Fink SNAD Anomaly Detection Model
Project description
Fink anomaly detection model
A set of modules for training models for finding anomalies in photometric data. There are currently two entry points via the console: fink_ad_model_train and get_anomaly_reactions.
fink_ad_model_train
The module trains the AADForest model using expert reactions from the C055ZJJ6N2AE channels in Slack and -1001898265997 in Telegram. It creates the following files:
- _g_means.csv and _r_means.csv -- averages over the training dataset;
- _reactions_g.csv and _reactions_r.csv -- training datasets for additional training of the AADForest algorithm, based on expert reactions from Slack and Telegram channels;
- forest_g_AAD.onnx -- model for _g filter
- forest_r_AAD.onnx -- model for _r filter
optional arguments:
--dataset_dir DATASET_DIR Input dir for dataset (default: './lc_features_20210617_photometry_corrected.parquet')
--n_jobs N_JOBS
Number of threads (default: -1)
usage: fink_ad_model_train [-h] [--dataset_dir DATASET_DIR] [--n_jobs N_JOBS]
get_anomaly_reactions
Uploading anomaly reactions from messengers. It creates the following files:
- _reactions_g.csv and _reactions_r.csv -- training datasets for additional training of the AADForest algorithm, based on expert reactions from Slack and Telegram channels;
optional arguments:
--slack_channel SLACK_CHANNEL Slack Channel ID (default: 'C055ZJJ6N2AE') --tg_channel TG_CHANNEL Telegram Channel ID (default: -1001898265997)
usage: get_anomaly_reactions [-h] [--slack_channel SLACK_CHANNEL] [--tg_channel TG_CHANNEL]
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