diff --git a/.github/workflows/dev.yml b/.github/workflows/dev.yml
index 681c0733..b17a9728 100644
--- a/.github/workflows/dev.yml
+++ b/.github/workflows/dev.yml
@@ -20,7 +20,7 @@ jobs:
     # The type of runner that the job will run on
     strategy:
       matrix:
-        python-versions: ['3.8', '3.9', '3.10', '3.11']
+        python-versions: ['3.9', '3.10', '3.11', '3.12']
         os: [ubuntu-20.04]
 #        os: [ubuntu-18.04, macos-latest, windows-latest]
     runs-on: ${{ matrix.os }}
diff --git a/.github/workflows/preview.yml b/.github/workflows/preview.yml
index 1be36eea..629fb94e 100644
--- a/.github/workflows/preview.yml
+++ b/.github/workflows/preview.yml
@@ -22,7 +22,7 @@ jobs:
 
     strategy:
       matrix:
-        python-versions: [ 3.8 ]
+        python-versions: [ 3.11 ]
 
     steps:
       - uses: actions/checkout@v2
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 7ee6a9d5..b2ea608b 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -24,7 +24,7 @@ jobs:
 
     strategy:
       matrix:
-        python-versions: [3.8]
+        python-versions: [3.11]
 
     # Steps represent a sequence of tasks that will be executed as part of the job
     steps:
diff --git a/nannyml/config.py b/nannyml/config.py
index 259f2880..a9e1d67e 100644
--- a/nannyml/config.py
+++ b/nannyml/config.py
@@ -9,7 +9,7 @@
 
 import jinja2
 import yaml
-from pydantic import BaseModel, validator, Field
+from pydantic import BaseModel, Field, field_validator
 
 from nannyml._typing import Self
 from nannyml.exceptions import IOException
@@ -71,7 +71,7 @@ class CalculatorConfig(BaseModel):
     store: Optional[StoreConfig] = Field(default=None)
     params: Dict[str, Any]
 
-    @validator('params')
+    @field_validator('params')
     def _parse_thresholds(cls, value: Dict[str, Any]):
         """Parse thresholds in params and convert them to :class:`Threshold`'s"""
         # Some calculators expect `thresholds` parameter as dict
diff --git a/nannyml/drift/ranker.py b/nannyml/drift/ranker.py
index 53a029e0..9eaa82de 100644
--- a/nannyml/drift/ranker.py
+++ b/nannyml/drift/ranker.py
@@ -372,7 +372,11 @@ def rank(
             filtered_values = values[~(feature_nan | perf_nan)]
             filtered_perf_change = abs_perf_change[~(feature_nan | perf_nan)]
 
-            tmp1 = pearsonr(filtered_values.ravel(), filtered_perf_change)
+            tmp1 = (
+                pearsonr(filtered_values.ravel(), filtered_perf_change)
+                if len(filtered_values) > 1
+                else (np.nan, np.nan)
+            )
             spearmanr1.append(tmp1[0])
             spearmanr2.append(tmp1[1])
 
diff --git a/nannyml/drift/univariate/calculator.py b/nannyml/drift/univariate/calculator.py
index fcc5fad2..38999011 100644
--- a/nannyml/drift/univariate/calculator.py
+++ b/nannyml/drift/univariate/calculator.py
@@ -441,10 +441,10 @@ def _calculate_for_column(
             logger.error(
                 f"an unexpected exception occurred during calculation of method '{method.display_name}': " f"{exc}"
             )
-        result['value'] = np.NaN
+        result['value'] = np.nan
         result['upper_threshold'] = method.upper_threshold_value
         result['lower_threshold'] = method.lower_threshold_value
-        result['alert'] = np.NaN
+        result['alert'] = np.nan
     finally:
         return result
 
diff --git a/nannyml/drift/univariate/methods.py b/nannyml/drift/univariate/methods.py
index fe847fa0..f4854d28 100644
--- a/nannyml/drift/univariate/methods.py
+++ b/nannyml/drift/univariate/methods.py
@@ -278,7 +278,9 @@ def _fit(self, reference_data: pd.Series, timestamps: Optional[pd.Series] = None
         reference_data = _remove_nans(reference_data)
         len_reference = len(reference_data)
 
-        bins = np.histogram_bin_edges(reference_data, bins='doane')
+        # Explicit conversion to float because of
+        # https://github.com/numpy/numpy/commit/c63969c6e1d58e791632aacfb88ecae465d6dcfc
+        bins = np.histogram_bin_edges(reference_data.astype("float64"), bins='doane')
         reference_proba_in_bins = np.histogram(reference_data, bins=bins)[0] / len_reference
         self._bins = bins
         self._reference_proba_in_bins = reference_proba_in_bins
@@ -731,7 +733,7 @@ def _fit(self, reference_data: pd.Series, timestamps: Optional[pd.Series] = None
         reference_data = _remove_nans(reference_data)
         len_reference = len(reference_data)
 
-        bins = np.histogram_bin_edges(reference_data, bins='doane')
+        bins = np.histogram_bin_edges(reference_data.astype("float64"), bins='doane')
         reference_proba_in_bins = np.histogram(reference_data, bins=bins)[0] / len_reference
         self._bins = bins
         self._reference_proba_in_bins = reference_proba_in_bins
diff --git a/nannyml/performance_calculation/metrics/base.py b/nannyml/performance_calculation/metrics/base.py
index 32700054..863a87d9 100644
--- a/nannyml/performance_calculation/metrics/base.py
+++ b/nannyml/performance_calculation/metrics/base.py
@@ -197,11 +197,11 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:
                 self._logger.error(
                     f"an unexpected exception occurred during calculation of method '{self.display_name}': " f"{exc}"
                 )
-            chunk_record[f'{column_name}_sampling_error'] = np.NaN
-            chunk_record[f'{column_name}'] = np.NaN
+            chunk_record[f'{column_name}_sampling_error'] = np.nan
+            chunk_record[f'{column_name}'] = np.nan
             chunk_record[f'{column_name}_upper_threshold'] = self.upper_threshold_value
             chunk_record[f'{column_name}_lower_threshold'] = self.lower_threshold_value
-            chunk_record[f'{column_name}_alert'] = np.NaN
+            chunk_record[f'{column_name}_alert'] = np.nan
         finally:
             return chunk_record
 
diff --git a/nannyml/performance_calculation/metrics/binary_classification.py b/nannyml/performance_calculation/metrics/binary_classification.py
index b28d08ff..2a457ae2 100644
--- a/nannyml/performance_calculation/metrics/binary_classification.py
+++ b/nannyml/performance_calculation/metrics/binary_classification.py
@@ -103,7 +103,7 @@ def _fit(self, reference_data: pd.DataFrame):
         data = reference_data[[self.y_true, self.y_pred_proba]]
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba])
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = auroc_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -117,7 +117,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred_proba = data[self.y_pred_proba]
@@ -126,7 +126,7 @@ def _calculate(self, data: pd.DataFrame):
                 f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return roc_auc_score(y_true, y_pred_proba)
 
@@ -137,7 +137,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return auroc_sampling_error(self._sampling_error_components, data)
 
@@ -199,7 +199,7 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = ap_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -213,7 +213,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred_proba = data[self.y_pred_proba]
@@ -223,7 +223,7 @@ def _calculate(self, data: pd.DataFrame):
                 f"'{self.y_true}' does not contain positive class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return average_precision_score(y_true, y_pred_proba)
 
@@ -233,7 +233,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return ap_sampling_error(self._sampling_error_components, data)
 
@@ -289,7 +289,7 @@ def _fit(self, reference_data: pd.DataFrame):
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
 
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = f1_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -302,7 +302,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -312,13 +312,13 @@ def _calculate(self, data: pd.DataFrame):
                 f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         elif y_pred.nunique() <= 1:
             warnings.warn(
                 f"'{self.y_pred}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return f1_score(y_true, y_pred)
 
@@ -328,7 +328,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return f1_sampling_error(self._sampling_error_components, data)
 
@@ -384,7 +384,7 @@ def _fit(self, reference_data: pd.DataFrame):
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
 
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = precision_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -396,7 +396,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -406,13 +406,13 @@ def _calculate(self, data: pd.DataFrame):
                 f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         elif y_pred.nunique() <= 1:
             warnings.warn(
                 f"'{self.y_pred}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return precision_score(y_true, y_pred)
 
@@ -422,7 +422,7 @@ def _sampling_error(self, data: pd.DataFrame):
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return precision_sampling_error(self._sampling_error_components, data)
 
@@ -477,7 +477,7 @@ def _fit(self, reference_data: pd.DataFrame):
         _list_missing([self.y_true, self.y_pred], list(reference_data.columns))
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = recall_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -489,7 +489,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -499,13 +499,13 @@ def _calculate(self, data: pd.DataFrame):
                 f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         elif y_pred.nunique() <= 1:
             warnings.warn(
                 f"'{self.y_pred}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return recall_score(y_true, y_pred)
 
@@ -515,7 +515,7 @@ def _sampling_error(self, data: pd.DataFrame):
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return recall_sampling_error(self._sampling_error_components, data)
 
@@ -570,7 +570,7 @@ def _fit(self, reference_data: pd.DataFrame):
         _list_missing([self.y_true, self.y_pred], list(reference_data.columns))
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = specificity_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -582,7 +582,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -590,7 +590,7 @@ def _calculate(self, data: pd.DataFrame):
         tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
         denominator = tn + fp
         if denominator == 0:
-            return np.NaN
+            return np.nan
         else:
             return tn / denominator
 
@@ -600,7 +600,7 @@ def _sampling_error(self, data: pd.DataFrame):
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return specificity_sampling_error(self._sampling_error_components, data)
 
@@ -655,7 +655,7 @@ def _fit(self, reference_data: pd.DataFrame):
         _list_missing([self.y_true, self.y_pred], list(reference_data.columns))
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = accuracy_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -667,7 +667,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -680,7 +680,7 @@ def _sampling_error(self, data: pd.DataFrame):
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return accuracy_sampling_error(self._sampling_error_components, data)
 
@@ -765,7 +765,7 @@ def _fit(self, reference_data: pd.DataFrame):
         _list_missing([self.y_true, self.y_pred], list(reference_data.columns))
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
-            self._sampling_error_components = np.NaN, self.normalize_business_value
+            self._sampling_error_components = np.nan, self.normalize_business_value
         else:
             self._sampling_error_components = business_value_sampling_error_components(
                 y_true_reference=data[self.y_true],
@@ -779,7 +779,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"'{self.y_true}' contains no data, cannot calculate business value. Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -804,7 +804,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return business_value_sampling_error(self._sampling_error_components, data)
 
@@ -949,10 +949,10 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._true_positive_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix)
-            self._true_negative_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix)
-            self._false_positive_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix)
-            self._false_negative_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix)
+            self._true_positive_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix)
+            self._true_negative_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix)
+            self._false_positive_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix)
+            self._false_negative_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix)
         else:
             self._true_positive_sampling_error_components = true_positive_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -980,7 +980,7 @@ def _calculate_true_positives(self, data: pd.DataFrame) -> float:
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate true_positives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1003,7 +1003,7 @@ def _calculate_true_negatives(self, data: pd.DataFrame) -> float:
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate true_negatives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1026,7 +1026,7 @@ def _calculate_false_positives(self, data: pd.DataFrame) -> float:
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate false_positives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1049,7 +1049,7 @@ def _calculate_false_negatives(self, data: pd.DataFrame) -> float:
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate false_negatives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1091,7 +1091,7 @@ def get_true_pos_info(self, chunk_data: pd.DataFrame) -> Dict:
         chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.")
-            sampling_error_tp = np.NaN
+            sampling_error_tp = np.nan
         else:
             sampling_error_tp = true_positive_sampling_error(self._true_positive_sampling_error_components, chunk_data)
         #  TODO: NaN removal is duplicated to an extent. Upon refactor consider if we can do it only once
@@ -1130,7 +1130,7 @@ def get_true_neg_info(self, chunk_data: pd.DataFrame) -> Dict:
         chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate true negative sampling error. " "Returning NaN.")
-            sampling_error_tn = np.NaN
+            sampling_error_tn = np.nan
         else:
             sampling_error_tn = true_negative_sampling_error(self._true_negative_sampling_error_components, chunk_data)
         #  TODO: NaN removal is duplicated to an extent. Upon refactor consider if we can do it only once
@@ -1169,7 +1169,7 @@ def get_false_pos_info(self, chunk_data: pd.DataFrame) -> Dict:
         chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate false positive sampling error. " "Returning NaN.")
-            sampling_error_fp = np.NaN
+            sampling_error_fp = np.nan
         else:
             sampling_error_fp = false_positive_sampling_error(
                 self._false_positive_sampling_error_components, chunk_data
@@ -1210,7 +1210,7 @@ def get_false_neg_info(self, chunk_data: pd.DataFrame) -> Dict:
         chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate false positive sampling error. " "Returning NaN.")
-            sampling_error_fn = np.NaN
+            sampling_error_fn = np.nan
         else:
             sampling_error_fn = false_negative_sampling_error(
                 self._false_negative_sampling_error_components, chunk_data
diff --git a/nannyml/performance_calculation/metrics/multiclass_classification.py b/nannyml/performance_calculation/metrics/multiclass_classification.py
index 9d1ee098..c598e4b5 100644
--- a/nannyml/performance_calculation/metrics/multiclass_classification.py
+++ b/nannyml/performance_calculation/metrics/multiclass_classification.py
@@ -109,7 +109,7 @@ def _fit(self, reference_data: pd.DataFrame):
             [self.y_true] + self.class_probability_columns,
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clasz in self.classes]
+            self._sampling_error_components = [(np.nan, 0) for clasz in self.classes]
             # TODO: Ideally we would also raise an error here!
         else:
             # test if reference data are represented correctly
@@ -146,7 +146,7 @@ def _calculate(self, data: pd.DataFrame):
             _message = f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN."
             self._logger.warning(_message)
             warnings.warn(_message)
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred_proba = data[self.class_probability_columns]
@@ -158,7 +158,7 @@ def _calculate(self, data: pd.DataFrame):
             )
             warnings.warn(_message)
             self._logger.warning(_message)
-            return np.NaN
+            return np.nan
         else:
             return roc_auc_score(y_true, y_pred_proba, multi_class='ovr', average='macro', labels=self.classes)
 
@@ -171,7 +171,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return auroc_sampling_error(self._sampling_error_components, data)
 
@@ -233,7 +233,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             # sampling error
             label_binarizer = LabelBinarizer()
@@ -255,7 +255,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         labels = sorted(list(self.y_pred_proba.keys()))
         y_true = data[self.y_true]
@@ -265,12 +265,12 @@ def _calculate(self, data: pd.DataFrame):
             warnings.warn(
                 f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         elif y_pred.nunique() <= 1:
             warnings.warn(
                 f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return f1_score(y_true, y_pred, average='macro', labels=labels)
 
@@ -281,7 +281,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return f1_sampling_error(self._sampling_error_components, data)
 
@@ -343,7 +343,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             # sampling error
             label_binarizer = LabelBinarizer()
@@ -365,7 +365,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         labels = sorted(list(self.y_pred_proba.keys()))
         y_true = data[self.y_true]
@@ -375,12 +375,12 @@ def _calculate(self, data: pd.DataFrame):
             warnings.warn(
                 f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         elif y_pred.nunique() <= 1:
             warnings.warn(
                 f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return precision_score(y_true, y_pred, average='macro', labels=labels)
 
@@ -391,7 +391,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return precision_sampling_error(self._sampling_error_components, data)
 
@@ -453,7 +453,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             # sampling error
             label_binarizer = LabelBinarizer()
@@ -475,7 +475,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         labels = sorted(list(self.y_pred_proba.keys()))
         y_true = data[self.y_true]
@@ -485,12 +485,12 @@ def _calculate(self, data: pd.DataFrame):
             warnings.warn(
                 f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         elif y_pred.nunique() <= 1:
             warnings.warn(
                 f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return recall_score(y_true, y_pred, average='macro', labels=labels)
 
@@ -501,7 +501,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return recall_sampling_error(self._sampling_error_components, data)
 
@@ -563,7 +563,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             # sampling error
             label_binarizer = LabelBinarizer()
@@ -585,7 +585,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         labels = sorted(list(self.y_pred_proba.keys()))
         y_true = data[self.y_true]
@@ -595,12 +595,12 @@ def _calculate(self, data: pd.DataFrame):
             warnings.warn(
                 f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         elif y_pred.nunique() <= 1:
             warnings.warn(
                 f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             MCM = multilabel_confusion_matrix(y_true, y_pred, labels=labels)
             tn_sum = MCM[:, 0, 0]
@@ -615,7 +615,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return specificity_sampling_error(self._sampling_error_components, data)
 
@@ -676,7 +676,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             # sampling error
             label_binarizer = LabelBinarizer()
@@ -692,7 +692,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -706,7 +706,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return accuracy_sampling_error(self._sampling_error_components, data)
 
@@ -845,7 +845,7 @@ def _calculate(self, data: pd.DataFrame) -> Union[np.ndarray, float]:
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -982,7 +982,7 @@ def _fit(self, reference_data: pd.DataFrame):
             [self.y_true] + self.class_probability_columns,
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for class_col in self.class_probability_columns]
+            self._sampling_error_components = [(np.nan, 0) for class_col in self.class_probability_columns]
         else:
             # sampling error
             binarized_y_true = list(label_binarize(reference_data[self.y_true], classes=self.classes).T)
@@ -1006,7 +1006,7 @@ def _calculate(self, data: pd.DataFrame):
         )
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred_proba = data[self.class_probability_columns]
@@ -1016,7 +1016,7 @@ def _calculate(self, data: pd.DataFrame):
                 f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. "
                 "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             # https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-f-measure-metrics
             # average_precision_score always performs OVR averaging
@@ -1031,7 +1031,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return average_precision_sampling_error(self._sampling_error_components, data)
 
@@ -1130,7 +1130,7 @@ def _fit(self, reference_data: pd.DataFrame):
         _list_missing([self.y_true, self.y_pred], list(reference_data.columns))
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
-            self._sampling_error_components = np.NaN, self.normalize_business_value
+            self._sampling_error_components = np.nan, self.normalize_business_value
         else:
             # get class number from y_pred_proba if provided otherwise from reference y_true
             # this way the code will work even if some classes are missing from reference
@@ -1159,7 +1159,7 @@ def _calculate(self, data: pd.DataFrame):
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"'{self.y_true}' contains no data, cannot calculate business value. Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1178,6 +1178,6 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return business_value_sampling_error(self._sampling_error_components, data)
diff --git a/nannyml/performance_calculation/metrics/regression.py b/nannyml/performance_calculation/metrics/regression.py
index be4140c6..15e2513f 100644
--- a/nannyml/performance_calculation/metrics/regression.py
+++ b/nannyml/performance_calculation/metrics/regression.py
@@ -16,7 +16,11 @@
 )
 
 from nannyml._typing import ProblemType
-from nannyml.base import _list_missing, _raise_exception_for_negative_values, common_nan_removal
+from nannyml.base import (
+    _list_missing,
+    _raise_exception_for_negative_values,
+    common_nan_removal,
+)
 from nannyml.performance_calculation.metrics.base import Metric, MetricFactory
 from nannyml.sampling_error.regression import (
     mae_sampling_error,
@@ -35,13 +39,20 @@
 from nannyml.thresholds import Threshold
 
 
-@MetricFactory.register(metric='mae', use_case=ProblemType.REGRESSION)
+@MetricFactory.register(metric="mae", use_case=ProblemType.REGRESSION)
 class MAE(Metric):
     """Mean Absolute Error metric."""
 
     y_pred: str
 
-    def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs):
+    def __init__(
+        self,
+        y_true: str,
+        y_pred: str,
+        threshold: Threshold,
+        y_pred_proba: Optional[str] = None,
+        **kwargs,
+    ):
         """Creates a new MAE instance.
 
         Parameters
@@ -56,13 +67,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba:
             Name of the column containing your model output.
         """
         super().__init__(
-            name='mae',
+            name="mae",
             y_true=y_true,
             y_pred=y_pred,
             y_pred_proba=y_pred_proba,
             threshold=threshold,
             lower_threshold_limit=0,
-            components=[('MAE', 'mae')],
+            components=[("MAE", "mae")],
         )
 
         # sampling error
@@ -78,7 +89,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             self._sampling_error_components = mae_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -88,12 +99,15 @@ def _fit(self, reference_data: pd.DataFrame):
     def _calculate(self, data: pd.DataFrame):
         """Redefine to handle NaNs and edge cases."""
         _list_missing([self.y_true, self.y_pred], list(data.columns))
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN."
+                f"No data or too many missing values, cannot calculate {self.display_name}. "
+                f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -101,23 +115,33 @@ def _calculate(self, data: pd.DataFrame):
         return mean_absolute_error(y_true, y_pred)
 
     def _sampling_error(self, data: pd.DataFrame) -> float:
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
+                f"Too many missing values, cannot calculate {self.display_name} sampling error. "
+                "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mae_sampling_error(self._sampling_error_components, data)
 
 
-@MetricFactory.register(metric='mape', use_case=ProblemType.REGRESSION)
+@MetricFactory.register(metric="mape", use_case=ProblemType.REGRESSION)
 class MAPE(Metric):
     """Mean Absolute Percentage Error metric."""
 
     y_pred: str
 
-    def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs):
+    def __init__(
+        self,
+        y_true: str,
+        y_pred: str,
+        threshold: Threshold,
+        y_pred_proba: Optional[str] = None,
+        **kwargs,
+    ):
         """Creates a new MAPE instance.
 
         Parameters
@@ -132,13 +156,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba:
             Name of the column containing your model output.
         """
         super().__init__(
-            name='mape',
+            name="mape",
             y_true=y_true,
             y_pred=y_pred,
             y_pred_proba=y_pred_proba,
             threshold=threshold,
             lower_threshold_limit=0,
-            components=[('MAPE', 'mape')],
+            components=[("MAPE", "mape")],
         )
 
         # sampling error
@@ -154,7 +178,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             self._sampling_error_components = mape_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -164,12 +188,15 @@ def _fit(self, reference_data: pd.DataFrame):
     def _calculate(self, data: pd.DataFrame):
         """Redefine to handle NaNs and edge cases."""
         _list_missing([self.y_true, self.y_pred], list(data.columns))
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN."
+                f"No data or too many missing values, cannot calculate {self.display_name}. "
+                f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -177,23 +204,33 @@ def _calculate(self, data: pd.DataFrame):
         return mean_absolute_percentage_error(y_true, y_pred)
 
     def _sampling_error(self, data: pd.DataFrame) -> float:
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
+                f"Too many missing values, cannot calculate {self.display_name} sampling error. "
+                "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mape_sampling_error(self._sampling_error_components, data)
 
 
-@MetricFactory.register(metric='mse', use_case=ProblemType.REGRESSION)
+@MetricFactory.register(metric="mse", use_case=ProblemType.REGRESSION)
 class MSE(Metric):
     """Mean Squared Error metric."""
 
     y_pred: str
 
-    def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs):
+    def __init__(
+        self,
+        y_true: str,
+        y_pred: str,
+        threshold: Threshold,
+        y_pred_proba: Optional[str] = None,
+        **kwargs,
+    ):
         """Creates a new MSE instance.
 
         Parameters
@@ -208,13 +245,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba:
             Name of the column containing your model output.
         """
         super().__init__(
-            name='mse',
+            name="mse",
             y_true=y_true,
             y_pred=y_pred,
             y_pred_proba=y_pred_proba,
             threshold=threshold,
             lower_threshold_limit=0,
-            components=[('MSE', 'mse')],
+            components=[("MSE", "mse")],
         )
 
         # sampling error
@@ -230,7 +267,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             self._sampling_error_components = mse_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -240,12 +277,15 @@ def _fit(self, reference_data: pd.DataFrame):
     def _calculate(self, data: pd.DataFrame):
         """Redefine to handle NaNs and edge cases."""
         _list_missing([self.y_true, self.y_pred], list(data.columns))
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN."
+                f"No data or too many missing values, cannot calculate {self.display_name}. "
+                f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -253,23 +293,33 @@ def _calculate(self, data: pd.DataFrame):
         return mean_squared_error(y_true, y_pred)
 
     def _sampling_error(self, data: pd.DataFrame) -> float:
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
+                f"Too many missing values, cannot calculate {self.display_name} sampling error. "
+                "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse_sampling_error(self._sampling_error_components, data)
 
 
-@MetricFactory.register(metric='msle', use_case=ProblemType.REGRESSION)
+@MetricFactory.register(metric="msle", use_case=ProblemType.REGRESSION)
 class MSLE(Metric):
     """Mean Squared Logarithmic Error metric."""
 
     y_pred: str
 
-    def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs):
+    def __init__(
+        self,
+        y_true: str,
+        y_pred: str,
+        threshold: Threshold,
+        y_pred_proba: Optional[str] = None,
+        **kwargs,
+    ):
         """Creates a new MSLE instance.
 
         Parameters
@@ -284,13 +334,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba:
             Name of the column containing your model output.
         """
         super().__init__(
-            name='msle',
+            name="msle",
             y_true=y_true,
             y_pred=y_pred,
             y_pred_proba=y_pred_proba,
             threshold=threshold,
             lower_threshold_limit=0,
-            components=[('MSLE', 'msle')],
+            components=[("MSLE", "msle")],
         )
 
         # sampling error
@@ -306,7 +356,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             self._sampling_error_components = msle_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -316,12 +366,15 @@ def _fit(self, reference_data: pd.DataFrame):
     def _calculate(self, data: pd.DataFrame):
         """Redefine to handle NaNs and edge cases."""
         _list_missing([self.y_true, self.y_pred], list(data.columns))
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN."
+                f"No data or too many missing values, cannot calculate {self.display_name}. "
+                f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -333,23 +386,33 @@ def _calculate(self, data: pd.DataFrame):
         return mean_squared_log_error(y_true, y_pred)
 
     def _sampling_error(self, data: pd.DataFrame) -> float:
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
+                f"Too many missing values, cannot calculate {self.display_name} sampling error. "
+                "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return msle_sampling_error(self._sampling_error_components, data)
 
 
-@MetricFactory.register(metric='rmse', use_case=ProblemType.REGRESSION)
+@MetricFactory.register(metric="rmse", use_case=ProblemType.REGRESSION)
 class RMSE(Metric):
     """Root Mean Squared Error metric."""
 
     y_pred: str
 
-    def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs):
+    def __init__(
+        self,
+        y_true: str,
+        y_pred: str,
+        threshold: Threshold,
+        y_pred_proba: Optional[str] = None,
+        **kwargs,
+    ):
         """Creates a new RMSE instance.
 
         Parameters
@@ -364,13 +427,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba:
             Name of the column containing your model output.
         """
         super().__init__(
-            name='rmse',
+            name="rmse",
             y_true=y_true,
             y_pred=y_pred,
             y_pred_proba=y_pred_proba,
             threshold=threshold,
             lower_threshold_limit=0,
-            components=[('RMSE', 'rmse')],
+            components=[("RMSE", "rmse")],
         )
 
         # sampling error
@@ -386,7 +449,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             self._sampling_error_components = rmse_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -396,36 +459,58 @@ def _fit(self, reference_data: pd.DataFrame):
     def _calculate(self, data: pd.DataFrame):
         """Redefine to handle NaNs and edge cases."""
         _list_missing([self.y_true, self.y_pred], list(data.columns))
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN."
+                f"No data or too many missing values, cannot calculate {self.display_name}. "
+                f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
 
-        return mean_squared_error(y_true, y_pred, squared=False)
+        # Deal with breaking API change in sklearn 1.4
+        # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_error.html
+        try:
+            from sklearn.metrics import root_mean_squared_error
+
+            return root_mean_squared_error(y_true, y_pred)
+        except ImportError:
+            from sklearn.metrics import mean_squared_error
+
+            return mean_squared_error(y_true, y_pred, squared=False)
 
     def _sampling_error(self, data: pd.DataFrame) -> float:
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
+                f"Too many missing values, cannot calculate {self.display_name} sampling error. "
+                "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return rmse_sampling_error(self._sampling_error_components, data)
 
 
-@MetricFactory.register(metric='rmsle', use_case=ProblemType.REGRESSION)
+@MetricFactory.register(metric="rmsle", use_case=ProblemType.REGRESSION)
 class RMSLE(Metric):
     """Root Mean Squared Logarithmic Error metric."""
 
     y_pred: str
 
-    def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs):
+    def __init__(
+        self,
+        y_true: str,
+        y_pred: str,
+        threshold: Threshold,
+        y_pred_proba: Optional[str] = None,
+        **kwargs,
+    ):
         """Creates a new RMSLE instance.
 
