Initial commit

app.py

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+"""
+Demo is based on https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html
+"""
+from sklearn.svm import SVC
+from sklearn.datasets import load_digits
+from sklearn.feature_selection import RFE
+import matplotlib.pyplot as plt
+
+# Load the digits dataset
+digits = load_digits()
+X = digits.images.reshape((len(digits.images), -1))
+y = digits.target
+
+# Create the RFE object and rank each pixel
+svc = SVC(kernel="linear", C=1)
+
+
+def recursive_feature_elimination(n_features_to_select, step, esimator=svc):
+    # Plot the results
+    fig = plt.figure()
+    rfe = RFE(estimator=esimator, n_features_to_select=1, step=1)
+    # step : Number of feature to remove at each iteration, least important are removed
+    # n_features_to_select : Number of features to be selected after repeated elimination
+    rfe.fit(X, y)
+    ranking = rfe.ranking_.reshape(digits.images[0].shape)
+
+    # Plot pixel ranking
+    plt.matshow(ranking, cmap=plt.cm.Blues)
+    plt.colorbar()
+    plt.title("Ranking of pixels with RFE")
+    # plt.show()
+    return plt
+
+
+import gradio as gr
+
+title = " Illustration of Recursive feature elimination.🌲 "
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(
+        " This example the feature importnace by using  Recursive feature elimination <br>"
+        " Dataset  is load_digits() which is images of size 8 X 8 hand-written digits  <br>"
+        " **Parameters** <br> <br> **Number of features to select** : Represents the features left at the end of feature selection process. <br>"
+        " **Step** : Number of feature to remove at each iteration, least important are removed. <br>"
+    )
+
+    gr.Markdown(
+        " Support Vector classifier is used as estimator to rank features. <br>"
+    )
+
+    gr.Markdown(
+        " **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html)**"
+    )
+    with gr.Row():
+        n_features_to_select = gr.Slider(
+            minimum=0, maximum=20, step=1, value=1, label="Number of features to select"
+        )
+        step = gr.Slider(minimum=0, maximum=20, step=1, value=1, label="Step")
+
+    btn = gr.Button(value="Submit")
+
+    btn.click(
+        recursive_feature_elimination,
+        inputs=[n_features_to_select, step],
+        outputs=gr.Plot(
+            label="Recursive feature elimination of pixels in digit classification"
+        ),
+    )  #
+
+    gr.Markdown(
+        " Plot shows the importance of each pixel in the classification of the digits. <br>"
+    )
+
+demo.launch()

requirements.txt

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+scikit-learn==1.2.1