Full Code of dholzmueller/pytabkit for AI

gitextract_xlrx7g0c/

├── .github/
│   └── workflows/
│       └── testing.yml
├── .gitignore
├── .readthedocs.yaml
├── LICENSE.txt
├── README.md
├── docs/
│   ├── Makefile
│   ├── make.bat
│   ├── requirements.txt
│   └── source/
│       ├── bench/
│       │   ├── 00_installation.md
│       │   ├── 01_running_the_benchmark.md
│       │   ├── 02_stored_data.md
│       │   ├── 03_code.md
│       │   ├── adding_models.md
│       │   ├── download_results.md
│       │   ├── refine_then_calibrate.md
│       │   └── using_the_scheduler.md
│       ├── conf.py
│       ├── index.rst
│       └── models/
│           ├── 00_overview.md
│           ├── 01_sklearn_interfaces.rst
│           ├── 02_hpo.md
│           ├── 03_training_implementation.md
│           ├── examples.md
│           ├── nn_classes.md
│           └── quantile_reg.md
├── examples/
│   └── tutorial_notebook.ipynb
├── original_requirements/
│   ├── conda_env_2024_06_25.yml
│   ├── conda_env_2024_10_28.yml
│   ├── conda_env_2025_01_15.yml
│   └── requirements_2024_06_25.txt
├── pyproject.toml
├── pytabkit/
│   ├── __about__.py
│   ├── __init__.py
│   ├── bench/
│   │   ├── __init__.py
│   │   ├── alg_wrappers/
│   │   │   ├── __init__.py
│   │   │   ├── general.py
│   │   │   └── interface_wrappers.py
│   │   ├── data/
│   │   │   ├── __init__.py
│   │   │   ├── common.py
│   │   │   ├── get_uci.py
│   │   │   ├── import_talent_benchmark.py
│   │   │   ├── import_tasks.py
│   │   │   ├── paths.py
│   │   │   ├── tasks.py
│   │   │   └── uci_file_ops.py
│   │   ├── eval/
│   │   │   ├── __init__.py
│   │   │   ├── analysis.py
│   │   │   ├── colors.py
│   │   │   ├── evaluation.py
│   │   │   ├── plotting.py
│   │   │   ├── runtimes.py
│   │   │   └── tables.py
│   │   ├── run/
│   │   │   ├── __init__.py
│   │   │   ├── results.py
│   │   │   └── task_execution.py
│   │   └── scheduling/
│   │       ├── __init__.py
│   │       ├── execution.py
│   │       ├── jobs.py
│   │       ├── resource_manager.py
│   │       ├── resources.py
│   │       └── schedulers.py
│   └── models/
│       ├── __init__.py
│       ├── alg_interfaces/
│       │   ├── __init__.py
│       │   ├── alg_interfaces.py
│       │   ├── autogluon_model_interfaces.py
│       │   ├── base.py
│       │   ├── calibration.py
│       │   ├── catboost_interfaces.py
│       │   ├── ensemble_interfaces.py
│       │   ├── lightgbm_interfaces.py
│       │   ├── nn_interfaces.py
│       │   ├── other_interfaces.py
│       │   ├── resource_computation.py
│       │   ├── resource_params.py
│       │   ├── rtdl_interfaces.py
│       │   ├── sub_split_interfaces.py
│       │   ├── tabm_interface.py
│       │   ├── tabr_interface.py
│       │   ├── xgboost_interfaces.py
│       │   └── xrfm_interfaces.py
│       ├── data/
│       │   ├── __init__.py
│       │   ├── conversion.py
│       │   ├── data.py
│       │   ├── nested_dict.py
│       │   └── splits.py
│       ├── hyper_opt/
│       │   ├── __init__.py
│       │   ├── coord_opt.py
│       │   └── hyper_optimizers.py
│       ├── nn_models/
│       │   ├── __init__.py
│       │   ├── activations.py
│       │   ├── base.py
│       │   ├── categorical.py
│       │   ├── models.py
│       │   ├── nn.py
│       │   ├── pipeline.py
│       │   ├── rtdl_num_embeddings.py
│       │   ├── rtdl_resnet.py
│       │   ├── tabm.py
│       │   ├── tabr.py
│       │   ├── tabr_context_freeze.py
│       │   └── tabr_lib.py
│       ├── optim/
│       │   ├── __init__.py
│       │   ├── adopt.py
│       │   ├── optimizers.py
│       │   └── scheduling_adam.py
│       ├── sklearn/
│       │   ├── __init__.py
│       │   ├── default_params.py
│       │   ├── sklearn_base.py
│       │   └── sklearn_interfaces.py
│       ├── torch_utils.py
│       ├── training/
│       │   ├── __init__.py
│       │   ├── auc_mu.py
│       │   ├── coord.py
│       │   ├── lightning_callbacks.py
│       │   ├── lightning_modules.py
│       │   ├── logging.py
│       │   ├── metrics.py
│       │   ├── nn_creator.py
│       │   └── scheduling.py
│       └── utils.py
├── scripts/
│   ├── analyze_hpo_best_params.py
│   ├── analyze_tasks.py
│   ├── check_missing_values.py
│   ├── copy_algs.py
│   ├── create_plots_and_tables.py
│   ├── create_probclass_plots.py
│   ├── create_xrfm_ablations_table.py
│   ├── custom_paths.py.default
│   ├── download_data.py
│   ├── estimate_resource_params.py
│   ├── get_sklearn_names.py
│   ├── make_plot_animation.py
│   ├── meta_hyperopt.py
│   ├── move_algs.py
│   ├── move_many_algs.py
│   ├── print_complete_results.py
│   ├── print_runtimes.py
│   ├── ray_slurm_launch.py
│   ├── ray_slurm_template.sh
│   ├── rename_alg.py
│   ├── rename_tag.py
│   ├── run_evaluation.py
│   ├── run_experiments.py
│   ├── run_experiments_unused.py
│   ├── run_probclass_experiments.py
│   ├── run_single_task.py
│   ├── run_slurm.py
│   ├── run_time_measurement.py
│   └── run_xrfm_large_ablations.py
└── tests/
    ├── __init__.py
    ├── test_bench.py
    ├── test_ensemble.py
    ├── test_metrics.py
    ├── test_rtdl_nns.py
    ├── test_sklearn_interfaces.py
    ├── test_tabr.py
    └── test_variants.py

SYMBOL INDEX (2572 symbols across 99 files)

FILE: pytabkit/bench/alg_wrappers/general.py
  class AlgWrapper (line 13) | class AlgWrapper:
    method __init__ (line 17) | def __init__(self, **config):
    method run (line 25) | def run(self, task_package: TaskPackage, logger: Logger, assigned_reso...
    method get_required_resources (line 40) | def get_required_resources(self, task_package: TaskPackage) -> Require...
    method get_max_n_vectorized (line 49) | def get_max_n_vectorized(self, task_info: TaskInfo) -> int:
    method get_pred_param_names (line 58) | def get_pred_param_names(self, task_package: TaskPackage) -> List[str]:

FILE: pytabkit/bench/alg_wrappers/interface_wrappers.py
  function get_prep_factory (line 55) | def get_prep_factory(**config):
  class AlgInterfaceWrapper (line 59) | class AlgInterfaceWrapper(AlgWrapper):
    method __init__ (line 64) | def __init__(self, create_alg_interface_fn: Optional[Callable[[...], A...
    method _create_alg_interface_impl (line 75) | def _create_alg_interface_impl(self, task_package: TaskPackage) -> Alg...
    method create_alg_interface (line 89) | def create_alg_interface(self, task_package: TaskPackage) -> AlgInterf...
    method run (line 112) | def run(self, task_package: TaskPackage, logger: Logger, assigned_reso...
    method get_required_resources (line 222) | def get_required_resources(self, task_package: TaskPackage) -> Require...
    method get_pred_param_names (line 235) | def get_pred_param_names(self, task_package: TaskPackage) -> List[str]:
  class LoadResultsWrapper (line 239) | class LoadResultsWrapper(AlgInterfaceWrapper):
    method __init__ (line 240) | def __init__(self, alg_name: str, **config):
    method _create_alg_interface_impl (line 244) | def _create_alg_interface_impl(self, task_package: TaskPackage) -> Alg...
    method get_required_resources (line 267) | def get_required_resources(self, task_package: TaskPackage) -> Require...
  class CaruanaEnsembleWrapper (line 272) | class CaruanaEnsembleWrapper(AlgInterfaceWrapper):
    method __init__ (line 273) | def __init__(self, sub_wrappers: List[AlgInterfaceWrapper], **config):
    method _create_alg_interface_impl (line 277) | def _create_alg_interface_impl(self, task_package: TaskPackage) -> Alg...
    method get_required_resources (line 289) | def get_required_resources(self, task_package: TaskPackage) -> Require...
  class AlgorithmSelectionWrapper (line 295) | class AlgorithmSelectionWrapper(AlgInterfaceWrapper):
    method __init__ (line 296) | def __init__(self, sub_wrappers: List[AlgInterfaceWrapper], **config):
    method _create_alg_interface_impl (line 300) | def _create_alg_interface_impl(self, task_package: TaskPackage) -> Alg...
    method get_required_resources (line 312) | def get_required_resources(self, task_package: TaskPackage) -> Require...
  class MultiSplitAlgInterfaceWrapper (line 319) | class MultiSplitAlgInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 320) | def __init__(self, **config):
    method create_single_alg_interface (line 323) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
    method _create_alg_interface_impl (line 327) | def _create_alg_interface_impl(self, task_package: TaskPackage) -> Alg...
  class SubSplitInterfaceWrapper (line 336) | class SubSplitInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method __init__ (line 337) | def __init__(self, create_sub_split_learner_fn: Optional[Callable[[......
    method create_sub_split_interface (line 341) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
    method create_single_alg_interface (line 346) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class NNInterfaceWrapper (line 352) | class NNInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 353) | def __init__(self, **config):
    method get_max_n_vectorized (line 356) | def get_max_n_vectorized(self, task_info: TaskInfo) -> int:
  class NNHyperoptInterfaceWrapper (line 373) | class NNHyperoptInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 374) | def __init__(self, **config):
    method get_max_n_vectorized (line 377) | def get_max_n_vectorized(self, task_info: TaskInfo) -> int:
  class RandomParamsNNInterfaceWrapper (line 394) | class RandomParamsNNInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 395) | def __init__(self, model_idx: int, **config):
  class LGBMSklearnInterfaceWrapper (line 400) | class LGBMSklearnInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 401) | def create_sub_split_interface(self, task_type: TaskType):
  class LGBMInterfaceWrapper (line 405) | class LGBMInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 406) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class LGBMHyperoptInterfaceWrapper (line 410) | class LGBMHyperoptInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method create_single_alg_interface (line 411) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class RandomParamsLGBMInterfaceWrapper (line 416) | class RandomParamsLGBMInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method create_single_alg_interface (line 417) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class XGBSklearnInterfaceWrapper (line 422) | class XGBSklearnInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 423) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class XGBInterfaceWrapper (line 427) | class XGBInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 428) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class RandomParamsXGBInterfaceWrapper (line 432) | class RandomParamsXGBInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method create_single_alg_interface (line 433) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class XGBHyperoptInterfaceWrapper (line 438) | class XGBHyperoptInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method create_single_alg_interface (line 439) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class CatBoostSklearnInterfaceWrapper (line 444) | class CatBoostSklearnInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 445) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class CatBoostInterfaceWrapper (line 449) | class CatBoostInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 450) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class CatBoostHyperoptInterfaceWrapper (line 454) | class CatBoostHyperoptInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method create_single_alg_interface (line 455) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class RandomParamsCatBoostInterfaceWrapper (line 460) | class RandomParamsCatBoostInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method create_single_alg_interface (line 461) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class RFInterfaceWrapper (line 466) | class RFInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 467) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class ExtraTreesInterfaceWrapper (line 471) | class ExtraTreesInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 472) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class KNNInterfaceWrapper (line 476) | class KNNInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 477) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class LinearModelInterfaceWrapper (line 481) | class LinearModelInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 482) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class GBTInterfaceWrapper (line 486) | class GBTInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 487) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class SklearnMLPInterfaceWrapper (line 491) | class SklearnMLPInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 492) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class KANInterfaceWrapper (line 496) | class KANInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 497) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class GrandeInterfaceWrapper (line 501) | class GrandeInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 502) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class TabPFN2InterfaceWrapper (line 506) | class TabPFN2InterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 507) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class TabICLInterfaceWrapper (line 511) | class TabICLInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 512) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class MLPRTDLInterfaceWrapper (line 516) | class MLPRTDLInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 517) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class ResNetRTDLInterfaceWrapper (line 521) | class ResNetRTDLInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 522) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class FTTransformerInterfaceWrapper (line 526) | class FTTransformerInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 527) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class TabRInterfaceWrapper (line 531) | class TabRInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 532) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class TabMInterfaceWrapper (line 536) | class TabMInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 537) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class RandomParamsResnetInterfaceWrapper (line 541) | class RandomParamsResnetInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 542) | def __init__(self, model_idx: int, **config):
  class RandomParamsRTDLMLPInterfaceWrapper (line 547) | class RandomParamsRTDLMLPInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 548) | def __init__(self, model_idx: int, **config):
  class RandomParamsFTTransformerInterfaceWrapper (line 553) | class RandomParamsFTTransformerInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 554) | def __init__(self, model_idx: int, **config):
  class AutoGluonModelInterfaceWrapper (line 559) | class AutoGluonModelInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 560) | def __init__(self, **config):
  class RandomParamsTabRInterfaceWrapper (line 565) | class RandomParamsTabRInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_single_alg_interface (line 566) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \
  class RandomParamsRFInterfaceWrapper (line 571) | class RandomParamsRFInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 572) | def __init__(self, model_idx: int, **config):
  class RandomParamsExtraTreesInterfaceWrapper (line 577) | class RandomParamsExtraTreesInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 578) | def __init__(self, model_idx: int, **config):
  class RandomParamsKNNInterfaceWrapper (line 583) | class RandomParamsKNNInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 584) | def __init__(self, model_idx: int, **config):
  class RandomParamsLinearModelInterfaceWrapper (line 589) | class RandomParamsLinearModelInterfaceWrapper(AlgInterfaceWrapper):
    method __init__ (line 590) | def __init__(self, model_idx: int, **config):
  class xRFMInterfaceWrapper (line 595) | class xRFMInterfaceWrapper(SubSplitInterfaceWrapper):
    method create_sub_split_interface (line 596) | def create_sub_split_interface(self, task_type: TaskType) -> AlgInterf...
  class RandomParamsxRFMInterfaceWrapper (line 600) | class RandomParamsxRFMInterfaceWrapper(MultiSplitAlgInterfaceWrapper):
    method create_single_alg_interface (line 601) | def create_single_alg_interface(self, n_cv: int, task_type: TaskType) \

FILE: pytabkit/bench/data/common.py
  class TaskSource (line 2) | class TaskSource:
  class SplitType (line 15) | class SplitType:

FILE: pytabkit/bench/data/get_uci.py
  function get_skill_craft (line 32) | def get_skill_craft():
  function get_cargo_2000 (line 49) | def get_cargo_2000():
  function get_KDC_4007 (line 60) | def get_KDC_4007():
  function get_sml2010 (line 71) | def get_sml2010():
  function get_wine_quality (line 97) | def get_wine_quality():
  function get_parkinson (line 151) | def get_parkinson():
  function get_insurance_benchmark (line 176) | def get_insurance_benchmark():
  function get_EEG_steady_state (line 200) | def get_EEG_steady_state():
  function get_air_quality (line 212) | def get_air_quality():
  function get_cycle_power_plant (line 257) | def get_cycle_power_plant():
  function get_carbon_nanotubes (line 280) | def get_carbon_nanotubes():
  function get_naval_propulsion (line 304) | def get_naval_propulsion():
  function get_blood_pressure (line 340) | def get_blood_pressure():
  function get_gas_sensor_drift (line 356) | def get_gas_sensor_drift():
  function get_bike_sharing (line 394) | def get_bike_sharing():
  function get_appliances_energy (line 419) | def get_appliances_energy():
  function get_indoor_loc (line 444) | def get_indoor_loc():
  function get_online_news_popularity (line 500) | def get_online_news_popularity():
  function get_facebook_comment_volume (line 518) | def get_facebook_comment_volume():
  function get_bejing_pm25 (line 539) | def get_bejing_pm25():
  function get_protein_tertiary_structure (line 560) | def get_protein_tertiary_structure():
  function get_tamilnadu_electricity (line 574) | def get_tamilnadu_electricity():
  function get_metro_interstate_traffic_volume (line 584) | def get_metro_interstate_traffic_volume():
  function get_facebook_live_sellers_thailand (line 642) | def get_facebook_live_sellers_thailand():
  function get_parking_birmingham (line 680) | def get_parking_birmingham():
  function get_tarvel_review_ratings (line 734) | def get_tarvel_review_ratings():
  function get_superconductivity (line 770) | def get_superconductivity():
  function get_gnfuv_unmanned_surface_vehicles (line 803) | def get_gnfuv_unmanned_surface_vehicles():
  function get_five_cities_pm25 (line 815) | def get_five_cities_pm25():
  function get_phishing (line 873) | def get_phishing():
  function get_ozone_level (line 895) | def get_ozone_level():
  function get_opportunity_activity (line 917) | def get_opportunity_activity():
  function get_australian_sign_language (line 932) | def get_australian_sign_language():
  function get_seismic_bumps (line 945) | def get_seismic_bumps():
  function get_meu_mobile_ksd (line 968) | def get_meu_mobile_ksd():
  function get_character_trajectories (line 981) | def get_character_trajectories():
  function get_vicon_physical_action (line 996) | def get_vicon_physical_action():
  function get_simulated_falls (line 1008) | def get_simulated_falls():
  function get_chess (line 1021) | def get_chess():
  function get_abalone (line 1048) | def get_abalone():
  function get_madelon (line 1066) | def get_madelon():
  function get_spambase (line 1100) | def get_spambase():
  function get_wilt (line 1117) | def get_wilt():
  function get_waveform (line 1144) | def get_waveform():
  function get_wall_following_robot (line 1176) | def get_wall_following_robot():
  function get_page_blocks (line 1219) | def get_page_blocks():
  function get_optical_recognition_handwritten_digits (line 1248) | def get_optical_recognition_handwritten_digits():
  function get_bach_chorals_harmony (line 1269) | def get_bach_chorals_harmony():
  function get_turkiye_student_evaluation (line 1282) | def get_turkiye_student_evaluation():
  function get_smartphone_human_activity (line 1299) | def get_smartphone_human_activity():
  function get_artificial_characters (line 1332) | def get_artificial_characters():
  function get_first_order_theorem_proving (line 1345) | def get_first_order_theorem_proving():
  function get_landsat_satimage (line 1381) | def get_landsat_satimage():
  function get_hiv_1_protease (line 1400) | def get_hiv_1_protease():
  function get_musk (line 1413) | def get_musk():
  function get_ble_rssi_indoor_location (line 1435) | def get_ble_rssi_indoor_location():
  function get_australian_sign_language (line 1447) | def get_australian_sign_language():
  function get_anuran_calls (line 1460) | def get_anuran_calls():
  function get_thyroids (line 1523) | def get_thyroids():
  function get_isolet (line 1769) | def get_isolet():
  function get_mushroom (line 1783) | def get_mushroom():
  function get_assamese_characters (line 1802) | def get_assamese_characters():
  function get_arabic_digit (line 1815) | def get_arabic_digit():
  function get_eeg_steady_state_visual (line 1829) | def get_eeg_steady_state_visual():
  function get_gesture_phase_segmentation (line 1843) | def get_gesture_phase_segmentation():
  function get_emg_physical_action (line 1891) | def get_emg_physical_action():
  function get_human_activity_smartphone (line 1904) | def get_human_activity_smartphone():
  function get_polish_companies_bankruptcy (line 1943) | def get_polish_companies_bankruptcy():
  function get_crowd_sourced_mapping (line 1969) | def get_crowd_sourced_mapping():
  function get_firm_teacher_clave (line 2008) | def get_firm_teacher_clave():
  function get_smartphone_human_activity_postural (line 2042) | def get_smartphone_human_activity_postural():
  function get_pen_recognition_handwritten_characters (line 2088) | def get_pen_recognition_handwritten_characters():
  function get_epileptic_seizure_recognition (line 2108) | def get_epileptic_seizure_recognition():
  function get_nursery (line 2124) | def get_nursery():
  function get_indoor_user_movement_prediction (line 2175) | def get_indoor_user_movement_prediction():
  function get_eeg_eye_state (line 2189) | def get_eeg_eye_state():
  function get_htru2 (line 2203) | def get_htru2():
  function get_magic_gamma_telescope (line 2230) | def get_magic_gamma_telescope():
  function get_letter_recognition (line 2248) | def get_letter_recognition():
  function get_occupancy_detection (line 2267) | def get_occupancy_detection():
  function get_avila (line 2289) | def get_avila():
  function get_grammatical_facial_expressions (line 2323) | def get_grammatical_facial_expressions():
  function get_chess_krvk (line 2335) | def get_chess_krvk():
  function get_default_credit_card (line 2362) | def get_default_credit_card():
  function get_nomao (line 2380) | def get_nomao():
  function get_indoor_loc_mag (line 2414) | def get_indoor_loc_mag():
  function get_activity_recognition (line 2427) | def get_activity_recognition():
  function get_bank_marketing (line 2441) | def get_bank_marketing():
  function get_census_income (line 2543) | def get_census_income():
  function get_emg_for_gestures (line 2604) | def get_emg_for_gestures():
  function get_indoor_channel_measurements (line 2617) | def get_indoor_channel_measurements():
  function get_electrical_grid_stability_simulated (line 2629) | def get_electrical_grid_stability_simulated():
  function get_online_shoppers_attention (line 2657) | def get_online_shoppers_attention():
  function get_pmu_ud (line 2685) | def get_pmu_ud():
  function get_seoul_bike_data (line 2697) | def get_seoul_bike_data():
  function get_south_german_credit (line 2727) | def get_south_german_credit():
  function get_shill_bidding (line 2744) | def get_shill_bidding():
  function get_gas_turbine (line 2764) | def get_gas_turbine():
  function get_oral_toxicity (line 2789) | def get_oral_toxicity():
  function get_wave_energy (line 2812) | def get_wave_energy():
  function get_firewall (line 2850) | def get_firewall():
  function get_real_estate_value (line 2869) | def get_real_estate_value():
  function get_crop_mapping (line 2887) | def get_crop_mapping():
  function get_bitcoin_heist (line 2901) | def get_bitcoin_heist():
  function get_query_analytics (line 2928) | def get_query_analytics():
  function download_all_uci (line 2961) | def download_all_uci(paths: Paths):

FILE: pytabkit/bench/data/import_talent_benchmark.py
  function import_talent_benchmark (line 14) | def import_talent_benchmark(paths: Paths, talent_folder: str, source_nam...

FILE: pytabkit/bench/data/import_tasks.py
  function download_if_not_exists (line 17) | def download_if_not_exists(url: str, dest: str):
  function extract_categories (line 39) | def extract_categories(X):
  function check_zero_hot (line 72) | def check_zero_hot(uci_base_path):
  function convert_to_class_numbers (line 92) | def convert_to_class_numbers(y):
  function import_from_csv (line 102) | def import_from_csv(ds_path: Union[Path, str], task_type: TaskType, task...
  function import_uci_tasks (line 140) | def import_uci_tasks(paths: Paths, remove_duplicates: bool = False, reru...
  function get_openml_task_ids (line 164) | def get_openml_task_ids(suite_id: Union[str, int]) -> List[int]:
  class PandasTask (line 170) | class PandasTask:
    method __init__ (line 171) | def __init__(self, x_df: pd.DataFrame, y_df: pd.Series, cat_indicator:...
    method get_n_classes (line 190) | def get_n_classes(self):
    method get_n_samples (line 197) | def get_n_samples(self):
    method deduplicate (line 200) | def deduplicate(self):
    method limit_n_classes (line 207) | def limit_n_classes(self, max_n_classes: int):
    method subsample (line 223) | def subsample(self, max_size: int):
    method remove_missing_cont (line 231) | def remove_missing_cont(self):
    method normalize_regression_y (line 239) | def normalize_regression_y(self):
    method get_task (line 244) | def get_task(self, task_desc: TaskDescription) -> Task:
    method from_openml_task_id (line 286) | def from_openml_task_id(task_id: int):
  function set_openml_cache_dir (line 304) | def set_openml_cache_dir(dir_name: Union[str, Path]):
  function get_openml_ds_names (line 314) | def get_openml_ds_names(task_ids: List[int]):
  function import_openml (line 325) | def import_openml(task_ids: List[int], task_source_name: str, paths: Pat...

