From c958a7c593abf77f67ae364a480529df90dbf860 Mon Sep 17 00:00:00 2001 From: Trenton Holmes <797416+stumpylog@users.noreply.github.com> Date: Wed, 22 Feb 2023 20:03:23 -0800 Subject: [PATCH] Changes from a hash based system to a time based system to prevent extra retrains --- src/documents/classifier.py | 54 ++++---- src/documents/tests/data/model.pickle | Bin 156104 -> 0 bytes src/documents/tests/test_classifier.py | 170 ++++++++++++++++++------- 3 files changed, 151 insertions(+), 73 deletions(-) delete mode 100644 src/documents/tests/data/model.pickle diff --git a/src/documents/classifier.py b/src/documents/classifier.py index 66958087a..9a5728124 100644 --- a/src/documents/classifier.py +++ b/src/documents/classifier.py @@ -1,10 +1,10 @@ -import hashlib import logging import os import pickle import re import shutil import warnings +from datetime import datetime from typing import Iterator from typing import List from typing import Optional @@ -62,12 +62,13 @@ class DocumentClassifier: # v7 - Updated scikit-learn package version # v8 - Added storage path classifier - FORMAT_VERSION = 8 + # v9 - Changed from hash to time for training data check + FORMAT_VERSION = 9 def __init__(self): - # hash of the training data. used to prevent re-training when the + # last time training data was calculated. used to prevent re-training when the # training data has not changed. - self.data_hash: Optional[bytes] = None + self.last_data_change: Optional[datetime] = None self.data_vectorizer = None self.tags_binarizer = None @@ -91,7 +92,7 @@ class DocumentClassifier: ) else: try: - self.data_hash = pickle.load(f) + self.last_data_change = pickle.load(f) self.data_vectorizer = pickle.load(f) self.tags_binarizer = pickle.load(f) @@ -121,7 +122,7 @@ class DocumentClassifier: with open(target_file_temp, "wb") as f: pickle.dump(self.FORMAT_VERSION, f) - pickle.dump(self.data_hash, f) + pickle.dump(self.last_data_change, f) pickle.dump(self.data_vectorizer, f) pickle.dump(self.tags_binarizer, f) @@ -137,35 +138,40 @@ class DocumentClassifier: def train(self): + # Get non-inbox documents + docs_queryset = Document.objects.exclude(tags__is_inbox_tag=True) + + # No documents exit to train against + if docs_queryset.count() == 0: + raise ValueError("No training data available.") + + # No documents have changed since classifier was trained + latest_doc_change = docs_queryset.latest("modified").modified + if ( + self.last_data_change is not None + and self.last_data_change >= latest_doc_change + ): + return False + labels_tags = [] labels_correspondent = [] labels_document_type = [] labels_storage_path = [] - docs_queryset = Document.objects.order_by("pk").exclude(tags__is_inbox_tag=True) - - if docs_queryset.count() == 0: - raise ValueError("No training data available.") - # Step 1: Extract and preprocess training data from the database. logger.debug("Gathering data from database...") - m = hashlib.sha1() for doc in docs_queryset: - preprocessed_content = self.preprocess_content(doc.content) - m.update(preprocessed_content.encode("utf-8")) 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 = sorted( @@ -174,22 +180,14 @@ class DocumentClassifier: matching_algorithm=MatchingModel.MATCH_AUTO, ) ) - for tag in tags: - m.update(tag.to_bytes(4, "little", signed=True)) labels_tags.append(tags) y = -1 sd = doc.storage_path if sd and sd.matching_algorithm == MatchingModel.MATCH_AUTO: y = sd.pk - m.update(y.to_bytes(4, "little", signed=True)) labels_storage_path.append(y) - new_data_hash = m.digest() - - if self.data_hash and new_data_hash == self.data_hash: - return False - labels_tags_unique = {tag for tags in labels_tags for tag in tags} num_tags = len(labels_tags_unique) @@ -216,12 +214,16 @@ class DocumentClassifier: from sklearn.feature_extraction.text import CountVectorizer from sklearn.neural_network import MLPClassifier - from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer + from sklearn.preprocessing import LabelBinarizer + from sklearn.preprocessing import MultiLabelBinarizer # Step 2: vectorize data logger.debug("Vectorizing data...") def content_generator() -> Iterator[str]: + """ + Generates the content for documents, but once at a time + """ for doc in docs_queryset: yield self.preprocess_content(doc.content) @@ -299,7 +301,7 @@ class DocumentClassifier: "There are no storage paths. 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