update pre-commit and fix formatting

.pre-commit-config.yaml

@@ -4,17 +4,17 @@ fail_fast: true
 
 repos:
   - repo: https://github.com/psf/black
-    rev: 22.8.0
+    rev: 23.3.0
     hooks:
       - id: black
 
   - repo: https://github.com/timothycrosley/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
       - id: isort
 
   - repo: https://github.com/pycqa/flake8
-    rev: 5.0.4
+    rev: 6.0.0
     hooks:
       - id: flake8
         additional_dependencies: [flake8-isort]

znnl/distance_metrics/__init__.py

@@ -26,12 +26,12 @@
 """
 from znnl.distance_metrics.angular_distance import AngularDistance
 from znnl.distance_metrics.cosine_distance import CosineDistance
+from znnl.distance_metrics.cross_entropy_distance import CrossEntropyDistance
 from znnl.distance_metrics.distance_metric import DistanceMetric
 from znnl.distance_metrics.hyper_sphere_distance import HyperSphere
 from znnl.distance_metrics.l_p_norm import LPNorm
 from znnl.distance_metrics.mahalanobis_distance import MahalanobisDistance
 from znnl.distance_metrics.order_n_difference import OrderNDifference
-from znnl.distance_metrics.cross_entropy_distance import CrossEntropyDistance
 
 __all__ = [
     DistanceMetric.__name__,
@@ -41,5 +41,5 @@
     OrderNDifference.__name__,
     MahalanobisDistance.__name__,
     HyperSphere.__name__,
-    CrossEntropyDistance.__name__
+    CrossEntropyDistance.__name__,
 ]

znnl/distance_metrics/angular_distance.py

@@ -49,7 +49,7 @@ def __init__(self, points: int = None):
             self.normalization = points / np.pi
         else:
             raise ValueError("Invalid points input.")
-        
+
     def __name__(self):
         """
         Name of the class.
@@ -59,7 +59,7 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"angular_distance"
+        return "angular_distance"
 
     def __call__(self, point_1: np.ndarray, point_2: np.ndarray, **kwargs):
         """

znnl/distance_metrics/cosine_distance.py

@@ -68,7 +68,7 @@ def __call__(self, point_1: np.ndarray, point_2: np.ndarray, **kwargs):
         )
 
         return 1 - abs(np.divide(numerator, denominator))
-    
+
     def __name__(self):
         """
         Name of the class.
@@ -78,4 +78,4 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"cosine_distance"
+        return "cosine_distance"

znnl/distance_metrics/cross_entropy_distance.py

@@ -34,6 +34,17 @@ class CrossEntropyDistance(DistanceMetric):
     Class for the cross entropy distance
     """
 
+    def __name__(self):
+        """
+        Name of the class.
+
+        Returns
+        -------
+        name : str
+                The name of the class.
+        """
+        return "cross_entropy_distance"
+
     def __call__(self, prediction, target):
         """
 

znnl/distance_metrics/distance_metric.py

@@ -44,7 +44,7 @@ def __name__(self) -> str:
                 The name of the class.
         """
         return "distance_metric"
-    
+
     def __signature__(self) -> tuple:
         """
         Signature of the class.

znnl/distance_metrics/hyper_sphere_distance.py

@@ -73,7 +73,7 @@ def __call__(self, point_1: np.ndarray, point_2: np.ndarray, **kwargs):
         return LPNorm(order=self.order)(point_1, point_2) * CosineDistance()(
             point_1, point_2
         )
-    
+
     def __name__(self):
         """
         Name of the class.

znnl/distance_metrics/l_p_norm.py

@@ -69,7 +69,7 @@ def __call__(self, point_1: np.ndarray, point_2: np.ndarray, **kwargs):
                 Array of distances for each point.
         """
         return np.linalg.norm(point_1 - point_2, axis=1, ord=self.order)
-    
+
     def __name__(self):
         """
         Name of the class.

znnl/distance_metrics/mahalanobis_distance.py

@@ -66,7 +66,7 @@ def __call__(self, point_1: np.array, point_2: np.array, **kwargs) -> np.array:
             distances.append(distance)
 
         return distances
-    
+
     def __name__(self):
         """
         Name of the class.
@@ -76,4 +76,4 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"mahalanobis_distance"
+        return "mahalanobis_distance"

znnl/distance_metrics/order_n_difference.py

@@ -79,7 +79,7 @@ def __call__(self, point_1: np.ndarray, point_2: np.ndarray, **kwargs):
             return np.sum(np.power(diff, self.order), axis=1)
         else:
             raise ValueError(f"Invalid reduction operation: {self.reduce_operation}")
-        
+
     def __name__(self):
         """
         Name of the class.
@@ -89,4 +89,4 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"order_{self.order}_difference_{self.reduce_operation}"
+        return "order_{self.order}_difference_{self.reduce_operation}"