FILE: pytabkit/bench/data/paths.py
  class TmpPathContextManager (line 10) | class TmpPathContextManager:
    method __init__ (line 14) | def __init__(self, path: Path):
    method __enter__ (line 17) | def __enter__(self) -> Path:
    method __exit__ (line 23) | def __exit__(self, type, value, traceback):
  class Paths (line 27) | class Paths:
    method __init__ (line 35) | def __init__(self, base_folder: str, tasks_folder: Optional[str] = Non...
    method from_env_variables (line 46) | def from_env_variables() -> 'Paths':
    method base (line 67) | def base(self) -> Path:
    method algs (line 70) | def algs(self) -> Path:
    method tasks (line 73) | def tasks(self) -> Path:
    method task_collections (line 76) | def task_collections(self) -> Path:
    method results (line 79) | def results(self) -> Path:
    method result_summaries (line 82) | def result_summaries(self) -> Path:
    method eval (line 85) | def eval(self) -> Path:
    method plots (line 88) | def plots(self) -> Path:
    method tmp (line 91) | def tmp(self) -> Path:
    method uci_download (line 94) | def uci_download(self) -> Path:
    method resources (line 97) | def resources(self):
    method times (line 100) | def times(self) -> Path:
    method new_tmp_folder (line 103) | def new_tmp_folder(self) -> TmpPathContextManager:
    method results_alg_task (line 107) | def results_alg_task(self, task_desc: 'TaskDescription', alg_name: str...
    method summary_alg_task (line 110) | def summary_alg_task(self, task_desc: 'TaskDescription', alg_name: str...
    method results_alg_task_split (line 114) | def results_alg_task_split(self, task_desc: 'TaskDescription', alg_nam...
    method tasks_task (line 118) | def tasks_task(self, task_desc: 'TaskDescription') -> Path:
    method results_task (line 121) | def results_task(self, task_desc: 'TaskDescription') -> Path:
    method resources_exp_it (line 124) | def resources_exp_it(self, exp_name: str, iteration: int) -> Path:
    method task_source (line 127) | def task_source(self, task_source_name: str) -> Path:
    method times_alg_task (line 130) | def times_alg_task(self, alg_name: str, task_desc: 'TaskDescription'):

FILE: pytabkit/bench/data/tasks.py
  class TaskDescription (line 25) | class TaskDescription:
    method __init__ (line 30) | def __init__(self, task_source: str, task_name: str):
    method load_info (line 38) | def load_info(self, paths: Paths) -> 'TaskInfo':
    method load_task (line 47) | def load_task(self, paths: Paths):
    method exists_task (line 56) | def exists_task(self, paths: Paths):
    method __str__ (line 65) | def __str__(self):
    method to_dict (line 71) | def to_dict(self) -> Dict:
    method from_dict (line 80) | def from_dict(data: Dict) -> 'TaskDescription':
    method __hash__ (line 89) | def __hash__(self):
    method __eq__ (line 92) | def __eq__(self, other):
  class TaskCollection (line 98) | class TaskCollection:
    method __init__ (line 104) | def __init__(self, coll_name: str, task_descs: List[TaskDescription]):
    method save (line 112) | def save(self, paths: Paths):
    method load_infos (line 117) | def load_infos(self, paths: Paths) -> List['TaskInfo']:
    method from_name (line 121) | def from_name(coll_name: str, paths: Paths) -> 'TaskCollection':
    method from_source (line 128) | def from_source(task_source: str, paths: Paths) -> 'TaskCollection':
  class TaskInfo (line 145) | class TaskInfo:
    method __init__ (line 149) | def __init__(self, task_desc: TaskDescription, n_samples: int, tensor_...
    method get_n_classes (line 170) | def get_n_classes(self) -> int:
    method load_task (line 176) | def load_task(self, paths: Paths) -> 'Task':
    method get_ds_size_gb (line 191) | def get_ds_size_gb(self) -> float:
    method save (line 200) | def save(self, paths: Paths):
    method load (line 210) | def load(paths: Paths, task_desc: TaskDescription):
    method from_ds (line 221) | def from_ds(task_desc: TaskDescription, ds: DictDataset, default_split...
    method get_random_splits (line 226) | def get_random_splits(self, n_splits: int, trainval_fraction: float = ...
    method get_default_splits (line 234) | def get_default_splits(self, n_splits) -> List[SplitInfo]:
  class Task (line 243) | class Task:
    method __init__ (line 248) | def __init__(self, task_info: TaskInfo, ds: DictDataset):
    method save (line 252) | def save(self, paths: Paths):
  class TaskPackage (line 262) | class TaskPackage:
    method __init__ (line 266) | def __init__(self, task_info: TaskInfo, split_infos: List[SplitInfo], ...

FILE: pytabkit/bench/data/uci_file_ops.py
  class UCIVars (line 35) | class UCIVars:
  function prepare_new_data_set_group_id (line 59) | def prepare_new_data_set_group_id():
  function make_folder (line 68) | def make_folder(folder):
  function download_and_save (line 79) | def download_and_save(url, filename):
  function unzip_raw_data (line 94) | def unzip_raw_data(filename):
  function unrar_raw_data (line 104) | def unrar_raw_data(filename):
  function my_decode (line 113) | def my_decode(x):
  function unarff_raw_data (line 124) | def unarff_raw_data(filename):
  function un_z_raw_data (line 142) | def un_z_raw_data(filename):
  function untar_raw_data (line 156) | def untar_raw_data(filename):
  function ungz_raw_data (line 167) | def ungz_raw_data(filename):
  function replace_chars_in_file (line 184) | def replace_chars_in_file(filename, old_char, new_char):
  function get_category_replace_string (line 201) | def get_category_replace_string(category_size, position, separator):
  function replace_categories_in_file (line 224) | def replace_categories_in_file(filename, categories, separator):
  function convert_replace_string_to_vector (line 233) | def convert_replace_string_to_vector(string, separator):
  function get_categories_in_mixed_data (line 246) | def get_categories_in_mixed_data(data, column):
  function auto_replace_categories_in_mixed_data (line 258) | def auto_replace_categories_in_mixed_data(data, column, separator, unkno...
  function auto_replace_missing_in_mixed_data (line 274) | def auto_replace_missing_in_mixed_data(data, unknown_string = '?'):
  function replace_categories_in_mixed_data (line 299) | def replace_categories_in_mixed_data(data, categories, column, separator...
  function replace_bin_cats_in_mixed_data (line 330) | def replace_bin_cats_in_mixed_data(data, categories, column, separator, ...
  function replace_ordinals_in_mixed_data (line 359) | def replace_ordinals_in_mixed_data(data, categories, column, separator, ...
  function replace_manual_in_mixed_data (line 387) | def replace_manual_in_mixed_data(data, categories, column, replacement, ...
  function replace_circulars_in_mixed_data (line 414) | def replace_circulars_in_mixed_data(data, categories, column, separator,...
  function replace_isodate_by_day_in_mixed_data (line 447) | def replace_isodate_by_day_in_mixed_data(data, column):
  function replace_time_by_seconds_in_mixed_data (line 466) | def replace_time_by_seconds_in_mixed_data(data, column, sep, rounded = 1):
  function remove_files (line 483) | def remove_files(folder, filename_pattern):
  function concat_files (line 493) | def concat_files(source_filename_pattern, target_filename):
  function load_mixed_raw_data (line 508) | def load_mixed_raw_data(filename, sep, header = False):
  function write_mixed_raw_data (line 535) | def write_mixed_raw_data(filename, data, sep):
  function load_raw_data (line 548) | def load_raw_data(filename, sep, description_columns = 0, date_column = ...
  function remove_rows_with_label (line 670) | def remove_rows_with_label(data, label):
  function remove_empty_columns (line 683) | def remove_empty_columns(data):
  function save_data_to_file (line 702) | def save_data_to_file(data, filename, is_classification, is_regression =...
  function save_data_stats (line 795) | def save_data_stats(data_stats):
  function is_number (line 812) | def is_number(string, german_decimal):
  function remove_columns (line 835) | def remove_columns(data, columns):
  function move_label_in_front (line 843) | def move_label_in_front(data, label_column):
  function count_bin_columns (line 857) | def count_bin_columns(data):
  function convert_time_to_seconds (line 873) | def convert_time_to_seconds(time, sep):

FILE: pytabkit/bench/eval/analysis.py
  class ResultsTables (line 14) | class ResultsTables:
    method __init__ (line 15) | def __init__(self, paths: Paths):
    method get (line 19) | def get(self, coll_name: str, n_cv: int = 1, tag: str = 'paper') -> Mu...
  function _get_t_mean_confidence_interval_single (line 32) | def _get_t_mean_confidence_interval_single(values: np.ndarray) -> Tuple[...
  function get_t_mean_confidence_interval (line 46) | def get_t_mean_confidence_interval(values: np.ndarray) -> Tuple[np.ndarr...
  function get_benchmark_results (line 59) | def get_benchmark_results(paths: Paths, table: MultiResultsTable, coll_n...
  function get_opt_groups (line 221) | def get_opt_groups(task_type_name: str) -> Dict[str, List[str]]:
  function get_ensemble_groups (line 246) | def get_ensemble_groups(task_type_name: str) -> Dict[str, List[str]]:
  function get_simplified_name (line 266) | def get_simplified_name(alg_name: str):
  function get_display_name (line 282) | def get_display_name(alg_name: str) -> str:

FILE: pytabkit/bench/eval/colors.py
  function bilin_int (line 4) | def bilin_int(x: float, values: List[Tuple[float, float]]) -> float:
  function bisection_find (line 21) | def bisection_find(f: Callable[[float], float], y: float, xmin: float, x...
  function more_percep_uniform_hue (line 48) | def more_percep_uniform_hue(x: float) -> float:

FILE: pytabkit/bench/eval/evaluation.py
  class AlgFilter (line 13) | class AlgFilter:
    method __call__ (line 14) | def __call__(self, alg_name: str, tags: List[str], alg_config: Dict[st...
  class FunctionAlgFilter (line 18) | class FunctionAlgFilter(AlgFilter):
    method __init__ (line 19) | def __init__(self, f):
    method __call__ (line 22) | def __call__(self, alg_name: str, tags: List[str], alg_config: Dict[st...
  class EvalModeSelector (line 26) | class EvalModeSelector:  # base class
    method select_eval_modes (line 27) | def select_eval_modes(self, eval_modes: List[Tuple[str, str, str]]) ->...
    method select (line 32) | def select(self, alg_name: str, task_results: List) -> Tuple[List[str]...
  class DefaultEvalModeSelector (line 57) | class DefaultEvalModeSelector(EvalModeSelector):
    method select_eval_modes (line 58) | def select_eval_modes(self, eval_modes: List[Tuple[str, str, str]]) ->...
  class AlgTaskTable (line 89) | class AlgTaskTable:
    method __init__ (line 90) | def __init__(self, alg_names: List[str], task_infos: List[TaskInfo], a...
    method map (line 95) | def map(self, f):
    method filter_n_splits (line 100) | def filter_n_splits(self, n_splits: int) -> 'AlgTaskTable':
    method to_array (line 114) | def to_array(self) -> np.ndarray:
    method rename_algs (line 117) | def rename_algs(self, f: Callable[[str], str]) -> 'AlgTaskTable':
    method filter_algs (line 121) | def filter_algs(self, alg_names: List[str]) -> 'AlgTaskTable':
  class MultiResultsTable (line 127) | class MultiResultsTable:
    method __init__ (line 128) | def __init__(self, train_table: AlgTaskTable, val_table: AlgTaskTable,...
    method get_test_results_table (line 138) | def get_test_results_table(self, eval_mode_selector: EvalModeSelector,...
    method load (line 290) | def load(task_collection: TaskCollection, n_cv: int, paths: Paths, alg...
  class TableAnalyzer (line 356) | class TableAnalyzer:
    method __init__ (line 357) | def __init__(self, post_f: Optional[Callable[[float], float]] = None):
    method _print_table (line 360) | def _print_table(self, alg_names: List[str], means, stds=None, is_high...
    method print_analysis (line 379) | def print_analysis(self, alg_task_table: AlgTaskTable):
  class TaskWeighting (line 383) | class TaskWeighting:
    method __init__ (line 384) | def __init__(self, task_infos: List[TaskInfo], separate_task_names: Op...
    method get_n_groups (line 404) | def get_n_groups(self) -> int:
    method get_task_weights (line 407) | def get_task_weights(self) -> np.ndarray:
  class MeanTableAnalyzer (line 411) | class MeanTableAnalyzer(TableAnalyzer):
    method __init__ (line 412) | def __init__(self, f=None, use_weighting=False, separate_task_names: O...
    method print_analysis (line 418) | def print_analysis(self, alg_task_table: AlgTaskTable) -> None:
    method get_means (line 438) | def get_means(self, alg_task_table: AlgTaskTable) -> List[float]:
    method get_intervals (line 451) | def get_intervals(self, alg_task_table: AlgTaskTable, std_factor: floa...
  class ArrayTableAnalyzer (line 471) | class ArrayTableAnalyzer(TableAnalyzer):
    method __init__ (line 476) | def __init__(self, f=None, use_weighting=False, separate_task_names: O...
    method _is_higher_better (line 482) | def _is_higher_better(self) -> bool:
    method _process_losses (line 486) | def _process_losses(self, loss_arr: np.ndarray, val_loss_arr: Optional...
    method print_analysis (line 491) | def print_analysis(self, alg_task_table: AlgTaskTable, val_table: Opti...
  class WinsTableAnalyzer (line 526) | class WinsTableAnalyzer(ArrayTableAnalyzer):
    method _process_losses (line 527) | def _process_losses(self, loss_arr: np.ndarray, val_loss_arr: Optional...
    method _is_higher_better (line 531) | def _is_higher_better(self) -> bool:
  function get_ranks (line 535) | def get_ranks(values: np.ndarray) -> np.ndarray:
  class RankTableAnalyzer (line 544) | class RankTableAnalyzer(ArrayTableAnalyzer):
    method _process_losses (line 545) | def _process_losses(self, loss_arr: np.ndarray, val_loss_arr: Optional...
  class NormalizedLossTableAnalyzer (line 550) | class NormalizedLossTableAnalyzer(ArrayTableAnalyzer):
    method _process_losses (line 551) | def _process_losses(self, loss_arr: np.ndarray, val_loss_arr: Optional...
  class GreedyAlgSelectionTableAnalyzer (line 558) | class GreedyAlgSelectionTableAnalyzer(ArrayTableAnalyzer):
    method _process_losses (line 563) | def _process_losses(self, loss_arr: np.ndarray, val_loss_arr: Optional...
  function alg_results_str (line 591) | def alg_results_str(alg_task_table: AlgTaskTable, alg_name: str):
  function alg_comparison_str (line 607) | def alg_comparison_str(alg_task_table: AlgTaskTable, alg_names: List[str]):

FILE: pytabkit/bench/eval/plotting.py
  function get_plot_color_idx (line 55) | def get_plot_color_idx(alg_name: str):
  function gg_color_hue (line 72) | def gg_color_hue(n: int, saturation: float = 1.0, value: float = 0.65):
  function get_plot_color (line 80) | def get_plot_color(alg_name: str):
  function coll_name_to_title (line 89) | def coll_name_to_title(coll_name: str) -> str:
  function plot_schedule (line 112) | def plot_schedule(paths: Paths, filename: str, sched_name: str) -> None:
  function plot_schedules (line 128) | def plot_schedules(paths: Paths, filename: str, sched_names: List[str], ...
  function _create_benchmark_result_plot (line 146) | def _create_benchmark_result_plot(file_path: Path, benchmark_results: Di...
  function _create_benchmark_result_plot_with_intervals (line 207) | def _create_benchmark_result_plot_with_intervals(file_path: Path, benchm...
  function get_equidistant_colors (line 279) | def get_equidistant_colors(n: int):
  function plot_benchmark_bars (line 286) | def plot_benchmark_bars(paths: Paths, tables: ResultsTables, filename: s...
  function plot_scatter_ax (line 354) | def plot_scatter_ax(paths: Paths, tables: ResultsTables, ax: matplotlib....
  function plot_scatter (line 419) | def plot_scatter(paths: Paths, filename: str, tables: ResultsTables, col...
  function _plot_scatter_with_labels (line 455) | def _plot_scatter_with_labels(x_dict: Dict[str, float], y_dict: Dict[str...
  function extend_runtimes (line 651) | def extend_runtimes(times: Dict[str, float], task_type_name: str, keep_g...
  function plot_pareto_ax (line 718) | def plot_pareto_ax(ax: matplotlib.axes.Axes, paths: Paths, tables: Resul...
  function shorten_coll_names (line 853) | def shorten_coll_names(coll_names: List[str]) -> List[str]:
  function plot_pareto (line 862) | def plot_pareto(paths: Paths, tables: ResultsTables, coll_names: List[st...
  function plot_winrates (line 951) | def plot_winrates(paths: Paths, tables: ResultsTables, coll_name: str, a...
  function plot_stopping_ax (line 1029) | def plot_stopping_ax(ax: plt.Axes, paths: Paths, tables: ResultsTables, ...
  function plot_stopping (line 1062) | def plot_stopping(paths: Paths, tables: ResultsTables, classification: b...
  function get_equidistant_blue_colors (line 1093) | def get_equidistant_blue_colors(n: int):
  function _create_cumul_abl_plot (line 1103) | def _create_cumul_abl_plot(file_path: Path, benchmark_results: Dict[str,...
  function plot_cumulative_ablations (line 1266) | def plot_cumulative_ablations(paths: Paths, tables: ResultsTables, filen...
  function plot_cdd_ax (line 1375) | def plot_cdd_ax(ax: matplotlib.axes.Axes, paths: Paths, tables: ResultsT...
  function plot_cdd (line 1418) | def plot_cdd(paths: Paths, tables: ResultsTables, coll_names: List[str],...

FILE: pytabkit/bench/eval/runtimes.py
  function get_avg_train_times (line 10) | def get_avg_train_times(paths: Paths, coll_name: str, per_1k_samples: bo...
  function get_avg_predict_times (line 28) | def get_avg_predict_times(paths: Paths, coll_name: str, per_1k_samples: ...

FILE: pytabkit/bench/eval/tables.py
  function _get_table_str (line 14) | def _get_table_str(*parts: List[List[str]]):
  function generate_ds_table (line 29) | def generate_ds_table(paths: Paths, task_collection: TaskCollection, inc...
  function generate_collections_table (line 67) | def generate_collections_table(paths: Paths):
  function generate_individual_results_table (line 131) | def generate_individual_results_table(paths: Paths, tables: ResultsTable...
  function generate_ablations_table (line 181) | def generate_ablations_table(paths: Paths, tables: ResultsTables):
  function generate_refit_table (line 303) | def generate_refit_table(paths: Paths, tables: ResultsTables, alg_family...
  function generate_preprocessing_table (line 355) | def generate_preprocessing_table(paths: Paths, tables: ResultsTables):
  function generate_stopping_table (line 404) | def generate_stopping_table(paths: Paths, tables: ResultsTables):
  function generate_architecture_table (line 449) | def generate_architecture_table(paths: Paths, tables: ResultsTables):

FILE: pytabkit/bench/run/results.py
  class ResultManager (line 11) | class ResultManager:
    method __init__ (line 16) | def __init__(self):
    method add_results (line 29) | def add_results(self, is_cv: bool, results_dict: Dict) -> None:
    method save (line 51) | def save(self, path: Path) -> None:
    method load (line 66) | def load(path: Path, load_other: bool = True, load_preds: bool = True):
  function save_summaries (line 94) | def save_summaries(paths: Paths, task_infos: List[TaskInfo], alg_name: s...

FILE: pytabkit/bench/run/task_execution.py
  class TabBenchJob (line 24) | class TabBenchJob(AbstractJob):
    method __init__ (line 29) | def __init__(self, alg_name: str, alg_wrapper: AlgWrapper, task_packag...
    method get_group (line 43) | def get_group(self) -> str:
    method __call__ (line 49) | def __call__(self, assigned_resources: NodeResources) -> bool:
    method get_required_resources (line 92) | def get_required_resources(self) -> RequiredResources:
    method get_desc (line 95) | def get_desc(self) -> str:
  class RunConfig (line 106) | class RunConfig:
    method __init__ (line 111) | def __init__(self, n_tt_splits: int, n_cv: int = 1, n_refit: int = 0, ...
  class TabBenchJobManager (line 141) | class TabBenchJobManager:
    method __init__ (line 146) | def __init__(self, paths: Paths):
    method add_jobs (line 154) | def add_jobs(self, task_infos: List[TaskInfo], run_config: RunConfig, ...
    method run_jobs (line 252) | def run_jobs(self, scheduler: BaseJobScheduler) -> None:
  function run_alg_selection (line 269) | def run_alg_selection(paths: Paths, config: RunConfig, task_infos: List[...

FILE: pytabkit/bench/scheduling/execution.py
  function get_gpu_rams_gb (line 16) | def get_gpu_rams_gb(use_reserved: bool = True):
  function measure_node_resources (line 48) | def measure_node_resources(node_id: int) -> Tuple[NodeResources, NodeRes...
  function node_runner (line 88) | def node_runner(feedback_queue, job_queue, node_id: int):
  class NodeManager (line 143) | class NodeManager:
    method start (line 144) | def start(self):
    method terminate (line 147) | def terminate(self):
  class RayJobManager (line 151) | class RayJobManager(NodeManager):
    method __init__ (line 152) | def __init__(self, max_n_threads: Optional[int] = None, available_cpu_...
    method start (line 163) | def start(self) -> None:
    method get_resource_manager (line 208) | def get_resource_manager(self) -> ResourceManager:
    method submit_job (line 213) | def submit_job(self, job_info: JobInfo) -> None:
    method pop_finished_job_infos (line 228) | def pop_finished_job_infos(self, timeout_s: float = -1.0) -> List[JobI...
    method terminate (line 252) | def terminate(self) -> None:

FILE: pytabkit/bench/scheduling/jobs.py
  class JobResult (line 10) | class JobResult:
    method __init__ (line 14) | def __init__(self, job_id: int, time_s: float,
    method set_max_cpu_ram_gb (line 39) | def set_max_cpu_ram_gb(self, value: float) -> None:
  class AbstractJob (line 47) | class AbstractJob:
    method get_group (line 51) | def get_group(self) -> str:
    method __call__ (line 58) | def __call__(self, assigned_resources: NodeResources) -> bool:
    method get_required_resources (line 72) | def get_required_resources(self) -> RequiredResources:
    method get_desc (line 78) | def get_desc(self) -> str:
  class JobRunner (line 85) | class JobRunner:
    method __init__ (line 89) | def __init__(self, job: AbstractJob, job_id: int, assigned_resources: ...
    method __call__ (line 99) | def __call__(self) -> JobResult:

FILE: pytabkit/bench/scheduling/resource_manager.py
  class JobStatus (line 10) | class JobStatus(enum.Enum):
  class JobInfo (line 17) | class JobInfo:
    method __init__ (line 18) | def __init__(self, job: AbstractJob, job_id: int, start_time: Optional...
    method get_status (line 27) | def get_status(self) -> JobStatus:
    method set_started (line 37) | def set_started(self, assigned_resources: NodeResources):
    method set_finished (line 41) | def set_finished(self, job_result: JobResult):
    method is_remaining (line 44) | def is_remaining(self):
    method is_running (line 47) | def is_running(self):
    method is_finished (line 50) | def is_finished(self):
    method is_failed (line 53) | def is_failed(self):
    method is_succeed (line 56) | def is_succeed(self):
  class ResourceManager (line 60) | class ResourceManager:
    method __init__ (line 64) | def __init__(self, total_resources: SystemResources, fixed_resources: ...
    method get_fixed_resources (line 69) | def get_fixed_resources(self):
    method get_total_resources (line 72) | def get_total_resources(self):
    method get_free_resources (line 75) | def get_free_resources(self):
    method job_started (line 84) | def job_started(self, job_info: JobInfo):
    method job_finished (line 90) | def job_finished(self, job_result: JobResult) -> JobInfo:

FILE: pytabkit/bench/scheduling/resources.py
  class NodeResources (line 15) | class NodeResources:
    method __init__ (line 19) | def __init__(self, node_id: int, n_threads: float, cpu_ram_gb: float, ...
    method get_n_threads (line 27) | def get_n_threads(self) -> int:
    method set_n_threads (line 30) | def set_n_threads(self, n_threads: int):
    method get_cpu_ram_gb (line 35) | def get_cpu_ram_gb(self) -> float:
    method set_cpu_ram_gb (line 38) | def set_cpu_ram_gb(self, cpu_ram_gb: float) -> None:
    method set_gpu_rams_gb (line 43) | def set_gpu_rams_gb(self, gpu_rams_gb: np.ndarray) -> None:
    method get_gpu_usages (line 48) | def get_gpu_usages(self) -> np.ndarray:
    method get_gpu_rams_gb (line 51) | def get_gpu_rams_gb(self) -> np.ndarray:
    method get_physical_core_usages (line 54) | def get_physical_core_usages(self) -> np.ndarray:
    method get_n_physical_cores (line 57) | def get_n_physical_cores(self) -> int:
    method get_total_gpu_ram_gb (line 60) | def get_total_gpu_ram_gb(self) -> float:
    method get_total_gpu_usage (line 63) | def get_total_gpu_usage(self) -> float:
    method get_used_gpu_ids (line 66) | def get_used_gpu_ids(self) -> np.ndarray:  # todo: naming
    method get_used_physical_cores (line 69) | def get_used_physical_cores(self) -> np.ndarray:
    method get_resource_vector (line 72) | def get_resource_vector(self) -> np.ndarray:
    method get_interface_resources (line 76) | def get_interface_resources(self) -> InterfaceResources:
    method __iadd__ (line 80) | def __iadd__(self, other: 'NodeResources') -> 'NodeResources':  # oper...
    method __isub__ (line 84) | def __isub__(self, other: 'NodeResources') -> 'NodeResources':
    method __imul__ (line 88) | def __imul__(self, other: 'NodeResources') -> 'NodeResources':
    method __itruediv__ (line 92) | def __itruediv__(self, other: 'NodeResources') -> 'NodeResources':
    method __add__ (line 96) | def __add__(self, other: 'NodeResources') -> 'NodeResources':
    method __sub__ (line 101) | def __sub__(self, other: 'NodeResources') -> 'NodeResources':
    method __mul__ (line 106) | def __mul__(self, other: 'NodeResources') -> 'NodeResources':
    method __truediv__ (line 111) | def __truediv__(self, other: 'NodeResources') -> 'NodeResources':
    method try_assign (line 116) | def try_assign(self, required_resources: RequiredResources,
    method zeros_like (line 172) | def zeros_like(node_resources: 'NodeResources') -> 'NodeResources':
  class SystemResources (line 178) | class SystemResources:
    method __init__ (line 182) | def __init__(self, resources: List[NodeResources]):
    method __getitem__ (line 185) | def __getitem__(self, index: int):
    method __len__ (line 188) | def __len__(self):
    method __iadd__ (line 191) | def __iadd__(self, other):
    method __isub__ (line 196) | def __isub__(self, other):
    method __imul__ (line 201) | def __imul__(self, other):
    method __itruediv__ (line 206) | def __itruediv__(self, other):
    method __add__ (line 211) | def __add__(self, other):
    method __sub__ (line 216) | def __sub__(self, other):
    method __mul__ (line 221) | def __mul__(self, other):
    method __truediv__ (line 226) | def __truediv__(self, other):
    method get_n_threads (line 231) | def get_n_threads(self):
    method get_cpu_ram_gb (line 234) | def get_cpu_ram_gb(self):
    method get_gpu_usage (line 237) | def get_gpu_usage(self):
    method get_gpu_ram_gb (line 240) | def get_gpu_ram_gb(self):
    method get_num_gpus (line 243) | def get_num_gpus(self):
    method get_resource_vector (line 246) | def get_resource_vector(self):

FILE: pytabkit/bench/scheduling/schedulers.py
  function format_length_s (line 13) | def format_length_s(duration: float) -> str:
  function format_date_s (line 33) | def format_date_s(time_s: float) -> str:
  class BaseJobScheduler (line 37) | class BaseJobScheduler:
    method __init__ (line 42) | def __init__(self, job_manager: RayJobManager):
    method _submit_more_jobs (line 47) | def _submit_more_jobs(self) -> None:
    method add_jobs (line 51) | def add_jobs(self, jobs: List[AbstractJob]):
    method run (line 55) | def run(self):
    method _has_unfinished_jobs (line 84) | def _has_unfinished_jobs(self) -> bool:
    method _print_start (line 87) | def _print_start(self):
    method _print_end (line 97) | def _print_end(self):
    method _compute_group_stats (line 123) | def _compute_group_stats(self) -> Dict[str, Dict[str, Union[int, float...
    method _get_time_estimates (line 156) | def _get_time_estimates(self, job_infos: List[JobInfo], group_stats: D...
    method _print_progress (line 174) | def _print_progress(self):
    method _print_running_jobs (line 226) | def _print_running_jobs(self):
  class SimpleJobScheduler (line 264) | class SimpleJobScheduler(BaseJobScheduler):
    method _submit_more_jobs (line 271) | def _submit_more_jobs(self) -> None:
  class CustomJobScheduler (line 336) | class CustomJobScheduler(BaseJobScheduler):
    method _submit_more_jobs (line 342) | def _submit_more_jobs(self) -> None:

FILE: pytabkit/models/alg_interfaces/alg_interfaces.py
  class AlgInterface (line 19) | class AlgInterface:
    method __init__ (line 44) | def __init__(self, fit_params: Optional[List[Dict[str, Any]]] = None, ...
    method fit (line 58) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method fit_and_eval (line 87) | def fit_and_eval(self, ds: DictDataset, idxs_list: List[SplitIdxs], in...
    method eval (line 104) | def eval(self, ds: DictDataset, idxs_list: List[SplitIdxs], metrics: O...
    method predict (line 191) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_refit_interface (line 203) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method get_fit_params (line 215) | def get_fit_params(self) -> Optional[List[Dict]]:
    method get_required_resources (line 221) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method get_available_predict_params (line 237) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:
    method get_current_predict_params_name (line 241) | def get_current_predict_params_name(self):
    method get_current_predict_params_dict (line 244) | def get_current_predict_params_dict(self):
    method set_current_predict_params (line 247) | def set_current_predict_params(self, name: str) -> None:
    method to (line 250) | def to(self, device: str) -> None:
  class MultiSplitWrapperAlgInterface (line 254) | class MultiSplitWrapperAlgInterface(AlgInterface):
    method __init__ (line 256) | def __init__(self, single_split_interfaces: List[AlgInterface], **conf...
    method get_refit_interface (line 261) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 273) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method fit_and_eval (line 285) | def fit_and_eval(self, ds: DictDataset, idxs_list: List[SplitIdxs], in...
    method predict (line 300) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 303) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method get_available_predict_params (line 310) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:
    method set_current_predict_params (line 313) | def set_current_predict_params(self, name: str) -> None:
  class SingleSplitAlgInterface (line 319) | class SingleSplitAlgInterface(AlgInterface):
  class OptAlgInterface (line 323) | class OptAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 324) | def __init__(self, hyper_optimizer: HyperOptimizer, max_resource_confi...
    method create_alg_interface (line 345) | def create_alg_interface(self, n_sub_splits: int, **config) -> AlgInte...
    method get_refit_interface (line 348) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method objective (line 360) | def objective(self, params, ds: DictDataset, idxs_list: List[SplitIdxs...
    method fit_and_eval (line 418) | def fit_and_eval(self, ds: DictDataset, idxs_list: List[SplitIdxs], in...
    method predict (line 444) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 447) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsAlgInterface (line 456) | class RandomParamsAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 457) | def __init__(self, model_idx: int, fit_params: Optional[List[Dict[str,...
    method _sample_params (line 468) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 471) | def _create_interface_from_config(self, n_tv_splits: int, **config):
    method get_refit_interface (line 474) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method _create_sub_interface (line 479) | def _create_sub_interface(self, ds: DictDataset, seed: int, n_train: i...
    method fit (line 489) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 500) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 504) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...

