Create app.py

app.py

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+import gradio as gr
+import time
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.feature_selection import SelectKBest, f_classif
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.svm import LinearSVC
+
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+)
+model_card = f"""
+## Description
+
+**Univariate feature selection** can be used to improve classification accuracy on a noisy dataset.
+In **univariate feature selection**, each feature is evaluated independently, and a statistical test is used to determine its strength of association with the target variable. 
+The most important features are then selected based on their statistical significance, typically using a threshold p-value or a pre-defined number of top features to select.
+
+In this demo, some noisy (non informative) features are added to the iris dataset then use **Support vector machine (SVM)** to classify the Iris dataset both before and after applying univariate feature selection.
+The results of the feature selection are presented through p-values and weights of SVMs, which are plotted for comparison. 
+The objective of this demo is to evaluate the accuracy of the models and assess the impact of univariate feature selection on the model weights.
+You can play around with different ``number of top features`` and ``random seed``.
+
+## Dataset
+
+Iris dataset
+"""
+# The iris dataset
+X, y = load_iris(return_X_y=True)
+
+# Some noisy data not correlated
+E = np.random.RandomState(42).uniform(0, 0.1, size=(X.shape[0], 20))
+
+# Add the noisy data to the informative features
+X = np.hstack((X, E))
+
+
+def do_train(k_features, random_state):
+    # Split dataset to select feature and evaluate the classifier
+    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=random_state)
+    selector = SelectKBest(f_classif, k=k_features)
+    selector.fit(X_train, y_train)
+    scores = -np.log10(selector.pvalues_)
+    scores /= scores.max()
+    
+
+    fig1, axes1 = plt.subplots()
+    X_indices = np.arange(X.shape[-1])
+    axes1.bar(X_indices - 0.05, scores, width=0.2)
+    axes1.set_title("Feature univariate score")
+    axes1.set_xlabel("Feature number")
+    axes1.set_ylabel(r"Univariate score ($-Log(p_{value})$)")
+
+    clf = make_pipeline(MinMaxScaler(), LinearSVC())
+    clf.fit(X_train, y_train)
+
+    svm_weights = np.abs(clf[-1].coef_).sum(axis=0)
+    svm_weights /= svm_weights.sum()
+
+    clf_selected = make_pipeline(SelectKBest(f_classif, k=k_features), MinMaxScaler(), LinearSVC())
+    clf_selected.fit(X_train, y_train)
+
+    svm_weights_selected = np.abs(clf_selected[-1].coef_).sum(axis=0)
+    svm_weights_selected /= svm_weights_selected.sum()
+
+    fig2, axes2 = plt.subplots()
+    axes2.bar(
+        X_indices - 0.45, scores, width=0.2, label=r"Univariate score ($-Log(p_{value})$)"
+    )
+
+    axes2.bar(X_indices - 0.25, svm_weights, width=0.2, label="SVM weight")
+
+    axes2.bar(
+        X_indices[selector.get_support()] - 0.05,
+        svm_weights_selected,
+        width=0.2,
+        label="SVM weights after selection",
+    )
+
+    axes2.set_title("Comparing feature selection")
+    axes2.set_xlabel("Feature number")
+    axes2.set_yticks(())
+    axes2.axis("tight")
+    axes2.legend(loc="upper right")
+
+    text = f"Classification accuracy without selecting features: {clf.score(X_test, y_test)*100:.2f}%. Classification accuracy after univariate feature selection: {clf_selected.score(X_test, y_test)*100:.2f}%"
+        
+    return fig1, fig2, text
+
+
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>Univariate Feature Selection</h1>
+            </div>
+        ''')
+    gr.Markdown(model_card)
+    gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py\">scikit-learn</a>")
+    k_features = gr.Slider(minimum=2, maximum=10, step=1, value=2, label="Number of top features to select")
+    random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random seed")
+    with gr.Row():
+        with gr.Column():
+            plot_1 = gr.Plot(label="Univariate score")
+        with gr.Column():
+            plot_2 = gr.Plot(label="Comparing feature selection")
+    with gr.Row():
+        resutls = gr.Textbox(label="Results")
+
+    k_features.change(fn=do_train, inputs=[k_features, random_state], outputs=[plot_1, plot_2, resutls])
+    random_state.change(fn=do_train, inputs=[k_features, random_state], outputs=[plot_1, plot_2, resutls])
+
+demo.launch()