From 2c5871608dd25a63b8970b6c7b3864c2117ce53e Mon Sep 17 00:00:00 2001
From: Alexis Drakopoulos <33128441+alexisdrakopoulos@users.noreply.github.com>
Date: Mon, 6 Jun 2022 15:49:13 +0100
Subject: [PATCH 1/7] quick pass

---
 shap/_explanation.py                 |  7 ++--
 shap/actions/_optimizer.py           | 54 +++++++++++++++++-----------
 shap/benchmark/_explanation_error.py |  2 +-
 shap/benchmark/_sequential.py        |  2 +-
 shap/benchmark/experiments.py        |  2 +-
 shap/explainers/_additive.py         |  4 +--
 shap/explainers/_deep/deep_tf.py     |  3 +-
 shap/explainers/_exact.py            |  6 ++--
 shap/explainers/_explainer.py        |  6 ++--
 shap/explainers/_linear.py           | 14 ++++----
 shap/explainers/_partition.py        |  2 +-
 shap/explainers/_permutation.py      |  4 +--
 shap/explainers/_tree.py             | 25 ++++++-------
 shap/explainers/other/_maple.py      |  2 +-
 shap/explainers/other/_random.py     |  2 +-
 shap/explainers/other/_treegain.py   |  2 +-
 shap/explainers/pytree.py            |  5 +--
 shap/maskers/_composite.py           |  6 ++--
 shap/maskers/_image.py               |  3 +-
 shap/maskers/_tabular.py             |  7 ++--
 shap/plots/_force.py                 |  2 +-
 shap/utils/_exceptions.py            | 16 +++++++++
 22 files changed, 106 insertions(+), 70 deletions(-)
 create mode 100644 shap/utils/_exceptions.py

diff --git a/shap/_explanation.py b/shap/_explanation.py
index a098034e4..ad30c4fd5 100644
--- a/shap/_explanation.py
+++ b/shap/_explanation.py
@@ -10,6 +10,7 @@
 from slicer import Slicer, Alias, Obj
 # from ._order import Order
 from .utils._general import OpChain
+from .utils._exceptions import DimensionError
 
 # slicer confuses pylint...
 # pylint: disable=no-member
@@ -298,7 +299,7 @@ def cohorts(self, cohorts):
         if isinstance(cohorts, (list, tuple, np.ndarray)):
             cohorts = np.array(cohorts)
             return Cohorts(**{name: self[cohorts == name] for name in np.unique(cohorts)})
-        raise Exception("The given set of cohort indicators is not recognized! Please give an array or int.")
+        raise TypeError("The given set of cohort indicators is not recognized! Please give an array or int.")
 
     def __repr__(self):
         """ Display some basic printable info, but not everything.
@@ -575,7 +576,7 @@ def sum(self, axis=None, grouping=None):
         elif axis == 1 or len(self.shape) == 1:
             return group_features(self, grouping)
         else:
-            raise Exception("Only axis = 1 is supported for grouping right now...")
+            raise DimensionError("Only axis = 1 is supported for grouping right now...")
 
     def hstack(self, other):
         """ Stack two explanations column-wise.
@@ -638,7 +639,7 @@ def hclust(self, metric="sqeuclidean", axis=0):
         values = self.values
 
         if len(values.shape) != 2:
-            raise Exception("The hclust order only supports 2D arrays right now!")
+            raise DimensionError("The hclust order only supports 2D arrays right now!")
 
         if axis == 1:
             values = values.T
diff --git a/shap/actions/_optimizer.py b/shap/actions/_optimizer.py
index 17263aa0b..d1e911a3d 100644
--- a/shap/actions/_optimizer.py
+++ b/shap/actions/_optimizer.py
@@ -5,70 +5,84 @@
 from ._action import Action
 
 
-class ActionOptimizer():
+class InvalidAction(Exception):
+    pass
+
+
+class ConvergenceError(Exception):
+    pass
+
+
+class ActionOptimizer:
     def __init__(self, model, actions):
         self.model = model
-        warnings.warn("Note that ActionOptimizer is still in an alpha state and is subjust to API changes.")
+        warnings.warn(
+            "Note that ActionOptimizer is still in an alpha state and is subjust to API changes."
+        )
         # actions go into mutually exclusive groups
         self.action_groups = []
         for group in actions:
-            
+
             if issubclass(type(group), Action):
                 group._group_index = len(self.action_groups)
                 group._grouped_index = 0
                 self.action_groups.append([copy.copy(group)])
             elif issubclass(type(group), list):
                 group = sorted([copy.copy(v) for v in group], key=lambda a: a.cost)
-                for i,v in enumerate(group):
+                for i, v in enumerate(group):
                     v._group_index = len(self.action_groups)
                     v._grouped_index = i
                 self.action_groups.append(group)
             else:
-                raise Exception("A passed action was not an Action or list of actions!")
-        
+                raise InvalidAction(
+                    "A passed action was not an Action or list of actions!"
+                )
+
     def __call__(self, *args, max_evals=10000):
-        
+
         # init our queue with all the least costly actions
         q = queue.PriorityQueue()
         for i in range(len(self.action_groups)):
             group = self.action_groups[i]
             q.put((group[0].cost, [group[0]]))
-        
+
         nevals = 0
         while not q.empty():
-            
+
             # see if we have exceeded our runtime budget
             nevals += 1
             if nevals > max_evals:
-                raise Exception(f"Failed to find a solution with max_evals={max_evals}! Try reducing the number of actions or increasing max_evals.")
-            
+                raise ConvergenceError(
+                    f"Failed to find a solution with max_evals={max_evals}! Try reducing the number of actions or increasing max_evals."
+                )
+
             # get the next cheapest set of actions we can do
             cost, actions = q.get()
-            
+
             # apply those actions
             args_tmp = copy.deepcopy(args)
             for a in actions:
                 a(*args_tmp)
-            
+
             # if the model is now satisfied we are done!!
             v = self.model(*args_tmp)
             if v:
                 return actions
-            
+
             # if not then we add all possible follow-on actions to our queue
             else:
                 for i in range(len(self.action_groups)):
                     group = self.action_groups[i]
-                    
+
                     # look to to see if we already have a action from this group, if so we need to
                     # move to a more expensive action in the same group
                     next_ind = 0
                     prev_in_group = -1
-                    for j,a in enumerate(actions):
+                    for j, a in enumerate(actions):
                         if a._group_index == i:
-                            next_ind = max(next_ind, a._grouped_index+1)
+                            next_ind = max(next_ind, a._grouped_index + 1)
                             prev_in_group = j
-                    
+
                     # we are adding a new action type
                     if prev_in_group == -1:
                         new_actions = actions + [group[next_ind]]
@@ -79,7 +93,7 @@ def __call__(self, *args, max_evals=10000):
                     # we don't have a more expensive action left in this group
                     else:
                         new_actions = None
-                    
+
                     # add the new option to our queue
                     if new_actions is not None:
-                        q.put((sum([a.cost for a in new_actions]), new_actions))
\ No newline at end of file
+                        q.put((sum([a.cost for a in new_actions]), new_actions))
diff --git a/shap/benchmark/_explanation_error.py b/shap/benchmark/_explanation_error.py
index e5feb4bc4..fa43133c5 100644
--- a/shap/benchmark/_explanation_error.py
+++ b/shap/benchmark/_explanation_error.py
@@ -115,7 +115,7 @@ def __call__(self, explanation, name, step_fraction=0.01, indices=[], silent=Fal
                 elif callable(self.masker.clustering):
                     row_clustering = self.masker.clustering(*args)
                 else:
-                    raise Exception("The masker passed has a .clustering attribute that is not yet supported by the ExplanationError benchmark!")
+                    raise NotImplementedError("The masker passed has a .clustering attribute that is not yet supported by the ExplanationError benchmark!")
 
