add data from tabrepo

README.md

@@ -21,6 +21,9 @@ cd TabArena-WIP
 # gradio only works with 3.10 
 uv sync --python 3.10
 PYTHONPATH=. uv run python src/generate_dummy_data.py
+
+# OR download data from tabrepo in ~/Downloads and then do
+# PYTHONPATH=. uv run python src/convert_tabrepo_data.py
 PYTHONPATH=. uv run python app.py  
 ```
 

app.py

@@ -24,10 +24,6 @@ def restart_space():
     API.restart_space(repo_id=REPO_ID)
 
 
-
-
-
-
 def init_leaderboard(ori_dataframe, model_info_df, sort_val: str|None = None):
     if ori_dataframe is None or ori_dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
@@ -108,6 +104,7 @@ def load_results():
     # print(f'Variate type dataframe is {variate_type_df}')
     return overall_df, model_info_df, domain_df
 
+
 def main():
 
     overall_df, model_info_df, domain_df = load_results()

results/AutoGluon (best)/all_results.csv

@@ -1,6 +0,0 @@
-dataset,model,raw-error,fit-time-per-1K-rows,inference-time-per-1K-rows
-airline,AutoGluon (best),0.5488135039273248,65,68
-electricity,AutoGluon (best),0.8579456176227568,10,84
-solar-energy,AutoGluon (best),0.6458941130666561,37,88
-traffic,AutoGluon (best),0.8917730007820798,89,13
-volcano,AutoGluon (best),0.3834415188257777,40,88

results/AutoGluon (best)/config.json

@@ -1 +0,0 @@
-{"model": "AutoGluon (best)", "method_type": "AutoML"}

results/AutoGluon 0.8 (4h)/all_results.csv

@@ -0,0 +1,192 @@
+dataset,fold,model,raw-error,metric,fit-time-per-1K-rows,inference-time-per-1K-rows
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+Diabetes130US,0,AutoGluon 0.8 (4h),0.825031,log_loss,12578.6,0.0013265727256647
+GAMETES_Epistasis_2-Way_1000atts_0_4H_EDM-1_EDM-1_1,0,AutoGluon 0.8 (4h),0.271406,roc_auc,13616.2,0.14375
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results/AutoGluon 0.8 (4h)/config.json

@@ -0,0 +1 @@
+{"model": "AutoGluon 0.8 (4h)", "method_type": "AutoML"}

results/AutoGluon 1.1 (4h)/all_results.csv

@@ -0,0 +1,192 @@
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+yprop_4_1,0,AutoGluon 1.1 (4h),0.0253948,rmse,9825.9,6.7

results/AutoGluon 1.1 (4h)/config.json

@@ -0,0 +1 @@
+{"model": "AutoGluon 1.1 (4h)", "method_type": "AutoML"}

results/Autosklearn2 (4h)/all_results.csv

@@ -0,0 +1,192 @@
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results/Autosklearn2 (4h)/config.json

@@ -0,0 +1 @@
+{"model": "Autosklearn2 (4h)", "method_type": "AutoML"}

results/CAT (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/CAT (default)/config.json

@@ -0,0 +1 @@
+{"model": "CAT (default)", "method_type": "Tree-based"}

results/CatBoost/all_results.csv

@@ -1,6 +0,0 @@
-dataset,model,raw-error,fit-time-per-1K-rows,inference-time-per-1K-rows
-airline,CatBoost,0.4799771723750573,82,38
-electricity,CatBoost,0.8360787635373775,73,10
-solar-energy,CatBoost,0.6481718720511972,81,70
-traffic,CatBoost,0.7781567509498505,48,65
-volcano,CatBoost,0.978618342232764,89,50

results/CatBoost/config.json

@@ -1 +0,0 @@
-{"model": "CatBoost", "method_type": "Boosted-tree"}

results/EBM (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/EBM (default)/config.json

