yptheangel/shap_streamlit_xgb.py

## 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)