             masked_model = MaskedModel(self.model, self.masker, self.link, self.linearize_link, *args)
 
diff --git a/shap/benchmark/_sequential.py b/shap/benchmark/_sequential.py
index 3ebb82ade..d7003a1bf 100644
--- a/shap/benchmark/_sequential.py
+++ b/shap/benchmark/_sequential.py
@@ -14,7 +14,7 @@ def __init__(self, mask_type, sort_order, masker, model, *model_args, batch_size
 
         for arg in model_args:
             if isinstance(arg, pd.DataFrame):
-                raise Exception("DataFrame arguments dont iterate correctly, pass numpy arrays instead!")
+                raise TypeError("DataFrame arguments dont iterate correctly, pass numpy arrays instead!")
 
         # convert any DataFrames to numpy arrays
         # self.model_arg_cols = []
diff --git a/shap/benchmark/experiments.py b/shap/benchmark/experiments.py
index 49a8ff3e5..8b43bd2be 100644
--- a/shap/benchmark/experiments.py
+++ b/shap/benchmark/experiments.py
@@ -413,7 +413,7 @@ def __run_remote_experiment(experiment, remote, cache_dir="/tmp", python_binary=
             #print(cache_id.replace("__", " ") + " ...loaded from remote after %f seconds" % (time.time() - start))
             return pickle.load(f)
     else:
-        raise Exception("Remote benchmark call finished but no local file was found!")
+        raise FileNotFoundError("Remote benchmark call finished but no local file was found!")
 
 def __gen_cache_id(experiment):
     dataset_name, model_name, method_name, metric_name = experiment
diff --git a/shap/explainers/_additive.py b/shap/explainers/_additive.py
index 9a8caa2d7..438f69fb9 100644
--- a/shap/explainers/_additive.py
+++ b/shap/explainers/_additive.py
@@ -48,7 +48,7 @@ def __init__(self, model, masker, link=None, feature_names=None):
                 # self.model(np.zeros(num_features))
                 # self._zero_offset = self.model(np.zeros(num_features))#model.intercept_#outputs[0]
                 # self._input_offsets = np.zeros(num_features) #* self._zero_offset
-                raise Exception("Masker not given and we don't yet support pulling the distribution centering directly from the EBM model!")
+                raise NotImplementedError("Masker not given and we don't yet support pulling the distribution centering directly from the EBM model!")
                 return
 
         # here we need to compute the offsets ourselves because we can't pull them directly from a model we know about
@@ -83,7 +83,7 @@ def supports_model_with_masker(model, masker):
         """
         if safe_isinstance(model, "interpret.glassbox.ExplainableBoostingClassifier"):
             if model.interactions is not 0:
-                raise Exception("Need to add support for interaction effects!")
+                raise NotImplementedError("Need to add support for interaction effects!")
             return True
             
         return False
diff --git a/shap/explainers/_deep/deep_tf.py b/shap/explainers/_deep/deep_tf.py
index e449d0ba8..ee67096e3 100644
--- a/shap/explainers/_deep/deep_tf.py
+++ b/shap/explainers/_deep/deep_tf.py
@@ -3,6 +3,7 @@
 from .._explainer import Explainer
 from packaging import version
 from ..tf_utils import _get_session, _get_graph, _get_model_inputs, _get_model_output
+from ...utils._exceptions import DimensionError
 keras = None
 tf = None
 tf_ops = None
@@ -173,7 +174,7 @@ def __init__(self, model, data, session=None, learning_phase_flags=None):
             if noutputs is not None:
                 self.phi_symbolics = [None for i in range(noutputs)]
             else:
-                raise Exception("The model output tensor to be explained cannot have a static shape in dim 1 of None!")
+                raise DimensionError("The model output tensor to be explained cannot have a static shape in dim 1 of None!")
 
     def _get_model_output(self, model):
         if len(model.layers[-1]._inbound_nodes) == 0:
diff --git a/shap/explainers/_exact.py b/shap/explainers/_exact.py
index 12658202a..4514e41ac 100644
--- a/shap/explainers/_exact.py
+++ b/shap/explainers/_exact.py
@@ -92,7 +92,7 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
 
             # make sure we have enough evals
             if max_evals is not None and max_evals != "auto" and max_evals < 2**len(inds):
-                raise Exception(
+                raise ValueError(
                     f"It takes {2**len(inds)} masked evaluations to run the Exact explainer on this instance, but max_evals={max_evals}!"
                 )
 
@@ -131,14 +131,14 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
                 _compute_grey_code_row_values_st(row_values, mask, inds, outputs, coeff, extended_delta_indexes, MaskedModel.delta_mask_noop_value)
 
             elif interactions > 2:
-                raise Exception("Currently the Exact explainer does not support interactions higher than order 2!")
+                raise NotImplementedError("Currently the Exact explainer does not support interactions higher than order 2!")
 