FILE: pytabkit/models/alg_interfaces/autogluon_model_interfaces.py
  class AutoGluonModelAlgInterface (line 16) | class AutoGluonModelAlgInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 20) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method _create_df (line 59) | def _create_df(self, X: pd.DataFrame, y: Optional[np.ndarray]):
    method get_required_resources (line 69) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method _fit_sklearn (line 91) | def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np...
    method _predict_sklearn (line 161) | def _predict_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:
    method _predict_proba_sklearn (line 164) | def _predict_proba_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:

FILE: pytabkit/models/alg_interfaces/base.py
  class SplitIdxs (line 7) | class SplitIdxs:
    method __init__ (line 11) | def __init__(self, train_idxs: torch.Tensor, val_idxs: Optional[torch....
    method get_sub_split_idxs (line 39) | def get_sub_split_idxs(self, i: int) -> 'SubSplitIdxs':
    method get_sub_split_idxs_alt (line 43) | def get_sub_split_idxs_alt(self, i: int) -> 'SplitIdxs':
  class SubSplitIdxs (line 48) | class SubSplitIdxs:
    method __init__ (line 52) | def __init__(self, train_idxs: torch.Tensor, val_idxs: Optional[torch....
  class InterfaceResources (line 66) | class InterfaceResources:
    method __init__ (line 70) | def __init__(self, n_threads: int, gpu_devices: List[str], time_in_sec...
  class RequiredResources (line 76) | class RequiredResources:
    method __init__ (line 80) | def __init__(self, time_s: float, n_threads: float, cpu_ram_gb: float,...
    method get_resource_vector (line 91) | def get_resource_vector(self, fixed_resource_vector: np.ndarray):
    method should_add_fixed_resources (line 99) | def should_add_fixed_resources(self) -> bool:
    method combine_sequential (line 103) | def combine_sequential(resources_list: List['RequiredResources']):

FILE: pytabkit/models/alg_interfaces/calibration.py
  class PostHocCalibrationAlgInterface (line 24) | class PostHocCalibrationAlgInterface(AlgInterface):
    method __init__ (line 25) | def __init__(self, alg_interface: AlgInterface, fit_params: Optional[L...
    method _transform_probs (line 31) | def _transform_probs(self, probs: np.ndarray) -> np.ndarray:
    method fit (line 38) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 95) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 127) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method to (line 131) | def to(self, device: str) -> None:

FILE: pytabkit/models/alg_interfaces/catboost_interfaces.py
  class CatBoostSklearnSubSplitInterface (line 24) | class CatBoostSklearnSubSplitInterface(SklearnSubSplitInterface):
    method _get_cat_indexes_arg_name (line 25) | def _get_cat_indexes_arg_name(self) -> str:
    method _create_sklearn_model (line 28) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 62) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class CatBoostCustomMetric (line 73) | class CatBoostCustomMetric:
    method __init__ (line 77) | def __init__(self, metric_name: str, is_classification: bool, is_highe...
    method is_max_optimal (line 84) | def is_max_optimal(self):
    method evaluate (line 87) | def evaluate(self, approxes, target, weight):
    method get_final_error (line 116) | def get_final_error(self, error, weight):
  class CatBoostSubSplitInterface (line 120) | class CatBoostSubSplitInterface(TreeBasedSubSplitInterface):
    method _get_params (line 121) | def _get_params(self):
    method get_refit_interface (line 166) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method _get_eval_metric (line 170) | def _get_eval_metric(self, val_metric_name: Optional[str], n_classes: ...
    method _preprocess_params (line 197) | def _preprocess_params(self, params: Dict[str, Any], n_classes: int) -...
    method _convert_ds (line 227) | def _convert_ds(self, ds: DictDataset) -> Any:
    method _fit (line 239) | def _fit(self, train_ds: DictDataset, val_ds: Optional[DictDataset], p...
    method _predict (line 272) | def _predict(self, bst, ds: DictDataset, n_classes: int,
    method get_required_resources (line 297) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class CatBoostHyperoptAlgInterface (line 308) | class CatBoostHyperoptAlgInterface(OptAlgInterface):
    method __init__ (line 309) | def __init__(self, space=None, n_hyperopt_steps: int = 50, **config):
    method create_alg_interface (line 392) | def create_alg_interface(self, n_sub_splits: int, **config) -> AlgInte...
  class RandomParamsCatBoostAlgInterface (line 396) | class RandomParamsCatBoostAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 397) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 758) | def _create_interface_from_config(self, n_tv_splits: int, **config):

FILE: pytabkit/models/alg_interfaces/ensemble_interfaces.py
  class WeightedPrediction (line 18) | class WeightedPrediction:
    method __init__ (line 19) | def __init__(self, y_pred_list: List[torch.Tensor], task_type: TaskType):
    method predict_for_weights (line 24) | def predict_for_weights(self, weights: np.ndarray):
  class CaruanaEnsembleAlgInterface (line 33) | class CaruanaEnsembleAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 39) | def __init__(self, alg_interfaces: List[AlgInterface], fit_params: Opt...
    method get_refit_interface (line 44) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 49) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 160) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 172) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method to (line 179) | def to(self, device: str) -> None:
  class AlgorithmSelectionAlgInterface (line 186) | class AlgorithmSelectionAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 191) | def __init__(self, alg_interfaces: List[AlgInterface], fit_params: Opt...
    method get_refit_interface (line 196) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 205) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 273) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 278) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method to (line 286) | def to(self, device: str) -> None:
  class PrecomputedPredictionsAlgInterface (line 293) | class PrecomputedPredictionsAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 294) | def __init__(self, y_preds_cv: torch.Tensor, y_preds_refit: Optional[t...
    method get_refit_interface (line 303) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 306) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 311) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 317) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...

FILE: pytabkit/models/alg_interfaces/lightgbm_interfaces.py
  class LGBMCustomMetric (line 23) | class LGBMCustomMetric:
    method __init__ (line 24) | def __init__(self, metric_name: str, is_classification: bool, is_highe...
    method __call__ (line 29) | def __call__(self, y_pred: np.ndarray, eval_data):
  class LGBMSklearnSubSplitInterface (line 61) | class LGBMSklearnSubSplitInterface(SklearnSubSplitInterface):
    method _get_cat_indexes_arg_name (line 62) | def _get_cat_indexes_arg_name(self) -> str:
    method _create_sklearn_model (line 65) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 96) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class LGBMSubSplitInterface (line 107) | class LGBMSubSplitInterface(TreeBasedSubSplitInterface):
    method _get_params (line 108) | def _get_params(self):
    method get_refit_interface (line 134) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method _preprocess_params (line 139) | def _preprocess_params(self, params: Dict[str, Any], n_classes: int) -...
    method _convert_ds (line 161) | def _convert_ds(self, ds: DictDataset) -> Any:
    method _fit (line 177) | def _fit(self, train_ds: DictDataset, val_ds: Optional[DictDataset], p...
    method _predict (line 250) | def _predict(self, bst, ds: DictDataset, n_classes: int, other_params:...
    method get_required_resources (line 266) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class LGBMHyperoptAlgInterface (line 277) | class LGBMHyperoptAlgInterface(OptAlgInterface):
    method __init__ (line 278) | def __init__(self, space=None, n_hyperopt_steps: int = 50, opt_method:...
    method create_alg_interface (line 356) | def create_alg_interface(self, n_sub_splits: int, **config) -> AlgInte...
  class RandomParamsLGBMAlgInterface (line 360) | class RandomParamsLGBMAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 361) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 704) | def _create_interface_from_config(self, n_tv_splits: int, **config):

FILE: pytabkit/models/alg_interfaces/nn_interfaces.py
  function get_lignting_accel_and_devices (line 33) | def get_lignting_accel_and_devices(device: str):
  class NNAlgInterface (line 52) | class NNAlgInterface(AlgInterface):
    method __init__ (line 53) | def __init__(self, fit_params: Optional[List[Dict[str, Any]]] = None, ...
    method get_refit_interface (line 58) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 61) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 155) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_available_predict_params (line 181) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:
    method get_required_resources (line 189) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method get_model_ram_gb (line 242) | def get_model_ram_gb(self, ds: DictDataset, n_cv: int, n_refit: int, n...
    method to (line 255) | def to(self, device: str) -> None:
    method get_importances (line 260) | def get_importances(self) -> torch.Tensor:
    method get_first_layer_weights (line 303) | def get_first_layer_weights(self, with_scale: bool) -> torch.Tensor:
  class NNHyperoptAlgInterface (line 325) | class NNHyperoptAlgInterface(OptAlgInterface):
    method __init__ (line 326) | def __init__(self, space: Optional[Union[str, Dict[str, Any]]] = None,...
    method create_alg_interface (line 355) | def create_alg_interface(self, n_sub_splits: int, **config) -> AlgInte...
    method get_required_resources (line 358) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RealMLPParamSampler (line 369) | class RealMLPParamSampler:
    method __init__ (line 370) | def __init__(self, is_classification: bool, hpo_space_name: str = 'def...
    method sample_params (line 374) | def sample_params(self, seed: int) -> Dict[str, Any]:
  class RandomParamsNNAlgInterface (line 984) | class RandomParamsNNAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 985) | def __init__(self, model_idx: int, fit_params: Optional[List[Dict[str,...
    method get_refit_interface (line 992) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method _create_sub_interface (line 997) | def _create_sub_interface(self, ds: DictDataset, seed: int):
    method fit (line 1011) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 1019) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 1023) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method get_available_predict_params (line 1029) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:
    method to (line 1032) | def to(self, device: str) -> None:

FILE: pytabkit/models/alg_interfaces/other_interfaces.py
  class RFSubSplitInterface (line 21) | class RFSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 22) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 55) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsRFAlgInterface (line 68) | class RandomParamsRFAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 69) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 508) | def _create_interface_from_config(self, n_tv_splits: int, **config):
  class ExtraTreesSubSplitInterface (line 512) | class ExtraTreesSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 513) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 546) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsExtraTreesAlgInterface (line 559) | class RandomParamsExtraTreesAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 560) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 773) | def _create_interface_from_config(self, n_tv_splits: int, **config):
  class GBTSubSplitInterface (line 777) | class GBTSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 778) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 802) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class KNNSubSplitInterface (line 815) | class KNNSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 816) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 830) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsKNNAlgInterface (line 843) | class RandomParamsKNNAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 844) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 866) | def _create_interface_from_config(self, n_tv_splits: int, **config):
  class LinearModelSubSplitInterface (line 870) | class LinearModelSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 871) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 906) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsLinearModelAlgInterface (line 919) | class RandomParamsLinearModelAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 920) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 984) | def _create_interface_from_config(self, n_tv_splits: int, **config):
  class SklearnMLPSubSplitInterface (line 988) | class SklearnMLPSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 989) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 998) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class KANSubSplitInterface (line 1011) | class KANSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 1012) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 1023) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method _fit_sklearn (line 1036) | def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np...
    method _predict_sklearn (line 1052) | def _predict_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:
    method _predict_proba_sklearn (line 1055) | def _predict_proba_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:
  class GrandeWrapper (line 1059) | class GrandeWrapper:
    method __init__ (line 1064) | def __init__(self, **config):
    method fit (line 1067) | def fit(self, X, y, X_val, y_val, cat_features: Optional[List[str]] = ...
    method predict_proba (line 1134) | def predict_proba(self, X):
    method predict (line 1137) | def predict(self, X):
  class GrandeSubSplitInterface (line 1145) | class GrandeSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 1146) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 1152) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method _fit_sklearn (line 1164) | def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np...
  class TabPFN2SubSplitInterface (line 1180) | class TabPFN2SubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 1181) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method _fit_sklearn (line 1208) | def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np...
    method get_required_resources (line 1220) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class TabICLSubSplitInterface (line 1233) | class TabICLSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 1234) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method _fit_sklearn (line 1262) | def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np...
    method _predict_sklearn (line 1284) | def _predict_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:
    method _predict_proba_sklearn (line 1292) | def _predict_proba_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:
    method get_required_resources (line 1300) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...

FILE: pytabkit/models/alg_interfaces/resource_computation.py
  function get_resource_features (line 24) | def get_resource_features(config: Dict, ds: DictDataset, n_cv: int, n_re...
  function process_resource_features (line 72) | def process_resource_features(raw_features: Dict[str, Any], feature_spec...
  function eval_linear_product_model (line 91) | def eval_linear_product_model(raw_features: Dict[str, Any], params: Dict...
  class FeatureSpec (line 108) | class FeatureSpec:
    method _listify (line 113) | def _listify(spec: Union[List, str]):
    method _product_str (line 122) | def _product_str(first: str, second: str) -> str:
    method concat (line 135) | def concat(*feature_specs):
    method product (line 141) | def product(*feature_specs):
    method powerset_products (line 154) | def powerset_products(*feature_specs):
  class NormalizedDataRegressor (line 166) | class NormalizedDataRegressor:
    method __init__ (line 167) | def __init__(self, sub_regressor):
    method fit (line 170) | def fit(self, X: np.ndarray, y: np.ndarray):
    method get_coefs (line 175) | def get_coefs(self) -> np.ndarray:
    method predict (line 178) | def predict(self, X: np.ndarray) -> np.ndarray:
  class LogLinearModule (line 182) | class LogLinearModule(nn.Module):
    method __init__ (line 183) | def __init__(self, n_features: int):
    method forward (line 187) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LogLinearRegressor (line 191) | class LogLinearRegressor:
    method __init__ (line 192) | def __init__(self, pessimistic: bool):
    method fit (line 195) | def fit(self, X: np.ndarray, y: np.ndarray):
    method get_coefs (line 221) | def get_coefs(self) -> np.ndarray:
  function fit_resource_factors (line 225) | def fit_resource_factors(data: List[Tuple[Dict[str, float], float]], pes...
  class TimeWrapper (line 254) | class TimeWrapper:
    method __init__ (line 255) | def __init__(self, f: Callable):
    method __call__ (line 258) | def __call__(self):
  function create_ds (line 265) | def create_ds(n_samples: int, n_cont: int, n_cat: int, cat_size: int, n_...
  class Sampler (line 281) | class Sampler:
    method sample (line 282) | def sample(self) -> Union[int, float]:
  class UniformSampler (line 286) | class UniformSampler(Sampler):
    method __init__ (line 287) | def __init__(self, low: Union[int, float], high: Union[int, float], lo...
    method sample (line 293) | def sample(self) -> Union[int, float]:
  function ds_to_xy (line 307) | def ds_to_xy(ds: DictDataset) -> Tuple[pd.DataFrame, np.ndarray]:
  class ResourcePredictor (line 313) | class ResourcePredictor:
    method __init__ (line 317) | def __init__(self, config: Dict[str, Any], time_params: Dict[str, floa...
    method get_required_resources (line 334) | def get_required_resources(self, ds: DictDataset, **extra_params) -> R...

FILE: pytabkit/models/alg_interfaces/resource_params.py
  class ResourceParams (line 1) | class ResourceParams:
  class ResourceParamsOld (line 141) | class ResourceParamsOld:

FILE: pytabkit/models/alg_interfaces/rtdl_interfaces.py
  function allow_single_underscore (line 23) | def allow_single_underscore(params_config: List[Tuple]) -> List[Tuple]:
  class SkorchSubSplitInterface (line 36) | class SkorchSubSplitInterface(SklearnSubSplitInterface):
    method _fit_sklearn (line 37) | def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np...
    method predict (line 87) | def predict(self, ds: DictDataset) -> torch.Tensor:
  class RTDL_MLPSubSplitInterface (line 124) | class RTDL_MLPSubSplitInterface(SkorchSubSplitInterface):
    method _create_sklearn_model (line 125) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 166) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class ResnetSubSplitInterface (line 181) | class ResnetSubSplitInterface(SkorchSubSplitInterface):
    method _create_sklearn_model (line 182) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 226) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class FTTransformerSubSplitInterface (line 243) | class FTTransformerSubSplitInterface(SkorchSubSplitInterface):
    method _create_sklearn_model (line 244) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 286) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  function choose_batch_size_rtdl (line 311) | def choose_batch_size_rtdl(train_size) -> int:
  function choose_batch_size_rtdl_new (line 324) | def choose_batch_size_rtdl_new(train_size: int) -> int:
  class RTDL_MLP_ParamSamplerNew (line 337) | class RTDL_MLP_ParamSamplerNew:
    method __init__ (line 338) | def __init__(self, is_classification: bool, train_size: int, num_emb_t...
    method sample_params (line 343) | def sample_params(self, seed: int) -> Dict[str, Any]:
  class RTDL_ResNet_ParamSampler (line 400) | class RTDL_ResNet_ParamSampler:
    method __init__ (line 401) | def __init__(self, is_classification: bool, train_size: int):
    method sample_params (line 405) | def sample_params(self, seed: int) -> Dict[str, Any]:
  class RTDL_ResNet_ParamSamplerNew (line 447) | class RTDL_ResNet_ParamSamplerNew:
    method __init__ (line 448) | def __init__(self, is_classification: bool, train_size: int):
    method sample_params (line 452) | def sample_params(self, seed: int) -> Dict[str, Any]:
  class RandomParamsResnetAlgInterface (line 493) | class RandomParamsResnetAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 494) | def __init__(self, model_idx: int, fit_params: Optional[List[Dict[str,...
    method get_refit_interface (line 499) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method _create_sub_interface (line 503) | def _create_sub_interface(self, ds: DictDataset, seed: int, n_train: i...
    method fit (line 513) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 519) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 522) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsFTTransformerAlgInterface (line 529) | class RandomParamsFTTransformerAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 530) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 561) | def _create_interface_from_config(self, n_tv_splits: int, **config):
  class RandomParamsRTDLMLPAlgInterface (line 565) | class RandomParamsRTDLMLPAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 566) | def __init__(self, model_idx: int, fit_params: Optional[List[Dict[str,...
    method get_refit_interface (line 571) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method _create_sub_interface (line 575) | def _create_sub_interface(self, ds: DictDataset, seed: int, n_train: i...
    method fit (line 587) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 594) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 597) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...

FILE: pytabkit/models/alg_interfaces/sub_split_interfaces.py
  class SingleSplitWrapperAlgInterface (line 19) | class SingleSplitWrapperAlgInterface(SingleSplitAlgInterface):
    method __init__ (line 25) | def __init__(self, sub_split_interfaces: List[AlgInterface], fit_param...
    method get_refit_interface (line 33) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 57) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 126) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 130) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
    method get_available_predict_params (line 140) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:
    method set_current_predict_params (line 143) | def set_current_predict_params(self, name: str) -> None:
  class SklearnSubSplitInterface (line 149) | class SklearnSubSplitInterface(SingleSplitAlgInterface):  # todo: have a...
    method __init__ (line 154) | def __init__(self, fit_params: Optional[List[Dict[str, Any]]] = None, ...
    method fit (line 161) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method _fit_sklearn (line 224) | def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np...
    method predict (line 237) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method _predict_sklearn (line 259) | def _predict_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:
    method _predict_proba_sklearn (line 262) | def _predict_proba_sklearn(self, x_df: pd.DataFrame) -> np.ndarray:
    method _create_sklearn_model (line 265) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method _get_cat_indexes_arg_name (line 269) | def _get_cat_indexes_arg_name(self) -> str:
  class TreeBasedSubSplitInterface (line 274) | class TreeBasedSubSplitInterface(SingleSplitAlgInterface):  # todo: inse...
    method __init__ (line 279) | def __init__(self, fit_params: Optional[List[Dict[str, Any]]] = None, ...
    method fit (line 286) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 350) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method _fit (line 364) | def _fit(self, train_ds: DictDataset, val_ds: Optional[DictDataset], p...
    method _predict (line 369) | def _predict(self, bst: Any, ds: DictDataset, n_classes: int, other_pa...
    method _get_params (line 372) | def _get_params(self) -> Dict[str, Any]:
    method get_available_predict_params (line 375) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:

FILE: pytabkit/models/alg_interfaces/tabm_interface.py
  function get_tabm_auto_batch_size (line 28) | def get_tabm_auto_batch_size(n_train: int) -> int:
  class TabMSubSplitInterface (line 43) | class TabMSubSplitInterface(SingleSplitAlgInterface):
    method __init__ (line 44) | def __init__(self, fit_params: Optional[List[Dict[str, Any]]] = None, ...
    method get_refit_interface (line 47) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 50) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 414) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 453) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsTabMAlgInterface (line 471) | class RandomParamsTabMAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 472) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 524) | def _create_interface_from_config(self, n_tv_splits: int, **config):
    method get_available_predict_params (line 527) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:
    method set_current_predict_params (line 530) | def set_current_predict_params(self, name: str) -> None:

FILE: pytabkit/models/alg_interfaces/tabr_interface.py
  class ExceptionPrintingCallback (line 37) | class ExceptionPrintingCallback(pl.callbacks.Callback):
    method on_exception (line 38) | def on_exception(self, trainer, pl_module, exception):
  class TabRSubSplitInterface (line 44) | class TabRSubSplitInterface(AlgInterface):
    method __init__ (line 45) | def __init__(self, **config):
    method create_model (line 52) | def create_model(self, n_num_features, n_bin_features,
    method infer_batch_size (line 95) | def infer_batch_size(self, n_samples_train: int) -> int:
    method fit (line 107) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 348) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 435) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class RandomParamsTabRAlgInterface (line 458) | class RandomParamsTabRAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 459) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 531) | def _create_interface_from_config(self, n_tv_splits: int, **config):

FILE: pytabkit/models/alg_interfaces/xgboost_interfaces.py
  class XGBCustomMetric (line 22) | class XGBCustomMetric:
    method __init__ (line 23) | def __init__(self, metric_names: Union[str, List[str]], is_classificat...
    method __call__ (line 28) | def __call__(self, y_pred: np.ndarray, dtrain):
  class XGBSklearnSubSplitInterface (line 71) | class XGBSklearnSubSplitInterface(SklearnSubSplitInterface):
    method _create_sklearn_model (line 72) | def _create_sklearn_model(self, seed: int, n_threads: int, gpu_devices...
    method get_required_resources (line 103) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class XGBSubSplitInterface (line 114) | class XGBSubSplitInterface(TreeBasedSubSplitInterface):
    method _get_params (line 116) | def _get_params(self):
    method get_refit_interface (line 144) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method _preprocess_params (line 149) | def _preprocess_params(self, params: Dict[str, Any], n_classes: int) -...
    method _convert_ds (line 179) | def _convert_ds(self, ds: DictDataset) -> Any:
    method _fit (line 188) | def _fit(self, train_ds: DictDataset, val_ds: Optional[DictDataset], p...
    method _predict (line 267) | def _predict(self, bst, ds: DictDataset, n_classes: int, other_params:...
    method get_required_resources (line 284) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  class XGBHyperoptAlgInterface (line 295) | class XGBHyperoptAlgInterface(OptAlgInterface):
    method __init__ (line 296) | def __init__(self, space=None, n_hyperopt_steps: int = 50, **config):
    method create_alg_interface (line 430) | def create_alg_interface(self, n_sub_splits: int, **config) -> AlgInte...
  class RandomParamsXGBAlgInterface (line 434) | class RandomParamsXGBAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 435) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 650) | def _create_interface_from_config(self, n_tv_splits: int, **config):
    method get_available_predict_params (line 653) | def get_available_predict_params(self) -> Dict[str, Dict[str, Any]]:
    method set_current_predict_params (line 656) | def set_current_predict_params(self, name: str) -> None:

FILE: pytabkit/models/alg_interfaces/xrfm_interfaces.py
  class xRFMSubSplitInterface (line 23) | class xRFMSubSplitInterface(SingleSplitAlgInterface):
    method __init__ (line 24) | def __init__(self, fit_params: Optional[List[Dict[str, Any]]] = None, ...
    method get_refit_interface (line 27) | def get_refit_interface(self, n_refit: int, fit_params: Optional[List[...
    method fit (line 30) | def fit(self, ds: DictDataset, idxs_list: List[SplitIdxs], interface_r...
    method predict (line 229) | def predict(self, ds: DictDataset) -> torch.Tensor:
    method get_required_resources (line 245) | def get_required_resources(self, ds: DictDataset, n_cv: int, n_refit: ...
  function sample_xrfm_params (line 275) | def sample_xrfm_params(seed: int, hpo_space_name: str = 'default'):
  class RandomParamsxRFMAlgInterface (line 517) | class RandomParamsxRFMAlgInterface(RandomParamsAlgInterface):
    method _sample_params (line 518) | def _sample_params(self, is_classification: bool, seed: int, n_train: ...
    method _create_interface_from_config (line 521) | def _create_interface_from_config(self, n_tv_splits: int, **config):

FILE: pytabkit/models/data/conversion.py
  class ToDictDatasetConverter (line 14) | class ToDictDatasetConverter:
    method __init__ (line 15) | def __init__(self, cat_features: Optional[Union[List[bool], np.ndarray...
    method fit_transform (line 25) | def fit_transform(self, x: Union[np.ndarray, pd.DataFrame, pd.Series, ...
    method transform (line 83) | def transform(self, x: Union[np.ndarray, pd.DataFrame, pd.Series, Dict...

