Merge pull request #18 from LSSTDESC/ceci2

Update for ceci version 2

pyproject.toml

@@ -16,7 +16,7 @@ classifiers = [
 ]
 dynamic = ["version"]
 dependencies = [
-    "pz-rail-base",
+    "pz-rail-base>=1.0.3",
     "scikit-learn",
 ]
 

src/rail/estimation/algos/k_nearneigh.py

@@ -65,10 +65,10 @@ class KNearNeighInformer(CatInformer):
                           nneigh_min=Param(int, 3, msg="int, min number of near neighbors to use for PDF fit"),
                           nneigh_max=Param(int, 7, msg="int, max number of near neighbors to use ofr PDF fit"))
 
-    def __init__(self, args, comm=None):
+    def __init__(self, args, **kwargs):
         """ Constructor
         Do CatInformer specific initialization, then check on bands """
-        CatInformer.__init__(self, args, comm=comm)
+        super().__init__(args, **kwargs)
 
         usecols = self.config.bands.copy()
         usecols.append(self.config.redshift_col)
@@ -150,15 +150,15 @@ class KNearNeighEstimator(CatEstimator):
                           mag_limits=SHARED_PARAMS,
                           redshift_col=SHARED_PARAMS)
 
-    def __init__(self, args, comm=None):
+    def __init__(self, args, **kwargs):
         """ Constructor:
         Do Estimator specific initialization """
         self.sigma = None
         self.numneigh = None
         self.model = None
         self.trainszs = None
         self.zgrid = None
-        CatEstimator.__init__(self, args, comm=comm)
+        super().__init__(args, **kwargs)
         usecols = self.config.bands.copy()
         usecols.append(self.config.redshift_col)
         self.usecols = usecols

src/rail/estimation/algos/nz_dir.py

@@ -41,11 +41,6 @@ class NZDirInformer(CatInformer):
                           distance_delta=Param(float, 1.e-6, msg="padding for distance calculation"),
                           hdf5_groupname=Param(str, "photometry", msg="name of hdf5 group for data, if None, then set to ''"))
 
-    def __init__(self, args, comm=None):
-        """ Constructor:
-        Do Informer specific initialization """
-        CatInformer.__init__(self, args, comm=comm)
-
     def run(self):
         from sklearn.neighbors import NearestNeighbors
 
@@ -118,15 +113,15 @@ class NZDirSummarizer(CatEstimator):
     outputs = [('output', QPHandle),
                ('single_NZ', QPHandle)]
 
-    def __init__(self, args, comm=None):
+    def __init__(self, args, **kwargs):
         self.zgrid = None
         self.model = None
         self.distances = None
         self.szusecols = None
         self.szweights = None
         self.sz_mag_data = None
         self.bincents = None
-        CatEstimator.__init__(self, args, comm=comm)
+        super().__init__(args, **kwargs)
 
     def open_model(self, **kwargs):
         CatEstimator.open_model(self, **kwargs)

src/rail/estimation/algos/random_forest.py

@@ -38,9 +38,6 @@ class RandomForestInformer(CatInformer):
         random_seed=Param(int, msg="random seed"),
         no_assign=Param(int, -99, msg="Value for no assignment flag"),)
     outputs = [('model', ModelHandle)]
-
-    def __init__(self, args, comm=None):
-        CatInformer.__init__(self, args, comm=comm)
 
     def run(self):
         # Load the training data
@@ -107,10 +104,6 @@ class RandomForestClassifier(CatClassifier):
         class_bands=Param(tuple, ["r","i","z"], msg="Which bands to use for classification"),
         bands=Param(dict, {"r":"mag_r_lsst", "i":"mag_i_lsst", "z":"mag_z_lsst"}, msg="column names for the the bands"),)
     outputs = [('output', Hdf5Handle)]
-
-    def __init__(self, args, comm=None):
-        CatClassifier.__init__(self, args, comm=comm)
-
 
     def open_model(self, **kwargs):
         CatClassifier.open_model(self, **kwargs)

src/rail/estimation/algos/sklearn_neurnet.py

@@ -85,10 +85,10 @@ class SklNeurNetInformer(CatInformer):
                                           "not optimally)"))
 
 
-    def __init__(self, args, comm=None):
+    def __init__(self, args, **kwargs):
         """ Constructor:
         Do CatInformer specific initialization """
-        CatInformer.__init__(self, args, comm=comm)
+        super().__init__(args, **kwargs)
         if self.config.ref_band not in self.config.bands:
             raise ValueError("ref_band not present in bands list! ")
 
@@ -127,10 +127,10 @@ class SklNeurNetEstimator(CatEstimator):
                           nondetect_val=SHARED_PARAMS,
                           bands=SHARED_PARAMS)
 
-    def __init__(self, args, comm=None):
+    def __init__(self, args, **kwargs):
         """ Constructor:
         Do CatEstimator specific initialization """
-        CatEstimator.__init__(self, args, comm=comm)
+        super().__init__(args, **kwargs)
         if self.config.ref_band not in self.config.bands:
             raise ValueError("ref_band is not in list of bands!")