znnl/loss_functions/__init__.py

@@ -28,9 +28,9 @@
 from znnl.loss_functions.cosine_distance import CosineDistanceLoss
 from znnl.loss_functions.cross_entropy_loss import CrossEntropyLoss
 from znnl.loss_functions.l_p_norm import LPNormLoss
+from znnl.loss_functions.loss import Loss
 from znnl.loss_functions.mahalanobis import MahalanobisLoss
 from znnl.loss_functions.mean_power_error import MeanPowerLoss
-from znnl.loss_functions.loss import Loss
 
 __all__ = [
     AngleDistanceLoss.__name__,

znnl/loss_functions/absolute_angle_difference.py

@@ -41,12 +41,12 @@ def __init__(self):
         self.metric = AngularDistance()
 
     def __name__(self):
-            """
-            Name of the class.
-
-            Returns
-            -------
-            name : str
-                    The name of the class.
-            """
-            return f"angle_distance_loss"
+        """
+        Name of the class.
+
+        Returns
+        -------
+        name : str
+                The name of the class.
+        """
+        return "angle_distance_loss"

znnl/loss_functions/cosine_distance.py

@@ -49,4 +49,4 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"cosine_distance_loss"
+        return "cosine_distance_loss"

znnl/loss_functions/cross_entropy_loss.py

@@ -24,10 +24,8 @@
 Summary
 -------
 """
-import optax
-
-from znnl.loss_functions.loss import Loss
 from znnl.distance_metrics.cross_entropy_distance import CrossEntropyDistance
+from znnl.loss_functions.loss import Loss
 
 
 class CrossEntropyLoss(Loss):
@@ -51,4 +49,4 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"cross_entropy_loss"
+        return "cross_entropy_loss"

znnl/loss_functions/loss.py

@@ -56,7 +56,7 @@ def __name__(self) -> str:
                 The name of the class.
         """
         return "loss_parent"
-    
+
     def __signature__(self) -> tuple:
         """
         Signature of the class.

znnl/loss_functions/mahalanobis.py

@@ -49,4 +49,4 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"mahalanobis_loss"
+        return "mahalanobis_loss"

znnl/loss_functions/mean_power_error.py

@@ -54,4 +54,4 @@ def __name__(self):
         name : str
                 The name of the class.
         """
-        return f"mean_power_loss_{self.order}"
+        return "mean_power_loss_{self.order}"

znnl/observables/__init__.py

@@ -27,6 +27,4 @@
 """
 from znnl.observables.observable import Observable
 
-__all__ = [
-    Observable.__name__
-]
+__all__ = [Observable.__name__]

znnl/observables/observable.py

@@ -46,7 +46,7 @@ def __name__(self) -> str:
                 The name of the observable.
         """
         raise NotImplementedError("Implemented in child class.")
-    
+
     @classmethod
     def __signature__(self, data_set: dict) -> tuple:
         """
@@ -63,7 +63,7 @@ def __signature__(self, data_set: dict) -> tuple:
                 The signature of the observable.
         """
         raise NotImplementedError("Implemented in child class.")
-    
+
     @classmethod
     def __call__(self, data_set: dict) -> Union[str, np.ndarray, float]:
         """
@@ -78,6 +78,6 @@ def __call__(self, data_set: dict) -> Union[str, np.ndarray, float]:
         -------
         value : Union[str, np.ndarray, float]
                 The value of the observable.
-            
+
         """
         raise NotImplementedError("Implemented in child class.")

znnl/training_strategies/partitioned_training.py

@@ -88,8 +88,8 @@ def __init__(
                 Random seed for the RNG. Uses a random int if not specified.
         recursive_mode : RecursiveMode
                 Defining the recursive mode that can be used in training.
-                If the recursive mode is used, the training will be performed until a
-                condition is fulfilled.
+                If the recursive mode is used, the training will be performed
+                until a condition is fulfilled.
                 The loss value at which point you consider the model trained.
         disable_loading_bar : bool
                 Disable the output visualization of the loading bar.
@@ -244,7 +244,7 @@ def train_model(
                 Number of epochs to train over.
                 Each epoch defines a training phase.
         train_ds_selection : list
-                             (default = [slice(-1, None, None), slice(None, None, None)])
+                             default = [slice(-1, None, None), slice(None, None, None)]
                 The train is selected by a np.array of indices or slices.
                 Each slice or array defines a training phase.
         batch_size : list (default = [1, 1])