@@ -0,0 +1 @@
+{"model": "EBM (default)", "method_type": "Other"}

results/FASTAI (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/FASTAI (default)/config.json

@@ -0,0 +1 @@
+{"model": "FASTAI (default)", "method_type": "Fine-tuned"}

results/FT_TRANSFORMER (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/FT_TRANSFORMER (default)/config.json

@@ -0,0 +1 @@
+{"model": "FT_TRANSFORMER (default)", "method_type": "Fine-tuned"}

results/GBM (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/GBM (default)/config.json

@@ -0,0 +1 @@
+{"model": "GBM (default)", "method_type": "Tree-based"}

results/KNN (default)/all_results.csv

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results/KNN (default)/config.json

@@ -0,0 +1 @@
+{"model": "KNN (default)", "method_type": "Other"}

results/KNN/all_results.csv

@@ -1,6 +0,0 @@
-dataset,model,raw-error,fit-time-per-1K-rows,inference-time-per-1K-rows
-airline,KNN,0.6531400357979377,12,47
-electricity,KNN,0.358152166969525,1,15
-solar-energy,KNN,0.6078306687154678,13,43
-traffic,KNN,0.43860151346232035,69,7
-volcano,KNN,0.10204481074802807,4,77

results/KNN/config.json

@@ -1 +0,0 @@
-{"model": "KNN", "method_type": "Other"}

results/LR (default)/all_results.csv

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results/LR (default)/config.json

@@ -0,0 +1 @@
+{"model": "LR (default)", "method_type": "Other"}

results/NN_TORCH (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/NN_TORCH (default)/config.json

@@ -0,0 +1 @@
+{"model": "NN_TORCH (default)", "method_type": "Other"}

results/REALMLP (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/REALMLP (default)/config.json

@@ -0,0 +1 @@
+{"model": "REALMLP (default)", "method_type": "Fine-tuned"}

results/RF (default)/all_results.csv

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results/RF (default)/config.json

@@ -0,0 +1 @@
+{"model": "RF (default)", "method_type": "Tree-based"}

results/TabPFN-v2/all_results.csv

@@ -1,6 +0,0 @@
-dataset,model,raw-error,fit-time-per-1K-rows,inference-time-per-1K-rows
-airline,TabPFN-v2,0.5684339488686485,1,37
-electricity,TabPFN-v2,0.6176354970758771,39,18
-solar-energy,TabPFN-v2,0.9437480785146242,43,59
-traffic,TabPFN-v2,0.6130634578841324,2,66
-volcano,TabPFN-v2,0.09928035035897387,58,36

results/TabPFN-v2/config.json

@@ -1 +0,0 @@
-{"model": "TabPFN-v2", "method_type": "Foundational"}

results/TabPFN/all_results.csv

@@ -1,6 +0,0 @@
-dataset,model,raw-error,fit-time-per-1K-rows,inference-time-per-1K-rows
-airline,TabPFN,0.46147936225293185,20,20
-electricity,TabPFN,0.11827442586893322,33,66
-solar-energy,TabPFN,0.1433532874090464,33,32
-traffic,TabPFN,0.10590760718779213,24,36
-volcano,TabPFN,0.18633234332675996,56,29

results/TabPFN/config.json

@@ -1 +0,0 @@
-{"model": "TabPFN", "method_type": "Foundational"}

results/XGB (default)/all_results.csv

@@ -0,0 +1,192 @@
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results/XGB (default)/config.json

@@ -0,0 +1 @@
+{"model": "XGB (default)", "method_type": "Tree-based"}

results/XT (default)/all_results.csv

