diff --git a/AGENTS.md b/AGENTS.md index 2bbe7cadb..3b6523ab1 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -8,12 +8,12 @@ TabArena is a living benchmark for tabular machine learning. It evaluates ML met ## Repository Layout -This repo is a **uv workspace** (root `pyproject.toml`) with three packages plus standalone tooling: +This repo is a **uv workspace** (root `pyproject.toml`; workspace members `tabarena` + `bencheval`) plus two more installable packages and supporting dirs: - `tabarena/` — Core package. Repository pattern for benchmark data, model wrappers, simulation, evaluation, plotting. Depends on AutoGluon and `bencheval`. - `bencheval/` — Standalone lightweight metrics/leaderboard package (ELO, win-rates, ranks, improvability). Computes leaderboards from results DataFrames. No dependency on `tabarena`. - `tabflow/` — AWS SageMaker workflow orchestration. CLI entry points: `tabflow` (launch jobs) and `tabflow-download` (download results). Depends on `tabarena`. -- `tabflow_slurm/` — Standalone scripts (not a package) for running experiments on SLURM clusters. +- `tabflow_slurm/` — Package (own `pyproject.toml`, not a uv-workspace member) for running experiments on SLURM clusters. See `tabflow_slurm/README.md` and `tabflow_slurm/AGENTS.md`. - `examples/` — Usage examples for benchmarking, plotting, meta-learning, custom models. - `tst/` — Tests (note: `tst/`, **not** `tests/`). @@ -64,7 +64,7 @@ Raw predictions → EvaluationRepository → Simulation/Portfolio → Results Da - **`EvaluationRepository`** (`tabarena/tabarena/repository/evaluation_repository.py`) — Central class combining config metadata/rankings (`ZeroshotSimulatorContext`), cached val/test predictions (`TabularModelPredictions`), and `GroundTruth`. Supports subsetting by datasets/folds/configs/problem_types and ensemble selection via mixins. - **`TabularModelPredictions`** (`tabarena/tabarena/predictions/`) — Abstract base for prediction storage. Implementations: `TabularPredictionsInMemory` (dict-based) and `TabularPredictionsMemmap` (disk-based memory-mapped for large benchmarks). Structure: `{dataset: {fold: {val/test: {config: predictions}}}}`. -- **`AbstractExecModel`** (`tabarena/tabarena/benchmark/models/wrapper/abstract_class.py`) — Base for benchmarked models. AutoGluon model wrappers live under `tabarena/tabarena/benchmark/models/ag//`; matching HPO search-space generators live under `tabarena/tabarena/models//generate.py`. Registry: `tabarena/tabarena/benchmark/models/model_registry.py`. +- **`AbstractExecModel`** (`tabarena/tabarena/benchmark/models/wrapper/abstract_class.py`) — Base for the benchmark *execution* wrappers. New benchmarked models live in one folder per model at `tabarena/tabarena/models//` (`model.py` = AutoGluon wrapper subclassing AG's `AbstractModel`/`AbstractTorchModel`, `hpo.py` = search-space generator, `info.py` = `ModelInfo`/`MethodMetadata` registry entry), auto-discovered by `tabarena/tabarena/models/_registry.py::discover_models()` (which `tabarena/tabarena/benchmark/models/model_registry.py` then derives the AG registry from). Use the **`add-model` skill** — there is no `benchmark/models/ag//` layout for new models. - **`ExperimentRunner` / `ExperimentBatchRunner`** (`tabarena/tabarena/benchmark/experiment/`) — Execute model fitting across tasks. Configured via YAML (`experiment_constructor.py`). - **`ZeroshotSimulatorContext`** (`tabarena/tabarena/simulation/`) — Manages config rankings for HPO simulation and portfolio generation. - **`TabArena`** (`bencheval/bencheval/tabarena.py`) — Leaderboard computation from results DataFrames. Independent of the core `tabarena` package. @@ -75,7 +75,7 @@ Artifacts download to `~/.cache/tabarena/` by default; override with `TABARENA_C ## Conventions -- **Add a new model**: touches ~7 locations (AG wrapper, search-space generator, two `__init__.py`s, `model_registry.py`, `models/utils.py` import map, `tabarena/pyproject.toml` extras, and a test). Use existing models in `tabarena/tabarena/benchmark/models/ag/` as templates — pick a structural neighbor (foundation/torch model vs. CPU/sklearn model). +- **Add a new model**: create one folder `tabarena/tabarena/models//` (`model.py`, `hpo.py`, `info.py`, `__init__.py`), then