fixed bug sorting on the fly

run_onthefly.py

@@ -70,17 +70,18 @@ def load_lc_csv(filename):
 
 
 def reformat_to_df(pred_probs, ids=None):
-    """
-    """
+    """ Reformat SNN predictions to a DataFrame
+
     # TO DO: suppport nb_inference != 1
+    """
     num_inference_samples = 1
 
     d_series = {}
     for i in range(pred_probs[0].shape[1]):
         d_series["SNID"] = []
         d_series[f"prob_class{i}"] = []
     for idx, value in enumerate(pred_probs):
-        d_series["SNID"] += ids[idx] if len(ids) > 0 else idx
+        d_series["SNID"] += [ids[idx]] if len(ids) > 0 else idx
         value = value.reshape((num_inference_samples, -1))
         value_dim = value.shape[1]
         for i in range(value_dim):
@@ -126,7 +127,8 @@ def reformat_to_df(pred_probs, ids=None):
 
     # Obtain predictions for full light-curve
     # Format: batch, nb_inference_samples, nb_classes
-    pred_probs = classify_lcs(df, args.model_file, args.device)
+    # Beware, ids are resorted while obtaining predictions!
+    ids_preds, pred_probs = classify_lcs(df, args.model_file, args.device)
 
     # ________________________
     # Optional

supernnova/validation/validate_onthefly.py

@@ -54,6 +54,7 @@ def format_data(df, settings):
     # fill dummies
     if "PEAKMJD" not in df.keys():
         df["PEAKMJD"] = np.zeros(len(df))
+
     # pivot
     df = pivot_dataframe_single_from_df(df, settings)
 
@@ -71,6 +72,16 @@ def format_data(df, settings):
 
 
 def classify_lcs(df, model_file, device):
+    """ Obtain predictions for light-curves
+    Args:
+        df (DataFrame): light-curves to classify
+        model_file (str): Path+name of model to use for predictions
+        device (str): wehter to use cuda or cpu
+
+    Returns:
+        idx (list): light-curve indices after classification (they are resorted)
+        preds (np.array): predictions for this model (shape= len(idx),model_nb_class)
+    """
     # init
     settings = get_settings(model_file)
     settings.use_cuda = True if "cuda" in str(device) else False
@@ -134,4 +145,4 @@ def classify_lcs(df, model_file, device):
     # B, inf_samples, nb_classes
     preds = np.stack(list_preds, axis=1)
 
-    return preds
+    return df.index.unique(), preds