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