@@ -0,0 +1,192 @@
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+wall-robot-navigation,0,XT (default),0.1417120305779524,log_loss,0.4323964118957519,0.0001716792933052
+waveform-5000,0,XT (default),0.4341503883451223,log_loss,0.4474794864654541,0.0001807498931884
+wilt,0,XT (default),0.0132264024185422,roc_auc,0.392575740814209,0.0001418859068335
+wind,0,XT (default),0.0555091548576891,roc_auc,0.5491375923156738,0.000162579058005
+wine-quality-red,0,XT (default),0.762673759869358,log_loss,0.4105851650238037,0.0004276116987255
+wine-quality-white,0,XT (default),0.7677662724743084,log_loss,0.6060824394226074,0.0001106319645561
+wine_quality,0,XT (default),0.5804142355918884,rmse,0.4224710464477539,0.0001065863523737
+yprop_4_1,0,XT (default),0.0257182008973559,rmse,7.20976996421814,0.0002256663380946

results/XT (default)/config.json

@@ -0,0 +1 @@
+{"model": "XT (default)", "method_type": "Tree-based"}

results/dataset_properties.csv

@@ -1,6 +1,192 @@
-dataset,problem_type,num_rows,num_features
-airline,Regression,10,12
-electricity,Classification,1020,2
-solar-energy,Multi-classification,100,3
-traffic,Multi-classification,10000,12
-volcano,Regression,100,12
+dataset,problem_type,num_rows,num_features,num_classes
+2dplanes,binary,40768,10,2
+APSFailure,binary,76000,170,2
+Allstate_Claims_Severity,regression,188318,130,0
+Amazon_employee_access,binary,32769,9,2
+Australian,binary,690,14,2
+Bioresponse,binary,3751,1776,2
+Brazilian_houses,regression,10692,12,0
+Click_prediction_small,binary,39948,11,2
+Diabetes130US,multiclass,101766,49,3
+GAMETES_Epistasis_2-Way_1000atts_0_4H_EDM-1_EDM-1_1,binary,1600,1000,2
+GAMETES_Epistasis_2-Way_20atts_0_1H_EDM-1_1,binary,1600,20,2
+GAMETES_Epistasis_2-Way_20atts_0_4H_EDM-1_1,binary,1600,20,2
+GAMETES_Epistasis_3-Way_20atts_0_2H_EDM-1_1,binary,1600,20,2
+GAMETES_Heterogeneity_20atts_1600_Het_0_4_0_2_50_EDM-2_001,binary,1600,20,2
+GAMETES_Heterogeneity_20atts_1600_Het_0_4_0_2_75_EDM-2_001,binary,1600,20,2
+GesturePhaseSegmentationProcessed,multiclass,9873,32,5
+Indian_pines,multiclass,9144,220,8
+Internet-Advertisements,binary,3279,1558,2
+LED-display-domain-7digit,multiclass,500,7,10
+MIP-2016-regression,regression,1090,144,0
+Mercedes_Benz_Greener_Manufacturing,regression,4209,376,0
+MiceProtein,multiclass,1080,81,8
+MiniBooNE,binary,130064,50,2
+Moneyball,regression,1232,14,0
+OnlineNewsPopularity,regression,39644,59,0
+PhishingWebsites,binary,11055,30,2
+Run_or_walk_information,binary,88588,6,2
+SAT11-HAND-runtime-regression,regression,4440,116,0
+Satellite,binary,5100,36,2
+SpeedDating,binary,8378,120,2
+Titanic,binary,2201,3,2
+Traffic_violations,multiclass,70340,20,3
+Yolanda,regression,400000,100,0
+abalone,regression,4177,8,0
+ada,binary,4147,48,2
+adult,binary,48842,14,2
+ailerons,binary,13750,40,2
+analcatdata_authorship,multiclass,841,70,4
+analcatdata_dmft,multiclass,797,4,6
+arsenic-female-bladder,binary,559,4,2
+artificial-characters,multiclass,10218,7,10
+autoUniv-au1-1000,binary,1000,20,2
+autoUniv-au6-750,multiclass,750,40,8
+autoUniv-au7-1100,multiclass,1100,12,5
