Add: first minimal version for the the lb computation

README.md

@@ -1,3 +1,7 @@
 ---
 license: cc-by-4.0
 ---
+
+### Submitting Results to the Leaderboard
+
+To submit results, please concat your model performance results to 'df_results.csv', rerun 'get_leaderboard_csv.py' and write a pull request using the provided template.

get_leaderboard_csv.py

@@ -0,0 +1,170 @@
+import os
+from pathlib import Path
+from tabrepo.tabarena.tabarena import TabArena
+from autogluon.common.loaders import load_pd, load_pkl
+from autogluon.common.savers import save_pd, save_pkl
+import pandas as pd
+
+def compute_normalized_error_dynamic(df_results: pd.DataFrame) -> pd.DataFrame:
+    df_results = df_results.copy(deep=True)
+    df_results_og = df_results.copy(deep=True)
+
+    df_results = df_results.drop(columns=["normalized-error-dataset", "normalized-error-task"], errors="ignore")
+
+    method_col = "method"
+
+    df_results_per_dataset = df_results.groupby([method_col, "dataset"])["metric_error"].mean().reset_index(
+        drop=False)
+
+    from tabrepo.utils.normalized_scorer import NormalizedScorer
+
+    # Alternative, this also incorporates Portfolios and HPO into the normalized scoring. This makes normalized-error dependent on what simulations we run.
+    # This is unbiased against very strong simulation results because the best method defines what is `0.0` on a dataset.
+    normalized_scorer_dataset = NormalizedScorer(
+        df_results_per_dataset,
+        tasks=list(df_results_per_dataset["dataset"].unique()),
+        baseline=None,
+        task_col="dataset",
+        framework_col=method_col,
+    )
+
+    all_tasks = df_results[["dataset", "fold"]].drop_duplicates().values.tolist()
+    all_tasks = [tuple(task) for task in all_tasks]
+
+    normalized_scorer_task = NormalizedScorer(
+        df_results,
+        tasks=all_tasks,
+        baseline=None,
+        task_col=["dataset", "fold"],
+        framework_col=method_col,
+    )
+
+    df_results["normalized-error-task"] = [normalized_scorer_task.rank(task=(dataset, fold), error=error) for
+                                            (dataset, fold, error) in
+                                            zip(df_results["dataset"], df_results["fold"],
+                                                df_results["metric_error"])]
+
+    df_results_per_dataset["normalized-error-dataset"] = [
+        normalized_scorer_dataset.rank(task=dataset, error=error) for (dataset, error) in
+        zip(df_results_per_dataset["dataset"], df_results_per_dataset["metric_error"])
+    ]
+
+    df_results_per_dataset = df_results_per_dataset.set_index(["dataset", method_col], drop=True)[
+        "normalized-error-dataset"]
+    df_results = df_results.merge(df_results_per_dataset, left_on=["dataset", method_col], right_index=True)
+
+    df_results_og["normalized-error-dataset"] = df_results["normalized-error-dataset"]
+    df_results_og["normalized-error-task"] = df_results["normalized-error-task"]
+    return df_results_og
+
+
+dataset_sizes = {'APSFailure': 76000.0,
+    'Amazon_employee_access': 32769.0,
+    'Another-Dataset-on-used-Fiat-500': 1538.0,
+    'Bank_Customer_Churn': 10000.0,
+    'Bioresponse': 3751.0,
+    'Diabetes130US': 71518.0,
+    'E-CommereShippingData': 10999.0,
+    'Fitness_Club': 1500.0,
+    'Food_Delivery_Time': 45451.0,
+    'GiveMeSomeCredit': 150000.0,
+    'HR_Analytics_Job_Change_of_Data_Scientists': 19158.0,
+    'Is-this-a-good-customer': 1723.0,
+    'MIC': 1699.0,
+    'Marketing_Campaign': 2240.0,
+    'NATICUSdroid': 7491.0,
+    'QSAR-TID-11': 5742.0,
+    'QSAR_fish_toxicity': 907.0,
+    'SDSS17': 78053.0,
+    'airfoil_self_noise': 1503.0,
+    'anneal': 898.0,
+    'bank-marketing': 45211.0,
+    'blood-transfusion-service-center': 748.0,