FILE: pytabkit/models/data/data.py
  class TaskType (line 12) | class TaskType:
  class TensorInfo (line 19) | class TensorInfo:
    method __init__ (line 20) | def __init__(self, feat_shape: Optional[Union[List, np.ndarray, torch....
    method get_feat_shape (line 27) | def get_feat_shape(self) -> np.ndarray:
    method get_cat_sizes (line 33) | def get_cat_sizes(self) -> torch.Tensor:
    method get_n_features (line 38) | def get_n_features(self) -> int:
    method get_cat_size_product (line 41) | def get_cat_size_product(self) -> int:
    method is_empty (line 44) | def is_empty(self) -> bool:
    method is_cont (line 47) | def is_cont(self) -> bool:
    method is_cat (line 51) | def is_cat(self) -> bool:
    method to_dict (line 54) | def to_dict(self) -> Dict:
    method from_dict (line 59) | def from_dict(data: Dict) -> 'TensorInfo':
    method concat (line 63) | def concat(tensor_infos: List['TensorInfo']) -> 'TensorInfo':
  class DictDataset (line 76) | class DictDataset:
    method __init__ (line 79) | def __init__(self, tensors: Optional[Dict[str, torch.Tensor]], tensor_...
    method split_xy (line 93) | def split_xy(self) -> Tuple['DictDataset', 'DictDataset']:
    method without_labels (line 98) | def without_labels(self) -> 'DictDataset':
    method to_df (line 101) | def to_df(self) -> pd.DataFrame:
    method get_batch (line 119) | def get_batch(self, idxs) -> Dict[str, torch.Tensor]:
    method get_sub_dataset (line 122) | def get_sub_dataset(self, idxs) -> 'DictDataset':
    method get_shuffled (line 125) | def get_shuffled(self, seed) -> 'DictDataset':
    method get_size_gb (line 128) | def get_size_gb(self) -> float:
    method join (line 136) | def join(*datasets):
    method to (line 140) | def to(self, device):
    method __getitem__ (line 143) | def __getitem__(self, key):
    method get_n_classes (line 150) | def get_n_classes(self):
  class ParallelDictDataLoader (line 158) | class ParallelDictDataLoader:
    method __init__ (line 159) | def __init__(self, ds: DictDataset, idxs: torch.Tensor, batch_size: in...
    method get_num_samples (line 194) | def get_num_samples(self):
    method get_num_iterated_samples (line 197) | def get_num_iterated_samples(self):
    method __len__ (line 202) | def __len__(self):
    method __iter__ (line 205) | def __iter__(self):
  class ValDictDataLoader (line 217) | class ValDictDataLoader:
    method __init__ (line 218) | def __init__(self, ds: DictDataset, val_idxs: torch.Tensor, val_batch_...
    method __len__ (line 228) | def __len__(self):
    method __iter__ (line 231) | def __iter__(self):

FILE: pytabkit/models/data/nested_dict.py
  class NestedDict (line 6) | class NestedDict:
    method __init__ (line 18) | def __init__(self, data_dict=None):
    method __getitem__ (line 21) | def __getitem__(self, idxs):
    method __setitem__ (line 29) | def __setitem__(self, idxs, value):
    method __contains__ (line 44) | def __contains__(self, item: Union[List, Tuple]):
    method get (line 53) | def get(self, idxs, default=None):
    method _dict_update_rec (line 59) | def _dict_update_rec(self, d1: dict, d2: dict):
    method update (line 66) | def update(self, other: 'NestedDict'):
    method __str__ (line 69) | def __str__(self):
    method __repr__ (line 72) | def __repr__(self):
    method get_dict (line 75) | def get_dict(self) -> Dict:
    method from_kwargs (line 79) | def from_kwargs(**kwargs):

FILE: pytabkit/models/data/splits.py
  class Split (line 14) | class Split:
    method __init__ (line 15) | def __init__(self, ds: DictDataset, idxs: Tuple[torch.Tensor, torch.Te...
    method get_sub_ds (line 23) | def get_sub_ds(self, i):
    method get_sub_idxs (line 26) | def get_sub_idxs(self, i):
  class Splitter (line 30) | class Splitter:
    method get_idxs (line 31) | def get_idxs(self, ds: DictDataset) -> Tuple[torch.Tensor, torch.Tensor]:
    method split_ds (line 34) | def split_ds(self, ds: DictDataset) -> Split:
    method get_split_sizes (line 38) | def get_split_sizes(self, n_samples: int) -> Tuple:
  class RandomSplitter (line 42) | class RandomSplitter(Splitter):
    method __init__ (line 43) | def __init__(self, seed, first_fraction=0.8, max_n_first: Optional[int...
    method get_idxs (line 48) | def get_idxs(self, ds: DictDataset) -> Tuple[torch.Tensor, torch.Tensor]:
    method get_split_sizes (line 56) | def get_split_sizes(self, n_samples: int) -> Tuple:
  class IndexSplitter (line 63) | class IndexSplitter(Splitter):
    method __init__ (line 64) | def __init__(self, index):
    method get_idxs (line 67) | def get_idxs(self, ds: DictDataset) -> Tuple[torch.Tensor, torch.Tensor]:
    method get_split_sizes (line 71) | def get_split_sizes(self, n_samples: int) -> Tuple:
  class AllNothingSplitter (line 75) | class AllNothingSplitter(Splitter):
    method get_idxs (line 76) | def get_idxs(self, ds: DictDataset) -> Tuple[torch.Tensor, torch.Tensor]:
    method split_ds (line 81) | def split_ds(self, ds: DictDataset) -> Split:
    method get_split_sizes (line 85) | def get_split_sizes(self, n_samples: int) -> Tuple:
  class MultiSplitter (line 89) | class MultiSplitter:
    method get_idxs (line 90) | def get_idxs(self, ds: DictDataset) -> List[Tuple[torch.Tensor, torch....
    method split_ds (line 93) | def split_ds(self, ds: DictDataset) -> List[Split]:
  class KFoldSplitter (line 98) | class KFoldSplitter(MultiSplitter):
    method __init__ (line 99) | def __init__(self, k: int, seed: int, stratified=False):
    method get_idxs (line 106) | def get_idxs(self, ds: DictDataset) -> List[Tuple[torch.Tensor, torch....
    method get_split_sizes (line 121) | def get_split_sizes(self, n_samples: int) -> Tuple:
  class SplitInfo (line 126) | class SplitInfo:
    method __init__ (line 127) | def __init__(self, splitter: Splitter, split_type: str, id: int, alg_s...
    method get_sub_seed (line 134) | def get_sub_seed(self, split_idx: int, is_cv: bool):
    method get_sub_splits (line 138) | def get_sub_splits(self, ds: DictDataset, n_splits: int, is_cv: bool) ...
    method get_train_and_val_size (line 149) | def get_train_and_val_size(self, n_samples: int, n_splits: int, is_cv:...

FILE: pytabkit/models/hyper_opt/coord_opt.py
  function identity (line 13) | def identity(x):
  class Hyperparameter (line 17) | class Hyperparameter:
    method __init__ (line 18) | def __init__(self, start_value: Union[int, float], min_step_size: Unio...
    method adjust_step_size (line 44) | def adjust_step_size(self, current_value: float, step_size: float) -> ...
    method apply_tfms (line 93) | def apply_tfms(self, x: Any) -> Any:
  class CoordOptimizerImpl (line 97) | class CoordOptimizerImpl:
    method __init__ (line 101) | def __init__(self, f: Callable[[Dict], Tuple[float, Any]], space: Dict...
    method suggest (line 140) | def suggest(self, new_hp_values) -> float:
    method convert_hp_values (line 156) | def convert_hp_values(self, values: np.ndarray) -> Dict[str, Any]:
    method eval (line 159) | def eval(self, new_hp_values: np.ndarray) -> Tuple[float, Any]:
    method already_evaluated (line 169) | def already_evaluated(self, new_hp_values: np.ndarray) -> bool:
    method coord_opt_idx (line 180) | def coord_opt_idx(self, idx: int):
    method run (line 226) | def run(self) -> None:
  class CoordOptimizer (line 252) | class CoordOptimizer(HyperOptimizer):
    class CoordOptFuncWrapper (line 253) | class CoordOptFuncWrapper:
      method __init__ (line 254) | def __init__(self, f: Callable[[dict], Tuple[float, Any]], fixed_par...
      method __call__ (line 258) | def __call__(self, params: Dict[str, Any], seed: int = 0):
    method __init__ (line 263) | def __init__(self, space: Dict[str, Hyperparameter], fixed_params: Dic...
    method _optimize_impl (line 270) | def _optimize_impl(self, f: Callable[[dict], Tuple[float, Any]], seed:...

FILE: pytabkit/models/hyper_opt/hyper_optimizers.py
  class FunctionEvaluationTracker (line 11) | class FunctionEvaluationTracker:
    method __init__ (line 15) | def __init__(self, f: Callable[[dict], Tuple[float, Any]], n_steps: in...
    method __call__ (line 24) | def __call__(self, params: dict) -> Tuple[float, Any]:
    method get_best_params_and_result (line 41) | def get_best_params_and_result(self) -> Tuple[Dict, Tuple[float, Any]]:
  class HyperOptimizer (line 45) | class HyperOptimizer:
    method __init__ (line 46) | def __init__(self, n_hyperopt_steps: int):
    method _optimize_impl (line 49) | def _optimize_impl(self, f: Callable[[dict], Tuple[float, Any]], seed:...
    method optimize (line 53) | def optimize(self, f: Callable[[dict], Tuple[float, Any]], seed: int, ...
    method get_n_hyperopt_steps (line 75) | def get_n_hyperopt_steps(self) -> int:
  class ConstantHyperOptimizer (line 85) | class ConstantHyperOptimizer(HyperOptimizer):
    method __init__ (line 86) | def __init__(self, params: dict):
    method _optimize_impl (line 90) | def _optimize_impl(self, f: Callable[[dict], Tuple[float, Any]], seed:...
  function f_unpack_dict (line 94) | def f_unpack_dict(dct):
  class HyperoptOptimizer (line 121) | class HyperoptOptimizer(HyperOptimizer):
    class HyperoptFuncWrapper (line 122) | class HyperoptFuncWrapper:
      method __init__ (line 123) | def __init__(self, f: Callable[[dict], Tuple[float, Any]], fixed_par...
      method __call__ (line 127) | def __call__(self, params: dict):
    method __init__ (line 136) | def __init__(self, space, fixed_params, n_hyperopt_steps: int = 50, **...
    method _optimize_impl (line 142) | def _optimize_impl(self, f: Callable[[dict], Tuple[float, Any]], seed:...
  class SMACOptimizer (line 165) | class SMACOptimizer(HyperOptimizer):
    class SMACFuncWrapper (line 166) | class SMACFuncWrapper:
      method __init__ (line 167) | def __init__(self, f: Callable[[dict], Tuple[float, Any]], fixed_par...
      method __call__ (line 171) | def __call__(self, params, seed: int = 0):
    method __init__ (line 178) | def __init__(self, space, fixed_params: Dict[str, Any], n_hyperopt_ste...
    method _optimize_impl (line 187) | def _optimize_impl(self, f: Callable[[dict], Tuple[float, Any]], seed:...

FILE: pytabkit/models/nn_models/activations.py
  function _swish_jit_fwd (line 13) | def _swish_jit_fwd(x): return x.mul(torch.sigmoid(x))
  function _swish_jit_bwd (line 17) | def _swish_jit_bwd(x, grad_output):
  class _SwishJitAutoFn (line 22) | class _SwishJitAutoFn(torch.autograd.Function):
    method forward (line 24) | def forward(ctx, x):
    method backward (line 29) | def backward(ctx, grad_output):
  function swish (line 36) | def swish(x): return x * torch.sigmoid(x)
  function _mish_jit_fwd (line 40) | def _mish_jit_fwd(x): return x.mul(torch.tanh(F.softplus(x)))
  function _mish_jit_bwd (line 44) | def _mish_jit_bwd(x, grad_output):
  class MishJitAutoFn (line 50) | class MishJitAutoFn(torch.autograd.Function):
    method forward (line 52) | def forward(ctx, x):
    method backward (line 57) | def backward(ctx, grad_output):
  function mish (line 64) | def mish(x): return x.mul(torch.tanh(F.softplus(x)))
  function golu (line 67) | def golu(x):
  class ParametricActivationLayer (line 73) | class ParametricActivationLayer(Layer):
    method __init__ (line 74) | def __init__(self, f, weight):
    method forward_cont (line 79) | def forward_cont(self, x):
    method _stack (line 83) | def _stack(self, layers):
  class ParametricActivationFitter (line 87) | class ParametricActivationFitter(Fitter):
    method __init__ (line 88) | def __init__(self, f, **config):
    method get_n_params (line 94) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 97) | def _fit(self, ds: DictDataset) -> Layer:
  class ActivationFactory (line 104) | class ActivationFactory(FitterFactory):
    method __init__ (line 105) | def __init__(self, **config):
    method _create (line 109) | def _create(self, tensor_infos) -> Fitter:

FILE: pytabkit/models/nn_models/base.py
  class Scope (line 62) | class Scope:
    method __init__ (line 63) | def __init__(self, names: Optional[List[str]] = None):
    method get_sub_scope (line 66) | def get_sub_scope(self, name: str) -> 'Scope':
    method __str__ (line 69) | def __str__(self):
    method matches (line 72) | def matches(self, regex: Union[str, re.Pattern]) -> bool:
  class TrainContext (line 78) | class TrainContext:
    method __init__ (line 82) | def __init__(self, scope: Optional[Scope] = None, hp_manager: Optional...
    method clone (line 86) | def clone(self):
    method get_global_context (line 90) | def get_global_context() -> 'TrainContext':
  function sub_scope_context (line 97) | def sub_scope_context(name: str):
  function sub_scopes_context (line 106) | def sub_scopes_context(names: List[str]):
  function set_scope_context (line 118) | def set_scope_context(scope: Scope):
  function set_hp_context (line 127) | def set_hp_context(hp_manager: Optional[HyperparamManager]):
  class ContextAware (line 136) | class ContextAware:
    method __init__ (line 137) | def __init__(self, scope_names: Optional[List[str]] = None):
    method add_scope (line 141) | def add_scope(self, name: str):
    method add_others_scope (line 145) | def add_others_scope(self, other: 'ContextAware'):
    method set_context (line 150) | def set_context(self):
  class ContextRecorder (line 155) | class ContextRecorder:
    method __init__ (line 156) | def __init__(self):
    method set_context (line 161) | def set_context(self):
  class StringConvertible (line 167) | class StringConvertible:
    method __init__ (line 168) | def __init__(self):
    method __repr__ (line 171) | def __repr__(self):
    method __str__ (line 174) | def __str__(self):
  class Variable (line 179) | class Variable(ContextRecorder, nn.Parameter):
    method __new__ (line 180) | def __new__(cls, data=None, trainable=True, requires_grad=None, hyper_...
    method __init__ (line 190) | def __init__(self, data=None, trainable=True, requires_grad=None, hype...
    method __deepcopy__ (line 193) | def __deepcopy__(self, memo):
    method __repr__ (line 202) | def __repr__(self):
    method stack (line 208) | def stack(vars: List['Variable'], dim=0):
  class Layer (line 220) | class Layer(ContextRecorder, StringConvertible, nn.Module):
    method __init__ (line 232) | def __init__(self, new_tensor_infos: Optional[Dict[str, TensorInfo]] =...
    method forward_tensor_infos (line 253) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]) ->...
    method forward (line 263) | def forward(self, data: Union[DictDataset, Dict[str, torch.Tensor]]) -...
    method forward_ds (line 275) | def forward_ds(self, ds: DictDataset) -> DictDataset:
    method forward_cont (line 280) | def forward_cont(self, x: torch.Tensor) -> torch.Tensor:
    method forward_tensors (line 287) | def forward_tensors(self, tensors: Dict[str, torch.Tensor]) -> Dict[st...
    method _stack (line 297) | def _stack(self, layers: List['Layer']) -> 'Layer':
    method stack (line 313) | def stack(self, layers: List['Layer']) -> 'Layer':
    method __setattr__ (line 323) | def __setattr__(self, name, value):
  class IdentityLayer (line 352) | class IdentityLayer(Layer):
    method forward_tensors (line 354) | def forward_tensors(self, x):
  class SequentialLayer (line 358) | class SequentialLayer(Layer):
    method __init__ (line 359) | def __init__(self, tfms: List[Layer]):
    method forward_tensor_infos (line 363) | def forward_tensor_infos(self, tensor_infos):
    method forward_ds (line 368) | def forward_ds(self, ds: DictDataset):
    method forward_tensors (line 373) | def forward_tensors(self, tensors):
    method _stack (line 378) | def _stack(self, seq_tfms):
    method __repr__ (line 382) | def __repr__(self):
    method __str__ (line 385) | def __str__(self):
  class ResidualLayer (line 390) | class ResidualLayer(Layer):
    method __init__ (line 391) | def __init__(self, inner_layer: Layer):
    method forward_tensor_infos (line 395) | def forward_tensor_infos(self, tensor_infos):
    method forward_tensors (line 398) | def forward_tensors(self, tensors: Dict[str, torch.Tensor]):
    method _stack (line 403) | def _stack(self, seq_tfms):
    method __repr__ (line 406) | def __repr__(self):
    method __str__ (line 409) | def __str__(self):
  class ConcatParallelLayer (line 414) | class ConcatParallelLayer(Layer):
    method __init__ (line 422) | def __init__(self, layers: List[Layer], fitter: 'Fitter'):
    method forward_tensors (line 426) | def forward_tensors(self, tensors):
    method _stack (line 433) | def _stack(self, tfms: List[Layer]):
    method __repr__ (line 437) | def __repr__(self):
    method __str__ (line 440) | def __str__(self):
  class FilterTensorsLayer (line 445) | class FilterTensorsLayer(Layer):
    method __init__ (line 449) | def __init__(self, include_keys: Optional[List[str]], exclude_keys: Op...
    method forward_tensors (line 457) | def forward_tensors(self, tensors: Dict[str, torch.Tensor]) -> Dict[st...
  class FunctionLayer (line 467) | class FunctionLayer(Layer):
    method __init__ (line 468) | def __init__(self, f):
    method forward_cont (line 472) | def forward_cont(self, x: torch.Tensor) -> torch.Tensor:
  class BiasLayer (line 476) | class BiasLayer(Layer):
    method __init__ (line 477) | def __init__(self, bias: Variable, factor: float = 1.0):
    method forward_cont (line 482) | def forward_cont(self, x):
    method _stack (line 489) | def _stack(self, tfms):
  class ScaleLayer (line 493) | class ScaleLayer(Layer):
    method __init__ (line 494) | def __init__(self, scale: Variable):
    method forward_cont (line 498) | def forward_cont(self, x):
    method _stack (line 502) | def _stack(self, tfms):
  class WeightLayer (line 506) | class WeightLayer(Layer):
    method __init__ (line 507) | def __init__(self, weight: Variable, factor: float = 1.0):
    method forward_cont (line 513) | def forward_cont(self, x):
    method _stack (line 519) | def _stack(self, tfms):
  class RenameTensorLayer (line 523) | class RenameTensorLayer(Layer):
    method __init__ (line 524) | def __init__(self, old_name: str, new_name: str, fitter: 'Fitter'):
    method forward_tensors (line 529) | def forward_tensors(self, tensors: Dict[str, torch.Tensor]) -> Dict[st...
    method _stack (line 539) | def _stack(self, layers: List['Layer']) -> 'Layer':
  class Fitter (line 546) | class Fitter(ContextAware, StringConvertible):
    method __init__ (line 551) | def __init__(self, needs_tensors: bool = True, is_individual: bool = T...
    method _get_n_values (line 572) | def _get_n_values(self, tensor_infos: Dict[str, TensorInfo], relevant_...
    method get_n_params (line 584) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method get_n_forward (line 592) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 602) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]) ->...
    method fit (line 610) | def fit(self, ds: DictDataset) -> Layer:
    method fit_transform (line 620) | def fit_transform(self, ds: DictDataset, needs_tensors: bool = True) -...
    method fit_transform_subsample (line 632) | def fit_transform_subsample(self, ds: DictDataset, ram_limit_gb: float...
    method _fit (line 645) | def _fit(self, ds: DictDataset) -> Layer:
    method _fit_transform (line 663) | def _fit_transform(self, ds: DictDataset, needs_tensors: bool) -> Tupl...
    method _fit_transform_subsample (line 681) | def _fit_transform_subsample(self, ds: DictDataset, ram_limit_gb: floa...
    method split_off_dynamic (line 696) | def split_off_dynamic(self) -> Tuple['Fitter', 'Fitter']:
    method split_off_individual (line 711) | def split_off_individual(self):
  class IdentityFitter (line 725) | class IdentityFitter(Fitter):
    method __init__ (line 726) | def __init__(self, **config):
    method _fit (line 729) | def _fit(self, ds: DictDataset) -> Layer:
  class SequentialFitter (line 733) | class SequentialFitter(Fitter):
    method __init__ (line 734) | def __init__(self, fitters: List[Fitter], **config):
    method forward_tensor_infos (line 740) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]):
    method get_n_params (line 745) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]):
    method get_n_forward (line 752) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]):
    method _fit_transform (line 759) | def _fit_transform(self, ds: DictDataset, needs_tensors: bool = True):
    method _fit_transform_subsample (line 768) | def _fit_transform_subsample(self, ds: DictDataset, ram_limit_gb: floa...
    method split_off_dynamic (line 778) | def split_off_dynamic(self):
    method split_off_individual (line 788) | def split_off_individual(self):
    method __str__ (line 798) | def __str__(self):
  class ResidualFitter (line 803) | class ResidualFitter(Fitter):
    method __init__ (line 804) | def __init__(self, inner_fitter: Fitter, **config):
    method forward_tensor_infos (line 809) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]):
    method get_n_params (line 812) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]):
    method get_n_forward (line 815) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]):
    method _fit_transform (line 818) | def _fit_transform(self, ds: DictDataset, needs_tensors=True):
    method split_off_dynamic (line 824) | def split_off_dynamic(self):
    method split_off_individual (line 830) | def split_off_individual(self):
    method __str__ (line 836) | def __str__(self):
  class FunctionFitter (line 841) | class FunctionFitter(Fitter):
    method __init__ (line 842) | def __init__(self, f, **config):
    method _fit (line 846) | def _fit(self, ds: DictDataset):
  class ConcatParallelFitter (line 850) | class ConcatParallelFitter(Fitter):
    method __init__ (line 852) | def __init__(self, fitters: List[Fitter]):
    method get_n_forward (line 857) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method get_n_params (line 863) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 866) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]) ->...
    method _fit (line 874) | def _fit(self, ds: DictDataset) -> Layer:
  class FitterFactory (line 880) | class FitterFactory(ContextAware, StringConvertible):
    method __init__ (line 885) | def __init__(self, scope_names: Optional[List[str]] = None):
    method create (line 888) | def create(self, tensor_infos: Dict[str, TensorInfo]) -> Fitter:
    method create_transform (line 900) | def create_transform(self, tensor_infos: Dict[str, TensorInfo]) -> Tup...
    method _create (line 912) | def _create(self, tensor_infos: Dict[str, TensorInfo]) -> Fitter:
    method _create_transform (line 926) | def _create_transform(self, tensor_infos: Dict[str, TensorInfo]) -> Tu...
  class SequentialFactory (line 931) | class SequentialFactory(FitterFactory):
    method __init__ (line 932) | def __init__(self, factories: List[FitterFactory]):
    method _create_transform (line 936) | def _create_transform(self, tensor_infos: Dict[str, TensorInfo]):
    method __str__ (line 943) | def __str__(self):
  class IdentityFactory (line 948) | class IdentityFactory(FitterFactory):
    method _create (line 949) | def _create(self, tensor_infos):
  class FunctionFactory (line 953) | class FunctionFactory(FitterFactory):
    method __init__ (line 954) | def __init__(self, f):
    method _create (line 958) | def _create(self, tensor_infos):
  class ConcatParallelFactory (line 962) | class ConcatParallelFactory(FitterFactory):
    method __init__ (line 963) | def __init__(self, factories: List[FitterFactory]):
    method _create (line 967) | def _create(self, tensor_infos) -> Fitter:
  class FilterTensorsFactory (line 971) | class FilterTensorsFactory(Fitter, FitterFactory):
    method __init__ (line 972) | def __init__(self, include_keys: Optional[List[str]] = None, exclude_k...
    method forward_tensor_infos (line 977) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]) ->...
    method _fit (line 983) | def _fit(self, ds: DictDataset) -> Layer:
  class RenameTensorFactory (line 987) | class RenameTensorFactory(Fitter, FitterFactory):
    method __init__ (line 988) | def __init__(self, old_name: str, new_name: str, **config):
    method get_n_forward (line 993) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 999) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]) ->...
    method _fit (line 1011) | def _fit(self, ds: DictDataset) -> Layer:

