Update AGN & SLSN models for ELASTICC (#353)

* Joblib models updated. Pkl file converted to joblib

* AGN classifier now reads joblib

* Fixed path

* Use joblib instead of pickle to load the model

fink_science/agn/classifier.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import pickle
+import joblib
 import fink_science.agn.kernel as k
 import fink_science.agn.feature_extraction as fe
 import os
@@ -27,7 +27,7 @@
 def load_classifier(source):
     """
     load the random forest classifier trained to recognize the AGN
-    on binary cases : AGNs vs non-AGNs  (pickle format).
+    on binary cases : AGNs vs non-AGNs  (joblib format).
 
     Parameters
     ----------
@@ -58,8 +58,7 @@ def load_classifier(source):
     elif source == 'ZTF':
         model_path = k.CLASSIFIER_ZTF
 
-    with open(model_path, "rb") as f:
-        clf = pickle.load(f)
+    clf = joblib.load(model_path)
 
     return clf
 

fink_science/agn/kernel.py

@@ -17,7 +17,7 @@
 from fink_science import __file__
 
 curdir = os.path.dirname(os.path.abspath(__file__))
-CLASSIFIER_ELASTICC = curdir + "/data/models/AGN_elasticc_alerts.pkl"
-CLASSIFIER_ZTF = curdir + "/data/models/AGN_binary.pkl"
+CLASSIFIER_ELASTICC = curdir + "/data/models/AGN_elasticc_alerts.joblib"
+CLASSIFIER_ZTF = curdir + "/data/models/AGN_binary.joblib"
 MINIMUM_POINTS = 4
 MAXFEV = 3000

fink_science/agn/unit_examples.py

@@ -1,11 +1,9 @@
 import pandas as pd
 import numpy as np
 import fink_science.agn.kernel as k
-import pickle
+import joblib
 
-
-with open(k.CLASSIFIER_ELASTICC, "rb") as f:
-    clf_unit = pickle.load(f)
+clf_unit = joblib.load(k.CLASSIFIER_ELASTICC)
 
 raw_ztf_unit = pd.DataFrame(
     {