Everything for space

app.py

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+from __future__ import annotations
+
+import numpy as np
+import gradio as gr
+from sklearn.svm import SVC
+import plotly.graph_objects as go
+from sklearn.datasets import load_digits
+from sklearn.model_selection import validation_curve
+
+def plot_validation_curve(x: np.array, ys: list[np.array], yerros: list[np.array], names: list[str], colors: list[str], log_x: bool=True, title: str=""):
+    fig = go.Figure()
+
+    for y, yerror, name, color in zip(ys, yerros, names, colors):
+        y_upper = y + yerror
+        y_lower = y - yerror
+        
+        fig.add_trace(
+            go.Scatter(
+                x=x,
+                y=y,
+                name=name,
+                line_color=color
+            )
+        )
+
+        fig.add_trace(
+            go.Scatter(
+                x=x.tolist()+x[::-1].tolist(), # x, then x reversed
+                y=y_upper.tolist()+y_lower[::-1].tolist(), # upper, then lower reversed
+                fill='toself',
+                fillcolor=color,
+                line=dict(color=color),
+                hoverinfo="skip",
+                showlegend=False,
+                opacity=0.2
+            )
+        )
+
+    if log_x:
+        fig.update_xaxes(type="log")
+
+    fig.update_layout(title=title, xaxis_title="gamma", yaxis_title="Accuracy")
+
+    return fig
+
+
+
+def app_fn(n_points: int):
+    X, y = load_digits(return_X_y=True)
+    subset_mask = np.isin(y, [1, 2])  # binary classification: 1 vs 2
+    X, y = X[subset_mask], y[subset_mask]
+
+    param_range = np.logspace(-6, -1, n_points)
+    train_scores, test_scores = validation_curve(
+        SVC(),
+        X,
+        y,
+        param_name="gamma",
+        param_range=param_range,
+        scoring="accuracy",
+        n_jobs=-1,
+    )
+
+    train_scores_mean = np.mean(train_scores, axis=1)
+    train_scores_std = np.std(train_scores, axis=1)
+    test_scores_mean = np.mean(test_scores, axis=1)
+    test_scores_std = np.std(test_scores, axis=1)
+
+    fig = plot_validation_curve(
+        param_range, 
+        [train_scores_mean, test_scores_mean], 
+        [train_scores_std, test_scores_std], 
+        ["Training score", "Cross-validation score"], 
+        ["orange", "navy"], 
+        title="Validation Curve with SVM for Gamma Hyperparameter"
+    )
+
+    return fig
+
+title = "Plotting Validation Curve"
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(
+        """
+        #### This example shows the usage of a validation curve to understand \
+        how the performance of a model, SVM in this case, changes with varying hyperparameters. \
+        The dataset used was the [digits dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) \
+        from scikit-learn. The hyperparameter varied was gamma. \
+        
+        [Original Example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py)
+        """
+    )
+
+    n_points = gr.inputs.Slider(5, 100, 5, 5,label="Number of points")
+    btn = gr.Button("Run")
+    fig = gr.Plot(label="Validation Curve")
+
+    btn.click(fn=app_fn, inputs=[n_points], outputs=[fig])
+    demo.load(fn=app_fn, inputs=[n_points], outputs=[fig])
+
+demo.launch()

requirements.txt

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+scikit-learn==1.2.2
+plotly==5.14.1