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from turtle import title |
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import gradio as gr |
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import matplotlib.pyplot as plt |
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from sklearn.datasets import load_diabetes |
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from sklearn.linear_model import LinearRegression |
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from sklearn.model_selection import cross_val_predict |
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from sklearn.metrics import PredictionErrorDisplay |
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def predict_diabetes(subsample, plot_type): |
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X, y = load_diabetes(return_X_y=True) |
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lr = LinearRegression() |
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y_pred = cross_val_predict(lr, X, y, cv=10) |
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fig, axs = plt.subplots(ncols=2, figsize=(8, 4)) |
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if "Actual vs. Predicted" in plot_type: |
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PredictionErrorDisplay.from_predictions( |
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y, |
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y_pred=y_pred, |
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kind="actual_vs_predicted", |
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subsample=subsample, |
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ax=axs[0], |
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random_state=0, |
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) |
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axs[0].set_title("Actual vs. Predicted values") |
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if "Residuals vs. Predicted" in plot_type: |
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PredictionErrorDisplay.from_predictions( |
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y, |
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y_pred=y_pred, |
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kind="residual_vs_predicted", |
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subsample=subsample, |
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ax=axs[1], |
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random_state=0, |
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) |
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axs[1].set_title("Residuals vs. Predicted Values") |
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fig.suptitle("Plotting cross-validated predictions") |
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plt.tight_layout() |
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plt.close(fig) |
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image_path = "predictions.png" |
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fig.savefig(image_path) |
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return image_path |
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inputs = [ |
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gr.inputs.Slider(minimum=1, maximum=100, step=1, default=100, label="Subsample"), |
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gr.inputs.CheckboxGroup(["Actual vs. Predicted", "Residuals vs. Predicted"], label="Plot Types", default=["Actual vs. Predicted", "Residuals vs. Predicted"]) |
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] |
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outputs = gr.outputs.Image(label="Cross-Validated Predictions", type="pil") |
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title = "Plotting Cross-Validated Predictions" |
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description="This app plots cross-validated predictions for a linear regression model trained on the diabetes dataset. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html" |
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examples = [ |
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[ |
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100, |
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["Actual vs. Predicted"], |
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"Plotting cross-validated predictions with Actual vs. Predicted plot.", |
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], |
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[ |
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50, |
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["Residuals vs. Predicted"], |
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"Plotting cross-validated predictions with Residuals vs. Predicted plot.", |
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], |
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[ |
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75, |
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["Actual vs. Predicted", "Residuals vs. Predicted"], |
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"Plotting cross-validated predictions with both Actual vs. Predicted and Residuals vs. Predicted plots.", |
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], |
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] |
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gr.Interface(fn=predict_diabetes, title=title, description=description, examples=examples, inputs=inputs, outputs=outputs).launch() |
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