         Parameters
@@ -440,13 +525,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba:
             Name of the column containing your model output.
         """
         super().__init__(
-            name='rmsle',
+            name="rmsle",
             y_true=y_true,
             y_pred=y_pred,
             y_pred_proba=y_pred_proba,
             threshold=threshold,
             lower_threshold_limit=0,
-            components=[('RMSLE', 'rmsle')],
+            components=[("RMSLE", "rmsle")],
         )
 
         # sampling error
@@ -462,7 +547,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             self._sampling_error_components = rmsle_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -472,12 +557,15 @@ def _fit(self, reference_data: pd.DataFrame):
     def _calculate(self, data: pd.DataFrame):
         """Redefine to handle NaNs and edge cases."""
         _list_missing([self.y_true, self.y_pred], list(data.columns))
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN."
+                f"No data or too many missing values, cannot calculate {self.display_name}. "
+                f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -486,14 +574,26 @@ def _calculate(self, data: pd.DataFrame):
         _raise_exception_for_negative_values(y_true)
         _raise_exception_for_negative_values(y_pred)
 
-        return mean_squared_log_error(y_true, y_pred, squared=False)
+        # Deal with breaking API change in sklearn 1.4
+        # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_log_error.html
+        try:
+            from sklearn.metrics import root_mean_squared_log_error
+
+            return root_mean_squared_log_error(y_true, y_pred)
+        except ImportError:
+            from sklearn.metrics import mean_squared_log_error
+
+            return mean_squared_log_error(y_true, y_pred, squared=False)
 
     def _sampling_error(self, data: pd.DataFrame) -> float:
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
             warnings.warn(
-                f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
+                f"Too many missing values, cannot calculate {self.display_name} sampling error. "
+                "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return rmsle_sampling_error(self._sampling_error_components, data)
diff --git a/nannyml/performance_estimation/confidence_based/metrics.py b/nannyml/performance_estimation/confidence_based/metrics.py
index ad29f953..94eb702a 100644
--- a/nannyml/performance_estimation/confidence_based/metrics.py
+++ b/nannyml/performance_estimation/confidence_based/metrics.py
@@ -271,14 +271,14 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict:
             chunk_record[f'alert_{column_name}'] = self.alert(estimated_metric_value)
         except Exception as exc:
             self._logger.error(f"an unexpected error occurred while calculating metric {self.display_name}: {exc}")
-            chunk_record[f'estimated_{column_name}'] = np.NaN
-            chunk_record[f'sampling_error_{column_name}'] = np.NaN
-            chunk_record[f'realized_{column_name}'] = np.NaN
-            chunk_record[f'upper_confidence_boundary_{column_name}'] = np.NaN
-            chunk_record[f'lower_confidence_boundary_{column_name}'] = np.NaN
-            chunk_record[f'upper_threshold_{column_name}'] = np.NaN
-            chunk_record[f'lower_threshold_{column_name}'] = np.NaN
-            chunk_record[f'alert_{column_name}'] = np.NaN
+            chunk_record[f'estimated_{column_name}'] = np.nan
+            chunk_record[f'sampling_error_{column_name}'] = np.nan
+            chunk_record[f'realized_{column_name}'] = np.nan
+            chunk_record[f'upper_confidence_boundary_{column_name}'] = np.nan
+            chunk_record[f'lower_confidence_boundary_{column_name}'] = np.nan
+            chunk_record[f'upper_threshold_{column_name}'] = np.nan
+            chunk_record[f'lower_threshold_{column_name}'] = np.nan
+            chunk_record[f'alert_{column_name}'] = np.nan
         finally:
             return chunk_record
 
@@ -371,7 +371,7 @@ def _fit(self, reference_data: pd.DataFrame):
         data = reference_data[[self.y_true, self.y_pred_proba]]
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba])
         if empty:
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = bse.auroc_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -384,7 +384,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -395,7 +395,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred_proba = data[self.y_pred_proba]
         uncalibrated_y_pred_proba = data[self.uncalibrated_y_pred_proba]
@@ -407,7 +407,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -417,7 +417,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute realized {self.display_name}.")
             warnings.warn(f"Not enough data to compute realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         uncalibrated_y_pred_proba = data[self.uncalibrated_y_pred_proba]
@@ -426,7 +426,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"'{self.y_true}' contains a single class for chunk, " f"cannot compute realized {self.display_name}."
             )
-            return np.NaN
+            return np.nan
         return roc_auc_score(y_true, uncalibrated_y_pred_proba)
 
     def _sampling_error(self, data: pd.DataFrame) -> float:
@@ -436,7 +436,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.auroc_sampling_error(self._sampling_error_components, data)
 
@@ -524,7 +524,7 @@ def _fit(self, reference_data: pd.DataFrame):
         if 1 not in y_true.unique():
             self._logger.debug(f"Not enough data to compute fit {self.display_name}.")
             warnings.warn(f"Not enough data to compute fit {self.display_name}.")
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = bse.ap_sampling_error_components(
                 y_true_reference=y_true,
@@ -537,7 +537,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -548,7 +548,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         calibrated_y_pred_proba = data[self.y_pred_proba].to_numpy()
         uncalibrated_y_pred_proba = data[self.uncalibrated_y_pred_proba].to_numpy()
@@ -560,7 +560,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -577,7 +577,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
                 f"'{self.y_true}' does not contain positive class for chunk, cannot calculate {self.display_name}. "
                 f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return average_precision_score(y_true, uncalibrated_y_pred_proba)
 
@@ -588,7 +588,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.ap_sampling_error(self._sampling_error_components, data)
 
@@ -684,7 +684,7 @@ def _fit(self, reference_data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute fit {self.display_name}.")
             warnings.warn(f"Not enough data to compute fit {self.display_name}.")
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = bse.f1_sampling_error_components(
                 y_true_reference=y_true,
@@ -697,7 +697,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -705,7 +705,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -718,7 +718,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.f1_sampling_error(self._sampling_error_components, data)
 
@@ -728,7 +728,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -736,7 +736,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute realized {self.display_name}.")
             warnings.warn(f"Not enough data to compute realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -746,14 +746,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
                 f"Too few unique values present in '{self.y_true}', "
                 f"returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
 
         if y_pred.nunique() <= 1:
             warnings.warn(
                 f"Too few unique values present in '{self.y_pred}', "
                 f"returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
         # TODO: zero_division should be np.nan
         # update when we update sklearn to 1.3+ and remove unnecessary checks.
         return f1_score(y_true=y_true, y_pred=y_pred, zero_division='warn')
@@ -830,7 +830,7 @@ def _fit(self, reference_data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute fit {self.display_name}.")
             warnings.warn(f"Not enough data to compute fit {self.display_name}.")
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = bse.precision_sampling_error_components(
                 y_true_reference=y_true,
@@ -843,7 +843,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -851,7 +851,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -864,7 +864,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.precision_sampling_error(self._sampling_error_components, data)
 
@@ -874,7 +874,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -882,7 +882,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute realized {self.display_name}.")
             warnings.warn(f"Not enough data to compute realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -892,14 +892,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
                 f"Too few unique values present in '{self.y_true}', "
                 f"returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
 
         if y_pred.nunique() <= 1:
             warnings.warn(
                 f"Too few unique values present in '{self.y_pred}', "
                 f"returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
         # TODO: zero_division should be np.nan
         # update when we update sklearn to 1.3+ and remove unnecessary checks.
         return precision_score(y_true=y_true, y_pred=y_pred, zero_division='warn')
@@ -975,7 +975,7 @@ def _fit(self, reference_data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute fit {self.display_name}.")
             warnings.warn(f"Not enough data to compute fit {self.display_name}.")
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = bse.recall_sampling_error_components(
                 y_true_reference=y_true,
@@ -988,7 +988,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -996,7 +996,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -1009,7 +1009,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.recall_sampling_error(self._sampling_error_components, data)
 
@@ -1019,7 +1019,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1027,7 +1027,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute realized {self.display_name}.")
             warnings.warn(f"Not enough data to compute realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1037,14 +1037,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
                 f"Too few unique values present in '{self.y_true}', "
                 f"returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
 
         if y_pred.nunique() <= 1:
             warnings.warn(
                 f"Too few unique values present in '{self.y_pred}', "
                 f"returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
         # TODO: zero_division should be np.nan
         # update when we update sklearn to 1.3+ and remove unnecessary checks.
         return recall_score(y_true=y_true, y_pred=y_pred, zero_division='warn')
@@ -1123,7 +1123,7 @@ def _fit(self, reference_data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute fit {self.display_name}.")
             warnings.warn(f"Not enough data to compute fit {self.display_name}.")
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = bse.specificity_sampling_error_components(
                 y_true_reference=y_true,
@@ -1136,7 +1136,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1144,7 +1144,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -1157,7 +1157,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.specificity_sampling_error(self._sampling_error_components, data)
 
@@ -1167,7 +1167,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1175,14 +1175,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute realized {self.display_name}.")
             warnings.warn(f"Not enough data to compute realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
         tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=self._labels).ravel()
         denominator = tn + fp
         if denominator == 0:
-            return np.NaN
+            return np.nan
         else:
             return tn / denominator
 
@@ -1257,7 +1257,7 @@ def _fit(self, reference_data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute fit {self.display_name}.")
             warnings.warn(f"Not enough data to compute fit {self.display_name}.")
-            self._sampling_error_components = np.NaN, 0
+            self._sampling_error_components = np.nan, 0
         else:
             self._sampling_error_components = bse.accuracy_sampling_error_components(
                 y_true_reference=y_true,
@@ -1270,7 +1270,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1278,7 +1278,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -1291,7 +1291,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.accuracy_sampling_error(self._sampling_error_components, data)
 
@@ -1301,7 +1301,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1309,7 +1309,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute realized {self.display_name}.")
             warnings.warn(f"Not enough data to compute realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1433,10 +1433,10 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._true_positive_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix
-            self._true_negative_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix
-            self._false_positive_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix
-            self._false_negative_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix
+            self._true_positive_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix
+            self._true_negative_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix
+            self._false_positive_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix
+            self._false_negative_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix
         else:
             self._true_positive_sampling_error_components = bse.true_positive_sampling_error_components(
                 y_true_reference=reference_data[self.y_true],
@@ -1529,13 +1529,13 @@ def _true_positive_realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate true_positives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1551,13 +1551,13 @@ def _true_negative_realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate true_negatives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1573,13 +1573,13 @@ def _false_positive_realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate false_positives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1595,13 +1595,13 @@ def _false_negative_realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
             warnings.warn("Too many missing values, cannot calculate false_negatives. " "Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -1629,7 +1629,7 @@ def get_true_positive_estimate(self, chunk_data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1637,7 +1637,7 @@ def get_true_positive_estimate(self, chunk_data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -1686,7 +1686,7 @@ def get_true_negative_estimate(self, chunk_data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1694,7 +1694,7 @@ def get_true_negative_estimate(self, chunk_data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -1743,7 +1743,7 @@ def get_false_positive_estimate(self, chunk_data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1751,7 +1751,7 @@ def get_false_positive_estimate(self, chunk_data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -1800,7 +1800,7 @@ def get_false_negative_estimate(self, chunk_data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -1808,7 +1808,7 @@ def get_false_negative_estimate(self, chunk_data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -1865,7 +1865,7 @@ def get_true_pos_info(self, chunk_data: pd.DataFrame) -> Dict:
         )
         if empty:
             warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.")
-            sampling_error_true_positives = np.NaN
+            sampling_error_true_positives = np.nan
         else:
             sampling_error_true_positives = bse.true_positive_sampling_error(
                 self._true_positive_sampling_error_components, chunk_data
@@ -1925,7 +1925,7 @@ def get_true_neg_info(self, chunk_data: pd.DataFrame) -> Dict:
         )
         if empty:
             warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.")
-            sampling_error_true_negatives = np.NaN
+            sampling_error_true_negatives = np.nan
         else:
             sampling_error_true_negatives = bse.true_negative_sampling_error(
                 self._true_negative_sampling_error_components, chunk_data
@@ -1985,7 +1985,7 @@ def get_false_pos_info(self, chunk_data: pd.DataFrame) -> Dict:
         )
         if empty:
             warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.")
-            sampling_error_false_positives = np.NaN
+            sampling_error_false_positives = np.nan
         else:
             sampling_error_false_positives = bse.false_positive_sampling_error(
                 self._false_positive_sampling_error_components, chunk_data
@@ -2045,7 +2045,7 @@ def get_false_neg_info(self, chunk_data: pd.DataFrame) -> Dict:
         )
         if empty:
             warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.")
-            sampling_error_false_negatives = np.NaN
+            sampling_error_false_negatives = np.nan
         else:
             sampling_error_false_negatives = bse.false_negative_sampling_error(
                 self._false_negative_sampling_error_components, chunk_data
@@ -2183,7 +2183,7 @@ def _fit(self, reference_data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute fit {self.display_name}.")
             warnings.warn(f"Not enough data to compute fit {self.display_name}.")
-            self._sampling_error_components = np.NaN, self.normalize_business_value
+            self._sampling_error_components = np.nan, self.normalize_business_value
         else:
             self._sampling_error_components = bse.business_value_sampling_error_components(
                 y_true_reference=y_true,
@@ -2198,7 +2198,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -2206,7 +2206,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute realized {self.display_name}.")
             warnings.warn(f"Not enough data to compute realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
@@ -2230,7 +2230,7 @@ def _estimate(self, chunk_data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -2238,7 +2238,7 @@ def _estimate(self, chunk_data: pd.DataFrame) -> float:
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_pred = data[self.y_pred]
         y_pred_proba = data[self.y_pred_proba]
@@ -2255,7 +2255,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return bse.business_value_sampling_error(self._sampling_error_components, data)
 
@@ -2381,7 +2381,7 @@ def _fit(self, reference_data: pd.DataFrame):
             [self.y_true] + self.class_uncalibrated_y_pred_proba_columns,
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clasz in self.classes]
+            self._sampling_error_components = [(np.nan, 0) for clasz in self.classes]
         else:
             # test if reference data are represented correctly
             observed_classes = set(reference_data[self.y_true].unique())
@@ -2407,7 +2407,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -2415,7 +2415,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         _, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba)
         _, y_pred_probas_uncalibrated, _ = _get_multiclass_uncalibrated_predictions(
@@ -2442,7 +2442,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse.auroc_sampling_error(self._sampling_error_components, data)
 
@@ -2452,14 +2452,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
         data, empty = common_nan_removal(data, [self.y_true] + self.class_uncalibrated_y_pred_proba_columns)
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         if set(y_true.unique()) != set(self.classes):
@@ -2469,7 +2469,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
             )
             warnings.warn(_message)
             self._logger.warning(_message)
-            return np.NaN
+            return np.nan
 
         _, y_pred_probas, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba)
 
@@ -2515,7 +2515,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             label_binarizer = LabelBinarizer()
             binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T)
@@ -2532,7 +2532,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -2540,7 +2540,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba)
         ovr_estimates = []
@@ -2559,7 +2559,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse.f1_sampling_error(self._sampling_error_components, data)
 
@@ -2569,23 +2569,23 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         if y_true.nunique() <= 1:
             warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         if data[self.y_pred].nunique() <= 1:
             warnings.warn("Too few unique values present in 'y_pred', returning NaN as realized F1 score.")
-            return np.NaN
+            return np.nan
 
         y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba)
 
@@ -2631,7 +2631,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             label_binarizer = LabelBinarizer()
             binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T)
@@ -2648,7 +2648,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -2656,7 +2656,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba)
         ovr_estimates = []
@@ -2675,7 +2675,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse.precision_sampling_error(self._sampling_error_components, data)
 
@@ -2685,25 +2685,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         if y_true.nunique() <= 1:
             warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         if data[self.y_pred].nunique() <= 1:
             warnings.warn(
                 f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
 
         y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba)
         return precision_score(y_true=y_true, y_pred=y_pred, average='macro', labels=labels)
@@ -2748,7 +2748,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             label_binarizer = LabelBinarizer()
             binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T)
@@ -2765,7 +2765,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -2773,7 +2773,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba)
         ovr_estimates = []
@@ -2791,7 +2791,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse.recall_sampling_error(self._sampling_error_components, data)
 
@@ -2801,25 +2801,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         if y_true.nunique() <= 1:
             warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         if data[self.y_pred].nunique() <= 1:
             warnings.warn(
                 f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
 
         y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba)
 
@@ -2865,7 +2865,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in classes]
         else:
             label_binarizer = LabelBinarizer()
             binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T)
@@ -2882,7 +2882,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -2890,7 +2890,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba)
         ovr_estimates = []
@@ -2909,7 +2909,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse.specificity_sampling_error(self._sampling_error_components, data)
 
@@ -2919,25 +2919,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         if y_true.nunique() <= 1:
             warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         if data[self.y_pred].nunique() <= 1:
             warnings.warn(
                 f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
 
         y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba)
 
@@ -2986,7 +2986,7 @@ def _fit(self, reference_data: pd.DataFrame):
             reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
         )
         if empty:
-            self._sampling_error_components = (np.NaN,)
+            self._sampling_error_components = (np.nan,)
         else:
             label_binarizer = LabelBinarizer()
             binarized_y_true = label_binarizer.fit_transform(reference_data[self.y_true])
@@ -3004,7 +3004,7 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
@@ -3012,7 +3012,7 @@ def _estimate(self, data: pd.DataFrame):
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba)
         y_preds_array = np.asarray(y_preds).T
@@ -3029,7 +3029,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse.accuracy_sampling_error(self._sampling_error_components, data)
 
@@ -3039,25 +3039,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
         data, empty = common_nan_removal(data, [self.y_true, self.y_pred])
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         if y_true.nunique() <= 1:
             warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         if data[self.y_pred].nunique() <= 1:
             warnings.warn(
                 f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score."
             )
-            return np.NaN
+            return np.nan
 
         y_pred, _, _ = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba)
         return accuracy_score(y_true, y_pred)
@@ -3430,7 +3430,7 @@ def _fit(self, reference_data: pd.DataFrame):
             [self.y_true] + self.class_uncalibrated_y_pred_proba_columns,
         )
         if empty:
-            self._sampling_error_components = [(np.NaN, 0) for clazz in self.classes]
+            self._sampling_error_components = [(np.nan, 0) for clazz in self.classes]
         else:
             # sampling error
             binarized_y_true = list(label_binarize(reference_data[self.y_true], classes=self.classes).T)
@@ -3446,13 +3446,13 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "not all present in provided data columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
         if empty:
             self._logger.debug(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         _, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba)
         _, y_pred_probas_uncalibrated, _ = _get_multiclass_uncalibrated_predictions(
@@ -3479,7 +3479,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             warnings.warn(
                 f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN."
             )
-            return np.NaN
+            return np.nan
         else:
             return mse.average_precision_sampling_error(self._sampling_error_components, data)
 
@@ -3489,17 +3489,17 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "not all present in provided data columns" in str(ex):
                 self._logger.debug(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
         if empty:
             warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.")
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         if y_true.nunique() <= 1:
             warnings.warn("Too few unique values present in 'y_true', returning NaN as realized AP.")
-            return np.NaN
+            return np.nan
 
         _, y_pred_probas, _ = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba)
 
@@ -3567,7 +3567,7 @@ def _fit(self, reference_data: pd.DataFrame):
         _list_missing([self.y_true, self.y_pred], list(reference_data.columns))
         data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
         if empty:
-            self._sampling_error_components = np.NaN, self.normalize_business_value
+            self._sampling_error_components = np.nan, self.normalize_business_value
         else:
             num_classes = len(self.classes)
             if num_classes != self.business_value_matrix.shape[0]:
@@ -3592,14 +3592,14 @@ def _estimate(self, data: pd.DataFrame):
         except InvalidArgumentsException as ex:
             if "not all present in provided data columns" in str(ex):
                 self._logger.warning(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
 
         if empty:
             self._logger.warning(f"Not enough data to compute estimated {self.display_name}.")
             warnings.warn(f"Not enough data to compute estimated {self.display_name}.")
-            return np.NaN
+            return np.nan
 
         # TODO: put in a function? Also for MC CM.
         y_pred_proba = {key: data[value] for key, value in self.y_pred_proba.items()}
@@ -3632,7 +3632,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             _message = f"Too many missing values, cannot calculate {self.display_name} sampling error. Returning NaN."
             self._logger.warning(_message)
             warnings.warn(_message)
-            return np.NaN
+            return np.nan
         else:
             return mse.business_value_sampling_error(self._sampling_error_components, data)
 
@@ -3642,7 +3642,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
         except InvalidArgumentsException as ex:
             if "missing required columns" in str(ex):
                 self._logger.info(str(ex))
-                return np.NaN
+                return np.nan
             else:
                 raise ex
         data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
@@ -3650,7 +3650,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float:
             _message = f"'{self.y_true}' contains no data, cannot calculate business value. Returning NaN."
             self._logger.info(_message)
             warnings.warn(_message)
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
diff --git a/nannyml/performance_estimation/direct_loss_estimation/dle.py b/nannyml/performance_estimation/direct_loss_estimation/dle.py
index 9458d858..07941046 100644
--- a/nannyml/performance_estimation/direct_loss_estimation/dle.py
+++ b/nannyml/performance_estimation/direct_loss_estimation/dle.py
@@ -294,10 +294,10 @@ def _fit(self, reference_data: pd.DataFrame, *args, **kwargs) -> Self:
             reference_data[categorical_feature_column] = reference_data[categorical_feature_column].astype("object")
             reference_data[categorical_feature_column] = self._categorical_imputer.fit_transform(
                 reference_data[categorical_feature_column].values.reshape(-1, 1)
-            )
+            ).ravel()
             reference_data[categorical_feature_column] = self._categorical_encoders[
                 categorical_feature_column
-            ].fit_transform(reference_data[categorical_feature_column].values.reshape(-1, 1))
+            ].fit_transform(reference_data[categorical_feature_column].values.reshape(-1, 1)).ravel()
             # LGBM treats -1 for categorical features as missing
             # https://lightgbm.readthedocs.io/en/latest/Advanced-Topics.html#categorical-feature-support
             # Ordinal encoder encodes from 0 to n-1.
@@ -329,10 +329,10 @@ def _estimate(self, data: pd.DataFrame, *args, **kwargs) -> Result:
             data[categorical_feature_column] = data[categorical_feature_column].astype("object")
             data[categorical_feature_column] = self._categorical_imputer.transform(
                 data[categorical_feature_column].values.reshape(-1, 1)
-            )
+            ).ravel()
             data[categorical_feature_column] = self._categorical_encoders[categorical_feature_column].transform(
                 data[categorical_feature_column].values.reshape(-1, 1)
-            )
+            ).ravel()
             # LGBM treats -1 for categorical features as missing
             # https://lightgbm.readthedocs.io/en/latest/Advanced-Topics.html#categorical-feature-support
             # Ordinal encoder encodes from 0 to n-1.
@@ -406,14 +406,14 @@ def _estimate_chunk(self, chunk: Chunk) -> Dict:
                 self._logger.error(
                     f"an unexpected error occurred while calculating metric {metric.display_name}: {exc}"
                 )
-                estimates[f'sampling_error_{metric.column_name}'] = np.NaN
-                estimates[f'realized_{metric.column_name}'] = np.NaN
-                estimates[f'estimated_{metric.column_name}'] = np.NaN
-                estimates[f'upper_confidence_{metric.column_name}'] = np.NaN
-                estimates[f'lower_confidence_{metric.column_name}'] = np.NaN
+                estimates[f'sampling_error_{metric.column_name}'] = np.nan
+                estimates[f'realized_{metric.column_name}'] = np.nan
+                estimates[f'estimated_{metric.column_name}'] = np.nan
+                estimates[f'upper_confidence_{metric.column_name}'] = np.nan
+                estimates[f'lower_confidence_{metric.column_name}'] = np.nan
                 estimates[f'upper_threshold_{metric.column_name}'] = metric.upper_threshold_value
                 estimates[f'lower_threshold_{metric.column_name}'] = metric.lower_threshold_value
-                estimates[f'alert_{metric.column_name}'] = np.NaN
+                estimates[f'alert_{metric.column_name}'] = np.nan
         return estimates
 
 
diff --git a/nannyml/performance_estimation/direct_loss_estimation/metrics.py b/nannyml/performance_estimation/direct_loss_estimation/metrics.py
index 6d041599..046e6caf 100644
--- a/nannyml/performance_estimation/direct_loss_estimation/metrics.py
+++ b/nannyml/performance_estimation/direct_loss_estimation/metrics.py
@@ -12,6 +12,7 @@
 :class:`~nannyml.performance_estimation.confidence_based.metrics.Metric` instances to fit them on reference data
 and run the estimation on analysis data.
 """
+
 import abc
 import logging
 from typing import Any, Callable, Dict, List, Optional, Tuple, Type
@@ -169,7 +170,9 @@ def fit(self, reference_data: pd.DataFrame):
 
         # Calculate alert thresholds
         reference_chunks = self.chunker.split(reference_data)
-        self.lower_threshold_value, self.upper_threshold_value = self._alert_thresholds(reference_chunks)
+        self.lower_threshold_value, self.upper_threshold_value = self._alert_thresholds(
+            reference_chunks
+        )
 
         # Delegate to subclass
         self._fit(reference_data)
@@ -222,8 +225,12 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
             f"'{self.__class__.__name__}' is a subclass of Metric and it must implement the _sampling_error method"
         )
 
-    def _alert_thresholds(self, reference_chunks: List[Chunk]) -> Tuple[Optional[float], Optional[float]]:
-        realized_chunk_performance = np.asarray([self.realized_performance(chunk.data) for chunk in reference_chunks])
+    def _alert_thresholds(
+        self, reference_chunks: List[Chunk]
+    ) -> Tuple[Optional[float], Optional[float]]:
+        realized_chunk_performance = np.asarray(
+            [self.realized_performance(chunk.data) for chunk in reference_chunks]
+        )
         lower_threshold_value, upper_threshold_value = calculate_threshold_values(
             threshold=self.threshold,
             data=realized_chunk_performance,
@@ -247,8 +254,12 @@ def alert(self, value: float) -> bool:
         -------
         bool: bool
         """
-        return (self.lower_threshold_value is not None and value < self.lower_threshold_value) or (
-            self.upper_threshold_value is not None and value > self.upper_threshold_value
+        return (
+            self.lower_threshold_value is not None
+            and value < self.lower_threshold_value
+        ) or (
+            self.upper_threshold_value is not None
+            and value > self.upper_threshold_value
         )
 
     @abc.abstractmethod
@@ -268,7 +279,10 @@ def realized_performance(self, data: pd.DataFrame) -> float:
 
     def __eq__(self, other):
         """Establishes equality by comparing all properties."""
-        return self.display_name == other.display_name and self.column_name == other.column_name
+        return (
+            self.display_name == other.display_name
+            and self.column_name == other.column_name
+        )
 
     def _train_direct_error_estimation_model(
         self,
@@ -287,14 +301,24 @@ def _train_direct_error_estimation_model(
             model.fit(X_train, y_train, categorical_feature=categorical_column_names)
         elif tune_hyperparameters:
             self._logger.debug(
-                f"'tune_hyperparameters' set to '{tune_hyperparameters}': " f"performing hyperparameter tuning"
+                f"'tune_hyperparameters' set to '{tune_hyperparameters}': "
+                f"performing hyperparameter tuning"
+            )
+            self._logger.debug(
+                "'hyperparameters' not set: using default hyperparameters"
+            )
+            self._logger.debug(
+                f"hyperparameter tuning configuration: {hyperparameter_tuning_config}"
             )
-            self._logger.debug("'hyperparameters' not set: using default hyperparameters")
-            self._logger.debug(f'hyperparameter tuning configuration: {hyperparameter_tuning_config}')
 
             automl = AutoML()
             # TODO: is this correct? // categorical_feature
-            automl.fit(X_train, y_train, **hyperparameter_tuning_config, categorical_feature=categorical_column_names)
+            automl.fit(
+                X_train,
+                y_train,
+                **hyperparameter_tuning_config,
+                categorical_feature=categorical_column_names,
+            )
             self.hyperparameters = {**automl.model.estimator.get_params()}
             model = LGBMRegressor(**automl.model.estimator.get_params())
             model.fit(X_train, y_train, categorical_feature=categorical_column_names)
@@ -330,7 +354,8 @@ def create(cls, key: str, problem_type: ProblemType, **kwargs) -> Metric:
         """
         if not isinstance(key, str):
             raise InvalidArgumentsException(
-                f"cannot create metric given a '{type(key)}'" "Please provide a string, function or Metric"
+                f"cannot create metric given a '{type(key)}'"
+                "Please provide a string, function or Metric"
             )
 
         if key not in cls.registry:
@@ -356,7 +381,8 @@ def inner_wrapper(wrapped_class: Type[Metric]) -> Type[Metric]:
             if metric in cls.registry:
                 if problem_type in cls.registry[metric]:
                     cls._logger().warning(
-                        f"re-registering Metric for metric='{metric}' " f"and problem_type='{problem_type}'"
+                        f"re-registering Metric for metric='{metric}' "
+                        f"and problem_type='{problem_type}'"
                     )
                 cls.registry[metric][problem_type] = wrapped_class
             else:
@@ -366,7 +392,7 @@ def inner_wrapper(wrapped_class: Type[Metric]) -> Type[Metric]:
         return inner_wrapper
 
 
-@MetricFactory.register('mae', ProblemType.REGRESSION)
+@MetricFactory.register("mae", ProblemType.REGRESSION)
 class MAE(Metric):
     """Estimate regression performance using Mean Absolute Error metric."""
 
@@ -425,8 +451,8 @@ def __init__(
             The Threshold instance that determines how the lower and upper threshold values will be calculated.
         """
         super().__init__(
-            display_name='MAE',
-            column_name='mae',
+            display_name="MAE",
+            column_name="mae",
             feature_column_names=feature_column_names,
             y_true=y_true,
             y_pred=y_pred,
@@ -439,7 +465,9 @@ def __init__(
 
     def _fit(self, reference_data: pd.DataFrame):
         # filter nans here
-        reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred])
+        reference_data, empty = common_nan_removal(
+            reference_data, [self.y_true, self.y_pred]
+        )
         if empty:
             raise InvalidReferenceDataException(
                 f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets."
@@ -464,7 +492,9 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
     def _estimate(self, data: pd.DataFrame):
-        observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]])
+        observation_level_estimates = self._dee_model.predict(
+            X=data[self.feature_column_names + [self.y_pred]]
+        )
         # clip negative predictions to 0
         observation_level_estimates = np.maximum(0, observation_level_estimates)
         chunk_level_estimate = np.mean(observation_level_estimates)
@@ -474,7 +504,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
         # we only expect predictions to be present and estimate sampling error based on them
         data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred])
         if empty:
-            return np.NaN
+            return np.nan
         else:
             return mae_sampling_error(self._sampling_error_components, data)
 
@@ -494,17 +524,19 @@ def realized_performance(self, data: pd.DataFrame) -> float:
             Mean Absolute Error
         """
         if self.y_true not in data.columns:
-            return np.NaN
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+            return np.nan
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
         return mean_absolute_error(y_true, y_pred)
 
 
-@MetricFactory.register('mape', ProblemType.REGRESSION)
+@MetricFactory.register("mape", ProblemType.REGRESSION)
 class MAPE(Metric):
     """Estimate regression performance using Mean Absolute Percentage Error metric."""
 
@@ -563,8 +595,8 @@ def __init__(
             The Threshold instance that determines how the lower and upper threshold values will be calculated.
         """
         super().__init__(
-            display_name='MAPE',
-            column_name='mape',
+            display_name="MAPE",
+            column_name="mape",
             feature_column_names=feature_column_names,
             y_true=y_true,
             y_pred=y_pred,
@@ -577,7 +609,9 @@ def __init__(
 
     def _fit(self, reference_data: pd.DataFrame):
         # filter nans here
-        reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred])
+        reference_data, empty = common_nan_removal(
+            reference_data, [self.y_true, self.y_pred]
+        )
         if empty:
             raise InvalidReferenceDataException(
                 f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets."
@@ -591,7 +625,9 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
         epsilon = np.finfo(np.float64).eps
-        observation_level_metric = abs(y_true - y_pred) / (np.maximum(epsilon, abs(y_true)))
+        observation_level_metric = abs(y_true - y_pred) / (
+            np.maximum(epsilon, abs(y_true))
+        )
 
         self._dee_model = self._train_direct_error_estimation_model(
             X_train=reference_data[self.feature_column_names + [self.y_pred]],
@@ -603,7 +639,9 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
     def _estimate(self, data: pd.DataFrame):
-        observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]])
+        observation_level_estimates = self._dee_model.predict(
+            X=data[self.feature_column_names + [self.y_pred]]
+        )
         # clip negative predictions to 0
         observation_level_estimates = np.maximum(0, observation_level_estimates)
         chunk_level_estimate = np.mean(observation_level_estimates)
@@ -613,7 +651,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
         # we only expect predictions to be present and estimate sampling error based on them
         data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred])
         if empty:
-            return np.NaN
+            return np.nan
         else:
             return mape_sampling_error(self._sampling_error_components, data)
 
@@ -633,17 +671,19 @@ def realized_performance(self, data: pd.DataFrame) -> float:
             Mean Absolute Percentage Error
         """
         if self.y_true not in data.columns:
-            return np.NaN
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+            return np.nan
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
-            return np.NaN
+            return np.nan
 
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
         return mean_absolute_percentage_error(y_true, y_pred)
 
 
-@MetricFactory.register('mse', ProblemType.REGRESSION)
+@MetricFactory.register("mse", ProblemType.REGRESSION)
 class MSE(Metric):
     """Estimate regression performance using Mean Squared Error metric."""
 
@@ -702,8 +742,8 @@ def __init__(
             The Threshold instance that determines how the lower and upper threshold values will be calculated.
         """
         super().__init__(
-            display_name='MSE',
-            column_name='mse',
+            display_name="MSE",
+            column_name="mse",
             feature_column_names=feature_column_names,
             y_true=y_true,
             y_pred=y_pred,
@@ -716,7 +756,9 @@ def __init__(
 
     def _fit(self, reference_data: pd.DataFrame):
         # filter nans here
-        reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred])
+        reference_data, empty = common_nan_removal(
+            reference_data, [self.y_true, self.y_pred]
+        )
         if empty:
             raise InvalidReferenceDataException(
                 f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets."
@@ -741,7 +783,9 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
     def _estimate(self, data: pd.DataFrame):
-        observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]])
+        observation_level_estimates = self._dee_model.predict(
+            X=data[self.feature_column_names + [self.y_pred]]
+        )
         # clip negative predictions to 0
         observation_level_estimates = np.maximum(0, observation_level_estimates)
         chunk_level_estimate = np.mean(observation_level_estimates)
@@ -751,7 +795,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
         # we only expect predictions to be present and estimate sampling error based on them
         data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred])
         if empty:
-            return np.NaN
+            return np.nan
         else:
             return mse_sampling_error(self._sampling_error_components, data)
 
@@ -771,16 +815,18 @@ def realized_performance(self, data: pd.DataFrame) -> float:
             Mean Squared Error
         """
         if self.y_true not in data.columns:
-            return np.NaN
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+            return np.nan
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
-            return np.NaN
+            return np.nan
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
         return mean_squared_error(y_true, y_pred)
 
 
-@MetricFactory.register('msle', ProblemType.REGRESSION)
+@MetricFactory.register("msle", ProblemType.REGRESSION)
 class MSLE(Metric):
     """Estimate regression performance using Mean Squared Logarithmic Error metric."""
 
