refactor: update pydantic and typer version.

fcmeans/main.py

@@ -4,7 +4,7 @@
 from joblib import Parallel, delayed
 import numpy as np
 from numpy.typing import NDArray
-from pydantic import BaseModel, Extra, Field, validate_arguments
+from pydantic import BaseModel, ConfigDict, Field, validate_call
 import tqdm
 
 
@@ -39,22 +39,20 @@ class FCM(BaseModel):
         ReferenceError: If called without the model being trained
     """
 
-    class Config:
-        extra = Extra.allow
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(extra="allow", arbitrary_types_allowed=True)
 
     n_clusters: int = Field(5, ge=1)
     max_iter: int = Field(150, ge=1, le=1000)
     m: float = Field(2.0, ge=1.0)
     error: float = Field(1e-5, ge=1e-9)
     random_state: Optional[int] = None
-    trained: bool = Field(False, const=True)
+    trained: bool = False
     n_jobs: int = Field(1, ge=1)
     verbose: Optional[bool] = False
     distance: Optional[Union[DistanceOptions, Callable]] = DistanceOptions.euclidean
     distance_params: Optional[Dict] = {}
 
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
+    @validate_call(config=dict(arbitrary_types_allowed=True))
     def fit(self, X: NDArray) -> None:
         """Train the fuzzy-c-means model
 
@@ -76,7 +74,7 @@ def fit(self, X: NDArray) -> None:
                 break
         self.trained = True
 
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
+    @validate_call(config=dict(arbitrary_types_allowed=True))
     def soft_predict(self, X: NDArray) -> NDArray:
         """Soft predict of FCM
 
@@ -95,7 +93,7 @@ def soft_predict(self, X: NDArray) -> NDArray:
         u_dist = np.vstack(u_dist).T
         return 1 / u_dist
 
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
+    @validate_call(config=dict(arbitrary_types_allowed=True))
     def predict(self, X: NDArray) -> NDArray:
         """Predict the closest cluster each sample in X belongs to.