Fink 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;
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]
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for fink_anomaly_detection_model-0.4.13.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1726b63b807f95b895c404db919c04d420043f886933e6512b01adc25a97ee89 |
|
MD5 | e60e0b5a83f23f4fe300038b80cecb10 |
|
BLAKE2b-256 | 4edb590446fe145c6ec05e2c88c40abb2d7c2513eeaceaf86097b2db5aceccc8 |
Hashes for fink_anomaly_detection_model-0.4.13-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 30943826b404d0c97e2ca9573592ee9906da1fd0fdb10dd80194d2f2175b8452 |
|
MD5 | d9449e67d47294dc8678aef4bd358899 |
|
BLAKE2b-256 | 1a4645a255a4aae7af5251e254a26f1bb76cbc8b88eb3772e6b28df85c663a58 |