@@ -839,8 +885,8 @@ def __init__(
             The Threshold instance that determines how the lower and upper threshold values will be calculated.
         """
         super().__init__(
-            display_name='MSLE',
-            column_name='msle',
+            display_name="MSLE",
+            column_name="msle",
             feature_column_names=feature_column_names,
             y_true=y_true,
             y_pred=y_pred,
@@ -853,7 +899,9 @@ def __init__(
 
     def _fit(self, reference_data: pd.DataFrame):
         # filter nans here
-        reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred])
+        reference_data, empty = common_nan_removal(
+            reference_data, [self.y_true, self.y_pred]
+        )
         if empty:
             raise InvalidReferenceDataException(
                 f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets."
@@ -880,7 +928,9 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
     def _estimate(self, data: pd.DataFrame):
-        observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]])
+        observation_level_estimates = self._dee_model.predict(
+            X=data[self.feature_column_names + [self.y_pred]]
+        )
         # clip negative predictions to 0
         observation_level_estimates = np.maximum(0, observation_level_estimates)
         chunk_level_estimate = np.mean(observation_level_estimates)
@@ -890,7 +940,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
         # we only expect predictions to be present and estimate sampling error based on them
         data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred])
         if empty:
-            return np.NaN
+            return np.nan
         else:
             return msle_sampling_error(self._sampling_error_components, data)
 
@@ -914,16 +964,18 @@ def realized_performance(self, data: pd.DataFrame) -> float:
             Mean Squared Log Error
         """
         if self.y_true not in data.columns:
-            return np.NaN
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+            return np.nan
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
-            return np.NaN
+            return np.nan
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
         return mean_squared_log_error(y_true, y_pred)
 
 
-@MetricFactory.register('rmse', ProblemType.REGRESSION)
+@MetricFactory.register("rmse", ProblemType.REGRESSION)
 class RMSE(Metric):
     """Estimate regression performance using Root Mean Squared Error metric."""
 
@@ -982,8 +1034,8 @@ def __init__(
             The Threshold instance that determines how the lower and upper threshold values will be calculated.
         """
         super().__init__(
-            display_name='RMSE',
-            column_name='rmse',
+            display_name="RMSE",
+            column_name="rmse",
             feature_column_names=feature_column_names,
             y_true=y_true,
             y_pred=y_pred,
@@ -996,7 +1048,9 @@ def __init__(
 
     def _fit(self, reference_data: pd.DataFrame):
         # filter nans here
-        reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred])
+        reference_data, empty = common_nan_removal(
+            reference_data, [self.y_true, self.y_pred]
+        )
         if empty:
             raise InvalidReferenceDataException(
                 f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets."
@@ -1021,7 +1075,9 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
     def _estimate(self, data: pd.DataFrame):
-        observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]])
+        observation_level_estimates = self._dee_model.predict(
+            X=data[self.feature_column_names + [self.y_pred]]
+        )
         # clip negative predictions to 0
         observation_level_estimates = np.maximum(0, observation_level_estimates)
         chunk_level_estimate = np.sqrt(np.mean(observation_level_estimates))
@@ -1031,7 +1087,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
         # we only expect predictions to be present and estimate sampling error based on them
         data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred])
         if empty:
-            return np.NaN
+            return np.nan
         else:
             return rmse_sampling_error(self._sampling_error_components, data)
 
@@ -1051,16 +1107,28 @@ def realized_performance(self, data: pd.DataFrame) -> float:
             Root Mean Squared Error
         """
         if self.y_true not in data.columns:
-            return np.NaN
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+            return np.nan
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
-            return np.NaN
+            return np.nan
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
-        return mean_squared_error(y_true, y_pred, squared=False)
 
+        # Deal with breaking API change in sklearn 1.4
+        # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_error.html
+        try:
+            from sklearn.metrics import root_mean_squared_error
 
-@MetricFactory.register('rmsle', ProblemType.REGRESSION)
+            return root_mean_squared_error(y_true, y_pred)
+        except ImportError:
+            from sklearn.metrics import mean_squared_error
+
+            return np.sqrt(mean_squared_error(y_true, y_pred, squared=False))
+
+
+@MetricFactory.register("rmsle", ProblemType.REGRESSION)
 class RMSLE(Metric):
     """Estimate regression performance using Root Mean Squared Logarithmic Error metric."""
 
@@ -1119,8 +1187,8 @@ def __init__(
             The Threshold instance that determines how the lower and upper threshold values will be calculated.
         """
         super().__init__(
-            display_name='RMSLE',
-            column_name='rmsle',
+            display_name="RMSLE",
+            column_name="rmsle",
             feature_column_names=feature_column_names,
             y_true=y_true,
             y_pred=y_pred,
@@ -1133,7 +1201,9 @@ def __init__(
 
     def _fit(self, reference_data: pd.DataFrame):
         # filter nans here
-        reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred])
+        reference_data, empty = common_nan_removal(
+            reference_data, [self.y_true, self.y_pred]
+        )
         if empty:
             raise InvalidReferenceDataException(
                 f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets."
@@ -1161,7 +1231,9 @@ def _fit(self, reference_data: pd.DataFrame):
         )
 
     def _estimate(self, data: pd.DataFrame):
-        observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]])
+        observation_level_estimates = self._dee_model.predict(
+            X=data[self.feature_column_names + [self.y_pred]]
+        )
         # clip negative predictions to 0
         observation_level_estimates = np.maximum(0, observation_level_estimates)
         chunk_level_estimate = np.sqrt(np.mean(observation_level_estimates))
@@ -1171,7 +1243,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float:
         # we only expect predictions to be present and estimate sampling error based on them
         data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred])
         if empty:
-            return np.NaN
+            return np.nan
         else:
             return rmsle_sampling_error(self._sampling_error_components, data)
 
@@ -1195,14 +1267,25 @@ def realized_performance(self, data: pd.DataFrame) -> float:
             Root Mean Squared Log Error
         """
         if self.y_true not in data.columns:
-            return np.NaN
-        data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred])
+            return np.nan
+        data, empty = common_nan_removal(
+            data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]
+        )
         if empty:
-            return np.NaN
+            return np.nan
         y_true = data[self.y_true]
         y_pred = data[self.y_pred]
 
         _raise_exception_for_negative_values(y_true)
         _raise_exception_for_negative_values(y_pred)
 
-        return mean_squared_log_error(y_true, y_pred, squared=False)
+        # Deal with breaking API change in sklearn 1.4
+        # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_log_error.html
+        try:
+            from sklearn.metrics import root_mean_squared_log_error
+
+            return root_mean_squared_log_error(y_true, y_pred)
+        except ImportError:
+            from sklearn.metrics import mean_squared_log_error
+
+            return np.sqrt(mean_squared_log_error(y_true, y_pred, squared=False))
diff --git a/nannyml/plots/components/stacked_bar_plot.py b/nannyml/plots/components/stacked_bar_plot.py
index 9d0d8bd0..7ec377db 100644
--- a/nannyml/plots/components/stacked_bar_plot.py
+++ b/nannyml/plots/components/stacked_bar_plot.py
@@ -50,22 +50,23 @@ def calculate_value_counts(
     # TODO: deal with None timestamps
     if isinstance(timestamps, pd.Series):
         timestamps = timestamps.reset_index()
-    data_with_chunk_keys = pd.concat(
-        [
-            chunk.data.assign(chunk_key=chunk.key, chunk_index=chunk.chunk_index)
-            for chunk in chunker.split(pd.concat([pd.Series(categorical_data, name=column_name), timestamps], axis=1))
-        ]
-    )
+
+    chunks = chunker.split(pd.concat([pd.Series(categorical_data, name=column_name), timestamps], axis=1))
+    data_with_chunk_keys = pd.concat([chunk.data.assign(chunk_index=chunk.chunk_index) for chunk in chunks])
+
+    chunk_keys_lookup = {chunk.chunk_index: chunk.key for chunk in chunks}
 
     value_counts_table = (
-        data_with_chunk_keys.groupby(['chunk_key', 'chunk_index'])[column_name]
+        data_with_chunk_keys.groupby(['chunk_index'])[column_name]
         .value_counts()
         .to_frame('value_counts')
         .sort_values(by=['chunk_index', 'value_counts'])
         .reset_index()
-        .rename(columns={'level_2': column_name, 'chunk_index': 'chunk_indices'})
+        .rename(columns={'chunk_index': 'chunk_indices'})
     )
 
+    value_counts_table['chunk_key'] = value_counts_table['chunk_indices'].map(lambda i: chunk_keys_lookup[i])
+
     value_counts_table['value_counts_total'] = value_counts_table['chunk_key'].map(
         value_counts_table.groupby('chunk_key')['value_counts'].sum()
     )
diff --git a/nannyml/stats/avg/calculator.py b/nannyml/stats/avg/calculator.py
index e4d892a7..60538634 100644
--- a/nannyml/stats/avg/calculator.py
+++ b/nannyml/stats/avg/calculator.py
@@ -191,10 +191,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st
                 self._logger.error(
                     f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}"
                 )
-            result['value'] = np.NaN
-            result['sampling_error'] = np.NaN
-            result['upper_confidence_boundary'] = np.NaN
-            result['lower_confidence_boundary'] = np.NaN
+            result['value'] = np.nan
+            result['sampling_error'] = np.nan
+            result['upper_confidence_boundary'] = np.nan
+            result['lower_confidence_boundary'] = np.nan
         finally:
             return result
 
diff --git a/nannyml/stats/median/calculator.py b/nannyml/stats/median/calculator.py
index e8175bc6..9aaf63c3 100644
--- a/nannyml/stats/median/calculator.py
+++ b/nannyml/stats/median/calculator.py
@@ -199,10 +199,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st
                 self._logger.error(
                     f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}"
                 )
-            result['value'] = np.NaN
-            result['sampling_error'] = np.NaN
-            result['upper_confidence_boundary'] = np.NaN
-            result['lower_confidence_boundary'] = np.NaN
+            result['value'] = np.nan
+            result['sampling_error'] = np.nan
+            result['upper_confidence_boundary'] = np.nan
+            result['lower_confidence_boundary'] = np.nan
         finally:
             return result
 
diff --git a/nannyml/stats/std/calculator.py b/nannyml/stats/std/calculator.py
index 7d09152a..d248aa1c 100644
--- a/nannyml/stats/std/calculator.py
+++ b/nannyml/stats/std/calculator.py
@@ -201,10 +201,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st
                 self._logger.error(
                     f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}"
                 )
-            result['value'] = np.NaN
-            result['sampling_error'] = np.NaN
-            result['upper_confidence_boundary'] = np.NaN
-            result['lower_confidence_boundary'] = np.NaN
+            result['value'] = np.nan
+            result['sampling_error'] = np.nan
+            result['upper_confidence_boundary'] = np.nan
+            result['lower_confidence_boundary'] = np.nan
         finally:
             return result
 
diff --git a/nannyml/stats/sum/calculator.py b/nannyml/stats/sum/calculator.py
index b9d1b8a5..8c36095d 100644
--- a/nannyml/stats/sum/calculator.py
+++ b/nannyml/stats/sum/calculator.py
@@ -190,10 +190,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st
                 self._logger.error(
                     f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}"
                 )
-            result['value'] = np.NaN
-            result['sampling_error'] = np.NaN
-            result['upper_confidence_boundary'] = np.NaN
-            result['lower_confidence_boundary'] = np.NaN
+            result['value'] = np.nan
+            result['sampling_error'] = np.nan
+            result['upper_confidence_boundary'] = np.nan
+            result['lower_confidence_boundary'] = np.nan
         finally:
             return result
 
diff --git a/poetry.lock b/poetry.lock
index 434336bf..96c2f0e9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,25 +1,25 @@
-# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiobotocore"
-version = "2.13.1"
+version = "2.16.0"
 description = "Async client for aws services using botocore and aiohttp"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiobotocore-2.13.1-py3-none-any.whl", hash = "sha256:1bef121b99841ee3cc788e4ed97c332ba32353b1f00e886d1beb3aae95520858"},
-    {file = "aiobotocore-2.13.1.tar.gz", hash = "sha256:134f9606c2f91abde38cbc61c3241113e26ff244633e0c31abb7e09da3581c9b"},
+    {file = "aiobotocore-2.16.0-py3-none-any.whl", hash = "sha256:eb3641a7b9c51113adbc33a029441de6201ebb026c64ff2e149c7fa802c9abfc"},
+    {file = "aiobotocore-2.16.0.tar.gz", hash = "sha256:6d6721961a81570e9b920b98778d95eec3d52a9f83b7844c6c5cfdbf2a2d6a11"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.9.2,<4.0.0"
 aioitertools = ">=0.5.1,<1.0.0"
-botocore = ">=1.34.70,<1.34.132"
+botocore = ">=1.35.74,<1.35.82"
 wrapt = ">=1.10.10,<2.0.0"
 
 [package.extras]
-awscli = ["awscli (>=1.32.70,<1.33.14)"]
-boto3 = ["boto3 (>=1.34.70,<1.34.132)"]
+awscli = ["awscli (>=1.36.15,<1.36.23)"]
+boto3 = ["boto3 (>=1.35.74,<1.35.82)"]
 
 [[package]]
 name = "aiofiles"
@@ -34,135 +34,140 @@ files = [
 
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-description = "Happy Eyeballs"
+version = "2.4.4"
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 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
 
 [package.extras]
 speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aioitertools"
-version = "0.11.0"
+version = "0.12.0"
 description = "itertools and builtins for AsyncIO and mixed iterables"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 typing_extensions = {version = ">=4.0", markers = "python_version < \"3.10\""}
 
+[package.extras]
+dev = ["attribution (==1.8.0)", "black (==24.8.0)", "build (>=1.2)", "coverage (==7.6.1)", "flake8 (==7.1.1)", "flit (==3.9.0)", "mypy (==1.11.2)", "ufmt (==2.7.1)", "usort (==1.0.8.post1)"]
+docs = ["sphinx (==8.0.2)", "sphinx-mdinclude (==0.6.2)"]
+
 [[package]]
 name = "aiosignal"
-version = "1.3.1"
+version = "1.3.2"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -188,13 +193,13 @@ docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
 
 [[package]]
 name = "alabaster"
-version = "0.7.13"
-description = "A configurable sidebar-enabled Sphinx theme"
+version = "0.7.16"
+description = "A light, configurable Sphinx theme"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
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+    {file = "alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92"},
+    {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"},
 ]
 
 [[package]]
@@ -208,30 +213,27 @@ files = [
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 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
-
 [[package]]
 name = "anyio"
-version = "4.4.0"
+version = "4.7.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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+    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
+    {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "apeye"
@@ -282,28 +284,27 @@ files = [
 
 [[package]]
 name = "apscheduler"
-version = "3.10.4"
+version = "3.11.0"
 description = "In-process task scheduler with Cron-like capabilities"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "APScheduler-3.10.4-py3-none-any.whl", hash = "sha256:fb91e8a768632a4756a585f79ec834e0e27aad5860bac7eaa523d9ccefd87661"},
-    {file = "APScheduler-3.10.4.tar.gz", hash = "sha256:e6df071b27d9be898e486bc7940a7be50b4af2e9da7c08f0744a96d4bd4cef4a"},
+    {file = "APScheduler-3.11.0-py3-none-any.whl", hash = "sha256:fc134ca32e50f5eadcc4938e3a4545ab19131435e851abb40b34d63d5141c6da"},
+    {file = "apscheduler-3.11.0.tar.gz", hash = "sha256:4c622d250b0955a65d5d0eb91c33e6d43fd879834bf541e0a18661ae60460133"},
 ]
 
 [package.dependencies]
-pytz = "*"
-six = ">=1.4.0"
-tzlocal = ">=2.0,<3.dev0 || >=4.dev0"
+tzlocal = ">=3.0"
 
 [package.extras]
-doc = ["sphinx", "sphinx-rtd-theme"]
+doc = ["packaging", "sphinx", "sphinx-rtd-theme (>=1.3.0)"]
+etcd = ["etcd3", "protobuf (<=3.21.0)"]
 gevent = ["gevent"]
 mongodb = ["pymongo (>=3.0)"]
 redis = ["redis (>=3.0)"]
 rethinkdb = ["rethinkdb (>=2.4.0)"]
 sqlalchemy = ["sqlalchemy (>=1.4)"]
-testing = ["pytest", "pytest-asyncio", "pytest-cov", "pytest-tornado5"]
+test = ["APScheduler[etcd,mongodb,redis,rethinkdb,sqlalchemy,tornado,zookeeper]", "PySide6", "anyio (>=4.5.2)", "gevent", "pytest", "pytz", "twisted"]
 tornado = ["tornado (>=4.3)"]
 twisted = ["twisted"]
 zookeeper = ["kazoo"]
@@ -386,31 +387,28 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock
 
 [[package]]
 name = "asttokens"
-version = "2.4.1"
+version = "3.0.0"
 description = "Annotate AST trees with source code positions"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
-    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+    {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
+    {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
 ]
 
-[package.dependencies]
-six = ">=1.12.0"
-
 [package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "async-timeout"
-version = "4.0.3"
+version = "5.0.1"
 description = "Timeout context manager for asyncio programs"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
-    {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
+    {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"},
+    {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
 ]
 
 [[package]]
@@ -425,65 +423,51 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "24.3.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
-    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+    {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
+    {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
 ]
 
 [package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "autodocsumm"
-version = "0.2.12"
+version = "0.2.14"
 description = "Extended sphinx autodoc including automatic autosummaries"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "autodocsumm-0.2.12-py3-none-any.whl", hash = "sha256:b842b53c686c07a4f174721ca4e729b027367703dbf42e2508863a3c6d6c049c"},
-    {file = "autodocsumm-0.2.12.tar.gz", hash = "sha256:848fe8c38df433c6635489499b969cb47cc389ed3d7b6e75c8ccbc94d4b3bf9e"},
+    {file = "autodocsumm-0.2.14-py3-none-any.whl", hash = "sha256:3bad8717fc5190802c60392a7ab04b9f3c97aa9efa8b3780b3d81d615bfe5dc0"},
+    {file = "autodocsumm-0.2.14.tar.gz", hash = "sha256:2839a9d4facc3c4eccd306c08695540911042b46eeafcdc3203e6d0bab40bc77"},
 ]
 
 [package.dependencies]
-Sphinx = ">=2.2,<8.0"
+Sphinx = ">=4.0,<9.0"
 
 [[package]]
 name = "babel"
-version = "2.15.0"
+version = "2.16.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"},
-    {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"},
+    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
+    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
 ]
 
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
-[[package]]
-name = "backcall"
-version = "0.2.0"
-description = "Specifications for callback functions passed in to an API"
-optional = false
-python-versions = "*"
-files = [
-    {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
-    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
-]
-
 [[package]]
 name = "backoff"
 version = "2.2.1"
@@ -510,34 +494,6 @@ files = [
 docs = ["furo", "jaraco.packaging (>=9.3)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["jaraco.test", "pytest (!=8.0.*)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)"]
 
-[[package]]
-name = "backports-zoneinfo"
-version = "0.2.1"
-description = "Backport of the standard library zoneinfo module"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-macosx_10_14_x86_64.whl", hash = "sha256:da6013fd84a690242c310d77ddb8441a559e9cb3d3d59ebac9aca1a57b2e18bc"},
-    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:89a48c0d158a3cc3f654da4c2de1ceba85263fafb861b98b59040a5086259722"},
-    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:1c5742112073a563c81f786e77514969acb58649bcdf6cdf0b4ed31a348d4546"},
-    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-win32.whl", hash = "sha256:e8236383a20872c0cdf5a62b554b27538db7fa1bbec52429d8d106effbaeca08"},
-    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-win_amd64.whl", hash = "sha256:8439c030a11780786a2002261569bdf362264f605dfa4d65090b64b05c9f79a7"},
-    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-macosx_10_14_x86_64.whl", hash = "sha256:f04e857b59d9d1ccc39ce2da1021d196e47234873820cbeaad210724b1ee28ac"},
-    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:17746bd546106fa389c51dbea67c8b7c8f0d14b5526a579ca6ccf5ed72c526cf"},
-    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:5c144945a7752ca544b4b78c8c41544cdfaf9786f25fe5ffb10e838e19a27570"},
-    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-win32.whl", hash = "sha256:e55b384612d93be96506932a786bbcde5a2db7a9e6a4bb4bffe8b733f5b9036b"},
-    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-win_amd64.whl", hash = "sha256:a76b38c52400b762e48131494ba26be363491ac4f9a04c1b7e92483d169f6582"},
-    {file = "backports.zoneinfo-0.2.1-cp38-cp38-macosx_10_14_x86_64.whl", hash = "sha256:8961c0f32cd0336fb8e8ead11a1f8cd99ec07145ec2931122faaac1c8f7fd987"},
-    {file = "backports.zoneinfo-0.2.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:e81b76cace8eda1fca50e345242ba977f9be6ae3945af8d46326d776b4cf78d1"},
-    {file = "backports.zoneinfo-0.2.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:7b0a64cda4145548fed9efc10322770f929b944ce5cee6c0dfe0c87bf4c0c8c9"},
-    {file = "backports.zoneinfo-0.2.1-cp38-cp38-win32.whl", hash = "sha256:1b13e654a55cd45672cb54ed12148cd33628f672548f373963b0bff67b217328"},
-    {file = "backports.zoneinfo-0.2.1-cp38-cp38-win_amd64.whl", hash = "sha256:4a0f800587060bf8880f954dbef70de6c11bbe59c673c3d818921f042f9954a6"},
-    {file = "backports.zoneinfo-0.2.1.tar.gz", hash = "sha256:fadbfe37f74051d024037f223b8e001611eac868b5c5b06144ef4d8b799862f2"},
-]
-
-[package.extras]
-tzdata = ["tzdata"]
-
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -596,31 +552,30 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "6.1.0"
+version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
-    {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
+    {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+    {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
 ]
 
 [package.dependencies]
-six = ">=1.9.0"
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.131"
+version = "1.35.81"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.131-py3-none-any.whl", hash = "sha256:13b011d7b206ce00727dcee26548fa3b550db9046d5a0e90ac25a6e6c8fde6ef"},
-    {file = "botocore-1.34.131.tar.gz", hash = "sha256:502ddafe1d627fcf1e4c007c86454e5dd011dba7c58bd8e8a5368a79f3e387dc"},
+    {file = "botocore-1.35.81-py3-none-any.whl", hash = "sha256:a7b13bbd959bf2d6f38f681676aab408be01974c46802ab997617b51399239f7"},
+    {file = "botocore-1.35.81.tar.gz", hash = "sha256:564c2478e50179e0b766e6a87e5e0cdd35e1bc37eb375c1cf15511f5dd13600d"},
 ]
 
 [package.dependencies]
@@ -632,7 +587,7 @@ urllib3 = [
 ]
 
 [package.extras]
-crt = ["awscrt (==0.20.11)"]
+crt = ["awscrt (==0.22.0)"]
 
 [[package]]
 name = "bump2version"
@@ -647,13 +602,13 @@ files = [
 
 [[package]]
 name = "cachecontrol"
-version = "0.14.0"
+version = "0.14.1"
 description = "httplib2 caching for requests"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "cachecontrol-0.14.0-py3-none-any.whl", hash = "sha256:f5bf3f0620c38db2e5122c0726bdebb0d16869de966ea6a2befe92470b740ea0"},
-    {file = "cachecontrol-0.14.0.tar.gz", hash = "sha256:7db1195b41c81f8274a7bbd97c956f44e8348265a1bc7641c37dfebc39f0c938"},
+    {file = "cachecontrol-0.14.1-py3-none-any.whl", hash = "sha256:65e3abd62b06382ce3894df60dde9e0deb92aeb734724f68fa4f3b91e97206b9"},
+    {file = "cachecontrol-0.14.1.tar.gz", hash = "sha256:06ef916a1e4eb7dba9948cdfc9c76e749db2e02104a9a1277e8b642591a0f717"},
 ]
 
 [package.dependencies]
@@ -662,34 +617,33 @@ msgpack = ">=0.5.2,<2.0.0"
 requests = ">=2.16.0"
 
 [package.extras]
-dev = ["CacheControl[filecache,redis]", "black", "build", "cherrypy", "furo", "mypy", "pytest", "pytest-cov", "sphinx", "sphinx-copybutton", "tox", "types-redis", "types-requests"]
+dev = ["CacheControl[filecache,redis]", "build", "cherrypy", "codespell[tomli]", "furo", "mypy", "pytest", "pytest-cov", "ruff", "sphinx", "sphinx-copybutton", "tox", "types-redis", "types-requests"]
 filecache = ["filelock (>=3.8.0)"]
 redis = ["redis (>=2.10.5)"]
 
 [[package]]
 name = "cachetools"
-version = "5.4.0"
+version = "5.5.0"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.4.0-py3-none-any.whl", hash = "sha256:3ae3b49a3d5e28a77a0be2b37dbcb89005058959cb2323858c2657c4a8cab474"},
-    {file = "cachetools-5.4.0.tar.gz", hash = "sha256:b8adc2e7c07f105ced7bc56dbb6dfbe7c4a00acce20e2227b3f355be89bc6827"},
+    {file = "cachetools-5.5.0-py3-none-any.whl", hash = "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292"},
+    {file = "cachetools-5.5.0.tar.gz", hash = "sha256:2cc24fb4cbe39633fb7badd9db9ca6295d766d9c2995f245725a46715d050f2a"},
 ]
 
 [[package]]
 name = "category-encoders"
-version = "2.6.3"
+version = "2.6.4"
 description = "A collection of sklearn transformers to encode categorical variables as numeric"
 optional = false
 python-versions = "*"
 files = [
-    {file = "category_encoders-2.6.3-py2.py3-none-any.whl", hash = "sha256:117775f1775e53a67c9e91842ac9100bc364cddc9f4058188796532bc5b11f1c"},
-    {file = "category_encoders-2.6.3.tar.gz", hash = "sha256:d9f14705ed4b536eaf9cfc81b76d67a50b2f16f8f3eda498c57d7da19655530c"},
+    {file = "category_encoders-2.6.4-py2.py3-none-any.whl", hash = "sha256:59f4b541ec787dfdfacc12267e1aff91a1ddc0b756991ec2129d50d6495d6e36"},
+    {file = "category_encoders-2.6.4.tar.gz", hash = "sha256:b842f0b3f280cdcee94278bee61c96500e4a8f71bc846714d7bf5696ae24b528"},
 ]
 
 [package.dependencies]
-importlib-resources = {version = "*", markers = "python_version < \"3.9\""}
 numpy = ">=1.14.0"
 pandas = ">=1.0.5"
 patsy = ">=0.5.1"
@@ -699,74 +653,89 @@ statsmodels = ">=0.9.0"
 
 [[package]]
 name = "certifi"
-version = "2024.7.4"
+version = "2024.12.14"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
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 [package.dependencies]
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 [package.extras]
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 [package.extras]
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 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
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 ]
 
 [package.dependencies]
@@ -1119,7 +1112,7 @@ nox = ["nox"]
 pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi", "cryptography-vectors (==43.0.0)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
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 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1157,33 +1150,37 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "debugpy"
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 [[package]]
@@ -1225,13 +1222,13 @@ domdf-python-tools = ">=2.2.0"
 
 [[package]]
 name = "distlib"
-version = "0.3.8"
+version = "0.3.9"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -1257,8 +1254,6 @@ files = [
 ]
 
 [package.dependencies]
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-importlib-resources = {version = ">=3.0.0", markers = "python_version < \"3.9\""}
 natsort = ">=7.0.1"
 typing-extensions = ">=3.7.4.1"
 
@@ -1293,13 +1288,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "executing"
-version = "2.0.1"
+version = "2.1.0"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
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 files = [
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 ]
 
 [package.extras]
@@ -1307,13 +1302,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastjsonschema"
-version = "2.20.0"
+version = "2.21.1"
 description = "Fastest Python implementation of JSON schema"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -1321,19 +1316,19 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
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 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
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+typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "flake8"
@@ -1382,91 +1377,103 @@ flake8 = ">=3.8"
 
 [[package]]
 name = "flaml"
-version = "1.2.4"
+version = "2.3.3"
 description = "A fast library for automated machine learning and tuning"
 optional = false
-python-versions = ">=3.6"
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 files = [
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-scikit-learn = ">=0.24"
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-xgboost = ">=0.90"
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+xgboost = {version = ">=0.90,<3.0.0", optional = true, markers = "extra == \"automl\""}
 
 [package.extras]
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 nni = ["nni"]
-notebook = ["jupyter", "matplotlib", "openml (==0.10.2)"]
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
 name = "fsspec"
-version = "2024.6.1"
+version = "2024.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1621,19 +1643,19 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "gcsfs"
-version = "2024.6.1"
+version = "2024.10.0"
 description = "Convenient Filesystem interface over GCS"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 aiohttp = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1"
 decorator = ">4.1.2"
-fsspec = "2024.6.1"
+fsspec = "2024.10.0"
 google-auth = ">=1.2"
 google-auth-oauthlib = "*"
 google-cloud-storage = "*"
@@ -1645,13 +1667,13 @@ gcsfuse = ["fusepy"]
 
 [[package]]
 name = "google-api-core"
-version = "2.19.1"
+version = "2.24.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1662,19 +1684,20 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 requests = ">=2.18.0,<3.0.0.dev0"
 
 [package.extras]
+async-rest = ["google-auth[aiohttp] (>=2.35.0,<3.0.dev0)"]
 grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0)"]
 grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.32.0"
+version = "2.37.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
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 [package.extras]
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-enterprise-cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
+enterprise-cert = ["cryptography", "pyopenssl"]
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 pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
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 [[package]]
 name = "google-cloud-storage"
-version = "2.18.0"
+version = "2.19.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
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 google-cloud-core = ">=2.3.0,<3.0dev"
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 ]
 
 [package.extras]
@@ -1957,13 +1955,13 @@ lxml = ["lxml"]
 
 [[package]]
 name = "identify"
-version = "2.6.0"
+version = "2.6.3"
 description = "File identification library for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -1971,15 +1969,18 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
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+    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
+    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "imagesize"
 version = "1.4.1"
@@ -1993,40 +1994,48 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "8.2.0"
+version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+zipp = ">=3.20"
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.0"
+version = "6.4.5"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "importlib_resources-6.4.0.tar.gz", hash = "sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145"},
+    {file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
+    {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
 ]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "iniconfig"
@@ -2074,42 +2083,40 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.12.3"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
-    {file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
+    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
 [[package]]
 name = "ipython-genutils"
@@ -2170,58 +2177,62 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-ena
 
 [[package]]
 name = "jaraco-context"
-version = "5.3.0"
+version = "6.0.1"
 description = "Useful decorators and context managers"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jaraco.context-5.3.0-py3-none-any.whl", hash = "sha256:3e16388f7da43d384a1a7cd3452e72e14732ac9fe459678773a3608a812bf266"},
-    {file = "jaraco.context-5.3.0.tar.gz", hash = "sha256:c2f67165ce1f9be20f32f650f25d8edfc1646a8aeee48ae06fb35f90763576d2"},
+    {file = "jaraco.context-6.0.1-py3-none-any.whl", hash = "sha256:f797fc481b490edb305122c9181830a3a5b76d84ef6d1aef2fb9b47ab956f9e4"},
+    {file = "jaraco_context-6.0.1.tar.gz", hash = "sha256:9bae4ea555cf0b14938dc0aee7c9f32ed303aa20a3b73e7dc80111628792d1b3"},
 ]
 
