Pass flake8

optbinning/binning/binning.py

@@ -1176,25 +1176,26 @@ def status(self):
         self._check_is_fitted()
 
         return self._status
-    
-    def to_json(self, path: str):
+
+    def to_json(self, path):
         """
-        Save optimal bins and/or splits points and transformation depending on the target type.
-        
+        Save optimal bins and/or splits points and transformation depending on
+        the target type.
+
         Parameters
         ----------
-        path: The path where the json is going to be saved
+        path: The path where the json is going to be saved.
         """
         if path is None:
             raise ValueError('Specify the path for the json file')
 
         table = self.binning_table
 
-        opt_bin_dict=dict()
+        opt_bin_dict = dict()
         opt_bin_dict['name'] = table.name
         opt_bin_dict['dtype'] = table.dtype
         opt_bin_dict['special_codes'] = table.special_codes
-        
+
         if table.dtype == 'numerical':
             opt_bin_dict['splits'] = table.splits.tolist()
         elif table.dtype == 'categorical':
@@ -1211,22 +1212,23 @@ def to_json(self, path: str):
 
         with open(path, "w") as write_file:
             json.dump(opt_bin_dict, write_file)
-            
-    def read_json(self, path: str):
+
+    def read_json(self, path):
         """
-        Read json file containing split points and set them as the new split points.
+        Read json file containing split points and set them as the new split
+        points.
 
         Parameters
-        ----------        
+        ----------
         path: The path of the json file.
         """
         self._is_fitted = True
-        
+
         with open(path, "r") as read_file:
             bin_table_attr = json.load(read_file)
 
         for key in bin_table_attr.keys():
             if isinstance(bin_table_attr[key], list):
                 bin_table_attr[key] = np.array(bin_table_attr[key])
-            
+
         self._binning_table = BinningTable(**bin_table_attr)

optbinning/binning/binning_statistics.py

@@ -1567,7 +1567,7 @@ class ContinuousBinningTable:
     def __init__(self, name, dtype, special_codes, splits, n_records, sums,
                  stds, min_target, max_target, n_zeros, min_x=None, max_x=None,
                  categories=None, cat_others=None, user_splits=None):
-        
+
         self.name = name
         self.dtype = dtype
         self.special_codes = special_codes
@@ -1696,8 +1696,11 @@ def plot(self, add_special=True, add_missing=True, style="bin",
         Parameters
         ----------
         metric : str, optional (default="mean")
-            Supported metrics are "mean" to show the Mean value of the target variable in each bin,
-            "iv" to show the IV of each bin and "woe" to show the Weight of Evidence (WoE) of each bin.
+            Supported metrics are "mean" to show the Mean value of the target
+            variable in each bin, "iv" to show the IV of each bin and "woe" to
+            show the Weight of Evidence (WoE) of each bin.
+
+            .. versionadded:: 0.19.0
 
         add_special : bool (default=True)
             Whether to add the special codes bin.
@@ -1763,7 +1766,7 @@ def plot(self, add_special=True, add_missing=True, style="bin",
             elif self.min_x is None or self.max_x is None:
                 raise ValueError('If style="actual", min_x and max_x must be '
                                  'provided.')
-        
+
         if metric == "mean":
             metric_values = self._mean
             metric_label = "Mean"
@@ -2084,4 +2087,3 @@ def quality_score(self):
         _check_is_analyzed(self)
 
         return self._quality_score
-

optbinning/binning/continuous_binning.py

@@ -978,25 +978,26 @@ def binning_table(self):
         self._check_is_fitted()
 
         return self._binning_table
-    
-    def to_json(self, path: str):
+
+    def to_json(self, path):
         """
-        Save optimal bins and/or splits points and transformation depending on the target type.
-        
+        Save optimal bins and/or splits points and transformation depending on
+        the target type.
+
         Parameters
         ----------
-        path: The path where the json is going to be saved
+        path: The path where the json is going to be saved.
         """
         if path is None:
             raise ValueError('Specify the path for the json file.')
 
         table = self.binning_table
 
-        opt_bin_dict=dict()
+        opt_bin_dict = dict()
         opt_bin_dict['name'] = table.name
         opt_bin_dict['dtype'] = table.dtype
         opt_bin_dict['special_codes'] = table.special_codes
-        
+
         if table.dtype == 'numerical':
             opt_bin_dict['splits'] = table.splits.tolist()
         elif table.dtype == 'categorical':
@@ -1017,25 +1018,26 @@ def to_json(self, path: str):
 
         with open(path, "w") as write_file:
             json.dump(opt_bin_dict, write_file)
-            
-    def read_json(self, path: str):
+
+    def read_json(self, path):
         """
-        Read json file containing split points and set them as the new split points.
+        Read json file containing split points and set them as the new split
+        points.
 
         Parameters
-        ----------        
+        ----------
         path: The path of the json file.
         """
         if path is None:
             raise ValueError('Specify the path for the json file.')
-        
+
         self._is_fitted = True
-        
+
         with open(path, "r") as read_file:
             cont_table_attr = json.load(read_file)
 
         for key in cont_table_attr.keys():
             if isinstance(cont_table_attr[key], list):
                 cont_table_attr[key] = np.array(cont_table_attr[key])
-            
+
         self._binning_table = ContinuousBinningTable(**cont_table_attr)

optbinning/binning/cp.py

@@ -248,7 +248,7 @@ def build_model_scenarios(self, n_nonevent, n_event, w):
         # Constraint: max-pvalue
         for s in range(n_scenarios):
             self.add_constraint_violation(model, x,
-                                           pvalue_violation_indices[s])
+                                          pvalue_violation_indices[s])
 
         # Constraint: min diff
         for s in range(n_scenarios):

optbinning/binning/multiclass_binning.py

@@ -874,51 +874,51 @@ def splits(self):
         self._check_is_fitted()
 
         return self._splits_optimal
-    
-    def to_json(self, path: str):
+
+    def to_json(self, path):
         """
-        Save optimal bins and/or splits points and transformation depending on the target type.
-        
+        Save optimal bins and/or splits points and transformation depending on
+        the target type.
+
         Parameters
         ----------
-        path: The path where the json is going to be saved
+        path: The path where the json is going to be saved.
         """
         if path is None:
             raise ValueError('Specify the path for the json file.')
 
         table = self.binning_table
 
-        opt_bin_dict=dict()
+        opt_bin_dict = dict()
         opt_bin_dict['name'] = table.name
         opt_bin_dict['special_codes'] = table.special_codes
-
-        opt_bin_dict['splits'] = table.splits.tolist()
 
+        opt_bin_dict['splits'] = table.splits.tolist()
         opt_bin_dict['n_event'] = table.n_event.tolist()
-
         opt_bin_dict['classes'] = table.classes.tolist()
 
         with open(path, "w") as write_file:
             json.dump(opt_bin_dict, write_file)
-            
-    def read_json(self, path: str):
+
+    def read_json(self, path):
         """
-        Read json file containing split points and set them as the new split points.
+        Read json file containing split points and set them as the new split
+        points.
 
         Parameters
-        ----------        
+        ----------
         path: The path of the json file.
         """
         if path is None:
             raise ValueError('Specify the path for the json file.')
-        
+
         self._is_fitted = True
-        
+
         with open(path, "r") as read_file:
             multi_table_attr = json.load(read_file)
 
         for key in multi_table_attr.keys():
             if isinstance(multi_table_attr[key], list):
                 multi_table_attr[key] = np.array(multi_table_attr[key])
-            
+
         self._binning_table = MulticlassBinningTable(**multi_table_attr)