FILE: pytabkit/models/nn_models/categorical.py
  class SingleEncodingFactory (line 14) | class SingleEncodingFactory(FitterFactory):
    method __init__ (line 15) | def __init__(self, create_fitter, min_cat_size=0, max_cat_size=-1):
    method apply_on (line 21) | def apply_on(self, cat_size: int, n_classes: int):
    method _create (line 25) | def _create(self, tensor_infos):
  class EncodingLayer (line 40) | class EncodingLayer(Layer):
    method __init__ (line 41) | def __init__(self, single_enc_layers: Iterable[Layer], enc_output_name...
    method forward_tensors (line 46) | def forward_tensors(self, tensors):
    method _stack (line 70) | def _stack(self, layers: List['EncodingLayer']):
  class EncodingFitter (line 75) | class EncodingFitter(Fitter):
    method __init__ (line 76) | def __init__(self, single_encoder_fitters: List[Fitter], enc_output_na...
    method get_n_params (line 83) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method get_n_forward (line 87) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _sub_tensor_infos (line 97) | def _sub_tensor_infos(self, tensor_infos):
    method forward_tensor_infos (line 103) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 122) | def _fit(self, ds: DictDataset) -> Layer:
  class EncodingFactory (line 147) | class EncodingFactory(FitterFactory):
    method __init__ (line 148) | def __init__(self, single_encoder_factory, enc_output_name: str = 'x_c...
    method _create (line 153) | def _create(self, tensor_infos):
  class SingleOneHotLayer (line 166) | class SingleOneHotLayer(Layer):
    method __init__ (line 167) | def __init__(self, fitter: Fitter, onoff, cat_size, use_missing_zero: ...
    method _binary (line 174) | def _binary(self, x_cat, values):
    method _multiple (line 180) | def _multiple(self, x_cat, on_value, off_value):
    method forward_tensors (line 188) | def forward_tensors(self, tensors):
  class SingleOneHotFitter (line 213) | class SingleOneHotFitter(Fitter):
    method __init__ (line 214) | def __init__(self, use_missing_zero: bool, bin_onoff: Tuple[float, flo...
    method forward_tensor_infos (line 222) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 230) | def _fit(self, ds: DictDataset) -> Layer:
  class SingleOneHotFactory (line 238) | class SingleOneHotFactory(SingleEncodingFactory):
    method __init__ (line 239) | def __init__(self, use_missing_zero=True, bin_onoff=(1.0, 0.0), multi_...
    method apply_on (line 249) | def apply_on(self, cat_size: int, n_classes: int):
  class SingleEmbeddingLayer (line 259) | class SingleEmbeddingLayer(Layer):
    method __init__ (line 260) | def __init__(self, emb: Variable):
    method forward_tensors (line 266) | def forward_tensors(self, tensors):
    method _stack (line 310) | def _stack(self, layers: List['SingleEmbeddingLayer']):
  function fastai_emb_size_fn (line 314) | def fastai_emb_size_fn(n_cat: int):
  class ConstantFunction (line 318) | class ConstantFunction:
    method __init__ (line 319) | def __init__(self, value: Any):
    method __call__ (line 322) | def __call__(self, *args, **kwargs) -> Any:
  function get_embedding_size (line 326) | def get_embedding_size(fn: Optional[Union[int, str, Callable[[int], int]...
  class SingleEmbeddingFitter (line 342) | class SingleEmbeddingFitter(Fitter):
    method __init__ (line 343) | def __init__(self, embedding_size=None, **config):
    method get_n_params (line 353) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 357) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 361) | def _fit(self, ds: DictDataset) -> Layer:
  class SingleEmbeddingFactory (line 380) | class SingleEmbeddingFactory(SingleEncodingFactory):
    method __init__ (line 381) | def __init__(self, embedding_size=None, min_embedding_cat_size=0, max_...
  class SingleTargetEncodingFitter (line 389) | class SingleTargetEncodingFitter(Fitter):
    method __init__ (line 390) | def __init__(self, n_classes, **config):
    method get_n_params (line 394) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 400) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 404) | def _fit(self, ds: DictDataset) -> Layer:
  class SingleTargetEncodingFactory (line 428) | class SingleTargetEncodingFactory(SingleEncodingFactory):
    method __init__ (line 429) | def __init__(self, min_targetenc_cat_size=0, max_targetenc_cat_size=-1...
  class SingleOrdinalEncodingLayer (line 438) | class SingleOrdinalEncodingLayer(Layer):
    method __init__ (line 439) | def __init__(self, fitter, cat_size: int, permute_ordinal_encoding: bo...
    method forward_tensors (line 447) | def forward_tensors(self, tensors):
  class SingleOrdinalEncodingFitter (line 454) | class SingleOrdinalEncodingFitter(Fitter):
    method __init__ (line 455) | def __init__(self, permute_ordinal_encoding: bool = False, **config):
    method forward_tensor_infos (line 459) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 462) | def _fit(self, ds: DictDataset) -> Layer:
  class SingleOrdinalEncodingFactory (line 467) | class SingleOrdinalEncodingFactory(SingleEncodingFactory):
    method __init__ (line 468) | def __init__(self, min_labelenc_cat_size=0, max_labelenc_cat_size=-1, ...

FILE: pytabkit/models/nn_models/models.py
  class BlockFactory (line 30) | class BlockFactory(FitterFactory):
    method __init__ (line 31) | def __init__(self, out_features: int, block_str: str = 'w-b-a', **conf...
    method _create_transform (line 39) | def _create_transform(self, tensor_infos):
  function smooth_clip_func (line 71) | def smooth_clip_func(x, max_abs_value: float = 3.0):
  function tanh_clip_func (line 75) | def tanh_clip_func(x):
  class PreprocessingFactory (line 79) | class PreprocessingFactory(FitterFactory):
    method __init__ (line 80) | def __init__(self, **config):
    method _create (line 84) | def _create(self, tensor_infos: Dict[str, TensorInfo]) -> Fitter:
  class NNFactory (line 157) | class NNFactory(FitterFactory):
    method __init__ (line 158) | def __init__(self, **config):
    method _create_transform (line 168) | def _create_transform(self, tensor_infos: Dict[str, TensorInfo]) -> Tu...

FILE: pytabkit/models/nn_models/nn.py
  class WeightFitter (line 16) | class WeightFitter(Fitter):
    method __init__ (line 17) | def __init__(self, out_features, **config):
    method get_n_params (line 46) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 49) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 52) | def _fit(self, ds: DictDataset):
  class BiasFitter (line 192) | class BiasFitter(Fitter):
    method __init__ (line 193) | def __init__(self, **config):
    method get_n_params (line 210) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method heplus_bias (line 213) | def heplus_bias(self, x, n_simplex):
    method _fit (line 222) | def _fit(self, ds: DictDataset):
  class ScaleFitter (line 279) | class ScaleFitter(Fitter):
    method __init__ (line 280) | def __init__(self, **config):
    method get_n_params (line 290) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 293) | def _fit(self, ds: DictDataset):
  class ScaleFactory (line 318) | class ScaleFactory(FitterFactory):
    method __init__ (line 319) | def __init__(self, **config):
    method _create (line 323) | def _create(self, tensor_infos: Dict[str, TensorInfo]) -> Fitter:
  class DropoutLayer (line 327) | class DropoutLayer(Layer):
    method __init__ (line 328) | def __init__(self):
    method forward_cont (line 332) | def forward_cont(self, x):
  class DropoutFitter (line 339) | class DropoutFitter(Fitter):
    method __init__ (line 340) | def __init__(self):
    method _fit (line 343) | def _fit(self, ds: DictDataset) -> Layer:
  class NoiseLayer (line 347) | class NoiseLayer(Layer):
    method __init__ (line 348) | def __init__(self):
    method forward_cont (line 352) | def forward_cont(self, x):
  class NoiseFitter (line 359) | class NoiseFitter(Fitter):
    method __init__ (line 360) | def __init__(self, **config):
    method _fit (line 363) | def _fit(self, ds: DictDataset) -> Layer:
  class ClampLayer (line 370) | class ClampLayer(Layer):
    method __init__ (line 371) | def __init__(self, low: Variable, high: Variable):
    method forward_cont (line 376) | def forward_cont(self, x):
    method _stack (line 382) | def _stack(self, layers):
  class ClampOutputFactory (line 387) | class ClampOutputFactory(Fitter, FitterFactory):
    method __init__ (line 388) | def __init__(self, **config):
    method get_n_params (line 392) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 395) | def _fit(self, ds: DictDataset) -> Layer:
  class NormalizeOutputLayer (line 401) | class NormalizeOutputLayer(Layer):
    method __init__ (line 402) | def __init__(self, mean: Variable, std: Variable):
    method forward_tensors (line 407) | def forward_tensors(self, tensors):
    method _stack (line 416) | def _stack(self, layers):
  class NormalizeOutputFactory (line 421) | class NormalizeOutputFactory(Fitter, FitterFactory):
    method __init__ (line 422) | def __init__(self, **config):
    method get_n_params (line 425) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 428) | def _fit(self, ds: DictDataset) -> Layer:
  class NormWeightLayer (line 434) | class NormWeightLayer(Layer):
    method __init__ (line 435) | def __init__(self, weight: Variable, factor: float, fitter: Fitter, tr...
    method forward_cont (line 441) | def forward_cont(self, x):
    method _stack (line 444) | def _stack(self, layers):
  class FixedScaleFactory (line 451) | class FixedScaleFactory(Fitter, FitterFactory):
    method __init__ (line 452) | def __init__(self, scale: torch.Tensor):
    method _fit (line 456) | def _fit(self, ds: DictDataset) -> Layer:
  class FeatureImportanceFactory (line 460) | class FeatureImportanceFactory(Fitter, FitterFactory):
    method __init__ (line 461) | def __init__(self):
    method _fit (line 464) | def _fit(self, ds: DictDataset) -> Layer:
  class FixedWeightFactory (line 469) | class FixedWeightFactory(Fitter, FitterFactory):
    method __init__ (line 470) | def __init__(self):
    method _fit (line 473) | def _fit(self, ds: DictDataset) -> Layer:
  class RFFeatureImportanceFactory (line 478) | class RFFeatureImportanceFactory(Fitter, FitterFactory):
    method __init__ (line 479) | def __init__(self):
    method _fit (line 482) | def _fit(self, ds: DictDataset) -> Layer:
  class PLREmbeddingsFactory (line 501) | class PLREmbeddingsFactory(Fitter, FitterFactory):
    method __init__ (line 503) | def __init__(self, plr_sigma: float = 1.0, plr_hidden_1: int = 8, plr_...
    method get_n_params (line 521) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method get_n_forward (line 531) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 546) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]) ->...
    method _fit (line 551) | def _fit(self, ds: DictDataset) -> Layer:
  class PLREmbeddingsLayer (line 594) | class PLREmbeddingsLayer(Layer):
    method __init__ (line 597) | def __init__(self, fitter: Fitter, weight_1: Variable, weight_2: Varia...
    method forward_cont (line 606) | def forward_cont(self, x):
    method _stack (line 630) | def _stack(self, layers):
  class PLREmbeddingsLayerCosBias (line 639) | class PLREmbeddingsLayerCosBias(Layer):
    method __init__ (line 642) | def __init__(self, fitter: Fitter, weight_1: Variable, bias_1: Variable,
    method forward_cont (line 653) | def forward_cont(self, x):
    method _stack (line 679) | def _stack(self, layers):
  class PeriodicEmbeddingsFactory (line 689) | class PeriodicEmbeddingsFactory(Fitter, FitterFactory):
    method __init__ (line 691) | def __init__(self, periodic_emb_sigma: float = 1.0, periodic_emb_dim: ...
    method get_n_params (line 702) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method get_n_forward (line 712) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 717) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]) ->...
    method _fit (line 722) | def _fit(self, ds: DictDataset) -> Layer:
  class PeriodicEmbeddingsLayerSinCos (line 745) | class PeriodicEmbeddingsLayerSinCos(Layer):
    method __init__ (line 748) | def __init__(self, fitter: Fitter, weight: Variable, periodic_emb_dens...
    method forward_cont (line 753) | def forward_cont(self, x):
    method _stack (line 769) | def _stack(self, layers):
  class ToSoftLabelLayer (line 775) | class ToSoftLabelLayer(Layer):
    method __init__ (line 776) | def __init__(self, y_tensor_info, fitter: Fitter):
    method forward_tensors (line 780) | def forward_tensors(self, tensors):
  class ToSoftLabelFitter (line 798) | class ToSoftLabelFitter(Fitter):
    method __init__ (line 799) | def __init__(self):
    method forward_tensor_infos (line 802) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 809) | def _fit(self, ds: DictDataset) -> Layer:
  class LabelSmoothingLayer (line 813) | class LabelSmoothingLayer(Layer):
    method __init__ (line 815) | def __init__(self, ls_dist: Variable):
    method forward_tensors (line 820) | def forward_tensors(self, tensors):
    method _stack (line 832) | def _stack(self, layers):
  class LabelSmoothingFitter (line 836) | class LabelSmoothingFitter(Fitter):
    method __init__ (line 837) | def __init__(self, use_ls_prior=False, **config):
    method _fit (line 847) | def _fit(self, ds: DictDataset) -> Layer:
  class LabelSmoothingFactory (line 860) | class LabelSmoothingFactory(FitterFactory):
    method __init__ (line 861) | def __init__(self, **config):
    method _create (line 865) | def _create(self, tensor_infos) -> Fitter:
  class StochasticLabelNoiseLayer (line 873) | class StochasticLabelNoiseLayer(Layer):
    method __init__ (line 874) | def __init__(self):
    method forward_tensors (line 878) | def forward_tensors(self, tensors):
  class StochasticLabelNoiseFitter (line 886) | class StochasticLabelNoiseFitter(Fitter):
    method __init__ (line 887) | def __init__(self):
    method _fit (line 890) | def _fit(self, ds: DictDataset) -> Layer:
  class StochasticLabelNoiseFactory (line 895) | class StochasticLabelNoiseFactory(FitterFactory):
    method _create (line 896) | def _create(self, tensor_infos) -> Fitter:
  class StochasticGateLayer (line 905) | class StochasticGateLayer(Layer):
    method __init__ (line 906) | def __init__(self, mu: Variable):
    method forward_cont (line 912) | def forward_cont(self, x):
    method _stack (line 923) | def _stack(self, layers):
  class StochasticGateFactory (line 927) | class StochasticGateFactory(Fitter, FitterFactory):
    method __init__ (line 928) | def __init__(self):
    method get_n_params (line 931) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method get_n_forward (line 934) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 938) | def _fit(self, ds: DictDataset) -> Layer:
  class AntisymmetricInitializationFactory (line 944) | class AntisymmetricInitializationFactory(FitterFactory):
    method __init__ (line 945) | def __init__(self, factory, **config):
    method _create (line 950) | def _create(self, tensor_infos) -> Fitter:
  class AntisymmetricInitializationFitter (line 959) | class AntisymmetricInitializationFitter(Fitter):
    method __init__ (line 964) | def __init__(self, fitter: Fitter, **config):
    method forward_tensor_infos (line 970) | def forward_tensor_infos(self, tensor_infos: Dict[str, TensorInfo]):
    method get_n_params (line 973) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]):
    method get_n_forward (line 976) | def get_n_forward(self, tensor_infos: Dict[str, TensorInfo]):
    method _fit (line 979) | def _fit(self, ds: DictDataset) -> Layer:
    method __str__ (line 992) | def __str__(self):
  class SubtractionLayer (line 997) | class SubtractionLayer(Layer):
    method __init__ (line 998) | def __init__(self, layer1: Layer, layer2: Layer):
    method forward_tensor_infos (line 1003) | def forward_tensor_infos(self, tensor_infos):
    method forward_tensors (line 1007) | def forward_tensors(self, tensors):
    method _stack (line 1015) | def _stack(self, layers):

FILE: pytabkit/models/nn_models/pipeline.py
  class ReplaceMissingContLayer (line 17) | class ReplaceMissingContLayer(Layer):
    method __init__ (line 18) | def __init__(self, means: Variable):
    method forward_cont (line 24) | def forward_cont(self, x):
    method _stack (line 27) | def _stack(self, layers: List['ReplaceMissingContLayer']):
  class MeanReplaceMissingContFactory (line 31) | class MeanReplaceMissingContFactory(Fitter, FitterFactory):
    method __init__ (line 32) | def __init__(self, trainable=False, **config):
    method get_n_params (line 36) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 39) | def _fit(self, ds: DictDataset) -> Layer:
  class MeanCenterFactory (line 50) | class MeanCenterFactory(Fitter, FitterFactory):
    method __init__ (line 51) | def __init__(self, trainable=False, **config):
    method get_n_params (line 55) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 58) | def _fit(self, ds: DictDataset) -> Layer:
  class MedianCenterFactory (line 65) | class MedianCenterFactory(Fitter, FitterFactory):
    method __init__ (line 66) | def __init__(self, median_center_trainable=False, **config):
    method get_n_params (line 70) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 73) | def _fit(self, ds: DictDataset) -> Layer:
  class L2NormalizeFactory (line 83) | class L2NormalizeFactory(Fitter, FitterFactory):
    method __init__ (line 84) | def __init__(self, trainable=False, l2_normalize_eps=1e-8, **config):
    method get_n_params (line 89) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 92) | def _fit(self, ds: DictDataset) -> Layer:
  class L1NormalizeFactory (line 100) | class L1NormalizeFactory(Fitter, FitterFactory):
    method __init__ (line 101) | def __init__(self, trainable=False, eps=1e-8, **config):
    method get_n_params (line 106) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 109) | def _fit(self, ds: DictDataset) -> Layer:
  class MinMaxScaleFactory (line 116) | class MinMaxScaleFactory(Fitter, FitterFactory):
    method __init__ (line 117) | def __init__(self, trainable=False, eps=1e-8, **config):
    method get_n_params (line 122) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 125) | def _fit(self, ds: DictDataset) -> Layer:
  class RobustScaleFactory (line 137) | class RobustScaleFactory(Fitter, FitterFactory):
    method __init__ (line 138) | def __init__(self, robust_scale_trainable=False, robust_scale_eps=1e-3...
    method get_n_params (line 143) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 146) | def _fit(self, ds: DictDataset) -> Layer:
  class RobustScaleV2Factory (line 160) | class RobustScaleV2Factory(Fitter, FitterFactory):
    method __init__ (line 161) | def __init__(self, robust_scale_trainable=False, **config):
    method get_n_params (line 165) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 168) | def _fit(self, ds: DictDataset) -> Layer:
  class GlobalScaleNormalizeFactory (line 183) | class GlobalScaleNormalizeFactory(Fitter, FitterFactory):
    method __init__ (line 184) | def __init__(self, global_scale_factor=1.0, **config):
    method get_n_params (line 188) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method _fit (line 191) | def _fit(self, ds: DictDataset) -> Layer:
  class ThermometerCodingLayer (line 199) | class ThermometerCodingLayer(Layer):
    method __init__ (line 200) | def __init__(self, centers: Variable, scale: float, fitter: Fitter):
    method forward_cont (line 205) | def forward_cont(self, x):
    method _stack (line 209) | def _stack(self, layers):
  class ThermometerCodingFactory (line 213) | class ThermometerCodingFactory(Fitter, FitterFactory):
    method __init__ (line 214) | def __init__(self, tc_low=-1.0, tc_high=1.0, tc_num=3, tc_scale=1.0, *...
    method get_n_params (line 221) | def get_n_params(self, tensor_infos: Dict[str, TensorInfo]) -> int:
    method forward_tensor_infos (line 224) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 228) | def _fit(self, ds: DictDataset) -> Layer:
  class CircleCodingLayer (line 236) | class CircleCodingLayer(Layer):
    method __init__ (line 237) | def __init__(self, scale: float, fitter: Fitter):
    method forward_cont (line 241) | def forward_cont(self, x):
    method _stack (line 246) | def _stack(self, layers):
  class CircleCodingFactory (line 250) | class CircleCodingFactory(Fitter, FitterFactory):
    method __init__ (line 251) | def __init__(self, circle_coding_scale=1.0, **config):
    method forward_tensor_infos (line 255) | def forward_tensor_infos(self, tensor_infos):
    method _fit (line 259) | def _fit(self, ds: DictDataset) -> Layer:
  function apply_tfms_rec (line 265) | def apply_tfms_rec(tfms: Union[BaseEstimator, List], x: torch.Tensor):
  class SklearnTransformLayer (line 272) | class SklearnTransformLayer(Layer):
    method __init__ (line 273) | def __init__(self, tfms: Union[BaseEstimator, List], fitter: Fitter):
    method forward_cont (line 277) | def forward_cont(self, x):
    method _stack (line 280) | def _stack(self, layers):
  class SklearnTransformFactory (line 284) | class SklearnTransformFactory(Fitter, FitterFactory):
    method __init__ (line 285) | def __init__(self, tfm: BaseEstimator, **config):
    method _fit (line 289) | def _fit(self, ds: DictDataset) -> Layer:

FILE: pytabkit/models/nn_models/rtdl_num_embeddings.py
  function _check_input_shape (line 36) | def _check_input_shape(x: Tensor, expected_n_features: int) -> None:
  class LinearEmbeddings (line 48) | class LinearEmbeddings(nn.Module):
    method __init__ (line 67) | def __init__(self, n_features: int, d_embedding: int) -> None:
    method reset_parameters (line 83) | def reset_parameters(self) -> None:
    method forward (line 88) | def forward(self, x: Tensor) -> Tensor:
  class LinearReLUEmbeddings (line 94) | class LinearReLUEmbeddings(nn.Sequential):
    method __init__ (line 114) | def __init__(self, n_features: int, d_embedding: int = 32) -> None:
  class _Periodic (line 128) | class _Periodic(nn.Module):
    method __init__ (line 137) | def __init__(self, n_features: int, k: int, sigma: float) -> None:
    method reset_parameters (line 146) | def reset_parameters(self):
    method forward (line 154) | def forward(self, x: Tensor) -> Tensor:
  class _NLinear (line 164) | class _NLinear(nn.Module):
    method __init__ (line 171) | def __init__(
    method reset_parameters (line 179) | def reset_parameters(self):
    method forward (line 186) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class PeriodicEmbeddings (line 203) | class PeriodicEmbeddings(nn.Module):
    method __init__ (line 234) | def __init__(
    method forward (line 272) | def forward(self, x: Tensor) -> Tensor:
  function _check_bins (line 281) | def _check_bins(bins: list[Tensor]) -> None:
  function compute_bins (line 319) | def compute_bins(
  class _PiecewiseLinearEncodingImpl (line 500) | class _PiecewiseLinearEncodingImpl(nn.Module):
    method __init__ (line 562) | def __init__(self, bins: list[Tensor]) -> None:
    method get_max_n_bins (line 624) | def get_max_n_bins(self) -> int:
    method forward (line 627) | def forward(self, x: Tensor) -> Tensor:
  class PiecewiseLinearEncoding (line 655) | class PiecewiseLinearEncoding(nn.Module):
    method __init__ (line 671) | def __init__(self, bins: list[Tensor]) -> None:
    method forward (line 679) | def forward(self, x: Tensor) -> Tensor:
  class PiecewiseLinearEmbeddings (line 685) | class PiecewiseLinearEmbeddings(nn.Module):
    method __init__ (line 694) | def __init__(
    method forward (line 748) | def forward(self, x: Tensor) -> Tensor:

FILE: pytabkit/models/nn_models/rtdl_resnet.py
  function reglu (line 33) | def reglu(x: Tensor) -> Tensor:
  function geglu (line 38) | def geglu(x: Tensor) -> Tensor:
  function get_activation_fn (line 43) | def get_activation_fn(name: str) -> ty.Callable[[Tensor], Tensor]:
  function get_nonglu_activation_fn (line 55) | def get_nonglu_activation_fn(name: str) -> ty.Callable[[Tensor], Tensor]:
  function print_but_serializable (line 65) | def print_but_serializable(*args, **kwargs):
  class RTDL_MLP (line 73) | class RTDL_MLP(nn.Module):
    method __init__ (line 75) | def __init__(
    method forward (line 151) | def forward(self, x):
  class ResNet (line 187) | class ResNet(nn.Module):
    method __init__ (line 188) | def __init__(
    method forward (line 257) | def forward(self, x) -> Tensor:
  class Tokenizer (line 298) | class Tokenizer(nn.Module):
    method __init__ (line 301) | def __init__(
    method n_tokens (line 340) | def n_tokens(self) -> int:
    method forward (line 345) | def forward(self, x_num: Tensor, x_cat: ty.Optional[Tensor]) -> Tensor:
  class MultiheadAttention (line 373) | class MultiheadAttention(nn.Module):
    method __init__ (line 374) | def __init__(
    method _reshape (line 397) | def _reshape(self, x: Tensor) -> Tensor:
    method forward (line 406) | def forward(
  class FT_Transformer (line 444) | class FT_Transformer(nn.Module):
    method __init__ (line 453) | def __init__(
    method _get_kv_compressions (line 539) | def _get_kv_compressions(self, layer):
    method _start_residual (line 550) | def _start_residual(self, x, layer, norm_idx):
    method _end_residual (line 558) | def _end_residual(self, x, x_residual, layer, norm_idx):
    method forward (line 566) | def forward(self, x) -> Tensor:
  class InputShapeSetterResnet (line 610) | class InputShapeSetterResnet(skorch.callbacks.Callback):
    method __init__ (line 611) | def __init__(
    method on_train_begin (line 619) | def on_train_begin(self, net, X, y):
  class LearningRateLogger (line 657) | class LearningRateLogger(Callback):
    method on_epoch_begin (line 658) | def on_epoch_begin(self, net, dataset_train=None, dataset_valid=None, ...
  class UniquePrefixCheckpoint (line 667) | class UniquePrefixCheckpoint(Checkpoint):
    method initialize (line 676) | def initialize(self):
    method on_train_end (line 683) | def on_train_end(self, net, **kwargs):
  class MyCustomError (line 719) | class MyCustomError(Exception):
  class EarlyStoppingCustomError (line 723) | class EarlyStoppingCustomError(EarlyStopping):
    method on_epoch_end (line 724) | def on_epoch_end(self, net, **kwargs):
  class NeuralNetRegressorWrapped (line 742) | class NeuralNetRegressorWrapped(NeuralNetRegressor):
    method __init__ (line 743) | def __init__(self, *args, **kwargs):
    method set_categorical_indicator (line 750) | def set_categorical_indicator(self, categorical_indicator):
    method set_predict_mean (line 753) | def set_predict_mean(self, predict_mean):
    method set_y_train_mean (line 756) | def set_y_train_mean(self, y_train_mean):
    method get_default_callbacks (line 759) | def get_default_callbacks(self):
    method fit (line 765) | def fit(self, X, y):
    method predict (line 771) | def predict(self, X):
    method partial_fit (line 781) | def partial_fit(self, X, y=None, classes=None, **fit_params):
  class NeuralNetClassifierWrapped (line 795) | class NeuralNetClassifierWrapped(NeuralNetClassifier):
    method __init__ (line 796) | def __init__(self, *args, **kwargs):
    method set_categorical_indicator (line 801) | def set_categorical_indicator(self, categorical_indicator):
    method set_n_classes (line 804) | def set_n_classes(self, n_classes):
    method fit (line 807) | def fit(self, X, y):
    method get_default_callbacks (line 811) | def get_default_callbacks(self):
    method partial_fit (line 821) | def partial_fit(self, X, y=None, classes=None, **fit_params):
  function initialize_optimizer_ft_transformer (line 836) | def initialize_optimizer_ft_transformer(self, triggered_directly=None):
  class NeuralNetClassifierCustomOptim (line 875) | class NeuralNetClassifierCustomOptim(NeuralNetClassifierWrapped):
    method initialize_optimizer (line 876) | def initialize_optimizer(self, triggered_directly=None):
  class NeuralNetRegressorCustomOptim (line 879) | class NeuralNetRegressorCustomOptim(NeuralNetRegressorWrapped):
    method initialize_optimizer (line 880) | def initialize_optimizer(self, triggered_directly=None):
  function mse_constant_predictor (line 883) | def mse_constant_predictor(model, X, y):
  function create_regressor_skorch (line 887) | def create_regressor_skorch(
  function create_classifier_skorch (line 1002) | def create_classifier_skorch(

