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Calculating anomaly_score and sending notifications for user models #929

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Calculating anomaly_score and sending notifications for user models #929

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91bbf7c
Calculating anomaly-score and sending notifications for user models
Knispel2 Dec 13, 2024
b688ba5
Merge branch 'astrolabsoftware:master' into master
Knispel2 Dec 13, 2024
23c9e2f
add def. model
Knispel2 Dec 13, 2024
e41a8f7
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Knispel2 Dec 13, 2024
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10 changes: 9 additions & 1 deletion bin/anomaly_archival.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
from fink_broker.spark_utils import init_sparksession, load_parquet_files

from fink_filters.filter_anomaly_notification.filter import anomaly_notification_

from fink_science.anomaly_detection.processor import ANOMALY_MODELS
from fink_broker.logging_utils import get_fink_logger, inspect_application

from fink_broker.hbase_utils import push_full_df_to_hbase
Expand Down Expand Up @@ -81,6 +81,14 @@ def main():
cut_coords=True,
)

for model in ANOMALY_MODELS:
df_proc = df.select(
'objectId', 'candidate.ra',
'candidate.dec', 'candidate.rb',
f'anomaly_score{model}', 'timestamp')
anomaly_notification_(df_proc, send_to_tg=False,
send_to_slack=False, send_to_anomaly_base=True, model=model)

# Keep only candidates of interest for all sky anomalies
oids = [int(i) for i in pdf["candid"].to_numpy()]
df_hbase = df.filter(df["candid"].isin(list(oids)))
Expand Down
7 changes: 5 additions & 2 deletions fink_broker/science.py
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Expand Up @@ -43,6 +43,7 @@
from fink_science.kilonova.processor import knscore
from fink_science.ad_features.processor import extract_features_ad
from fink_science.anomaly_detection.processor import anomaly_score
from fink_science.anomaly_detection.processor import ANOMALY_MODELS

from fink_science.random_forest_snia.processor import rfscore_rainbow_elasticc
from fink_science.snn.processor import snn_ia_elasticc, snn_broad_elasticc
Expand Down Expand Up @@ -410,10 +411,12 @@ def apply_science_modules(df: DataFrame, tns_raw_output: str = "") -> DataFrame:
]

df = df.withColumn("lc_features", extract_features_ad(*ad_args))

# Apply level one processor: anomaly_score
_LOG.info("New processor: Anomaly score")
df = df.withColumn("anomaly_score", anomaly_score("lc_features"))
ANOMALY_MODELS = [''] + ANOMALY_MODELS # '' - model for a public channel
for model in ANOMALY_MODELS:
_LOG.info(f"...Anomaly score{model}")
df = df.withColumn(f'anomaly_score{model}', anomaly_score("lc_features", F.lit(model)))

# split features
df = (
Expand Down