added _remove_outliers args to __init__

src/openqdc/datasets/base.py

@@ -97,17 +97,27 @@ class BaseDataset:
     __average_nb_atoms__ = None
     __stats__ = {}
 
+    avg_options = {
+        "median": np.median,
+        "mean": np.mean,
+    } # options for removing outliers
+
     def __init__(
         self,
         energy_unit: Optional[str] = None,
         distance_unit: Optional[str] = None,
         overwrite_local_cache: bool = False,
         cache_dir: Optional[str] = None,
         remove_outliers: bool = True,
+        avg_fn = "median",
+        num_stds: float = 3.0,
     ) -> None:
         set_cache_dir(cache_dir)
         self.data = None
         self.remove_outliers = remove_outliers
+        self.avg_fn = avg_fn
+        self.num_stds = num_stds
+
         if not self.is_preprocessed():
             raise DatasetNotAvailableError(self.__name__)
         else:
@@ -169,18 +179,19 @@ def _compute_average_nb_atoms(self):
     def _remove_outliers(
             self, 
             formation_E: np.array, 
-            mean_or_median: str = "median", 
+            avg_fn: str = "median", 
             num_stds: float = 3.0,
         ) -> np.array:
         assert(
-            mean_or_median in ["mean", "median"],
-            f"{mean_or_median} is not a valid option, should be one of ['mean', 'median']"
+            avg_fn in BaseDataset.avg_options.keys(),
+            f"{avg_fn} is not a valid option, should be one of {list(BaseDataset.avg_options.keys())}"
         )
-        logger.info(f"Removing outliers outside {mean_or_median} +/- {num_stds} stds")
-        fn = np.median if mean_or_median == "median" else np.mean
+        logger.info(f"Removing outliers outside {avg_fn} +/- {num_stds} stds")
+        fn = BaseDataset.avg_options[avg_fn]
         mid = fn(formation_E)
         mask = np.logical_or(formation_E < mid - num_stds * formation_E.std(), formation_E > mid + num_stds * formation_E.std())
         formation_E = formation_E[~mask] # TODO: Christian, your formation E values are different than the ones I calculated yesterday, not sure why?
+        print(self.data.keys())
         for key in self.data:
             # TODO: We need a way to map the mask to the 'atomic_inputs' array
             if key != "atomic_inputs":
@@ -203,7 +214,9 @@ def _precompute_E(self):
 
         # remove outliers if requested in __init__
         if self.remove_outliers:
-            E = self._remove_outliers(np.squeeze(E.T))
+            E = self._remove_outliers(np.squeeze(E.T),
+                                      avg_fn=self.avg_fn,
+                                      num_stds=self.num_stds)
 
         inter_E_mean = np.nanmean(E / self.data["n_atoms"][:, None], axis=0)
         inter_E_std = np.nanstd(E / self.data["n_atoms"][:, None], axis=0)