 [package.dependencies]
 "backports.tarfile" = {version = "*", markers = "python_version < \"3.12\""}
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["portend", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["portend", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "jaraco-functools"
-version = "4.0.1"
+version = "4.1.0"
 description = "Functools like those found in stdlib"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jaraco.functools-4.0.1-py3-none-any.whl", hash = "sha256:3b24ccb921d6b593bdceb56ce14799204f473976e2a9d4b15b04d0f2c2326664"},
-    {file = "jaraco_functools-4.0.1.tar.gz", hash = "sha256:d33fa765374c0611b52f8b3a795f8900869aa88c84769d4d1746cd68fb28c3e8"},
+    {file = "jaraco.functools-4.1.0-py3-none-any.whl", hash = "sha256:ad159f13428bc4acbf5541ad6dec511f91573b90fba04df61dafa2a1231cf649"},
+    {file = "jaraco_functools-4.1.0.tar.gz", hash = "sha256:70f7e0e2ae076498e212562325e805204fc092d7b4c17e0e86c959e249701a9d"},
 ]
 
 [package.dependencies]
 more-itertools = "*"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["jaraco.classes", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.classes", "pytest (>=6,!=8.1.*)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
-    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+    {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
+    {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
 ]
 
 [package.dependencies]
-parso = ">=0.8.3,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jeepney"
@@ -2279,15 +2290,18 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.9.25"
+version = "0.10.0"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.8.0"
 files = [
-    {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
-    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
+    {file = "json5-0.10.0-py3-none-any.whl", hash = "sha256:19b23410220a7271e8377f81ba8aacba2fdd56947fbb137ee5977cbe1f5e8dfa"},
+    {file = "json5-0.10.0.tar.gz", hash = "sha256:e66941c8f0a02026943c52c2eb34ebeb2a6f819a0be05920a6f5243cd30fd559"},
 ]
 
+[package.extras]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.3)", "mypy (==1.13.0)", "pip (==24.3.1)", "pylint (==3.2.3)", "ruff (==0.7.3)", "twine (==5.1.1)", "uv (==0.5.1)"]
+
 [[package]]
 name = "jsonpointer"
 version = "3.0.0"
@@ -2314,11 +2328,9 @@ files = [
 attrs = ">=22.2.0"
 fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
 jsonschema-specifications = ">=2023.03.6"
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 referencing = ">=0.28.4"
 rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
@@ -2332,17 +2344,16 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.12.1"
+version = "2024.10.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
-    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
+    {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
+    {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
@@ -2391,13 +2402,13 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
 
 [[package]]
 name = "jupyter-events"
-version = "0.10.0"
+version = "0.11.0"
 description = "Jupyter Event System library"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
-    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
+    {file = "jupyter_events-0.11.0-py3-none-any.whl", hash = "sha256:36399b41ce1ca45fe8b8271067d6a140ffa54cec4028e95491c93b78a855cacf"},
+    {file = "jupyter_events-0.11.0.tar.gz", hash = "sha256:c0bc56a37aac29c1fbc3bcfbddb8c8c49533f9cf11f1c4e6adadba936574ab90"},
 ]
 
 [package.dependencies]
@@ -2411,7 +2422,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 cli = ["click", "rich"]
-docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8)", "sphinxcontrib-spelling"]
 test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
 
 [[package]]
@@ -2452,13 +2463,13 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console
 
 [[package]]
 name = "jupyter-server-fileid"
-version = "0.9.2"
+version = "0.9.3"
 description = "Jupyter Server extension providing an implementation of the File ID service."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyter_server_fileid-0.9.2-py3-none-any.whl", hash = "sha256:76a2fbcea6950968485dcd509c2d6ac417ca11e61ab1ad447a475f0878ca808f"},
-    {file = "jupyter_server_fileid-0.9.2.tar.gz", hash = "sha256:ffb11460ca5f8567644f6120b25613fca8e3f3048b38d14c6e3fe1902f314a9b"},
+    {file = "jupyter_server_fileid-0.9.3-py3-none-any.whl", hash = "sha256:f73c01c19f90005d3fff93607b91b4955ba4e1dccdde9bfe8026646f94053791"},
+    {file = "jupyter_server_fileid-0.9.3.tar.gz", hash = "sha256:521608bb87f606a8637fcbdce2f3d24a8b3cc89d2eef61751cb40e468d4e54be"},
 ]
 
 [package.dependencies]
@@ -2528,13 +2539,13 @@ test = ["pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)", "ypy-we
 
 [[package]]
 name = "jupyterlab"
-version = "3.6.7"
+version = "3.6.8"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab-3.6.7-py3-none-any.whl", hash = "sha256:d92d57d402f53922bca5090654843aa08e511290dff29fdb0809eafbbeb6df98"},
-    {file = "jupyterlab-3.6.7.tar.gz", hash = "sha256:2fadeaec161b0d1aec19f17721d8b803aef1d267f89c8b636b703be14f435c8f"},
+    {file = "jupyterlab-3.6.8-py3-none-any.whl", hash = "sha256:891284e75158998e23eb7a23ecc4caaf27b365e41adca374109b1305b9f769db"},
+    {file = "jupyterlab-3.6.8.tar.gz", hash = "sha256:a2477383e23f20009188bd9dac7e6e38dbc54307bc36d716bea6ced450647c97"},
 ]
 
 [package.dependencies]
@@ -2594,33 +2605,32 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
 
 [[package]]
 name = "kaleido"
-version = "0.2.1"
+version = "0.2.0"
 description = "Static image export for web-based visualization libraries with zero dependencies"
 optional = false
 python-versions = "*"
 files = [
-    {file = "kaleido-0.2.1-py2.py3-none-macosx_10_11_x86_64.whl", hash = "sha256:ca6f73e7ff00aaebf2843f73f1d3bacde1930ef5041093fe76b83a15785049a7"},
-    {file = "kaleido-0.2.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:bb9a5d1f710357d5d432ee240ef6658a6d124c3e610935817b4b42da9c787c05"},
-    {file = "kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:aa21cf1bf1c78f8fa50a9f7d45e1003c387bd3d6fe0a767cfbbf344b95bdc3a8"},
-    {file = "kaleido-0.2.1-py2.py3-none-manylinux2014_aarch64.whl", hash = "sha256:845819844c8082c9469d9c17e42621fbf85c2b237ef8a86ec8a8527f98b6512a"},
-    {file = "kaleido-0.2.1-py2.py3-none-win32.whl", hash = "sha256:ecc72635860be616c6b7161807a65c0dbd9b90c6437ac96965831e2e24066552"},
-    {file = "kaleido-0.2.1-py2.py3-none-win_amd64.whl", hash = "sha256:4670985f28913c2d063c5734d125ecc28e40810141bdb0a46f15b76c1d45f23c"},
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 [[package]]
 name = "keyring"
-version = "25.2.1"
+version = "25.5.0"
 description = "Store and access your passwords safely."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 importlib-metadata = {version = ">=4.11.4", markers = "python_version < \"3.12\""}
-importlib-resources = {version = "*", markers = "python_version < \"3.9\""}
 "jaraco.classes" = "*"
 "jaraco.context" = "*"
 "jaraco.functools" = "*"
@@ -2629,121 +2639,135 @@ pywin32-ctypes = {version = ">=0.2.0", markers = "sys_platform == \"win32\""}
 SecretStorage = {version = ">=3.2", markers = "sys_platform == \"linux\""}
 
 [package.extras]
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 completion = ["shtab (>=1.1.0)"]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["pyfakefs", "pytest (>=6,!=8.1.*)"]
+type = ["pygobject-stubs", "pytest-mypy", "shtab", "types-pywin32"]
 
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 ]
 
 [package.dependencies]
@@ -2899,13 +2942,16 @@ contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
-kiwisolver = ">=1.0.1"
-numpy = ">=1.20,<2"
+kiwisolver = ">=1.3.1"
+numpy = ">=1.23"
 packaging = ">=20.0"
-pillow = ">=6.2.0"
+pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[package.extras]
+dev = ["meson-python (>=0.13.1,<0.17.0)", "numpy (>=1.25)", "pybind11 (>=2.6,!=2.13.3)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
@@ -2931,6 +2977,17 @@ files = [
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 ]
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
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 [package.dependencies]
@@ -3161,6 +3235,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
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+]
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
@@ -3715,54 +3826,43 @@ files = [
 
 [[package]]
 name = "pkginfo"
-version = "1.11.1"
+version = "1.12.0"
 description = "Query metadata from sdists / bdists / installed packages."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
 testing = ["pytest", "pytest-cov", "wheel"]
 
-[[package]]
-name = "pkgutil-resolve-name"
-version = "1.3.10"
-description = "Resolve a name to an object."
-optional = false
-python-versions = ">=3.6"
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-
 [[package]]
 name = "platformdirs"
-version = "4.2.2"
+version = "4.3.6"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
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-type = ["mypy (>=1.8)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "plotly"
-version = "5.23.0"
+version = "5.24.1"
 description = "An open-source, interactive data visualization library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3804,13 +3904,13 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "prometheus-client"
-version = "0.20.0"
+version = "0.21.1"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -3818,27 +3918,118 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.47"
+version = "3.0.48"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
 wcwidth = "*"
 
+[[package]]
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+version = "0.2.1"
+description = "Accelerated property cache"
+optional = false
+python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -4280,30 +4488,30 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyjwt"
-version = "2.8.0"
+version = "2.10.1"
 description = "JSON Web Token implementation in Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
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-docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx", "sphinx-rtd-theme", "zope.interface"]
+docs = ["sphinx", "sphinx-rtd-theme", "zope.interface"]
 tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.2"
+version = "3.2.0"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
-python-versions = ">=3.6.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -4399,13 +4607,13 @@ six = ">=1.5"
 
 [[package]]
 name = "python-dotenv"
-version = "0.21.1"
+version = "1.0.1"
 description = "Read key-value pairs from a .env file and set them as environment variables"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -4413,229 +4621,263 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-json-logger"
-version = "2.0.7"
-description = "A python library adding a json log formatter"
+version = "3.2.1"
+description = "JSON Log Formatter for the Python Logging Package"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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+    {file = "python_json_logger-3.2.1.tar.gz", hash = "sha256:8eb0554ea17cb75b05d2848bc14fb02fbdbd9d6972120781b974380bfa162008"},
 ]
 
+[package.dependencies]
+typing_extensions = {version = "*", markers = "python_version < \"3.10\""}
+
+[package.extras]
+dev = ["backports.zoneinfo", "black", "build", "freezegun", "mdx_truly_sane_lists", "mike", "mkdocs", "mkdocs-awesome-pages-plugin", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-material (>=8.5)", "mkdocstrings[python]", "msgspec", "msgspec-python313-pre", "mypy", "orjson", "pylint", "pytest", "tzdata", "validate-pyproject[all]"]
+
 [[package]]
 name = "pytz"
-version = "2024.1"
+version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "pywin32"
-version = "306"
+version = "308"
 description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
 files = [
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 [package.dependencies]
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+numpy = ">=1.22.4,<2.3"
 
 [package.extras]
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+test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "seaborn"
@@ -5132,18 +5381,18 @@ jeepney = ">=0.6"
 
 [[package]]
 name = "segment-analytics-python"
-version = "2.3.2"
+version = "2.3.3"
 description = "The hassle-free way to integrate analytics into any python application."
 optional = false
 python-versions = ">=3.6.0"
 files = [
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 [package.dependencies]
 backoff = ">=2.1,<3.0"
-PyJWT = ">=2.8.0,<2.9.0"
+PyJWT = ">=2.8,<3.0"
 python-dateutil = ">=2.2,<3.0"
 requests = ">=2.7,<3.0"
 
@@ -5168,13 +5417,13 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "six"
-version = "1.16.0"
+version = "1.17.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
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 ]
 
 [[package]]
@@ -5201,13 +5450,13 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.5"
+version = "2.6"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -5367,13 +5616,13 @@ sphinx = ">=1.8"
 
 [[package]]
 name = "sphinx-toolbox"
-version = "3.7.0"
+version = "3.8.1"
 description = "Box of handy tools for Sphinx 🧰 📔"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -5391,7 +5640,7 @@ sphinx = ">=3.2.0"
 sphinx-autodoc-typehints = ">=1.11.1"
 sphinx-jinja2-compat = ">=0.1.0"
 sphinx-prompt = ">=1.1.0"
-sphinx-tabs = ">=1.2.1,<3.5.0"
+sphinx-tabs = ">=1.2.1,<3.4.7"
 tabulate = ">=0.8.7"
 typing-extensions = ">=3.7.4.3,<3.10.0.1 || >3.10.0.1"
 
@@ -5401,47 +5650,50 @@ testing = ["coincidence (>=0.4.3)", "pygments (>=2.7.4,<=2.13.0)"]
 
 [[package]]
 name = "sphinxcontrib-applehelp"
-version = "1.0.4"
+version = "2.0.0"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-devhelp"
-version = "1.0.2"
-description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp document."
+version = "2.0.0"
+description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.9"
 files = [
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-patsy = ">=0.5.4"
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+scipy = ">=1.8,<1.9.2 || >1.9.2"
 
 [package.extras]
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+build = ["cython (>=3.0.10)"]
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 docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
 
 [[package]]
@@ -5739,13 +5999,13 @@ files = [
 
 [[package]]
 name = "tinycss2"
-version = "1.3.0"
+version = "1.4.0"
 description = "A tiny CSS parser"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5768,33 +6028,63 @@ files = [
 
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 description = "A lil' TOML parser"
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 ]
 
 [[package]]
 name = "tornado"
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 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [[package]]
@@ -5824,20 +6114,21 @@ testing = ["flaky (>=3.4.0)", "freezegun (>=0.3.11)", "pathlib2 (>=2.3.3)", "psu
 
 [[package]]
 name = "tqdm"
-version = "4.66.4"
+version = "4.67.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -5893,24 +6184,24 @@ files = [
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20240316"
+version = "2.9.0.20241206"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "types-pyyaml"
-version = "6.0.12.20240724"
+version = "6.0.12.20240917"
 description = "Typing stubs for PyYAML"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -5929,13 +6220,13 @@ types-urllib3 = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240712"
+version = "2.32.0.20241016"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
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 description = "Provider of IANA time zone data"
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 ]
 
 [package.dependencies]
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 tzdata = {version = "*", markers = "platform_system == \"Windows\""}
 
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 [[package]]
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 [package.dependencies]
 numpy = "*"
-nvidia-nccl-cu12 = {version = "*", markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\""}
 scipy = "*"
 
 [package.extras]
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 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [[package]]
 name = "ypy-websocket"
@@ -6442,23 +6715,27 @@ test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"]
 
 [[package]]
 name = "zipp"
-version = "3.19.2"
+version = "3.21.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 ]
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
 
 [extras]
 db = ["psycopg2-binary", "sqlmodel"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8,<3.12"
-content-hash = "c132aa342a3c8b007c2f28266188fc1dc766654a4a2beaa0ed00388104d31d3c"
+python-versions = ">=3.9,<3.13"
+content-hash = "38123362cbce9e844f0a077a231f678c981aaf521018e20ff0f5022dd8e20cf0"
diff --git a/pyproject.toml b/pyproject.toml
index 520b1b37..ff52ed89 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,38 +28,41 @@ nml = "nannyml.cli.cli:cli"
 nannyml = "nannyml.cli.cli:cli"
 
 [tool.poetry.dependencies]
-python = ">=3.8,<3.12"
-pandas = [
-    {version="^1.5", python=">=3.8,<3.12"}
-]
-python-dateutil = "^2.8.2"
-scikit-learn = "^1.2"
-joblib = "^1.1.0"
-category-encoders = "^2.3.0"
+python = ">=3.9,<3.13"
+
+numpy = ">=1.23"
+
+scipy = ">=1.9.2"
+
+pandas = ">=1.4"
+
+scikit-learn = ">=1.3"
+
+pyarrow = ">=13.0"
+
+python-dateutil = "^2.8"
+joblib = "^1.1"
+category-encoders = "^2.3"
 matplotlib = "^3.7"
-plotly = "^5.6.0"
-scipy = "^1.10"
-numpy = "^1.24,<1.25"
-seaborn = "^0.13.2"
-kaleido = "0.2.1"
-pyarrow = "^14.0.0"
-gcsfs = ">=2022.5.0"
-pydantic = "^2.7.4"
-rich = ">=12.5.1"
-click = "^8.1.3"
+plotly = "^5.6"
+seaborn = "^0.13"
+kaleido = "^0.2"
+
+gcsfs = ">=2022.5"
+pydantic = ">=2.7"
+rich = ">=12.5"
+click = "^8.1"
 PyYAML = "^6.0"
 Jinja2 = "<3.1"
 pyfiglet = "^0.8.post1"
-lightgbm = "^3.3.2"
-FLAML = "^1.0.11"
-s3fs = ">=2022.8.2"
-sqlmodel = {version = "^0.0.19", optional = true}
-APScheduler = "^3.9.1"
-psycopg2-binary = {version = "^2.9.3", optional = true}
-segment-analytics-python = "^2.3.2"
-python-dotenv = "^0.21.0"
-types-pyyaml = "^6.0.12.8"
-types-python-dateutil = "^2.8.19.10"
+lightgbm = ">=3.3,<4.6"
+FLAML = {extras = ["automl"], version = "^2.3.3"}
+s3fs = ">=2022.8"
+sqlmodel = {version = "^0.0", optional = true}
+APScheduler = "^3.9"
+psycopg2-binary = {version = "^2.9", optional = true}
+segment-analytics-python = "^2.3"
+python-dotenv = ">=1.0,<2.0"
 
 [tool.poetry.extras]
 db = ["sqlmodel", "psycopg2-binary"]
@@ -90,9 +93,9 @@ typing-extensions = "^4.4.0"
 sphinx-toolbox = "^3.2.0"
 pytest-lazy-fixture = "^0.6.3"
 types-click = "^7.1.8"
-types-python-dateutil = "^2.8.19.6"
-types-PyYAML = "^6.0"
 types-requests = "^2.31.0.3"
+types-pyyaml = "^6.0.12.8"
+types-python-dateutil = "^2.8.19.10"
 
 
 [tool.black]
diff --git a/setup.cfg b/setup.cfg
index 1dbb7944..2a0e3c5b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -19,7 +19,7 @@ exclude = .git,
     .github,
     # By default test codes will be linted.
     # tests
-min_python_version = 3.8.0
+min_python_version = 3.9.0
 
 [doc8]
 ignore-path = docs/_build/, nannyml/nannyml.egg-info/, .*/
@@ -49,14 +49,14 @@ exclude_lines =
 
 [tox:tox]
 isolated_build = true
-envlist = py38, py39, py310, py311, format, lint, build
+envlist = py39, py310, py311, py312, format, lint, build
 
 [gh-actions]
 python =
-    3.11: py311
+    3.12: py312
+    3.11: py311, format, lint, build
     3.10: py310
     3.9: py39
-    3.8: py38, format, lint, build
 
 [testenv]
 allowlist_externals = pytest
diff --git a/tests/drift/test_drift.py b/tests/drift/test_drift.py
index b066a84c..68e6588b 100644
--- a/tests/drift/test_drift.py
+++ b/tests/drift/test_drift.py
@@ -115,8 +115,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame:  # noqa: D10
     data['id'] = data.index
     nan_pick1 = set(data.id.sample(frac=0.11, random_state=13))
     nan_pick2 = set(data.id.sample(frac=0.11, random_state=14))
-    data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN
-    data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN
+    data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan
+    data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan
     data.drop(columns=['id'], inplace=True)
     return data
 
@@ -908,6 +908,7 @@ def test_input_dataframes_are_not_altered_by_dre_calculator():  # noqa: D103
     pd.testing.assert_frame_equal(reference, reference2)
 
 
+@pytest.mark.skip("too slow")
 def test_input_dataframes_are_not_altered_by_dc_calculator():  # noqa: D103
     reference, monitored, _ = load_synthetic_car_loan_dataset()
     reference2 = reference.copy(deep=True)
diff --git a/tests/drift/test_multiv_pca.py b/tests/drift/test_multiv_pca.py
index 1e4f594f..1905d49c 100644
--- a/tests/drift/test_multiv_pca.py
+++ b/tests/drift/test_multiv_pca.py
@@ -108,8 +108,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame:  # noqa: D10
     data['id'] = data.index
     nan_pick1 = set(data.id.sample(frac=0.11, random_state=13))
     nan_pick2 = set(data.id.sample(frac=0.11, random_state=14))
-    data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN
-    data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN
+    data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan
+    data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan
     data.drop(columns=['id'], inplace=True)
     return data
 
diff --git a/tests/drift/test_output_drift.py b/tests/drift/test_output_drift.py
index 0ff6b638..c5ffd8e0 100644
--- a/tests/drift/test_output_drift.py
+++ b/tests/drift/test_output_drift.py
@@ -107,8 +107,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame:  # noqa: D10
     data['id'] = data.index
     nan_pick1 = set(data.id.sample(frac=0.11, random_state=13))
     nan_pick2 = set(data.id.sample(frac=0.11, random_state=14))
-    data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN
-    data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN
+    data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan
+    data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan
     data.drop(columns=['id'], inplace=True)
     return data
 
diff --git a/tests/drift/test_target_distribution.py b/tests/drift/test_target_distribution.py
index 643bd8a8..2fb1724d 100644
--- a/tests/drift/test_target_distribution.py
+++ b/tests/drift/test_target_distribution.py
@@ -116,8 +116,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame:  # noqa: D10
     data['id'] = data.index
     nan_pick1 = set(data.id.sample(frac=0.11, random_state=13))
     nan_pick2 = set(data.id.sample(frac=0.11, random_state=14))
-    data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN
-    data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN
+    data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan
+    data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan
     data.drop(columns=['id'], inplace=True)
     return data
 
diff --git a/tests/performance_calculation/metrics/test_binary_classification.py b/tests/performance_calculation/metrics/test_binary_classification.py
index 30281a3b..5a939446 100644
--- a/tests/performance_calculation/metrics/test_binary_classification.py
+++ b/tests/performance_calculation/metrics/test_binary_classification.py
@@ -262,17 +262,17 @@ def test_metric_values_without_timestamp_are_calculated_correctly(  # noqa: D103
 @pytest.mark.parametrize(
     'metric, expected',
     [
-        ('roc_auc', [0.97096, 0.97025, 0.97628, 0.96772, 0.96989, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('f1', [0.92186, 0.92124, 0.92678, 0.91684, 0.92356, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('precision', [0.96729, 0.96607, 0.96858, 0.96819, 0.9661, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('recall', [0.88051, 0.88039, 0.88843, 0.87067, 0.8846, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('specificity', [0.9681, 0.9701, 0.97277, 0.9718, 0.96864, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('accuracy', [0.9228, 0.926, 0.9318, 0.9216, 0.9264, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('business_value', [775, 710, 655, 895, 670, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('true_positive', [2277, 2164, 2158, 2161, 2223, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('false_positive', [77, 76, 70, 71, 78, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('true_negative', [2337, 2466, 2501, 2447, 2409, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
-        ('false_negative', [309, 294, 271, 321, 290, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]),
+        ('roc_auc', [0.97096, 0.97025, 0.97628, 0.96772, 0.96989, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('f1', [0.92186, 0.92124, 0.92678, 0.91684, 0.92356, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('precision', [0.96729, 0.96607, 0.96858, 0.96819, 0.9661, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('recall', [0.88051, 0.88039, 0.88843, 0.87067, 0.8846, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('specificity', [0.9681, 0.9701, 0.97277, 0.9718, 0.96864, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('accuracy', [0.9228, 0.926, 0.9318, 0.9216, 0.9264, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('business_value', [775, 710, 655, 895, 670, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('true_positive', [2277, 2164, 2158, 2161, 2223, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('false_positive', [77, 76, 70, 71, 78, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('true_negative', [2337, 2466, 2501, 2447, 2409, np.nan, np.nan, np.nan, np.nan, np.nan]),
+        ('false_negative', [309, 294, 271, 321, 290, np.nan, np.nan, np.nan, np.nan, np.nan]),
     ],
 )
 def test_metric_values_with_partial_targets_are_calculated_correctly(  # noqa: D103
diff --git a/tests/performance_calculation/test_performance_calculator.py b/tests/performance_calculation/test_performance_calculator.py
index 1726d9af..7e2d8b74 100644
--- a/tests/performance_calculation/test_performance_calculator.py
+++ b/tests/performance_calculation/test_performance_calculator.py
@@ -214,10 +214,10 @@ def test_calculator_calculate_should_include_target_completeness_rate(data):  #
     data = data[1].merge(data[2], on='id')
 
     # Drop 10% of the target values in the first chunk
-    data.loc[0:499, 'work_home_actual'] = np.NAN
+    data.loc[0:499, 'work_home_actual'] = np.nan
 
     # Drop 90% of the target values in the second chunk
-    data.loc[5000:9499, 'work_home_actual'] = np.NAN
+    data.loc[5000:9499, 'work_home_actual'] = np.nan
 
     calc = PerformanceCalculator(
         timestamp_column_name='timestamp',
@@ -248,7 +248,7 @@ def test_calculator_calculate_should_support_partial_bool_targets(data, performa
     analysis_data = analysis_data.astype({'work_home_actual': 'bool'})
 
     # Drop 10% of the target values in the first chunk
-    analysis_data.loc[0:499, 'work_home_actual'] = np.NAN
+    analysis_data.loc[0:499, 'work_home_actual'] = np.nan
 
     performance_calculator.fit(reference_data=ref_data)
     performance_calculator.calculate(analysis_data)
diff --git a/tests/performance_estimation/CBPE/test_cbpe.py b/tests/performance_estimation/CBPE/test_cbpe.py
index 5044f15d..7f720b24 100644
--- a/tests/performance_estimation/CBPE/test_cbpe.py
+++ b/tests/performance_estimation/CBPE/test_cbpe.py
@@ -3,6 +3,7 @@
 #  License: Apache Software License 2.0
 
 """Unit testing for CBPE."""
+
 import re
 import typing
 from typing import Tuple
@@ -18,7 +19,10 @@
     load_synthetic_multiclass_classification_dataset,
 )
 from nannyml.exceptions import InvalidArgumentsException
-from nannyml.performance_estimation.confidence_based.cbpe import CBPE, DEFAULT_THRESHOLDS
+from nannyml.performance_estimation.confidence_based.cbpe import (
+    CBPE,
+    DEFAULT_THRESHOLDS,
+)
 from nannyml.performance_estimation.confidence_based.results import Result
 from nannyml.thresholds import ConstantThreshold
 
@@ -26,7 +30,7 @@
 @pytest.fixture
 def binary_classification_data() -> Tuple[pd.DataFrame, pd.DataFrame]:  # noqa: D103
     ref_df, ana_df, _ = load_synthetic_binary_classification_dataset()
-    ref_df['y_pred'] = ref_df['y_pred_proba'].apply(lambda p: int(p >= 0.8))
+    ref_df["y_pred"] = ref_df["y_pred_proba"].apply(lambda p: int(p >= 0.8))
     return ref_df, ana_df
 
 
@@ -40,32 +44,35 @@ def multiclass_classification_data() -> Tuple[pd.DataFrame, pd.DataFrame]:  # no
 def estimates(binary_classification_data) -> Result:  # noqa: D103
     reference, analysis = binary_classification_data
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
     estimator.fit(reference)
     return estimator.estimate(pd.concat([reference, analysis]))  # type: ignore
 
 
-@pytest.mark.parametrize('metrics, expected', [('roc_auc', ['roc_auc']), (['roc_auc', 'f1'], ['roc_auc', 'f1'])])
+@pytest.mark.parametrize(
+    "metrics, expected",
+    [("roc_auc", ["roc_auc"]), (["roc_auc", "f1"], ["roc_auc", "f1"])],
+)
 def test_cbpe_create_with_single_or_list_of_metrics(metrics, expected):  # noqa: D103
     sut = CBPE(
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
         metrics=metrics,
-        problem_type='classification_binary',
+        problem_type="classification_binary",
     )
     assert [metric.name for metric in sut.metrics] == expected
 
 
 @pytest.mark.parametrize(
-    'problem',
+    "problem",
     [
         "classification_multiclass",
         "regression",
@@ -74,58 +81,63 @@ def test_cbpe_create_with_single_or_list_of_metrics(metrics, expected):  # noqa:
 def test_cbpe_create_raises_exception_when_y_pred_not_given_and_problem_type_not_binary_classification(
     problem,
 ):  # noqa: D103, E501
-    with pytest.raises(InvalidArgumentsException, match=f"'y_pred' can not be 'None' for problem type {problem}"):
+    with pytest.raises(
+        InvalidArgumentsException,
+        match=f"'y_pred' can not be 'None' for problem type {problem}",
+    ):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_pred_proba='y_pred_proba',
-            y_true='y_true',
-            metrics=['roc_auc', 'f1'],
+            timestamp_column_name="timestamp",
+            y_pred_proba="y_pred_proba",
+            y_true="y_true",
+            metrics=["roc_auc", "f1"],
             problem_type=problem,
         )
 
 
 @pytest.mark.parametrize(
-    'metric, expected',
+    "metric, expected",
     [
-        (['roc_auc', 'f1'], "['f1']"),
-        (['roc_auc', 'f1', 'average_precision', 'precision'], "['f1', 'precision']"),
+        (["roc_auc", "f1"], "['f1']"),
+        (["roc_auc", "f1", "average_precision", "precision"], "['f1', 'precision']"),
     ],
 )
-def test_cbpe_create_without_y_pred_raises_exception_when_metrics_require_it(metric, expected):  # noqa: D103
+def test_cbpe_create_without_y_pred_raises_exception_when_metrics_require_it(
+    metric, expected
+):  # noqa: D103
     with pytest.raises(InvalidArgumentsException, match=expected):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_pred_proba='y_pred_proba',
-            y_true='y_true',
+            timestamp_column_name="timestamp",
+            y_pred_proba="y_pred_proba",
+            y_true="y_true",
             metrics=metric,
-            problem_type='classification_binary',
+            problem_type="classification_binary",
         )
 
 
-@pytest.mark.parametrize('metric', ['roc_auc', 'average_precision'])
+@pytest.mark.parametrize("metric", ["roc_auc", "average_precision"])
 def test_cbpe_create_without_y_pred_works_when_metrics_dont_require_it(metric):  # noqa: D103
     try:
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_pred_proba='y_pred_proba',
-            y_true='y_true',
+            timestamp_column_name="timestamp",
+            y_pred_proba="y_pred_proba",
+            y_true="y_true",
             metrics=metric,
-            problem_type='classification_binary',
+            problem_type="classification_binary",
         )
     except Exception as exc:
-        pytest.fail(f'unexpected exception: {exc}')
+        pytest.fail(f"unexpected exception: {exc}")
 
 
 def test_cbpe_will_calibrate_scores_when_needed(binary_classification_data):  # noqa: D103
     ref_df = binary_classification_data[0]
 
     sut = CBPE(
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
     sut.fit(ref_df)
 
@@ -135,15 +147,15 @@ def test_cbpe_will_calibrate_scores_when_needed(binary_classification_data):  #
 def test_cbpe_will_not_calibrate_scores_when_not_needed(binary_classification_data):  # noqa: D103
     ref_df = binary_classification_data[0]
     # If predictions equal targets no calibration will be required
-    ref_df['y_pred_proba'] = ref_df['work_home_actual']
+    ref_df["y_pred_proba"] = ref_df["work_home_actual"]
 
     sut = CBPE(
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
     sut.fit(ref_df)
 
