Initial Commit

app.py

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+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
+
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import cross_val_score
+
+plt.switch_backend("agg")
+
+def true_fn(X):
+    return np.cos(1.5 * np.pi * X)
+
+
+def modelData(n_samples: int, degree: int, cv: int) -> "plt.Figure":
+    """
+    This function demonstrate the principle of overfitting vs underfitting by
+    modeling a dataset using Linear Regression.
+
+    :param n_samples: the number of samples required in the data.
+    :param degree: the number of degrees for the polynomial features.
+
+    :returns: the matplotlib figures
+    """
+
+    X = np.sort(np.random.rand(n_samples))
+    y = true_fn(X) + np.random.randn(n_samples) * .1
+
+    fig, ax = plt.subplots(1, 1, figsize=(24, 15))
+
+    poly_feats = PolynomialFeatures(degree=degree, include_bias=False)
+    model = LinearRegression()
+
+    pipeline = Pipeline([
+        ("polynomial_feats", poly_feats),
+        ("lr", model)
+    ])
+
+    pipeline.fit(X[:, np.newaxis], y)
+    scores = cross_val_score(
+        pipeline, X[:, np.newaxis], y, scoring="neg_mean_squared_error", cv=cv
+    )
+
+    X_test = np.linspace(0, 1, 1000)
+    
+    ax.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), "--", linewidth=2.5, color="#C73E1D", label="Model")
+    ax.plot(X_test, true_fn(X_test), linewidth=2.5, color="#2E86AB", label="True function")
+    ax.scatter(X, y, s=20, alpha=.75, edgecolors="#3B1F2B", label="Samples")
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    ax.set_xticks(())
+    ax.set_yticks(())
+    ax.set_xlim((0, 1))
+    ax.set_ylim((-2, 2))
+    ax.legend(loc="best")
+    ax.set_title(f"Degree : {degree} \n MSE: {-scores.mean():.2e}(+/- {scores.std():.2e})")
+
+    return fig
+
+
+with gr.Blocks() as demo:
+
+    gr.Markdown(""" 
+    # Underfitting vs Overfitting
+
+    This space is a re-implementation of the original scikit-learn docs [Underfitting vs Overfitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)
+    In this space you can vary the sliders to get a picture of what an **underfitted** model looks like and what an **overfitted** model looks like.
+    If you want more details you can always head onto the scikit-learn doc mentioned above. 
+
+    Have fun enjoying the tool 🤗
+    """)
+    
+    n_samples = gr.Slider(30, 10_000, label="n_samples", info="number of samples", step=1, value=100)
+    degree = gr.Slider(1, 20, label="degree", info="the polynomial features degree", step=1, value=4)
+    cv = gr.Slider(1, 10, label="cv", info="number of cross-validation to run", step=1, value=5)
+
+    output = gr.Plot(label="Plot")
+
+    btn = gr.Button("Show")
+    btn.click(fn=modelData, inputs=[n_samples, degree, cv], outputs=output, api_name="overfitunderfit")
+
+

requirements.txt

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+scikit-learn
+scipy
+numpy
+pandas
+matplotlib