FILE: pytabkit/models/nn_models/tabm.py
  function init_rsqrt_uniform_ (line 22) | def init_rsqrt_uniform_(x: Tensor, d: int) -> Tensor:
  function init_random_signs_ (line 29) | def init_random_signs_(x: Tensor) -> Tensor:
  class NLinear (line 36) | class NLinear(nn.Module):
    method __init__ (line 53) | def __init__(
    method reset_parameters (line 61) | def reset_parameters(self):
    method forward (line 67) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class OneHotEncoding0d (line 79) | class OneHotEncoding0d(nn.Module):
    method __init__ (line 83) | def __init__(self, cardinalities: List[int]) -> None:
    method forward (line 87) | def forward(self, x: Tensor) -> Tensor:
  class ScaleEnsemble (line 114) | class ScaleEnsemble(nn.Module):
    method __init__ (line 115) | def __init__(
    method reset_parameters (line 127) | def reset_parameters(self) -> None:
    method forward (line 137) | def forward(self, x: Tensor) -> Tensor:
  class LinearEfficientEnsemble (line 142) | class LinearEfficientEnsemble(nn.Module):
    method __init__ (line 168) | def __init__(
    method reset_parameters (line 220) | def reset_parameters(self):
    method forward (line 243) | def forward(self, x: Tensor) -> Tensor:
  class MLP (line 260) | class MLP(nn.Module):
    method __init__ (line 261) | def __init__(
    method forward (line 286) | def forward(self, x: Tensor) -> Tensor:
  function make_efficient_ensemble (line 294) | def make_efficient_ensemble(module: nn.Module, EnsembleLayer, **kwargs) ...
  function _get_first_ensemble_layer (line 318) | def _get_first_ensemble_layer(backbone: MLP) -> LinearEfficientEnsemble:
  function _init_first_adapter (line 326) | def _init_first_adapter(
  function make_module (line 375) | def make_module(type: str, *args, **kwargs) -> nn.Module:
  function default_zero_weight_decay_condition (line 385) | def default_zero_weight_decay_condition(
  function make_parameter_groups (line 397) | def make_parameter_groups(
  class Model (line 433) | class Model(nn.Module):
    method __init__ (line 436) | def __init__(
    method forward (line 594) | def forward(

FILE: pytabkit/models/nn_models/tabr.py
  class NTPLinearLayer (line 24) | class NTPLinearLayer(nn.Module):
    method __init__ (line 25) | def __init__(self, in_features: int, out_features: int, bias: bool = T...
    method forward (line 41) | def forward(self, x):
  class ParametricMishActivationLayer (line 48) | class ParametricMishActivationLayer(nn.Module):
    method __init__ (line 49) | def __init__(self, n_features: int, lr_factor: float = 1.0):
    method f (line 54) | def f(self, x):
    method forward (line 57) | def forward(self, x):
  class ParametricReluActivationLayer (line 62) | class ParametricReluActivationLayer(nn.Module):
    method __init__ (line 63) | def __init__(self, n_features: int, lr_factor: float = 1.0):
    method f (line 68) | def f(self, x):
    method forward (line 71) | def forward(self, x):
  class ScalingLayer (line 76) | class ScalingLayer(nn.Module):
    method __init__ (line 77) | def __init__(self, n_features: int, lr_factor: float = 6.0):
    method forward (line 82) | def forward(self, x):
  function bce_with_logits_and_label_smoothing (line 86) | def bce_with_logits_and_label_smoothing(inputs, *args, ls_eps: float, **...
  class TabrModel (line 92) | class TabrModel(nn.Module):
    method __init__ (line 93) | def __init__(
    method reset_parameters (line 223) | def reset_parameters(self):
    method _encode (line 232) | def _encode(self, x_: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
    method forward (line 265) | def forward(
  function zero_wd_condition (line 403) | def zero_wd_condition(
  class TabrLightning (line 418) | class TabrLightning(pl.LightningModule):
    method __init__ (line 419) | def __init__(self, model, train_dataset,
    method setup (line 461) | def setup(self, stage=None):
    method get_Xy (line 474) | def get_Xy(self, part: str, idx) -> tuple[dict[str, Tensor], Tensor]:
    method training_step (line 496) | def training_step(self, batch, batch_idx):
    method validation_step (line 543) | def validation_step(self, batch, batch_idx):
    method predict_step (line 585) | def predict_step(self, batch, batch_idx, dataloader_idx=None):
    method configure_optimizers (line 614) | def configure_optimizers(self):
    method train_dataloader (line 621) | def train_dataloader(self):
    method val_dataloader (line 626) | def val_dataloader(self):

FILE: pytabkit/models/nn_models/tabr_context_freeze.py
  class TabrModelContextFreeze (line 29) | class TabrModelContextFreeze(nn.Module):
    class ForwardOutput (line 30) | class ForwardOutput(NamedTuple):
    method __init__ (line 35) | def __init__(
    method reset_parameters (line 155) | def reset_parameters(self):
    method _encode (line 164) | def _encode(self, x_: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
    method forward (line 197) | def forward(
  function zero_wd_condition (line 310) | def zero_wd_condition(
  class TabrLightningContextFreeze (line 325) | class TabrLightningContextFreeze(pl.LightningModule):
    method __init__ (line 326) | def __init__(self, model, train_dataset,
    method setup (line 370) | def setup(self, stage=None):
    method get_Xy (line 383) | def get_Xy(self, part: str, idx) -> tuple[dict[str, Tensor], Tensor]:
    method apply_model (line 405) | def apply_model(self, part, batch, batch_idx, training):
    method training_step (line 447) | def training_step(self, batch, batch_idx):
    method validation_step (line 517) | def validation_step(self, batch, batch_idx):
    method predict_step (line 572) | def predict_step(self, batch, batch_idx, dataloader_idx=None):
    method evaluate (line 612) | def evaluate(self, eval_batch_size: int, *, progress_bar: bool = False):
    method configure_optimizers (line 677) | def configure_optimizers(self):
    method train_dataloader (line 684) | def train_dataloader(self):
    method val_dataloader (line 689) | def val_dataloader(self):

FILE: pytabkit/models/nn_models/tabr_lib.py
  function _initialize_embeddings (line 27) | def _initialize_embeddings(weight: Tensor, d: Optional[int]) -> None:
  function make_trainable_vector (line 34) | def make_trainable_vector(d: int) -> Parameter:
  class OneHotEncoder (line 56) | class OneHotEncoder(nn.Module):
    method __init__ (line 59) | def __init__(self, cardinalities: list[int]) -> None:
    method forward (line 63) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CLSEmbedding (line 79) | class CLSEmbedding(nn.Module):
    method __init__ (line 80) | def __init__(self, d_embedding: int) -> None:
    method forward (line 84) | def forward(self, x: Tensor) -> Tensor:
  class CatEmbeddings (line 90) | class CatEmbeddings(nn.Module):
    method __init__ (line 91) | def __init__(
    method reset_parameters (line 123) | def reset_parameters(self) -> None:
    method forward (line 127) | def forward(self, x: Tensor) -> Tensor:
  class LinearEmbeddings (line 134) | class LinearEmbeddings(nn.Module):
    method __init__ (line 135) | def __init__(self, n_features: int, d_embedding: int, bias: bool = True):
    method reset_parameters (line 141) | def reset_parameters(self) -> None:
    method forward (line 146) | def forward(self, x: Tensor) -> Tensor:
  class PeriodicEmbeddings (line 154) | class PeriodicEmbeddings(nn.Module):
    method __init__ (line 155) | def __init__(
    method forward (line 163) | def forward(self, x: Tensor) -> Tensor:
  class NLinear (line 170) | class NLinear(nn.Module):
    method __init__ (line 171) | def __init__(
    method forward (line 184) | def forward(self, x):
  class LREmbeddings (line 193) | class LREmbeddings(nn.Sequential):
    method __init__ (line 196) | def __init__(self, n_features: int, d_embedding: int) -> None:
  class PLREmbeddings (line 200) | class PLREmbeddings(nn.Sequential):
    method __init__ (line 208) | def __init__(
  class PBLDEmbeddings (line 227) | class PBLDEmbeddings(nn.Module):
    method __init__ (line 228) | def __init__(self, n_features: int,
    method forward (line 245) | def forward(self, x):
  class MLP (line 272) | class MLP(nn.Module):
    class Block (line 273) | class Block(nn.Module):
      method __init__ (line 274) | def __init__(
      method forward (line 288) | def forward(self, x: Tensor) -> Tensor:
    method __init__ (line 293) | def __init__(
    method d_out (line 321) | def d_out(self) -> int:
    method forward (line 328) | def forward(self, x: Tensor) -> Tensor:
  function register_module (line 347) | def register_module(key: str, f: Callable[..., nn.Module]) -> None:
  function make_module (line 352) | def make_module(spec: ModuleSpec, *args, **kwargs) -> nn.Module:
  function get_n_parameters (line 377) | def get_n_parameters(m: nn.Module):
  function get_d_out (line 381) | def get_d_out(n_classes: Optional[int]) -> int:
  function default_zero_weight_decay_condition (line 388) | def default_zero_weight_decay_condition(
  function make_parameter_groups (line 404) | def make_parameter_groups(
  function make_optimizer (line 442) | def make_optimizer(
  function get_lr (line 460) | def get_lr(optimizer: optim.Optimizer) -> float:
  function set_lr (line 464) | def set_lr(optimizer: optim.Optimizer, lr: float) -> None:
  class Lambda (line 471) | class Lambda(torch.nn.Module):
    method __init__ (line 512) | def __init__(self, fn: Callable[..., torch.Tensor], /, **kwargs) -> None:
    method forward (line 561) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function _make_index_batches (line 568) | def _make_index_batches(
  function iter_batches (line 590) | def iter_batches(
  function cat (line 775) | def cat(data: List[T], /, dim: int = 0) -> T:
  function is_oom_exception (line 946) | def is_oom_exception(err: RuntimeError) -> bool:

FILE: pytabkit/models/optim/adopt.py
  class ADOPT (line 36) | class ADOPT(Optimizer):
    method __init__ (line 37) | def __init__(
    method __setstate__ (line 98) | def __setstate__(self, state):
    method _init_group (line 120) | def _init_group(
    method step (line 188) | def step(self, closure=None):
  function _single_tensor_adopt (line 244) | def _single_tensor_adopt(
  function _multi_tensor_adopt (line 318) | def _multi_tensor_adopt(
  function adopt (line 432) | def adopt(

FILE: pytabkit/models/optim/optimizers.py
  class OptimizerBase (line 15) | class OptimizerBase(torch.optim.Optimizer):
    method __init__ (line 16) | def __init__(self, opt, hyper_mappings, hp_manager: HyperparamManager):
    method get_hyper_values (line 31) | def get_hyper_values(self, name, i, use_hyper_factor=True):
    method step (line 38) | def step(self, closure=None, loss: Optional[torch.Tensor] = None):
    method train (line 69) | def train(self):
    method eval (line 74) | def eval(self):
    method _opt_step_with_loss (line 79) | def _opt_step_with_loss(self, loss: Optional[torch.Tensor]):
    method __getstate__ (line 82) | def __getstate__(self) -> Dict[str, Any]:
    method __setstate__ (line 86) | def __setstate__(self, state: Dict[str, Any]) -> None:
  class AdamOptimizer (line 91) | class AdamOptimizer(OptimizerBase):
    method __init__ (line 92) | def __init__(self, param_groups, hp_manager):
  class SchedulingAdamOptimizer (line 99) | class SchedulingAdamOptimizer(OptimizerBase):
    method __init__ (line 100) | def __init__(self, param_groups, hp_manager):
  class AMSGradOptimizer (line 107) | class AMSGradOptimizer(OptimizerBase):
    method __init__ (line 108) | def __init__(self, param_groups, hp_manager):
  class AdamaxOptimizer (line 115) | class AdamaxOptimizer(OptimizerBase):
    method __init__ (line 116) | def __init__(self, param_groups, hp_manager):
  class SGDOptimizer (line 123) | class SGDOptimizer(OptimizerBase):
    method __init__ (line 124) | def __init__(self, param_groups, hp_manager):
  class SFAdamOptimizer (line 129) | class SFAdamOptimizer(OptimizerBase):
    method __init__ (line 130) | def __init__(self, param_groups, hp_manager: HyperparamManager):
  class MoMoAdamOptimizer (line 140) | class MoMoAdamOptimizer(OptimizerBase):
    method __init__ (line 141) | def __init__(self, param_groups, hp_manager: HyperparamManager):
    method _opt_step_with_loss (line 148) | def _opt_step_with_loss(self, loss: Optional[torch.Tensor]):
  class AdoptOptimizer (line 152) | class AdoptOptimizer(OptimizerBase):
    method __init__ (line 153) | def __init__(self, param_groups, hp_manager: HyperparamManager):
  function get_opt_class (line 161) | def get_opt_class(opt_name):

FILE: pytabkit/models/optim/scheduling_adam.py
  class SchedulingAdam (line 7) | class SchedulingAdam(Optimizer):
    method __init__ (line 35) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
    method __setstate__ (line 51) | def __setstate__(self, state):
    method step (line 57) | def step(self, closure=None):

FILE: pytabkit/models/sklearn/default_params.py
  class DefaultParams (line 6) | class DefaultParams:

FILE: pytabkit/models/sklearn/sklearn_base.py
  function to_df (line 30) | def to_df(x) -> pd.DataFrame:
  function to_normal_type (line 39) | def to_normal_type(x) -> Any:
  function concat_arrays (line 45) | def concat_arrays(x1, x2) -> Any:
  function check_X_y_wrapper (line 53) | def check_X_y_wrapper(*args, **kwargs):
  function check_array_wrapper (line 66) | def check_array_wrapper(*args, **kwargs):
  class AlgInterfaceEstimator (line 79) | class AlgInterfaceEstimator(BaseEstimator):
    method _create_alg_interface (line 84) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 88) | def _supports_multioutput(self) -> bool:
    method _supports_single_class (line 92) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 97) | def _supports_single_sample(self) -> bool:
    method _non_deterministic_tag (line 100) | def _non_deterministic_tag(self) -> bool:
    method _is_classification (line 103) | def _is_classification(self) -> bool:
    method _get_default_params (line 106) | def _get_default_params(self) -> Dict[str, Any]:
    method _allowed_device_names (line 111) | def _allowed_device_names(self) -> List[str]:
    method _more_tags (line 115) | def _more_tags(self):
    method __sklearn_tags__ (line 118) | def __sklearn_tags__(self):
    method get_config (line 123) | def get_config(self) -> Dict[str, Any]:
    method fit (line 142) | def fit(self, X, y, X_val: Optional = None, y_val: Optional = None, va...
    method _predict_raw (line 462) | def _predict_raw(self, X) -> torch.Tensor:
    method to (line 486) | def to(self, device: str) -> None:
  class AlgInterfaceClassifier (line 496) | class AlgInterfaceClassifier(ClassifierMixin, AlgInterfaceEstimator):
    method _is_classification (line 499) | def _is_classification(self) -> bool:
    method predict_proba (line 502) | def predict_proba(self, X) -> np.ndarray:
    method predict_proba_ensemble (line 508) | def predict_proba_ensemble(self, X) -> np.ndarray:
    method predict (line 515) | def predict(self, X):
    method predict_ensemble (line 533) | def predict_ensemble(self, X):
  class AlgInterfaceRegressor (line 539) | class AlgInterfaceRegressor(RegressorMixin, AlgInterfaceEstimator):
    method _is_classification (line 542) | def _is_classification(self) -> bool:
    method _more_tags (line 545) | def _more_tags(self):
    method __sklearn_tags__ (line 548) | def __sklearn_tags__(self):
    method predict (line 554) | def predict(self, X):
    method predict_ensemble (line 565) | def predict_ensemble(self, X):