edit `models/__init__.py` (lazy class entry), `models/utils.py` (name→generator map), and `tabarena/pyproject.toml` (a per-model extra), plus a `tst/models/test_.py`. The registry auto-discovers the model from its `info.py` — no manual registry edit. **Use the `add-model` skill**, which encodes this and points to reference implementations (foundation / torch / sklearn). - **Imports**: `from __future__ import annotations` must be the first import in every `.py` file. Use absolute imports rooted at the package (e.g., `from tabarena.repository import EvaluationRepository`). - **Optional dependencies**: each model has its own pyproject extra under `tabarena/pyproject.toml`; the `benchmark` extra is the union. Heavy/optional libs must never be imported at module top-level in core paths — import inside the model wrapper. - **No new top-level docs files** unless the user asks. Edit existing files in place. diff --git a/CLAUDE.md b/CLAUDE.md index b94c9e912..4f3668f65 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -8,7 +8,7 @@ This file only documents Claude-specific extensions. ### Skills (`.claude/skills/`) -- **`add-model`** — Use whenever the user asks to add/integrate/wrap a new tabular ML model. It encodes the full 7-location change required (AG wrapper, search-space generator, registry entries, `pyproject.toml` extra, test) and points to reference implementations for each model class (foundation, torch, sklearn-like). +- **`add-model`** — Use whenever the user asks to add/integrate/wrap a new tabular ML model. It encodes the full change: a per-model folder (`model.py` wrapper, `hpo.py` search space, `info.py` registry entry) plus edits to `models/__init__.py`, `models/utils.py`, and the `pyproject.toml` extra, and a test — and points to reference implementations for each model class (foundation, torch, sklearn-like). The model is auto-discovered from its `info.py`. When the user describes work that matches a skill's trigger criteria, invoke the skill via the Skill tool instead of recreating the steps manually. diff --git a/examples/!experimental/run_tabarena_preprocessing.py b/examples/!experimental/run_tabarena_preprocessing.py index 5264ab413..5f6512c4a 100644 --- a/examples/!experimental/run_tabarena_preprocessing.py +++ b/examples/!experimental/run_tabarena_preprocessing.py @@ -39,7 +39,7 @@ TabArenaModelAgnosticPreprocessing, TabArenaModelSpecificPreprocessing, ) -from tabarena.benchmark.task.user_task import GroupLabelTypes +from tabarena.benchmark.task.metadata import GroupLabelTypes # --------------------------------------------------------------------------- # 1. Synthetic dataset diff --git a/scripts/generate_beyond_arena_metadata.py b/scripts/generate_beyond_arena_metadata.py new file mode 100644 index 000000000..d17fd3ab9 --- /dev/null +++ b/scripts/generate_beyond_arena_metadata.py @@ -0,0 +1,25 @@ +"""Regenerate the committed BeyondArena reference-metadata CSV (maintainer tool). + +Run this once when the ``BeyondArena`` collection contents change. It downloads +and converts every container (large, one-off), then writes a portable CSV to the +package-data location. Commit the resulting file so users can filter datasets +before downloading anything. + + python scripts/generate_beyond_arena_metadata.py + +Requires the optional ``data-foundry`` dependency (``tabarena[data-foundry]``). +""" + +from __future__ import annotations + +from tabarena.benchmark.task.data_foundry import ( + generate_reference_metadata, + get_beyond_arena_collection, + reference_metadata_package_path, +) + +if __name__ == "__main__": + collection = get_beyond_arena_collection() + out_path = reference_metadata_package_path(collection.name) + generate_reference_metadata(collection=collection, out_path=out_path) + print(f"Wrote reference metadata to {out_path}. Commit it to ship the fast filter path.") diff --git a/scripts/generate_beyond_arena_text_cache.py b/scripts/generate_beyond_arena_text_cache.py new file mode 100644 index 000000000..260204111 --- /dev/null +++ b/scripts/generate_beyond_arena_text_cache.py @@ -0,0 +1,57 @@ +"""Generate the semantic-text embedding caches for the BeyondArena text tasks (maintainer tool). + +For every BeyondArena task that carries text, this downloads/converts