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README.md

@@ -1,8 +1,8 @@
 ---
 title: Xgboost Income Prediction With Explainability
-emoji: 📈
-colorFrom: yellow
-colorTo: gray
+emoji: 💰
+colorFrom: blue
+colorTo: green
 sdk: gradio
 sdk_version: 3.1.7
 app_file: app.py

app.py

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+import random
+
+import gradio as gr
+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 example shows how to load data from the hugging face hub to train an XGBoost classifier and
+    demo the predictions with gradio.
+
+    The source is [here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability).
+    """)
+    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="Interpret")
+            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()

requirements.txt

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+matplotlib
+shap
+xgboost
+pandas
+datasets