Same as previous

python/shaprpy/explain.py

@@ -151,7 +151,7 @@ def explain(
         MSEv_uniform_comb_weights = MSEv_uniform_comb_weights,
         timing = timing,
         verbose = verbose,
-        is_python = True,
+        is_python=True,
         **kwargs
     )
 
@@ -232,7 +232,7 @@ def batch_prepare_vS_MC(S, rinternal, model, predict_model):
   dt_vS = shapr.compute_MCint(dt)
 
   if keep_samp_for_vS:
-    return ro.ListVector({'dt_vS': dt_vS, 'dt_samp_for_vS': dt})
+    return ro.ListVector({'dt_vS':dt_vS, 'dt_samp_for_vS':dt})
   else:
     return dt_vS
 
@@ -263,7 +263,7 @@ def get_feature_specs(get_model_specs, model):
 
   if not isinstance(feature_specs, dict):
     raise ValueError(f'`get_model_specs` returned an object of type `{type(feature_specs)}`, but it should be of type `dict`')
-  if set(feature_specs.keys()) != set(["labels", "classes", "factor_levels"]):
+  if set(feature_specs.keys()) != set(["labels","classes","factor_levels"]):
     raise ValueError(f'`get_model_specs` should return a `dict` with keys ["labels","classes","factor_levels"], but found keys {list(feature_specs.keys())}')
 
   if feature_specs is None:
@@ -277,7 +277,7 @@ def strvec_or_na(v):
       return strvec
     def listvec_or_na(v):
       if v is None: return NA
-      return ro.ListVector({k: list(val) for k,val in v.items()})
+      return ro.ListVector({k:list(val) for k,val in v.items()})
 
     rfeature_specs = ro.ListVector({
       'labels': py2r_or_na(feature_specs['labels']),
@@ -302,7 +302,7 @@ def get_predict_model(x_test, predict_model, model):
   try:
     tmp = py2r(predict_model(model, x_test))
   except Exception as e:
-    raise RuntimeError(f'The predict_model function of class `{model_class0}` is invalid.\nA basic function test threw the following error:\n{e}')
+      raise RuntimeError(f'The predict_model function of class `{model_class0}` is invalid.\nA basic function test threw the following error:\n{e}')
   if not all(base.is_numeric(tmp)):
     raise RuntimeError('The output of predict_model is expected to be numeric.')
   if not (len(tmp) == 2):
@@ -361,10 +361,9 @@ def prebuilt_predict_model(model):
   try:
     from keras.models import Model
     if isinstance(model, Model):
-      def predict_fn(m, x):
+      def predict_fn(m,x):
         pred = m.predict(x)
         return pred.reshape(pred.shape[0],)
-
       return predict_fn
   except:
     pass
@@ -373,6 +372,7 @@ def predict_fn(m, x):
 
 
 def compute_time(timing_list):
+
   timing_secs = {
       f'{key}': (timing_list[key] - timing_list[prev_key]).total_seconds()
       for key, prev_key in zip(list(timing_list.keys())[1:], list(timing_list.keys())[:-1])