问将`shap.summary_plot()`的渐变颜色更改为特定的2或3 RGB渐变调色板颜色

EN

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.model_selection import train_test_split
import xgboost as xgb
import shap

# import some data to play with
iris = datasets.load_iris()
Y = pd.DataFrame(iris.target, columns = ["Species"])
X = pd.DataFrame(iris.data, columns = iris.feature_names)


X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0, stratify=Y)

params = { # General Parameters
            'booster': 'gbtree',
            # Param for boosting
             'eta': 0.2, 
             'gamma': 1,
             'max_depth': 5,
             'min_child_weight': 5,
             'subsample': 0.5,
             'colsample_bynode': 0.5,             
             'lambda': 0,  #default = 0                                        
             'alpha': 1,    #default = 1            
            # Command line parameters
             'num_rounds': 10000,
            # Learning Task Parameters
             'objective': 'multi:softprob' #'multi:softprob'
             }


model = xgb.XGBClassifier(**params, verbose=0, cv=5 , )
# fitting the model
model.fit(X_train,np.ravel(Y_train), eval_set=[(X_test, np.ravel(Y_test))], early_stopping_rounds=20)
# Tree on XGBoost
explainerXGB = shap.TreeExplainer(model, data=X, model_output ="margin")
#recall one  can put "probablity"  then we explain the output of the model transformed 
#into probability space (note that this means the SHAP values now sum to the probability output of the model).
shap_values_XGB_test = explainerXGB.shap_values(X_test)
shap_values_XGB_train = explainerXGB.shap_values(X_train)

shap.summary_plot(shap_values_XGB_train, X_train, )#color=cmap

from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap

RGB_val = 255

color01= (0,150,200)  # Blue wanted
color04= (220,60,60)  # red wanted
Colors = [color01, color04]

# Creating a blue red palette transition for graphics
Colors= [(R/RGB_val,G/RGB_val,B/RGB_val) for idx, (R,G,B) in enumerate(Colors)]
n = 256

# Start of the creation of the gradient
Color01= ListedColormap(Colors[0], name='Color01', N=None)
Color04= ListedColormap(Colors[1], name='Color04', N=None)
top = cm.get_cmap(Color01,128)
bottom = cm.get_cmap(Color04,128)
newcolors = np.vstack((top(np.linspace(0, 1, 128)),
                       bottom(np.linspace(0, 1, 128))))

mymin0 = newcolors[0][0]
mymin1 = newcolors[0][1]
mymin2 = newcolors[0][2]
mymin3 = newcolors[0][3]
mymax0 = newcolors[255][0]
mymax1 = newcolors[255][1]
mymax2 = newcolors[255][2]
mymax3 = newcolors[255][3]

GradientBlueRed= [np.linspace(mymin0, mymax0,  n),
                   np.linspace(mymin1, mymax1,  n),
                   np.linspace(mymin2, mymax2,  n),
                   np.linspace(mymin3, mymax3,  n)]

GradientBlueRed_res =np.transpose(GradientBlueRed)

# End of the creation of the gradient

newcmp = ListedColormap(GradientBlueRed_res, name='BlueRed')

shap.summary_plot(shap_values_XGB_train, X_train, color=newcmp)