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import gradio as gr |
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import numpy as np |
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from sklearn.datasets import load_iris |
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from sklearn.pipeline import Pipeline |
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from sklearn.feature_selection import SelectPercentile, f_classif |
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from sklearn.preprocessing import StandardScaler |
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from sklearn.svm import SVC |
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import matplotlib.pyplot as plt |
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from sklearn.model_selection import cross_val_score |
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def svm_anova_app(percentiles): |
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X, y = load_iris(return_X_y=True) |
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rng = np.random.RandomState(0) |
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X = np.hstack((X, 2 * rng.random((X.shape[0], 36)))) |
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clf = Pipeline([ |
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("anova", SelectPercentile(f_classif)), |
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("scaler", StandardScaler()), |
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("svc", SVC(gamma="auto")), |
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]) |
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score_means = [] |
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score_stds = [] |
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for p in percentiles: |
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clf.set_params(anova__percentile=float(p)) |
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this_scores = cross_val_score(clf, X, y) |
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score_means.append(this_scores.mean()) |
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score_stds.append(this_scores.std()) |
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plt.errorbar(percentiles, score_means, np.array(score_stds)) |
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plt.title("Performance of the SVM-Anova varying the percentile of features selected") |
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plt.xticks(np.linspace(0, 100, 11, endpoint=True)) |
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plt.xlabel("Percentile") |
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plt.ylabel("Accuracy Score") |
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plt.axis("tight") |
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plt.savefig("plot.png") |
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plt.close() |
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return "plot.png" |
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iface = gr.Interface( |
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fn=svm_anova_app, |
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inputs=gr.inputs.CheckboxGroup(['1', '3', '6', '10', '15', '20', '25', '30', '35', '40', '45', '50', '55', '60', '65', '70', '75', '80', '85', '90', '95', '100'], label="Percentiles"), |
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outputs="image", |
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title="SVM-Anova Performance", |
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description="This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores. We use the iris dataset (4 features) and add 36 non-informative features. We can find that our model achieves best performance when we select around 10 percent of features. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html" |
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) |
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iface.launch() |
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