the task (via the +``BeyondArena`` metadata bundle), computes its semantic embeddings, and writes the per-task cache to +the canonical, encoder-versioned location +(:func:`~tabarena.benchmark.preprocessing.text_cache.text_cache_path`). This is the *producer* side; +end users instead download these caches (see +:func:`~tabarena.benchmark.task.data_foundry.text_cache.download_text_cache`). + +Heavy + GPU-bound (loads the sentence-transformer encoder) and requires the optional ``data-foundry`` +extra. Run once when the BeyondArena text tasks or the encoder change. + + python scripts/generate_beyond_arena_text_cache.py [--ignore-cache] [--dataset-names a b c] + +In production these caches are *shipped inside each Data Foundry container* (as +``tabarena_text_cache.parquet``) and imported automatically when the dataset is materialized — see +:mod:`tabarena.benchmark.task.data_foundry.text_cache`. This script is the local (re)generation path: +it writes each cache to the canonical, encoder-versioned location +(:func:`~tabarena.benchmark.preprocessing.text_cache.text_cache_path`), useful for regenerating a +cache to upload into a container, or for purely-local use without re-downloading. +""" + +from __future__ import annotations + +import argparse + + +def generate_beyond_arena_text_caches(*, dataset_names: list[str] | None = None, ignore_cache: bool = False) -> int: + """Generate caches for all (or the named) BeyondArena text tasks; returns the count generated.""" + from tabarena.benchmark.preprocessing.text_cache import generate_text_cache + from tabarena.benchmark.task.metadata import BeyondArenaMetadataBundle + from tabarena.benchmark.task.user_task import UserTask + + bundle = BeyondArenaMetadataBundle(dataset_names_to_run=dataset_names) + task_metadata = bundle.load_task_metadata() # downloads/converts the (filtered) tasks + text_tasks = [ttm for ttm in task_metadata if ttm.has_text] + print(f"Found {len(text_tasks)} BeyondArena text task(s) to cache (of {len(task_metadata)} total).") + + for ttm in text_tasks: + user_task = UserTask.from_task_id_str(ttm.task_id_str) + generate_text_cache(user_task, ignore_cache=ignore_cache) + return len(text_tasks) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--ignore-cache", action="store_true", help="Regenerate even if a cache already exists.") + parser.add_argument( + "--dataset-names", + nargs="+", + default=None, + help="Restrict to these dataset names (default: all BeyondArena text tasks).", + ) + args = parser.parse_args() + + n = generate_beyond_arena_text_caches(dataset_names=args.dataset_names, ignore_cache=args.ignore_cache) + print(f"Done. Generated/checked {n} text-task cache(s).") diff --git a/scripts/generate_tabarena_v0pt1_metadata.py b/scripts/generate_tabarena_v0pt1_metadata.py new file mode 100644 index 000000000..8d6239a27 --- /dev/null +++ b/scripts/generate_tabarena_v0pt1_metadata.py @@ -0,0 +1,18 @@ +"""Regenerate the committed TabArena v0.1 reference-metadata CSV (maintainer tool). + +Rebuilds the per-task x split metadata from the curated v0.1 metadata and writes it +to the package-data location read by ``TabArenaV0pt1TaskMetadataSource``. Run this +when the curated v0.1 metadata changes, then commit the resulting CSV. + + python scripts/generate_tabarena_v0pt1_metadata.py +""" + +from __future__ import annotations + +from tabarena.benchmark.task.metadata.sources.tabarena_v0pt1 import ( + generate_tabarena_v0_1_reference_metadata, +) + +if __name__ == "__main__": + out_path = generate_tabarena_v0_1_reference_metadata() + print(f"Wrote reference metadata to {out_path}. Commit it to skip the on-the-fly rebuild.") diff --git a/tabarena/pyproject.toml b/tabarena/pyproject.toml index 70000eb8b..6585d8e20 100644 --- a/tabarena/pyproject.toml +++ b/tabarena/pyproject.toml @@ -58,6 +58,12 @@ dependencies = [ ] [project.optional-dependencies] + +# Data Foundry integration: lets `tabarena.benchmark.task.data_foundry` convert +# curated containers into TabArena UserTasks. Included in the `benchmark` union. +# FIXME: To use `uv`, you need to do `uv pip install --prerelease=allow .