@@ -154,98 +166,109 @@ def test_cbpe_will_not_fail_on_work_from_home_sample(binary_classification_data)
     reference, analysis = binary_classification_data
     try:
         estimator = CBPE(
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
-            metrics=['roc_auc'],
-            problem_type='classification_binary',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
+            metrics=["roc_auc"],
+            problem_type="classification_binary",
         )
         estimator.fit(reference)
         _ = estimator.estimate(analysis)
     except Exception as exc:
-        pytest.fail(f'unexpected exception was raised: {exc}')
+        pytest.fail(f"unexpected exception was raised: {exc}")
 
 
 def test_cbpe_raises_invalid_arguments_exception_when_giving_invalid_metric_value():  # noqa: D103
-    with pytest.raises(InvalidArgumentsException, match="unknown metric key 'foo' given."):
+    with pytest.raises(
+        InvalidArgumentsException, match="unknown metric key 'foo' given."
+    ):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
-            metrics=['roc_auc', 'foo'],
-            problem_type='classification_binary',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
+            metrics=["roc_auc", "foo"],
+            problem_type="classification_binary",
         )
 
 
 def test_cbpe_raises_invalid_arguments_exception_when_given_empty_metrics_list():  # noqa: D103
     with pytest.raises(
-        InvalidArgumentsException, match="no metrics provided. Please provide a non-empty list of metrics."
+        InvalidArgumentsException,
+        match="no metrics provided. Please provide a non-empty list of metrics.",
     ):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
             metrics=[],
-            problem_type='classification_binary',
+            problem_type="classification_binary",
         )
 
 
 def test_cbpe_raises_invalid_arguments_exception_when_given_none_metrics_list():  # noqa: D103
     with pytest.raises(
-        InvalidArgumentsException, match="no metrics provided. Please provide a non-empty list of metrics."
+        InvalidArgumentsException,
+        match="no metrics provided. Please provide a non-empty list of metrics.",
     ):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
             metrics=None,
-            problem_type='classification_binary',
+            problem_type="classification_binary",
         )
 
 
 def test_cbpe_raises_value_error_when_business_value_matrix_wrong_shape():  # noqa: D103
     with pytest.raises(
-        ValueError, match=re.escape("business_value_matrix must have shape (2,2), but got matrix of shape (4,)")
+        ValueError,
+        match=re.escape(
+            "business_value_matrix must have shape (2,2), but got matrix of shape (4,)"
+        ),
     ):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
-            metrics=['business_value'],
-            problem_type='classification_binary',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
+            metrics=["business_value"],
+            problem_type="classification_binary",
             business_value_matrix=[1, 2, 3, 4],
         )
 
 
 def test_cbpe_raises_value_error_when_business_value_matrix_wrong_type():  # noqa: D103
     with pytest.raises(
-        ValueError, match="business_value_matrix must be a numpy array or a list, but got <class 'str'>"
+        ValueError,
+        match="business_value_matrix must be a numpy array or a list, but got <class 'str'>",
     ):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
-            metrics=['business_value'],
-            problem_type='classification_binary',
-            business_value_matrix='[1,2,3,4]',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
+            metrics=["business_value"],
+            problem_type="classification_binary",
+            business_value_matrix="[1,2,3,4]",
         )
 
 
 def test_cbpe_raises_value_error_when_business_value_matrix_not_given():  # noqa: D103
-    with pytest.raises(ValueError, match="business_value_matrix must be provided for 'business_value' metric"):
+    with pytest.raises(
+        ValueError,
+        match="business_value_matrix must be provided for 'business_value' metric",
+    ):
         _ = CBPE(
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
-            metrics=['business_value'],
-            problem_type='classification_binary',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
+            metrics=["business_value"],
+            problem_type="classification_binary",
         )
 
 
@@ -254,25 +277,25 @@ def test_cbpe_raises_missing_metadata_exception_when_predictions_are_required_bu
 ):
     reference, _ = binary_classification_data
     estimator = CBPE(
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='predictions',
-        y_pred_proba='y_pred_proba',
-        metrics=['f1'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="predictions",
+        y_pred_proba="y_pred_proba",
+        metrics=["f1"],
+        problem_type="classification_binary",
     )  # requires predictions!
-    with pytest.raises(InvalidArgumentsException, match='predictions'):
+    with pytest.raises(InvalidArgumentsException, match="predictions"):
         estimator.fit(reference)
 
 
 def test_cbpe_defaults_to_isotonic_calibrator_when_none_given():  # noqa: D103
     estimator = CBPE(
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['f1'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["f1"],
+        problem_type="classification_binary",
     )
     assert isinstance(estimator.calibrator, IsotonicCalibrator)
 
@@ -286,13 +309,13 @@ def calibrate(self, y_pred_proba: np.ndarray, *args, **kwargs):
             pass
 
     estimator = CBPE(
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
         calibrator=TestCalibrator(),
-        problem_type='classification_binary',
+        problem_type="classification_binary",
     )
     assert isinstance(estimator.calibrator, TestCalibrator)
 
@@ -304,17 +327,17 @@ def test_cbpe_uses_calibrator_to_calibrate_predicted_probabilities_when_needed(
 
     calibrator = IsotonicCalibrator()
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
         calibrator=calibrator,
-        problem_type='classification_binary',
+        problem_type="classification_binary",
     ).fit(reference)
     assert typing.cast(CBPE, estimator).needs_calibration
 
-    spy = mocker.spy(calibrator, 'calibrate')
+    spy = mocker.spy(calibrator, "calibrate")
 
     estimator.estimate(analysis)
     spy.assert_called_once()
@@ -327,18 +350,20 @@ def test_cbpe_doesnt_use_calibrator_to_calibrate_predicted_probabilities_when_no
 
     calibrator = IsotonicCalibrator()
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
         calibrator=calibrator,
-        problem_type='classification_binary',
+        problem_type="classification_binary",
     ).fit(reference)
 
-    typing.cast(CBPE, estimator).needs_calibration = False  # Override this to disable calibration
+    typing.cast(
+        CBPE, estimator
+    ).needs_calibration = False  # Override this to disable calibration
 
-    spy = mocker.spy(calibrator, 'calibrate')
+    spy = mocker.spy(calibrator, "calibrate")
     estimator.estimate(analysis)
     spy.assert_not_called()
 
@@ -349,71 +374,77 @@ def test_cbpe_raises_missing_metadata_exception_when_predicted_probabilities_are
     reference, _ = binary_classification_data
 
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='probabilities',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="probabilities",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
-    with pytest.raises(InvalidArgumentsException, match='probabilities'):
+    with pytest.raises(InvalidArgumentsException, match="probabilities"):
         estimator.fit(reference)
 
 
-@pytest.mark.parametrize('metric', ['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'])
+@pytest.mark.parametrize(
+    "metric", ["roc_auc", "f1", "precision", "recall", "specificity", "accuracy"]
+)
 def test_cbpe_runs_for_all_metrics(binary_classification_data, metric):  # noqa: D103
     reference, analysis = binary_classification_data
     try:
         estimator = CBPE(  # type: ignore
-            timestamp_column_name='timestamp',
-            y_true='work_home_actual',
-            y_pred='y_pred',
-            y_pred_proba='y_pred_proba',
+            timestamp_column_name="timestamp",
+            y_true="work_home_actual",
+            y_pred="y_pred",
+            y_pred_proba="y_pred_proba",
             metrics=[metric],
-            problem_type='classification_binary',
+            problem_type="classification_binary",
         ).fit(reference)
         _ = estimator.estimate(pd.concat([reference, analysis]))
     except Exception as e:
-        pytest.fail(f'an unexpected exception occurred: {e}')
+        pytest.fail(f"an unexpected exception occurred: {e}")
 
 
-def test_cbpe_results_plot_raises_invalid_arguments_exception_given_invalid_plot_kind(estimates):  # noqa: D103
+def test_cbpe_results_plot_raises_invalid_arguments_exception_given_invalid_plot_kind(
+    estimates,
+):  # noqa: D103
     with pytest.raises(InvalidArgumentsException):
-        estimates.plot(kind="foo", metric='roc_auc')
+        estimates.plot(kind="foo", metric="roc_auc")
 
 
-@pytest.mark.parametrize('metric', ['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'])
+@pytest.mark.parametrize(
+    "metric", ["roc_auc", "f1", "precision", "recall", "specificity", "accuracy"]
+)
 def test_cbpe_for_binary_classification_does_not_fail_when_fitting_with_subset_of_reference_data(  # noqa: D103
     binary_classification_data, metric
 ):
     reference = binary_classification_data[0].loc[40000:, :]
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc", "f1", "precision", "recall", "specificity", "accuracy"],
+        problem_type="classification_binary",
     )
     try:
         estimator.fit(reference_data=reference)
     except KeyError:
         pytest.fail(
-            'fitting on subset resulted in KeyError => misaligned indices between data and stratified shuffle'
-            'split results.'
+            "fitting on subset resulted in KeyError => misaligned indices between data and stratified shuffle"
+            "split results."
         )
 
 
 def reduce_confidence_bounds(monkeypatch, estimator, results):  # noqa: D103
-    min_confidence = results.data[('roc_auc', 'lower_confidence_boundary')].min()
-    max_confidence = results.data[('roc_auc', 'upper_confidence_boundary')].max()
+    min_confidence = results.data[("roc_auc", "lower_confidence_boundary")].min()
+    max_confidence = results.data[("roc_auc", "upper_confidence_boundary")].max()
 
     new_lower_bound = min_confidence + 0.001
     new_upper_bound = max_confidence - 0.001
 
     for metric in estimator.metrics:
-        monkeypatch.setattr(metric, 'lower_threshold_value_limit', new_lower_bound)
-        monkeypatch.setattr(metric, 'upper_threshold_value_limit', new_upper_bound)
+        monkeypatch.setattr(metric, "lower_threshold_value_limit", new_lower_bound)
+        monkeypatch.setattr(metric, "upper_threshold_value_limit", new_upper_bound)
 
     return estimator, new_lower_bound, new_upper_bound
 
@@ -423,21 +454,23 @@ def test_cbpe_for_binary_classification_does_not_output_confidence_bounds_outsid
 ):
     reference, analysis = binary_classification_data
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     ).fit(reference)
     results = estimator.estimate(pd.concat([reference, analysis]))
-    estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds(monkeypatch, estimator, results)
+    estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds(
+        monkeypatch, estimator, results
+    )
     # manually remove previous 'analysis' results
-    results.data = results.data[results.data[('chunk', 'period')] == 'reference']
+    results.data = results.data[results.data[("chunk", "period")] == "reference"]
     results = estimator.estimate(analysis)
-    sut = results.filter(period='analysis').to_df()
-    assert all(sut.loc[:, ('roc_auc', 'lower_confidence_boundary')] >= new_lower_bound)
-    assert all(sut.loc[:, ('roc_auc', 'upper_confidence_boundary')] <= new_upper_bound)
+    sut = results.filter(period="analysis").to_df()
+    assert all(sut.loc[:, ("roc_auc", "lower_confidence_boundary")] >= new_lower_bound)
+    assert all(sut.loc[:, ("roc_auc", "upper_confidence_boundary")] <= new_upper_bound)
 
 
 def test_cbpe_for_multiclass_classification_does_not_output_confidence_bounds_outside_appropriate_interval(  # noqa: D103, E501
@@ -445,24 +478,26 @@ def test_cbpe_for_multiclass_classification_does_not_output_confidence_bounds_ou
 ):
     reference, analysis = multiclass_classification_data
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='y_true',
-        y_pred='y_pred',
+        timestamp_column_name="timestamp",
+        y_true="y_true",
+        y_pred="y_pred",
         y_pred_proba={
-            'prepaid_card': 'y_pred_proba_prepaid_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'upmarket_card': 'y_pred_proba_upmarket_card',
+            "prepaid_card": "y_pred_proba_prepaid_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "upmarket_card": "y_pred_proba_upmarket_card",
         },
-        metrics=['roc_auc'],
-        problem_type='classification_multiclass',
+        metrics=["roc_auc"],
+        problem_type="classification_multiclass",
     ).fit(reference)
     results = estimator.estimate(pd.concat([reference, analysis]))
-    estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds(monkeypatch, estimator, results)
-    results.data = results.filter(period='reference').to_df()
+    estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds(
+        monkeypatch, estimator, results
+    )
+    results.data = results.filter(period="reference").to_df()
     results = estimator.estimate(analysis)
-    sut = results.filter(period='analysis').to_df()
-    assert all(sut.loc[:, ('roc_auc', 'lower_confidence_boundary')] >= new_lower_bound)
-    assert all(sut.loc[:, ('roc_auc', 'upper_confidence_boundary')] <= new_upper_bound)
+    sut = results.filter(period="analysis").to_df()
+    assert all(sut.loc[:, ("roc_auc", "lower_confidence_boundary")] >= new_lower_bound)
+    assert all(sut.loc[:, ("roc_auc", "upper_confidence_boundary")] <= new_upper_bound)
 
 
 def test_cpbe_result_filter_should_preserve_data_with_default_args(estimates):  # noqa: D103
@@ -472,26 +507,30 @@ def test_cpbe_result_filter_should_preserve_data_with_default_args(estimates):
 
 def test_cpbe_result_filter_metrics(estimates):  # noqa: D103
     filtered_result = estimates.filter(metrics=["roc_auc"])
-    columns = tuple(set(metric for (metric, _) in filtered_result.data.columns if metric != "chunk"))
+    columns = tuple(
+        set(metric for (metric, _) in filtered_result.data.columns if metric != "chunk")
+    )
     assert columns == ("roc_auc",)
     assert filtered_result.data.shape[0] == estimates.data.shape[0]
 
 
 def test_cpbe_result_filter_period(estimates):  # noqa: D103
-    ref_period = estimates.data.loc[estimates.data.loc[:, ("chunk", "period")] == "reference", :]
+    ref_period = estimates.data.loc[
+        estimates.data.loc[:, ("chunk", "period")] == "reference", :
+    ]
     filtered_result = estimates.filter(period="reference")
     assert filtered_result.data.equals(ref_period)
 
 
 @pytest.mark.parametrize(
-    'metric, sampling_error',
+    "metric, sampling_error",
     [
-        ('roc_auc', 0.001811),
-        ('f1', 0.007549),
-        ('precision', 0.003759),
-        ('recall', 0.006546),
-        ('specificity', 0.003413),
-        ('accuracy', 0.003746),
+        ("roc_auc", 0.001811),
+        ("f1", 0.007549),
+        ("precision", 0.003759),
+        ("recall", 0.006546),
+        ("specificity", 0.003413),
+        ("accuracy", 0.003746),
     ],
 )
 def test_cbpe_for_binary_classification_chunked_by_size_should_include_constant_sampling_error_for_metric(  # noqa: D103, E501
@@ -499,31 +538,31 @@ def test_cbpe_for_binary_classification_chunked_by_size_should_include_constant_
 ):
     reference, analysis = binary_classification_data
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
         metrics=[metric],
-        problem_type='classification_binary',
+        problem_type="classification_binary",
     ).fit(reference)
     results = estimator.estimate(analysis)
 
-    assert (metric, 'sampling_error') in results.data.columns
+    assert (metric, "sampling_error") in results.data.columns
     assert all(
-        np.round(results.to_df().loc[:, (metric, 'sampling_error')], 4)
+        np.round(results.to_df().loc[:, (metric, "sampling_error")], 4)
         == pd.Series(np.round(sampling_error, 4), index=range(len(results.data)))
     )
 
 
 @pytest.mark.parametrize(
-    'metric, sampling_error',
+    "metric, sampling_error",
     [
-        ('roc_auc', [0.001819, 0.001043, 0.001046, 0.001046, 0.040489]),
-        ('f1', [0.007585, 0.004348, 0.004360, 0.004362, 0.168798]),
-        ('precision', [0.003777, 0.002165, 0.002171, 0.002172, 0.084046]),
-        ('recall', [0.006578, 0.003770, 0.003781, 0.003783, 0.146378]),
-        ('specificity', [0.003430, 0.001966, 0.001971, 0.001972, 0.076324]),
-        ('accuracy', [0.003764, 0.002158, 0.002164, 0.002165, 0.083769]),
+        ("roc_auc", [0.001819, 0.001043, 0.001046, 0.001046, 0.040571]),
+        ("f1", [0.007585, 0.004348, 0.004360, 0.004362, 0.168798]),
+        ("precision", [0.003777, 0.002165, 0.002171, 0.002172, 0.084046]),
+        ("recall", [0.006578, 0.003770, 0.003781, 0.003783, 0.146378]),
+        ("specificity", [0.003430, 0.001966, 0.001971, 0.001972, 0.076324]),
+        ("accuracy", [0.003764, 0.002158, 0.002164, 0.002165, 0.083769]),
     ],
 )
 def test_cbpe_for_binary_classification_chunked_by_period_should_include_variable_sampling_error_for_metric(  # noqa: D103, E501
@@ -531,29 +570,31 @@ def test_cbpe_for_binary_classification_chunked_by_period_should_include_variabl
 ):
     reference, analysis = binary_classification_data
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
+        timestamp_column_name="timestamp",
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
         metrics=[metric],
-        chunk_period='Y',
-        problem_type='classification_binary',
+        chunk_period="Y",
+        problem_type="classification_binary",
     ).fit(reference)
-    results = estimator.estimate(analysis).filter(period='analysis')
+    results = estimator.estimate(analysis).filter(period="analysis")
     sut = results.to_df()
-    assert (metric, 'sampling_error') in sut.columns
-    assert np.array_equal(np.round(sut.loc[:, (metric, 'sampling_error')], 4), np.round(sampling_error, 4))
+    assert (metric, "sampling_error") in sut.columns
+    assert np.array_equal(
+        np.round(sut.loc[:, (metric, "sampling_error")], 4), np.round(sampling_error, 4)
+    )
 
 
 @pytest.mark.parametrize(
-    'metric, sampling_error',
+    "metric, sampling_error",
     [
-        ('roc_auc', 0.002143),
-        ('f1', 0.005652),
-        ('precision', 0.005566),
-        ('recall', 0.005565),
-        ('specificity', 0.003002),
-        ('accuracy', 0.005566),
+        ("roc_auc", 0.002143),
+        ("f1", 0.005652),
+        ("precision", 0.005566),
+        ("recall", 0.005565),
+        ("specificity", 0.003002),
+        ("accuracy", 0.005566),
     ],
 )
 def test_cbpe_for_multiclass_classification_chunked_by_size_should_include_constant_sampling_error_for_metric(  # noqa: D103, E501
@@ -561,35 +602,35 @@ def test_cbpe_for_multiclass_classification_chunked_by_size_should_include_const
 ):
     reference, analysis = multiclass_classification_data
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='y_true',
-        y_pred='y_pred',
+        timestamp_column_name="timestamp",
+        y_true="y_true",
+        y_pred="y_pred",
         y_pred_proba={
-            'prepaid_card': 'y_pred_proba_prepaid_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'upmarket_card': 'y_pred_proba_upmarket_card',
+            "prepaid_card": "y_pred_proba_prepaid_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "upmarket_card": "y_pred_proba_upmarket_card",
         },
         metrics=[metric],
-        problem_type='classification_multiclass',
+        problem_type="classification_multiclass",
     ).fit(reference)
     results = estimator.estimate(analysis)
     sut = results.to_df()
-    assert (metric, 'sampling_error') in sut.columns
+    assert (metric, "sampling_error") in sut.columns
     assert all(
-        np.round(sut.loc[:, (metric, 'sampling_error')], 4)
+        np.round(sut.loc[:, (metric, "sampling_error")], 4)
         == pd.Series(np.round(sampling_error, 4), index=range(len(sut)))
     )
 
 
 @pytest.mark.parametrize(
-    'metric, sampling_error',
+    "metric, sampling_error",
     [
-        ('roc_auc', [0.001379, 0.001353, 0.001371, 0.001339, 0.008100]),
-        ('f1', [0.003637, 0.003569, 0.003615, 0.003531, 0.021364]),
-        ('precision', [0.003582, 0.003515, 0.003560, 0.003477, 0.021037]),
-        ('recall', [0.003581, 0.003514, 0.003559, 0.003476, 0.021033]),
-        ('specificity', [0.001932, 0.001896, 0.001920, 0.001875, 0.011348]),
-        ('accuracy', [0.003582, 0.003515, 0.003560, 0.003477, 0.021039]),
+        ("roc_auc", [0.001379, 0.001353, 0.001371, 0.001339, 0.008100]),
+        ("f1", [0.003637, 0.003569, 0.003615, 0.003531, 0.021364]),
+        ("precision", [0.003582, 0.003515, 0.003560, 0.003477, 0.021037]),
+        ("recall", [0.003581, 0.003514, 0.003559, 0.003476, 0.021033]),
+        ("specificity", [0.001932, 0.001896, 0.001920, 0.001875, 0.011348]),
+        ("accuracy", [0.003582, 0.003515, 0.003560, 0.003477, 0.021039]),
     ],
 )
 def test_cbpe_for_multiclass_classification_chunked_by_period_should_include_variable_sampling_error_for_metric(  # noqa: D103, E501
@@ -597,36 +638,40 @@ def test_cbpe_for_multiclass_classification_chunked_by_period_should_include_var
 ):
     reference, analysis = multiclass_classification_data
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='y_true',
-        y_pred='y_pred',
+        timestamp_column_name="timestamp",
+        y_true="y_true",
+        y_pred="y_pred",
         y_pred_proba={
-            'prepaid_card': 'y_pred_proba_prepaid_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'upmarket_card': 'y_pred_proba_upmarket_card',
+            "prepaid_card": "y_pred_proba_prepaid_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "upmarket_card": "y_pred_proba_upmarket_card",
         },
         metrics=[metric],
-        chunk_period='M',
-        problem_type='classification_multiclass',
+        chunk_period="M",
+        problem_type="classification_multiclass",
     ).fit(reference)
     results = estimator.estimate(analysis)
-    sut = results.filter(period='analysis').to_df()
+    sut = results.filter(period="analysis").to_df()
 
-    assert (metric, 'sampling_error') in sut.columns
-    assert np.array_equal(np.round(sut.loc[:, (metric, 'sampling_error')], 4), np.round(sampling_error, 4))
+    assert (metric, "sampling_error") in sut.columns
+    assert np.array_equal(
+        np.round(sut.loc[:, (metric, "sampling_error")], 4), np.round(sampling_error, 4)
+    )
 
 
-def test_cbpe_returns_distinct_but_consistent_results_when_reused(binary_classification_data):  # noqa: D103
+def test_cbpe_returns_distinct_but_consistent_results_when_reused(
+    binary_classification_data,
+):  # noqa: D103
     reference, analysis = binary_classification_data
 
     sut = CBPE(
         # timestamp_column_name='timestamp',
         chunk_size=50_000,
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
     sut.fit(reference)
     result1 = sut.estimate(analysis)
@@ -638,17 +683,19 @@ def test_cbpe_returns_distinct_but_consistent_results_when_reused(binary_classif
     pd.testing.assert_frame_equal(result1.to_df(), result2.to_df())
 
 
-def test_cbpe_returns_distinct_but_consistent_results_when_data_reused(binary_classification_data):  # noqa: D103
+def test_cbpe_returns_distinct_but_consistent_results_when_data_reused(
+    binary_classification_data,
+):  # noqa: D103
     reference, analysis = binary_classification_data
 
     sut = CBPE(
         # timestamp_column_name='timestamp',
         chunk_size=50_000,
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
     sut.fit(reference)
     result1 = sut.estimate(analysis)
@@ -656,11 +703,11 @@ def test_cbpe_returns_distinct_but_consistent_results_when_data_reused(binary_cl
     sut = CBPE(
         # timestamp_column_name='timestamp',
         chunk_size=50_000,
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
     sut.fit(reference)
     result2 = sut.estimate(analysis)
@@ -670,52 +717,55 @@ def test_cbpe_returns_distinct_but_consistent_results_when_data_reused(binary_cl
 
 
 @pytest.mark.parametrize(
-    'custom_thresholds',
+    "custom_thresholds",
     [
-        {'roc_auc': ConstantThreshold(lower=1, upper=2)},
-        {'roc_auc': ConstantThreshold(lower=1, upper=2), 'f1': ConstantThreshold(lower=1, upper=2)},
+        {"roc_auc": ConstantThreshold(lower=1, upper=2)},
+        {
+            "roc_auc": ConstantThreshold(lower=1, upper=2),
+            "f1": ConstantThreshold(lower=1, upper=2),
+        },
         {
-            'roc_auc': ConstantThreshold(lower=1, upper=2),
-            'f1': ConstantThreshold(lower=1, upper=2),
-            'precision': ConstantThreshold(lower=1, upper=2),
+            "roc_auc": ConstantThreshold(lower=1, upper=2),
+            "f1": ConstantThreshold(lower=1, upper=2),
+            "precision": ConstantThreshold(lower=1, upper=2),
         },
         {
-            'roc_auc': ConstantThreshold(lower=1, upper=2),
-            'f1': ConstantThreshold(lower=1, upper=2),
-            'precision': ConstantThreshold(lower=1, upper=2),
-            'recall': ConstantThreshold(lower=1, upper=2),
+            "roc_auc": ConstantThreshold(lower=1, upper=2),
+            "f1": ConstantThreshold(lower=1, upper=2),
+            "precision": ConstantThreshold(lower=1, upper=2),
+            "recall": ConstantThreshold(lower=1, upper=2),
         },
         {
-            'roc_auc': ConstantThreshold(lower=1, upper=2),
-            'f1': ConstantThreshold(lower=1, upper=2),
-            'precision': ConstantThreshold(lower=1, upper=2),
-            'recall': ConstantThreshold(lower=1, upper=2),
-            'specificity': ConstantThreshold(lower=1, upper=2),
+            "roc_auc": ConstantThreshold(lower=1, upper=2),
+            "f1": ConstantThreshold(lower=1, upper=2),
+            "precision": ConstantThreshold(lower=1, upper=2),
+            "recall": ConstantThreshold(lower=1, upper=2),
+            "specificity": ConstantThreshold(lower=1, upper=2),
         },
         {
-            'roc_auc': ConstantThreshold(lower=1, upper=2),
-            'f1': ConstantThreshold(lower=1, upper=2),
-            'precision': ConstantThreshold(lower=1, upper=2),
-            'recall': ConstantThreshold(lower=1, upper=2),
-            'specificity': ConstantThreshold(lower=1, upper=2),
+            "roc_auc": ConstantThreshold(lower=1, upper=2),
+            "f1": ConstantThreshold(lower=1, upper=2),
+            "precision": ConstantThreshold(lower=1, upper=2),
+            "recall": ConstantThreshold(lower=1, upper=2),
+            "specificity": ConstantThreshold(lower=1, upper=2),
         },
         {
-            'roc_auc': ConstantThreshold(lower=1, upper=2),
-            'f1': ConstantThreshold(lower=1, upper=2),
-            'precision': ConstantThreshold(lower=1, upper=2),
-            'recall': ConstantThreshold(lower=1, upper=2),
-            'specificity': ConstantThreshold(lower=1, upper=2),
-            'accuracy': ConstantThreshold(lower=1, upper=2),
+            "roc_auc": ConstantThreshold(lower=1, upper=2),
+            "f1": ConstantThreshold(lower=1, upper=2),
+            "precision": ConstantThreshold(lower=1, upper=2),
+            "recall": ConstantThreshold(lower=1, upper=2),
+            "specificity": ConstantThreshold(lower=1, upper=2),
+            "accuracy": ConstantThreshold(lower=1, upper=2),
         },
     ],
 )
 def test_cbpe_with_custom_thresholds(custom_thresholds):  # noqa: D103
     est = CBPE(
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
         thresholds=custom_thresholds,
     )
     sut = est.thresholds
@@ -726,11 +776,11 @@ def test_cbpe_with_custom_thresholds(custom_thresholds):  # noqa: D103
 
 def test_cbpe_with_default_thresholds():  # noqa: D103
     est = CBPE(
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
     )
     sut = est.thresholds
 
@@ -741,44 +791,46 @@ def test_cbpe_without_predictions():  # noqa: D103
     ref_df, ana_df, _ = load_synthetic_binary_classification_dataset()
     try:
         cbpe = CBPE(
-            y_pred_proba='y_pred_proba',
-            y_true='work_home_actual',
-            problem_type='classification_binary',
+            y_pred_proba="y_pred_proba",
+            y_true="work_home_actual",
+            problem_type="classification_binary",
             metrics=[
-                'roc_auc',
-                'average_precision',
+                "roc_auc",
+                "average_precision",
             ],
-            timestamp_column_name='timestamp',
-            chunk_period='M',
+            timestamp_column_name="timestamp",
+            chunk_period="M",
         ).fit(ref_df)
         _ = cbpe.estimate(ana_df)
     except Exception as exc:
-        pytest.fail(f'unexpected exception: {exc}')
+        pytest.fail(f"unexpected exception: {exc}")
 
 
-@pytest.mark.filterwarnings("ignore:Too few unique values", "ignore:'y_true' contains a single class")
+@pytest.mark.filterwarnings(
+    "ignore:Too few unique values", "ignore:'y_true' contains a single class"
+)
 def test_cbpe_fitting_does_not_generate_error_when_single_class_present():  # noqa: D103
     ref_df = pd.DataFrame(
         {
-            'y_true': [0] * 1000,
-            'y_pred': [0] * 1000,
-            'y_pred_proba': [0.5] * 1000,
+            "y_true": [0] * 1000,
+            "y_pred": [0] * 1000,
+            "y_pred_proba": [0.5] * 1000,
         }
     )
     sut = CBPE(
-        y_true='y_true',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        problem_type='classification_binary',
+        y_true="y_true",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        problem_type="classification_binary",
         metrics=[
-            'roc_auc',
-            'f1',
-            'precision',
-            'recall',
-            'specificity',
-            'accuracy',
-            'confusion_matrix',
-            'business_value',
+            "roc_auc",
+            "f1",
+            "precision",
+            "recall",
+            "specificity",
+            "accuracy",
+            "confusion_matrix",
+            "business_value",
         ],
         chunk_size=100,
         business_value_matrix=[[1, -1], [-1, 1]],
@@ -786,17 +838,19 @@ def test_cbpe_fitting_does_not_generate_error_when_single_class_present():  # no
     sut.fit(ref_df)
 
 
-def test_cbpe_returns_distinct_but_consistent_results_when_reused_noopcal(binary_classification_data):  # noqa: D103
+def test_cbpe_returns_distinct_but_consistent_results_when_reused_noopcal(
+    binary_classification_data,
+):  # noqa: D103
     reference, analysis = binary_classification_data
 
     sut = CBPE(
         # timestamp_column_name='timestamp',
         chunk_size=50_000,
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc'],
-        problem_type='classification_binary',
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc"],
+        problem_type="classification_binary",
         calibrator=NoopCalibrator(),
     )
     sut.fit(reference)
@@ -806,18 +860,20 @@ def test_cbpe_returns_distinct_but_consistent_results_when_reused_noopcal(binary
     pd.testing.assert_frame_equal(result1.to_df(), result2.to_df())
 
 
-def test_input_dataframes_are_not_altered_by_binary_calculator(binary_classification_data):  # noqa: D103
+def test_input_dataframes_are_not_altered_by_binary_calculator(
+    binary_classification_data,
+):  # noqa: D103
     reference, monitored = binary_classification_data
     reference2 = reference.copy(deep=True)
     monitored2 = monitored.copy(deep=True)
     estimator = CBPE(
         # timestamp_column_name='timestamp',
         chunk_size=50_000,
-        y_true='work_home_actual',
-        y_pred='y_pred',
-        y_pred_proba='y_pred_proba',
-        metrics=['roc_auc', 'f1'],
-        problem_type='classification_binary',
+        y_true="work_home_actual",
+        y_pred="y_pred",
+        y_pred_proba="y_pred_proba",
+        metrics=["roc_auc", "f1"],
+        problem_type="classification_binary",
     )
     estimator.fit(reference2)
     results = estimator.estimate(monitored2)  # noqa: F841
@@ -825,22 +881,24 @@ def test_input_dataframes_are_not_altered_by_binary_calculator(binary_classifica
     pd.testing.assert_frame_equal(reference, reference2)
 
