Restructured code to use classes compatible with joblib and minimize hacks when saving

pes_to_spec/model.py

@@ -87,6 +87,16 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator):
         high_res_gc = fftconvolve(X, gaussian, mode="same", axes=1)
         return high_res_gc
 
+    def inverse_transform(self, Xt: np.ndarray) -> np.ndarray:
+        """
+        Dummy function to allow for an inverse transform.
+
+        Args:
+          Xt: Transformed variable.
+
+        Returns: Xt.
+        """
+        return Xt
 
 class UncertaintyHolder(TransformerMixin, BaseEstimator):
     """
@@ -573,7 +583,7 @@ class Model(TransformerMixin, BaseEstimator):
         pca_y = np.concatenate((high_pca["Y"],
                                 high_pca["Y"] + high_pca["Y_eps"]),
                                axis=0)
-        high_res_predicted = self.y_model.inverse_transform(pca_y)
+        high_res_predicted = self.y_model["pca"].inverse_transform(pca_y)
         expected = high_res_predicted[:n_trains, :]
         unc = high_res_predicted[n_trains:, :] - expected
         return dict(expected=expected,