` so it recognizes pre-release data-foundry +data-foundry = ["data-foundry>=0.0.3"] + # Core model extras — installed together via the `benchmark` union below. tabpfn = [ "tabpfn>=8.0.3", @@ -98,6 +104,7 @@ benchmark = [ "tabarena[realmlp]", "tabarena[tabdpt]", "tabarena[tabm]", + "tabarena[data-foundry]", ] # Extended set: extra models layered on top of `benchmark` when needed. @@ -144,6 +151,7 @@ tabarena = [ "metrics/_roc_auc_cpp/compile.sh", "metrics/_roc_auc_cpp/cpp_auc.cpp", "nips2025_utils/metadata/task_metadata_tabarena51.csv", + "benchmark/task/metadata/sources/data/*.csv", "benchmark/models/ag/limix/_vendor/config/*.json", "benchmark/models/ag/limix/_vendor/LICENSE.txt", "benchmark/models/ag/limix/_vendor/README.md", diff --git a/tabarena/tabarena/benchmark/experiment/__init__.py b/tabarena/tabarena/benchmark/experiment/__init__.py index 0654ceabf..847e4597e 100644 --- a/tabarena/tabarena/benchmark/experiment/__init__.py +++ b/tabarena/tabarena/benchmark/experiment/__init__.py @@ -1,5 +1,11 @@ from __future__ import annotations # noqa: I001 +from tabarena.benchmark.experiment.bundle import ( + ModelConstraints, + TabArenaExperimentBundle, + TabArenaV0pt1ExperimentBundle, + BeyondArenaExperimentBundle, +) from tabarena.benchmark.experiment.experiment_constructor import ( AGExperiment, AGModelBagExperiment, @@ -25,10 +31,14 @@ "AGModelBagExperiment", "AGModelExperiment", "AGModelOuterExperiment", + "BeyondArenaExperimentBundle", "Experiment", "ExperimentBatchRunner", "ExperimentRunner", + "ModelConstraints", "OOFExperimentRunner", + "TabArenaExperimentBundle", + "TabArenaV0pt1ExperimentBundle", "YamlExperimentSerializer", "run_experiments", "run_experiments_new", diff --git a/tabarena/tabarena/benchmark/experiment/bundle.py b/tabarena/tabarena/benchmark/experiment/bundle.py new file mode 100644 index 000000000..155228de5 --- /dev/null +++ b/tabarena/tabarena/benchmark/experiment/bundle.py @@ -0,0 +1,452 @@ +"""Experiment selection and config generation: the TabArenaExperimentBundle.""" + +from __future__ import annotations + +from copy import deepcopy +from dataclasses import dataclass, field +from pathlib import Path +from typing import ClassVar + +from tabarena.benchmark.experiment.experiment_constructor import Experiment + + +@dataclass(frozen=True) +class ModelConstraints: + """Per-model dataset-compatibility constraints. + + A constraint is "active" only when its corresponding field is set + (non-`None`); unset fields impose no restriction. `regression_support` + defaults to True — set False for classification-only models. + """ + + max_n_features: int | None = None + max_n_samples_train_per_fold: int | None = None + min_n_samples_train_per_fold: int | None = None + max_n_classes: int | None = None + regression_support: bool = True + + def applies( + self, + *, + n_features: int, + n_classes: int, + n_samples_train_per_fold: int, + problem_type: str | None = None, + ) -> bool: + """True if a dataset with these properties is compatible with the model. + + For regression datasets, `problem_type == "regression"` is the + authoritative signal — `n_classes` from metadata can be 0/-1/None. + """ + if problem_type == "regression" and not self.regression_support: + return False + if self.max_n_features is not None and n_features > self.max_n_features: + return False + if ( + self.max_n_samples_train_per_fold is not None + and n_samples_train_per_fold > self.max_n_samples_train_per_fold + ): + return False + if ( + self.min_n_samples_train_per_fold is not None + and n_samples_train_per_fold < self.min_n_samples_train_per_fold + ): + return False + return not (self.max_n_classes is not None and n_classes > self.max_n_classes) + + +# Shared constraints for model families (used by TabArenaExperimentBundle.DEFAULT_MODEL_CONSTRAINTS). +TABICL_CONSTRAINTS = ModelConstraints( + max_n_samples_train_per_fold=100_000, + max_n_features=500, + regression_support=False, +) +TABPFNV2_CONSTRAINTS = ModelConstraints( + max_n_samples_train_per_fold=10_000, + max_n_features=500, + max_n_classes=10, +) + + +@dataclass(kw_only=True) +class TabArenaExperimentBundle: + """Defines which models/experiments to run in a benchmark and builds them. + + Encapsulates the list of models with per-model config counts, the + preprocessing pipelines applied to each model, and miscellaneous model-level + settings (predict