gradio/demo/xgboost-income-prediction-with-explainability/run.py

import gradio as gr
import random
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import shap
import xgboost as xgb
from datasets import load_dataset


matplotlib.use("Agg")
dataset = load_dataset("scikit-learn/adult-census-income")
X_train = dataset["train"].to_pandas()
_ = X_train.pop("fnlwgt")
_ = X_train.pop("race")
y_train = X_train.pop("income")
y_train = (y_train == ">50K").astype(int)
categorical_columns = [
    "workclass",
    "education",
    "marital.status",
    "occupation",
    "relationship",
    "sex",
    "native.country",
]
X_train = X_train.astype({col: "category" for col in categorical_columns})
data = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)
model = xgb.train(params={"objective": "binary:logistic"}, dtrain=data)
explainer = shap.TreeExplainer(model)

def predict(*args):
    df = pd.DataFrame([args], columns=X_train.columns)
    df = df.astype({col: "category" for col in categorical_columns})
    pos_pred = model.predict(xgb.DMatrix(df, enable_categorical=True))
    return {">50K": float(pos_pred[0]), "<=50K": 1 - float(pos_pred[0])}


def interpret(*args):
    df = pd.DataFrame([args], columns=X_train.columns)
    df = df.astype({col: "category" for col in categorical_columns})
    shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True))
    scores_desc = list(zip(shap_values[0], X_train.columns))
    scores_desc = sorted(scores_desc)
    fig_m = plt.figure(tight_layout=True)
    plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
    plt.title("Feature Shap Values")
    plt.ylabel("Shap Value")
    plt.xlabel("Feature")
    plt.tight_layout()
    return fig_m


unique_class = sorted(X_train["workclass"].unique())
unique_education = sorted(X_train["education"].unique())
unique_marital_status = sorted(X_train["marital.status"].unique())
unique_relationship = sorted(X_train["relationship"].unique())
unique_occupation = sorted(X_train["occupation"].unique())
unique_sex = sorted(X_train["sex"].unique())
unique_country = sorted(X_train["native.country"].unique())

with gr.Blocks() as demo:
    gr.Markdown("""
    **Income Classification with XGBoost 💰**:  This demo uses an XGBoost classifier predicts income based on demographic factors, along with Shapley value-based *explanations*. The [source code for this Gradio demo is here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability/blob/main/app.py).
    """)
    with gr.Row():
        with gr.Column():
            age = gr.Slider(label="Age", minimum=17, maximum=90, step=1, randomize=True)
            work_class = gr.Dropdown(
                label="Workclass",
                choices=unique_class,
                value=lambda: random.choice(unique_class),
            )
            education = gr.Dropdown(
                label="Education Level",
                choices=unique_education,
                value=lambda: random.choice(unique_education),
            )
            years = gr.Slider(
                label="Years of schooling",
                minimum=1,
                maximum=16,
                step=1,
                randomize=True,
            )
            marital_status = gr.Dropdown(
                label="Marital Status",
                choices=unique_marital_status,
                value=lambda: random.choice(unique_marital_status),
            )
            occupation = gr.Dropdown(
                label="Occupation",
                choices=unique_occupation,
                value=lambda: random.choice(unique_occupation),
            )
            relationship = gr.Dropdown(
                label="Relationship Status",
                choices=unique_relationship,
                value=lambda: random.choice(unique_relationship),
            )
            sex = gr.Dropdown(
                label="Sex", choices=unique_sex, value=lambda: random.choice(unique_sex)
            )
            capital_gain = gr.Slider(
                label="Capital Gain",
                minimum=0,
                maximum=100000,
                step=500,
                randomize=True,
            )
            capital_loss = gr.Slider(
                label="Capital Loss", minimum=0, maximum=10000, step=500, randomize=True
            )
            hours_per_week = gr.Slider(
                label="Hours Per Week Worked", minimum=1, maximum=99, step=1
            )
            country = gr.Dropdown(
                label="Native Country",
                choices=unique_country,
                value=lambda: random.choice(unique_country),
            )
        with gr.Column():
            label = gr.Label()
            plot = gr.Plot()
            with gr.Row():
                predict_btn = gr.Button(value="Predict")
                interpret_btn = gr.Button(value="Explain")
            predict_btn.click(
                predict,
                inputs=[
                    age,
                    work_class,
                    education,
                    years,
                    marital_status,
                    occupation,
                    relationship,
                    sex,
                    capital_gain,
                    capital_loss,
                    hours_per_week,
                    country,
                ],
                outputs=[label],
            )
            interpret_btn.click(
                interpret,
                inputs=[
                    age,
                    work_class,
                    education,
                    years,
                    marital_status,
                    occupation,
                    relationship,
                    sex,
                    capital_gain,
                    capital_loss,
                    hours_per_week,
                    country,
                ],
                outputs=[plot],
            )

demo.launch()