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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_
# 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)
# 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")
# if min_slider_samples_range:
min_slider_samples_range.change(generate_plots, inputs=[min_slider_samples_range,max_slider_samples_range], outputs= gr.Plot() )
# elif max_slider_samples_range:
max_slider_samples_range.input(generate_plots, inputs=[min_slider_samples_range,max_slider_samples_range], outputs= gr.Plot() )
# else:
# pass
demo.launch()
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