catboost is unable to calculate shap values when set_scale_and_bias is used

[antipisa]

catboost version: 1.0.6
Operating System: Linux
CPU: Y

Problem:
catboost cannot compute shap values if the model has set a scale and bias via the set_scale_and_bias function.

import catboost as cb
import shap
import numpy as np
import pandas as pd
N = 1000
p = 100
train_data = np.random.randn(N, p)
train_label = np.random.randn(N)


model = cb.CatBoostRegressor(num_boost_round=500, learning_rate=0.05)
pool = cb.Pool(train_data, train_label)
model.fit(pool, verbose_eval=500)
model.set_scale_and_bias(0.5, 0.0)
print(model.get_scale_and_bias())


explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(pool)

output:

0:	learn: 1.0010493	total: 9.97ms	remaining: 4.97s
499:	learn: 0.2587147	total: 2.02s	remaining: 0us
(0.5, 0.0)
---------------------------------------------------------------------------
CatBoostError                             Traceback (most recent call last)
/var/folders/n2/yv9l5srn20563nwmmh5yrk_w0000gn/T/ipykernel_26189/3254303356.py in <module>
     13 
     14 explainer = shap.TreeExplainer(model)
---> 15 shap_values = explainer.shap_values(pool)

~/miniconda3/lib/python3.7/site-packages/shap/explainers/_tree.py in shap_values(self, X, y, tree_limit, approximate, check_additivity, from_call)
    366                 if type(X) != catboost.Pool:
    367                     X = catboost.Pool(X, cat_features=self.model.cat_feature_indices)
--> 368                 phi = self.model.original_model.get_feature_importance(data=X, fstr_type='ShapValues')
    369 
    370             # note we pull off the last column and keep it as our expected_value

~/miniconda3/lib/python3.7/site-packages/catboost/core.py in get_feature_importance(self, data, type, prettified, thread_count, verbose, fstr_type, shap_mode, model_output, interaction_indices, shap_calc_type, reference_data, log_cout, log_cerr)
   3055             shap_calc_type = enum_from_enum_or_str(EShapCalcType, shap_calc_type).value
   3056             fstr, feature_names = self._calc_fstr(type, data, reference_data, thread_count, verbose, model_output, shap_mode, interaction_indices,
-> 3057                                                   shap_calc_type)
   3058         if type in (EFstrType.PredictionValuesChange, EFstrType.LossFunctionChange, EFstrType.PredictionDiff):
   3059             feature_importances = [value[0] for value in fstr]

~/miniconda3/lib/python3.7/site-packages/catboost/core.py in _calc_fstr(self, type, pool, reference_data, thread_count, verbose, model_output, shap_mode, interaction_indices, shap_calc_type)
   1774 
   1775     def _calc_fstr(self, type, pool, reference_data, thread_count, verbose, model_output, shap_mode, interaction_indices, shap_calc_type):
-> 1776         return self._object._calc_fstr(type.name, pool, reference_data, thread_count, verbose, model_output, shap_mode, interaction_indices, shap_calc_type)
   1777 
   1778     def _calc_ostr(self, train_pool, test_pool, top_size, ostr_type, update_method, importance_values_sign, thread_count, verbose):

_catboost.pyx in _catboost._CatBoost._calc_fstr()

_catboost.pyx in _catboost._CatBoost._calc_fstr()

CatBoostError: catboost/libs/fstr/calc_fstr.cpp:485: Non-identity {Scale} for feature importance is not supported