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import gradio as gr |
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import numpy as np |
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import pandas as pd |
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import seaborn as sns |
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cm = sns.light_palette("green", as_cmap=True) |
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data = pd.read_csv("all_res.csv") |
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data["coherence"] = np.sqrt(data["c_in"] * data["c_ex"]) |
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data["interpretability"] = np.sqrt(data["coherence"] * data["diversity"]) |
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data["v_measure"] = ( |
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2 |
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* data["homogeneity_score"] |
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* data["completeness_score"] |
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/ (data["homogeneity_score"] + data["completeness_score"]) |
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) |
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metrics = [ |
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"interpretability", |
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"coherence", |
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"diversity", |
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"fowlkes_mallows_score", |
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"adjusted_mutual_info_score", |
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"v_measure", |
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] |
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summary = data.groupby("task")[metrics].rank(pct=True) |
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summary["model"] = data["model"] |
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summary = ( |
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summary.groupby("model").mean().sort_values("interpretability", ascending=False) |
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) |
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summary = summary.reset_index() |
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summary.columns = [ |
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"Model", |
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"Interpretability", |
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"Coherence", |
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"Diversity", |
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"FMI", |
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"AMI", |
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"V-score", |
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] |
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styler = summary.style.format(precision=2).background_gradient(cmap="Greens") |
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with gr.Blocks() as demo: |
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table = gr.DataFrame(styler, show_search="filter", pinned_columns=1) |
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demo.launch( |
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theme=gr.themes.Base(font=[gr.themes.GoogleFont("Ubuntu"), "Arial", "sans-serif"]), |
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) |
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