FILE: pytabkit/models/sklearn/sklearn_interfaces.py
  class RealMLPConstructorMixin (line 43) | class RealMLPConstructorMixin:
    method __init__ (line 44) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class RealMLP_TD_Classifier (line 343) | class RealMLP_TD_Classifier(RealMLPConstructorMixin, AlgInterfaceClassif...
    method _get_default_params (line 348) | def _get_default_params(self):
    method _create_alg_interface (line 351) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 354) | def _allowed_device_names(self) -> List[str]:
  class RealMLP_TD_S_Classifier (line 358) | class RealMLP_TD_S_Classifier(RealMLPConstructorMixin, AlgInterfaceClass...
    method _get_default_params (line 363) | def _get_default_params(self):
    method _create_alg_interface (line 366) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 369) | def _allowed_device_names(self) -> List[str]:
  class RealMLP_TD_Regressor (line 373) | class RealMLP_TD_Regressor(RealMLPConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 378) | def _get_default_params(self):
    method _create_alg_interface (line 381) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 384) | def _allowed_device_names(self) -> List[str]:
  class RealMLP_TD_S_Regressor (line 388) | class RealMLP_TD_S_Regressor(RealMLPConstructorMixin, AlgInterfaceRegres...
    method _get_default_params (line 393) | def _get_default_params(self):
    method _create_alg_interface (line 396) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 399) | def _allowed_device_names(self) -> List[str]:
  class LGBMConstructorMixin (line 406) | class LGBMConstructorMixin:
    method __init__ (line 407) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class LGBM_TD_Classifier (line 457) | class LGBM_TD_Classifier(LGBMConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 458) | def _get_default_params(self):
    method _create_alg_interface (line 461) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class LGBM_D_Classifier (line 466) | class LGBM_D_Classifier(LGBMConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 467) | def _get_default_params(self):
    method _create_alg_interface (line 470) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class LGBM_TD_Regressor (line 475) | class LGBM_TD_Regressor(LGBMConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 476) | def _get_default_params(self):
    method _create_alg_interface (line 479) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 483) | def _supports_multioutput(self) -> bool:
  class LGBM_D_Regressor (line 487) | class LGBM_D_Regressor(LGBMConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 488) | def _get_default_params(self):
    method _create_alg_interface (line 491) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 495) | def _supports_multioutput(self) -> bool:
  class XGBConstructorMixin (line 499) | class XGBConstructorMixin:
    method __init__ (line 500) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class XGB_TD_Classifier (line 555) | class XGB_TD_Classifier(XGBConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 556) | def _get_default_params(self):
    method _create_alg_interface (line 559) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 563) | def _allowed_device_names(self) -> List[str]:
  class XGB_D_Classifier (line 567) | class XGB_D_Classifier(XGBConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 568) | def _get_default_params(self):
    method _create_alg_interface (line 571) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 575) | def _allowed_device_names(self) -> List[str]:
  class XGB_PBB_D_Classifier (line 579) | class XGB_PBB_D_Classifier(XGBConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 580) | def _get_default_params(self):
    method _create_alg_interface (line 583) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 587) | def _allowed_device_names(self) -> List[str]:
  class XGB_TD_Regressor (line 591) | class XGB_TD_Regressor(XGBConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 592) | def _get_default_params(self):
    method _create_alg_interface (line 595) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 599) | def _allowed_device_names(self) -> List[str]:
    method _supports_multioutput (line 602) | def _supports_multioutput(self) -> bool:
  class XGB_D_Regressor (line 606) | class XGB_D_Regressor(XGBConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 607) | def _get_default_params(self):
    method _create_alg_interface (line 610) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 614) | def _allowed_device_names(self) -> List[str]:
    method _supports_multioutput (line 617) | def _supports_multioutput(self) -> bool:
  class CatBoostConstructorMixin (line 621) | class CatBoostConstructorMixin:
    method __init__ (line 622) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class CatBoost_TD_Classifier (line 677) | class CatBoost_TD_Classifier(CatBoostConstructorMixin, AlgInterfaceClass...
    method _get_default_params (line 678) | def _get_default_params(self):
    method _create_alg_interface (line 681) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_single_class (line 685) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 688) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 691) | def _allowed_device_names(self) -> List[str]:
  class CatBoost_D_Classifier (line 695) | class CatBoost_D_Classifier(CatBoostConstructorMixin, AlgInterfaceClassi...
    method _get_default_params (line 696) | def _get_default_params(self):
    method _create_alg_interface (line 699) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_single_class (line 703) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 706) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 709) | def _allowed_device_names(self) -> List[str]:
  class CatBoost_TD_Regressor (line 713) | class CatBoost_TD_Regressor(CatBoostConstructorMixin, AlgInterfaceRegres...
    method _get_default_params (line 714) | def _get_default_params(self):
    method _create_alg_interface (line 717) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 721) | def _supports_multioutput(self) -> bool:
    method _supports_single_sample (line 724) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 727) | def _allowed_device_names(self) -> List[str]:
  class CatBoost_D_Regressor (line 731) | class CatBoost_D_Regressor(CatBoostConstructorMixin, AlgInterfaceRegress...
    method _get_default_params (line 732) | def _get_default_params(self):
    method _create_alg_interface (line 735) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 739) | def _supports_multioutput(self) -> bool:
    method _supports_single_sample (line 742) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 745) | def _allowed_device_names(self) -> List[str]:
  class RFConstructorMixin (line 749) | class RFConstructorMixin:
    method __init__ (line 750) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class RF_SKL_D_Classifier (line 786) | class RF_SKL_D_Classifier(RFConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 787) | def _get_default_params(self):
    method _create_alg_interface (line 790) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class RF_SKL_D_Regressor (line 795) | class RF_SKL_D_Regressor(RFConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 796) | def _get_default_params(self):
    method _create_alg_interface (line 799) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class MLPSKLConstructorMixin (line 804) | class MLPSKLConstructorMixin:
    method __init__ (line 805) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class MLP_SKL_D_Classifier (line 823) | class MLP_SKL_D_Classifier(MLPSKLConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 824) | def _get_default_params(self):
    method _create_alg_interface (line 827) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class MLP_SKL_D_Regressor (line 832) | class MLP_SKL_D_Regressor(MLPSKLConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 833) | def _get_default_params(self):
    method _create_alg_interface (line 836) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class GBDTHPOConstructorMixin (line 843) | class GBDTHPOConstructorMixin:
    method __init__ (line 844) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class XGB_HPO_Classifier (line 872) | class XGB_HPO_Classifier(GBDTHPOConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 873) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 876) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 886) | def _allowed_device_names(self) -> List[str]:
  class XGB_HPO_TPE_Classifier (line 890) | class XGB_HPO_TPE_Classifier(GBDTHPOConstructorMixin, AlgInterfaceClassi...
    method _get_default_params (line 891) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 896) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 900) | def _allowed_device_names(self) -> List[str]:
  class XGB_HPO_Regressor (line 904) | class XGB_HPO_Regressor(GBDTHPOConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 905) | def _get_default_params(self) -> Dict[str, Any]:
    method _allowed_device_names (line 908) | def _allowed_device_names(self) -> List[str]:
    method _create_alg_interface (line 911) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 921) | def _supports_multioutput(self) -> bool:
  class XGB_HPO_TPE_Regressor (line 925) | class XGB_HPO_TPE_Regressor(GBDTHPOConstructorMixin, AlgInterfaceRegress...
    method _get_default_params (line 926) | def _get_default_params(self) -> Dict[str, Any]:
    method _allowed_device_names (line 931) | def _allowed_device_names(self) -> List[str]:
    method _create_alg_interface (line 934) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 938) | def _supports_multioutput(self) -> bool:
  class LGBM_HPO_Classifier (line 942) | class LGBM_HPO_Classifier(GBDTHPOConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 943) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 946) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class LGBM_HPO_TPE_Classifier (line 957) | class LGBM_HPO_TPE_Classifier(GBDTHPOConstructorMixin, AlgInterfaceClass...
    method _get_default_params (line 958) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 963) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
  class LGBM_HPO_Regressor (line 968) | class LGBM_HPO_Regressor(GBDTHPOConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 969) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 972) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 982) | def _supports_multioutput(self) -> bool:
  class LGBM_HPO_TPE_Regressor (line 986) | class LGBM_HPO_TPE_Regressor(GBDTHPOConstructorMixin, AlgInterfaceRegres...
    method _get_default_params (line 987) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 992) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 996) | def _supports_multioutput(self) -> bool:
  class CatBoost_HPO_Classifier (line 1000) | class CatBoost_HPO_Classifier(GBDTHPOConstructorMixin, AlgInterfaceClass...
    method _get_default_params (line 1001) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 1004) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_single_class (line 1015) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1018) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 1021) | def _allowed_device_names(self) -> List[str]:
  class CatBoost_HPO_TPE_Classifier (line 1025) | class CatBoost_HPO_TPE_Classifier(GBDTHPOConstructorMixin, AlgInterfaceC...
    method _get_default_params (line 1026) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 1031) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_single_class (line 1035) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1038) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 1041) | def _allowed_device_names(self) -> List[str]:
  class CatBoost_HPO_Regressor (line 1045) | class CatBoost_HPO_Regressor(GBDTHPOConstructorMixin, AlgInterfaceRegres...
    method _get_default_params (line 1046) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 1049) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 1060) | def _supports_multioutput(self) -> bool:
    method _supports_single_sample (line 1063) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 1066) | def _allowed_device_names(self) -> List[str]:
  class CatBoost_HPO_TPE_Regressor (line 1070) | class CatBoost_HPO_TPE_Regressor(GBDTHPOConstructorMixin, AlgInterfaceRe...
    method _get_default_params (line 1071) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 1076) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 1080) | def _supports_multioutput(self) -> bool:
    method _supports_single_sample (line 1083) | def _supports_single_sample(self) -> bool:
    method _allowed_device_names (line 1086) | def _allowed_device_names(self) -> List[str]:
  class RF_HPO_Classifier (line 1090) | class RF_HPO_Classifier(GBDTHPOConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 1091) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 1094) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_single_class (line 1105) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1108) | def _supports_single_sample(self) -> bool:
  class RF_HPO_Regressor (line 1112) | class RF_HPO_Regressor(GBDTHPOConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 1113) | def _get_default_params(self) -> Dict[str, Any]:
    method _create_alg_interface (line 1116) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _supports_multioutput (line 1127) | def _supports_multioutput(self) -> bool:
    method _supports_single_sample (line 1130) | def _supports_single_sample(self) -> bool:
  class RealMLPHPOConstructorMixin (line 1134) | class RealMLPHPOConstructorMixin:
    method __init__ (line 1135) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class RealMLP_HPO_Classifier (line 1224) | class RealMLP_HPO_Classifier(RealMLPHPOConstructorMixin, AlgInterfaceCla...
    method _get_default_params (line 1225) | def _get_default_params(self):
    method _create_alg_interface (line 1228) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1236) | def _allowed_device_names(self) -> List[str]:
  class RealMLP_HPO_Regressor (line 1240) | class RealMLP_HPO_Regressor(RealMLPHPOConstructorMixin, AlgInterfaceRegr...
    method _get_default_params (line 1241) | def _get_default_params(self):
    method _create_alg_interface (line 1244) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1252) | def _allowed_device_names(self) -> List[str]:
  class ResnetConstructorMixin (line 1256) | class ResnetConstructorMixin:
    method __init__ (line 1257) | def __init__(self,
  class Resnet_RTDL_D_Classifier (line 1321) | class Resnet_RTDL_D_Classifier(ResnetConstructorMixin, AlgInterfaceClass...
    method _get_default_params (line 1322) | def _get_default_params(self):
    method _create_alg_interface (line 1325) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1329) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_class (line 1332) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1335) | def _supports_single_sample(self) -> bool:
    method _non_deterministic_tag (line 1338) | def _non_deterministic_tag(self) -> bool:
  class Resnet_RTDL_D_Regressor (line 1345) | class Resnet_RTDL_D_Regressor(ResnetConstructorMixin, AlgInterfaceRegres...
    method _get_default_params (line 1346) | def _get_default_params(self):
    method _create_alg_interface (line 1349) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1353) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_sample (line 1356) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 1359) | def _supports_multioutput(self) -> bool:
    method _non_deterministic_tag (line 1362) | def _non_deterministic_tag(self) -> bool:
  class FTTransformerConstructorMixin (line 1368) | class FTTransformerConstructorMixin:
    method __init__ (line 1369) | def __init__(self,
  class FTT_D_Classifier (line 1442) | class FTT_D_Classifier(FTTransformerConstructorMixin, AlgInterfaceClassi...
    method _get_default_params (line 1443) | def _get_default_params(self):
    method _create_alg_interface (line 1446) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1451) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_class (line 1454) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1457) | def _supports_single_sample(self) -> bool:
    method _non_deterministic_tag (line 1460) | def _non_deterministic_tag(self) -> bool:
  class FTT_D_Regressor (line 1467) | class FTT_D_Regressor(FTTransformerConstructorMixin, AlgInterfaceRegress...
    method _get_default_params (line 1468) | def _get_default_params(self):
    method _create_alg_interface (line 1471) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1476) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_sample (line 1479) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 1482) | def _supports_multioutput(self) -> bool:
    method _non_deterministic_tag (line 1485) | def _non_deterministic_tag(self) -> bool:
  class RTDL_MLPConstructorMixin (line 1491) | class RTDL_MLPConstructorMixin:
    method __init__ (line 1492) | def __init__(self,
  class MLP_RTDL_D_Classifier (line 1561) | class MLP_RTDL_D_Classifier(RTDL_MLPConstructorMixin, AlgInterfaceClassi...
    method _get_default_params (line 1562) | def _get_default_params(self):
    method _create_alg_interface (line 1565) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1569) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_class (line 1572) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1575) | def _supports_single_sample(self) -> bool:
    method _non_deterministic_tag (line 1578) | def _non_deterministic_tag(self) -> bool:
  class MLP_RTDL_D_Regressor (line 1585) | class MLP_RTDL_D_Regressor(RTDL_MLPConstructorMixin, AlgInterfaceRegress...
    method _get_default_params (line 1586) | def _get_default_params(self):
    method _create_alg_interface (line 1589) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1593) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_sample (line 1596) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 1599) | def _supports_multioutput(self) -> bool:
    method _non_deterministic_tag (line 1602) | def _non_deterministic_tag(self) -> bool:
  class MLP_PLR_D_Classifier (line 1608) | class MLP_PLR_D_Classifier(RTDL_MLPConstructorMixin, AlgInterfaceClassif...
    method _get_default_params (line 1609) | def _get_default_params(self):
    method _create_alg_interface (line 1612) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1616) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_class (line 1619) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1622) | def _supports_single_sample(self) -> bool:
    method _non_deterministic_tag (line 1625) | def _non_deterministic_tag(self) -> bool:
  class MLP_PLR_D_Regressor (line 1632) | class MLP_PLR_D_Regressor(RTDL_MLPConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 1633) | def _get_default_params(self):
    method _create_alg_interface (line 1636) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1640) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_sample (line 1643) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 1646) | def _supports_multioutput(self) -> bool:
    method _non_deterministic_tag (line 1649) | def _non_deterministic_tag(self) -> bool:
  class TabrConstructorMixin (line 1655) | class TabrConstructorMixin:
    method __init__ (line 1656) | def __init__(self,
  class TabR_S_D_Classifier (line 1737) | class TabR_S_D_Classifier(TabrConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 1738) | def _get_default_params(self):
    method _create_alg_interface (line 1741) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1745) | def _allowed_device_names(self) -> List[str]:
  class TabR_S_D_Regressor (line 1749) | class TabR_S_D_Regressor(TabrConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 1750) | def _get_default_params(self):
    method _create_alg_interface (line 1753) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1757) | def _allowed_device_names(self) -> List[str]:
  class RealTabR_D_Classifier (line 1761) | class RealTabR_D_Classifier(TabrConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 1762) | def _get_default_params(self):
    method _create_alg_interface (line 1765) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1769) | def _allowed_device_names(self) -> List[str]:
  class RealTabR_D_Regressor (line 1773) | class RealTabR_D_Regressor(TabrConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 1774) | def _get_default_params(self):
    method _create_alg_interface (line 1777) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1781) | def _allowed_device_names(self) -> List[str]:
  class TabMConstructorMixin (line 1785) | class TabMConstructorMixin:
    method __init__ (line 1786) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class TabM_D_Classifier (line 1912) | class TabM_D_Classifier(TabMConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 1913) | def _get_default_params(self):
    method _create_alg_interface (line 1916) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1920) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_class (line 1923) | def _supports_single_class(self) -> bool:
    method _supports_single_sample (line 1926) | def _supports_single_sample(self) -> bool:
  class TabM_D_Regressor (line 1930) | class TabM_D_Regressor(TabMConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 1931) | def _get_default_params(self):
    method _create_alg_interface (line 1934) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1938) | def _allowed_device_names(self) -> List[str]:
    method _supports_multioutput (line 1941) | def _supports_multioutput(self) -> bool:
    method _supports_single_sample (line 1944) | def _supports_single_sample(self) -> bool:
  class TabM_HPO_Classifier (line 1948) | class TabM_HPO_Classifier(RealMLPHPOConstructorMixin, AlgInterfaceClassi...
    method _get_default_params (line 1952) | def _get_default_params(self):
    method _create_alg_interface (line 1955) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1964) | def _allowed_device_names(self) -> List[str]:
  class TabM_HPO_Regressor (line 1968) | class TabM_HPO_Regressor(RealMLPHPOConstructorMixin, AlgInterfaceRegress...
    method _get_default_params (line 1972) | def _get_default_params(self):
    method _create_alg_interface (line 1975) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 1984) | def _allowed_device_names(self) -> List[str]:
  class XRFMConstructorMixin (line 1990) | class XRFMConstructorMixin:
    method __init__ (line 1991) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class XRFM_D_Classifier (line 2115) | class XRFM_D_Classifier(XRFMConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 2116) | def _get_default_params(self):
    method _create_alg_interface (line 2119) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2123) | def _allowed_device_names(self) -> List[str]:
    method _non_deterministic_tag (line 2126) | def _non_deterministic_tag(self) -> bool:
    method _supports_single_sample (line 2131) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 2134) | def _supports_multioutput(self) -> bool:
  class XRFM_D_Regressor (line 2139) | class XRFM_D_Regressor(XRFMConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 2140) | def _get_default_params(self):
    method _create_alg_interface (line 2143) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2147) | def _allowed_device_names(self) -> List[str]:
    method _non_deterministic_tag (line 2150) | def _non_deterministic_tag(self) -> bool:
    method _supports_single_sample (line 2155) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 2158) | def _supports_multioutput(self) -> bool:
  class XRFMHPOConstructorMixin (line 2162) | class XRFMHPOConstructorMixin:
    method __init__ (line 2163) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class XRFM_HPO_Classifier (line 2264) | class XRFM_HPO_Classifier(XRFMHPOConstructorMixin, AlgInterfaceClassifier):
    method _get_default_params (line 2268) | def _get_default_params(self):
    method _create_alg_interface (line 2271) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2280) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_sample (line 2283) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 2286) | def _supports_multioutput(self) -> bool:
  class XRFM_HPO_Regressor (line 2290) | class XRFM_HPO_Regressor(XRFMHPOConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 2294) | def _get_default_params(self):
    method _create_alg_interface (line 2297) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2306) | def _allowed_device_names(self) -> List[str]:
    method _supports_single_sample (line 2309) | def _supports_single_sample(self) -> bool:
    method _supports_multioutput (line 2312) | def _supports_multioutput(self) -> bool:
  class MLP_RTDL_HPO_Classifier (line 2319) | class MLP_RTDL_HPO_Classifier(RealMLPHPOConstructorMixin, AlgInterfaceCl...
    method _get_default_params (line 2320) | def _get_default_params(self):
    method _create_alg_interface (line 2323) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2332) | def _allowed_device_names(self) -> List[str]:
  class MLP_RTDL_HPO_Regressor (line 2336) | class MLP_RTDL_HPO_Regressor(RealMLPHPOConstructorMixin, AlgInterfaceReg...
    method _get_default_params (line 2337) | def _get_default_params(self):
    method _create_alg_interface (line 2340) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2349) | def _allowed_device_names(self) -> List[str]:
  class MLP_PLR_HPO_Classifier (line 2353) | class MLP_PLR_HPO_Classifier(RealMLPHPOConstructorMixin, AlgInterfaceCla...
    method _get_default_params (line 2354) | def _get_default_params(self):
    method _create_alg_interface (line 2357) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2367) | def _allowed_device_names(self) -> List[str]:
  class MLP_PLR_HPO_Regressor (line 2371) | class MLP_PLR_HPO_Regressor(RealMLPHPOConstructorMixin, AlgInterfaceRegr...
    method _get_default_params (line 2372) | def _get_default_params(self):
    method _create_alg_interface (line 2375) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2385) | def _allowed_device_names(self) -> List[str]:
  class Resnet_RTDL_HPO_Classifier (line 2389) | class Resnet_RTDL_HPO_Classifier(RealMLPHPOConstructorMixin, AlgInterfac...
    method _get_default_params (line 2390) | def _get_default_params(self):
    method _create_alg_interface (line 2393) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2402) | def _allowed_device_names(self) -> List[str]:
  class Resnet_RTDL_HPO_Regressor (line 2406) | class Resnet_RTDL_HPO_Regressor(RealMLPHPOConstructorMixin, AlgInterface...
    method _get_default_params (line 2407) | def _get_default_params(self):
    method _create_alg_interface (line 2410) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2419) | def _allowed_device_names(self) -> List[str]:
  class FTT_HPO_Classifier (line 2423) | class FTT_HPO_Classifier(RealMLPHPOConstructorMixin, AlgInterfaceClassif...
    method _get_default_params (line 2424) | def _get_default_params(self):
    method _create_alg_interface (line 2427) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2436) | def _allowed_device_names(self) -> List[str]:
  class FTT_HPO_Regressor (line 2440) | class FTT_HPO_Regressor(RealMLPHPOConstructorMixin, AlgInterfaceRegressor):
    method _get_default_params (line 2441) | def _get_default_params(self):
    method _create_alg_interface (line 2444) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2453) | def _allowed_device_names(self) -> List[str]:
  class TabR_HPO_Classifier (line 2457) | class TabR_HPO_Classifier(RealMLPHPOConstructorMixin, AlgInterfaceClassi...
    method _get_default_params (line 2458) | def _get_default_params(self):
    method _create_alg_interface (line 2461) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2470) | def _allowed_device_names(self) -> List[str]:
  class TabR_HPO_Regressor (line 2474) | class TabR_HPO_Regressor(RealMLPHPOConstructorMixin, AlgInterfaceRegress...
    method _get_default_params (line 2475) | def _get_default_params(self):
    method _create_alg_interface (line 2478) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2487) | def _allowed_device_names(self) -> List[str]:
  class Ensemble_TD_Classifier (line 2493) | class Ensemble_TD_Classifier(AlgInterfaceClassifier):
    method __init__ (line 2494) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
    method _create_alg_interface (line 2513) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2539) | def _allowed_device_names(self) -> List[str]:
  class Ensemble_TD_Regressor (line 2543) | class Ensemble_TD_Regressor(AlgInterfaceRegressor):
    method __init__ (line 2544) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
    method _create_alg_interface (line 2560) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2582) | def _allowed_device_names(self) -> List[str]:
  class EnsembleHPOConstructorMixin (line 2586) | class EnsembleHPOConstructorMixin:
    method __init__ (line 2587) | def __init__(self, device: Optional[str] = None, random_state: Optiona...
  class Ensemble_HPO_Classifier (line 2638) | class Ensemble_HPO_Classifier(EnsembleHPOConstructorMixin, AlgInterfaceC...
    method _create_alg_interface (line 2639) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2670) | def _allowed_device_names(self) -> List[str]:
  class Ensemble_HPO_Regressor (line 2674) | class Ensemble_HPO_Regressor(EnsembleHPOConstructorMixin, AlgInterfaceRe...
    method _create_alg_interface (line 2675) | def _create_alg_interface(self, n_cv: int) -> AlgInterface:
    method _allowed_device_names (line 2703) | def _allowed_device_names(self) -> List[str]:

FILE: pytabkit/models/torch_utils.py
  function get_available_device_names (line 7) | def get_available_device_names() -> List['str']:
  function seeded_randperm (line 14) | def seeded_randperm(n, device, seed):
  function permute_idxs (line 21) | def permute_idxs(idxs, seed):
  function batch_randperm (line 25) | def batch_randperm(n_batch, n, device='cpu'):
  function gauss_cdf (line 33) | def gauss_cdf(x):
  class ClampWithIdentityGradientFunc (line 37) | class ClampWithIdentityGradientFunc(torch.autograd.Function):
    method forward (line 39) | def forward(ctx, input: torch.Tensor, low: torch.Tensor, high: torch.T...
    method backward (line 43) | def backward(ctx, grad_output: torch.Tensor):
  function clamp_with_identity_gradient_func (line 47) | def clamp_with_identity_gradient_func(x, low, high):
  function cat_if_necessary (line 51) | def cat_if_necessary(tensors: List[torch.Tensor], dim: int):
  function hash_tensor (line 64) | def hash_tensor(tensor: torch.Tensor) -> int:
  function torch_np_quantile (line 72) | def torch_np_quantile(tensor: torch.Tensor, q: float, dim: int, keepdim:...
  function _cuda_in_use (line 93) | def _cuda_in_use() -> bool:
  class TorchTimer (line 104) | class TorchTimer:
    method __init__ (line 121) | def __init__(self, use_cuda: Optional[bool] = None, record_history: bo...
    method _do_cuda_sync (line 138) | def _do_cuda_sync(self) -> bool:
    method __enter__ (line 148) | def __enter__(self):
    method __exit__ (line 152) | def __exit__(self, exc_type, exc_val, exc_tb):
    method start (line 157) | def start(self):
    method stop (line 162) | def stop(self):
  function get_available_memory_gb (line 173) | def get_available_memory_gb(device: Union[str, torch.device]) -> float:

FILE: pytabkit/models/training/auc_mu.py
  function auc_mu_impl (line 26) | def auc_mu_impl(y_true, y_score, A=None, W=None):

FILE: pytabkit/models/training/coord.py
  class HyperparamManager (line 8) | class HyperparamManager:
    class HyperGetter (line 9) | class HyperGetter:
      method __init__ (line 10) | def __init__(self, tc: 'HyperparamManager', hyper_name: str, base_va...
      method __call__ (line 16) | def __call__(self):
    method __init__ (line 20) | def __init__(self, **config):
    method get_more_info_dict (line 30) | def get_more_info_dict(self) -> Dict:
    method _find_pattern (line 33) | def _find_pattern(self, d: dict, scope):
    method register_hyper (line 43) | def register_hyper(self, name: str, scope, default=None, default_sched...
    method get_hyper_sched_values (line 70) | def get_hyper_sched_values(self):
    method update_hyper_sched_values (line 74) | def update_hyper_sched_values(self):
    method add_reg_term (line 81) | def add_reg_term(self, loss):
    method update_hypers (line 84) | def update_hypers(self, learner):