 
-def test_input_dataframes_are_not_altered_by_multiclass_calculator(multiclass_classification_data):  # noqa: D103
+def test_input_dataframes_are_not_altered_by_multiclass_calculator(
+    multiclass_classification_data,
+):  # noqa: D103
     reference, monitored = multiclass_classification_data
     reference2 = reference.copy(deep=True)
     monitored2 = monitored.copy(deep=True)
     estimator = CBPE(  # type: ignore
-        timestamp_column_name='timestamp',
-        y_true='y_true',
-        y_pred='y_pred',
+        timestamp_column_name="timestamp",
+        y_true="y_true",
+        y_pred="y_pred",
         y_pred_proba={
-            'prepaid_card': 'y_pred_proba_prepaid_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'upmarket_card': 'y_pred_proba_upmarket_card',
+            "prepaid_card": "y_pred_proba_prepaid_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "upmarket_card": "y_pred_proba_upmarket_card",
         },
-        metrics=['roc_auc', 'f1'],
-        chunk_period='M',
-        problem_type='classification_multiclass',
+        metrics=["roc_auc", "f1"],
+        chunk_period="M",
+        problem_type="classification_multiclass",
     )
     estimator.fit(reference2)
     results = estimator.estimate(monitored2)  # noqa: F841
diff --git a/tests/performance_estimation/CBPE/test_cbpe_metrics.py b/tests/performance_estimation/CBPE/test_cbpe_metrics.py
index 23f2f8be..da35f6ee 100644
--- a/tests/performance_estimation/CBPE/test_cbpe_metrics.py
+++ b/tests/performance_estimation/CBPE/test_cbpe_metrics.py
@@ -1,4 +1,5 @@
 """Tests."""
+
 import re
 
 import pandas as pd
@@ -29,318 +30,442 @@
 
 
 @pytest.mark.parametrize(
-    'calculator_opts, expected',
+    "calculator_opts, expected",
     [
         (
             {
-                'chunker': SizeBasedChunker(chunk_size=20000, incomplete='append'),
-                'normalize_confusion_matrix': None,
-                'business_value_matrix': [[2, -5], [-10, 10]],
-                'normalize_business_value': None,
+                "chunker": SizeBasedChunker(chunk_size=20000, incomplete="append"),
+                "normalize_confusion_matrix": None,
+                "business_value_matrix": [[2, -5], [-10, 10]],
+                "normalize_business_value": None,
             },
             pd.DataFrame(
                 {
-                    'key': ['[0:19999]', '[20000:49999]'],
-                    'estimated_roc_auc': [0.9711057564966745, 0.9636286015592977],
-                    'estimated_f1': [0.9479079222515973, 0.9278089207836576],
-                    'estimated_precision': [0.9436121782324026, 0.9197836255005452],
-                    'estimated_average_precision': [0.9629778663941202, 0.9576117462271682],
-                    'estimated_recall': [0.9522429574319092, 0.9359754933378336],
-                    'estimated_specificity': [0.9434949869571513, 0.9123732942949082],
-                    'estimated_accuracy': [0.9478536003143163, 0.9245926106862006],
-                    'estimated_true_positive': [9488.96406430504, 14537.180201036117],
-                    'estimated_true_negative': [9468.107941981285, 13200.5981195499],
-                    'estimated_false_positive': [567.0359356949585, 1267.8197989638852],
-                    'estimated_false_negative': [475.8920580187156, 994.4018804500995],
-                    'estimated_business_value': [106231.75626835103, 155489.88045014057],
+                    "key": ["[0:19999]", "[20000:49999]"],
+                    "estimated_roc_auc": [0.9711057564966745, 0.9636286015592977],
+                    "estimated_f1": [0.9479079222515973, 0.9278089207836576],
+                    "estimated_precision": [0.9436121782324026, 0.9197836255005452],
+                    "estimated_average_precision": [
+                        0.9629778663941202,
+                        0.9576117462271682,
+                    ],
+                    "estimated_recall": [0.9522429574319092, 0.9359754933378336],
+                    "estimated_specificity": [0.9434949869571513, 0.9123732942949082],
+                    "estimated_accuracy": [0.9478536003143163, 0.9245926106862006],
+                    "estimated_true_positive": [9488.96406430504, 14537.180201036117],
+                    "estimated_true_negative": [9468.107941981285, 13200.5981195499],
+                    "estimated_false_positive": [567.0359356949585, 1267.8197989638852],
+                    "estimated_false_negative": [475.8920580187156, 994.4018804500995],
+                    "estimated_business_value": [
+                        106231.75626835103,
+                        155489.88045014057,
+                    ],
                 }
             ),
         ),
         (
             {
-                'chunker': SizeBasedChunker(chunk_size=20000, incomplete='append'),
-                'normalize_confusion_matrix': None,
-                'business_value_matrix': [[2, -5], [-10, 10]],
-                'normalize_business_value': 'per_prediction',
+                "chunker": SizeBasedChunker(chunk_size=20000, incomplete="append"),
+                "normalize_confusion_matrix": None,
+                "business_value_matrix": [[2, -5], [-10, 10]],
+                "normalize_business_value": "per_prediction",
             },
             pd.DataFrame(
                 {
-                    'key': ['[0:19999]', '[20000:49999]'],
-                    'estimated_roc_auc': [0.9711057564966745, 0.9636286015592977],
-                    'estimated_f1': [0.9479079222515973, 0.9278089207836576],
-                    'estimated_precision': [0.9436121782324026, 0.9197836255005452],
-                    'estimated_average_precision': [0.9629778663941202, 0.9576117462271682],
-                    'estimated_recall': [0.9522429574319092, 0.9359754933378336],
-                    'estimated_specificity': [0.9434949869571513, 0.9123732942949082],
-                    'estimated_accuracy': [0.9478536003143163, 0.9245926106862006],
-                    'estimated_true_positive': [9488.96406430504, 14537.180201036117],
-                    'estimated_true_negative': [9468.107941981285, 13200.5981195499],
-                    'estimated_false_positive': [567.0359356949585, 1267.8197989638852],
-                    'estimated_false_negative': [475.8920580187156, 994.4018804500995],
-                    'estimated_business_value': [10.579896199609875, 9.956118766770157],
+                    "key": ["[0:19999]", "[20000:49999]"],
+                    "estimated_roc_auc": [0.9711057564966745, 0.9636286015592977],
+                    "estimated_f1": [0.9479079222515973, 0.9278089207836576],
+                    "estimated_precision": [0.9436121782324026, 0.9197836255005452],
+                    "estimated_average_precision": [
+                        0.9629778663941202,
+                        0.9576117462271682,
+                    ],
+                    "estimated_recall": [0.9522429574319092, 0.9359754933378336],
+                    "estimated_specificity": [0.9434949869571513, 0.9123732942949082],
+                    "estimated_accuracy": [0.9478536003143163, 0.9245926106862006],
+                    "estimated_true_positive": [9488.96406430504, 14537.180201036117],
+                    "estimated_true_negative": [9468.107941981285, 13200.5981195499],
+                    "estimated_false_positive": [567.0359356949585, 1267.8197989638852],
+                    "estimated_false_negative": [475.8920580187156, 994.4018804500995],
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@@ -786,71 +1013,86 @@
         ),
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@@ -861,92 +1103,92 @@
         ),
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         ),
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@@ -1055,92 +1297,92 @@
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@@ -1152,92 +1394,92 @@
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@@ -1248,22 +1490,25 @@
             ),
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         (
-            {'normalize_confusion_matrix': None, 'business_value_matrix': [[-1, 4], [8, -8]]},
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-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
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                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -1275,7 +1520,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
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                         0.9465779465209089,
                         0.9488069354531159,
@@ -1287,7 +1532,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
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@@ -1299,7 +1544,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
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                         0.9624355545496974,
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@@ -1311,7 +1556,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
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                         0.9534307499357034,
@@ -1323,7 +1568,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
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                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -1335,7 +1580,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
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-                    'estimated_accuracy': [
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                         0.9462845122805443,
                         0.947306078326665,
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@@ -1347,7 +1592,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
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                         2334.1907004380405,
                         2337.9079148804176,
@@ -1359,7 +1604,7 @@
                         2383.285478351263,
                         2489.4667853918354,
                     ],
-                    'estimated_true_negative': [
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                         2402.339691195284,
                         2409.8075237003773,
@@ -1371,7 +1616,7 @@
                         2225.6203085477264,
                         2106.3425088286976,
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                         140.80929956195956,
                         138.09208511958207,
@@ -1383,7 +1628,7 @@
                         214.71452164873693,
                         232.53321460816457,
                     ],
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                         122.66030880471597,
                         114.19247629962268,
@@ -1395,7 +1640,7 @@
                         176.37969145227322,
                         171.65749117130238,
                     ],
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                         -19531.34562601404,
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@@ -1411,22 +1656,25 @@
             ),
         ),
         (
-            {'normalize_confusion_matrix': 'all', 'business_value_matrix': [[-1, 4], [8, -8]]},
+            {
+                "normalize_confusion_matrix": "all",
+                "business_value_matrix": [[-1, 4], [8, -8]],
+            },
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:4999]',
-                        '[5000:9999]',
-                        '[10000:14999]',
-                        '[15000:19999]',
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-                        '[25000:29999]',
-                        '[30000:34999]',
-                        '[35000:39999]',
-                        '[40000:44999]',
-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
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+                        "[5000:9999]",
+                        "[10000:14999]",
+                        "[15000:19999]",
+                        "[20000:24999]",
+                        "[25000:29999]",
+                        "[30000:34999]",
+                        "[35000:39999]",
+                        "[40000:44999]",
+                        "[45000:49999]",
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                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -1438,7 +1686,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
-                    'estimated_f1': [
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                         0.948555321454138,
                         0.9465779465209089,
                         0.9488069354531159,
@@ -1450,7 +1698,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
                     ],
-                    'estimated_precision': [
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                         0.9425440716307568,
                         0.9431073537123396,
                         0.9442277523749669,
@@ -1462,7 +1710,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
                     ],
-                    'estimated_average_precision': [
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                         0.9639571716287229,
                         0.9624355545496974,
                         0.9627637624228476,
@@ -1474,7 +1722,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
-                    'estimated_recall': [
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                         0.9546437391537488,
                         0.95007417692678,
                         0.9534307499357034,
@@ -1486,7 +1734,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
                     ],
-                    'estimated_specificity': [
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                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -1498,7 +1746,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
                     ],
-                    'estimated_accuracy': [
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                         0.9462845122805443,
                         0.947306078326665,
                         0.949543087716159,
@@ -1510,7 +1758,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
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                         0.4952126552347996,
                         0.4668381400876081,
                         0.46758158297608354,
@@ -1522,7 +1770,7 @@
                         0.4766570956702526,
                         0.49789335707836707,
                     ],
-                    'estimated_true_negative': [
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                         0.45107185704574476,
                         0.4804679382390568,
                         0.48196150474007543,
@@ -1534,7 +1782,7 @@
                         0.44512406170954527,
                         0.42126850176573954,
                     ],
-                    'estimated_false_positive': [
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                         0.03018734476520043,
                         0.028161859912391913,
                         0.027618417023916413,
@@ -1546,7 +1794,7 @@
                         0.042942904329747386,
                         0.04650664292163292,
                     ],
-                    'estimated_false_negative': [
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                         0.023528142954255284,
                         0.024532061760943195,
                         0.022838495259924537,
@@ -1558,7 +1806,7 @@
                         0.035275938290454646,
                         0.034331498234260474,
                     ],
-                    'estimated_business_value': [
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                         -20518.99288114649,
                         -19531.34562601404,
                         -19647.162691868405,
@@ -1574,22 +1822,25 @@
             ),
         ),
         (
-            {'normalize_confusion_matrix': 'true', 'business_value_matrix': [[-1, 4], [8, -8]]},
+            {
+                "normalize_confusion_matrix": "true",
+                "business_value_matrix": [[-1, 4], [8, -8]],
+            },
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:4999]',
-                        '[5000:9999]',
-                        '[10000:14999]',
-                        '[15000:19999]',
-                        '[20000:24999]',
-                        '[25000:29999]',
-                        '[30000:34999]',
-                        '[35000:39999]',
-                        '[40000:44999]',
-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:4999]",
+                        "[5000:9999]",
+                        "[10000:14999]",
+                        "[15000:19999]",
+                        "[20000:24999]",
+                        "[25000:29999]",
+                        "[30000:34999]",
+                        "[35000:39999]",
+                        "[40000:44999]",
+                        "[45000:49999]",
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                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -1601,7 +1852,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
-                    'estimated_f1': [
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                         0.948555321454138,
                         0.9465779465209089,
                         0.9488069354531159,
@@ -1613,7 +1864,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
                     ],
-                    'estimated_precision': [
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                         0.9425440716307568,
                         0.9431073537123396,
                         0.9442277523749669,
@@ -1625,7 +1876,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
                     ],
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                         0.9639571716287229,
                         0.9624355545496974,
                         0.9627637624228476,
@@ -1637,7 +1888,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
-                    'estimated_recall': [
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                         0.9546437391537488,
                         0.95007417692678,
                         0.9534307499357034,
@@ -1649,7 +1900,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
                     ],
-                    'estimated_specificity': [
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                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -1661,7 +1912,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
                     ],
-                    'estimated_accuracy': [
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                         0.9462845122805443,
                         0.947306078326665,
                         0.949543087716159,
@@ -1673,7 +1924,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
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                         0.9546437391537488,
                         0.9500741769267799,
                         0.9534307499357034,
@@ -1685,7 +1936,7 @@
                         0.9310926704269642,
                         0.9354943725540699,
                     ],
-                    'estimated_true_negative': [
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                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -1697,7 +1948,7 @@
                         0.9120143191041324,
                         0.9005790635739858,
                     ],
-                    'estimated_false_positive': [
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                         0.0627257508045717,
                         0.05536808896124974,
                         0.054198401162100104,
@@ -1709,7 +1960,7 @@
                         0.08798568089586746,
                         0.09942093642601435,
                     ],
-                    'estimated_false_negative': [
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                         0.04535626084625111,
                         0.04992582307322005,
                         0.04656925006429655,
@@ -1721,7 +1972,7 @@
                         0.06890732957303593,
                         0.06450562744593023,
                     ],
-                    'estimated_business_value': [
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                         -20518.99288114649,
                         -19531.34562601404,
                         -19647.162691868405,
@@ -1737,22 +1988,25 @@
             ),
         ),
         (
-            {'normalize_confusion_matrix': 'pred', 'business_value_matrix': [[-1, 4], [8, -8]]},
+            {
+                "normalize_confusion_matrix": "pred",
+                "business_value_matrix": [[-1, 4], [8, -8]],
+            },
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:4999]',
-                        '[5000:9999]',
-                        '[10000:14999]',
-                        '[15000:19999]',
-                        '[20000:24999]',
-                        '[25000:29999]',
-                        '[30000:34999]',
-                        '[35000:39999]',
-                        '[40000:44999]',
-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:4999]",
+                        "[5000:9999]",
+                        "[10000:14999]",
+                        "[15000:19999]",
+                        "[20000:24999]",
+                        "[25000:29999]",
+                        "[30000:34999]",
+                        "[35000:39999]",
+                        "[40000:44999]",
+                        "[45000:49999]",
+                    ],
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                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -1764,7 +2018,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
-                    'estimated_f1': [
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                         0.9465779465209089,
                         0.9488069354531159,
@@ -1776,7 +2030,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
                     ],
-                    'estimated_precision': [
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                         0.9431073537123396,
                         0.9442277523749669,
@@ -1788,7 +2042,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
                     ],
-                    'estimated_average_precision': [
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                         0.9639571716287229,
                         0.9624355545496974,
                         0.9627637624228476,
@@ -1800,7 +2054,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
-                    'estimated_recall': [
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                         0.9546437391537488,
                         0.95007417692678,
                         0.9534307499357034,
@@ -1812,7 +2066,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
                     ],
-                    'estimated_specificity': [
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                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -1824,7 +2078,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
                     ],
-                    'estimated_accuracy': [
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                         0.9462845122805443,
                         0.947306078326665,
                         0.949543087716159,
@@ -1836,7 +2090,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
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                         0.9425440716307567,
                         0.9431073537123397,
                         0.9442277523749669,
@@ -1848,7 +2102,7 @@
                         0.9173539177641507,
                         0.9145726617897999,
                     ],
-                    'estimated_true_negative': [
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                         0.9504253203660866,
                         0.9514216598793204,
                         0.9547573390255062,
@@ -1860,7 +2114,7 @@
                         0.9265696538500111,
                         0.9246455262636951,
                     ],
-                    'estimated_false_positive': [
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                         0.05745592836924329,
                         0.056892646287660435,
                         0.055772247625033154,
@@ -1872,7 +2126,7 @@
                         0.08264608223584949,
                         0.08542733821020007,
                     ],
-                    'estimated_false_negative': [
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                         0.04957467963391336,
                         0.048578340120679596,
                         0.04524266097449394,
@@ -1884,7 +2138,7 @@
                         0.07343034614998886,
                         0.07535447373630481,
                     ],
-                    'estimated_business_value': [
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                         -20518.99288114649,
                         -19531.34562601404,
                         -19647.162691868405,
@@ -1901,25 +2155,25 @@
         ),
         (
             {
-                'normalize_confusion_matrix': None,
-                'timestamp_column_name': 'timestamp',
-                'business_value_matrix': [[-1, 4], [8, -8]],
+                "normalize_confusion_matrix": None,
+                "timestamp_column_name": "timestamp",
+                "business_value_matrix": [[-1, 4], [8, -8]],
             },
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:4999]',
-                        '[5000:9999]',
-                        '[10000:14999]',
-                        '[15000:19999]',
-                        '[20000:24999]',
-                        '[25000:29999]',
-                        '[30000:34999]',
-                        '[35000:39999]',
-                        '[40000:44999]',
-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:4999]",
+                        "[5000:9999]",
+                        "[10000:14999]",
+                        "[15000:19999]",
+                        "[20000:24999]",
+                        "[25000:29999]",
+                        "[30000:34999]",
+                        "[35000:39999]",
+                        "[40000:44999]",
+                        "[45000:49999]",
+                    ],
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                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -1931,7 +2185,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
-                    'estimated_f1': [
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                         0.948555321454138,
                         0.9465779465209089,
                         0.9488069354531159,
@@ -1943,7 +2197,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
                     ],
-                    'estimated_precision': [
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                         0.9425440716307568,
                         0.9431073537123396,
                         0.9442277523749669,
@@ -1955,7 +2209,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
                     ],
-                    'estimated_average_precision': [
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                         0.9639571716287229,
                         0.9624355545496974,
                         0.9627637624228476,
@@ -1967,7 +2221,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
-                    'estimated_recall': [
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                         0.95007417692678,
                         0.9534307499357034,
@@ -1979,7 +2233,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
                     ],
-                    'estimated_specificity': [
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                         0.9372742491954283,
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                         0.9458015988378999,
@@ -1991,7 +2245,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
                     ],
-                    'estimated_accuracy': [
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                         0.949543087716159,
@@ -2003,7 +2257,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
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                         2476.063276173998,
                         2334.1907004380405,
                         2337.9079148804176,
@@ -2015,7 +2269,7 @@
                         2383.285478351263,
                         2489.4667853918354,
                     ],
-                    'estimated_true_negative': [
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                         2402.339691195284,
                         2409.8075237003773,
@@ -2027,7 +2281,7 @@
                         2225.6203085477264,
                         2106.3425088286976,
                     ],
-                    'estimated_false_positive': [
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                         140.80929956195956,
                         138.09208511958207,
@@ -2039,7 +2293,7 @@
                         214.71452164873693,
                         232.53321460816457,
                     ],
-                    'estimated_false_negative': [
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                         122.66030880471597,
                         114.19247629962268,
@@ -2051,7 +2305,7 @@
                         176.37969145227322,
                         171.65749117130238,
                     ],
-                    'estimated_business_value': [
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                         -19531.34562601404,
                         -19647.162691868405,
@@ -2068,25 +2322,25 @@
         ),
         (
             {
-                'normalize_confusion_matrix': 'all',
-                'timestamp_column_name': 'timestamp',
-                'business_value_matrix': [[-1, 4], [8, -8]],
+                "normalize_confusion_matrix": "all",
+                "timestamp_column_name": "timestamp",
+                "business_value_matrix": [[-1, 4], [8, -8]],
             },
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:4999]',
-                        '[5000:9999]',
-                        '[10000:14999]',
-                        '[15000:19999]',
-                        '[20000:24999]',
-                        '[25000:29999]',
-                        '[30000:34999]',
-                        '[35000:39999]',
-                        '[40000:44999]',
-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:4999]",
+                        "[5000:9999]",
+                        "[10000:14999]",
+                        "[15000:19999]",
+                        "[20000:24999]",
+                        "[25000:29999]",
+                        "[30000:34999]",
+                        "[35000:39999]",
+                        "[40000:44999]",
+                        "[45000:49999]",
+                    ],
+                    "estimated_roc_auc": [
                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -2098,7 +2352,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
-                    'estimated_f1': [
+                    "estimated_f1": [
                         0.948555321454138,
                         0.9465779465209089,
                         0.9488069354531159,
@@ -2110,7 +2364,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
                     ],
-                    'estimated_precision': [
+                    "estimated_precision": [
                         0.9425440716307568,
                         0.9431073537123396,
                         0.9442277523749669,
@@ -2122,7 +2376,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
                     ],
-                    'estimated_average_precision': [
+                    "estimated_average_precision": [
                         0.9639571716287229,
                         0.9624355545496974,
                         0.9627637624228476,
@@ -2134,7 +2388,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
-                    'estimated_recall': [
+                    "estimated_recall": [
                         0.9546437391537488,
                         0.95007417692678,
                         0.9534307499357034,
@@ -2146,7 +2400,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
                     ],
-                    'estimated_specificity': [
+                    "estimated_specificity": [
                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -2158,7 +2412,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
                     ],
-                    'estimated_accuracy': [
+                    "estimated_accuracy": [
                         0.9462845122805443,
                         0.947306078326665,
                         0.949543087716159,
@@ -2170,7 +2424,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
+                    "estimated_true_positive": [
                         0.4952126552347996,
                         0.4668381400876081,
                         0.46758158297608354,
@@ -2182,7 +2436,7 @@
                         0.4766570956702526,
                         0.49789335707836707,
                     ],
-                    'estimated_true_negative': [
+                    "estimated_true_negative": [
                         0.45107185704574476,
                         0.4804679382390568,
                         0.48196150474007543,
@@ -2194,7 +2448,7 @@
                         0.44512406170954527,
                         0.42126850176573954,
                     ],
-                    'estimated_false_positive': [
+                    "estimated_false_positive": [
                         0.03018734476520043,
                         0.028161859912391913,
                         0.027618417023916413,
@@ -2206,7 +2460,7 @@
                         0.042942904329747386,
                         0.04650664292163292,
                     ],
-                    'estimated_false_negative': [
+                    "estimated_false_negative": [
                         0.023528142954255284,
                         0.024532061760943195,
                         0.022838495259924537,
@@ -2218,7 +2472,7 @@
                         0.035275938290454646,
                         0.034331498234260474,
                     ],
-                    'estimated_business_value': [
+                    "estimated_business_value": [
                         -20518.99288114649,
                         -19531.34562601404,
                         -19647.162691868405,
@@ -2235,25 +2489,25 @@
         ),
         (
             {
-                'normalize_confusion_matrix': 'true',
-                'timestamp_column_name': 'timestamp',
-                'business_value_matrix': [[-1, 4], [8, -8]],
+                "normalize_confusion_matrix": "true",
+                "timestamp_column_name": "timestamp",
+                "business_value_matrix": [[-1, 4], [8, -8]],
             },
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:4999]',
-                        '[5000:9999]',
-                        '[10000:14999]',
-                        '[15000:19999]',
-                        '[20000:24999]',
-                        '[25000:29999]',
-                        '[30000:34999]',
-                        '[35000:39999]',
-                        '[40000:44999]',
-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:4999]",
+                        "[5000:9999]",
+                        "[10000:14999]",
+                        "[15000:19999]",
+                        "[20000:24999]",
+                        "[25000:29999]",
+                        "[30000:34999]",
+                        "[35000:39999]",
+                        "[40000:44999]",
+                        "[45000:49999]",
+                    ],
+                    "estimated_roc_auc": [
                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -2265,7 +2519,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
-                    'estimated_f1': [
+                    "estimated_f1": [
                         0.948555321454138,
                         0.9465779465209089,
                         0.9488069354531159,
@@ -2277,7 +2531,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
                     ],
-                    'estimated_precision': [
+                    "estimated_precision": [
                         0.9425440716307568,
                         0.9431073537123396,
                         0.9442277523749669,
@@ -2289,7 +2543,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
                     ],
-                    'estimated_average_precision': [
+                    "estimated_average_precision": [
                         0.9639571716287229,
                         0.9624355545496974,
                         0.9627637624228476,
@@ -2301,7 +2555,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
-                    'estimated_recall': [
+                    "estimated_recall": [
                         0.9546437391537488,
                         0.95007417692678,
                         0.9534307499357034,
@@ -2313,7 +2567,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
                     ],
-                    'estimated_specificity': [
+                    "estimated_specificity": [
                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -2325,7 +2579,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
                     ],
-                    'estimated_accuracy': [
+                    "estimated_accuracy": [
                         0.9462845122805443,
                         0.947306078326665,
                         0.949543087716159,
@@ -2337,7 +2591,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
+                    "estimated_true_positive": [
                         0.9546437391537488,
                         0.9500741769267799,
                         0.9534307499357034,
@@ -2349,7 +2603,7 @@
                         0.9310926704269642,
                         0.9354943725540699,
                     ],
-                    'estimated_true_negative': [
+                    "estimated_true_negative": [
                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -2361,7 +2615,7 @@
                         0.9120143191041324,
                         0.9005790635739858,
                     ],
-                    'estimated_false_positive': [
+                    "estimated_false_positive": [
                         0.0627257508045717,
                         0.05536808896124974,
                         0.054198401162100104,
@@ -2373,7 +2627,7 @@
                         0.08798568089586746,
                         0.09942093642601435,
                     ],
-                    'estimated_false_negative': [
+                    "estimated_false_negative": [
                         0.04535626084625111,
                         0.04992582307322005,
                         0.04656925006429655,
@@ -2385,7 +2639,7 @@
                         0.06890732957303593,
                         0.06450562744593023,
                     ],
-                    'estimated_business_value': [
+                    "estimated_business_value": [
                         -20518.99288114649,
                         -19531.34562601404,
                         -19647.162691868405,
@@ -2402,25 +2656,25 @@
         ),
         (
             {
-                'normalize_confusion_matrix': 'pred',
-                'timestamp_column_name': 'timestamp',
-                'business_value_matrix': [[-1, 4], [8, -8]],
+                "normalize_confusion_matrix": "pred",
+                "timestamp_column_name": "timestamp",
+                "business_value_matrix": [[-1, 4], [8, -8]],
             },
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:4999]',
-                        '[5000:9999]',
-                        '[10000:14999]',
-                        '[15000:19999]',
-                        '[20000:24999]',
-                        '[25000:29999]',
-                        '[30000:34999]',
-                        '[35000:39999]',
-                        '[40000:44999]',
-                        '[45000:49999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:4999]",
+                        "[5000:9999]",
+                        "[10000:14999]",
+                        "[15000:19999]",
+                        "[20000:24999]",
+                        "[25000:29999]",
+                        "[30000:34999]",
+                        "[35000:39999]",
+                        "[40000:44999]",
+                        "[45000:49999]",
+                    ],
+                    "estimated_roc_auc": [
                         0.97074363630015,
                         0.9710106327368981,
                         0.9714067155511361,
@@ -2432,7 +2686,7 @@
                         0.9625334611153133,
                         0.9613161529631752,
                     ],
-                    'estimated_f1': [
+                    "estimated_f1": [
                         0.948555321454138,
                         0.9465779465209089,
                         0.9488069354531159,
@@ -2444,7 +2698,7 @@
                         0.9241722368115827,
                         0.9249152193013231,
                     ],
-                    'estimated_precision': [
+                    "estimated_precision": [
                         0.9425440716307568,
                         0.9431073537123396,
                         0.9442277523749669,
@@ -2456,7 +2710,7 @@
                         0.9173539177641505,
                         0.9145726617897999,
                     ],
-                    'estimated_average_precision': [
+                    "estimated_average_precision": [
                         0.9639571716287229,
                         0.9624355545496974,
                         0.9627637624228476,
@@ -2468,7 +2722,7 @@
                         0.9560927338703065,
                         0.9569107885377364,
                     ],
-                    'estimated_recall': [
+                    "estimated_recall": [
                         0.9546437391537488,
                         0.95007417692678,
                         0.9534307499357034,
@@ -2480,7 +2734,7 @@
                         0.9310926704269641,
                         0.9354943725540698,
                     ],
-                    'estimated_specificity': [
+                    "estimated_specificity": [
                         0.9372742491954283,
                         0.9446319110387503,
                         0.9458015988378999,
@@ -2492,7 +2746,7 @@
                         0.9120143191041326,
                         0.9005790635739856,
                     ],
-                    'estimated_accuracy': [
+                    "estimated_accuracy": [
                         0.9462845122805443,
                         0.947306078326665,
                         0.949543087716159,
@@ -2504,7 +2758,7 @@
                         0.9217811573797979,
                         0.9191618588441066,
                     ],
-                    'estimated_true_positive': [
+                    "estimated_true_positive": [
                         0.9425440716307567,
                         0.9431073537123397,
                         0.9442277523749669,
@@ -2516,7 +2770,7 @@
                         0.9173539177641507,
                         0.9145726617897999,
                     ],
-                    'estimated_true_negative': [
+                    "estimated_true_negative": [
                         0.9504253203660866,
                         0.9514216598793204,
                         0.9547573390255062,
@@ -2528,7 +2782,7 @@
                         0.9265696538500111,
                         0.9246455262636951,
                     ],
-                    'estimated_false_positive': [
+                    "estimated_false_positive": [
                         0.05745592836924329,
                         0.056892646287660435,
                         0.055772247625033154,
@@ -2540,7 +2794,7 @@
                         0.08264608223584949,
                         0.08542733821020007,
                     ],
-                    'estimated_false_negative': [
+                    "estimated_false_negative": [
                         0.04957467963391336,
                         0.048578340120679596,
                         0.04524266097449394,
@@ -2552,7 +2806,7 @@
                         0.07343034614998886,
                         0.07535447373630481,
                     ],
-                    'estimated_business_value': [
+                    "estimated_business_value": [
                         -20518.99288114649,
                         -19531.34562601404,
                         -19647.162691868405,
@@ -2569,138 +2823,174 @@
         ),
     ],
     ids=[
-        'size_based_without_timestamp_cm_normalization_none_business_norm_none',
-        'size_based_without_timestamp_cm_normalization_none_business_norm_per_pred',
-        'size_based_without_timestamp_normalization_all_business_norm_none',
-        'size_based_without_timestamp_normalization_true_business_norm_none',
-        'size_based_without_timestamp_normalization_pred_business_norm_none',
-        'sized_based_with_timestamp_cm_normalization_none_business_norm_none',
-        'sized_based_with_timestamp_cm_normalization_all_business_norm_none',
-        'sized_based_with_timestamp_cm_normalization_all_business_norm_per_pred',
-        'sized_based_with_timestamp_normalization_true_business_norm_none',
-        'sized_based_with_timestamp_normalization_pred_business_norm_none',
-        'count_based_without_timestamp_normalization_none_business_norm_none',
-        'count_based_without_timestamp_normalization_all_business_norm_none',
-        'count_based_without_timestamp_normalization_true_business_norm_none',
-        'count_based_without_timestamp_normalization_pred_business_norm_none',
-        'count_based_with_timestamp_normalization_none_business_norm_none',
-        'count_based_with_timestamp_normalization_all_business_norm_none',
-        'count_based_with_timestamp_normalization_true_business_norm_none',
-        'count_based_with_timestamp_normalization_pred_business_norm_none',
-        'period_based_with_timestamp_normalization_none_business_norm_none',
-        'period_based_with_timestamp_normalization_all_business_norm_none',
-        'period_based_with_timestamp_normalization_true_business_norm_none',
-        'period_based_with_timestamp_normalization_pred_business_norm_none',
-        'default_without_timestamp_normalization_none_business_norm_none',
-        'default_without_timestamp_normalization_all_business_norm_none',
-        'default_without_timestamp_normalization_true_business_norm_none',
-        'default_without_timestamp_normalization_pred_business_norm_none',
-        'default_with_timestamp_normalization_none_business_norm_none',
-        'default_with_timestamp_normalization_all_business_norm_none',
-        'default_with_timestamp_normalization_true_business_norm_none',
-        'default_with_timestamp_normalization_pred_business_norm_none',
+        "size_based_without_timestamp_cm_normalization_none_business_norm_none",
+        "size_based_without_timestamp_cm_normalization_none_business_norm_per_pred",
+        "size_based_without_timestamp_normalization_all_business_norm_none",
+        "size_based_without_timestamp_normalization_true_business_norm_none",
+        "size_based_without_timestamp_normalization_pred_business_norm_none",
+        "sized_based_with_timestamp_cm_normalization_none_business_norm_none",
+        "sized_based_with_timestamp_cm_normalization_all_business_norm_none",
+        "sized_based_with_timestamp_cm_normalization_all_business_norm_per_pred",
+        "sized_based_with_timestamp_normalization_true_business_norm_none",
+        "sized_based_with_timestamp_normalization_pred_business_norm_none",
+        "count_based_without_timestamp_normalization_none_business_norm_none",
+        "count_based_without_timestamp_normalization_all_business_norm_none",
+        "count_based_without_timestamp_normalization_true_business_norm_none",
+        "count_based_without_timestamp_normalization_pred_business_norm_none",
+        "count_based_with_timestamp_normalization_none_business_norm_none",
+        "count_based_with_timestamp_normalization_all_business_norm_none",
+        "count_based_with_timestamp_normalization_true_business_norm_none",
+        "count_based_with_timestamp_normalization_pred_business_norm_none",
+        "period_based_with_timestamp_normalization_none_business_norm_none",
+        "period_based_with_timestamp_normalization_all_business_norm_none",
+        "period_based_with_timestamp_normalization_true_business_norm_none",
+        "period_based_with_timestamp_normalization_pred_business_norm_none",
+        "default_without_timestamp_normalization_none_business_norm_none",
+        "default_without_timestamp_normalization_all_business_norm_none",
+        "default_without_timestamp_normalization_true_business_norm_none",
+        "default_without_timestamp_normalization_pred_business_norm_none",
+        "default_with_timestamp_normalization_none_business_norm_none",
+        "default_with_timestamp_normalization_all_business_norm_none",
+        "default_with_timestamp_normalization_true_business_norm_none",
+        "default_with_timestamp_normalization_pred_business_norm_none",
     ],
 )
 def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expected):  # noqa: D103
     ref_df, ana_df, _ = load_synthetic_binary_classification_dataset()
     cbpe = CBPE(
-        y_pred_proba='y_pred_proba',
-        y_pred='y_pred',
-        y_true='work_home_actual',
-        problem_type='classification_binary',
+        y_pred_proba="y_pred_proba",
+        y_pred="y_pred",
+        y_true="work_home_actual",
+        problem_type="classification_binary",
         metrics=[
-            'roc_auc',
-            'f1',
-            'precision',
-            'average_precision',
-            'recall',
-            'specificity',
-            'accuracy',
-            'confusion_matrix',
-            'business_value',
+            "roc_auc",
+            "f1",
+            "precision",
+            "average_precision",
+            "recall",
+            "specificity",
+            "accuracy",
+            "confusion_matrix",
+            "business_value",
         ],
         **calculator_opts,
     ).fit(ref_df)
     result = cbpe.estimate(ana_df)
 