         # do a partition tree constrained version of Shapley values
         else:
 
             # make sure we have enough evals
             if max_evals is not None and max_evals != "auto" and max_evals < len(fm)**2:
-                raise Exception(
+                raise ValueError(
                     f"It takes {len(fm)**2} masked evaluations to run the Exact explainer on this instance, but max_evals={max_evals}!"
                 )
 
diff --git a/shap/explainers/_explainer.py b/shap/explainers/_explainer.py
index 1710dfc31..3647273f6 100644
--- a/shap/explainers/_explainer.py
+++ b/shap/explainers/_explainer.py
@@ -127,7 +127,7 @@ def __init__(self, model, masker=None, link=links.identity, algorithm="auto", ou
         if callable(link):
             self.link = link
         else:
-            raise Exception("The passed link function needs to be callable!")
+            raise TypeError("The passed link function needs to be callable!")
         self.linearize_link = linearize_link
 
         # if we are called directly (as opposed to through super()) then we convert ourselves to the subclass
@@ -165,7 +165,7 @@ def __init__(self, model, masker=None, link=links.identity, algorithm="auto", ou
 
                 # if we get here then we don't know how to handle what was given to us
                 else:
-                    raise Exception("The passed model is not callable and cannot be analyzed directly with the given masker! Model: " + str(model))
+                    raise TypeError("The passed model is not callable and cannot be analyzed directly with the given masker! Model: " + str(model))
 
             # build the right subclass
             if algorithm == "exact":
@@ -190,7 +190,7 @@ def __init__(self, model, masker=None, link=links.identity, algorithm="auto", ou
                 self.__class__ = explainers.Deep
                 explainers.Deep.__init__(self, self.model, self.masker, link=self.link, feature_names=self.feature_names, linearize_link=linearize_link, **kwargs)
             else:
-                raise Exception("Unknown algorithm type passed: %s!" % algorithm)
+                raise ValueError("Unknown algorithm type passed: %s!" % algorithm)
 
 
     def __call__(self, *args, max_evals="auto", main_effects=False, error_bounds=False, batch_size="auto",
diff --git a/shap/explainers/_linear.py b/shap/explainers/_linear.py
index b07925be8..7cc0b5346 100644
--- a/shap/explainers/_linear.py
+++ b/shap/explainers/_linear.py
@@ -92,7 +92,7 @@ def __init__(self, model, masker, link=links.identity, nsamples=1000, feature_pe
         elif issubclass(type(self.masker), maskers.Impute):
             self.feature_perturbation = "correlation_dependent"
         else:
-            raise Exception("The Linear explainer only supports the Independent, Partition, and Impute maskers right now!")
+            raise NotImplementedError("The Linear explainer only supports the Independent, Partition, and Impute maskers right now!")
         data = getattr(self.masker, "data", None)
 
         # convert DataFrame's to numpy arrays
@@ -120,12 +120,12 @@ def __init__(self, model, masker, link=links.identity, nsamples=1000, feature_pe
             if safe_isinstance(self.cov, "pandas.core.frame.DataFrame"):
                 self.cov = self.cov.values
         elif data is None:
-            raise Exception("A background data distribution must be provided!")
+            raise ValueError("A background data distribution must be provided!")
         else:
             if sp.sparse.issparse(data):
                 self.mean = np.array(np.mean(data, 0))[0]
                 if self.feature_perturbation != "interventional":
-                    raise Exception("Only feature_perturbation = 'interventional' is supported for sparse data")
+                    raise NotImplementedError("Only feature_perturbation = 'interventional' is supported for sparse data")
             else:
                 self.mean = np.array(np.mean(data, 0)).flatten() # assumes it is an array
                 if self.feature_perturbation == "correlation_dependent":
@@ -173,7 +173,7 @@ def __init__(self, model, masker, link=links.identity, nsamples=1000, feature_pe
             if nsamples != 1000:
                 warnings.warn("Setting nsamples has no effect when feature_perturbation = 'interventional'!")
         else:
-            raise Exception("Unknown type of feature_perturbation provided: " + self.feature_perturbation)
+            raise ValueError("Unknown type of feature_perturbation provided: " + self.feature_perturbation)
 
     def _estimate_transforms(self, nsamples):
         """ Uses block matrix inversion identities to quickly estimate transforms.
@@ -261,7 +261,7 @@ def _parse_model(model):
                 coef = model.coef_
                 intercept = model.intercept_
         else:
-            raise Exception("An unknown model type was passed: " + str(type(model)))
+            raise ValueError("An unknown model type was passed: " + str(type(model)))
 
         return coef,intercept
 
@@ -300,7 +300,7 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
 
         if self.feature_perturbation == "correlation_dependent":
             if sp.sparse.issparse(X):
-                raise Exception("Only feature_perturbation = 'interventional' is supported for sparse data")
+                raise NotImplementedError("Only feature_perturbation = 'interventional' is supported for sparse data")
             phi = np.matmul(np.matmul(X[:,self.valid_inds], self.avg_proj.T), self.x_transform.T) - self.mean_transformed
             phi = np.matmul(phi, self.avg_proj)
 
@@ -363,7 +363,7 @@ def shap_values(self, X):
 
         if self.feature_perturbation == "correlation_dependent":
             if sp.sparse.issparse(X):
-                raise Exception("Only feature_perturbation = 'interventional' is supported for sparse data")
+                raise ValueError("Only feature_perturbation = 'interventional' is supported for sparse data")
             phi = np.matmul(np.matmul(X[:,self.valid_inds], self.avg_proj.T), self.x_transform.T) - self.mean_transformed
             phi = np.matmul(phi, self.avg_proj)
 
diff --git a/shap/explainers/_partition.py b/shap/explainers/_partition.py
index f7ff202c9..6a2b9238b 100644
--- a/shap/explainers/_partition.py
+++ b/shap/explainers/_partition.py
@@ -148,7 +148,7 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
             # else:
             fixed_context = None
         elif fixed_context not in [0, 1, None]:
-            raise Exception("Unknown fixed_context value passed (must be 0, 1 or None): %s" %fixed_context)
+            raise ValueError("Unknown fixed_context value passed (must be 0, 1 or None): %s" %fixed_context)
 
         # build a masked version of the model for the current input sample
         fm = MaskedModel(self.model, self.masker, self.link, self.linearize_link, *row_args)
diff --git a/shap/explainers/_permutation.py b/shap/explainers/_permutation.py
index cfbe04d1f..c5cb2af1b 100644
--- a/shap/explainers/_permutation.py
+++ b/shap/explainers/_permutation.py
@@ -96,7 +96,7 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
             elif callable(self.masker.clustering):
                 row_clustering = self.masker.clustering(*row_args)
             else:
-                raise Exception("The masker passed has a .clustering attribute that is not yet supported by the Permutation explainer!")
+                raise NotImplementedError("The masker passed has a .clustering attribute that is not yet supported by the Permutation explainer!")
 
