issue1: type error,fix bug on sntypes

supernnova/data/make_dataset.py

@@ -105,7 +105,7 @@ def build_traintestval_splits(settings):
     # Save a dataframe to record train/test/val split for
     # binary, ternary and all-classes classification
     for dataset in ["saltfit", "photometry"]:
-        for nb_classes in list(set([2, len(settings.sntypes.keys())])):
+        for nb_classes in list([2, len(settings.sntypes.keys())]):
             logging_utils.print_green(
                 f"Computing {dataset} splits for {nb_classes}-way classification"
             )
@@ -470,7 +470,7 @@ def process_single_csv(file_path, settings):
     # Merge left on df: len(df) will not change and will now include
     # relevant columns from df_SNID
     merge_columns = ["SNID"]
-    for c_ in [2, list(set(len(settings.sntypes.keys())))]:
+    for c_ in [2, len(settings.sntypes.keys())]:
         merge_columns += [f"target_{c_}classes"]
         for dataset in ["photometry", "saltfit"]:
             merge_columns += [f"dataset_{dataset}_{c_}classes"]
@@ -528,8 +528,9 @@ def preprocess_data(settings):
             # Need to cast to list because executor returns an iterator
             host_spe_tmp += list(executor.map(parallel_fn,
                                               list_files[start:end]))
-    # for debugging
+    # for debugging only (if lines 520-531 are commented)
     # host_spe_tmp.append(process_single_FITS(list_files[0], settings))
+    # host_spe_tmp.append(process_single_csv(list_files[0], settings))
     # Save host spe for plotting and performance tests
     host_spe = [item for sublist in host_spe_tmp for item in sublist]
     pd.DataFrame(host_spe, columns=["SNID"]).to_pickle(
@@ -597,7 +598,7 @@ def pivot_dataframe_single(filename, settings):
     # drop columns that won"t be used onwards
     df = df.drop(["MJD", "delta_time"], 1)
     class_columns = []
-    for c_ in list(set([2, len(settings.sntypes.keys())])):
+    for c_ in list([2, len(settings.sntypes.keys())]):
         class_columns += [f"target_{c_}classes"]
         for dataset in ["photometry", "saltfit"]:
             class_columns += [f"dataset_{dataset}_{c_}classes"]