Merge pull request #277 from xopt-org/imports

update init py files

xopt/generators/bayesian/bayesian_generator.py

@@ -173,7 +173,7 @@ def validate_torch_modules(cls, v):
             if v.startswith("base64:"):
                 v = decode_torch_module(v)
             elif os.path.exists(v):
-                v = torch.load(v)
+                v = torch.load(v, weights_only=False)
         return v
 
     @field_validator("gp_constructor", mode="before")

xopt/generators/bayesian/models/standard.py

@@ -102,7 +102,7 @@ def validate_torch_modules(cls, v):
                     if val.startswith("base64:"):
                         v[key] = decode_torch_module(val)
                     elif os.path.exists(val):
-                        v[key] = torch.load(val)
+                        v[key] = torch.load(val, weights_only=False)
 
         return v
 

xopt/generators/es/__init__.py

@@ -0,0 +1,3 @@
+from xopt.generators.es.extremumseeking import ExtremumSeekingGenerator
+
+__all__ = ["ExtremumSeekingGenerator"]

xopt/generators/rcds/__init__.py

@@ -0,0 +1,3 @@
+from xopt.generators.rcds.rcds import RCDSGenerator
+
+__all__ = ["RCDSGenerator"]

xopt/generators/scipy/__init__.py

@@ -0,0 +1,4 @@
+from xopt.generators.scipy.latin_hypercube import LatinHypercubeGenerator
+from xopt.generators.scipy.neldermead import NelderMeadGenerator
+
+__all__ = ["LatinHypercubeGenerator", "NelderMeadGenerator"]

xopt/pydantic.py

@@ -181,7 +181,7 @@ def decode_torch_module(modulestr: str):
     decoded = base64.standard_b64decode(base64str)
     decompressed = gzip.decompress(decoded)
     bytestream = io.BytesIO(decompressed)
-    module = torch.load(bytestream)
+    module = torch.load(bytestream, weights_only=False)
     return module
 
 
@@ -194,7 +194,7 @@ def validate_files(cls, value, info: ValidationInfo):
             if os.path.exists(value):
                 extension = value.split(".")[-1]
                 if extension == "pt":
-                    value = torch.load(value)
+                    value = torch.load(value, weights_only=False)
 
         return value