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all_res.csv

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+,encoder,task,model,auto,runtime,dps,n_components,true_n,fowlkes_mallows_score,homogeneity_score,completeness_score,adjusted_mutual_info_score,diversity,c_in,c_ex,error
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+77,all-MiniLM-L6-v2,ArXivHierarchicalClusteringP2P,KeyNMF,False,4.439034938812256,461.36154101728687,23.0,23.0,0.22822201498342976,0.3701407312817881,0.3055608488462428,0.30511376982530736,0.8652173913043478,0.49104830622673035,0.24870648980140686,
+78,all-MiniLM-L6-v2,BiorxivClusteringP2P.v2,KeyNMF,False,78.63146233558655,684.0391670505333,26.0,26.0,0.23880008974271053,0.2896319428943089,0.26442284809223043,0.2750595292265646,0.8269230769230769,0.5859355926513672,0.4052533805370331,
+79,all-MiniLM-L6-v2,MedrxivClusteringP2P.v2,KeyNMF,False,64.05746483802795,585.4118656556321,51.0,51.0,0.13682445488937375,0.2456724085196874,0.19831320410176417,0.21119534430181694,0.6686274509803921,0.48587313294410706,0.30105718970298767,
+80,all-MiniLM-L6-v2,StackExchangeClusteringP2P.v2,KeyNMF,False,4797.140148639679,15.616387614033602,524.0,524.0,0.06419719276433514,0.27096901249078037,0.20041283541629296,0.16728584813846928,0.5633587786259542,0.3503369688987732,0.19716752691696046,
+81,all-MiniLM-L6-v2,TwentyNewsgroupsClustering.v2,KeyNMF,False,35.40333580970764,1681.903657894087,20.0,20.0,0.15690236237509309,0.21359656097807833,0.2512768257180566,0.23006324155606714,0.89,0.11630143225193024,0.1420399248600006,
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+90,all-MiniLM-L6-v2,BBCNewsClustering,GMM,False,1.6434226036071777,1353.273342546526,5.0,5.0,0.9104146251466987,0.8533403824897017,0.8529081083477215,0.852792832832457,1.0,0.6716110110282898,0.21536627411842346,

app.py

@@ -0,0 +1,48 @@
+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"]),
+)