Delete app.py

app.py

@@ -1,156 +0,0 @@
-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 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()
-    # 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_
-    #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()