+autoUniv-au7-700,multiclass,700,12,3
+balance-scale,multiclass,625,4,3
+bank-marketing,binary,45211,16,2
+bank32nh,binary,8192,32,2
+bank8FM,binary,8192,8,2
+baseball,multiclass,1340,16,3
+black_friday,regression,166821,9,0
+blood-transfusion-service-center,binary,748,4,2
+boston,regression,506,13,0
+boston_corrected,binary,506,20,2
+car,multiclass,1728,6,4
+cardiotocography,multiclass,2126,35,10
+churn,binary,5000,20,2
+climate-model-simulation-crashes,binary,540,20,2
+cmc,multiclass,1473,9,3
+cnae-9,multiclass,1080,856,9
+colleges,regression,7063,44,0
+colleges_usnews,binary,1302,33,2
+collins,multiclass,1000,23,30
+connect-4,multiclass,67557,42,3
+cpu_act,binary,8192,21,2
+cpu_small,binary,8192,12,2
+credit-g,binary,1000,20,2
+cylinder-bands,binary,540,39,2
+delta_ailerons,binary,7129,5,2
+delta_elevators,binary,9517,6,2
+diabetes,binary,768,8,2
+diamonds,regression,53940,9,0
+dilbert,multiclass,10000,2000,5
+dna,multiclass,3186,180,3
+dresses-sales,binary,500,12,2
+eeg-eye-state,binary,14980,14,2
+electricity,binary,45312,8,2
+elevators,regression,16599,18,0
+eucalyptus,multiclass,736,19,5
+eye_movements,multiclass,10936,27,3
+fabert,multiclass,8237,800,7
+first-order-theorem-proving,multiclass,6118,51,6
+fri_c0_1000_5,binary,1000,5,2
+fri_c0_500_5,binary,500,5,2
+fri_c1_1000_50,binary,1000,50,2
+fri_c2_1000_25,binary,1000,25,2
+fri_c2_500_50,binary,500,50,2
+fri_c3_1000_10,binary,1000,10,2
+fri_c3_1000_25,binary,1000,25,2
+fri_c3_500_10,binary,500,10,2
+fri_c3_500_50,binary,500,50,2
+fri_c4_500_100,binary,500,100,2
+fried,binary,40768,10,2
+gina,binary,3153,970,2
+har,multiclass,10299,561,6
+hill-valley,binary,1212,100,2
+hiva_agnostic,binary,4229,1617,2
+house_16H,regression,22784,16,0
+house_prices_nominal,regression,1460,79,0
+house_sales,regression,21613,21,0
+houses,binary,20640,8,2
+ilpd,binary,583,10,2
+isolet,multiclass,7797,617,26
+jannis,multiclass,83733,54,4
+jasmine,binary,2984,144,2
+jm1,binary,10885,21,2
+jungle_chess_2pcs_raw_endgame_complete,multiclass,44819,6,3
+kc1,binary,2109,21,2
+kc2,binary,522,21,2
+kdd_el_nino-small,binary,782,8,2
+kdd_internet_usage,binary,10108,68,2
+kick,binary,72983,32,2
+kin8nm,binary,8192,8,2
+kr-vs-k,multiclass,28056,6,18
+kropt,multiclass,28056,6,18
+led24,multiclass,3200,24,10
+letter,multiclass,20000,16,26
+madeline,binary,3140,259,2
+madelon,binary,2600,500,2
+mammography,binary,11183,6,2
+mc1,binary,9466,38,2
+meta,binary,528,21,2
+mfeat-factors,multiclass,2000,216,10
+micro-mass,multiclass,571,1300,20
+microaggregation2,multiclass,20000,20,5
+mozilla4,binary,15545,5,2
+no2,binary,500,7,2
+nomao,binary,34465,118,2
+numerai28_6,binary,96320,21,2
+okcupid-stem,multiclass,50789,19,3
+optdigits,multiclass,5620,64,10
+ozone-level-8hr,binary,2534,72,2
+page-blocks,multiclass,5473,10,5
+parity5_plus_5,binary,1124,10,2
+pbcseq,binary,1945,18,2
+pc1,binary,1109,21,2
+pc2,binary,5589,36,2
+pc3,binary,1563,37,2
+pc4,binary,1458,37,2
+pendigits,multiclass,10992,16,10
+philippine,binary,5832,308,2
+phoneme,binary,5404,5,2
+pm10,binary,500,7,2
+pol,regression,15000,48,0
+pollen,binary,3848,5,2
+puma32H,binary,8192,32,2
