Update constants.py

constants.py

@@ -59,10 +59,26 @@ Hard labels are categorical, having the same format of the real dataset. Soft la
 """
 
 WEIGHT_ADJUSTMENT_INTRODUCTION = """
-The score for ranking in the following table is computed by $score = w HLR - (1 - w) IOR$, where $w$ is the weight for the HLR metric.
+The score for ranking (DD-Ranking Score, DDRS) in the following table is computed by DDRS = \\frac{e^{w IOR - (1 - w) HLR} - e^{-1}}{e - e^{-1}}$, where $w$ is the weight for the HLR metric.
 **You can specify the weight $w$ below.**
 """
 
+METRIC_DEFINITION_INTRODUCTION = """
+$\\text{Acc.}$: The accuracy of models trained on different samples.
+
+$\\text{full-hard}$: Full dataset with hard labels.
+
+$\\text{syn-hard}$: Synthetic dataset with hard labels.
+
+$\\text{syn-any}$: Synthetic dataset with personalized evaluation methods (hard or soft labels).
+
+$\\text{rdm-any}$: Randomly selected dataset (under the same compression ratio) with the same personalized evaluation methods.
+
+$\\text{HLR} = \\text{Acc.} \\text{full-hard} - \\text{Acc.} \\text{syn-hard}$: The degree to which the original dataset is recovered under hard labels (hard label recovery).
+
+$\\text{IOR} = \\text{Acc.} \\text{syn-any} - \\text{Acc.} \\text{rdm-any}$: The improvement over random selection when using personalized evaluation methods (improvement over random).
+"""
+
 DATASET_LIST = ["CIFAR-10", "CIFAR-100", "Tiny-ImageNet"]
 IPC_LIST = ["IPC-1", "IPC-10", "IPC-50"]
 DATASET_IPC_LIST = {
@@ -74,7 +90,7 @@ LABEL_TYPE_LIST = ["Hard Label", "Soft Label"]
 
 METRICS = ["HLR", "IOR"]
 METRICS_SIGN = [1.0, -1.0]
-COLUMN_NAMES = ["Ranking", "Method", "Verified", "Date", "Label Type", "HLR", "IOR", "Score"]
+COLUMN_NAMES = ["Ranking", "Method", "Verified", "Date", "Label Type", "HLR%", "IOR%", "DDRS"]
 DATA_TITLE_TYPE = ['number', 'markdown', 'markdown', 'markdown', 'markdown', 'number', 'number', 'number']
 
 DATASET_MAPPING = {
@@ -92,4 +108,4 @@ IPC_MAPPING = {
 LABEL_MAPPING = {
     "Hard Label": 0,
     "Soft Label": 1,
-}
+}