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from __future__ import annotations |
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import numpy as np |
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import gradio as gr |
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import plotly.graph_objects as go |
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from sklearn.datasets import load_diabetes |
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from sklearn.ensemble import GradientBoostingRegressor |
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from sklearn.ensemble import RandomForestRegressor |
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from sklearn.linear_model import LinearRegression |
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from sklearn.ensemble import VotingRegressor |
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def plot_votes(preds: list[tuple[str, np.array]], markers: list[str]=None) -> go.Figure: |
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fig = go.Figure() |
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for idx, (name, pred) in enumerate(preds): |
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if not markers: |
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symbol = "diamond" |
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else: |
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symbol = markers[idx] |
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fig.add_trace( |
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go.Scatter( |
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y=pred, |
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mode="markers", |
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name=name, |
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marker=dict(symbol=symbol, size=10, line=dict(width=2, color="DarkSlateGrey")) |
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) |
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) |
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fig.update_layout( |
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title="Regressor predictions and their average", |
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yaxis_title="Predicted", |
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xaxis_title="Training Samples", |
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height=500, |
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xaxis=dict(showticklabels=False), |
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hovermode="x unified" |
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) |
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return fig |
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def app_fn(n: int) -> go.Figure: |
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X, y = load_diabetes(return_X_y=True) |
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reg1 = GradientBoostingRegressor(random_state=1) |
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reg2 = RandomForestRegressor(random_state=1) |
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reg3 = LinearRegression() |
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reg1.fit(X, y) |
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reg2.fit(X, y) |
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reg3.fit(X, y) |
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ereg = VotingRegressor([("gb", reg1), ("rf", reg2), ("lr", reg3)]) |
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ereg.fit(X, y) |
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xt = X[:n] |
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pred1 = reg1.predict(xt) |
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pred2 = reg2.predict(xt) |
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pred3 = reg3.predict(xt) |
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pred4 = ereg.predict(xt) |
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preds = [ |
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("Gradient Boosting", pred1), |
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("Random Forest", pred2), |
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("Linear Regression", pred3), |
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("Voting Regressor", pred4) |
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] |
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markers = ["diamond-tall", "triangle-up", "square", "star"] |
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fig = plot_votes(preds, markers) |
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return fig |
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title="Individual and Voting Regression Predictions 🗳️" |
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with gr.Blocks() as demo: |
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gr.Markdown(f"# {title}") |
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gr.Markdown( |
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""" |
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A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. \ |
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Then it averages the individual predictions to form a final prediction. This example will use three different regressors to \ |
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predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression. Then the 3 regressors will be used for the VotingRegressor. \ |
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The dataset used consists of 10 features collected from a cohort of diabetes patients. The target is a quantitative measure of disease progression one year after baseline. |
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[Original example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py) |
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""" |
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) |
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n = gr.inputs.Slider(10, 30, 5, 20, "Number of training samples") |
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plot = gr.Plot(label="Individual & Voting Predictions") |
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n.change(fn=app_fn, inputs=[n], outputs=[plot]) |
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demo.load(fn=app_fn, inputs=[n], outputs=[plot]) |
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demo.launch() |
