Superluminous supernovae classifier for ZTF (#552)

* first commit superluminous ztf

* Documentation

* pep8 formating

* Add extra dependencies for the tests

* Fixed remove None

* Removed useless imports

* added header + minor changes

* Only compute lc>30 days

* fixed indentation consistency

* fix indentation fr

* Minor consistency changes

* Updated classifier with more SLSN-I

* Fixed tests

* Fixed doctests

* Added is_transient filtering

* Code optimization

* pep8 fix

* All invalid alerts=-1

---------

Co-authored-by: Etienne Russeil <[email protected]>

Author: erusseil

fink_science/data/models/superluminous_classifier.joblib

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+# Copyright 2025 AstroLab Software
+# Author: Etienne Russeil
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from fink_science import __file__
+
+curdir = os.path.dirname(os.path.abspath(__file__))
+
+classifier_path = curdir + "/data/models/superluminous_classifier.joblib"
+band_wave_aa = {1: 4770.0, 2: 6231.0, 3: 7625.0}
+temperature = "sigmoid"
+bolometric = "bazin"
+min_points_total = 7
+min_points_perband = 3
+min_duration = 30

fink_science/ztf/superluminous/__init__.py

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+# Copyright 2020-2023 AstroLab Software
+# Author: Etienne Russeil
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from line_profiler import profile
+from fink_science import __file__
+from pyspark.sql.functions import pandas_udf, PandasUDFType
+from pyspark.sql.types import DoubleType
+from fink_science.tester import spark_unit_tests
+import numpy as np
+import pandas as pd
+import fink_science.ztf.superluminous.slsn_classifier as slsn
+from fink_science.ztf.superluminous.kernel import classifier_path
+import joblib
+import os
+
+
+@pandas_udf(DoubleType())
+@profile
+def superluminous_score(
+    cjd: pd.Series,
+    cfid: pd.Series,
+    cmagpsf: pd.Series,
+    csigmapsf: pd.Series,
+    distnr: pd.Series,
+    is_transient: pd.Series,
+) -> pd.Series:
+    """High level spark wrapper for the superluminous classifier on ztf data
+
+    Parameters
+    ----------
+    cjd: Spark DataFrame Column
+        JD times (vectors of floats)
+    cfid: Spark DataFrame Column
+        Filter IDs (vectors of str)
+    cmagpsf, csigmapsf: Spark DataFrame Columns
+        Magnitude and magnitude error from photometry (vectors of floats)
+    distnr: Spark DataFrame Column
+        The angular distance to the nearest reference source.
+    is_transient: Spark DataFrame Column
+        Is the source likely a transient.
+
+    Returns
+    -------
+    np.array
+        Superluminous supernovae classification probability vector
+        Return 0 if not enough points were available for feature extraction
+
+    Examples
+    --------
+    >>> from fink_utils.spark.utils import concat_col
+    >>> from pyspark.sql import functions as F
+    >>> from fink_filters.ztf.filter_transient_complete.filter import transient_complete_filter
+    >>> from fink_science.ztf.transient_features.processor import extract_transient_features
+    >>> sdf = spark.read.load(ztf_alert_sample)
+    >>> sdf = extract_transient_features(sdf)
+    >>> sdf = sdf.withColumn(
+    ... "is_transient",
+    ... transient_complete_filter(
+    ... "faint", "positivesubtraction", "real", "pointunderneath",
+    ... "brightstar", "variablesource", "stationary", "roid"))
+
+    # Required alert columns
+    >>> what = ['jd', 'fid', 'magpsf', 'sigmapsf']
+
+    # Use for creating temp name
+    >>> prefix = 'c'
+    >>> what_prefix = [prefix + i for i in what]
+
+    # Append temp columns with historical + current measurements
+    >>> for colname in what:
+    ...     sdf = concat_col(sdf, colname, prefix=prefix)
+
+    # Perform the fit + classification (default model)
+    >>> args = [F.col(i) for i in what_prefix]
+    >>> args += ["candidate.distnr", "is_transient"]
+    >>> sdf = sdf.withColumn('proba', superluminous_score(*args))
+    >>> sdf.filter(sdf['proba']==-1.0).count()
+    57
+    """
+    pdf = pd.DataFrame(
+        {
+            "cjd": cjd,
+            "cmagpsf": cmagpsf,
+            "csigmapsf": csigmapsf,
+            "cfid": cfid,
+            "distnr": distnr,
+            "is_transient": is_transient,
+        }
+    )
+
+    # If no alert pass the transient filter,
+    # directly return invalid value for everyone.
+    if sum(pdf["is_transient"]) == 0:
+        return pd.Series([-1.0]*len(pdf))
+
+    else:
+
+        # Initialise all probas to -1
+        probas_total = np.zeros(len(pdf), dtype=float) - 1
+        mask_valid = pdf["is_transient"]
+
+        # select only trasnient alerts
+        pdf_valid = pdf[mask_valid]
+
+        # Assign default -1 proba for every valid alert
+        probas = np.zeros(len(pdf_valid), dtype=float) - 1
+
+        pdf_valid = slsn.compute_flux(pdf_valid)
+        pdf_valid = slsn.remove_nan(pdf_valid)
+
+        # Perform feature extraction
+        features = slsn.extract_features(pdf_valid)
+
+        # Load classifier
+        clf = joblib.load(classifier_path)
+
+        # Modify proba for alerts that were feature extracted
+        extracted = np.sum(features.isnull(), axis=1) == 0
+        probas[extracted] = clf.predict_proba(
+            features.loc[extracted, clf.feature_names_in_]
+        )[:, 1]
+
+        probas_total[mask_valid] = probas
+
+        return pd.Series(probas_total)
+
+
+if __name__ == "__main__":
+    globs = globals()
+    path = os.path.dirname(__file__)
+
+    ztf_alert_sample = "file://{}/data/alerts/datatest/part-00003-bdab8e46-89c4-4ac1-8603-facd71833e8a-c000.snappy.parquet".format(
+        path
+    )
+    globs["ztf_alert_sample"] = ztf_alert_sample
+
+    # Run the test suite
+    spark_unit_tests(globs)