.content))
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f.write("\n")
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with open("dataset_correspondents.txt", "w") as f:
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for doc in Document.objects.exclude(tags__is_inbox_tag=True):
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f.write(doc.correspondent.name if doc.correspondent is not None else "None")
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f.write(doc.correspondent.name if doc.correspondent is not None and doc.correspondent.automatic_classification else "-")
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f.write(";")
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f.write(preprocess_content(doc.content))
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f.write("\n")
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