+puma8NH,binary,8192,8,2
+qsar-biodeg,binary,1055,41,2
+quake,regression,2178,3,0
+ringnorm,binary,7400,20,2
+rmftsa_ladata,binary,508,10,2
+satimage,multiclass,6430,36,6
+segment,multiclass,2310,19,7
+semeion,multiclass,1593,256,10
+sensory,regression,576,11,0
+shuttle,multiclass,58000,9,7
+socmob,regression,1156,5,0
+soybean,multiclass,683,35,19
+space_ga,regression,3107,6,0
+spambase,binary,4601,57,2
+splice,multiclass,3190,60,3
+steel-plates-fault,multiclass,1941,27,7
+sylvine,binary,5124,20,2
+synthetic_control,multiclass,600,60,6
+tecator,regression,240,124,0
+texture,multiclass,5500,40,11
+tokyo1,binary,959,44,2
+topo_2_1,regression,8885,266,0
+twonorm,binary,7400,20,2
+us_crime,regression,1994,126,0
+vehicle,multiclass,846,18,4
+visualizing_soil,binary,8641,4,2
+volcanoes-a2,multiclass,1623,3,5
+volcanoes-a3,multiclass,1521,3,5
+volcanoes-a4,multiclass,1515,3,5
+volcanoes-b1,multiclass,10176,3,5
+volcanoes-b2,multiclass,10668,3,5
+volcanoes-b5,multiclass,9989,3,5
+volcanoes-b6,multiclass,10130,3,5
+volcanoes-d1,multiclass,8753,3,5
+volcanoes-d4,multiclass,8654,3,5
+volcanoes-e1,multiclass,1183,3,5
+volkert,multiclass,58310,180,10
+wall-robot-navigation,multiclass,5456,24,4
+waveform-5000,multiclass,5000,40,3
+wilt,binary,4839,5,2
+wind,binary,6574,14,2
+wine-quality-red,multiclass,1599,11,6
+wine-quality-white,multiclass,4898,11,7
+wine_quality,regression,6497,11,0
+yprop_4_1,regression,8885,251,0

src/constants.py

@@ -18,7 +18,7 @@ class ProblemTypes:
 class MethodTypes:
     col_name: str = "method_type"
     automl: str = "AutoML"
-    boosted_tree: str = "Boosted-tree"
+    tree: str = "Tree-based"
     foundational: str = "Foundational"
     finetuned: str = "Fine-tuned"
     other: str = "Other"

src/convert_tabrepo_data.py

@@ -0,0 +1,99 @@
+from dataclasses import dataclass
+import json
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from src.constants import MethodTypes, ProblemTypes, DatasetInfo, MetricNames, DatasetInfoRow
+
+np.random.seed(0)
+
+
+def generate_dummy_data(method: str, model_type: str, datasets: list[DatasetInfoRow]):
+    # Create a sample DataFrame with schema 
+    # "dataset,model,eval_metrics/normalized-error,eval_metrics/rank,eval_metrics/ELO,eval_metrics/fit_time_per_1K_rows,eval_metrics/inference_time_per_1K_rows,domain,num_variates"
+    
+    rows = []
+    for dataset in datasets:
+        rows.append({
+            "dataset": dataset.name,
+            "model": method,
+            f"{MetricNames.raw_error}": np.random.rand(),
+            f"{MetricNames.fit_time_per_1K_rows}": np.random.randint(1, 100),
+            f"{MetricNames.inference_time_per_1K_rows}": np.random.randint(1, 100),
+        })
+    df = pd.DataFrame(rows)
+    result_path = Path(__file__).parent.parent / "results" / method
+    csv_path = result_path / f"all_results.csv"
+    csv_path.parent.mkdir(parents=True, exist_ok=True)
+    df.to_csv(csv_path, index=False)
+
+    with open(result_path / "config.json", "w") as f:
+        f.write(json.dumps(
+            {
+                "model": method,
