(MA) implemented endpointOverride parameter to allow user to specif…

…y the base URL to use and point to custom provider or a specific sub-database of a provider

pysipfenn/core/modelAdjusters.py

@@ -667,6 +667,14 @@ class OPTIMADEAdjuster(LocalAdjuster):
         maxResults: The maximum number of results to be fetched from the OPTIMADE API for a given query. Default is
             ``10000`` which is a very high number for most re-training tasks. If you are fetching a lot of data, it's
             possible the query is too broad, and you should consider narrowing it down.
+        endpointOverride: List of URL strings with the endpoint to be used for the OPTIMADE queries. This is an advanced
+            option allowing you to ignore the ``provider`` parameter and directly specify the endpoint to be used. It is
+            useful if you want to use a specific version of the provider's endpoint or narrow down the query to a
+            sub-database (Alexandria has two different endpoints for PBEsol and SCAN, for instance). You can also use it
+            to query unofficial endpoints. Make sure to (a) include protocol (``http://`` or ``https://``) and (b) not
+            include version (``/v1/``), nor the specific endpoint (``/structures``) as the client will add them. I.e.,
+            you want ``https://alexandria.icams.rub.de/pbesol`` rather than
+            ``alexandria.icams.rub.de/pbesol/v1/structures``. Default is ``None`` which has no effect.
     """
 
     def __init__(
@@ -704,15 +712,16 @@ def __init__(
             descriptor: Literal["Ward2017", "KS2022"] = "KS2022",
             useClearML: bool = False,
             taskName: str = "OPTIMADEFineTuning",
-            maxResults: int = 10000
+            maxResults: int = 10000,
+            endpointOverride: List[str] = None
     ) -> None:
         from optimade.client import OptimadeClient
 
         assert isinstance(calculator, Calculator), "The calculator must be an instance of the Calculator class."
         assert isinstance(model, str), "The model must be a string with the name of the model to be adjusted."
         assert isinstance(provider, str), "The provider must be a string with the name of the provider to be used."
         assert len(provider) != 0, "The provider must not be an empty string."
-        assert targetPath and isinstance(targetPath, list), "The target path must be a list of strings pointing to the target data in the OPTIMADE response."
+        assert targetPath and isinstance(targetPath, list) or isinstance(targetPath, tuple), "The target path must be a list of strings pointing to the target data in the OPTIMADE response."
         assert len(targetPath) > 0, "The target path must not be empty, i.e., it cannot point to no data."
         if provider != "mp" and targetPath == ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'):
             raise ValueError("You are utilizing the default (example) property target path specific to the Materials "
@@ -734,11 +743,20 @@ def __init__(
         self.descriptor = descriptor
         self.targetPath = targetPath
         self.provider = provider
-        self.client = OptimadeClient(
-            use_async=False,
-            include_providers=[provider],
-            max_results_per_provider=maxResults
-        )
+        if endpointOverride is None:
+            self.client = OptimadeClient(
+                use_async=False,
+                include_providers=[provider],
+                max_results_per_provider=maxResults
+            )
+        else:
+            assert isinstance(endpointOverride, list) or isinstance(endpointOverride, tuple), "The endpoint override must be a list of strings."
+            assert len(endpointOverride) != 0, "The endpoint override must not be an empty list."
+            self.client = OptimadeClient(
+                use_async=False,
+                base_urls=endpointOverride,
+                max_results_per_provider=maxResults
+            )
 
         if self.descriptor == "Ward2017":
             self.descriptorData: np.ndarray = np.empty((0, 271))