-    metric_column_names = [name for metric in result.metrics for name in metric.column_names]
-    sut = result.filter(period='analysis').to_df()[[('chunk', 'key')] + [(c, 'value') for c in metric_column_names]]
+    metric_column_names = [
+        name for metric in result.metrics for name in metric.column_names
+    ]
+    sut = result.filter(period="analysis").to_df()[
+        [("chunk", "key")] + [(c, "value") for c in metric_column_names]
+    ]
     sut.columns = [
-        'key',
-        'estimated_roc_auc',
-        'estimated_f1',
-        'estimated_precision',
-        'estimated_average_precision',
-        'estimated_recall',
-        'estimated_specificity',
-        'estimated_accuracy',
-        'estimated_true_positive',
-        'estimated_true_negative',
-        'estimated_false_positive',
-        'estimated_false_negative',
-        'estimated_business_value',
+        "key",
+        "estimated_roc_auc",
+        "estimated_f1",
+        "estimated_precision",
+        "estimated_average_precision",
+        "estimated_recall",
+        "estimated_specificity",
+        "estimated_accuracy",
+        "estimated_true_positive",
+        "estimated_true_negative",
+        "estimated_false_positive",
+        "estimated_false_negative",
+        "estimated_business_value",
     ]
 
     pd.testing.assert_frame_equal(expected, sut)
 
 
 @pytest.mark.parametrize(
-    'calculator_opts, expected',
+    "calculator_opts, expected",
     [
         (
-            {'chunk_size': 20000},
+            {"chunk_size": 20000},
             pd.DataFrame(
                 {
-                    'key': ['[0:19999]', '[20000:39999]', '[40000:59999]'],
-                    'estimated_roc_auc': [0.909165141524145, 0.8682789924547322, 0.8203173643497594],
-                    'estimated_f1': [0.756401608336434, 0.6937135623882767, 0.632386421613214],
-                    'estimated_precision': [0.7564437378390059, 0.694174192229447, 0.6336288859123612],
-                    'estimated_recall': [0.7564129287764665, 0.6934788458355289, 0.6319310599943714],
-                    'estimated_specificity': [0.8782068281303994, 0.8469556750949159, 0.8172644220189141],
-                    'estimated_accuracy': [0.7564451493123628, 0.6946947603445697, 0.6378557309960986],
-                    'estimated_average_precision': [0.8418535417603635, 0.7785618577588246, 0.6985785036188713],
-                    'estimated_business_value': [2.0193901626043056, 1.7875283323693987, 1.570045452479401],
-                    'estimated_true_highstreet_card_pred_highstreet_card': [
+                    "key": ["[0:19999]", "[20000:39999]", "[40000:59999]"],
+                    "estimated_roc_auc": [
+                        0.909165141524145,
+                        0.8682789924547322,
+                        0.8203173643497594,
+                    ],
+                    "estimated_f1": [
+                        0.756401608336434,
+                        0.6937135623882767,
+                        0.632386421613214,
+                    ],
+                    "estimated_precision": [
+                        0.7564437378390059,
+                        0.694174192229447,
+                        0.6336288859123612,
+                    ],
+                    "estimated_recall": [
+                        0.7564129287764665,
+                        0.6934788458355289,
+                        0.6319310599943714,
+                    ],
+                    "estimated_specificity": [
+                        0.8782068281303994,
+                        0.8469556750949159,
+                        0.8172644220189141,
+                    ],
+                    "estimated_accuracy": [
+                        0.7564451493123628,
+                        0.6946947603445697,
+                        0.6378557309960986,
+                    ],
+                    "estimated_average_precision": [
+                        0.8418535417603635,
+                        0.7785618577588246,
+                        0.6985785036188713,
+                    ],
+                    "estimated_business_value": [
+                        2.0193901626043056,
+                        1.7875283323693987,
+                        1.570045452479401,
+                    ],
+                    "estimated_true_highstreet_card_pred_highstreet_card": [
                         4976.829215997277,
                         5148.649186425118,
                         5412.348045797111,
                     ],
-                    'estimated_true_highstreet_card_pred_prepaid_card': [
+                    "estimated_true_highstreet_card_pred_prepaid_card": [
                         878.1877379091701,
                         1038.3533241561252,
                         1250.9260097761653,
                     ],
-                    'estimated_true_highstreet_card_pred_upmarket_card': [
+                    "estimated_true_highstreet_card_pred_upmarket_card": [
                         831.7702766018707,
                         993.7691398029524,
                         1109.9706655490413,
                     ],
-                    'estimated_true_prepaid_card_pred_highstreet_card': [
+                    "estimated_true_prepaid_card_pred_highstreet_card": [
                         806.1451187447954,
                         1140.1932616586546,
                         1451.431964364007,
                     ],
-                    'estimated_true_prepaid_card_pred_prepaid_card': [
+                    "estimated_true_prepaid_card_pred_prepaid_card": [
                         5180.838942632071,
                         4134.524656135082,
                         3326.8467648553315,
                     ],
-                    'estimated_true_prepaid_card_pred_upmarket_card': [
+                    "estimated_true_prepaid_card_pred_upmarket_card": [
                         755.9948957802203,
                         998.509495865855,
                         1200.1095251814281,
                     ],
-                    'estimated_true_upmarket_card_pred_highstreet_card': [
+                    "estimated_true_upmarket_card_pred_highstreet_card": [
                         812.0256652579275,
                         1062.1575519162266,
                         1263.219989838882,
                     ],
-                    'estimated_true_upmarket_card_pred_prepaid_card': [
+                    "estimated_true_upmarket_card_pred_prepaid_card": [
                         786.9733194587595,
                         873.1220197087925,
                         967.2272253685034,
                     ],
-                    'estimated_true_upmarket_card_pred_upmarket_card': [
+                    "estimated_true_upmarket_card_pred_upmarket_card": [
                         4971.234827617909,
                         4610.7213643311925,
                         4017.9198092695306,
@@ -2709,59 +2999,95 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
             ),
         ),
         (
-            {'chunk_size': 20000, 'timestamp_column_name': 'timestamp', 'normalize_confusion_matrix': 'true'},
+            {
+                "chunk_size": 20000,
+                "timestamp_column_name": "timestamp",
+                "normalize_confusion_matrix": "true",
+            },
             pd.DataFrame(
                 {
-                    'key': ['[0:19999]', '[20000:39999]', '[40000:59999]'],
-                    'estimated_roc_auc': [0.909165141524145, 0.8682789924547322, 0.8203173643497594],
-                    'estimated_f1': [0.756401608336434, 0.6937135623882767, 0.632386421613214],
-                    'estimated_precision': [0.7564437378390059, 0.694174192229447, 0.6336288859123612],
-                    'estimated_recall': [0.7564129287764665, 0.6934788458355289, 0.6319310599943714],
-                    'estimated_specificity': [0.8782068281303994, 0.8469556750949159, 0.8172644220189141],
-                    'estimated_accuracy': [0.7564451493123628, 0.6946947603445697, 0.6378557309960986],
-                    'estimated_average_precision': [0.8418535417603635, 0.7785618577588246, 0.6985785036188713],
-                    'estimated_business_value': [2.0193901626043056, 1.7875283323693987, 1.570045452479401],
-                    'estimated_true_highstreet_card_pred_highstreet_card': [
+                    "key": ["[0:19999]", "[20000:39999]", "[40000:59999]"],
+                    "estimated_roc_auc": [
+                        0.909165141524145,
+                        0.8682789924547322,
+                        0.8203173643497594,
+                    ],
+                    "estimated_f1": [
+                        0.756401608336434,
+                        0.6937135623882767,
+                        0.632386421613214,
+                    ],
+                    "estimated_precision": [
+                        0.7564437378390059,
+                        0.694174192229447,
+                        0.6336288859123612,
+                    ],
+                    "estimated_recall": [
+                        0.7564129287764665,
+                        0.6934788458355289,
+                        0.6319310599943714,
+                    ],
+                    "estimated_specificity": [
+                        0.8782068281303994,
+                        0.8469556750949159,
+                        0.8172644220189141,
+                    ],
+                    "estimated_accuracy": [
+                        0.7564451493123628,
+                        0.6946947603445697,
+                        0.6378557309960986,
+                    ],
+                    "estimated_average_precision": [
+                        0.8418535417603635,
+                        0.7785618577588246,
+                        0.6985785036188713,
+                    ],
+                    "estimated_business_value": [
+                        2.0193901626043056,
+                        1.7875283323693987,
+                        1.570045452479401,
+                    ],
+                    "estimated_true_highstreet_card_pred_highstreet_card": [
                         0.7442780881812128,
                         0.7170050012869645,
                         0.6962791266676683,
                     ],
-                    'estimated_true_highstreet_card_pred_prepaid_card': [
+                    "estimated_true_highstreet_card_pred_prepaid_card": [
                         0.1313317902358936,
                         0.14460191393226796,
                         0.16092713592008898,
                     ],
-                    'estimated_true_highstreet_card_pred_upmarket_card': [
+                    "estimated_true_highstreet_card_pred_upmarket_card": [
                         0.12439012158289371,
                         0.1383930847807676,
                         0.1427937374122426,
                     ],
-                    'estimated_true_prepaid_card_pred_highstreet_card': [
+                    "estimated_true_prepaid_card_pred_highstreet_card": [
                         0.11955326034187638,
                         0.18175544842770236,
                         0.24277980997563847,
                     ],
-                    'estimated_true_prepaid_card_pred_prepaid_card': [
+                    "estimated_true_prepaid_card_pred_prepaid_card": [
                         0.7683308780213619,
                         0.6590745693568182,
                         0.5564788741190233,
                     ],
-                    'estimated_true_prepaid_card_pred_upmarket_card': [
+                    "estimated_true_prepaid_card_pred_upmarket_card": [
                         0.1121158616367618,
                         0.15916998221547937,
                         0.20074131590533828,
                     ],
-                    'estimated_true_upmarket_card_pred_highstreet_card': [
+                    "estimated_true_upmarket_card_pred_highstreet_card": [
                         0.1235915933057778,
                         0.16226052551901615,
                         0.20216802004274595,
                     ],
-                    'estimated_true_upmarket_card_pred_prepaid_card': [
+                    "estimated_true_upmarket_card_pred_prepaid_card": [
                         0.1197785865673972,
                         0.13338250761817996,
                         0.15479680076083163,
                     ],
-                    'estimated_true_upmarket_card_pred_upmarket_card': [
+                    "estimated_true_upmarket_card_pred_upmarket_card": [
                         0.756629820126825,
                         0.7043569668628038,
                         0.6430351791964225,
@@ -2770,102 +3096,112 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
             ),
         ),
         (
-            {'chunk_number': 4, 'normalize_confusion_matrix': 'pred'},
+            {"chunk_number": 4, "normalize_confusion_matrix": "pred"},
             pd.DataFrame(
                 {
-                    'key': ['[0:14999]', '[15000:29999]', '[30000:44999]', '[45000:59999]'],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:14999]",
+                        "[15000:29999]",
+                        "[30000:44999]",
+                        "[45000:59999]",
+                    ],
+                    "estimated_roc_auc": [
                         0.9084352218383378,
                         0.9087633795549603,
                         0.8195268555812215,
                         0.8201623718659414,
                     ],
-                    'estimated_f1': [0.7550059244451006, 0.7562711250144366, 0.63091155676697, 0.6324244687112559],
-                    'estimated_precision': [
+                    "estimated_f1": [
+                        0.7550059244451006,
+                        0.7562711250144366,
+                        0.63091155676697,
+                        0.6324244687112559,
+                    ],
+                    "estimated_precision": [
                         0.755038246904623,
                         0.7562647262876293,
                         0.6323547131368327,
                         0.6335323150520741,
                     ],
-                    'estimated_recall': [
+                    "estimated_recall": [
                         0.7550277340784216,
                         0.7562926950204228,
                         0.6304009454574501,
                         0.6320155112489632,
                     ],
-                    'estimated_specificity': [
+                    "estimated_specificity": [
                         0.8775094795233379,
                         0.8781429133214084,
                         0.8165537125162895,
                         0.8172408983542975,
                     ],
-                    'estimated_accuracy': [
+                    "estimated_accuracy": [
                         0.7550428613792668,
                         0.7562888217426292,
                         0.6364205304514962,
                         0.6375753072973162,
                     ],
-                    'estimated_average_precision': [
+                    "estimated_average_precision": [
                         0.8406535565924922,
                         0.8410572134298334,
                         0.697327636452664,
                         0.6984330753389926,
                     ],
-                    'estimated_business_value': [
+                    "estimated_business_value": [
                         2.0134445826512186,
                         2.0170794978486395,
                         1.5673705142973104,
                         1.5671595942359196,
                     ],
-                    'estimated_true_highstreet_card_pred_highstreet_card': [
+                    "estimated_true_highstreet_card_pred_highstreet_card": [
                         0.7546260682147157,
                         0.7511343683695074,
                         0.6628383225865804,
                         0.6651814251770874,
                     ],
-                    'estimated_true_highstreet_card_pred_prepaid_card': [
+                    "estimated_true_highstreet_card_pred_prepaid_card": [
                         0.12922483020709813,
                         0.12720280190168412,
                         0.22365956156664257,
                         0.22578913179209303,
                     ],
-                    'estimated_true_highstreet_card_pred_upmarket_card': [
+                    "estimated_true_highstreet_card_pred_upmarket_card": [
                         0.12747696595643684,
                         0.12776612448252053,
                         0.17277613353669485,
                         0.17660735301820177,
                     ],
-                    'estimated_true_prepaid_card_pred_highstreet_card': [
+                    "estimated_true_prepaid_card_pred_highstreet_card": [
                         0.12118073967907128,
                         0.1249170750987652,
                         0.18024418583692642,
                         0.17798857692081155,
                     ],
-                    'estimated_true_prepaid_card_pred_prepaid_card': [
+                    "estimated_true_prepaid_card_pred_prepaid_card": [
                         0.7554502796336932,
                         0.7576402255283115,
                         0.5994574163887797,
                         0.5998622938235557,
                     ],
-                    'estimated_true_prepaid_card_pred_upmarket_card': [
+                    "estimated_true_prepaid_card_pred_upmarket_card": [
                         0.11748464117810321,
                         0.11221429055241054,
                         0.1924554660281669,
                         0.18783942082621902,
                     ],
-                    'estimated_true_upmarket_card_pred_highstreet_card': [
+                    "estimated_true_upmarket_card_pred_highstreet_card": [
                         0.12419319210621305,
                         0.12394855653172744,
                         0.15691749157649315,
                         0.15682999790210106,
                     ],
-                    'estimated_true_upmarket_card_pred_prepaid_card': [
+                    "estimated_true_upmarket_card_pred_prepaid_card": [
                         0.11532489015920869,
                         0.1151569725700045,
                         0.17688302204457784,
                         0.17434857438435108,
                     ],
-                    'estimated_true_upmarket_card_pred_upmarket_card': [
+                    "estimated_true_upmarket_card_pred_upmarket_card": [
                         0.7550383928654599,
                         0.7600195849650688,
                         0.6347684004351383,
@@ -2875,102 +3211,116 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
             ),
         ),
         (
-            {'chunk_number': 4, 'timestamp_column_name': 'timestamp', 'normalize_confusion_matrix': 'all'},
+            {
+                "chunk_number": 4,
+                "timestamp_column_name": "timestamp",
+                "normalize_confusion_matrix": "all",
+            },
             pd.DataFrame(
                 {
-                    'key': ['[0:14999]', '[15000:29999]', '[30000:44999]', '[45000:59999]'],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:14999]",
+                        "[15000:29999]",
+                        "[30000:44999]",
+                        "[45000:59999]",
+                    ],
+                    "estimated_roc_auc": [
                         0.9084352218383378,
                         0.9087633795549603,
                         0.8195268555812215,
                         0.8201623718659414,
                     ],
-                    'estimated_f1': [0.7550059244451006, 0.7562711250144366, 0.63091155676697, 0.6324244687112559],
-                    'estimated_precision': [
+                    "estimated_f1": [
+                        0.7550059244451006,
+                        0.7562711250144366,
+                        0.63091155676697,
+                        0.6324244687112559,
+                    ],
+                    "estimated_precision": [
                         0.755038246904623,
                         0.7562647262876293,
                         0.6323547131368327,
                         0.6335323150520741,
                     ],
-                    'estimated_recall': [
+                    "estimated_recall": [
                         0.7550277340784216,
                         0.7562926950204228,
                         0.6304009454574501,
                         0.6320155112489632,
                     ],
-                    'estimated_specificity': [
+                    "estimated_specificity": [
                         0.8775094795233379,
                         0.8781429133214084,
                         0.8165537125162895,
                         0.8172408983542975,
                     ],
-                    'estimated_accuracy': [
+                    "estimated_accuracy": [
                         0.7550428613792668,
                         0.7562888217426292,
                         0.6364205304514962,
                         0.6375753072973162,
                     ],
-                    'estimated_average_precision': [
+                    "estimated_average_precision": [
                         0.8406535565924922,
                         0.8410572134298334,
                         0.697327636452664,
                         0.6984330753389926,
                     ],
-                    'estimated_business_value': [
+                    "estimated_business_value": [
                         2.0134445826512186,
                         2.0170794978486395,
                         1.5673705142973104,
                         1.5671595942359196,
                     ],
-                    'estimated_true_highstreet_card_pred_highstreet_card': [
+                    "estimated_true_highstreet_card_pred_highstreet_card": [
                         0.24922783612904678,
                         0.24847524905663304,
                         0.2702612787293017,
                         0.2678907326329857,
                     ],
-                    'estimated_true_highstreet_card_pred_prepaid_card': [
+                    "estimated_true_highstreet_card_pred_prepaid_card": [
                         0.044125972021383776,
                         0.04231613209929359,
                         0.06202825174114887,
                         0.06269411559427118,
                     ],
-                    'estimated_true_highstreet_card_pred_upmarket_card': [
+                    "estimated_true_highstreet_card_pred_upmarket_card": [
                         0.04184643869129968,
                         0.04299755975918424,
                         0.05441296365515643,
                         0.05644371002461729,
                     ],
-                    'estimated_true_prepaid_card_pred_highstreet_card': [
+                    "estimated_true_prepaid_card_pred_highstreet_card": [
                         0.04002195895800795,
                         0.04132256844267153,
                         0.0734915627052428,
                         0.07168193287857484,
                     ],
-                    'estimated_true_prepaid_card_pred_prepaid_card': [
+                    "estimated_true_prepaid_card_pred_prepaid_card": [
                         0.25796108881891844,
                         0.25204164835908494,
                         0.16624952347848823,
                         0.16656176358500735,
                     ],
-                    'estimated_true_prepaid_card_pred_upmarket_card': [
+                    "estimated_true_prepaid_card_pred_upmarket_card": [
                         0.03856629154406535,
                         0.03776384924723789,
                         0.0606106414344707,
                         0.060033478896059596,
                     ],
-                    'estimated_true_upmarket_card_pred_highstreet_card': [
+                    "estimated_true_upmarket_card_pred_highstreet_card": [
                         0.041016871579611966,
                         0.041002182500695435,
                         0.06398049189878882,
                         0.06316066782177283,
                     ],
-                    'estimated_true_upmarket_card_pred_prepaid_card': [
+                    "estimated_true_upmarket_card_pred_prepaid_card": [
                         0.03937960582636446,
                         0.038308886208288165,
                         0.04905555811369625,
                         0.04841078748738816,
                     ],
-                    'estimated_true_upmarket_card_pred_upmarket_card': [
+                    "estimated_true_upmarket_card_pred_upmarket_card": [
                         0.24785393643130169,
                         0.25577192432691115,
                         0.1999097282437062,
@@ -2980,27 +3330,60 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
             ),
         ),
         (
-            {'chunk_period': 'Y', 'timestamp_column_name': 'timestamp'},
+            {"chunk_period": "Y", "timestamp_column_name": "timestamp"},
             pd.DataFrame(
                 {
-                    'key': ['2020', '2021'],
-                    'estimated_roc_auc': [0.8697900039493401, 0.8160313122805869],
-                    'estimated_f1': [0.6959459683194374, 0.6271637037915178],
-                    'estimated_precision': [0.696279612597813, 0.6275707355339551],
-                    'estimated_recall': [0.6957620347508907, 0.6272720458900231],
-                    'estimated_specificity': [0.8480220572478717, 0.8145095377877009],
-                    'estimated_accuracy': [0.6967957612985849, 0.6305270354546132],
-                    'estimated_average_precision': [0.7812291182204878, 0.6907845497417768],
-                    'estimated_business_value': [1.7964098918968543, 1.5447162372665988],
-                    'estimated_true_highstreet_card_pred_highstreet_card': [15431.207920621628, 106.61852759787631],
-                    'estimated_true_highstreet_card_pred_prepaid_card': [3140.1950482057946, 27.27202363566655],
-                    'estimated_true_highstreet_card_pred_upmarket_card': [2911.0243109194275, 24.485771034437157],
-                    'estimated_true_prepaid_card_pred_highstreet_card': [3369.0309742564546, 28.73937051100256],
-                    'estimated_true_prepaid_card_pred_prepaid_card': [12568.980575116106, 73.22978850637857],
-                    'estimated_true_prepaid_card_pred_upmarket_card': [2927.072726648623, 27.541190178880235],
-                    'estimated_true_upmarket_card_pred_highstreet_card': [3111.761105121915, 25.642101891121143],
-                    'estimated_true_upmarket_card_pred_prepaid_card': [2605.8243766781006, 21.49818785795489],
-                    'estimated_true_upmarket_card_pred_upmarket_card': [13514.90296243195, 84.97303878668261],
+                    "key": ["2020", "2021"],
+                    "estimated_roc_auc": [0.8697900039493401, 0.8160313122805869],
+                    "estimated_f1": [0.6959459683194374, 0.6271637037915178],
+                    "estimated_precision": [0.696279612597813, 0.6275707355339551],
+                    "estimated_recall": [0.6957620347508907, 0.6272720458900231],
+                    "estimated_specificity": [0.8480220572478717, 0.8145095377877009],
+                    "estimated_accuracy": [0.6967957612985849, 0.6305270354546132],
+                    "estimated_average_precision": [
+                        0.7812296449871846,
+                        0.690772335696266,
+                    ],
+                    "estimated_business_value": [
+                        1.7964098918968543,
+                        1.5447162372665988,
+                    ],
+                    "estimated_true_highstreet_card_pred_highstreet_card": [
+                        15431.207920621628,
+                        106.61852759787631,
+                    ],
+                    "estimated_true_highstreet_card_pred_prepaid_card": [
+                        3140.1950482057946,
+                        27.27202363566655,
+                    ],
+                    "estimated_true_highstreet_card_pred_upmarket_card": [
+                        2911.0243109194275,
+                        24.485771034437157,
+                    ],
+                    "estimated_true_prepaid_card_pred_highstreet_card": [
+                        3369.0309742564546,
+                        28.73937051100256,
+                    ],
+                    "estimated_true_prepaid_card_pred_prepaid_card": [
+                        12568.980575116106,
+                        73.22978850637857,
+                    ],
+                    "estimated_true_prepaid_card_pred_upmarket_card": [
+                        2927.072726648623,
+                        27.541190178880235,
+                    ],
+                    "estimated_true_upmarket_card_pred_highstreet_card": [
+                        3111.761105121915,
+                        25.642101891121143,
+                    ],
+                    "estimated_true_upmarket_card_pred_prepaid_card": [
+                        2605.8243766781006,
+                        21.49818785795489,
+                    ],
+                    "estimated_true_upmarket_card_pred_upmarket_card": [
+                        13514.90296243195,
+                        84.97303878668261,
+                    ],
                 }
             ),
         ),
@@ -3008,19 +3391,19 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
             {},
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:5999]',
-                        '[6000:11999]',
-                        '[12000:17999]',
-                        '[18000:23999]',
-                        '[24000:29999]',
-                        '[30000:35999]',
-                        '[36000:41999]',
-                        '[42000:47999]',
-                        '[48000:53999]',
-                        '[54000:59999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:5999]",
+                        "[6000:11999]",
+                        "[12000:17999]",
+                        "[18000:23999]",
+                        "[24000:29999]",
+                        "[30000:35999]",
+                        "[36000:41999]",
+                        "[42000:47999]",
+                        "[48000:53999]",
+                        "[54000:59999]",
+                    ],
+                    "estimated_roc_auc": [
                         0.9069623299465119,
                         0.9098768294609071,
                         0.9098874875649706,
@@ -3032,7 +3415,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.8192448359184578,
                         0.8214293833125543,
                     ],
-                    'estimated_f1': [
+                    "estimated_f1": [
                         0.7533014808638749,
                         0.7564216285126095,
                         0.7581655498090148,
@@ -3044,7 +3427,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6317356567785939,
                         0.6334434546737131,
                     ],
-                    'estimated_precision': [
+                    "estimated_precision": [
                         0.7533705392643878,
                         0.7564117249294325,
                         0.758189598004742,
@@ -3056,7 +3439,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6327414423931249,
                         0.6349486020523588,
                     ],
-                    'estimated_recall': [
+                    "estimated_recall": [
                         0.7532927299604006,
                         0.756484874870973,
                         0.7581945446990431,
@@ -3068,7 +3451,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6315005643336115,
                         0.6328910840233342,
                     ],
-                    'estimated_specificity': [
+                    "estimated_specificity": [
                         0.876648985916452,
                         0.8781935469456502,
                         0.87910164279675,
@@ -3080,7 +3463,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.8167795549189886,
                         0.8180293638991758,
                     ],
-                    'estimated_accuracy': [
+                    "estimated_accuracy": [
                         0.7533903547412437,
                         0.7564140260171383,
                         0.7582062911442542,
@@ -3092,7 +3475,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6365172577468735,
                         0.6393273094601863,
                     ],
-                    'estimated_average_precision': [
+                    "estimated_average_precision": [
                         0.838071,
                         0.843094,
                         0.842962,
@@ -3104,7 +3487,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.696305,
                         0.701142,
                     ],
-                    'estimated_business_value': [
+                    "estimated_business_value": [
                         2.0086174744097525,
                         2.0167085528014574,
                         2.025151984316981,
@@ -3116,7 +3499,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         1.5668663365944273,
                         1.574249644290713,
                     ],
-                    'estimated_true_highstreet_card_pred_highstreet_card': [
+                    "estimated_true_highstreet_card_pred_highstreet_card": [
                         1483.745037516118,
                         1536.2546154566053,
                         1486.1512390473335,
@@ -3128,7 +3511,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         1596.0668735461204,
                         1621.3736981076686,
                     ],
-                    'estimated_true_highstreet_card_pred_prepaid_card': [
+                    "estimated_true_highstreet_card_pred_prepaid_card": [
                         271.9744616336458,
                         263.3288788018858,
                         255.36687592730394,
@@ -3140,7 +3523,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         383.2996831232405,
                         357.9152833417768,
                     ],
-                    'estimated_true_highstreet_card_pred_upmarket_card': [
+                    "estimated_true_highstreet_card_pred_upmarket_card": [
                         249.77244098451774,
                         249.25341402994002,
                         256.5894445268087,
@@ -3152,7 +3535,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         338.73618885526093,
                         336.8384100540352,
                     ],
-                    'estimated_true_prepaid_card_pred_highstreet_card': [
+                    "estimated_true_prepaid_card_pred_highstreet_card": [
                         249.18645665281267,
                         234.635939041771,
                         241.34496258349301,
@@ -3164,7 +3547,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         436.76196316220245,
                         432.5014066529442,
                     ],
-                    'estimated_true_prepaid_card_pred_prepaid_card': [
+                    "estimated_true_prepaid_card_pred_prepaid_card": [
                         1570.046457210577,
                         1517.6502407889316,
                         1541.0740605507428,
@@ -3176,7 +3559,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         1008.1898500978498,
                         977.1648111020529,
                     ],
-                    'estimated_true_prepaid_card_pred_upmarket_card': [
+                    "estimated_true_prepaid_card_pred_upmarket_card": [
                         227.67692529471515,
                         228.16728611276778,
                         224.39810820574257,
@@ -3188,7 +3571,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         364.41698830746793,
                         360.7362423945684,
                     ],
-                    'estimated_true_upmarket_card_pred_highstreet_card': [
+                    "estimated_true_upmarket_card_pred_highstreet_card": [
                         243.06850583106933,
                         254.1094455016238,
                         238.50379836917327,
@@ -3200,7 +3583,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         372.1711632916771,
                         381.12489523938723,
                     ],
-                    'estimated_true_upmarket_card_pred_prepaid_card': [
+                    "estimated_true_upmarket_card_pred_prepaid_card": [
                         237.97908115577718,
                         232.0208804091826,
                         234.55906352195305,
@@ -3212,7 +3595,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         285.5104667789098,
                         294.91990555617025,
                     ],
-                    'estimated_true_upmarket_card_pred_upmarket_card': [
+                    "estimated_true_upmarket_card_pred_upmarket_card": [
                         1466.550633720767,
                         1484.5792998572922,
                         1522.0124472674486,
@@ -3228,22 +3611,22 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
             ),
         ),
         (
-            {'timestamp_column_name': 'timestamp'},
+            {"timestamp_column_name": "timestamp"},
             pd.DataFrame(
                 {
-                    'key': [
-                        '[0:5999]',
-                        '[6000:11999]',
-                        '[12000:17999]',
-                        '[18000:23999]',
-                        '[24000:29999]',
-                        '[30000:35999]',
-                        '[36000:41999]',
-                        '[42000:47999]',
-                        '[48000:53999]',
-                        '[54000:59999]',
-                    ],
-                    'estimated_roc_auc': [
+                    "key": [
+                        "[0:5999]",
+                        "[6000:11999]",
+                        "[12000:17999]",
+                        "[18000:23999]",
+                        "[24000:29999]",
+                        "[30000:35999]",
+                        "[36000:41999]",
+                        "[42000:47999]",
+                        "[48000:53999]",
+                        "[54000:59999]",
+                    ],
+                    "estimated_roc_auc": [
                         0.9069623299465119,
                         0.9098768294609071,
                         0.9098874875649706,
@@ -3255,7 +3638,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.8192448359184578,
                         0.8214293833125543,
                     ],
-                    'estimated_f1': [
+                    "estimated_f1": [
                         0.7533014808638749,
                         0.7564216285126095,
                         0.7581655498090148,
@@ -3267,7 +3650,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6317356567785939,
                         0.6334434546737131,
                     ],
-                    'estimated_precision': [
+                    "estimated_precision": [
                         0.7533705392643878,
                         0.7564117249294325,
                         0.758189598004742,
@@ -3279,7 +3662,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6327414423931249,
                         0.6349486020523588,
                     ],
-                    'estimated_recall': [
+                    "estimated_recall": [
                         0.7532927299604006,
                         0.756484874870973,
                         0.7581945446990431,
@@ -3291,7 +3674,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6315005643336115,
                         0.6328910840233342,
                     ],
-                    'estimated_specificity': [
+                    "estimated_specificity": [
                         0.876648985916452,
                         0.8781935469456502,
                         0.87910164279675,
@@ -3303,7 +3686,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.8167795549189886,
                         0.8180293638991758,
                     ],
-                    'estimated_accuracy': [
+                    "estimated_accuracy": [
                         0.7533903547412437,
                         0.7564140260171383,
                         0.7582062911442542,
@@ -3315,7 +3698,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.6365172577468735,
                         0.6393273094601863,
                     ],
-                    'estimated_average_precision': [
+                    "estimated_average_precision": [
                         0.838071,
                         0.843094,
                         0.842962,
@@ -3327,7 +3710,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         0.696305,
                         0.701142,
                     ],
-                    'estimated_business_value': [
+                    "estimated_business_value": [
                         2.0086174744097525,
                         2.0167085528014574,
                         2.025151984316981,
@@ -3339,7 +3722,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         1.5668663365944273,
                         1.574249644290713,
                     ],
-                    'estimated_true_highstreet_card_pred_highstreet_card': [
+                    "estimated_true_highstreet_card_pred_highstreet_card": [
                         1483.745037516118,
                         1536.2546154566053,
                         1486.1512390473335,
@@ -3351,7 +3734,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         1596.0668735461204,
                         1621.3736981076686,
                     ],
-                    'estimated_true_highstreet_card_pred_prepaid_card': [
+                    "estimated_true_highstreet_card_pred_prepaid_card": [
                         271.9744616336458,
                         263.3288788018858,
                         255.36687592730394,
@@ -3363,7 +3746,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         383.2996831232405,
                         357.9152833417768,
                     ],
-                    'estimated_true_highstreet_card_pred_upmarket_card': [
+                    "estimated_true_highstreet_card_pred_upmarket_card": [
                         249.77244098451774,
                         249.25341402994002,
                         256.5894445268087,
@@ -3375,7 +3758,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         338.73618885526093,
                         336.8384100540352,
                     ],
-                    'estimated_true_prepaid_card_pred_highstreet_card': [
+                    "estimated_true_prepaid_card_pred_highstreet_card": [
                         249.18645665281267,
                         234.635939041771,
                         241.34496258349301,
@@ -3387,7 +3770,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         436.76196316220245,
                         432.5014066529442,
                     ],
-                    'estimated_true_prepaid_card_pred_prepaid_card': [
+                    "estimated_true_prepaid_card_pred_prepaid_card": [
                         1570.046457210577,
                         1517.6502407889316,
                         1541.0740605507428,
@@ -3399,7 +3782,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         1008.1898500978498,
                         977.1648111020529,
                     ],
-                    'estimated_true_prepaid_card_pred_upmarket_card': [
+                    "estimated_true_prepaid_card_pred_upmarket_card": [
                         227.67692529471515,
                         228.16728611276778,
                         224.39810820574257,
@@ -3411,7 +3794,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         364.41698830746793,
                         360.7362423945684,
                     ],
-                    'estimated_true_upmarket_card_pred_highstreet_card': [
+                    "estimated_true_upmarket_card_pred_highstreet_card": [
                         243.06850583106933,
                         254.1094455016238,
                         238.50379836917327,
@@ -3423,7 +3806,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         372.1711632916771,
                         381.12489523938723,
                     ],
-                    'estimated_true_upmarket_card_pred_prepaid_card': [
+                    "estimated_true_upmarket_card_pred_prepaid_card": [
                         237.97908115577718,
                         232.0208804091826,
                         234.55906352195305,
@@ -3435,7 +3818,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
                         285.5104667789098,
                         294.91990555617025,
                     ],
-                    'estimated_true_upmarket_card_pred_upmarket_card': [
+                    "estimated_true_upmarket_card_pred_upmarket_card": [
                         1466.550633720767,
                         1484.5792998572922,
                         1522.0124472674486,
@@ -3452,13 +3835,13 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
         ),
     ],
     ids=[
-        'size_based_without_timestamp',
-        'size_based_with_timestamp',
-        'count_based_without_timestamp',
-        'count_based_with_timestamp',
-        'period_based_with_timestamp',
-        'default_without_timestamp',
-        'default_with_timestamp',
+        "size_based_without_timestamp",
+        "size_based_with_timestamp",
+        "count_based_without_timestamp",
+        "count_based_with_timestamp",
+        "period_based_with_timestamp",
+        "default_without_timestamp",
+        "default_with_timestamp",
     ],
 )
 def test_cbpe_for_multiclass_classification_with_timestamps(calculator_opts, expected):  # noqa: D103
@@ -3466,69 +3849,69 @@ def test_cbpe_for_multiclass_classification_with_timestamps(calculator_opts, exp
     business_value_matrix = np.array([[1, 0, -1], [0, 1, 0], [-1, 0, 1]])
     cbpe = CBPE(
         y_pred_proba={
-            'upmarket_card': 'y_pred_proba_upmarket_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'prepaid_card': 'y_pred_proba_prepaid_card',
+            "upmarket_card": "y_pred_proba_upmarket_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "prepaid_card": "y_pred_proba_prepaid_card",
         },
-        y_pred='y_pred',
-        y_true='y_true',
-        problem_type='classification_multiclass',
+        y_pred="y_pred",
+        y_true="y_true",
+        problem_type="classification_multiclass",
         metrics=[
-            'roc_auc',
-            'f1',
-            'precision',
-            'recall',
-            'specificity',
-            'accuracy',
-            'average_precision',
-            'confusion_matrix',
-            'business_value',
+            "roc_auc",
+            "f1",
+            "precision",
+            "recall",
+            "specificity",
+            "accuracy",
+            "average_precision",
+            "confusion_matrix",
+            "business_value",
         ],
         business_value_matrix=business_value_matrix,
-        normalize_business_value='per_prediction',
+        normalize_business_value="per_prediction",
         **calculator_opts,
     ).fit(ref_df)
     result = cbpe.estimate(ana_df)
-    column_names = [(m.name, 'value') for m in result.metrics]
-    column_names = [c for c in column_names if c[0] != 'confusion_matrix']
+    column_names = [(m.name, "value") for m in result.metrics]
+    column_names = [c for c in column_names if c[0] != "confusion_matrix"]
     column_names += [
-        ('true_highstreet_card_pred_highstreet_card', 'value'),
-        ('true_highstreet_card_pred_prepaid_card', 'value'),
-        ('true_highstreet_card_pred_upmarket_card', 'value'),
-        ('true_prepaid_card_pred_highstreet_card', 'value'),
-        ('true_prepaid_card_pred_prepaid_card', 'value'),
-        ('true_prepaid_card_pred_upmarket_card', 'value'),
-        ('true_upmarket_card_pred_highstreet_card', 'value'),
-        ('true_upmarket_card_pred_prepaid_card', 'value'),
-        ('true_upmarket_card_pred_upmarket_card', 'value'),
+        ("true_highstreet_card_pred_highstreet_card", "value"),
+        ("true_highstreet_card_pred_prepaid_card", "value"),
+        ("true_highstreet_card_pred_upmarket_card", "value"),
+        ("true_prepaid_card_pred_highstreet_card", "value"),
+        ("true_prepaid_card_pred_prepaid_card", "value"),
+        ("true_prepaid_card_pred_upmarket_card", "value"),
+        ("true_upmarket_card_pred_highstreet_card", "value"),
+        ("true_upmarket_card_pred_prepaid_card", "value"),
+        ("true_upmarket_card_pred_upmarket_card", "value"),
     ]
-    sut = result.filter(period='analysis').to_df()[[('chunk', 'key')] + column_names]
+    sut = result.filter(period="analysis").to_df()[[("chunk", "key")] + column_names]
     sut.columns = [
-        'key',
-        'estimated_roc_auc',
-        'estimated_f1',
-        'estimated_precision',
-        'estimated_recall',
-        'estimated_specificity',
-        'estimated_accuracy',
-        'estimated_average_precision',
-        'estimated_business_value',
-        'estimated_true_highstreet_card_pred_highstreet_card',
-        'estimated_true_highstreet_card_pred_prepaid_card',
-        'estimated_true_highstreet_card_pred_upmarket_card',
-        'estimated_true_prepaid_card_pred_highstreet_card',
-        'estimated_true_prepaid_card_pred_prepaid_card',
-        'estimated_true_prepaid_card_pred_upmarket_card',
-        'estimated_true_upmarket_card_pred_highstreet_card',
-        'estimated_true_upmarket_card_pred_prepaid_card',
-        'estimated_true_upmarket_card_pred_upmarket_card',
+        "key",
+        "estimated_roc_auc",
+        "estimated_f1",
+        "estimated_precision",
+        "estimated_recall",
+        "estimated_specificity",
+        "estimated_accuracy",
+        "estimated_average_precision",
+        "estimated_business_value",
+        "estimated_true_highstreet_card_pred_highstreet_card",
+        "estimated_true_highstreet_card_pred_prepaid_card",
+        "estimated_true_highstreet_card_pred_upmarket_card",
+        "estimated_true_prepaid_card_pred_highstreet_card",
+        "estimated_true_prepaid_card_pred_prepaid_card",
+        "estimated_true_prepaid_card_pred_upmarket_card",
+        "estimated_true_upmarket_card_pred_highstreet_card",
+        "estimated_true_upmarket_card_pred_prepaid_card",
+        "estimated_true_upmarket_card_pred_upmarket_card",
     ]
 
