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https://github.com/snehankekre/streamlit-shap
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
https://github.com/snehankekre/streamlit-shap
explainability interpretability machine-learning shap shapley streamlit streamlit-component
Last synced: about 1 month ago
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streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
- Host: GitHub
- URL: https://github.com/snehankekre/streamlit-shap
- Owner: snehankekre
- License: mit
- Created: 2022-01-31T13:41:19.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-07-21T06:52:56.000Z (over 2 years ago)
- Last Synced: 2024-12-10T05:51:42.265Z (about 2 months ago)
- Topics: explainability, interpretability, machine-learning, shap, shapley, streamlit, streamlit-component
- Language: Python
- Homepage: https://pypi.org/project/streamlit-shap/
- Size: 4.52 MB
- Stars: 84
- Watchers: 4
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# streamlit-shap
This component provides a wrapper to display [SHAP](https://github.com/slundberg/shap) plots in [Streamlit](https://streamlit.io/).
### Installation
First install Streamlit (of course!) then pip install this library:
```bash
pip install streamlit
pip install streamlit-shap
```
### Example
```python
import streamlit as st
from streamlit_shap import st_shap
import shap
from sklearn.model_selection import train_test_split
import xgboost
import numpy as np
import pandas as pd
@st.experimental_memo
def load_data():
return shap.datasets.adult()
@st.experimental_memo
def load_model(X, y):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)
d_train = xgboost.DMatrix(X_train, label=y_train)
d_test = xgboost.DMatrix(X_test, label=y_test)
params = {
"eta": 0.01,
"objective": "binary:logistic",
"subsample": 0.5,
"base_score": np.mean(y_train),
"eval_metric": "logloss",
"n_jobs": -1,
}
model = xgboost.train(params, d_train, 10, evals = [(d_test, "test")], verbose_eval=100, early_stopping_rounds=20)
return model
st.title("SHAP in Streamlit")
# train XGBoost model
X,y = load_data()
X_display,y_display = shap.datasets.adult(display=True)
model = load_model(X, y)
# compute SHAP values
explainer = shap.Explainer(model, X)
shap_values = explainer(X)
st_shap(shap.plots.waterfall(shap_values[0]), height=300)
st_shap(shap.plots.beeswarm(shap_values), height=300)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
st_shap(shap.force_plot(explainer.expected_value, shap_values[0,:], X_display.iloc[0,:]), height=200, width=1000)
st_shap(shap.force_plot(explainer.expected_value, shap_values[:1000,:], X_display.iloc[:1000,:]), height=400, width=1000)
```
#### Notes
Colorbar changes in `matplotlib>3.4.3` introduced bugs ([#22625](https://github.com/matplotlib/matplotlib/issues/22625), [#22087](https://github.com/matplotlib/matplotlib/issues/22087)) that cause the colorbar of certain shap plots (e.g. `beeswarm`) to not display properly. If colorbars are not displayed properly, try downgrading `matplotlib` to `3.4.3`.