update trust safety metrics

src/display/utils.py

@@ -77,8 +77,11 @@ ts_eval_column_dict = []
 # Init
 ts_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model Name", "markdown", True, never_hidden=True)])
 ts_eval_column_dict.append(["model_provider", ColumnContent, ColumnContent("LLM Provider", "markdown", True)])
-ts_eval_column_dict.append(["truthfulness", ColumnContent, ColumnContent("Truthfulness", "markdown", True)])
-ts_eval_column_dict.append(["safety", ColumnContent, ColumnContent("Safety", "markdown", True)])
+ts_eval_column_dict.append(["ts", ColumnContent, ColumnContent("Trust & Safety", "markdown", True)])
+ts_eval_column_dict.append(["safety", ColumnContent, ColumnContent("Safety", "markdown", False)])
+ts_eval_column_dict.append(["privacy", ColumnContent, ColumnContent("Privacy", "markdown", False)])
+ts_eval_column_dict.append(["truthfulness", ColumnContent, ColumnContent("Truthfulness", "markdown", False)])
+
 TSEvalColumn = make_dataclass("TSEvalColumn", ts_eval_column_dict, frozen=True)
 
 

src/populate.py

@@ -2,10 +2,6 @@ import os
 
 import pandas as pd
 
-# from src.display.formatting import has_no_nan_values, make_clickable_model
-# from src.display.utils import AutoEvalColumn, EvalQueueColumn
-# from src.leaderboard.read_evals import get_raw_eval_results
-
 
 def get_leaderboard_df_crm(
     crm_results_path: str, accuracy_cols: list, cost_cols: list
@@ -18,9 +14,6 @@ def get_leaderboard_df_crm(
     # leaderboard_accuracy_df = leaderboard_accuracy_df.sort_values(
     #     by=[AutoEvalColumn.accuracy_metric_average.name], ascending=False
     # )
-    # print(leaderboard_accuracy_df)
-    # print(leaderboard_accuracy_df.columns)
-    # print(leaderboard_accuracy_df["Model Name"].nunique())
     leaderboard_accuracy_df = leaderboard_accuracy_df[accuracy_cols].round(decimals=2)
 
     ref_df = leaderboard_accuracy_df[["Model Name", "LLM Provider"]].drop_duplicates()
@@ -34,54 +27,24 @@ def get_leaderboard_df_crm(
     leaderboard_ts_df = pd.read_csv(os.path.join(crm_results_path, "hf_leaderboard_ts.csv"))
     leaderboard_ts_df = leaderboard_ts_df[~leaderboard_ts_df["Model Name"].isin(sf_finetuned_models)]
     leaderboard_ts_df = leaderboard_ts_df.join(ref_df.set_index("Model Name"), on="Model Name")
+    privacy_cols = leaderboard_ts_df[
+        [
+            "Privacy Zero-Shot Match Avoidance",
+            "Privacy Zero-Shot Reveal Avoidance",
+            "Privacy Five-Shot Match Avoidance",
+            "Privacy Five-Shot Reveal Avoidance",
+        ]
+    ].apply(lambda x: x.str.rstrip("%").astype("float") / 100.0, axis=1)
+
+    leaderboard_ts_df["Privacy"] = privacy_cols.mean(axis=1).transform(lambda x: "{:,.2%}".format(x))
+
+    ts_cols = leaderboard_ts_df[
+        [
+            "Safety",
+            "Privacy",
+            "Truthfulness",
+        ]
+    ].apply(lambda x: x.str.rstrip("%").astype("float") / 100.0, axis=1)
+    leaderboard_ts_df["Trust & Safety"] = ts_cols.mean(axis=1).transform(lambda x: "{:,.2%}".format(x))
 
     return leaderboard_accuracy_df, leaderboard_cost_df, leaderboard_ts_df
-
-
-# def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
-#     """Creates a dataframe from all the individual experiment results"""
-#     raw_data = get_raw_eval_results(results_path, requests_path)
-#     all_data_json = [v.to_dict() for v in raw_data]
-
-#     df = pd.DataFrame.from_records(all_data_json)
-#     df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
-#     df = df[cols].round(decimals=2)
-
-#     # filter out if any of the benchmarks have not been produced
-#     df = df[has_no_nan_values(df, benchmark_cols)]
-#     return raw_data, df
-
-# def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
-#     """Creates the different dataframes for the evaluation queues requestes"""
-#     entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
-#     all_evals = []
-
-#     for entry in entries:
-#         if ".json" in entry:
-#             file_path = os.path.join(save_path, entry)
-#             with open(file_path) as fp:
-#                 data = json.load(fp)
-
-#             data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
-#             data[EvalQueueColumn.revision.name] = data.get("revision", "main")
-
-#             all_evals.append(data)
-#         elif ".md" not in entry:
-#             # this is a folder
-#             sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")]
-#             for sub_entry in sub_entries:
-#                 file_path = os.path.join(save_path, entry, sub_entry)
-#                 with open(file_path) as fp:
-#                     data = json.load(fp)
-
-#                 data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
-#                 data[EvalQueueColumn.revision.name] = data.get("revision", "main")
-#                 all_evals.append(data)
-
-#     pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
-#     running_list = [e for e in all_evals if e["status"] == "RUNNING"]
-#     finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
-#     df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
-#     df_running = pd.DataFrame.from_records(running_list, columns=cols)
-#     df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
-#     return df_finished[cols], df_running[cols], df_pending[cols]