         # loop over many permutations
         inds = fm.varying_inputs()
@@ -154,7 +154,7 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
                 history_pos += 1
 
             if npermutations == 0:
-                raise Exception(f"max_evals={max_evals} is too low for the Permutation explainer, it must be at least 2 * num_features + 1 = {2 * len(inds) + 1}!")
+                raise ValueError(f"max_evals={max_evals} is too low for the Permutation explainer, it must be at least 2 * num_features + 1 = {2 * len(inds) + 1}!")
 
             expected_value = outputs[0]
 
diff --git a/shap/explainers/_tree.py b/shap/explainers/_tree.py
index b17a1e081..142c640bd 100644
--- a/shap/explainers/_tree.py
+++ b/shap/explainers/_tree.py
@@ -11,6 +11,7 @@
 from ._explainer import Explainer
 from ..utils import assert_import, record_import_error, safe_isinstance
 from ..utils._legacy import DenseData
+from ..utils._exceptions import InvalidMaskerError, ExplainerError
 from .._explanation import Explanation
 from .. import maskers
 import warnings
@@ -102,10 +103,10 @@ def __init__(self, model, data = None, model_output="raw", feature_perturbation=
         if type(self.masker) is maskers.Independent:
             data = self.masker.data
         elif masker is not None:
-            raise Exception("Unsupported masker type: %s!" % str(type(self.masker)))
+            raise InvalidMaskerError("Unsupported masker type: %s!" % str(type(self.masker)))
 
         if getattr(self.masker, "clustering", None) is not None:
-            raise Exception("TreeExplainer does not support clustered data inputs! Please use shap.Explainer or pass an unclustered masker!")
+            raise ExplainerError("TreeExplainer does not support clustered data inputs! Please use shap.Explainer or pass an unclustered masker!")
 
         # check for deprecated options
         if model_output == "margin":
@@ -123,10 +124,10 @@ def __init__(self, model, data = None, model_output="raw", feature_perturbation=
                 warnings.warn("feature_dependence = \"independent\" has been renamed to feature_perturbation" \
                     " = \"interventional\", you can't supply both options! See GitHub issue #882.")
             if dep_val == "tree_path_dependent" and feature_perturbation == "interventional":
-                raise Exception("The feature_dependence option has been renamed to feature_perturbation! " \
+                raise ValueError("The feature_dependence option has been renamed to feature_perturbation! " \
                     "Please update the option name before calling TreeExplainer. See GitHub issue #882.")
         if feature_perturbation == "independent":
-            raise Exception("feature_perturbation = \"independent\" is not a valid option value, please use " \
+            raise ValueError("feature_perturbation = \"independent\" is not a valid option value, please use " \
                 "feature_perturbation = \"interventional\" instead. See GitHub issue #882.")
 
 
@@ -179,7 +180,7 @@ def __init__(self, model, data = None, model_output="raw", feature_perturbation=
             try:
                 self.expected_value = self.model.predict(self.data).mean(0)
             except ValueError:
-                raise Exception("Currently TreeExplainer can only handle models with categorical splits when " \
+                raise ExplainerError("Currently TreeExplainer can only handle models with categorical splits when " \
                                 "feature_perturbation=\"tree_path_dependent\" and no background data is passed. Please try again using " \
                                 "shap.TreeExplainer(model, feature_perturbation=\"tree_path_dependent\").")
             if hasattr(self.expected_value, '__len__') and len(self.expected_value) == 1:
@@ -534,7 +535,7 @@ def check_sum(sum_val, model_output):
                 err_msg += " This check failed because for one of the samples the sum of the SHAP values" \
                            " was %f, while the model output was %f. If this difference is acceptable" \
                            " you can set check_additivity=False to disable this check." % (sum_val[ind], model_output[ind])
-                raise Exception(err_msg)
+                raise ExplainerError(err_msg)
 
         if type(phi) is list:
             for i in range(len(phi)):
@@ -744,7 +745,7 @@ def __init__(self, model, data=None, data_missing=None, model_output=None):
                 self.base_offset = self.base_offset[0, 0]
                 has_len = False
             if has_len and self.model_output != "raw":
-                raise Exception("Multi-output HistGradientBoostingClassifier models are not yet supported unless model_output=\"raw\". See GitHub issue #1028")
+                raise NotImplementedError("Multi-output HistGradientBoostingClassifier models are not yet supported unless model_output=\"raw\". See GitHub issue #1028")
             self.input_dtype = sklearn.ensemble._hist_gradient_boosting.common.X_DTYPE
             self.num_stacked_models = len(model._predictors[0])
             if self.model_output == "predict_proba":
@@ -989,7 +990,7 @@ def __init__(self, model, data=None, data_missing=None, model_output=None):
             self.tree_output = "raw_value"
             self.base_offset = model.init_params[param_idx]
         else:
-            raise Exception("Model type not yet supported by TreeExplainer: " + str(type(model)))
+            raise ExplainerError("Model type not yet supported by TreeExplainer: " + str(type(model)))
 
         # build a dense numpy version of all the tree objects
         if self.trees is not None and self.trees:
@@ -1051,7 +1052,7 @@ def get_transform(self):
             elif self.tree_output == "probability":
                 transform = "identity"
             else:
-                raise Exception("model_output = \"probability\" is not yet supported when model.tree_output = \"" + self.tree_output + "\"!")
+                raise NotImplementedError("model_output = \"probability\" is not yet supported when model.tree_output = \"" + self.tree_output + "\"!")
         elif self.model_output == "log_loss":
 
             if self.objective == "squared_error":
@@ -1059,9 +1060,9 @@ def get_transform(self):
             elif self.objective == "binary_crossentropy":
                 transform = "logistic_nlogloss"
             else:
-                raise Exception("model_output = \"log_loss\" is not yet supported when model.objective = \"" + self.objective + "\"!")
+                raise NotImplementedError("model_output = \"log_loss\" is not yet supported when model.objective = \"" + self.objective + "\"!")
         else:
-            raise Exception("Unrecognized model_output parameter value: %s! If model.%s is a valid function open a github issue to ask that this method be supported. If you want 'predict_proba' just use 'probability' for now." % (str(self.model_output), str(self.model_output)))
+            raise TypeError("Unrecognized model_output parameter value: %s! If model.%s is a valid function open a github issue to ask that this method be supported. If you want 'predict_proba' just use 'probability' for now." % (str(self.model_output), str(self.model_output)))
 
         return transform
 
@@ -1360,7 +1361,7 @@ def extract_data(node, tree):
             self.values = values[:,np.newaxis] * scaling
             self.node_sample_weight = node_sample_weight
         else:
-            raise Exception("Unknown input to SingleTree constructor: " + str(tree))
+            raise TypeError("Unknown input to SingleTree constructor: " + str(tree))
 