+    'churn': 5000.0,
+    'coil2000_insurance_policies': 9822.0,
+    'concrete_compressive_strength': 1030.0,
+    'credit-g': 1000.0,
+    'credit_card_clients_default': 30000.0,
+    'customer_satisfaction_in_airline': 129880.0,
+    'diabetes': 768.0,
+    'diamonds': 53940.0,
+    'hazelnut-spread-contaminant-detection': 2400.0,
+    'healthcare_insurance_expenses': 1338.0,
+    'heloc': 10459.0,
+    'hiva_agnostic': 3845.0,
+    'houses': 20640.0,
+    'in_vehicle_coupon_recommendation': 12684.0,
+    'jm1': 10885.0,
+    'kddcup09_appetency': 50000.0,
+    'maternal_health_risk': 1014.0,
+    'miami_housing': 13776.0,
+    'online_shoppers_intention': 12330.0,
+    'physiochemical_protein': 45730.0,
+    'polish_companies_bankruptcy': 5910.0,
+    'qsar-biodeg': 1054.0,
+    'seismic-bumps': 2584.0,
+    'splice': 3190.0,
+    'students_dropout_and_academic_success': 4424.0,
+    'superconductivity': 21263.0,
+    'taiwanese_bankruptcy_prediction': 6819.0,
+    'website_phishing': 1353.0,
+    'wine_quality': 6497.0
+    }
+
+if __name__ == '__main__':
+    elo_bootstrap_rounds = 10
+
+    df_results = pd.read_csv("df_results.csv")
+
+    df_results = compute_normalized_error_dynamic(df_results)
+    df_results["normalized-error"] = df_results["normalized-error-dataset"]
+
+    df_results["num_instances"] = df_results["dataset"].map(dataset_sizes)
+
+    df_results['time_train_s_per_1K'] = df_results['time_train_s'] * 1000 / (2 / 3 * df_results['num_instances'])
+    df_results['time_infer_s_per_1K'] = df_results['time_infer_s'] * 1000 / (1 / 3 * df_results['num_instances'])
+
+
+    tabarena = TabArena(
+        method_col="method",
+        task_col="dataset",
+        seed_column="fold",
+        error_col="metric_error",
+        columns_to_agg_extra=[
+            "time_train_s",
+            "time_infer_s",
+            "time_train_s_per_1K",
+            "time_infer_s_per_1K",
+            "normalized-error",
+            "normalized-error-task",
+        ],
+        groupby_columns=[
+            "metric",
+            "problem_type",
+        ],
+    )
+
+    calibration_framework = "RF (default)"
+
+    # configs_all_success = ["TabPFNv2_c1_BAG_L1"]
+    # datasets_tabpfn_valid = self.repo.datasets(configs=configs_all_success, union=False)
+    # df_results_rank_compare3 = df_results_rank_compare[df_results_rank_compare["dataset"].isin(datasets_tabpfn_valid)]
+
+    leaderboard = tabarena.leaderboard(
+        data=df_results,
+        # data=df_results_rank_compare3,
+        include_winrate=True,
+        include_mrr=True,
+        # include_failure_counts=True,
+        include_rank_counts=True,
+        include_elo=True,
+        elo_kwargs=dict(
+            calibration_framework=calibration_framework,
+            calibration_elo=1000,
+            BOOTSTRAP_ROUNDS=elo_bootstrap_rounds,
+        )
+    )
+    elo_map = leaderboard["elo"]
+    leaderboard = leaderboard.reset_index(drop=False)
+
+    save_pd.save(path=f"tabarena_leaderboard.csv", df=leaderboard)

tabarena_leaderboard.csv

@@ -0,0 +1,45 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+REALMLP (tuned + ensemble),82918.85938235045,50.75538799222518,8670.919775777696,17.379653590906823,0.4168638276936593,0.46522486543163427,0.061688216426284394,0.04228497913106759,179788.54928349206,846.8926595159494,8.372549019607844,0.20859,30826.14291326205,23.329282654656303,6566.618256034785,10.263652729721208,0.3818665550412475,0.43309678028435356,0.027798517685924562,0.020583223407182,136595.87091904954,808.3809230744695,7.0,2,7,2,40,1565.4,18.3,33.7,0.8285453716370269,0.22323515264691735
