Delete app.py

app.py

@@ -1,112 +0,0 @@
-# Authors: Jona Sassenhagen
-# License: BSD 3 clause
-
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.decomposition import FactorAnalysis, PCA
-from sklearn.preprocessing import StandardScaler
-from sklearn.datasets import load_iris
-import gradio as gr
-from matplotlib import style
-plt.switch_backend("agg")
-style.use('ggplot')
-
-font1 = {'family':'DejaVu Sans','color':'#2563EB','size': 14}
-
-#load and transform the data
-data = load_iris()
-X = StandardScaler().fit_transform(data["data"])
-feature_names = data["feature_names"]
-
-methods = {
-    "PCA": PCA(),
-    "Unrotated FA": FactorAnalysis(),
-    "Varimax FA": FactorAnalysis(rotation="varimax")
-}
-
-def factor_analysis(method):
-    #figure1
-    fig1, ax = plt.subplots(figsize=(10, 6), facecolor='none', dpi = 200)
-    im = ax.imshow(np.corrcoef(X.T), cmap="Spectral", vmin=-1, vmax=1)
-
-    ax.set_xticks([0, 1, 2, 3])
-    ax.set_xticklabels(list(feature_names), 
-                       rotation=90, fontdict = font1)
-    ax.set_yticks([0, 1, 2, 3])
-    ax.set_yticklabels(list(feature_names), fontdict = font1)
-    plt.grid(False)
-    plt.colorbar(im).ax.tick_params()
-    ax.set_title("Iris feature correlation matrix", 
-                 fontdict=font1, size = 18, 
-                 color = "white", pad = 20,
-                 bbox=dict(boxstyle="round,pad=0.3", 
-                           color = "#2563EB"))
-    plt.tight_layout()
-    plt.close('all')
-
-    n_comps = 2
-
-    #figure2
-    fig2, axs = plt.subplots(figsize=(8, 5), facecolor='none', dpi = 200)
-    plt.grid(False)
-    fa = methods[method]
-    fa.set_params(n_components=n_comps)
-    fa.fit(X)
-
-    components = fa.components_
-
-    vmax = np.abs(components).max()
-    axs.imshow(components, cmap="Spectral", vmax=vmax, vmin=-vmax)
-    axs.set_xticks(np.arange(len(feature_names)))
-    axs.set_xticklabels(feature_names, fontdict=font1)
-    axs.set_title(method, 
-                  fontdict=font1, size = 18, 
-                  color = "white", pad = 20,
-                  bbox=dict(boxstyle="round,pad=0.3", 
-                           color = "#2563EB"))
-    axs.set_yticks([0, 1])
-    axs.set_yticklabels(["Comp. 1", "Comp. 2"], fontdict=font1)
-    
-    plt.tight_layout()
-    plt.close('all')
-    
-    return fig1, fig2, components
-
-intro = """<h1 style="text-align: center;">🤗 <strong>Factor Analysis (with rotation) to visualize patterns</strong> 🤗</h1>
-"""
-desc = """<h3 style="text-align: left;"> Investigating the Iris dataset, we see that sepal length, petal length and petal width are highly correlated. 
-Sepal width is less redundant. Matrix decomposition techniques can uncover these latent patterns. 
-<br><br>Applying rotations to the resulting components does not inherently improve the predictive value of the derived latent space,
-but can help visualise their structure; here, for example, the varimax rotation, 
-which is found by maximizing the squared variances of the weights, 
-finds a structure where the second component only loads positively on sepal width.
-<br></h3>
-"""
-
-made ="""<div style="text-align: center;">
-  <p>Made with ❤</p>"""
-
-link = """<div style="text-align: center;">
-<a href="https://scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html#sphx-glr-auto-examples-decomposition-plot-varimax-fa-py" target="_blank" rel="noopener noreferrer">
-Demo is based on this script from scikit-learn documentation</a>"""
-
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue",
-                                    secondary_hue="sky",
-                                    neutral_hue="neutral",
-                                    font =  gr.themes.GoogleFont("Roboto")),
-               title="Factor-Analysis-with-rotation") as demo:
-    gr.HTML(intro)
-    gr.HTML(desc)
-    method = gr.Radio(["PCA", "Unrotated FA", "Varimax FA"], label = "Choose method to show on the plot:", value = "PCA")
-    with gr.Box():
-        with gr.Column():
-            components = gr.Dataframe(headers= feature_names,label = "Loadings")
-            with gr.Row():
-                fig1 = gr.Plot(label="Plot covariance of Iris features")
-                fig2 = gr.Plot(label="Factor analysis")
-    method.change(fn=factor_analysis, inputs=method, outputs=[fig1, fig2, components])
-    demo.load(fn=factor_analysis, inputs=method, outputs=[fig1, fig2, components])
-    gr.HTML(made)
-    gr.HTML(link)
-    
-demo.launch()