         # Re-compute the number of samples that pass through each node if we are given data
         if data is not None and data_missing is not None:
diff --git a/shap/explainers/other/_maple.py b/shap/explainers/other/_maple.py
index 7c544a569..e792b158a 100644
--- a/shap/explainers/other/_maple.py
+++ b/shap/explainers/other/_maple.py
@@ -87,7 +87,7 @@ def __init__(self, model, data):
         # elif str(type(model)).endswith("xgboost.sklearn.XGBClassifier'>"):
         #     pass
         else:
-            raise Exception("The passed model is not yet supported by TreeMapleExplainer: " + str(type(model)))
+            raise NotImplementedError("The passed model is not yet supported by TreeMapleExplainer: " + str(type(model)))
 
         if str(type(data)).endswith("pandas.core.frame.DataFrame'>"):
             data = data.values
diff --git a/shap/explainers/other/_random.py b/shap/explainers/other/_random.py
index d612e9aed..8fa5ba9bd 100644
--- a/shap/explainers/other/_random.py
+++ b/shap/explainers/other/_random.py
@@ -37,7 +37,7 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
             elif callable(self.masker.clustering):
                 row_clustering = self.masker.clustering(*row_args)
             else:
-                raise Exception("The masker passed has a .clustering attribute that is not yet supported by the Permutation explainer!")
+                raise NotImplementedError("The masker passed has a .clustering attribute that is not yet supported by the Permutation explainer!")
 
         # compute the correct expected value
         masks = np.zeros(1, dtype=np.int)
diff --git a/shap/explainers/other/_treegain.py b/shap/explainers/other/_treegain.py
index 6c3329495..76ae5ee4b 100644
--- a/shap/explainers/other/_treegain.py
+++ b/shap/explainers/other/_treegain.py
@@ -20,7 +20,7 @@ def __init__(self, model):
         elif str(type(model)).endswith("xgboost.sklearn.XGBClassifier'>"):
             pass
         else:
-            raise Exception("The passed model is not yet supported by TreeGainExplainer: " + str(type(model)))
+            raise NotImplementedError("The passed model is not yet supported by TreeGainExplainer: " + str(type(model)))
         assert hasattr(model, "feature_importances_"), "The passed model does not have a feature_importances_ attribute!"
         self.model = model
 
diff --git a/shap/explainers/pytree.py b/shap/explainers/pytree.py
index 96bc59c14..f345bffef 100644
--- a/shap/explainers/pytree.py
+++ b/shap/explainers/pytree.py
@@ -6,6 +6,7 @@
 import numpy as np
 #import numba
 from .explainer import Explainer
+from ..utils._exceptions import ExplainerError
 
 # class TreeExplainer(Explainer):
 #     def __init__(self, model, **kwargs):
@@ -153,7 +154,7 @@ def __init__(self, model, **kwargs):
             self.model_type = "lightgbm"
             self.trees = model
         else:
-            raise Exception("Model type not supported by TreeExplainer: " + str(type(model)))
+            raise ExplainerError("Model type not supported by TreeExplainer: " + str(type(model)))
 
         if self.model_type == "internal":
             # Preallocate space for the unique path data
@@ -227,7 +228,7 @@ def shap_interaction_values(self, X, tree_limit=-1, **kwargs):
                 tree_limit=0
             return self.trees.predict(X, ntree_limit=tree_limit, pred_interactions=True)
         else:
-            raise Exception("Interaction values not yet supported for model type: " + str(type(X)))
+            raise NotImplementedError("Interaction values not yet supported for model type: " + str(type(X)))
 
     def tree_shap(self, tree, x, x_missing, phi, condition=0, condition_feature=0):
 
diff --git a/shap/maskers/_composite.py b/shap/maskers/_composite.py
index b1b23361d..ca7867e39 100644
--- a/shap/maskers/_composite.py
+++ b/shap/maskers/_composite.py
@@ -1,6 +1,6 @@
 import types
 from ._masker import Masker
-
+from ..utils._exceptions import InvalidMaskerError
 
 class Composite(Masker):
     """ This merges several maskers for different inputs together into a single composite masker.
@@ -103,7 +103,7 @@ def __call__(self, mask, *args):
                 num_rows = shapes[-1][0]
 
             if shapes[-1][0] != num_rows and shapes[-1][0] != 1 and shapes[-1][0] is not None:
-                raise Exception("The composite masker can only join together maskers with a compatible number of background rows!")
+                raise InvalidMaskerError("The composite masker can only join together maskers with a compatible number of background rows!")
             arg_pos += self.arg_counts[i]
 
         # call all the submaskers and combine their outputs
@@ -138,5 +138,5 @@ def joint_clustering(self, *args):
         if len(single_clustering) == 0:
             single_clustering = clustering
         elif len(clustering) != 0:
-            raise Exception("Joining two non-trivial clusterings is not yet implemented in the Composite masker!")
+            raise NotImplementedError("Joining two non-trivial clusterings is not yet implemented in the Composite masker!")
     return single_clustering
diff --git a/shap/maskers/_image.py b/shap/maskers/_image.py
index e39d4bb19..93aa75399 100644
--- a/shap/maskers/_image.py
+++ b/shap/maskers/_image.py
@@ -1,5 +1,6 @@
 import numpy as np
 from ..utils import assert_import, record_import_error, safe_isinstance
+from ..utils._exceptions import DimensionError
 from ._masker import Masker
 from .._serializable import Serializer, Deserializer
 import heapq
@@ -76,7 +77,7 @@ def __call__(self, mask, x):
             x = x.cpu().numpy()
 
         if np.prod(x.shape) != np.prod(self.input_shape):
-            raise Exception("The length of the image to be masked must match the shape given in the " + \
+            raise DimensionError("The length of the image to be masked must match the shape given in the " + \
                             "ImageMasker contructor: "+" * ".join([str(i) for i in x.shape])+ \
                             " != "+" * ".join([str(i) for i in self.input_shape]))
 
diff --git a/shap/maskers/_tabular.py b/shap/maskers/_tabular.py
index 820ef56c6..f72eda75d 100644
--- a/shap/maskers/_tabular.py
+++ b/shap/maskers/_tabular.py
@@ -4,6 +4,7 @@
 from numba import jit
 from .. import utils
 from ..utils import safe_isinstance, MaskedModel
+from ..utils._exceptions import DimensionError
 from ._masker import Masker
 from .._serializable import Serializer, Deserializer
 
@@ -70,7 +71,7 @@ def __init__(self, data, max_samples=100, clustering=None):
             elif safe_isinstance(clustering, "numpy.ndarray"):
                 self.clustering = clustering
             else:
-                raise Exception(
+                raise ValueError(
                     "Unknown clustering given! Make sure you pass a distance metric as a string, or a clustering as a numpy.ndarray."
                 )
         else:
@@ -92,7 +93,7 @@ def __call__(self, mask, x):
 
         # make sure we are given a single sample
         if len(x.shape) != 1 or x.shape[0] != self.data.shape[1]:
-            raise Exception("The input passed for tabular masking does not match the background data shape!")
+            raise DimensionError("The input passed for tabular masking does not match the background data shape!")
 
