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import gradio as gr |
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import pandas as pd |
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import plotly.express as px |
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from sklearn.svm import LinearSVC |
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from sklearn.pipeline import make_pipeline |
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from sklearn.datasets import make_classification |
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from sklearn.metrics import classification_report |
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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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def app_fn(k: int, n_features: int, n_informative: int, n_redundant: int): |
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X, y = make_classification( |
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n_features=n_features, |
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n_informative=n_informative, |
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n_redundant=n_redundant, |
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n_classes=2, |
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n_clusters_per_class=2, |
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random_state=42, |
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) |
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) |
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anova_filter = SelectKBest(f_classif, k=k) |
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clf = LinearSVC() |
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anova_svm = make_pipeline(anova_filter, clf) |
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anova_svm.fit(X_train, y_train) |
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y_pred = anova_svm.predict(X_test) |
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report = classification_report(y_test, y_pred, output_dict=True) |
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report_df = pd.DataFrame(report).transpose() |
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report_df = report_df.reset_index().rename(columns={"index": "class"}).round(2) |
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report_df["accuracy"] = report_df.loc[report_df["class"]=="accuracy"].values.flatten()[-1] |
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report_df = report_df.loc[report_df["class"]!="accuracy"] |
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features = anova_svm[:-1].inverse_transform(anova_svm[-1].coef_).flatten() > 0 |
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features = features.astype(int) |
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fig = px.bar(y=features) |
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fig.update_yaxes(ticktext=["False", "True"], tickvals=[0, 1]) |
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fig.update_layout( |
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title="Selected Features", |
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xaxis_title="Feature Index", |
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yaxis_title="Selected", |
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legend_title="Selected", |
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) |
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return report_df, fig |
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title = "Pipeline ANOVA SVM" |
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with gr.Blocks() as demo: |
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gr.Markdown(f"# {title}") |
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gr.Markdown( |
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""" |
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### This example creates a pipeline where in the first step k features are selected with ANOVA and then we pass the selected features \ |
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to a Linear SVM. This pipeline is then trained using a synthetic dataset and evaluated on a test holdout. \ |
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A table displaying the classification report with the metrics and a char showing the index of the selected features are shown at the bottom. |
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See original example [here](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py) |
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""" |
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) |
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with gr.Row(): |
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k = gr.inputs.Slider(minimum=1, maximum=20, default=3, step=1, label="Number of Features to Select") |
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n_features = gr.inputs.Slider(minimum=1, maximum=20, default=20, step=1, label="Total Features") |
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n_informative = gr.inputs.Slider(minimum=1, maximum=20, default=3, step=1, label="Informative Features") |
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n_redundant = gr.inputs.Slider(minimum=0, maximum=20, default=0, step=1, label="Redundant Features") |
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btn = gr.Button(label="Run") |
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with gr.Row(): |
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report = gr.DataFrame(label="Classification Report") |
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features = gr.Plot(label="Selected Features") |
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btn.click( |
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fn=app_fn, |
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inputs=[k, n_features, n_informative, n_redundant], |
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outputs=[report, features], |
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) |
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demo.load( |
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fn=app_fn, |
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inputs=[k, n_features, n_informative, n_redundant], |
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outputs=[report, features], |
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) |
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demo.launch() |
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