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rework most of the logging

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jonaswinkler
2021-02-05 01:10:29 +01:00
parent 0d3ab3aaf7
commit 431d4fd8e4
18 changed files with 80 additions and 71 deletions
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20
src/documents/classifier.py
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@@ -13,7 +13,7 @@ class IncompatibleClassifierVersionError(Exception):
pass
logger = logging.getLogger(__name__)
logger = logging.getLogger("paperless.classifier")
def preprocess_content(content):
@@ -115,7 +115,7 @@ class DocumentClassifier(object):
labels_document_type = list()
# Step 1: Extract and preprocess training data from the database.
logging.getLogger(__name__).debug("Gathering data from database...")
logger.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)
@@ -162,7 +162,7 @@ class DocumentClassifier(object):
num_correspondents = len(set(labels_correspondent) | {-1}) - 1
num_document_types = len(set(labels_document_type) | {-1}) - 1
logging.getLogger(__name__).debug(
logger.debug(
"{} documents, {} tag(s), {} correspondent(s), "
"{} document type(s).".format(
len(data),
@@ -173,7 +173,7 @@ class DocumentClassifier(object):
)
# Step 2: vectorize data
logging.getLogger(__name__).debug("Vectorizing data...")
logger.debug("Vectorizing data...")
self.data_vectorizer = CountVectorizer(
analyzer="word",
ngram_range=(1, 2),
@@ -183,7 +183,7 @@ class DocumentClassifier(object):
# Step 3: train the classifiers
if num_tags > 0:
logging.getLogger(__name__).debug("Training tags classifier...")
logger.debug("Training tags classifier...")
if num_tags == 1:
# Special case where only one tag has auto:
@@ -202,12 +202,12 @@ class DocumentClassifier(object):
self.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
else:
self.tags_classifier = None
logging.getLogger(__name__).debug(
logger.debug(
"There are no tags. Not training tags classifier."
)
if num_correspondents > 0:
logging.getLogger(__name__).debug(
logger.debug(
"Training correspondent classifier..."
)
self.correspondent_classifier = MLPClassifier(tol=0.01)
@@ -217,13 +217,13 @@ class DocumentClassifier(object):
)
else:
self.correspondent_classifier = None
logging.getLogger(__name__).debug(
logger.debug(
"There are no correspondents. Not training correspondent "
"classifier."
)
if num_document_types > 0:
logging.getLogger(__name__).debug(
logger.debug(
"Training document type classifier..."
)
self.document_type_classifier = MLPClassifier(tol=0.01)
@@ -233,7 +233,7 @@ class DocumentClassifier(object):
)
else:
self.document_type_classifier = None
logging.getLogger(__name__).debug(
logger.debug(
"There are no document types. Not training document type "
"classifier."
)