         # if mask is an array of integers then we are doing delta masking
         if np.issubdtype(mask.dtype, np.integer):
@@ -141,7 +142,7 @@ def invariants(self, x):
 
         # make sure we got valid data
         if x.shape != self.data.shape[1:]:
-            raise Exception(
+            raise DimensionError(
                 "The passed data does not match the background shape expected by the masker! The data of shape " + \
                 str(x.shape) + " was passed while the masker expected data of shape " + str(self.data.shape[1:]) + "."
             )
diff --git a/shap/plots/_force.py b/shap/plots/_force.py
index 5e55b92d8..bfcfe4416 100644
--- a/shap/plots/_force.py
+++ b/shap/plots/_force.py
@@ -171,7 +171,7 @@ def force(base_value, shap_values=None, features=None, feature_names=None, out_n
         
     else:
         if matplotlib:
-            raise Exception("matplotlib = True is not yet supported for force plots with multiple samples!")
+            raise NotImplementedError("matplotlib = True is not yet supported for force plots with multiple samples!")
         
         if shap_values.shape[0] > 3000:
             warnings.warn("shap.plots.force is slow for many thousands of rows, try subsampling your data.")
diff --git a/shap/utils/_exceptions.py b/shap/utils/_exceptions.py
new file mode 100644
index 000000000..31f0290dc
--- /dev/null
+++ b/shap/utils/_exceptions.py
@@ -0,0 +1,16 @@
+class DimensionError(Exception):
+    """
+    Used for instances where dimensions are either
+    not supported or cause errors.
+    """
+
+    pass
+
+class InvalidMaskerError(Exception):
+    pass
+
+class ExplainerError(Exception):
+    """
+    Generic errors related to Explainers
+    """
+    pass
\ No newline at end of file

From d55eceae3677fe11454f0cf522b80f5bffcce5d8 Mon Sep 17 00:00:00 2001
From: Alexis Drakopoulos <33128441+alexisdrakopoulos@users.noreply.github.com>
Date: Wed, 15 Jun 2022 08:47:08 +0100
Subject: [PATCH 2/7] refactored errors further

---
 shap/explainers/_explainer.py |  5 +++--
 shap/explainers/_linear.py    | 12 +++++++-----
 shap/explainers/_tree.py      | 10 +++++-----
 shap/maskers/_tabular.py      |  4 ++--
 shap/utils/_exceptions.py     | 17 ++++++++++++++++-
 5 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/shap/explainers/_explainer.py b/shap/explainers/_explainer.py
index 3647273f6..00f283c8b 100644
--- a/shap/explainers/_explainer.py
+++ b/shap/explainers/_explainer.py
@@ -1,4 +1,5 @@
 import copy
+from sqlite3 import NotSupportedError
 import time
 import numpy as np
 import scipy as sp
@@ -13,7 +14,7 @@
 from .._serializable import Serializable
 from .. import explainers
 from .._serializable import Serializer, Deserializer
-
+from ..utils._exceptions import InvalidAlgorithmError
 
 
 class Explainer(Serializable):
@@ -190,7 +191,7 @@ def __init__(self, model, masker=None, link=links.identity, algorithm="auto", ou
                 self.__class__ = explainers.Deep
                 explainers.Deep.__init__(self, self.model, self.masker, link=self.link, feature_names=self.feature_names, linearize_link=linearize_link, **kwargs)
             else:
-                raise ValueError("Unknown algorithm type passed: %s!" % algorithm)
+                raise InvalidAlgorithmError("Unknown algorithm type passed: %s!" % algorithm)
 
 
     def __call__(self, *args, max_evals="auto", main_effects=False, error_bounds=False, batch_size="auto",
diff --git a/shap/explainers/_linear.py b/shap/explainers/_linear.py
index 7cc0b5346..18e48f8b8 100644
--- a/shap/explainers/_linear.py
+++ b/shap/explainers/_linear.py
@@ -4,6 +4,7 @@
 from tqdm.autonotebook import tqdm
 from ._explainer import Explainer
 from ..utils import safe_isinstance
+from ..utils._exceptions import InvalidFeaturePerturbationError, InvalidModelError, DimensionError
 from .. import maskers
 from .. import links
 
@@ -173,7 +174,7 @@ def __init__(self, model, masker, link=links.identity, nsamples=1000, feature_pe
             if nsamples != 1000:
                 warnings.warn("Setting nsamples has no effect when feature_perturbation = 'interventional'!")
         else:
-            raise ValueError("Unknown type of feature_perturbation provided: " + self.feature_perturbation)
+            raise InvalidFeaturePerturbationError("Unknown type of feature_perturbation provided: " + self.feature_perturbation)
 
     def _estimate_transforms(self, nsamples):
         """ Uses block matrix inversion identities to quickly estimate transforms.
@@ -261,7 +262,7 @@ def _parse_model(model):
                 coef = model.coef_
                 intercept = model.intercept_
         else:
-            raise ValueError("An unknown model type was passed: " + str(type(model)))
+            raise InvalidModelError("An unknown model type was passed: " + str(type(model)))
 
         return coef,intercept
 
@@ -296,11 +297,12 @@ def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_si
             X = X.values
 
         #assert str(type(X)).endswith("'numpy.ndarray'>"), "Unknown instance type: " + str(type(X))
-        assert len(X.shape) == 1 or len(X.shape) == 2, "Instance must have 1 or 2 dimensions!"
+        if len(X.shape) not in (1, 2):
+            raise DimensionError("Instance must have 1 or 2 dimensions! Not: %s" %len(X.shape))
 
         if self.feature_perturbation == "correlation_dependent":
             if sp.sparse.issparse(X):
-                raise NotImplementedError("Only feature_perturbation = 'interventional' is supported for sparse data")
+                raise InvalidFeaturePerturbationError("Only feature_perturbation = 'interventional' is supported for sparse data")
             phi = np.matmul(np.matmul(X[:,self.valid_inds], self.avg_proj.T), self.x_transform.T) - self.mean_transformed
             phi = np.matmul(phi, self.avg_proj)
 