+GBM (tuned + ensemble),3088.2834950947295,22.058380506500956,771.4696177956965,4.079511267084072,0.4965444286779091,0.5395142186069455,0.08092857018345104,0.05687307313004266,11617.562781863639,427.3461655076809,9.696078431372548,0.2112,1667.667911251386,3.7860276963975696,417.0474735648702,2.6386738309580253,0.5416588157338842,0.5593505098057872,0.031447285784914514,0.021931422867572554,9501.568897853982,118.56327192619683,9.0,2,1,9,39,1531.1,20.3,14.0,0.797765617875057,0.1902967415863126
+TABM (tuned + ensemble),309945.85844461835,131.41347681909863,50992.23186278812,25.966007479697893,0.4833937084822297,0.527662991629337,0.06971132771025715,0.06300675126368147,1062292.4422391057,1826.3188257642526,9.813725490196079,0.20583,261121.6058749623,56.5677855014801,38348.59823876663,18.19368855657421,0.4799388525946232,0.5336364991195935,0.03234201709565321,0.029389991826645936,684372.7347620113,1502.185192005209,9.0,3,2,5,41,1529.3,20.7,21.4,0.7950296397628819,0.20732572406081626
+CAT (tuned + ensemble),20021.3498029566,3.148214934334516,4131.157990402895,0.9789028273051836,0.5176588402536559,0.5461494497192764,0.07465084960629444,0.05143820974657852,58497.647265017345,62.60491239485124,11.529411764705882,0.21079,7127.110804247856,1.3727677133348253,1658.431106942659,0.65278892715772,0.5077954495106399,0.5465689951015877,0.041621845573322824,0.025036082174698975,22349.139522535355,41.43902270876455,9.0,0,3,3,45,1488.3,16.9,12.6,0.7551299589603283,0.1396848765821662
+CAT (tuned),20021.3498029566,0.45918073721700764,4131.157990402895,0.1326399433788993,0.5340939510792267,0.560241189670243,0.07657642344207914,0.052142309382478255,58497.647265017345,8.05853748827096,12.07843137254902,0.21123,7127.110804247856,0.1666894753774007,1658.431106942659,0.08101450844552295,0.5302077268378067,0.5897290925717864,0.031525996057162775,0.029191919960694195,22349.139522535355,5.582269457200406,10.0,2,3,2,44,1478.1,4.9,17.9,0.7423620611035112,0.16767987499496947
+GBM (tuned),3088.2834950947295,3.280151636356362,771.4696177956965,0.7006538439498573,0.5855939719295641,0.6054436635097997,0.08824390076126835,0.06705477487523903,11617.562781863639,67.66975356224873,12.950980392156863,0.21181,1667.667911251386,0.5984517203436958,417.0474735648702,0.33376035314871977,0.6166809796992984,0.6457913506687698,0.040799476189841366,0.028946281575058085,9501.568897853982,17.56175518068717,11.0,0,0,0,51,1451.7,7.7,23.7,0.7220702234382125,0.09658870454529672
+XGB (tuned + ensemble),6066.175009788823,8.000445660682546,1229.3396535120692,3.119617584418379,0.592736096344163,0.6155854071545386,0.08897455703950505,0.06687166623000446,14800.692905333455,192.56405868832493,13.411764705882353,0.21458,2256.9276883072325,3.0598979949951173,693.4907982506384,1.6894451629541258,0.6371593498471637,0.6462976286741621,0.04112798968870923,0.03124154502861207,11769.825378159281,82.2877578838671,13.0,1,2,0,48,1441.9,9.1,14.9,0.7113543091655267,0.12808129533333062
+TABM (tuned),309945.85844461835,14.40000900593718,50992.23186278812,2.82530864865144,0.5961366156178172,0.6068470822608255,0.08197172744109267,0.07732216051615445,1062292.4422391057,184.73418386890836,13.794117647058824,0.21083,261121.6058749623,4.427005794313219,38348.59823876663,2.038203656717485,0.6353903270183762,0.6219311591470645,0.03979882553447056,0.034176546224293385,684372.7347620113,130.23219507457006,13.0,0,3,3,45,1432.7,18.6,6.5,0.7024623803009576,0.12602986562329124
+CAT (default),190.03885268302784,0.2709793741147242,87.85477829976735,0.13556345001146092,0.5824430026546973,0.6170029837677764,0.08789056287422015,0.0594755591282597,428.26912869207985,7.186903437353155,13.892156862745098,0.21142,28.268621895048355,0.18547145525614417,6.827020885706225,0.08026752106160412,0.574532210998386,0.5948951171173775,0.04297936180174722,0.029179578269185175,113.47556087857686,6.139565341592894,15.0,2,2,1,46,1432.4,20.6,22.8,0.7001823985408117,0.14580420869437943
