library(reticulate)

repl_python()

import numpy as np
import pandas as pd

train = pd.DataFrame(np.random.randn(100, 4), columns = list('ABCD'))
test = pd.DataFrame(np.random.randn(10, 4), columns = list('ABCD'))

target = 'A'

y_train, y_test = train[target].values, test[target].values
X_train, X_test = train.loc[:, train.columns != target], test.loc[:, test.columns != target]

import xgboost

params = {
  'nrounds': 50,
  'max_depth':8,
  'eta': 0.2,
  'gamma':0,
  'colsample_bytree': 0.8,
  'min_child_weight': 4,
  'subsample': 1,
  'objective':'reg:linear'
}

dtrain = xgboost.DMatrix(X_train, label = y_train)
dtest = xgboost.DMatrix(X_test, label = y_test)

num_boost_round = 999
early_str = 10

model = xgboost.train(
  params,
  dtrain,
  num_boost_round = num_boost_round,
  evals = [(dtest, "Test")],
  early_stopping_rounds = early_str
)

import shap
shap.initjs()
shap_values = shap.TreeExplainer(model).shap_values(X_train)

results = shap.force_plot(shap_values, X_train)

exit

py$results$data