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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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from sklearn.svm import SVC |
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import plotly.graph_objects as go |
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from sklearn.datasets import load_digits |
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from sklearn.model_selection import validation_curve |
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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=""): |
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fig = go.Figure() |
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for y, yerror, name, color in zip(ys, yerros, names, colors): |
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y_upper = y + yerror |
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y_lower = y - yerror |
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fig.add_trace( |
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go.Scatter( |
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x=x, |
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y=np.round(y, 3), |
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name=name, |
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line_color=color |
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) |
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) |
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fig.add_trace( |
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go.Scatter( |
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x=x.tolist()+x[::-1].tolist(), |
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y=y_upper.tolist()+y_lower[::-1].tolist(), |
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fill='toself', |
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fillcolor=color, |
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line=dict(color=color), |
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hoverinfo="skip", |
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showlegend=False, |
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opacity=0.2 |
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) |
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) |
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if log_x: |
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fig.update_xaxes(type="log") |
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fig.update_layout( |
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title=title, |
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xaxis_title="Hyperparameter", |
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yaxis_title="Accuracy", |
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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_points: int, param_name: str): |
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X, y = load_digits(return_X_y=True) |
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subset_mask = np.isin(y, [1, 2]) |
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X, y = X[subset_mask], y[subset_mask] |
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if param_name=="gamma": |
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param_range = np.logspace(-6, -1, n_points) |
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log_x = True |
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elif param_name=="C": |
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param_range = np.logspace(-2, 0, n_points) |
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log_x = True |
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elif param_name=="kernel": |
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param_range = np.array(["rbf", "linear", "poly", "sigmoid"]) |
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log_x = False |
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train_scores, test_scores = validation_curve( |
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SVC(), |
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X, |
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y, |
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param_name=param_name, |
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param_range=param_range, |
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scoring="accuracy", |
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n_jobs=-1, |
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) |
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train_scores_mean = np.mean(train_scores, axis=1) |
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train_scores_std = np.std(train_scores, axis=1) |
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test_scores_mean = np.mean(test_scores, axis=1) |
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test_scores_std = np.std(test_scores, axis=1) |
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fig = plot_validation_curve( |
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param_range, |
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[train_scores_mean, test_scores_mean], |
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[train_scores_std, test_scores_std], |
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["Training score", "Cross-validation score"], |
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["orange", "navy"], |
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title=f"Validation Curve with SVM for {param_name} Hyperparameter", |
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log_x=log_x |
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) |
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return fig |
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title = "Plotting Validation Curve" |
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with gr.Blocks(title=title) as demo: |
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gr.Markdown(f"# {title}") |
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gr.Markdown( |
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""" |
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#### This example shows the usage of a validation curve to understand \ |
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how the performance of a model, SVM in this case, changes with varying hyperparameters. \ |
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The dataset used was the [digits dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) \ |
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from scikit-learn. The hyperparameter varied was gamma. \ |
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[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) |
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""" |
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) |
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with gr.Row(): |
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n_points = gr.inputs.Slider(5, 100, 5, 5,label="Number of points") |
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param_name = gr.inputs.Dropdown(["gamma", "C", "kernel"], label="Hyperparameter", default="gamma") |
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fig = gr.Plot(label="Validation Curve") |
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n_points.release(fn=app_fn, inputs=[n_points, param_name], outputs=[fig]) |
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param_name.change(fn=app_fn, inputs=[n_points, param_name], outputs=[fig]) |
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demo.load(fn=app_fn, inputs=[n_points, param_name], outputs=[fig]) |
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demo.launch() |
