PyplotGlobalUseWarning

Using Streamlit
7

hello,
i have same issue in my code :slight_smile:
PyplotGlobalUseWarning : You are calling st.pyplot() without any arguments. After December 1st, 2020, we will remove the ability to do this as it requires the use of Matplotlib’s global figure object, which is not thread-safe.

code

import streamlit as st

import pandas as pd

import shap

import matplotlib.pyplot as plt

from sklearn import datasets

from sklearn.ensemble import RandomForestRegressor

st.write("""

# Boston House Price Prediction App

This app predicts the **Boston House Price**!

""")

st.write('---')

# Loads the Boston House Price Dataset

boston = datasets.load_boston()

X = pd.DataFrame(boston.data, columns=boston.feature_names)

Y = pd.DataFrame(boston.target, columns=["MEDV"])

# Sidebar

# Header of Specify Input Parameters

st.sidebar.header('Specify Input Parameters')

def user_input_features():

    CRIM = st.sidebar.slider('CRIM', X.CRIM.min(), X.CRIM.max(), X.CRIM.mean())

    ZN = st.sidebar.slider('ZN', X.ZN.min(), X.ZN.max(), X.ZN.mean())

    INDUS = st.sidebar.slider('INDUS', X.INDUS.min(), X.INDUS.max(), X.INDUS.mean())

    CHAS = st.sidebar.slider('CHAS', X.CHAS.min(), X.CHAS.max(), X.CHAS.mean())

    NOX = st.sidebar.slider('NOX', X.NOX.min(), X.NOX.max(), X.NOX.mean())

    RM = st.sidebar.slider('RM', X.RM.min(), X.RM.max(), X.RM.mean())

    AGE = st.sidebar.slider('AGE', X.AGE.min(), X.AGE.max(), X.AGE.mean())

    DIS = st.sidebar.slider('DIS', X.DIS.min(), X.DIS.max(), X.DIS.mean())

    RAD = st.sidebar.slider('RAD', X.RAD.min(), X.RAD.max(), X.RAD.mean())

    TAX = st.sidebar.slider('TAX', X.TAX.min(), X.TAX.max(), X.TAX.mean())

    PTRATIO = st.sidebar.slider('PTRATIO', X.PTRATIO.min(), X.PTRATIO.max(), X.PTRATIO.mean())

    B = st.sidebar.slider('B', X.B.min(), X.B.max(), X.B.mean())

    LSTAT = st.sidebar.slider('LSTAT', X.LSTAT.min(), X.LSTAT.max(), X.LSTAT.mean())

    data = {'CRIM': CRIM,

            'ZN': ZN,

            'INDUS': INDUS,

            'CHAS': CHAS,

            'NOX': NOX,

            'RM': RM,

            'AGE': AGE,

            'DIS': DIS,

            'RAD': RAD,

            'TAX': TAX,

            'PTRATIO': PTRATIO,

            'B': B,

            'LSTAT': LSTAT}

    features = pd.DataFrame(data, index=[0])

    return features

df = user_input_features()

# Main Panel

# Print specified input parameters

st.header('Specified Input parameters')

st.write(df)

st.write('---')

# Build Regression Model

model = RandomForestRegressor()

model.fit(X, Y)

# Apply Model to Make Prediction

prediction = model.predict(df)

st.header('Prediction of MEDV')

st.write(prediction)

st.write('---')

# Explaining the model's predictions using SHAP values

# https://github.com/slundberg/shap

explainer = shap.TreeExplainer(model)

shap_values = explainer.shap_values(X)

st.header('Feature Importance')

plt.title('Feature importance based on SHAP values')

shap.summary_plot(shap_values, X)

st.pyplot(bbox_inches='tight')

st.write('---')

plt.title('Feature importance based on SHAP values (Bar)')

shap.summary_plot(shap_values, X, plot_type="bar")

st.pyplot(bbox_inches='tight')