Python shap values

linear_shap.py

import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
import numpy as np
import shap
shap.initjs()

# select a set of background examples to take an expectation over
background = np.array(data)
#[np.random.choice(X_train.shape[0],100,replace=False)]

model = sklearn.linear_model.LinearRegression()
model.fit(data.iloc[:,1:], data.iloc[:,0])

# explain predictions of the model on four images
e = shap.LinearExplainer(model, data.iloc[:,1:])
#explainer = shap.KernelExplainer(model, X_train, link="logit")
shap_values = e.shap_values(np.array(data.iloc[:,1:]))
shap.summary_plot(shap_values, -np.array(data.iloc[:,1:]))
explainer = shap.Explainer(model, data.iloc[:,1:])
shap.plots.heatmap(explainer(data.iloc[:,1:]))