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import gradio as gr |
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import time |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from sklearn.datasets import load_iris |
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from sklearn.model_selection import train_test_split |
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from sklearn.feature_selection import SelectKBest, f_classif |
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from sklearn.pipeline import make_pipeline |
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from sklearn.preprocessing import MinMaxScaler |
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from sklearn.svm import LinearSVC |
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theme = gr.themes.Monochrome( |
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primary_hue="indigo", |
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secondary_hue="blue", |
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neutral_hue="slate", |
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) |
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model_card = f""" |
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## Description |
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**Univariate feature selection** can be used to improve classification accuracy on a noisy dataset. |
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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. |
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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. |
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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. |
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The results of the feature selection are presented through p-values and weights of SVMs, which are plotted for comparison. |
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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. |
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You can play around with different ``number of top features`` and ``random seed``. |
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## Dataset |
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Iris dataset |
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""" |
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X, y = load_iris(return_X_y=True) |
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E = np.random.RandomState(42).uniform(0, 0.1, size=(X.shape[0], 20)) |
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X = np.hstack((X, E)) |
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def do_train(k_features, random_state): |
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X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=random_state) |
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selector = SelectKBest(f_classif, k=k_features) |
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selector.fit(X_train, y_train) |
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scores = -np.log10(selector.pvalues_) |
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scores /= scores.max() |
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fig1, axes1 = plt.subplots() |
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X_indices = np.arange(X.shape[-1]) |
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axes1.bar(X_indices - 0.05, scores, width=0.2) |
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axes1.set_title("Feature univariate score") |
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axes1.set_xlabel("Feature number") |
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axes1.set_ylabel(r"Univariate score ($-Log(p_{value})$)") |
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clf = make_pipeline(MinMaxScaler(), LinearSVC()) |
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clf.fit(X_train, y_train) |
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svm_weights = np.abs(clf[-1].coef_).sum(axis=0) |
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svm_weights /= svm_weights.sum() |
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clf_selected = make_pipeline(SelectKBest(f_classif, k=k_features), MinMaxScaler(), LinearSVC()) |
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clf_selected.fit(X_train, y_train) |
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svm_weights_selected = np.abs(clf_selected[-1].coef_).sum(axis=0) |
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svm_weights_selected /= svm_weights_selected.sum() |
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fig2, axes2 = plt.subplots() |
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axes2.bar( |
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X_indices - 0.45, scores, width=0.2, label=r"Univariate score ($-Log(p_{value})$)" |
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) |
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axes2.bar(X_indices - 0.25, svm_weights, width=0.2, label="SVM weight") |
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axes2.bar( |
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X_indices[selector.get_support()] - 0.05, |
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svm_weights_selected, |
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width=0.2, |
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label="SVM weights after selection", |
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) |
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axes2.set_title("Comparing feature selection") |
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axes2.set_xlabel("Feature number") |
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axes2.set_yticks(()) |
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axes2.axis("tight") |
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axes2.legend(loc="upper right") |
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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}%" |
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return fig1, fig2, text |
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with gr.Blocks(theme=theme) as demo: |
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gr.Markdown(''' |
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<div> |
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<h1 style='text-align: center'>Univariate Feature Selection</h1> |
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</div> |
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''') |
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gr.Markdown(model_card) |
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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>") |
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k_features = gr.Slider(minimum=2, maximum=10, step=1, value=2, label="Number of top features to select") |
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random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random seed") |
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with gr.Row(): |
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with gr.Column(): |
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plot_1 = gr.Plot(label="Univariate score") |
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with gr.Column(): |
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plot_2 = gr.Plot(label="Comparing feature selection") |
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with gr.Row(): |
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resutls = gr.Textbox(label="Results") |
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k_features.change(fn=do_train, inputs=[k_features, random_state], outputs=[plot_1, plot_2, resutls]) |
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random_state.change(fn=do_train, inputs=[k_features, random_state], outputs=[plot_1, plot_2, resutls]) |
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demo.launch() |