@@ -363,7 +365,7 @@ def shap_values(self, X):
 
         if self.feature_perturbation == "correlation_dependent":
             if sp.sparse.issparse(X):
-                raise ValueError("Only feature_perturbation = 'interventional' is supported for sparse data")
+                raise InvalidFeaturePerturbationError("Only feature_perturbation = 'interventional' is supported for sparse data")
             phi = np.matmul(np.matmul(X[:,self.valid_inds], self.avg_proj.T), self.x_transform.T) - self.mean_transformed
             phi = np.matmul(phi, self.avg_proj)
 
diff --git a/shap/explainers/_tree.py b/shap/explainers/_tree.py
index 142c640bd..8bf7d1367 100644
--- a/shap/explainers/_tree.py
+++ b/shap/explainers/_tree.py
@@ -11,7 +11,7 @@
 from ._explainer import Explainer
 from ..utils import assert_import, record_import_error, safe_isinstance
 from ..utils._legacy import DenseData
-from ..utils._exceptions import InvalidMaskerError, ExplainerError
+from ..utils._exceptions import InvalidMaskerError, ExplainerError, InvalidFeaturePerturbationError, InvalidModelError
 from .._explanation import Explanation
 from .. import maskers
 import warnings
@@ -127,7 +127,7 @@ def __init__(self, model, data = None, model_output="raw", feature_perturbation=
                 raise ValueError("The feature_dependence option has been renamed to feature_perturbation! " \
                     "Please update the option name before calling TreeExplainer. See GitHub issue #882.")
         if feature_perturbation == "independent":
-            raise ValueError("feature_perturbation = \"independent\" is not a valid option value, please use " \
+            raise InvalidFeaturePerturbationError("feature_perturbation = \"independent\" is not a valid option value, please use " \
                 "feature_perturbation = \"interventional\" instead. See GitHub issue #882.")
 
 
@@ -153,7 +153,7 @@ def __init__(self, model, data = None, model_output="raw", feature_perturbation=
         self.approximate = approximate
 
         if feature_perturbation not in feature_perturbation_codes:
-            raise ValueError("Invalid feature_perturbation option!")
+            raise InvalidFeaturePerturbationError("Invalid feature_perturbation option!")
 
         # check for unsupported combinations of feature_perturbation and model_outputs
         if feature_perturbation == "tree_path_dependent":
@@ -990,7 +990,7 @@ def __init__(self, model, data=None, data_missing=None, model_output=None):
             self.tree_output = "raw_value"
             self.base_offset = model.init_params[param_idx]
         else:
-            raise ExplainerError("Model type not yet supported by TreeExplainer: " + str(type(model)))
+            raise InvalidModelError("Model type not yet supported by TreeExplainer: " + str(type(model)))
 
         # build a dense numpy version of all the tree objects
         if self.trees is not None and self.trees:
@@ -1062,7 +1062,7 @@ def get_transform(self):
             else:
                 raise NotImplementedError("model_output = \"log_loss\" is not yet supported when model.objective = \"" + self.objective + "\"!")
         else:
-            raise TypeError("Unrecognized model_output parameter value: %s! If model.%s is a valid function open a github issue to ask that this method be supported. If you want 'predict_proba' just use 'probability' for now." % (str(self.model_output), str(self.model_output)))
+            raise ValueError("Unrecognized model_output parameter value: %s! If model.%s is a valid function open a github issue to ask that this method be supported. If you want 'predict_proba' just use 'probability' for now." % (str(self.model_output), str(self.model_output)))
 
         return transform
 
diff --git a/shap/maskers/_tabular.py b/shap/maskers/_tabular.py
index f72eda75d..92ec6e3a1 100644
--- a/shap/maskers/_tabular.py
+++ b/shap/maskers/_tabular.py
@@ -4,7 +4,7 @@
 from numba import jit
 from .. import utils
 from ..utils import safe_isinstance, MaskedModel
-from ..utils._exceptions import DimensionError
+from ..utils._exceptions import DimensionError, InvalidClusteringError
 from ._masker import Masker
 from .._serializable import Serializer, Deserializer
 
@@ -71,7 +71,7 @@ def __init__(self, data, max_samples=100, clustering=None):
             elif safe_isinstance(clustering, "numpy.ndarray"):
                 self.clustering = clustering
             else:
-                raise ValueError(
+                raise InvalidClusteringError(
                     "Unknown clustering given! Make sure you pass a distance metric as a string, or a clustering as a numpy.ndarray."
                 )
         else:
diff --git a/shap/utils/_exceptions.py b/shap/utils/_exceptions.py
index 31f0290dc..7fbb6e660 100644
--- a/shap/utils/_exceptions.py
+++ b/shap/utils/_exceptions.py
@@ -1,3 +1,6 @@
+from multiprocessing.sharedctypes import Value
+
+
 class DimensionError(Exception):
     """
     Used for instances where dimensions are either
@@ -6,11 +9,23 @@ class DimensionError(Exception):
 
     pass
 
-class InvalidMaskerError(Exception):
+class InvalidMaskerError(ValueError):
     pass
 
 class ExplainerError(Exception):
     """
     Generic errors related to Explainers
     """
+    pass
+
+class InvalidAlgorithmError(ValueError):
+    pass
+
+class InvalidFeaturePerturbationError(ValueError):
+    pass
+
+class InvalidModelError(ValueError):
+    pass
+
+class InvalidClusteringError(ValueError):
     pass
\ No newline at end of file

From ad2e347a632f182f526d701d1774cca34a8fa693 Mon Sep 17 00:00:00 2001
From: Alexis Drakopoulos <33128441+alexisdrakopoulos@users.noreply.github.com>
Date: Wed, 15 Jun 2022 08:50:17 +0100
Subject: [PATCH 3/7] accidental import removed

---
 shap/utils/_exceptions.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/shap/utils/_exceptions.py b/shap/utils/_exceptions.py
index 7fbb6e660..baf73425a 100644
--- a/shap/utils/_exceptions.py
+++ b/shap/utils/_exceptions.py
@@ -1,6 +1,3 @@
-from multiprocessing.sharedctypes import Value
-
-
 class DimensionError(Exception):
     """
     Used for instances where dimensions are either

