improve: replace class configs

Author: samedii

lantern/early_stopping.py

@@ -1,19 +1,17 @@
 from typing import Optional
+
 import torch.utils.tensorboard
 
 from lantern import FunctionalBase
 
 
-class EarlyStopping(FunctionalBase):
+class EarlyStopping(FunctionalBase, arbitrary_types_allowed=True):
     """Keeps track of the best score and how long ago it was calculated."""
 
     tensorboard_logger: torch.utils.tensorboard.SummaryWriter
     best_score: Optional[float] = None
     scores_since_improvement: int = -1
 
-    class Config:
-        arbitrary_types_allowed = True
-
     def score(self, value):
         if self.best_score is None or value > self.best_score:
             return self.replace(

lantern/metric.py

@@ -1,17 +1,15 @@
-import numpy as np
 import functools
+from typing import Any, Callable, List, Optional, Union
+
+import numpy as np
+
 from lantern import FunctionalBase, star
-from typing import Callable, Any, Optional, List, Union
 
 
 class MapMetric(FunctionalBase):
     map_fn: Optional[Callable[..., Any]]
     state: List[Any]
 
-    class Config:
-        arbitrary_types_allowed = True
-        allow_mutation = True
-
     def __init__(self, state=list(), map_fn=None):
         super().__init__(
             state=state,
@@ -101,14 +99,10 @@ def __iter__(self):
 Metric = MapMetric
 
 
-class ReduceMetric(FunctionalBase):
+class ReduceMetric(FunctionalBase, arbitrary_types_allowed=True):
     reduce_fn: Callable[..., Any]
     state: Any
 
-    class Config:
-        arbitrary_types_allowed = True
-        allow_mutation = True
-
     def update_(self, *args, **kwargs):
         self.state = self.reduce_fn(self.state, *args, **kwargs)
         return self
@@ -141,10 +135,6 @@ class AggregateMetric(FunctionalBase):
     metric: Union[MapMetric, ReduceMetric]
     aggregate_fn: Callable
 
-    class Config:
-        arbitrary_types_allowed = True
-        allow_mutation = True
-
     def map(self, fn):
         return self.replace(aggregate_fn=lambda state: fn(self.aggregate_fn(state)))
 

lantern/metric_table.py

@@ -1,18 +1,14 @@
 import textwrap
+from typing import Any, Dict, Union
+
 import pandas as pd
-from lantern import FunctionalBase
-from typing import Dict, Union, Any
 
-# from wire_damage.tools import MapMetric, ReduceMetric, AggregateMetric
+from lantern import FunctionalBase
 
 
-class MetricTable(FunctionalBase):
+class MetricTable(FunctionalBase, arbitrary_types_allowed=True):
     name: str
     metrics: Dict[str, Any]
-    # metrics: Dict[str, Union[MapMetric, ReduceMetric, AggregateMetric]]
-
-    class Config:
-        arbitrary_types_allowed = True
 
     def __init__(self, name, metrics):
         super().__init__(