Feat: Spam Detection Feature #693
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feat: rewrite UserPipeline to include user id feat: correct user pipeline for user id feat: fix user id column in dataframes
- use 'first_name', 'last_name', 'is_active', 'email', 'affiliations', and 'bio' from MemberProfile - update user pipeline to fix latency issues - small fix in all_users_df(). - Convert df.value from markup to string - Fix name of df.columns fix: modify the partial_train to use the correct tokenizer
feat: - save to database from df - save recommendations - add load_labels() function to curator/spam_detect.py - load_labels() will take filepath of dataset and laod a dataframe consist of user__id and is_spam columns. Then it will initialize the SpamRecommendation table. - get all unlabelled users in dataframe chore: - migrations for altering SpamRecommendation fix:fixed SpamRecommendation __str__ function fix: - using None instead of an extra column in SpamRecommendation - Aiko had the idea of using None instead of an extra field which specified if a model has been labelled before. So, we are going to switch to that.
refactor: Move BioSpamClassifier to spam_detection_model.py and change functions names in SpamClassifier chore:removed print statements feat: add stub dataset for initial traning. This dataset should be replaced by an actual dataset with correct labels add 'TODO' comments on the parts to be fixed.
chore: todo noel chore: organized imports fix: fix the issue that data in database is not updated
…ile is created feat: - fit text spam classifier - prediction function in classifiers
…lass SpamClassifier refactor: refactored UserMetadataSpamClassifier, integrate TextSpamClassifier, change filenames, UserPipeline to UserSpamStatusProcessor, and SpamRecommendation to UserSpamStatus
feat: added model validation in TextClassifier fix: fixed positional argument bug - fixed positional argument bug - dataset.csv replaced and create a directory in shared folder for spam detection related files fix: fix typing issue in df[labelled_by_curator] column - manual tests on curator_spam_detection management command passed only for UserMetadataSpamClassifier.
…m spam_detection_models.py to spam.py, dataset.csv added under django/curator/
fix: fixed KeyError bug in TextSpamClassifier refactor: - create new file 'spam_processor.py' for UserSpamStatusProcessor. - change name from dataset.csv to spam_detaset.csv
fix: - move SPAM_DIR_PATH into settings - set SPAM_DIR_PATH as a pathlib.Path - remove last reference to update_labelled_by_curator - adjust test curator labelling references - use assertCountEqual for order independent comparison - could also convert to sets because there shouldn't be any duplicates refactor: restructure code and tests - tests should use SpamDetector entrypoint instead of instantiating - individual components to ensure proper initialization - move initial training dataset path into settings - move UserSpamStatusProcessor from detected file into curator/models.py as a collaborating class of UserSpamStatus. Should consider integrating more tightly into the UserSpamStatus objects manager Co-Authored-By: Allen Lee <[email protected]>
…al_fit() because CountVectorizer requires a model to fit the entire training dataset. Fix the management commands and tests accordingly. Also re-generated migration files
also clean up duplicate / dead imports
… to the spam feature. - adding headline comments for the functions. - Cleaning up the management command code and clarifying code responsibilities. - Bettering execution messages.
…d_by_curator=None - using get_all_users_df() instead of get_unlabelled_by_curator_df() to obtain dataframe, because a user previously labelled as ham may turn into spam. - improved the management command messages. - added exception handling for file operations. - replaced MultinomialNB with XGboost.
…ated architecture.
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Attempts to close https://github.com/comses/planning/issues/113
Squashed commits and solved merge conflicts.
Summary
Management commands for Machine Learning spam detection.
Features
Before running the commands, make sure spam_dataset.csv is located in the curator folder.
spam_dataset.csv consist of user_id and is_spam columns. is_spam column contains 1(Spam) or 0(Ham).
XGBoostClassifier() ... Uses XGboost as a classifier. Takes a data frame that has columns "user_id" and "input_data." The "input_data" column is a numerical vector where the selected fields are encoded by an encoder.
CountVectEncoder() ... Uses CountVectorizer as an encoder. Takes selected fields from "user_id," "labelled_by_curator," "first_name," "last_name," "is_active," "email," "affiliations," "bio," "research_interests" of the MemberProfiles as input.
Run the following command to get a list of spam users.
./manage.py curator_spam_detection --predictoptions
./manage.py curator_spam_detection --fit./manage.py curator_spam_detection --get_model_metrics./manage.py curator_spam_detection --load_labelsTests
Wrote 16 unit tests using Django tests