update problem and pipeline

tensorneat/problem/func_fit/__init__.py

@@ -1,4 +1,4 @@
-from .func_fit import FuncFit
 from .xor import XOR
 from .xor3d import XOR3d
-from .custom import CustomFuncFit
+from .custom import CustomFuncFit
+from .func_fit import FuncFit

tensorneat/problem/func_fit/custom.py

@@ -1,6 +1,4 @@
-from typing import Callable, Union, List, Tuple, Sequence
-
-import jax
+from typing import Callable, Union, List, Tuple
 from jax import vmap, Array, numpy as jnp
 import numpy as np
 

tensorneat/problem/func_fit/func_fit.py

@@ -1,19 +1,18 @@
 import jax
 import jax.numpy as jnp
 
+from ..base import BaseProblem
 from tensorneat.common import State
-from .. import BaseProblem
 
 
 class FuncFit(BaseProblem):
     jitable = True
 
-    def __init__(self, error_method: str = "mse", return_data: bool = False):
+    def __init__(self, error_method: str = "mse"):
         super().__init__()
 
         assert error_method in {"mse", "rmse", "mae", "mape"}
         self.error_method = error_method
-        self.return_data = return_data
 
     def setup(self, state: State = State()):
         return state
@@ -39,21 +38,16 @@ class FuncFit(BaseProblem):
         else:
             raise NotImplementedError
 
-        if self.return_data:
-            return -loss, self.inputs
-        else:
-            return -loss
+        return -loss
 
     def show(self, state, randkey, act_func, params, *args, **kwargs):
         predict = jax.vmap(act_func, in_axes=(None, None, 0))(
             state, params, self.inputs
         )
         inputs, target, predict = jax.device_get([self.inputs, self.targets, predict])
-        if self.return_data:
-            loss, _ = self.evaluate(state, randkey, act_func, params)
-        else:
-            loss = self.evaluate(state, randkey, act_func, params)
-        loss = -loss
+        fitness = self.evaluate(state, randkey, act_func, params)
+
+        loss = -fitness
 
         msg = ""
         for i in range(inputs.shape[0]):