batching, verbosity, artifact paths, fold fitting). + + `build_experiments` / `generate_configs_yaml` turn this into ready-to-run + `Experiment` objects, baking the compute resources (passed at build time) + into each experiment so the resulting YAML is self-contained. + """ + + models: list[tuple[str, int | str | dict]] = field(default_factory=list) + """List of models to run in the benchmark with metadata. + Metadata keys from left to right: + - model name: str + - number of random hyperparameter configurations to run: int or str + Some special cases are: + - If 0, only the default configuration is run. + - If "all", `n_random_configs`-many configurations are run. + - If dict, kwargs for AGExperiment + Example usage: + # Run all random configs for LightGBM, 10 random configs for Random Forest, + and only the default for TabDPT. + default_factory=lambda: [ + ("LightGBM", "all"), + ("RandomForest", 10), + ("TabDPT", 0), + ] + ) + + Models one can use: "CatBoost", "EBM", "ExtraTrees", "FastaiMLP", "KNN", "LightGBM", + "Linear", "ModernNCA", "TorchMLP", "RandomForest", "RealMLP", "TabDPT", "TabICL", + "TabM", "TabPFNv2", "XGBoost", "Mitra", "xRFM", "RealTabPFN-v2.5", "SAP-RPT-OSS", + "TabICLv2", "PerpetualBooster", "TabSTAR" + + For the newest set of available models, see: + `tabarena.models.utils.get_configs_generator_from_name` + """ + n_random_configs: int + """Number of random hyperparameter configurations to run for each model""" + preprocessing_pipelines: list[str] + """EXPERIMENTAL! + Preprocessing pipelines to add to the configurations we want to run. + + Each options multiplies the number of configurations to run by the number of + pipelines. For example, if we have 10 configurations and 2 pipelines, we will + run 20 configurations. + + Options: + - "default": Use the default preprocessing pipeline. + - "tabarena_default": new model agnostic and model specific preprocessing + updates for TabArena (experimental, can be buggy!). + - Any other string points to custom experimental code for now. + """ + model_agnostic_preprocessing: bool = True + """Whether to use model-agnostic preprocessing or not. + By default, we use AutoGluon's automatic preprocessing for all models. + This can be disabled by setting this to False. Warning: the model then needs + to be able to handle this! + """ + + max_predict_batch_size: int | None = None + """Maximal batch size for the predict function of the models. + This is used at validation and test predict time. Thus, it trades off speed for memory usage. + If None, no limit is applied. + """ + sequential_local_fold_fitting: bool = False + """Use Ray for local fold fitting. This is used to speed up the local fold fitting + and force this behavior if True. If False the default strategy of running the + local fold fitting is used, as determined by AutoGluon and the model's + default_ag_args_ensemble parameters.""" + model_artifacts_base_path: str | Path | None = "/tmp" # noqa: S108 + """Adapt the default temporary directory used for model artifacts in TabArena. + - If None, the default temporary directory is used: "./AutoGluonModels". + - If a string or Path, the directory is used as the base path for the temporary + and any model artifacts will be stored in time-stamped subdirectories. + """ + verbosity: int = 2 + """AutoGluon verbosity level for the fit, passed via `init_kwargs['verbosity']`.""" + model_verbosity: int | None = 4 + """Verbosity level passed to the model via model_hyperparameters['verbose']. + Controls model-level logging (e.g. CatBoost iteration logs, LightGBM verbosity) + independently of AutoGluon's overall verbosity. If None, no model-level verbosity is set.""" + adapt_num_folds_to_n_classes: bool = True + """Whether to adapt the number of folds to the number of classes for classification tasks. + Ensures that each fold has at least one sample of each class. + """ + shuffle_features: bool = True + """Whether to shuffle the features of the datasets. Only here for backward compatibility + with the original TabArena setup, but not recommended to change.""" + dynamic_tabarena_validation_protocol: bool = True + """If True, experiments built by this bundle adapt their validation data + dynamically based on the task at run time (handled by `run_experiments_new`). + WARNING: this can overwrite the configured validation of a configuration!""" + + custom_model_constraints: dict[str, ModelConstraints] = field(default_factory=dict) + """Per-model overrides of dataset compatibility, keyed by AG model key. + + Entries here are merged on top of `DEFAULT_MODEL_CONSTRAINTS` (custom wins + on key collisions). Models not listed in either map are considered + compatible with every dataset. Consumed by the benchmark orchestration to + skip incompatible `(model, dataset)` jobs. + """ + + DEFAULT_MODEL_CONSTRAINTS: ClassVar[dict[str, ModelConstraints]] = { + "TABICL": TABICL_CONSTRAINTS, + "TA-TABICL": TABICL_CONSTRAINTS, + "TABPFNV2": TABPFNV2_CONSTRAINTS, + "TA-TABPFNV2": TABPFNV2_CONSTRAINTS, + "MITRA": TABPFNV2_CONSTRAINTS, + } + + @property + def model_constraints(self) -> dict[str, ModelConstraints]: + """Effective model constraints (defaults overridden by `custom_model_constraints`).""" + return {**self.DEFAULT_MODEL_CONSTRAINTS, **self.custom_model_constraints} + + def generate_configs_yaml( + self, + *, + configs_path: str, + time_limit: int, + num_cpus: int | None, + num_gpus: int, + memory_limit: int | None, + time_limit_with_preprocessing: bool, + ) -> list[dict]: + """Build experiments, write them to `configs_path`, and return the parsed methods list. + + Compute resources (`num_cpus`/`num_gpus`/`memory_limit`/`time_limit`) are + baked into each experiment alongside the model-specific overrides this + bundle owns (including `verbosity`). + """ + import yaml + + from tabarena.benchmark.experiment import YamlExperimentSerializer + + experiments_all = self.build_experiments( + time_limit=time_limit, + num_cpus=num_cpus, + num_gpus=num_gpus, + memory_limit=memory_limit, + time_limit_with_preprocessing=time_limit_with_preprocessing, + ) + + # Verify no duplicate names + experiment_names = set() + for experiment in experiments_all: + if experiment.name in experiment_names: + raise AssertionError( + f"Found multiple instances of experiment named {experiment.name}. " + f"All experiment names must be unique!", + ) + experiment_names.add(experiment.name) + + YamlExperimentSerializer.to_yaml( + experiments=experiments_all, + path=configs_path, + ) + + # Read YAML file and return the methods list. + with Path(configs_path).open() as file: + return yaml.safe_load(file)["methods"] + + def build_experiments( + self, + *, + time_limit: int, + num_cpus: int | None, + num_gpus: int, + memory_limit: int | None, + time_limit_with_preprocessing: bool, + ) -> list[Experiment]: + """Build the full list of experiments (one per model x pipeline x random config).""" + method_kwargs = self._init_base_method_kwargs( + num_cpus=num_cpus, + num_gpus=num_gpus, + memory_limit=memory_limit, + ) + self._print_experiment_summary(method_kwargs) + return [ + exp + for pipeline_name in self.preprocessing_pipelines + for exp in self._build_experiments_for_pipeline( + pipeline_name, + method_kwargs, + time_limit=time_limit, + time_limit_with_preprocessing=time_limit_with_preprocessing, + ) + ] + + def _init_base_method_kwargs( + self, + *, + num_cpus: int | None, + num_gpus: int, + memory_limit: int | None, + ) -> dict: + """Build the per-method kwargs: the bench-level base, plus this bundle's + model-level overrides and the (baked) compute resources. + """ + mk = { + "init_kwargs": {"verbosity": self.verbosity}, + "shuffle_features": self.shuffle_features, + "fit_kwargs": {}, + "extra_model_hyperparameters": {}, + } + if self.model_artifacts_base_path is not None: + mk["init_kwargs"]["default_base_path"] = self.model_artifacts_base_path + if not self.model_agnostic_preprocessing: + mk["fit_kwargs"]["feature_generator"] = None + if self.adapt_num_folds_to_n_classes: + mk["fit_kwargs"]["adapt_num_bag_folds_to_n_classes"] = True + if self.max_predict_batch_size is not None: + mk["extra_model_hyperparameters"]["ag.max_batch_size"] = self.max_predict_batch_size + if self.model_verbosity is not None: + mk["extra_model_hyperparameters"]["ag.verbosity"] = self.model_verbosity + # Bake compute resources into the experiment so the serialized config is + # self-contained. `None` num_cpus/memory_limit are auto-detected at run + # time by the Experiment (see `Experiment._autodetect_resources`). + mk["fit_kwargs"]["num_cpus"] = num_cpus + mk["fit_kwargs"]["num_gpus"] = num_gpus + mk["fit_kwargs"]["memory_limit"] = memory_limit + return mk + + def _print_experiment_summary(self, method_kwargs: dict) -> None: + print( + "Generating experiments for models...", + f"\n\t`all` := number of configs: {self.n_random_configs}", + f"\n\t{len(self.models)} models: {self.models}", + f"\n\t{len(self.preprocessing_pipelines)} preprocessing pipelines: {self.preprocessing_pipelines}", + f"\n\tMethod kwargs: {method_kwargs}", + ) + + def _build_experiments_for_pipeline( + self, + pipeline_name: str, + method_kwargs: dict, + *, + time_limit: int, + time_limit_with_preprocessing: bool, + ) -> list: + """Per-pipeline overrides + per-model dispatch.""" + pipeline_kwargs = deepcopy(method_kwargs) + name_id_suffix = "" + preprocessing_pipeline = None + if self.model_agnostic_preprocessing: + if pipeline_name != "default": + preprocessing_pipeline = pipeline_name + if pipeline_name != "tabarena_default": + name_id_suffix = f"_{pipeline_name}" + + experiments: list = [] + for model in self.models: + experiments.extend( + self._build_experiments_for_model( + model, + pipeline_kwargs, + name_id_suffix, + preprocessing_pipeline=preprocessing_pipeline, + time_limit=time_limit, + time_limit_with_preprocessing=time_limit_with_preprocessing, + ) + ) + return experiments + + def _build_experiments_for_model( + self, + model: Experiment | tuple, + pipeline_method_kwargs: dict, + name_id_suffix: str, + *, + preprocessing_pipeline: str | None, + time_limit: int, + time_limit_with_preprocessing: bool, + ) -> list: + if isinstance(model, Experiment): + return [model] + model_name, n_configs_or_kwargs = model[0], model[1] + if isinstance(model_name, str) and model_name.startswith("AutoGluon"): + return self._generate_autogluon_config( + model_name=model_name, + agexp_kwargs=n_configs_or_kwargs, + pipeline_method_kwargs=pipeline_method_kwargs, + time_limit=time_limit, + ) + return self._generate_model_configs( + model_name=model_name, + n_configs=n_configs_or_kwargs, + pipeline_method_kwargs=pipeline_method_kwargs, + name_id_suffix=name_id_suffix, + preprocessing_pipeline=preprocessing_pipeline, + time_limit=time_limit, + time_limit_with_preprocessing=time_limit_with_preprocessing, + ) + + @staticmethod + def _generate_autogluon_config( + *, + model_name: str, + agexp_kwargs: dict, + pipeline_method_kwargs: dict, + time_limit: int, + ) -> list: + """Parse the AutoGluon config from the models.""" + from tabarena.benchmark.experiment.experiment_constructor import ( + AGExperiment, + ) + + # deepcopy: shallow .copy() leaves nested `fit_kwargs` / `init_kwargs` shared with + # the caller's dict, and the subsequent .update() / item assignment would mutate + # the user's `self.models` entry across calls. + agexp_kwargs = deepcopy(agexp_kwargs) + for key in ["fit_kwargs", "init_kwargs"]: + agexp_kwargs.setdefault(key, {}) + if key in pipeline_method_kwargs: + agexp_kwargs[key].update(pipeline_method_kwargs[key]) + agexp_kwargs["fit_kwargs"]["time_limit"] = time_limit + + return [AGExperiment(name=model_name, **agexp_kwargs)] + + def _generate_model_configs( + self, + *, + model_name: str, + n_configs: int | str, + pipeline_method_kwargs: dict, + name_id_suffix: str, + preprocessing_pipeline: str | None, + time_limit: int, + time_limit_with_preprocessing: bool, + default_seed_config: str = "fold-config-wise", + ) -> list: + from tabarena.models.utils import get_configs_generator_from_name + + if isinstance(n_configs, str) and n_configs == "all": + n_configs = self.n_random_configs + elif not isinstance(n_configs, int): + raise ValueError( + f"Invalid number of configurations for model {model_name}: {n_configs}. Must be an integer or 'all'." + ) + if isinstance(model_name, str): + config_generator = get_configs_generator_from_name(model_name) + else: + config_generator = deepcopy(model_name) + return config_generator.generate_all_bag_experiments( + num_random_configs=n_configs, + add_seed=default_seed_config, + name_id_suffix=name_id_suffix, + method_kwargs=pipeline_method_kwargs, + time_limit=time_limit, + time_limit_with_preprocessing=time_limit_with_preprocessing, + preprocessing_pipeline=preprocessing_pipeline, + fold_fitting_strategy="sequential_local" if