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Added code that trains models based on data from the databasae

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Jonas Winkler
2018-09-03 15:55:41 +02:00
parent 350da81081
commit ca315ba76c
2 changed files with 149 additions and 50 deletions
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100
src/documents/management/commands/document_create_classifier.py Executable file
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@@ -0,0 +1,100 @@
import logging
import os.path
import pickle
from django.core.management.base import BaseCommand
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder
from documents.models import Document
from ...mixins import Renderable
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 Command(Renderable, BaseCommand):
help = """
There is no help.
""".replace(" ", "")
def __init__(self, *args, **kwargs):
BaseCommand.__init__(self, *args, **kwargs)
def handle(self, *args, **options):
data = list()
labels_tags = list()
labels_correspondent = list()
labels_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_type.append(doc.document_type.name if doc.document_type is not None else "-")
labels_correspondent.append(doc.correspondent.name if doc.correspondent is not None else "-")
tags = [tag.name for tag in doc.tags.all()]
labels_tags.append(tags)
# Step 2: vectorize data
logging.getLogger(__name__).info("Vectorizing data...")
data_vectorizer = CountVectorizer(analyzer='char', ngram_range=(1, 5), min_df=0.05)
data_vectorized = data_vectorizer.fit_transform(data)
tags_binarizer = MultiLabelBinarizer()
labels_tags_vectorized = tags_binarizer.fit_transform(labels_tags)
correspondent_binarizer = LabelEncoder()
labels_correspondent_vectorized = correspondent_binarizer.fit_transform(labels_correspondent)
type_binarizer = LabelEncoder()
labels_type_vectorized = type_binarizer.fit_transform(labels_type)
# Step 3: train the classifiers
if len(tags_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training tags classifier")
tags_classifier = OneVsRestClassifier(MultinomialNB())
tags_classifier.fit(data_vectorized, labels_tags_vectorized)
else:
tags_classifier = None
logging.getLogger(__name__).info("There are no tags. Not training tags classifier.")
if len(correspondent_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training correspondent classifier")
correspondent_classifier = MultinomialNB()
correspondent_classifier.fit(data_vectorized, labels_correspondent_vectorized)
else:
correspondent_classifier = None
logging.getLogger(__name__).info("There are no correspondents. Not training correspondent classifier.")
if len(type_binarizer.classes_) > 0:
logging.getLogger(__name__).info("Training document type classifier")
type_classifier = MultinomialNB()
type_classifier.fit(data_vectorized, labels_type_vectorized)
else:
type_classifier = None
logging.getLogger(__name__).info("There are no document types. Not training document type classifier.")
models_root = os.path.abspath(os.path.join(os.path.dirname(__name__), "..", "models", "models.pickle"))
logging.getLogger(__name__).info("Saving models to " + models_root + "...")
with open(models_root, "wb") as f:
pickle.dump(data_vectorizer, f)
pickle.dump(tags_binarizer, f)
pickle.dump(correspondent_binarizer, f)
pickle.dump(type_binarizer, f)
pickle.dump(tags_classifier, f)
pickle.dump(correspondent_classifier, f)
pickle.dump(type_classifier, f)

29
src/documents/management/commands/document_create_dataset.py
View File

@@ -1,11 +1,19 @@
from collections import Counter
from django.core.management.base import BaseCommand
from documents.models import Document, Tag
from documents.models import Document
from ...mixins import Renderable
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 Command(Renderable, BaseCommand):
help = """
@@ -15,15 +23,6 @@ class Command(Renderable, BaseCommand):
def __init__(self, *args, **kwargs):
BaseCommand.__init__(self, *args, **kwargs)
def preprocess_content(self, content):
content = content.lower()
content = content.strip()
content = content.replace("\n", " ")
content = content.replace("\r", " ")
while content.find(" ") > -1:
content = content.replace(" ", " ")
return content
def handle(self, *args, **options):
with open("dataset_tags.txt", "w") as f:
for doc in Document.objects.exclude(tags__is_inbox_tag=True):
@@ -32,19 +31,19 @@ class Command(Renderable, BaseCommand):
labels.append(tag.name)
f.write(",".join(labels))
f.write(";")
f.write(self.preprocess_content(doc.content))
f.write(preprocess_content(doc.content))
f.write("\n")
with open("dataset_types.txt", "w") as f:
for doc in Document.objects.exclude(tags__is_inbox_tag=True):
f.write(doc.document_type.name if doc.document_type is not None else "None")
f.write(";")
f.write(self.preprocess_content(doc.content))
f.write(preprocess_content(doc.content))
f.write("\n")
with open("dataset_correspondents.txt", "w") as f:
for doc in Document.objects.exclude(tags__is_inbox_tag=True):
f.write(doc.correspondent.name if doc.correspondent is not None else "None")
f.write(";")
f.write(self.preprocess_content(doc.content))
f.write(preprocess_content(doc.content))
f.write("\n")