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import gradio as gr |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from sklearn.datasets import make_blobs |
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis |
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from sklearn.covariance import OAS |
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def generate_data(n_samples, n_features): |
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X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]]) |
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if n_features > 1: |
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X = np.hstack([X, np.random.randn(n_samples, n_features - 1)]) |
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return X, y |
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def classify(n_train, n_test, n_averages, n_features_max, step): |
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acc_clf1, acc_clf2, acc_clf3 = [], [], [] |
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n_features_range = range(1, n_features_max + 1, step) |
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for n_features in n_features_range: |
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score_clf1, score_clf2, score_clf3 = 0, 0, 0 |
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for _ in range(n_averages): |
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X, y = generate_data(n_train, n_features) |
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clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y) |
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clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y) |
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oa = OAS(store_precision=False, assume_centered=False) |
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clf3 = LinearDiscriminantAnalysis(solver="lsqr", covariance_estimator=oa).fit(X, y) |
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X, y = generate_data(n_test, n_features) |
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score_clf1 += clf1.score(X, y) |
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score_clf2 += clf2.score(X, y) |
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score_clf3 += clf3.score(X, y) |
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acc_clf1.append(score_clf1 / n_averages) |
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acc_clf2.append(score_clf2 / n_averages) |
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acc_clf3.append(score_clf3 / n_averages) |
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features_samples_ratio = np.array(n_features_range) / n_train |
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plt.plot( |
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features_samples_ratio, |
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acc_clf1, |
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linewidth=2, |
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label="LDA", |
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color="gold", |
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linestyle="solid", |
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) |
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plt.plot( |
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features_samples_ratio, |
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acc_clf2, |
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linewidth=2, |
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label="LDA with Ledoit Wolf", |
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color="navy", |
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linestyle="dashed", |
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) |
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plt.plot( |
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features_samples_ratio, |
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acc_clf3, |
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linewidth=2, |
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label="LDA with OAS", |
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color="red", |
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linestyle="dotted", |
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) |
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plt.xlabel("n_features / n_samples") |
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plt.ylabel("Classification accuracy") |
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plt.legend(loc="lower left") |
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plt.ylim((0.65, 1.0)) |
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plt.suptitle( |
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"LDA (Linear Discriminant Analysis) vs. " |
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+ "\n" |
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+ "LDA with Ledoit Wolf vs. " |
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+ "\n" |
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+ "LDA with OAS (1 discriminative feature)" |
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) |
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plt.tight_layout() |
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plt.savefig("plot.png") |
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return "plot.png" |
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inputs = [ |
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gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_train", default=20), |
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gr.inputs.Slider(minimum=1, maximum=500, step=1, label="n_test", default=200), |
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gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_averages", default=50), |
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gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_features_max", default=75), |
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gr.inputs.Slider(minimum=1, maximum=20, step=1, label="step", default=4), |
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] |
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output = gr.outputs.Image(type="pil") |
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examples = [ |
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[20, 200, 50, 75, 4], |
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[30, 250, 60, 80, 5], |
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[40, 300, 70, 90, 6], |
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] |
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title = "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification" |
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description = "This example illustrates how the Ledoit-Wolf and Oracle Shrinkage Approximating (OAS) estimators of covariance can improve classification. See the original example: https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html" |
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gr.Interface(classify, inputs, output, examples=examples, title=title, description=description).launch() |
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