yptheangel · October 17, 2022 09:33
diff --git a/shap_streamlit_xgb.py b/shap_streamlit_xgb.py
 import shap
 import streamlit as st
 import streamlit.components.v1 as components
 import xgboost
 import matplotlib.pyplot as plt

 @st.cache
 def load_data():
    return shap.datasets.boston()

 def st_shap(plot, height=None):
    shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>"
    components.html(shap_html, height=height)

 st.title("SHAP in Streamlit")

 # train XGBoost model
 X,y = load_data()
 model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)

 # explain the model's predictions using SHAP
 # (same syntax works for LightGBM, CatBoost, scikit-learn and spark models)
 explainer = shap.TreeExplainer(model)
 shap_values = explainer.shap_values(X)


 # actual plotting
 st_shap(shap.force_plot(explainer.expected_value, shap_values[0,:], X.iloc[0,:]))

 st.set_option('deprecation.showPyplotGlobalUse', False)
 shap.summary_plot(shap_values, X)
 st.pyplot(bbox_inches='tight')
 plt.clf()


 # shap_object = shap.Explanation(base_values = shap_values[0][0].base_values,
 #                                     values = shap_values[0].values,
 #                                     feature_names = X.columns,
 #                                     data = shap_values[0].data)

 # class ShapObject:
    
 #     def __init__(self, base_values, data, values, feature_names):
 #         self.base_values = base_values # Single value
 #         self.data = data # Raw feature values for 1 row of data
 #         self.values = values # SHAP values for the same row of data
 #         self.feature_names = feature_names # Column names
        
 # row = 10
 # shap_object = ShapObject(base_values = explainer.expected_value[1],
 #                          values = explainer.shap_values(X)[1][row,:],
 #                          feature_names = X.columns,
 #                          data = X.iloc[row,:])

 # shap_object = ShapObject(base_values = shap_values[0][0].base_values,
 #                          values = shap_values[0].values,
 #                          feature_names = X.columns,
 #                          data = shap_values[0].data)

 # shap.plots.waterfall_plot(shap_object)

 # shap.plots.waterfall(shap_object)
 # shap.plots.waterfall(shap_values[0])
 # shap.plots._waterfall.waterfall_legacy(explainer.expected_value, shap_values)
 # st.pyplot(bbox_inches='tight')
 # plt.clf()

 # visualize the training set predictions
 st_shap(shap.force_plot(explainer.expected_value, shap_values, X), 400)
	import shap
	import streamlit as st
	import streamlit.components.v1 as components
	import xgboost
	import matplotlib.pyplot as plt

	@st.cache
	def load_data():
	return shap.datasets.boston()

	def st_shap(plot, height=None):
	shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>"
	components.html(shap_html, height=height)

	st.title("SHAP in Streamlit")

	# train XGBoost model
	X,y = load_data()
	model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)

	# explain the model's predictions using SHAP
	# (same syntax works for LightGBM, CatBoost, scikit-learn and spark models)
	explainer = shap.TreeExplainer(model)
	shap_values = explainer.shap_values(X)


	# actual plotting
	st_shap(shap.force_plot(explainer.expected_value, shap_values[0,:], X.iloc[0,:]))

	st.set_option('deprecation.showPyplotGlobalUse', False)
	shap.summary_plot(shap_values, X)
	st.pyplot(bbox_inches='tight')
	plt.clf()


	# shap_object = shap.Explanation(base_values = shap_values[0][0].base_values,
	# values = shap_values[0].values,
	# feature_names = X.columns,
	# data = shap_values[0].data)

	# class ShapObject:

	# def __init__(self, base_values, data, values, feature_names):
	# self.base_values = base_values # Single value
	# self.data = data # Raw feature values for 1 row of data
	# self.values = values # SHAP values for the same row of data
	# self.feature_names = feature_names # Column names

	# row = 10
	# shap_object = ShapObject(base_values = explainer.expected_value[1],
	# values = explainer.shap_values(X)[1][row,:],
	# feature_names = X.columns,
	# data = X.iloc[row,:])

	# shap_object = ShapObject(base_values = shap_values[0][0].base_values,
	# values = shap_values[0].values,
	# feature_names = X.columns,
	# data = shap_values[0].data)

	# shap.plots.waterfall_plot(shap_object)

	# shap.plots.waterfall(shap_object)
	# shap.plots.waterfall(shap_values[0])
	# shap.plots._waterfall.waterfall_legacy(explainer.expected_value, shap_values)
	# st.pyplot(bbox_inches='tight')
	# plt.clf()

	# visualize the training set predictions
	st_shap(shap.force_plot(explainer.expected_value, shap_values, X), 400)