isort and minor refactoring.

alibi/explainers/pd_variance.py

@@ -1,20 +1,21 @@
 import copy
 import logging
-import sys
 import math
 import numbers
-import numpy as np
-import matplotlib.pyplot as plt
-from typing import Callable, List, Optional, Dict, Union, Tuple, Any
-from itertools import combinations
+import sys
 from enum import Enum
-from alibi.api.defaults import DEFAULT_META_PDVARIANCE, DEFAULT_DATA_PDVARIANCE
+from itertools import combinations
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import matplotlib.pyplot as plt
+import numpy as np
+from alibi.api.defaults import DEFAULT_DATA_PDVARIANCE, DEFAULT_META_PDVARIANCE
 from alibi.api.interfaces import Explainer, Explanation
-from alibi.explainers.partial_dependence import Kind, PartialDependence, TreePartialDependence
+from alibi.explainers.partial_dependence import (Kind, PartialDependence,
+                                                 TreePartialDependence)
 from alibi.explainers.similarity.grad import get_options_string
 from sklearn.base import BaseEstimator
 
-
 logger = logging.getLogger(__name__)
 
 if sys.version_info >= (3, 8):
@@ -360,7 +361,7 @@ def _build_explanation(self, buffers: dict) -> Explanation:
                     feature_values=buffers['feature_values'],
                     feature_names=buffers['feature_names'])
 
-        if self.meta['params']['method'] == 'importance':
+        if self.meta['params']['method'] == Method.IMPORTANCE:
             data.update(feature_importance=buffers['feature_importance'])
         else:
             data.update(feature_interaction=buffers['feature_interaction'])