add save and load function for classes.

tensorneat/algorithm/base.py

@@ -1,12 +1,7 @@
-from utils import State
+from utils import State, StatefulBaseClass
 
 
-class BaseAlgorithm:
+class BaseAlgorithm(StatefulBaseClass):
-    def setup(self, state=State()):
-        """initialize the state of the algorithm"""
-
-        raise NotImplementedError
-
     def ask(self, state: State):
         """require the population to be evaluated"""
         raise NotImplementedError

tensorneat/algorithm/hyperneat/substrate/base.py

@@ -1,9 +1,7 @@
-from utils import State
+from utils import StatefulBaseClass
 
 
-class BaseSubstrate:
+class BaseSubstrate(StatefulBaseClass):
-    def setup(self, state=State()):
-        return state
 
     def make_nodes(self, query_res):
         raise NotImplementedError

tensorneat/algorithm/neat/ga/crossover/base.py

@@ -1,9 +1,6 @@
-from utils import State
+from utils import StatefulBaseClass
 
 
-class BaseCrossover:
+class BaseCrossover(StatefulBaseClass):
-    def setup(self, state=State()):
-        return state
-
     def __call__(self, state, randkey, genome, nodes1, nodes2, conns1, conns2):
         raise NotImplementedError

tensorneat/algorithm/neat/ga/mutation/base.py

@@ -1,9 +1,6 @@
-from utils import State
+from utils import StatefulBaseClass
 
 
-class BaseMutation:
+class BaseMutation(StatefulBaseClass):
-    def setup(self, state=State()):
-        return state
-
     def __call__(self, state, randkey, genome, nodes, conns, new_node_key):
         raise NotImplementedError

tensorneat/algorithm/neat/gene/base.py

@@ -1,8 +1,8 @@
 import jax, jax.numpy as jnp
-from utils import State
+from utils import State, StatefulBaseClass
 
 
-class BaseGene:
+class BaseGene(StatefulBaseClass):
     "Base class for node genes or connection genes."
     fixed_attrs = []
     custom_attrs = []
@@ -10,9 +10,6 @@ class BaseGene:
     def __init__(self):
         pass
 
-    def setup(self, state=State()):
-        return state
-
     def new_identity_attrs(self, state):
         # the attrs which do identity transformation, used in mutate add node
         raise NotImplementedError

tensorneat/algorithm/neat/gene/conn/base.py

@@ -1,4 +1,4 @@
-import jax, jax.numpy as jnp
+import jax
 from .. import BaseGene
 
 

tensorneat/algorithm/neat/genome/base.py

@@ -1,10 +1,10 @@
 import jax, jax.numpy as jnp
 from ..gene import BaseNodeGene, BaseConnGene
 from ..ga import BaseMutation, BaseCrossover
-from utils import State
+from utils import State, StatefulBaseClass
 
 
-class BaseGenome:
+class BaseGenome(StatefulBaseClass):
     network_type = None
 
     def __init__(

tensorneat/algorithm/neat/species/base.py

@@ -1,15 +1,12 @@
-from utils import State
+from utils import State, StatefulBaseClass
 from ..genome import BaseGenome
 
 
-class BaseSpecies:
+class BaseSpecies(StatefulBaseClass):
     genome: BaseGenome
     pop_size: int
     species_size: int
 
-    def setup(self, state=State()):
-        return state
-
     def ask(self, state: State):
         raise NotImplementedError
 

tensorneat/pipeline.py

@@ -6,10 +6,10 @@ from algorithm import BaseAlgorithm
 from problem import BaseProblem
 from problem.rl_env import RLEnv
 from problem.func_fit import FuncFit
-from utils import State
+from utils import State, StatefulBaseClass
 
 
-class Pipeline:
+class Pipeline(StatefulBaseClass):
     def __init__(
         self,
         algorithm: BaseAlgorithm,

tensorneat/problem/base.py

@@ -1,15 +1,11 @@
 from typing import Callable
 
-from utils import State
+from utils import State, StatefulBaseClass
 
 
-class BaseProblem:
+class BaseProblem(StatefulBaseClass):
     jitable = None
 
-    def setup(self, state: State = State()):
-        """initialize the state of the problem"""
-        return state
-
     def evaluate(self, state: State, randkey, act_func: Callable, params):
         """evaluate one individual"""
         raise NotImplementedError

tensorneat/utils/__init__.py

@@ -3,3 +3,4 @@ from .aggregation import Agg, agg_func, AGG_ALL
 from .tools import *
 from .graph import *
 from .state import State
+from .stateful_class import StatefulBaseClass

tensorneat/utils/state.py

@@ -30,6 +30,12 @@ class State:
     def __repr__(self):
         return f"State ({self.state_dict})"
 
+    def __getstate__(self):
+        return self.state_dict.copy()
+
+    def __setstate__(self, state):
+        self.__dict__["state_dict"] = state
+
     def tree_flatten(self):
         children = list(self.state_dict.values())
         aux_data = list(self.state_dict.keys())

tensorneat/utils/stateful_class.py

@@ -0,0 +1,44 @@
+from typing import Optional
+from . import State
+import pickle
+import datetime
+import warnings
+
+
+class StatefulBaseClass:
+    def setup(self, state=State()):
+        return state
+
+    def save(self, state: Optional[State] = None, path: Optional[str] = None):
+        if path is None:
+            time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+            path = f"./{self.__class__.__name__} {time}.pkl"
+        if state is not None:
+            self.__dict__["aux_for_state"] = state
+        with open(path, "wb") as f:
+            pickle.dump(self, f)
+
+    @classmethod
+    def load(cls, path: str, with_state: bool = False, warning: bool = True):
+        with open(path, "rb") as f:
+            obj = pickle.load(f)
+        if with_state:
+            if "aux_for_state" not in obj.__dict__:
+                if warning:
+                    warnings.warn(
+                        "This object does not have state to load, return empty state",
+                        category=UserWarning,
+                    )
+                return obj, State()
+            state = obj.__dict__["aux_for_state"]
+            del obj.__dict__["aux_for_state"]
+            return obj, state
+        else:
+            if "aux_for_state" in obj.__dict__:
+                if warning:
+                    warnings.warn(
+                        "This object state to load, ignore it",
+                        category=UserWarning,
+                    )
+                del obj.__dict__["aux_for_state"]
+            return obj