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| import gradio as gr | |
| import time | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from scipy.linalg import toeplitz, cholesky | |
| from sklearn.covariance import LedoitWolf, OAS | |
| np.random.seed(0) | |
| def generate_plots(min_slider_samples_range,max_slider_samples_range): | |
| # print("slider_samples_range:",slider_samples_range) | |
| slider_samples_range =np.arange(min_slider_samples_range,max_slider_samples_range,1) | |
| n_features = 100 | |
| repeat = 100 | |
| lw_mse = np.zeros((slider_samples_range.size, repeat)) | |
| oa_mse = np.zeros((slider_samples_range.size, repeat)) | |
| lw_shrinkage = np.zeros((slider_samples_range.size, repeat)) | |
| oa_shrinkage = np.zeros((slider_samples_range.size, repeat)) | |
| for i, n_samples in enumerate(slider_samples_range): | |
| for j in range(repeat): | |
| X = np.dot(np.random.normal(size=(n_samples, n_features)), coloring_matrix.T) | |
| lw = LedoitWolf(store_precision=False, assume_centered=True) | |
| lw.fit(X) | |
| lw_mse[i, j] = lw.error_norm(real_cov, scaling=False) | |
| lw_shrinkage[i, j] = lw.shrinkage_ | |
| oa = OAS(store_precision=False, assume_centered=True) | |
| oa.fit(X) | |
| oa_mse[i, j] = oa.error_norm(real_cov, scaling=False) | |
| oa_shrinkage[i, j] = oa.shrinkage_ | |
| return | |
| def plot_mse(): | |
| # plot MSE | |
| plt.clf() | |
| plt.subplot(2, 1, 1) | |
| plt.errorbar( | |
| slider_samples_range, | |
| lw_mse.mean(1), | |
| yerr=lw_mse.std(1), | |
| label="Ledoit-Wolf", | |
| color="navy", | |
| lw=2, | |
| ) | |
| plt.errorbar( | |
| slider_samples_range, | |
| oa_mse.mean(1), | |
| yerr=oa_mse.std(1), | |
| label="OAS", | |
| color="darkorange", | |
| lw=2, | |
| ) | |
| plt.ylabel("Squared error") | |
| plt.legend(loc="upper right") | |
| plt.title("Comparison of covariance estimators") | |
| plt.xlim(5, 31) | |
| return plt | |
| def plot_shrinkage(): | |
| # plot shrinkage coefficient | |
| plt.subplot(2, 1, 2) | |
| plt.errorbar( | |
| slider_samples_range, | |
| lw_shrinkage.mean(1), | |
| yerr=lw_shrinkage.std(1), | |
| label="Ledoit-Wolf", | |
| color="navy", | |
| lw=2, | |
| ) | |
| plt.errorbar( | |
| slider_samples_range, | |
| oa_shrinkage.mean(1), | |
| yerr=oa_shrinkage.std(1), | |
| label="OAS", | |
| color="darkorange", | |
| lw=2, | |
| ) | |
| plt.xlabel("n_samples") | |
| plt.ylabel("Shrinkage") | |
| plt.legend(loc="lower right") | |
| plt.ylim(plt.ylim()[0], 1.0 + (plt.ylim()[1] - plt.ylim()[0]) / 10.0) | |
| plt.xlim(5, 31) | |
| # plt.show() | |
| return plt | |
| title = "Ledoit-Wolf vs OAS estimation" | |
| # def greet(name): | |
| # return "Hello " + name + "!" | |
| with gr.Blocks(title=title, theme=gr.themes.Default(font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as demo: | |
| gr.Markdown(f"# {title}") | |
| gr.Markdown( | |
| """ | |
| The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a MSE criterion), yielding the Ledoit-Wolf covariance estimate. | |
| Chen et al. proposed an improvement of the Ledoit-Wolf shrinkage parameter, the OAS coefficient, whose convergence is significantly better under the assumption that the data are Gaussian. | |
| This example, inspired from Chen’s publication [1], shows a comparison of the estimated MSE of the LW and OAS methods, using Gaussian distributed data. | |
| [1] “Shrinkage Algorithms for MMSE Covariance Estimation” Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010. | |
| """) | |
| n_features = 100 | |
| min_slider_samples_range = gr.Slider(6, 31, value=6, step=1, label="min_samples_range", info="Choose between 6 and 31") | |
| max_slider_samples_range = gr.Slider(6, 31, value=31, step=1, label="max_samples_range", info="Choose between 6 and 31") | |
| r = 0.1 | |
| real_cov = toeplitz(r ** np.arange(n_features)) | |
| coloring_matrix = cholesky(real_cov) | |
| gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html)**") | |
| # name = "hardy" | |
| # greet_btn = gr.Button("Greet") | |
| # output = gr.Textbox(label="Output Box") | |
| # greet_btn.click(fn=greet, inputs=name, outputs=output) | |
| gr.Label(value="Comparison of Covariance Estimators") | |
| generate_plots() | |
| #if min_slider_samples_range: | |
| min_slider_samples_range.change(plot_mse, outputs= gr.Plot() ) | |
| max_slider_samples_range.change(plot_shrinkage, outputs= gr.Plot() ) | |
| #elif max_slider_samples_range: | |
| # elif changed == False: | |
| # min_slider_samples_range.change(generate_plots, inputs=[min_slider_samples_range,max_slider_samples_range], outputs= gr.Plot() ) | |
| # max_slider_samples_range.change(generate_plots, inputs=[min_slider_samples_range,max_slider_samples_range], outputs= gr.Plot() ) | |
| # changed = True | |
| # else: | |
| # pass | |
| demo.launch() |