+                MethodTypes.col_name: model_type,
+            }
+        ))
+
+    return df
+
+
+def get_model_type(model: str):
+    pattern_families = {
+        "tuned": MethodTypes.automl,
+        "tuned + ensemble": MethodTypes.automl,
+        "FT": MethodTypes.finetuned,
+        "AutoGluon": MethodTypes.automl,
+        "Autosklearn2": MethodTypes.automl,
+        "CAT": MethodTypes.tree,
+        "EBM": MethodTypes.other,
+        "FASTAI": MethodTypes.finetuned,
+        "FT_TRANSFORMER": MethodTypes.finetuned,
+        "GBM": MethodTypes.tree,
+        "KNN": MethodTypes.other,
+        "REALMLP": MethodTypes.finetuned,
+        "RF": MethodTypes.tree,
+        "XGB": MethodTypes.tree,
+        "XT": MethodTypes.tree,
+    }
+
+    for pattern, family in pattern_families.items():
+        if pattern in model:
+            return family
+    return MethodTypes.other
+
+
+if __name__ == "__main__":
+    tabrepo_results_root = Path("~/Downloads/tabrepo_temp_results").expanduser()
+    results_root = Path(__file__).parent.parent / "results"
+    results_root.mkdir(exist_ok=True)
+    df_datasets = pd.read_csv(tabrepo_results_root / "dataset_properties.csv")
+    df_datasets.to_csv(results_root / "dataset_properties.csv", index=False)
+
+    df_models = pd.read_csv(tabrepo_results_root / "all_results.csv")
+
+    # For now discard tuned and ensemble
+    df_models = df_models[~df_models.model.str.contains("tuned")]
+    for model in df_models.loc[:, "model"].unique():
+        result_path = results_root / model
+        result_path.mkdir(exist_ok=True)
+        model_type = get_model_type(model)
+        df_all_results = df_models.loc[df_models.model == model].copy()
+        # dataset,model,raw-error,fit-time-per-1K-rows,inference-time-per-1K-rows
+        df_all_results.rename(columns={
+            "dataset": "dataset",
+            "model": "model",
+            "metric_error": "raw-error",
+            # TODO divide by number of rows and multiply by 1K
+            "time_train_s": "fit-time-per-1K-rows",
+            "time_infer_s": "inference-time-per-1K-rows",
+        }, inplace=True)
+        df_all_results.to_csv(result_path / "all_results.csv", index=False)
+
+        with open(result_path / "config.json", "w") as f:
+            f.write(json.dumps(
+                {
+                    "model": model,
+                    MethodTypes.col_name: model_type,
+                }
+            ))

src/display/utils.py

@@ -59,7 +59,7 @@ model_type_emoji = {
     MethodTypes.foundational: "🟢",
     MethodTypes.finetuned: "🔶",
     MethodTypes.automl: "🤖",
-    MethodTypes.boosted_tree: "🌴",
+    MethodTypes.tree: "🌴",
     MethodTypes.other: "❓",
 }
 
@@ -69,7 +69,7 @@ class ModelType(Enum):
     T1 = _make_model_details(MethodTypes.foundational)
     T2 = _make_model_details(MethodTypes.finetuned)
     T3 = _make_model_details(MethodTypes.automl)
-    T4 = _make_model_details(MethodTypes.boosted_tree)
+    T4 = _make_model_details(MethodTypes.tree)
     T5 = _make_model_details(MethodTypes.other)
 
     Unknown = ModelDetails(name="", symbol="?")