+MNCA (tuned + ensemble),236280.2154733103,1702.196323570239,30006.94935586565,242.28091421135778,0.5505767660456689,0.5586240829218156,0.08664207766754953,0.06754385113988148,652156.5791152017,15602.161928533395,14.137254901960784,0.21292,121087.6539443069,180.97370640436807,20604.601796717565,62.20216151532996,0.541453039808286,0.5456102653473301,0.0430773666280555,0.02715435675098635,411805.39657275786,5363.328713863691,11.0,3,2,3,43,1429.9,4.5,32.2,0.6944824441404469,0.17900509017688904
+TABPFNV2 (tuned + ensemble),10256.531407853461,95.46951768190513,2959.3267031650266,47.66674133700557,0.503197928773918,0.5700964346025057,0.08122967375518858,0.08144792307465215,64069.99714449901,3721.4147607998016,14.784313725490197,0.22709,4578.3020022392275,13.798940539360046,3031.497965050595,21.441745344797773,0.5337673254910522,0.5850719212832521,0.038662968410838605,0.03246599803688503,29840.566580772273,861.2724496865974,11.0,11,5,3,32,1412.3,22.0,21.0,0.6794345645234838,0.32754813047254516
+XGB (tuned),6066.175009788823,1.6190793797341305,1229.3396535120692,0.700121681977388,0.6399553616600582,0.6523914608653362,0.09242539010249577,0.0711168313478587,14800.692905333455,40.1709192350484,15.0,0.21499,2256.9276883072325,0.41774741808573407,693.4907982506384,0.30807601554053166,0.6669546265555522,0.70210928999892,0.05170619453009351,0.03462028858330469,11769.825378159281,12.136706817415504,15.0,0,0,0,51,1401.4,21.0,18.3,0.6744186046511628,0.08427229700319627
+TABICL (default),82.80334805787777,14.446106202929627,7.466879203260337,1.6843418131047034,0.5918583004865708,0.642337482634756,0.0875419134792788,0.08838426144985995,132.8309504851054,173.47404322746522,15.92156862745098,0.21659,20.0619904200236,1.316457470258077,6.6270115137100225,1.479660415649414,0.6122367359437335,0.7288871207662829,0.05183434289459943,0.02747963525474764,110.37274820175764,91.25701004379175,12.0,7,3,1,40,1387.8,26.7,20.7,0.6529867761057911,0.23354563591767594
+MNCA (tuned),236280.2154733103,132.95273180870188,30006.94935586565,12.11660203671404,0.6802004436773026,0.6525246439940443,0.09271379272963949,0.07057393673192208,652156.5791152017,1042.4108253893155,15.92156862745098,0.21326,121087.6539443069,7.72004418902927,20604.601796717565,3.084316739055195,0.6677351461060136,0.6505615420190676,0.05810835963747607,0.04513587282105141,411805.39657275786,246.05730829827976,15.0,0,2,1,48,1387.6,27.7,17.8,0.6529867761057911,0.10031778370165712
+REALMLP (tuned),82918.85938235045,2.265432894048088,8670.919775777696,0.9264900528190781,0.7389586445041083,0.70029023973971,0.09659892461491905,0.08207609716020928,179788.54928349206,40.77005886388897,17.647058823529413,0.21508,30826.14291326205,1.0006839964124892,6566.618256034785,0.48749297777811695,0.8387978690853825,0.745244032216585,0.06316180783476799,0.053202622492465136,136595.87091904954,36.17393097895673,17.0,0,0,0,51,1349.4,25.0,12.4,0.612859097127223,0.06790542415647745
+TABPFNV2 (tuned),10256.531407853461,3.508518432739773,2959.3267031650266,1.6627901692072438,0.6232375524417093,0.6563921255920102,0.10222872451877378,0.09869357539741673,64069.99714449901,134.38769780419096,17.95098039215686,0.22751,4578.3020022392275,0.5097733656565349,3031.497965050595,0.46198440414964803,0.7318822746796125,0.7030050854033848,0.0763369368627963,0.04641495405969184,29840.566580772273,22.486278308901746,13.5,1,9,2,39,1346.3,13.5,16.0,0.6057911536707706,0.17720105680966697
+TABM (default),892.3161711748909,11.073448471150368,115.89421747045174,1.8536708133194066,0.6964894973438743,0.7139990788254938,0.11184424398862675,0.10286402245372848,2275.1270097443276,125.71608963592972,18.215686274509803,0.21634,409.2738807996114,3.2030898465050592,65.59975180574475,1.0119200928441636,0.7971619826286321,0.7916271358030448,0.05542898892892967,0.039773366059758904,1333.0629108506891,91.60318880657768,17.0,0,1,0,50,1337.5,19.7,10.4,0.5996352029183767,0.08429884996590406
