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app.py

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+# 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':'Comic Sans MS','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")
+    btn = gr.Button()
+    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")
+    btn.click(fn=factor_analysis, inputs=method, outputs=[fig1, fig2, components])
+    gr.HTML(made)
+    gr.HTML(link)
+    
+demo.launch()

requirements.txt.txt

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+gradio
+matplotlib
+numpy
+scikit-learn