FILE: pytabki

[
  {
    "path": ".github/workflows/testing.yml",
    "chars": 965,
    "preview": "name: 'test'\n\non:\n  push:\n    branches:\n      - \"main\"\n      - \"dev\"\n  pull_request:\n    branches:\n      - '*'\n\njobs:\n  "
  },
  {
    "path": ".gitignore",
    "chars": 333,
    "preview": "*.pyc\n*.pdf\n*.zip\n*.ckpt\n\nexperiments/*/\nexperiments/trace.json\n!experiments/meta_hpo\n!experiments/prototypes\npublic_exp"
  },
  {
    "path": ".readthedocs.yaml",
    "chars": 1122,
    "preview": "# Read the Docs configuration file for Sphinx projects\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html f"
  },
  {
    "path": "LICENSE.txt",
    "chars": 11357,
    "preview": "                                 Apache License\n                           Version 2.0, January 2004\n                   "
  },
  {
    "path": "README.md",
    "chars": 18721,
    "preview": "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dh"
  },
  {
    "path": "docs/Makefile",
    "chars": 638,
    "preview": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the "
  },
  {
    "path": "docs/make.bat",
    "chars": 804,
    "preview": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sp"
  },
  {
    "path": "docs/requirements.txt",
    "chars": 498,
    "preview": "adjustText>=1.0\nautorank>=1.0\ncatboost>=1.2\ndask[dataframe]>=2023\ndill\nfire\nlightgbm>=4.1\nmatplotlib>=3.0\nmsgpack>=1.0\nm"
  },
  {
    "path": "docs/source/bench/00_installation.md",
    "chars": 1556,
    "preview": "# Overview and Installation of the Benchmarking code\n\nOur benchmarking code contains several features:\n\n- Automatic data"
  },
  {
    "path": "docs/source/bench/01_running_the_benchmark.md",
    "chars": 5086,
    "preview": "# Running the benchmark\n\n## Configuration of data paths\n\nThe paths for storing data and results are configured\nthrough t"
  },
  {
    "path": "docs/source/bench/02_stored_data.md",
    "chars": 3657,
    "preview": "# Data format\n\nHere, we describe how the main data is stored \ninside the main data folder configured in the `tab_bench.d"
  },
  {
    "path": "docs/source/bench/03_code.md",
    "chars": 2434,
    "preview": "# Code structure\n\n## Algorithm wrappers\n\nTo run methods in `tab_bench`, one needs to \nprovide them as a subclass of `tab"
  },
  {
    "path": "docs/source/bench/adding_models.md",
    "chars": 1066,
    "preview": "# Adding your own models to the benchmark\n\nTo run your own models,\n- implement an `AlgInterface` subclass. There are num"
  },
  {
    "path": "docs/source/bench/download_results.md",
    "chars": 1693,
    "preview": "# Downloading the benchmark results\n\nThe benchmark data (as well as the code)\nis archived at [DaRUS](https://doi.org/10."
  },
  {
    "path": "docs/source/bench/refine_then_calibrate.md",
    "chars": 1252,
    "preview": "# Reproducing results of \"Rethinking Early Stopping: Refine, Then Calibrate\"\n\nHere, we document how to reproduce results"
  },
  {
    "path": "docs/source/bench/using_the_scheduler.md",
    "chars": 3053,
    "preview": "# Using the scheduler\n\n`pytabkit` includes a flexible scheduler that can schedule jobs within python using `ray` and `mu"
  },
  {
    "path": "docs/source/conf.py",
    "chars": 1621,
    "preview": "# Configuration file for the Sphinx documentation builder.\n#\n# For the full list of built-in configuration values, see t"
  },
  {
    "path": "docs/source/index.rst",
    "chars": 783,
    "preview": "Welcome to PyTabKit's documentation!\n======================================\n\n.. toctree::\n   :maxdepth: 2\n   :caption: C"
  },
  {
    "path": "docs/source/models/00_overview.md",
    "chars": 4225,
    "preview": "# Overview of the `models` part\n\n## Scikit-learn interfaces\n\nWe provide scikit-learn interfaces for various methods in \n"
  },
  {
    "path": "docs/source/models/01_sklearn_interfaces.rst",
    "chars": 4590,
    "preview": "Scikit-learn interfaces\n=======================\n\nWe provide scikit-learn interfaces for numerous methods in\n``pytabkit.m"
  },
  {
    "path": "docs/source/models/02_hpo.md",
    "chars": 5667,
    "preview": "# Hyperparameter optimization\n\nThis is a guide how to perform hyperparameter optimization (HPO) \nto get the best results"
  },
  {
    "path": "docs/source/models/03_training_implementation.md",
    "chars": 4326,
    "preview": "# Training directly with PyTorch Lightning\n\n## Using PyTorch Lightning\n\nThe TabNN models are implemented using [Pytorch "
  },
  {
    "path": "docs/source/models/examples.md",
    "chars": 2349,
    "preview": "# Examples\n\n## Refitting RealMLP on train+val data using the best epoch from a previous run\n\nYou can refit RealMLP by si"
  },
  {
    "path": "docs/source/models/nn_classes.md",
    "chars": 6187,
    "preview": "# NN implementation\n\nWhile RealMLP is implemented in PyTorch, \nwe extend the conventional `nn.Module` logic. \nTraditiona"
  },
  {
    "path": "docs/source/models/quantile_reg.md",
    "chars": 788,
    "preview": "# (Multi)quantile regression with RealMLP\n\nRealMLP supports multiquantile regression, for example by using\n```python\nfro"
  },
  {
    "path": "examples/tutorial_notebook.ipynb",
    "chars": 9905,
    "preview": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"enZVuzCHCy1n\"\n      },\n      \"sou"
  },
  {
    "path": "original_requirements/conda_env_2024_06_25.yml",
    "chars": 7670,
    "preview": "name: tab_bench_venv_3\nchannels:\n  - pytorch\n  - nvidia\n  - defaults\ndependencies:\n  - _libgcc_mutex=0.1\n  - _openmp_mut"
  },
  {
    "path": "original_requirements/conda_env_2024_10_28.yml",
    "chars": 7838,
    "preview": "name: tab_bench_conda\nchannels:\n  - pytorch\n  - nvidia\n  - defaults\ndependencies:\n  - _libgcc_mutex=0.1\n  - _openmp_mute"
  },
  {
    "path": "original_requirements/conda_env_2025_01_15.yml",
    "chars": 5194,
    "preview": "name: probclass\nchannels:\n  - pytorch\n  - nvidia\n  - defaults\ndependencies:\n  - _libgcc_mutex=0.1\n  - _openmp_mutex=5.1\n"
  },
  {
    "path": "original_requirements/requirements_2024_06_25.txt",
    "chars": 4314,
    "preview": "adjustText==1.0.4\naiohttp==3.9.1\naiosignal==1.3.1\nannotated-types==0.6.0\nargon2-cffi==23.1.0\nargon2-cffi-bindings==21.2."
  },
  {
    "path": "pyproject.toml",
    "chars": 5616,
    "preview": "[build-system]\nrequires = [\"hatchling>=1.26.1\"]  # https://github.com/pypa/hatch/issues/1818\nbuild-backend = \"hatchling."
  },
  {
    "path": "pytabkit/__about__.py",
    "chars": 119,
    "preview": "# SPDX-FileCopyrightText: 2024-present David Holzmüller\n#\n# SPDX-License-Identifier: Apache-2.0\n\n__version__ = \"1.7.3\"\n"
  },
  {
    "path": "pytabkit/__init__.py",
    "chars": 49,
    "preview": "from .models.sklearn.sklearn_interfaces import *\n"
  },
  {
    "path": "pytabkit/bench/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/bench/alg_wrappers/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/bench/alg_wrappers/general.py",
    "chars": 3701,
    "preview": "from pathlib import Path\nfrom typing import List, Dict, Optional\n\nfrom pytabkit.bench.data.tasks import TaskPackage, Tas"
  },
  {
    "path": "pytabkit/bench/alg_wrappers/interface_wrappers.py",
    "chars": 30352,
    "preview": "import shutil\nfrom pathlib import Path\nfrom typing import Callable, List, Optional, Dict\n\nimport torch\n\nfrom pytabkit.be"
  },
  {
    "path": "pytabkit/bench/data/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/bench/data/common.py",
    "chars": 468,
    "preview": "\nclass TaskSource:\n    UCI_BIN_CLASS = 'uci-bin-class'\n    UCI_MULTI_CLASS = 'uci-multi-class'\n    UCI_REGRESSION = 'uci"
  },
  {
    "path": "pytabkit/bench/data/get_uci.py",
    "chars": 143346,
    "preview": "#!/usr/bin/python3\nimport os\nimport shutil\nimport ssl\n\nimport pandas\n\nfrom pytabkit.bench.data.paths import Paths\nfrom p"
  },
  {
    "path": "pytabkit/bench/data/import_talent_benchmark.py",
    "chars": 6209,
    "preview": "from pathlib import Path\nfrom typing import Optional\n\nimport numpy as np\nimport pandas as pd\n\nfrom pytabkit.bench.data.i"
  },
  {
    "path": "pytabkit/bench/data/import_tasks.py",
    "chars": 16946,
    "preview": "from typing import Union, Optional, List, Dict\n\nimport sklearn.model_selection\n\nimport torch\nfrom pathlib import Path\nim"
  },
  {
    "path": "pytabkit/bench/data/paths.py",
    "chars": 5391,
    "preview": "import os\nimport uuid\nfrom pathlib import Path\nfrom typing import Optional\n\nfrom pytabkit.models import utils\nimport shu"
  },
  {
    "path": "pytabkit/bench/data/tasks.py",
    "chars": 12090,
    "preview": "from typing import Dict, List, Optional\n\nfrom pytabkit.bench.data.common import SplitType\nfrom pytabkit.bench.data.paths"
  },
  {
    "path": "pytabkit/bench/data/uci_file_ops.py",
    "chars": 30747,
    "preview": "\n\n\nimport os as os\nimport re as re\nimport csv as csv\nimport math as math\nfrom pathlib import Path\n\nimport pandas as pand"
  },
  {
    "path": "pytabkit/bench/eval/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/bench/eval/analysis.py",
    "chars": 14221,
    "preview": "from typing import Optional, Callable, Tuple, Dict, List, Union\n\nimport numpy as np\nimport scipy\n\nfrom pytabkit.bench.da"
  },
  {
    "path": "pytabkit/bench/eval/colors.py",
    "chars": 1546,
    "preview": "from typing import List, Tuple, Callable\n\n\ndef bilin_int(x: float, values: List[Tuple[float, float]]) -> float:\n    # in"
  },
  {
    "path": "pytabkit/bench/eval/evaluation.py",
    "chars": 32782,
    "preview": "import distutils.command.build_ext\nfrom typing import List, Dict, Any, Tuple, Optional, Callable, Union\n\nimport numpy as"
  },
  {
    "path": "pytabkit/bench/eval/plotting.py",
    "chars": 70654,
    "preview": "import copy\nfrom pathlib import Path\nfrom typing import List, Dict, Optional, Tuple, Callable\n\nimport matplotlib\nimport "
  },
  {
    "path": "pytabkit/bench/eval/runtimes.py",
    "chars": 2224,
    "preview": "from typing import Dict\n\nimport numpy as np\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytabkit.bench.data.tasks "
  },
  {
    "path": "pytabkit/bench/eval/tables.py",
    "chars": 24989,
    "preview": "from typing import List, Optional\n\nimport numpy as np\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytabkit.bench.d"
  },
  {
    "path": "pytabkit/bench/run/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/bench/run/results.py",
    "chars": 5873,
    "preview": "from pathlib import Path\nfrom typing import Dict, List\n\nimport numpy as np\n\nfrom pytabkit.bench.data.paths import Paths\n"
  },
  {
    "path": "pytabkit/bench/run/task_execution.py",
    "chars": 17562,
    "preview": "import shutil\nimport traceback\nfrom typing import List, Optional\n\nimport numpy as np\n\nfrom pytabkit.bench.alg_wrappers.g"
  },
  {
    "path": "pytabkit/bench/scheduling/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/bench/scheduling/execution.py",
    "chars": 11079,
    "preview": "import os\n\nimport time\nimport multiprocessing as mp\nimport traceback\nfrom typing import Tuple, Optional, List\n\nimport nu"
  },
  {
    "path": "pytabkit/bench/scheduling/jobs.py",
    "chars": 5020,
    "preview": "import time\nimport traceback\nimport sys\nfrom typing import Optional\n\nfrom pytabkit.bench.scheduling.resources import Nod"
  },
  {
    "path": "pytabkit/bench/scheduling/resource_manager.py",
    "chars": 3210,
    "preview": "import copy\nimport enum\nimport time\nfrom typing import Optional\n\nfrom pytabkit.bench.scheduling.jobs import AbstractJob,"
  },
  {
    "path": "pytabkit/bench/scheduling/resources.py",
    "chars": 9296,
    "preview": "from typing import Optional, List\n\nimport numpy as np\nimport copy\n\nfrom pytabkit.models.alg_interfaces.base import Inter"
  },
  {
    "path": "pytabkit/bench/scheduling/schedulers.py",
    "chars": 25305,
    "preview": "import copy\nimport sys\nimport time\nfrom typing import List, Dict, Union\n\nimport numpy as np\n\nfrom pytabkit.bench.schedul"
  },
  {
    "path": "pytabkit/models/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/models/alg_interfaces/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/models/alg_interfaces/alg_interfaces.py",
    "chars": 28484,
    "preview": "import functools\nimport warnings\nfrom pathlib import Path\nfrom typing import List, Tuple, Any, Optional, Dict\n\nimport to"
  },
  {
    "path": "pytabkit/models/alg_interfaces/autogluon_model_interfaces.py",
    "chars": 8019,
    "preview": "import copy\nimport os\nfrom typing import List, Any, Optional\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom p"
  },
  {
    "path": "pytabkit/models/alg_interfaces/base.py",
    "chars": 5462,
    "preview": "from typing import Optional, List\n\nimport numpy as np\nimport torch\n\n\nclass SplitIdxs:\n    \"\"\"\n    Represents multiple tr"
  },
  {
    "path": "pytabkit/models/alg_interfaces/calibration.py",
    "chars": 6367,
    "preview": "import traceback\nfrom pathlib import Path\nfrom typing import List, Optional, Tuple, Dict, Any, Callable\n\nimport numpy as"
  },
  {
    "path": "pytabkit/models/alg_interfaces/catboost_interfaces.py",
    "chars": 40972,
    "preview": "import copy\nimport warnings\nfrom pathlib import Path\nfrom typing import Optional, Dict, Any, List, Tuple, Union\n\nimport "
  },
  {
    "path": "pytabkit/models/alg_interfaces/ensemble_interfaces.py",
    "chars": 16187,
    "preview": "import copy\nimport time\nfrom pathlib import Path\nfrom typing import List, Optional, Dict\n\nimport numpy as np\nimport torc"
  },
  {
    "path": "pytabkit/models/alg_interfaces/lightgbm_interfaces.py",
    "chars": 39463,
    "preview": "import copy\nfrom pathlib import Path\nfrom typing import Optional, Dict, Tuple, Any, List\n\nimport numpy as np\nimport torc"
  },
  {
    "path": "pytabkit/models/alg_interfaces/nn_interfaces.py",
    "chars": 59875,
    "preview": "import copy\nimport warnings\nfrom pathlib import Path\nfrom typing import List, Optional, Dict, Any, Union\n\nimport numpy a"
  },
  {
    "path": "pytabkit/models/alg_interfaces/other_interfaces.py",
    "chars": 69487,
    "preview": "import os\nfrom typing import Any, List, Optional\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom sklearn.compo"
  },
  {
    "path": "pytabkit/models/alg_interfaces/resource_computation.py",
    "chars": 16343,
    "preview": "import numbers\nimport time\nfrom collections.abc import Callable\nfrom typing import Dict, Union, List, Any, Tuple, Option"
  },
  {
    "path": "pytabkit/models/alg_interfaces/resource_params.py",
    "chars": 29301,
    "preview": "class ResourceParams:\n    # determined using estimate_resource_params.py\n    cb_class_time = {'': 1.1074866100217955, 'd"
  },
  {
    "path": "pytabkit/models/alg_interfaces/rtdl_interfaces.py",
    "chars": 29938,
    "preview": "import copy\nfrom typing import List, Any, Optional, Dict, Tuple\nfrom pathlib import Path\n\nimport numpy as np\nimport pand"
  },
  {
    "path": "pytabkit/models/alg_interfaces/sub_split_interfaces.py",
    "chars": 19208,
    "preview": "import copy\nimport random\nfrom pathlib import Path\nfrom typing import List, Optional, Dict, Any, Tuple\n\nimport numpy as "
  },
  {
    "path": "pytabkit/models/alg_interfaces/tabm_interface.py",
    "chars": 23183,
    "preview": "import functools\nimport math\nimport random\nfrom pathlib import Path\n\nimport scipy\nimport sklearn\nimport torch\nimport num"
  },
  {
    "path": "pytabkit/models/alg_interfaces/tabr_interface.py",
    "chars": 23872,
    "preview": "from typing import List, Any, Optional, Dict, Tuple\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimp"
  },
  {
    "path": "pytabkit/models/alg_interfaces/xgboost_interfaces.py",
    "chars": 36025,
    "preview": "import copy\nfrom pathlib import Path\nfrom typing import Optional, Dict, Any, Tuple, List, Union\n\nimport numpy as np\nimpo"
  },
  {
    "path": "pytabkit/models/alg_interfaces/xrfm_interfaces.py",
    "chars": 25769,
    "preview": "import contextlib\nimport random\nfrom pathlib import Path\nfrom typing import Optional, List, Any, Tuple, Dict\n\nimport num"
  },
  {
    "path": "pytabkit/models/data/__init__.py",
    "chars": 1,
    "preview": "\n"
  },
  {
    "path": "pytabkit/models/data/conversion.py",
    "chars": 5574,
    "preview": "import warnings\nfrom typing import Union, List, Optional\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom panda"
  },
  {
    "path": "pytabkit/models/data/data.py",
    "chars": 9984,
    "preview": "import math\nfrom typing import Optional, Union, List, Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\n"
  },
  {
    "path": "pytabkit/models/data/nested_dict.py",
    "chars": 2384,
    "preview": "from typing import Union, List, Tuple, Dict\n\nfrom pytabkit.models import utils\n\n\nclass NestedDict:\n    \"\"\"\n    Dictionar"
  },
  {
    "path": "pytabkit/models/data/splits.py",
    "chars": 6126,
    "preview": "import math\nfrom typing import Tuple, List, Optional\n\nimport torch\n\nfrom pytabkit.models import utils\nfrom pytabkit.mode"
  },
  {
    "path": "pytabkit/models/hyper_opt/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/models/hyper_opt/coord_opt.py",
    "chars": 12881,
    "preview": "from pathlib import Path\n\nimport numpy as np\nfrom typing import Union, Callable, Any, Optional, Dict, Tuple\n\nfrom pytabk"
  },
  {
    "path": "pytabkit/models/hyper_opt/hyper_optimizers.py",
    "chars": 9637,
    "preview": "import time\nfrom pathlib import Path\nfrom typing import Callable, Tuple, Any, Dict, Union, Optional\n\nimport numpy as np\n"
  },
  {
    "path": "pytabkit/models/nn_models/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/models/nn_models/activations.py",
    "chars": 4357,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom typing import Dict\n\n# ------ from fastai2\nfrom torch.jit import script"
  },
  {
    "path": "pytabkit/models/nn_models/base.py",
    "chars": 44144,
    "preview": "from pytabkit.models import torch_utils, utils\nfrom pytabkit.models.data.data import TensorInfo, DictDataset\nfrom pytabk"
  },
  {
    "path": "pytabkit/models/nn_models/categorical.py",
    "chars": 22693,
    "preview": "from typing import Iterable, List, Dict, Tuple, Any, Callable, Optional, Union\n\nimport numpy as np\nimport torch\nimport t"
  },
  {
    "path": "pytabkit/models/nn_models/models.py",
    "chars": 15968,
    "preview": "import copy\nimport functools\nfrom typing import Dict, Tuple\n\nimport numpy as np\nimport torch\nfrom sklearn.preprocessing "
  },
  {
    "path": "pytabkit/models/nn_models/nn.py",
    "chars": 48672,
    "preview": "import copy\nfrom typing import Dict\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom sklearn.ensemb"
  },
  {
    "path": "pytabkit/models/nn_models/pipeline.py",
    "chars": 12576,
    "preview": "from typing import List, Dict, Union\n\nimport sklearn\nimport torch\nfrom sklearn.base import BaseEstimator, TransformerMix"
  },
  {
    "path": "pytabkit/models/nn_models/rtdl_num_embeddings.py",
    "chars": 28432,
    "preview": "# taken from https://github.com/yandex-research/rtdl-num-embeddings/blob/main/package/rtdl_num_embeddings.py\n\"\"\"On Embed"
  },
  {
    "path": "pytabkit/models/nn_models/rtdl_resnet.py",
    "chars": 41592,
    "preview": "import math\nimport numbers\nimport typing as ty\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimpor"
  },
  {
    "path": "pytabkit/models/nn_models/tabm.py",
    "chars": 21909,
    "preview": "# License: https://github.com/yandex-research/tabm/blob/main/LICENSE\n\n# NOTE\n# The minimum required versions of the depe"
  },
  {
    "path": "pytabkit/models/nn_models/tabr.py",
    "chars": 25644,
    "preview": "import os\nimport inspect\nimport warnings\nimport math\nfrom functools import partial\n\nimport numpy as np\nimport torch\nfrom"
  },
  {
    "path": "pytabkit/models/nn_models/tabr_context_freeze.py",
    "chars": 27891,
    "preview": "import os\nimport inspect\nimport warnings\nimport math\nfrom functools import partial\n\nimport torch\nfrom torch import Tenso"
  },
  {
    "path": "pytabkit/models/nn_models/tabr_lib.py",
    "chars": 33311,
    "preview": "import math\nimport inspect\nimport warnings\nimport dataclasses\nfrom typing import Any, Callable, Optional, Union, cast, I"
  },
  {
    "path": "pytabkit/models/optim/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/models/optim/adopt.py",
    "chars": 18446,
    "preview": "# taken from https://github.com/iShohei220/adopt/blob/main/adopt.py\n# Apache 2.0 license\n# requires torch >= 2.4\n\n# mypy"
  },
  {
    "path": "pytabkit/models/optim/optimizers.py",
    "chars": 7834,
    "preview": "import warnings\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom itertools import chain\nfrom typing im"
  },
  {
    "path": "pytabkit/models/optim/scheduling_adam.py",
    "chars": 6250,
    "preview": "import torch\nfrom torch.optim import Optimizer\nimport math\n\n\n#  modification of normal adam to properly handle varying b"
  },
  {
    "path": "pytabkit/models/sklearn/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/models/sklearn/default_params.py",
    "chars": 14995,
    "preview": "import numpy as np\n\nfrom pytabkit.models import utils\n\n\nclass DefaultParams:\n    RealMLP_TD_CLASS = dict(\n        hidden"
  },
  {
    "path": "pytabkit/models/sklearn/sklearn_base.py",
    "chars": 25371,
    "preview": "import copy\nfrom pathlib import Path\nfrom typing import Dict, Any, Optional, Union, List\nfrom warnings import warn\nfrom "
  },
  {
    "path": "pytabkit/models/sklearn/sklearn_interfaces.py",
    "chars": 139895,
    "preview": "import pathlib\nfrom typing import Optional, Any, Union, List, Dict, Literal\n\nimport numpy as np\n\nfrom pytabkit.models im"
  },
  {
    "path": "pytabkit/models/torch_utils.py",
    "chars": 7322,
    "preview": "from typing import List, Union, Optional\n\nimport torch\nimport numpy as np\n\n\ndef get_available_device_names() -> List['st"
  },
  {
    "path": "pytabkit/models/training/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "pytabkit/models/training/auc_mu.py",
    "chars": 5287,
    "preview": "# taken from https://github.com/kleimanr/auc_mu/blob/master/auc_mu.py\n\n\"\"\"\nComputation of the measure 'AUC Mu'. This mea"
  },
  {
    "path": "pytabkit/models/training/coord.py",
    "chars": 3937,
    "preview": "from typing import Dict\n\nfrom pytabkit.models.training.scheduling import ConstantSchedule, get_schedule\n\n# layers are cr"
  },
  {
    "path": "pytabkit/models/training/lightning_callbacks.py",
    "chars": 8851,
    "preview": "from typing import List, Any, Optional, Union, Dict\n\nimport numpy as np\nimport torch\n\ntry:\n    from lightning.pytorch.ca"
  },
  {
    "path": "pytabkit/models/training/lightning_modules.py",
    "chars": 16199,
    "preview": "from pytabkit.models.training.lightning_callbacks import ModelCheckpointCallback\n\ntry:\n    import lightning.pytorch as p"
  },
  {
    "path": "pytabkit/models/training/logging.py",
    "chars": 624,
    "preview": "class Logger:\n    def __init__(self, verbosity_level):\n        # higher verbosity level means more verbose\n        self."
  },
  {
    "path": "pytabkit/models/training/metrics.py",
    "chars": 27846,
    "preview": "import traceback\nfrom typing import Dict, Any, List, Optional, Tuple, Callable\n\nimport numpy as np\nfrom sklearn.metrics "
  },
  {
    "path": "pytabkit/models/training/nn_creator.py",
    "chars": 12780,
    "preview": "import functools\nfrom typing import List, Optional, Tuple, Callable, Dict, Any\n\nimport numpy as np\nimport torch\n\nfrom py"
  },
  {
    "path": "pytabkit/models/training/scheduling.py",
    "chars": 19523,
    "preview": "import numpy as np\nimport math\n\n\nclass LearnerProgress:\n    def __init__(self):\n        self.epoch = 0\n        self.epoc"
  },
  {
    "path": "pytabkit/models/utils.py",
    "chars": 18095,
    "preview": "import multiprocessing as mp\nimport os\nimport os.path\nimport heapq\nimport glob\nimport gzip\nimport shutil\nimport timeit\nf"
  },
  {
    "path": "scripts/analyze_hpo_best_params.py",
    "chars": 4029,
    "preview": "import msgpack_numpy as m\nm.patch()\nimport numbers\nfrom typing import Optional\n\nimport fire\nimport numpy as np\n\nfrom pyt"
  },
  {
    "path": "scripts/analyze_tasks.py",
    "chars": 5366,
    "preview": "from pathlib import Path\nfrom typing import List, Optional\n\nimport fire\nimport matplotlib.pyplot as plt\nimport numpy as "
  },
  {
    "path": "scripts/check_missing_values.py",
    "chars": 2448,
    "preview": "from typing import Optional\n\nimport fire\nimport openml\n\nfrom pytabkit.bench.data.import_tasks import set_openml_cache_di"
  },
  {
    "path": "scripts/copy_algs.py",
    "chars": 1262,
    "preview": "import shutil\nfrom typing import List\n\nimport fire\n\nfrom pytabkit.bench.data.paths import Paths\n\n\ndef copy_algs_in_paths"
  },
  {
    "path": "scripts/create_plots_and_tables.py",
    "chars": 14403,
    "preview": "from pytabkit.bench.data.paths import Paths\nfrom pytabkit.bench.data.tasks import TaskCollection\nfrom pytabkit.bench.eva"
  },
  {
    "path": "scripts/create_probclass_plots.py",
    "chars": 33978,
    "preview": "from typing import Optional, List\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom adjustText import adjust_tex"
  },
  {
    "path": "scripts/create_xrfm_ablations_table.py",
    "chars": 5140,
    "preview": "from typing import List, Optional\n\nimport numpy as np\nfrom pytabkit.bench.run.results import ResultManager\n\nfrom pytabki"
  },
  {
    "path": "scripts/custom_paths.py.default",
    "chars": 51,
    "preview": "def get_base_folder():\n    return 'tab_bench_data'\n"
  },
  {
    "path": "scripts/download_data.py",
    "chars": 18954,
    "preview": "from typing import Optional\n\nimport fire\n\nfrom pytabkit.bench.data.common import TaskSource\nfrom pytabkit.bench.data.get"
  },
  {
    "path": "scripts/estimate_resource_params.py",
    "chars": 18138,
    "preview": "import multiprocessing\nimport time\nfrom typing import List, Dict, Any, Callable\n\nimport numpy as np\nimport sklearn\nimpor"
  },
  {
    "path": "scripts/get_sklearn_names.py",
    "chars": 655,
    "preview": "import importlib\n# get the names of all sklearn interfaces, for exporting them in __all__ to import them from a higher-l"
  },
  {
    "path": "scripts/make_plot_animation.py",
    "chars": 8566,
    "preview": "from typing import List\n\nfrom pytabkit.bench.eval.plotting import plot_pareto\nfrom pytabkit.bench.data.paths import Path"
  },
  {
    "path": "scripts/meta_hyperopt.py",
    "chars": 30505,
    "preview": "from typing import Optional, Tuple, Any, Dict\n\nimport numpy as np\n\nfrom pytabkit.bench.alg_wrappers.interface_wrappers i"
  },
  {
    "path": "scripts/move_algs.py",
    "chars": 2033,
    "preview": "import shutil\nfrom typing import Optional\n\nimport fire\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytabkit.models"
  },
  {
    "path": "scripts/move_many_algs.py",
    "chars": 880,
    "preview": "from typing import Optional\n\nimport fire\n\nfrom pytabkit.models import utils\nfrom scripts.move_algs import move_algs\n\n\nde"
  },
  {
    "path": "scripts/print_complete_results.py",
    "chars": 908,
    "preview": "import fire\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytabkit.bench.eval.analysis import ResultsTables\nfrom pyt"
  },
  {
    "path": "scripts/print_runtimes.py",
    "chars": 876,
    "preview": "from pytabkit.bench.data.paths import Paths\nfrom pytabkit.bench.eval.runtimes import get_avg_train_times, get_avg_predic"
  },
  {
    "path": "scripts/ray_slurm_launch.py",
    "chars": 5320,
    "preview": "# from https://docs.ray.io/en/latest/cluster/examples/slurm-launch.html#slurm-launch\n# slurm-launch.py\n# Usage:\n# python"
  },
  {
    "path": "scripts/ray_slurm_template.sh",
    "chars": 2534,
    "preview": "#!/bin/bash\n# shellcheck disable=SC2206\n# THIS FILE IS GENERATED BY AUTOMATION SCRIPT! PLEASE REFER TO ORIGINAL SCRIPT!\n"
  },
  {
    "path": "scripts/rename_alg.py",
    "chars": 2239,
    "preview": "import os\nimport shutil\nfrom pathlib import Path\n\nimport fire\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytabkit"
  },
  {
    "path": "scripts/rename_tag.py",
    "chars": 557,
    "preview": "import fire\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytabkit.models import utils\n\n\ndef rename_tag(old_name: st"
  },
  {
    "path": "scripts/run_evaluation.py",
    "chars": 11882,
    "preview": "import time\nfrom typing import Optional\n\nimport numpy as np\n\nimport fire\n\nfrom pytabkit.bench.data.common import SplitTy"
  },
  {
    "path": "scripts/run_experiments.py",
    "chars": 94094,
    "preview": "from typing import Optional, Dict, Any, List\n\nimport numpy as np\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytab"
  },
  {
    "path": "scripts/run_experiments_unused.py",
    "chars": 28054,
    "preview": "from typing import List, Optional, Dict, Any\n\nimport numpy as np\n\nfrom pytabkit.bench.alg_wrappers.interface_wrappers im"
  },
  {
    "path": "scripts/run_probclass_experiments.py",
    "chars": 16437,
    "preview": "import copy\nimport time\nfrom typing import List, Optional, Dict, Any\n\nimport numpy as np\nimport pandas as pd\nimport skle"
  },
  {
    "path": "scripts/run_single_task.py",
    "chars": 5463,
    "preview": "import time\n\nimport numpy as np\nimport torch\n\nfrom pytabkit.bench.data.paths import Paths\nfrom pytabkit.bench.data.tasks"
  },
  {
    "path": "scripts/run_slurm.py",
    "chars": 253,
    "preview": "import functools\n\nimport fire\n\nfrom run_experiments import run_gbdt_rs_configs\nfrom pytabkit.bench.data.paths import Pat"
  },
  {
    "path": "scripts/run_time_measurement.py",
    "chars": 16192,
    "preview": "import random\nimport time\nimport torch\n\nimport numpy as np\nimport sklearn\n\nfrom pytabkit.bench.data.paths import Paths\nf"
  },
  {
    "path": "scripts/run_xrfm_large_ablations.py",
    "chars": 8178,
    "preview": "import fire\n\nfrom pytabkit.bench.alg_wrappers.interface_wrappers import RandomParamsxRFMInterfaceWrapper\nfrom pytabkit.b"
  },
  {
    "path": "tests/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "tests/test_bench.py",
    "chars": 2829,
    "preview": "from pathlib import Path\n\nfrom sklearn.datasets import make_classification\nimport torch\n\nfrom pytabkit import XGB_TD_Cla"
  },
  {
    "path": "tests/test_ensemble.py",
    "chars": 826,
    "preview": "import pytest\nimport sklearn.base\nimport numpy as np\n\nfrom pytabkit import Ensemble_TD_Classifier, Ensemble_TD_Regressor"
  },
  {
    "path": "tests/test_metrics.py",
    "chars": 415,
    "preview": "import numpy as np\nimport torch\nimport sklearn\n\nfrom pytabkit.models.training.metrics import Metrics\n\n\ndef test_pinball("
  },
  {
    "path": "tests/test_rtdl_nns.py",
    "chars": 10972,
    "preview": "import numpy as np\nimport pandas as pd\nfrom sklearn.utils.estimator_checks import check_estimator\nfrom sklearn.model_sel"
  },
  {
    "path": "tests/test_sklearn_interfaces.py",
    "chars": 2517,
    "preview": "import pytest\nfrom sklearn.utils.estimator_checks import parametrize_with_checks\n\nfrom pytabkit import XRFM_D_Classifier"
  },
  {
    "path": "tests/test_tabr.py",
    "chars": 6013,
    "preview": "import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import "
  },
  {
    "path": "tests/test_variants.py",
    "chars": 3045,
    "preview": "import pytest\nimport numpy as np\nimport pandas as pd\nimport sklearn\nfrom sklearn.base import ClassifierMixin\nimport torc"
  }
]