+NN_TORCH (tuned + ensemble),29590.123960411,13.183612702147375,3730.4883371756378,3.5605725894053775,0.748551089057436,0.7410830400076495,0.10163089676455901,0.08787484128894504,97942.50427646749,194.19437074218936,18.431372549019606,0.21479,10848.660208092795,3.946354971991645,2875.520340774676,1.9516254299583915,0.8918402507857954,0.7854962777205111,0.0655838819050707,0.05785446391646682,61441.834728019094,144.99924838142883,18.0,0,0,1,50,1329.6,13.9,9.5,0.5946192430460556,0.07544818131413467
+MNCA (default),795.306494966,33.3562901808026,90.93203020141935,7.463237374429941,0.7842454576104358,0.7715129001192104,0.12756645405022168,0.0928250470149411,1891.7439043706402,401.9459402385641,19.30392156862745,0.22115,168.86419762505426,2.495272477467855,43.5328308061671,1.4533180631966292,0.9036980011670422,0.8397745521998465,0.0581075785329781,0.057037982337386,1003.1000523368327,103.14689436289233,21.0,1,0,0,50,1318.9,8.3,15.4,0.574327405380757,0.08989311812182865
+TABPFNV2 (default),12.045264010803372,0.825882379421741,4.479779889710856,0.45291413655168883,0.6574942806370782,0.7097934044927047,0.11248713479463117,0.11011936574776773,69.53545044274783,29.750306503263065,19.333333333333332,0.22887,9.117408725950453,0.42119165261586505,3.3572004182270923,0.31292319242745453,0.9332080994174107,0.8423307481260834,0.06834049124367259,0.04813768599620556,52.33126667019037,18.51200905119134,20.0,4,1,4,42,1320.8,18.3,16.7,0.5736434108527132,0.18137568921761685
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+XT (tuned + ensemble),1289.1519403179227,3.0578905248434194,465.042745028218,1.2896231314620477,0.8535500678824685,0.8419856402373398,0.13902458643572385,0.11519988693813606,5501.829777288319,78.9471514852573,22.92156862745098,0.21814,763.5855970117781,1.8364481396145291,183.01944048073585,0.761281055543471,1.0,0.9501297831996555,0.08482265400407019,0.07079319457267828,3537.1622158448413,68.2023166779235,26.0,0,0,2,49,1239.9,13.2,21.1,0.49019607843137253,0.06709444078720042
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+NN_TORCH (tuned),29590.123960411,0.7234875786018787,3730.4883371756378,0.20866384944268937,0.8601642719013879,0.8300843960980898,0.12229829141381855,0.1108390862933938,97942.50427646749,10.830632864633378,23.833333333333332,0.21784,10848.660208092795,0.2631075382232666,2875.520340774676,0.1311128328263719,1.0,0.9141296613785662,0.09365465607587997,0.0689682938764631,61441.834728019094,8.236695976463636,24.0,0,0,0,51,1220.7,20.0,15.7,0.4689922480620155,0.049800303347480715
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+NN_TORCH (default),74.85835825680128,0.5412627521423472,14.699152435241754,0.19913845161978772,0.9747521976215978,0.9514464669707063,0.17085143338983064,0.1566783899750688,327.5182175918235,9.710846453772232,30.637254901960784,0.22759,34.192402362823486,0.22645958264668778,9.99256321379882,0.12580467878727217,1.0,0.992329781710752,0.13913423122778046,0.10253030727386281,204.15320945265734,7.844934898940224,32.0,0,0,0,51,1060.6,29.6,10.1,0.31076151390788875,0.03593632811563251
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+XT (default),3.067954011524425,0.20851136391458944,0.7601454087869071,0.07664730662224606,0.9684602587576486,0.9570945182917132,0.22654446396491512,0.23723350012294867,6.922794409042175,5.082769758447122,34.009803921568626,0.24428,1.029017792807685,0.09326590432061087,0.24740086254260138,0.04980371561279119,1.0,1.0,0.1737557797158895,0.15315330767061192,5.707021421143386,4.456198846093203,37.0,0,0,0,51,967.1,11.1,18.6,0.23233014135886912,0.03301807620066852
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