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__': if os.path.exists("benchmark_results/df_results.parquet"): df_results = load_pd.load(path="benchmark_results/df_results.parquet") else: print(f"Loading results...") context_name = "tabarena_paper_full_51" s3_prefix_public = "https://tabarena.s3.us-west-2.amazonaws.com/evaluation" df_result_save_path = f"{context_name}/data/df_results.parquet" df_results = load_pd.load(path=f"{s3_prefix_public}/{df_result_save_path}") df_results.rename({"framework": "method"}, inplace=True,axis=1) df_results["method"] = df_results["method"].map({ "AutoGluon_bq_4h8c": "AutoGluon 1.3 (4h)", "AutoGluon_bq_1h8c": "AutoGluon 1.3 (1h)", "AutoGluon_bq_5m8c": "AutoGluon 1.3 (5m)", "LightGBM_c1_BAG_L1": "GBM (default)", "XGBoost_c1_BAG_L1": "XGB (default)", "CatBoost_c1_BAG_L1": "CAT (default)", "NeuralNetTorch_c1_BAG_L1": "NN_TORCH (default)", "NeuralNetFastAI_c1_BAG_L1": "FASTAI (default)", "KNeighbors_c1_BAG_L1": "KNN (default)", "RandomForest_c1_BAG_L1": "RF (default)", "ExtraTrees_c1_BAG_L1": "XT (default)", "LinearModel_c1_BAG_L1": "LR (default)", "TabPFN_c1_BAG_L1": "TABPFN (default)", "RealMLP_c1_BAG_L1": "REALMLP (default)", "ExplainableBM_c1_BAG_L1": "EBM (default)", "FTTransformer_c1_BAG_L1": "FT_TRANSFORMER (default)", "TabPFNv2_c1_BAG_L1": "TABPFNV2 (default)", "TabICL_c1_BAG_L1": "TABICL (default)", 'TabDPT_c1_BAG_L1': "TABDPT (default)", 'TabM_c1_BAG_L1': "TABM (default)", 'ModernNCA_c1_BAG_L1': "MNCA (default)", }).fillna(df_results["method"]) df_results = df_results.loc[df_results["method"].apply(lambda x: "default" in x or "(tuned)" in x or "(tuned + ensemble)" in x or "AutoGluon 1.3 (4h)" in x)] df_results.loc[:, "seed"] = 0 df_results.drop(columns=["config_selected", "metadata", "rank"], inplace=True, errors="ignore") save_pd.save(path="benchmark_results/df_results.parquet", df=df_results) elo_bootstrap_rounds = 100 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"benchmark_results/tabarena_leaderboard.csv", df=leaderboard)