From 2055ff55e98adbdae0dd92a5f1f41ad848a5e73f Mon Sep 17 00:00:00 2001
From: Alexis Drakopoulos <33128441+alexisdrakopoulos@users.noreply.github.com>
Date: Wed, 15 Jun 2022 08:52:54 +0100
Subject: [PATCH 4/7] updating tests

---
 shap/actions/_optimizer.py      | 9 +--------
 shap/utils/_exceptions.py       | 7 +++++++
 tests/actions/_optimizer.py     | 6 +++---
 tests/explainers/test_linear.py | 4 ++--
 4 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/shap/actions/_optimizer.py b/shap/actions/_optimizer.py
index d1e911a3d..845e1dda3 100644
--- a/shap/actions/_optimizer.py
+++ b/shap/actions/_optimizer.py
@@ -3,14 +3,7 @@
 import warnings
 import copy
 from ._action import Action
-
-
-class InvalidAction(Exception):
-    pass
-
-
-class ConvergenceError(Exception):
-    pass
+from ..utils._exceptions import InvalidAction, ConvergenceError
 
 
 class ActionOptimizer:
diff --git a/shap/utils/_exceptions.py b/shap/utils/_exceptions.py
index baf73425a..762ef2ac9 100644
--- a/shap/utils/_exceptions.py
+++ b/shap/utils/_exceptions.py
@@ -6,6 +6,13 @@ class DimensionError(Exception):
 
     pass
 
+class InvalidAction(Exception):
+    pass
+
+
+class ConvergenceError(Exception):
+    pass
+
 class InvalidMaskerError(ValueError):
     pass
 
diff --git a/tests/actions/_optimizer.py b/tests/actions/_optimizer.py
index 870d17a29..b0ab5c2f1 100644
--- a/tests/actions/_optimizer.py
+++ b/tests/actions/_optimizer.py
@@ -6,7 +6,7 @@
 import pandas as pd
 import pytest
 import shap
-
+from shap.utils._exceptions import ConvergenceError, InvalidAction
 
 def create_basic_scenario():
     X = pd.DataFrame({"feature1": np.ones(5), "feature2": np.ones(5), "feature3": np.ones(5)})
@@ -80,7 +80,7 @@ def test_too_few_evals():
         [IncreaseFeature3(i) for i in range(1,20)]
     ]
     optimizer = shap.ActionOptimizer(passed, possible_actions)
-    with pytest.raises(Exception):
+    with pytest.raises(ConvergenceError):
         optimizer(X.iloc[0], max_evals=3)
 
 
@@ -98,5 +98,5 @@ def test_run_out_of_group():
 
 
 def test_bad_action():
-    with pytest.raises(Exception):
+    with pytest.raises(InvalidAction):
         shap.ActionOptimizer(None, [None])
\ No newline at end of file
diff --git a/tests/explainers/test_linear.py b/tests/explainers/test_linear.py
index 052d38a35..5a06c0dc4 100644
--- a/tests/explainers/test_linear.py
+++ b/tests/explainers/test_linear.py
@@ -6,7 +6,7 @@
 import scipy
 import pytest
 import shap
-
+from shap.utils._exceptions import InvalidMaskerError
 
 def test_tied_pair():
     np.random.seed(0)
@@ -36,7 +36,7 @@ def test_tied_pair_new():
     assert np.abs(explainer.shap_values(X) - np.array([0.5, 0.5, 0])).max() < 0.05
 
 def test_wrong_masker():
-    with pytest.raises(Exception):
+    with pytest.raises(InvalidMaskerError):
         shap.explainers.Linear((0, 0), shap.maskers.Image("blur(10,10)", (10, 10, 3)))
 
 def test_tied_triple():

From 1f599845729fdcc5c4aef30aaba7b6cc1bde7230 Mon Sep 17 00:00:00 2001
From: Alexis Drakopoulos <33128441+alexisdrakopoulos@users.noreply.github.com>
Date: Wed, 15 Jun 2022 17:03:08 +0100
Subject: [PATCH 5/7] wrong error

---
 tests/explainers/test_linear.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/explainers/test_linear.py b/tests/explainers/test_linear.py
index d47201027..e14ef7300 100644
--- a/tests/explainers/test_linear.py
+++ b/tests/explainers/test_linear.py
@@ -6,7 +6,7 @@
 import scipy
 import pytest
 import shap
-from shap.utils._exceptions import InvalidMaskerError
+from shap.utils._exceptions import NotImplementedError
 
 def test_tied_pair():
     np.random.seed(0)
@@ -36,7 +36,7 @@ def test_tied_pair_new():
     assert np.abs(explainer.shap_values(X) - np.array([0.5, 0.5, 0])).max() < 0.05
 
 def test_wrong_masker():
-    with pytest.raises(InvalidMaskerError):
+    with pytest.raises(NotImplementedError):
         shap.explainers.Linear((0, 0), shap.maskers.Image("blur(10,10)", (10, 10, 3)))
 
 def test_tied_triple():

From ecc5af43650149cfd4031820a13600290b9156c9 Mon Sep 17 00:00:00 2001
From: Alexis Drakopoulos <33128441+alexisdrakopoulos@users.noreply.github.com>
Date: Wed, 15 Jun 2022 17:30:04 +0100
Subject: [PATCH 6/7] accidental import

---
 tests/explainers/test_linear.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/explainers/test_linear.py b/tests/explainers/test_linear.py
index e14ef7300..509eabcb7 100644
--- a/tests/explainers/test_linear.py
+++ b/tests/explainers/test_linear.py
@@ -6,7 +6,6 @@
 import scipy
 import pytest
 import shap
-from shap.utils._exceptions import NotImplementedError
 
 def test_tied_pair():
     np.random.seed(0)

From 348dc7d8256b1f1fd874512a5ce3826476e442ab Mon Sep 17 00:00:00 2001
From: Alexis Drakopoulos <33128441+alexisdrakopoulos@users.noreply.github.com>
Date: Wed, 15 Jun 2022 21:58:17 +0100
Subject: [PATCH 7/7] accidental import

---
 shap/explainers/_explainer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/shap/explainers/_explainer.py b/shap/explainers/_explainer.py
index 6c660d837..f490190d8 100644
--- a/shap/explainers/_explainer.py
+++ b/shap/explainers/_explainer.py
@@ -1,5 +1,4 @@
 import copy
-from sqlite3 import NotSupportedError
 import time
 import numpy as np
 import scipy as sp