import update

Signed-off-by: giandos200 <giando95menico@gmail.com>

Author: giandos200

dice_ml/__init__.py

@@ -1,6 +1,6 @@
 from .data import Data
-from .model import Model
 from .dice import Dice
+from .model import Model
 
 __all__ = ["Data",
            "Model",

dice_ml/counterfactual_explanations.py

@@ -1,11 +1,12 @@
 import json
-import jsonschema
 import os
 
-from dice_ml.diverse_counterfactuals import CounterfactualExamples
-from dice_ml.utils.exception import UserConfigValidationException
-from dice_ml.diverse_counterfactuals import _DiverseCFV2SchemaConstants
+import jsonschema
+
 from dice_ml.constants import _SchemaVersions
+from dice_ml.diverse_counterfactuals import (CounterfactualExamples,
+                                             _DiverseCFV2SchemaConstants)
+from dice_ml.utils.exception import UserConfigValidationException
 
 
 class _CommonSchemaConstants:

dice_ml/data_interfaces/private_data_interface.py

@@ -1,10 +1,11 @@
 """Module containing meta data information about private data."""
 
-import sys
-import pandas as pd
-import numpy as np
 import collections
 import logging
+import sys
+
+import numpy as np
+import pandas as pd
 
 from dice_ml.data_interfaces.base_data_interface import _BaseData
 

dice_ml/data_interfaces/public_data_interface.py

@@ -1,14 +1,15 @@
 """Module containing all required information about the interface between raw (or transformed)
 public data and DiCE explainers."""
 
-import pandas as pd
-import numpy as np
 import logging
 from collections import defaultdict
 
-from dice_ml.data_interfaces.base_data_interface import _BaseData
-from dice_ml.utils.exception import SystemException, UserConfigValidationException
+import numpy as np
+import pandas as pd
 
+from dice_ml.data_interfaces.base_data_interface import _BaseData
+from dice_ml.utils.exception import (SystemException,
+                                     UserConfigValidationException)
 
 class PublicData(_BaseData):
     """A data interface for public data. This class is an interface to DiCE explainers
@@ -258,7 +259,7 @@ def get_valid_feature_range(self, feature_range_input, normalized=True):
         """
         feature_range = {}
 
-        for idx, feature_name in enumerate(self.feature_names):
+        for _, feature_name in enumerate(self.feature_names):
             feature_range[feature_name] = []
             if feature_name in self.continuous_feature_names:
                 max_value = self.data_df[feature_name].max()

dice_ml/dice.py

@@ -3,9 +3,9 @@
    such as RandomSampling, DiCEKD or DiCEGenetic"""
 
 from dice_ml.constants import BackEndTypes, SamplingStrategy
-from dice_ml.utils.exception import UserConfigValidationException
 from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 from dice_ml.data_interfaces.private_data_interface import PrivateData
+from dice_ml.utils.exception import UserConfigValidationException
 
 
 class Dice(ExplainerBase):
@@ -67,12 +67,14 @@ def decide(model_interface, method):
 
     elif model_interface.backend == BackEndTypes.Tensorflow1:
         # pretrained Keras Sequential model with Tensorflow 1.x backend
-        from dice_ml.explainer_interfaces.dice_tensorflow1 import DiceTensorFlow1
+        from dice_ml.explainer_interfaces.dice_tensorflow1 import \
+            DiceTensorFlow1
         return DiceTensorFlow1
 
     elif model_interface.backend == BackEndTypes.Tensorflow2:
         # pretrained Keras Sequential model with Tensorflow 2.x backend
-        from dice_ml.explainer_interfaces.dice_tensorflow2 import DiceTensorFlow2
+        from dice_ml.explainer_interfaces.dice_tensorflow2 import \
+            DiceTensorFlow2
         return DiceTensorFlow2
 
     elif model_interface.backend == BackEndTypes.Pytorch:

dice_ml/diverse_counterfactuals.py

@@ -1,8 +1,10 @@
-import pandas as pd
 import copy
 import json
+
+import pandas as pd
+
+from dice_ml.constants import ModelTypes, _SchemaVersions
 from dice_ml.utils.serialize import DummyDataInterface
-from dice_ml.constants import _SchemaVersions, ModelTypes
 
 
 class _DiverseCFV1SchemaConstants:
@@ -115,6 +117,7 @@ def _visualize_internal(self, display_sparse_df=True, show_only_changes=False,
 
     def visualize_as_dataframe(self, display_sparse_df=True, show_only_changes=False):
         from IPython.display import display
+
         # original instance
         print('Query instance (original outcome : %i)' % round(self.test_pred))
         display(self.test_instance_df)  # works only in Jupyter notebook

dice_ml/explainer_interfaces/dice_KD.py

@@ -2,14 +2,15 @@
 Module to generate counterfactual explanations from a KD-Tree
 This code is similar to 'Interpretable Counterfactual Explanations Guided by Prototypes': https://arxiv.org/pdf/1907.02584.pdf
 """
-from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
-import numpy as np
+import copy
 import timeit
+
+import numpy as np
 import pandas as pd
-import copy
 
 from dice_ml import diverse_counterfactuals as exp
 from dice_ml.constants import ModelTypes
+from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 
 
 class DiceKD(ExplainerBase):

dice_ml/explainer_interfaces/dice_genetic.py

@@ -2,16 +2,17 @@
 Module to generate diverse counterfactual explanations based on genetic algorithm
 This code is similar to 'GeCo: Quality Counterfactual Explanations in Real Time': https://arxiv.org/pdf/2101.01292.pdf
 """
-from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
-import numpy as np
-import pandas as pd
+import copy
 import random
 import timeit
-import copy
+
+import numpy as np
+import pandas as pd
 from sklearn.preprocessing import LabelEncoder
 
 from dice_ml import diverse_counterfactuals as exp
 from dice_ml.constants import ModelTypes
+from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 
 
 class DiceGenetic(ExplainerBase):

dice_ml/explainer_interfaces/dice_pytorch.py

@@ -1,16 +1,16 @@
 """
 Module to generate diverse counterfactual explanations based on PyTorch framework
 """
-from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
-import torch
-
-import numpy as np
+import copy
 import random
 import timeit
-import copy
+import numpy as np
+
+import torch
 
 from dice_ml import diverse_counterfactuals as exp
 from dice_ml.counterfactual_explanations import CounterfactualExplanations
+from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 
 
 class DicePyTorch(ExplainerBase):

dice_ml/explainer_interfaces/dice_random.py

@@ -3,14 +3,15 @@
 Module to generate diverse counterfactual explanations based on random sampling.
 A simple implementation.
 """
-from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
-import numpy as np
-import pandas as pd
 import random
 import timeit
 
+import numpy as np
+import pandas as pd
+
 from dice_ml import diverse_counterfactuals as exp
 from dice_ml.constants import ModelTypes
+from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
 
 
 class DiceRandom(ExplainerBase):