Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import numpy as np
 import pandas as pd
 
 from constants import *
@@ -11,6 +12,8 @@ def get_data(verified, dataset, ipc, label_type, metric_weights=None):
         label_type = [label_type]
 
     data = pd.read_csv("data.csv")
+    # filter data with no hlr or ior (no nan)
+    data = data.dropna(subset=["hlr", "ior"])
     data["verified"] = data["verified"].apply(lambda x: bool(x))
     data["dataset"] = data["dataset"].apply(lambda x: DATASET_LIST[x])
     data["ipc"] = data["ipc"].apply(lambda x: IPC_LIST[x])
@@ -28,12 +31,13 @@ def get_data(verified, dataset, ipc, label_type, metric_weights=None):
     data["score"] = data[METRICS[0].lower()] * 0.0
     for i, metric in enumerate(METRICS):
         data["score"] += data[metric.lower()] * metric_weights[i] * METRICS_SIGN[i]
+    data["score"] = (np.exp(-0.01 * data["score"]) - np.exp(-1.0)) / (np.exp(1.0) - np.exp(-1.0))
     data = data.sort_values(by="score", ascending=False)
     data["ranking"] = range(1, len(data) + 1)
 
     for metric in METRICS:
-        data[metric.lower()] = data[metric.lower()].apply(lambda x: round(x, 2))
-    data["score"] = data["score"].apply(lambda x: round(x, 2))
+        data[metric.lower()] = data[metric.lower()].apply(lambda x: round(x, 3))
+    data["score"] = data["score"].apply(lambda x: round(x, 3))
 
     # formatting
     data["method"] = "[" + data["method"] + "](" + data["method_reference"] + ")"
@@ -41,9 +45,9 @@ def get_data(verified, dataset, ipc, label_type, metric_weights=None):
     data = data.drop(columns=["method_reference", "dataset", "ipc"])
     data = data[['ranking', 'method', 'verified', 'date', 'label_type', 'hlr', 'ior', 'score']]
     if label_type == "Hard Label":
-        data = data.rename(columns={"ranking": "Ranking", "method": "Method", "date": "Date", "label_type": "Label Type", "hlr": "HLR↓", "ior": "IOR↑", "score": "Score↓", "verified": "Verified"})
+        data = data.rename(columns={"ranking": "Ranking", "method": "Method", "date": "Date", "label_type": "Label Type", "hlr": "HLR%↓", "ior": "IOR%↑", "score": "DDRS↑", "verified": "Verified"})
     else:
-        data = data.rename(columns={"ranking": "Ranking", "method": "Method", "date": "Date", "label_type": "Label Type", "hlr": "HLR↓", "ior": "IOR↑", "score": "Score↓", "verified": "Verified"})
+        data = data.rename(columns={"ranking": "Ranking", "method": "Method", "date": "Date", "label_type": "Label Type", "hlr": "HLR%↓", "ior": "IOR%↑", "score": "DDRS↑", "verified": "Verified"})
     return data
 
 
@@ -92,6 +96,13 @@ with gr.Blocks() as leaderboard:
             gr.Slider(label=f"Weight for HLR", minimum=0.0, maximum=1.0, value=0.5, interactive=True))
         adjust_btn = gr.Button("Adjust Weights")
 
+    with gr.Accordion("Metric Definitions", open=False):
+        gr.Markdown(METRIC_DEFINITION_INTRODUCTION, latex_delimiters=[
+              {'left': '$$', 'right': '$$', 'display': True},
+              {'left': '$', 'right': '$', 'display': False},
+              {'left': '\\(', 'right': '\\)', 'display': False},
+              {'left': '\\[', 'right': '\\]', 'display': True}
+          ])
 
     # metric_weights = [s.value for s in metric_sliders]
     metric_weights = [metric_sliders[0].value, 1.0 - metric_sliders[0].value]