@@ -85,7 +85,7 @@ class ModelType(Enum):
             return ModelType.T2
         if MethodTypes.automl in type or model_type_emoji[MethodTypes.automl] in type:
             return ModelType.T3
-        if MethodTypes.boosted_tree in type or model_type_emoji[MethodTypes.boosted_tree] in type:
+        if MethodTypes.tree in type or model_type_emoji[MethodTypes.tree] in type:
             return ModelType.T4
         if MethodTypes.other in type or model_type_emoji[MethodTypes.other] in type:
             return ModelType.T5

src/generate_dummy_data.py

@@ -50,7 +50,7 @@ if __name__ == "__main__":
 
     methods = [
         ("AutoGluon (best)", MethodTypes.automl),
-        ("CatBoost", MethodTypes.boosted_tree),
+        ("CatBoost", MethodTypes.tree),
         ("TabPFN", MethodTypes.foundational),
         ("TabPFN-v2", MethodTypes.foundational),
         ("KNN", MethodTypes.other),
@@ -65,8 +65,6 @@ if __name__ == "__main__":
     row_datasets = []
     for dataset in datasets:
         row_datasets.append({
-        # dataset,domain,frequency,num_variates
-            # M4 Yearly,Econ/Fin,A-DEC,1
             DatasetInfo.col_name: dataset.name,
             ProblemTypes.col_name: dataset.problem_type,
             DatasetInfo.num_rows: dataset.num_rows,

src/populate.py

@@ -1,12 +1,7 @@
-import json
-import os
-
 import pandas as pd
 
-from src.display.formatting import has_no_nan_values, make_clickable_model
-from src.display.utils import EvalQueueColumn
 from src.leaderboard.read_evals import get_model_info
-import ipdb
+
 
 def get_model_info_df(results_path: str, requests_path: str, cols: list=[], benchmark_cols: list=[]) -> pd.DataFrame:
     """Creates a dataframe from all the individual experiment results"""
@@ -22,72 +17,4 @@ def get_merged_df(result_df: pd.DataFrame, model_info_df: pd.DataFrame) -> pd.Da
     merged_df = pd.merge(model_info_df, result_df, on='model', how='inner')
     merged_df = merged_df.drop(columns=['model'])
     merged_df = merged_df.rename(columns={'model_w_link': 'model'})
-    return merged_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)
-    # raw_data = get_raw_eval_results(results_path, requests_path)
-    # print('results_path:', results_path)
-    # all_data_json = [v.to_dict() for v in raw_data]
-    # print(f"The raw data is {all_data_json}")
-    #
-    # df = pd.DataFrame.from_records(all_data_json)
-    df = pd.read_csv(results_path)
-    # df = pd.read_csv('LOTSAv2_EvalBenchmark(Long).csv')
-    # Step 2: Pivot the DataFrame
-    df = df.pivot_table(index='model',
-                                    columns='dataset',
-                                    values='eval_metrics/MAE[0.5]',
-                                    aggfunc='first')
-    df.drop(columns=['ALL'], inplace=True)
-    df['Average'] = df.mean(axis=1)
-    # Reset the index if you want the model column to be part of the DataFrame
-    df.reset_index(inplace=True)
-    print(f"DF at stage 1 ********** {df}")
-    # ipdb.set_trace()
-    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
-    # df = df.sort_values(by=[AutoEvalColumn.__dataclass_fields__['average'].name], ascending=False)
-    print(f"DF at stage 2 ********** {df}")
-    df = df[cols].round(decimals=2)
-    print(f"DF at stage 3 ********** {df}")
-
-    # filter out if any of the benchmarks have not been produced
-    df = df[has_no_nan_values(df, benchmark_cols)]
-    return 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]
+    return merged_df

src/utils.py

@@ -167,7 +167,7 @@ def get_grouped_dfs(root_dir=None):
     df['ELO'] = df.groupby(['dataset', ProblemTypes.col_name])[f'{RANKING_METRIC}'].rank(method='first',
                                                                                           ascending=True) * 100
     # group by domain
-    grouped_results_overall = df.groupby(['model'])[METRIC_CHOICES].agg(stats.gmean)
+    grouped_results_overall = df.groupby(['model'])[METRIC_CHOICES].agg("mean")
     grouped_results_overall_rank = df.groupby(['model'])[['rank']].mean()
     grouped_results_overall_elo = df.groupby(['model'])[['ELO']].mean()
     grouped_results_overall = pd.concat([
@@ -200,7 +200,8 @@ def standardize_df(df):
 
 
 def group_by(df, col_name):
-    grouped_results = df.groupby([col_name, 'model'])[METRIC_CHOICES].agg(stats.gmean)
+    #grouped_results = df.groupby([col_name, 'model'])[METRIC_CHOICES].agg(stats.gmean)
+    grouped_results = df.groupby([col_name, 'model'])[METRIC_CHOICES].mean()
     grouped_results_rank = df.groupby([col_name, 'model'])[['rank']].mean()
     grouped_results = pd.concat([grouped_results, grouped_results_rank], axis=1)
     # Display the results