add results By Metric Type

app.py

@@ -66,9 +66,9 @@ LEADERBOARD_DF = get_leaderboard_df(
 LEADERBOARD_DF_DOMAIN = get_leaderboard_df(
     EVAL_RESULTS_PATH + "/leaderboards/BOOM_domain_leaderboard.csv", EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS
 )
-# LEADERBOARD_DF_METRIC_TYPE = get_leaderboard_df(
-#     EVAL_RESULTS_PATH + "/leaderboards/BOOM_metric_type_leaderboard.csv", EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS
-# )
+LEADERBOARD_DF_METRIC_TYPE = get_leaderboard_df(
+    EVAL_RESULTS_PATH + "/leaderboards/BOOM_metric_type_leaderboard.csv", EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS
+)
 LEADERBOARD_DF_TERM = get_leaderboard_df(
     EVAL_RESULTS_PATH + "/leaderboards/BOOM_term_leaderboard.csv", EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS
 )
@@ -143,8 +143,8 @@ with demo:
         with gr.TabItem("🏅 By Domain", elem_id="boom-benchmark-tab-table", id=1):
             leaderboard = init_leaderboard(LEADERBOARD_DF_DOMAIN, model_info_df)
 
-        # with gr.TabItem("🏅 By Metric Type", elem_id="boom-benchmark-tab-table", id=2):
-        #     leaderboard = init_leaderboard(LEADERBOARD_DF_METRIC_TYPE, model_info_df)
+        with gr.TabItem("🏅 By Metric Type", elem_id="boom-benchmark-tab-table", id=2):
+            leaderboard = init_leaderboard(LEADERBOARD_DF_METRIC_TYPE, model_info_df)
 
         with gr.TabItem("🏅 By Forecast Horizon", elem_id="boom-benchmark-tab-table", id=3):
             leaderboard = init_leaderboard(LEADERBOARD_DF_TERM, model_info_df)

results/leaderboards/BOOM_metric_type_leaderboard.csv

@@ -0,0 +1,15 @@
+model,Count (MASE),Distribution (MASE),Gauge (MASE),Rate (MASE),Count (CRPS),Distribution (CRPS),Gauge (CRPS),Rate (CRPS)
+Toto-Open-Base-1.0,0.687,0.658,0.583,0.634,0.317,0.382,0.382,0.369
+autoets,1.074,0.854,0.774,0.907,1.557,1.22,3.059,1.496
+autoarima,1.031,0.864,0.755,0.88,0.767,0.736,0.701,0.777
+autotheta,1.578,1.144,1.062,1.158,1.132,0.976,1.015,1.034
+chronos_bolt_base,0.88,0.753,0.696,0.739,0.402,0.446,0.463,0.443
+chronos_bolt_small,0.883,0.759,0.706,0.742,0.403,0.452,0.469,0.445
+moirai_1.1_base,0.795,0.724,0.686,0.728,0.353,0.422,0.444,0.418
+moirai_1.1_large,0.813,0.729,0.7,0.733,0.372,0.428,0.456,0.422
+moirai_1.1_small,0.814,0.741,0.72,0.753,0.37,0.434,0.471,0.433
+naive,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+time-moe,0.652,0.878,0.76,0.846,0.651,0.604,0.694,0.619
+timer,0.663,0.89,0.721,0.864,0.662,0.608,0.658,0.63
+timesfm_2_0_500m,0.919,0.745,0.706,0.726,0.403,0.44,0.466,0.431
+visionts,1.22,1.034,0.922,1.041,0.603,0.674,0.672,0.687