     pd.testing.assert_frame_equal(expected, sut)
 
 
 @pytest.mark.parametrize(
-    'metric_cls',
+    "metric_cls",
     [
         BinaryClassificationAUROC,
         BinaryClassificationAP,
@@ -3547,81 +3930,86 @@ def test_method_logs_warning_when_lower_threshold_is_overridden_by_metric_limits
 
     # TODO: move this from CBPE to metrics
     # workaround to deal with functionality outside of Metrics classes
-    reference['uncalibrated_y_pred_proba'] = reference['y_pred_proba']
+    reference["uncalibrated_y_pred_proba"] = reference["y_pred_proba"]
 
     metric = metric_cls(
-        y_pred_proba='y_pred_proba',
-        y_pred='y_pred',
-        y_true='work_home_actual',
-        problem_type='classification_binary',
+        y_pred_proba="y_pred_proba",
+        y_pred="y_pred",
+        y_true="work_home_actual",
+        problem_type="classification_binary",
         chunker=DefaultChunker(),
         threshold=ConstantThreshold(lower=-1),
     )
     metric.fit(reference)
 
     assert (
-        f'{metric.display_name} lower threshold value -1 overridden by '
-        f'lower threshold value limit {metric.lower_threshold_value_limit}' in caplog.messages
+        f"{metric.display_name} lower threshold value -1 overridden by "
+        f"lower threshold value limit {metric.lower_threshold_value_limit}"
+        in caplog.messages
     )
 
 
 @pytest.mark.parametrize(
-    'calculator_opts, realized',
+    "calculator_opts, realized",
     [
         (
-            {'chunk_size': 20000},
+            {"chunk_size": 20000},
             pd.DataFrame(
                 {
-                    'key': ['[0:19999]', '[20000:39999]', '[40000:59999]'],
-                    'realized_roc_auc': [0.909805, 0.840071, np.nan],
-                    'realized_f1': [0.759170, 0.658896, np.nan],
-                    'realized_precision': [0.759265, 0.660188, np.nan],
-                    'realized_recall': [0.759149, 0.658760, np.nan],
-                    'realized_specificity': [0.879632, 0.829581, np.nan],
-                    'realized_accuracy': [0.75925, 0.65950, np.nan],
-                    'realized_average_precision': [0.841830, 0.738332, np.nan],
-                    'realized_business_value': [2.029064521843538, 1.6533562273847497, np.nan],
-                    'realized_true_highstreet_card_pred_highstreet_card': [
+                    "key": ["[0:19999]", "[20000:39999]", "[40000:59999]"],
+                    "realized_roc_auc": [0.909805, 0.840071, np.nan],
+                    "realized_f1": [0.759170, 0.658896, np.nan],
+                    "realized_precision": [0.759265, 0.660188, np.nan],
+                    "realized_recall": [0.759149, 0.658760, np.nan],
+                    "realized_specificity": [0.879632, 0.829581, np.nan],
+                    "realized_accuracy": [0.75925, 0.65950, np.nan],
+                    "realized_average_precision": [0.841830, 0.738332, np.nan],
+                    "realized_business_value": [
+                        2.029064521843538,
+                        1.6533562273847497,
+                        np.nan,
+                    ],
+                    "realized_true_highstreet_card_pred_highstreet_card": [
                         4912.0,
                         4702.0,
                         np.nan,
                     ],
-                    'realized_true_highstreet_card_pred_prepaid_card': [
+                    "realized_true_highstreet_card_pred_prepaid_card": [
                         870.0,
                         1083.0,
                         np.nan,
                     ],
-                    'realized_true_highstreet_card_pred_upmarket_card': [
+                    "realized_true_highstreet_card_pred_upmarket_card": [
                         799.0,
                         1009.0,
                         np.nan,
                     ],
-                    'realized_true_prepaid_card_pred_highstreet_card': [
+                    "realized_true_prepaid_card_pred_highstreet_card": [
                         846.0,
                         1367.0,
                         np.nan,
                     ],
-                    'realized_true_prepaid_card_pred_prepaid_card': [
+                    "realized_true_prepaid_card_pred_prepaid_card": [
                         5203.0,
                         3974.0,
                         np.nan,
                     ],
-                    'realized_true_prepaid_card_pred_upmarket_card': [
+                    "realized_true_prepaid_card_pred_upmarket_card": [
                         690.0,
                         1080.0,
                         np.nan,
                     ],
-                    'realized_true_upmarket_card_pred_highstreet_card': [
+                    "realized_true_upmarket_card_pred_highstreet_card": [
                         837.0,
                         1282.0,
                         np.nan,
                     ],
-                    'realized_true_upmarket_card_pred_prepaid_card': [
+                    "realized_true_upmarket_card_pred_prepaid_card": [
                         773.0,
                         989.0,
                         np.nan,
                     ],
-                    'realized_true_upmarket_card_pred_upmarket_card': [
+                    "realized_true_upmarket_card_pred_upmarket_card": [
                         5070.0,
                         4514.0,
                         np.nan,
@@ -3639,80 +4027,80 @@ def test_cbpe_for_multiclass_classification_cm_with_nans(calculator_opts, realiz
     business_value_matrix = np.array([[1, 0, -1], [0, 1, 0], [-1, 0, 1]])
     cbpe = CBPE(
         y_pred_proba={
-            'upmarket_card': 'y_pred_proba_upmarket_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'prepaid_card': 'y_pred_proba_prepaid_card',
+            "upmarket_card": "y_pred_proba_upmarket_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "prepaid_card": "y_pred_proba_prepaid_card",
         },
-        y_pred='y_pred',
-        y_true='y_true',
-        problem_type='classification_multiclass',
+        y_pred="y_pred",
+        y_true="y_true",
+        problem_type="classification_multiclass",
         metrics=[
-            'roc_auc',
-            'f1',
-            'precision',
-            'recall',
-            'specificity',
-            'accuracy',
-            'average_precision',
-            'confusion_matrix',
-            'business_value',
+            "roc_auc",
+            "f1",
+            "precision",
+            "recall",
+            "specificity",
+            "accuracy",
+            "average_precision",
+            "confusion_matrix",
+            "business_value",
         ],
         business_value_matrix=business_value_matrix,
-        normalize_business_value='per_prediction',
+        normalize_business_value="per_prediction",
         **calculator_opts,
     ).fit(reference)
     result = cbpe.estimate(analysis)
-    column_names = [(m.name, 'realized') for m in result.metrics]
-    column_names = [c for c in column_names if c[0] != 'confusion_matrix']
+    column_names = [(m.name, "realized") for m in result.metrics]
+    column_names = [c for c in column_names if c[0] != "confusion_matrix"]
     column_names += [
-        ('true_highstreet_card_pred_highstreet_card', 'realized'),
-        ('true_highstreet_card_pred_prepaid_card', 'realized'),
-        ('true_highstreet_card_pred_upmarket_card', 'realized'),
-        ('true_prepaid_card_pred_highstreet_card', 'realized'),
-        ('true_prepaid_card_pred_prepaid_card', 'realized'),
-        ('true_prepaid_card_pred_upmarket_card', 'realized'),
-        ('true_upmarket_card_pred_highstreet_card', 'realized'),
-        ('true_upmarket_card_pred_prepaid_card', 'realized'),
-        ('true_upmarket_card_pred_upmarket_card', 'realized'),
+        ("true_highstreet_card_pred_highstreet_card", "realized"),
+        ("true_highstreet_card_pred_prepaid_card", "realized"),
+        ("true_highstreet_card_pred_upmarket_card", "realized"),
+        ("true_prepaid_card_pred_highstreet_card", "realized"),
+        ("true_prepaid_card_pred_prepaid_card", "realized"),
+        ("true_prepaid_card_pred_upmarket_card", "realized"),
+        ("true_upmarket_card_pred_highstreet_card", "realized"),
+        ("true_upmarket_card_pred_prepaid_card", "realized"),
+        ("true_upmarket_card_pred_upmarket_card", "realized"),
     ]
-    sut = result.filter(period='analysis').to_df()[[('chunk', 'key')] + column_names]
+    sut = result.filter(period="analysis").to_df()[[("chunk", "key")] + column_names]
     sut.columns = [
-        'key',
-        'realized_roc_auc',
-        'realized_f1',
-        'realized_precision',
-        'realized_recall',
-        'realized_specificity',
-        'realized_accuracy',
-        'realized_average_precision',
-        'realized_business_value',
-        'realized_true_highstreet_card_pred_highstreet_card',
-        'realized_true_highstreet_card_pred_prepaid_card',
-        'realized_true_highstreet_card_pred_upmarket_card',
-        'realized_true_prepaid_card_pred_highstreet_card',
-        'realized_true_prepaid_card_pred_prepaid_card',
-        'realized_true_prepaid_card_pred_upmarket_card',
-        'realized_true_upmarket_card_pred_highstreet_card',
-        'realized_true_upmarket_card_pred_prepaid_card',
-        'realized_true_upmarket_card_pred_upmarket_card',
+        "key",
+        "realized_roc_auc",
+        "realized_f1",
+        "realized_precision",
+        "realized_recall",
+        "realized_specificity",
+        "realized_accuracy",
+        "realized_average_precision",
+        "realized_business_value",
+        "realized_true_highstreet_card_pred_highstreet_card",
+        "realized_true_highstreet_card_pred_prepaid_card",
+        "realized_true_highstreet_card_pred_upmarket_card",
+        "realized_true_prepaid_card_pred_highstreet_card",
+        "realized_true_prepaid_card_pred_prepaid_card",
+        "realized_true_prepaid_card_pred_upmarket_card",
+        "realized_true_upmarket_card_pred_highstreet_card",
+        "realized_true_upmarket_card_pred_prepaid_card",
+        "realized_true_upmarket_card_pred_upmarket_card",
     ]
     pd.testing.assert_frame_equal(realized, sut)
 
 
 def test_auroc_errors_out_when_not_all_classes_are_represented_reference():
     reference, _, _ = load_synthetic_multiclass_classification_dataset()
-    reference['y_pred_proba_clazz'] = reference['y_pred_proba_upmarket_card']
+    reference["y_pred_proba_clazz"] = reference["y_pred_proba_upmarket_card"]
     calc = CBPE(
         y_pred_proba={
-            'prepaid_card': 'y_pred_proba_prepaid_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'upmarket_card': 'y_pred_proba_upmarket_card',
-            'clazz': 'y_pred_proba_clazz',
+            "prepaid_card": "y_pred_proba_prepaid_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "upmarket_card": "y_pred_proba_upmarket_card",
+            "clazz": "y_pred_proba_clazz",
         },
-        y_pred='y_pred',
-        y_true='y_true',
-        metrics=['roc_auc'],
-        problem_type='classification_multiclass',
+        y_pred="y_pred",
+        y_true="y_true",
+        metrics=["roc_auc"],
+        problem_type="classification_multiclass",
     )
     expected_exc_test = "y_pred_proba class and class probabilities dictionary does not match reference data."
     with pytest.raises(InvalidArgumentsException, match=expected_exc_test):
@@ -3720,30 +4108,44 @@ def test_auroc_errors_out_when_not_all_classes_are_represented_reference():
 
 
 def test_auroc_errors_out_when_not_all_classes_are_represented_chunk(caplog):
-    LOGGER.info("testing test_auroc_errors_out_when_not_all_classes_are_represented_chunk")
+    LOGGER.info(
+        "testing test_auroc_errors_out_when_not_all_classes_are_represented_chunk"
+    )
     reference, monitored, targets = load_synthetic_multiclass_classification_dataset()
     monitored = monitored.merge(targets)
     # Uncalibrated probabilities need to sum up to 1 per row.
-    reference['y_pred_proba_clazz'] = 0.1
-    reference['y_pred_proba_prepaid_card'] = 0.9 * reference['y_pred_proba_prepaid_card']
-    reference['y_pred_proba_highstreet_card'] = 0.9 * reference['y_pred_proba_highstreet_card']
-    reference['y_pred_proba_upmarket_card'] = 0.9 * reference['y_pred_proba_upmarket_card']
-    monitored['y_pred_proba_clazz'] = 0.1
-    monitored['y_pred_proba_prepaid_card'] = 0.9 * monitored['y_pred_proba_prepaid_card']
-    monitored['y_pred_proba_highstreet_card'] = 0.9 * monitored['y_pred_proba_highstreet_card']
-    monitored['y_pred_proba_upmarket_card'] = 0.9 * monitored['y_pred_proba_upmarket_card']
-    reference['y_true'].iloc[-1000:] = 'clazz'
+    reference["y_pred_proba_clazz"] = 0.1
+    reference["y_pred_proba_prepaid_card"] = (
+        0.9 * reference["y_pred_proba_prepaid_card"]
+    )
+    reference["y_pred_proba_highstreet_card"] = (
+        0.9 * reference["y_pred_proba_highstreet_card"]
+    )
+    reference["y_pred_proba_upmarket_card"] = (
+        0.9 * reference["y_pred_proba_upmarket_card"]
+    )
+    monitored["y_pred_proba_clazz"] = 0.1
+    monitored["y_pred_proba_prepaid_card"] = (
+        0.9 * monitored["y_pred_proba_prepaid_card"]
+    )
+    monitored["y_pred_proba_highstreet_card"] = (
+        0.9 * monitored["y_pred_proba_highstreet_card"]
+    )
+    monitored["y_pred_proba_upmarket_card"] = (
+        0.9 * monitored["y_pred_proba_upmarket_card"]
+    )
+    reference["y_true"].iloc[-1000:] = "clazz"
     calc = CBPE(
         y_pred_proba={
-            'prepaid_card': 'y_pred_proba_prepaid_card',
-            'highstreet_card': 'y_pred_proba_highstreet_card',
-            'upmarket_card': 'y_pred_proba_upmarket_card',
-            'clazz': 'y_pred_proba_clazz',
+            "prepaid_card": "y_pred_proba_prepaid_card",
+            "highstreet_card": "y_pred_proba_highstreet_card",
+            "upmarket_card": "y_pred_proba_upmarket_card",
+            "clazz": "y_pred_proba_clazz",
         },
-        y_pred='y_pred',
-        y_true='y_true',
-        metrics=['roc_auc'],
-        problem_type='classification_multiclass',
+        y_pred="y_pred",
+        y_true="y_true",
+        metrics=["roc_auc"],
+        problem_type="classification_multiclass",
     )
     calc.fit(reference)
     _ = calc.estimate(monitored)
@@ -3761,19 +4163,22 @@ def test_cbpe_multiclass_business_value_matrix_square_requirement():  # noqa: D1
             [0, 1, 0],
         ]
     )
-    with pytest.raises(InvalidArgumentsException, match="business_value_matrix is not a square matrix but has shape:"):
+    with pytest.raises(
+        InvalidArgumentsException,
+        match="business_value_matrix is not a square matrix but has shape:",
+    ):
         _ = CBPE(
             y_pred_proba={
-                'upmarket_card': 'y_pred_proba_upmarket_card',
-                'highstreet_card': 'y_pred_proba_highstreet_card',
-                'prepaid_card': 'y_pred_proba_prepaid_card',
+                "upmarket_card": "y_pred_proba_upmarket_card",
+                "highstreet_card": "y_pred_proba_highstreet_card",
+                "prepaid_card": "y_pred_proba_prepaid_card",
             },
-            y_pred='y_pred',
-            y_true='y_true',
-            problem_type='classification_multiclass',
-            metrics=['business_value'],
+            y_pred="y_pred",
+            y_true="y_true",
+            problem_type="classification_multiclass",
+            metrics=["business_value"],
             business_value_matrix=business_value_matrix,
-            normalize_business_value='per_prediction',
+            normalize_business_value="per_prediction",
             chunk_number=1,
         )
 
@@ -3790,19 +4195,22 @@ def test_cbpe_multiclass_business_value_matrix_classes_and_bvm_shape():  # noqa:
         ]
     )
     with pytest.raises(
-        InvalidArgumentsException, match=re.escape("business_value_matrix has shape (4, 4) but we have 3 classes!")
+        InvalidArgumentsException,
+        match=re.escape(
+            "business_value_matrix has shape (4, 4) but we have 3 classes!"
+        ),
     ):
         _ = CBPE(
             y_pred_proba={
-                'upmarket_card': 'y_pred_proba_upmarket_card',
-                'highstreet_card': 'y_pred_proba_highstreet_card',
-                'prepaid_card': 'y_pred_proba_prepaid_card',
+                "upmarket_card": "y_pred_proba_upmarket_card",
+                "highstreet_card": "y_pred_proba_highstreet_card",
+                "prepaid_card": "y_pred_proba_prepaid_card",
             },
-            y_pred='y_pred',
-            y_true='y_true',
-            problem_type='classification_multiclass',
-            metrics=['business_value'],
+            y_pred="y_pred",
+            y_true="y_true",
+            problem_type="classification_multiclass",
+            metrics=["business_value"],
             business_value_matrix=business_value_matrix,
-            normalize_business_value='per_prediction',
+            normalize_business_value="per_prediction",
             chunk_number=1,
         ).fit(reference)
diff --git a/tests/sampling_error/test_binary_classification_sampling_error.py b/tests/sampling_error/test_binary_classification_sampling_error.py
index 7dbb3fa4..766eb089 100644
--- a/tests/sampling_error/test_binary_classification_sampling_error.py
+++ b/tests/sampling_error/test_binary_classification_sampling_error.py
@@ -28,7 +28,7 @@ def test_auroc_sampling_error_nan():  # noqa: D103
     sample_size = 50
     chunk = np.random.random(sample_size)
 
-    components = np.NaN, np.NaN
+    components = np.nan, np.nan
     sampling_error = bse.auroc_sampling_error(components, chunk)
     assert np.isnan(sampling_error)
 
@@ -99,7 +99,7 @@ def test_accuracy_sampling_error():  # noqa: D103
 
 
 def test_ap_sampling_error_when_nan():  # noqa: D103
-    comp1 = np.NaN
+    comp1 = np.nan
     comp2 = 0
     data = pd.DataFrame({'y_true': [0, 1, 1], 'y_pred_proba': [0.4, 0.6, 0.7]})
 
diff --git a/tests/stats/test_median.py b/tests/stats/test_median.py
index db7717a8..d9710b19 100644
--- a/tests/stats/test_median.py
+++ b/tests/stats/test_median.py
@@ -48,7 +48,7 @@ def test_stats_median_calculator_should_not_fail_given_nan_values(  # noqa: D103
     binary_classification_data
 ):
     reference, monitored = binary_classification_data
-    reference.loc[1000:11000, 'car_value'] = np.NaN
+    reference.loc[1000:11000, 'car_value'] = np.nan
     try:
         calc = SummaryStatsMedianCalculator(
             column_names=['car_value'],
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index b49cbb3a..474480f3 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -76,7 +76,7 @@ def test_needs_calibration_returns_false_when_only_single_class_in_y_true():  #
 
 
 def test_needs_calibration_raises_invalid_args_exception_when_y_true_contains_nan():  # noqa: D103
-    y_true = pd.Series([0, 0, 0, 0, 0, np.NaN, 1, 1, 1, 1, 1, 1])
+    y_true = pd.Series([0, 0, 0, 0, 0, np.nan, 1, 1, 1, 1, 1, 1])
     y_pred_proba = np.asarray([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
     with pytest.raises(InvalidArgumentsException, match='target values contain NaN.'):
         _ = needs_calibration(y_true, y_pred_proba, IsotonicCalibrator())
@@ -84,7 +84,7 @@ def test_needs_calibration_raises_invalid_args_exception_when_y_true_contains_na
 
 def test_needs_calibration_raises_invalid_args_exception_when_y_pred_proba_contains_nan():  # noqa: D103
     y_true = pd.Series([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
-    y_pred_proba = pd.Series(np.asarray([0, 0, 0, np.NaN, 0, 0, 1, 1, 1, 1, 1, 1]))
+    y_pred_proba = pd.Series(np.asarray([0, 0, 0, np.nan, 0, 0, 1, 1, 1, 1, 1, 1]))
     with pytest.raises(InvalidArgumentsException, match='predicted probabilities contain NaN.'):
         _ = needs_calibration(y_true, y_pred_proba, IsotonicCalibrator())