History

xopt/base.py

@@ -11,6 +11,7 @@
 
 # from xopt.generators import get_generator_and_defaults
 from xopt.pydantic import XoptBaseModel
+from xopt.utils import build_generator_from_saved_state
 from xopt.vocs import VOCS
 
 __version__ = _version.get_versions()["version"]
@@ -66,7 +67,8 @@ def __init__(
         Initialize Xopt object using either a config dictionary or explicitly
 
         Args:
-            config: dict, or YAML or JSON str or file. This overrides all other arguments.
+            config: dict, or YAML or JSON str or file.
+            This overrides all other arguments.
 
             generator: Generator object
             evaluator: Evaluator object
@@ -310,10 +312,17 @@ def check_components(self):
         if self.vocs is None:
             raise XoptError("Xopt VOCS is not specified")
 
+    def rebuild_from_previous_state(self, index):
+        """rebuild generator from saved history"""
+        if self.options.dump_file is not None:
+            return build_generator_from_saved_state(
+                index=index, dump_file=self.options.dump_file
+            )
+
     def dump_state(self):
         """dump data to file"""
         if self.options.dump_file is not None:
-            output = state_to_dict(self)
+            output = state_to_dict(self, include_history=True)
             with open(self.options.dump_file, "w") as f:
                 yaml.dump(output, f)
             logger.debug(f"Dumped state to YAML file: {self.options.dump_file}")
@@ -394,7 +403,8 @@ def yaml(self, filename=None, *, include_data=False):
 
     def __repr__(self):
         """
-        Returns infor about the Xopt object, including the YAML representation without data.
+        Returns infor about the Xopt object,
+        including the YAML representation without data.
         """
         return f"""
             Xopt
@@ -489,7 +499,7 @@ def xopt_kwargs_from_dict(config: dict) -> dict:
     }
 
 
-def state_to_dict(X, include_data=True):
+def state_to_dict(X, include_data=True, include_history=False):
     # dump data to dict with config metadata
     output = {
         "xopt": json.loads(X.options.json()),
@@ -503,4 +513,7 @@ def state_to_dict(X, include_data=True):
     if include_data:
         output["data"] = json.loads(X.data.to_json())
 
+    if include_history:
+        output["history"] = json.loads(X.generator.json())
+
     return output

xopt/generator.py

@@ -46,6 +46,10 @@ def __init__(self, **kwargs):
             vocs: The vocs to use.
             options: The options to use.
         """
+        print("start")
+        for key, value in kwargs.items():
+            print(key, value)
+        print("end")
         super().__init__(**kwargs)
         _check_vocs(self.vocs, self.supports_multi_objective)
         logger.info(f"Initialized generator {self.name}")

xopt/pydantic.py

@@ -421,7 +421,8 @@ def map(self, fn, *iter: Iterable, **kwargs) -> Iterable[Future]:
 
 
 def get_callable_from_string(callable: str, bind: Any = None) -> Callable:
-    """Get callable from a string. In the case that the callable points to a bound method,
+    """Get callable from a string.
+    In the case that the callable points to a bound method,
     the function returns a callable taking the bind instance as the first arg.
 
     Args:

xopt/utils.py

@@ -8,6 +8,9 @@
 import pandas as pd
 import torch
 import yaml
+from pydantic import parse_obj_as
+
+from xopt.generators import get_generator
 
 from .pydantic import get_descriptions_defaults
 from .vocs import VOCS
@@ -172,6 +175,21 @@ def read_xopt_csv(*files):
     return pd.concat(dfs)
 
 
+def build_generator_from_saved_state(index, dump_file):
+    """rebuild generator from saved history"""
+    with open(dump_file, "r") as file:
+        data = yaml.safe_load(file)
+
+    list_of_saved_generators = data["history"]
+    desired_state = list_of_saved_generators[index]
+
+    # desired_state['vocs'] = data['vocs']
+    generator_class = get_generator(data["generator"].pop("name"))
+    rebuilt_generator = parse_obj_as(generator_class, desired_state)
+
+    return rebuilt_generator
+
+
 def visualize_model(generator, data, axes=None):
     test_x = torch.linspace(*torch.tensor(generator.vocs.bounds.flatten()), 100)
     generator.add_data(data)