I'm running xgboost for machine learning, and after successful completion of my machine learning using XGBClassifier , I want to make plots of the results.
A minimal working example of my input data in JSON format:
[{"age":58,"Deceased":"False","sex":"False"},{"Deceased":"False","age":59,"sex":"False"},{"sex":"False","age":"68","Deceased":"False"},{"Deceased":"False","age":"26","sex":"False"},{"Deceased":"False","age":87,"sex":"False"},{"sex":"True","age":31,"Deceased":"False"},{"Deceased":"False","age":"35","sex":"False"},{"sex":"False","Deceased":"False","age":41},{"age":"78","Deceased":"False","sex":"True"},{"Deceased":"False","age":"45","sex":"True"},{"sex":"False","age":56,"Deceased":"False"},{"sex":"False","Deceased":"False","age":"26"},{"sex":"True","age":"64","Deceased":"False"},{"sex":"False","age":"37","Deceased":"False"},{"age":"86","Deceased":"True","sex":"False"},{"age":76,"Deceased":"True","sex":"True"},{"Deceased":"True","age":69,"sex":"False"},{"Deceased":"True","age":79,"sex":"True"}]
Following advice from https://evgenypogorelov.com/multiclass-xgb-shap.html
my script:

import mlflow
import sys
import json
import mlflow.sklearn
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split, KFold, cross_val_score
import xgboost
import shap
from sklearn.metrics import accuracy_score, precision_score, plot_roc_curve

def ref_to_json_file(data, filename):
    json1=json.dumps(data)
    f = open(filename,"w+")
    print(json1,file=f)

def xgbclassifier_wrapper( json_file, dependent_var, output_stem):
  #https://xgboost.readthedocs.io/en/latest/parameter.html
  pandasDF = pd.read_json(json_file)
  bool_cols = ["Deceased", "sex"]#, 'Hospitalized', 'Respiratory_Support', 'sex']
  for col in bool_cols:
    pandasDF[col] = pandasDF[col]=='True'
  Y = pandasDF[dependent_var]
  X = pandasDF.drop([dependent_var], axis=1)
  
  X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
  mlflow.sklearn.autolog()

  # With autolog() enabled, all model parameters, a model score, and the fitted model are automatically logged.  
  with mlflow.start_run():
    # Set the model parameters. 
    n_estimators = 200
    colsample_bytree = 0.3
    learning_rate = 0.05
    max_depth = 6# default 6; max. depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in lossguided growing policy when tree_method is set as hist or gpu_hist and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.
    #min_child_rate = 0
    gamma = 0 # default = 0; Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger gamma is, the more conservative the algorithm will be.

    # Create and train model.
    xg_clf = xgboost.XGBClassifier( n_estimators=n_estimators, colsample_bytree=colsample_bytree, learning_rate=learning_rate, max_depth=max_depth)
    xg_clf.fit(X_train, y_train)
    # Use the model to make predictions on the test dataset.
    predictions = xg_clf.predict(X_test)
  accuracy = accuracy_score(y_test, predictions)
  pre_score  = precision_score(y_test, predictions)
  feature_importances = pd.DataFrame(xg_clf.feature_importances_, index=X.columns, columns=['importance'])
  feature_importances.to_json("data/" + output_stem + '.feature_importances.json')
  kfold = KFold(n_splits=10)
  results = cross_val_score(xg_clf, X, Y, cv=kfold)
  accuracy = results.mean() * 100
  roc = plot_roc_curve(xg_clf, X_test, y_test, name = dependent_var)
  return accuracy

json_file = 'debug.json'#"/home/con/covid_study2065/data/pat.data.array.json"
if not os.path.isfile(json_file):
    sys.exit("json file doesn't exist.")
deceased = xgbclassifier_wrapper(json_file, "Deceased", 'debug')
explainer = shap.TreeExplainer(deceased.xg_clf, model_output = "raw", feature_perturbation="interventional", data = deceased.X)

explainer = shap.TreeExplainer(deceased.xg_clf, model_output = "probability", feature_perturbation="interventional", data = deceased.X)

gives an error:

Exception ignored in: 'array_dealloc'
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_tree.py", line 1353, in __init__
    _cext.dense_tree_update_weights(
SystemError: <class 'DeprecationWarning'> returned a result with an error set
Found a NULL input array in _cext_dense_tree_update_weights!
Traceback (most recent call last):
  File "debug.py", line 97, in <module>
    explainer = shap.TreeExplainer(deceased.xg_clf, model_output = "probability", feature_perturbation="interventional", data = deceased.X)
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_tree.py", line 147, in __init__
    self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_tree.py", line 827, in __init__
    self.trees = xgb_loader.get_trees(data=data, data_missing=data_missing)
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_tree.py", line 1522, in get_trees
    trees.append(SingleTree({
  File "/usr/local/lib/python3.8/dist-packages/shap/explainers/_tree.py", line 1353, in __init__
    _cext.dense_tree_update_weights(
SystemError: <built-in function dense_tree_update_weights> returned NULL without setting an error

When I look at deceased.xg_clf , which is input to shap.TreeExplainer :

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=0.3, gamma=0, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=0.05, max_delta_step=0, max_depth=6,
              min_child_weight=1, missing=nan, monotone_constraints='()',
              n_estimators=200, n_jobs=1, num_parallel_tree=1, random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
              tree_method='exact', validate_parameters=1, verbosity=None)

Adjusting the input to XGBClassifer to the same parameters that the tutorial used, viz.

xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1.0,
         gamma=0.0, max_delta_step=0.0, min_child_weight=1.0,
         missing=None, n_jobs=-1, objective='binary:logistic', random_state=42, reg_alpha=0.0,
         reg_lambda=1.0, scale_pos_weight=1.0, tree_method='auto')

also gives the same error as my parameters.
I have literally no idea what's causing this error, and this message isn't helpful: I never did anything like array_alloc , which I thought was a C-level thing to do.
this error also occurs when doing a parameter grid_search.
I'm running Python 3.8.0 on Ubuntu 18.04 on a VM, using shap 0.38.1 The error also occurs on Python 3.8.5. The error also occurs with Ubuntu 20.04.2 LTS (Focal Fossa) 64-bit, linux kernel 5.8.044-generic x86_64.
Updating to shap version 0.39.0 did not help.
I tried updating to Python 3.8.8, but that made the situation even worse, because one of the dependencies of shap isn't compatible with that version:

Collecting slicer==0.0.7 (from shap)
  Could not find a version that satisfies the requirement slicer==0.0.7 (from shap) (from versions: )
No matching distribution found for slicer==0.0.7 (from shap)

I've opened an issue on their GitHub page: https://github.com/slundberg/shap/issues/1844
also, my versions of xgboost, numpy, and scipy are all up-to-date:

Requirement already up-to-date: xgboost in /usr/local/lib/python3.8/dist-packages (1.3.3)
Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.19.5)
Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.6.1)

How can I run the shap library?
or... is there some competitor to shap that I could use?