self.sequential_local_fold_fitting else None, + dynamic_tabarena_validation_protocol=self.dynamic_tabarena_validation_protocol, + ) + +@dataclass(kw_only=True) +class TabArenaV0pt1ExperimentBundle(TabArenaExperimentBundle): + """The original TabArena-v0.1 experiment bundle, for backward compatibility.""" + + n_random_configs: int = 200 + """TabArena-v0.1 used 200 configs per model.""" + shuffle_features: bool = False + """TabArena-v0.1 default""" + dynamic_tabarena_validation_protocol: bool = False + """Only used in v0.2 or larger with new data foundry task metadata integration.""" + preprocessing_pipelines: list[str] = field(default_factory=lambda: ["default"]) + """Use AutoGluon default preprocessing only.""" + adapt_num_folds_to_n_classes: bool = False + """TabArena-v0.1 did not adapt the number of folds to the number of classes.""" + +@dataclass(kw_only=True) +class BeyondArenaExperimentBundle(TabArenaExperimentBundle): + """Experiment bundle for the BeyondArena paper. + It used the current defaults, 25 configs, and the new TabArena preprocessing. + """ + + n_random_configs: int = 25 + preprocessing_pipelines: list[str] = field(default_factory=lambda: ["tabarena_default"]) \ No newline at end of file diff --git a/tabarena/tabarena/benchmark/experiment/experiment_constructor.py b/tabarena/tabarena/benchmark/experiment/experiment_constructor.py index bbaa92638..8db407f59 100644 --- a/tabarena/tabarena/benchmark/experiment/experiment_constructor.py +++ b/tabarena/tabarena/benchmark/experiment/experiment_constructor.py @@ -17,7 +17,7 @@ from tabarena.benchmark.models.model_registry import infer_model_cls from tabarena.utils.cache import AbstractCacheFunction, CacheFunctionDummy from tabarena.benchmark.task.openml import OpenMLTaskWrapper -from tabarena.benchmark.task.user_task import GroupLabelTypes, SplitTimeHorizonTypes, SplitTimeHorizonUnitTypes +from tabarena.benchmark.task.metadata import GroupLabelTypes, SplitTimeHorizonTypes, SplitTimeHorizonUnitTypes class Experiment: """ @@ -109,6 +109,8 @@ def __init__( *, experiment_cls: Type[ExperimentRunner] = OOFExperimentRunner, experiment_kwargs: dict = None, + preprocessing_pipeline: str | None = None, + dynamic_tabarena_validation_protocol: bool = False, ): if experiment_kwargs is None: experiment_kwargs = {} @@ -123,6 +125,13 @@ def __init__( self.method_kwargs = method_kwargs self.experiment_cls = experiment_cls self.experiment_kwargs = experiment_kwargs + # Name of an optional preprocessing pipeline to apply, resolved lazily at + # run time via `_resolve_preprocessing` (see that method). + self.preprocessing_pipeline = preprocessing_pipeline + # Whether `run_experiments_new` should adapt this experiment's validation + # data dynamically based on the task it runs on (task-dependent, so applied + # at run time rather than baked into method_kwargs). + self.dynamic_tabarena_validation_protocol = dynamic_tabarena_validation_protocol def construct_method(self, problem_type: str, eval_metric) -> AbstractExecModel: return self.method_cls( @@ -145,19 +154,21 @@ def run( ) -> dict: if cacher is None: cacher = CacheFunctionDummy(verbose=verbose) + # Resolve the preprocessing pipeline (if any) into a ready-to-run experiment. + resolved = self._resolve_preprocessing() if task is not None: out = cacher.cache( - fun=self.experiment_cls.init_and_run, + fun=resolved.experiment_cls.init_and_run, fun_kwargs=dict( - method_cls=self.method_cls, + method_cls=resolved.method_cls, task=task, fold=fold, repeat=repeat, sample=sample, task_name=task_name, - method=self.name, - fit_args=self.method_kwargs, - **self.experiment_kwargs, + method=resolved.name, + fit_args=self._autodetect_resources(resolved.method_kwargs), + **resolved.experiment_kwargs, **experiment_kwargs, ), ignore_cache=ignore_cache, @@ -167,6 +178,90 @@ def run( out = cacher.cache(fun=None, fun_kwargs=None, ignore_cache=ignore_cache) return out + def _resolve_preprocessing(self) -> Experiment: + """Resolve `self.preprocessing_pipeline` into a ready-to-run Experiment. + + Returns `self` when there is nothing to resolve; otherwise returns a + deep-copied Experiment with the pipeline applied and + `preprocessing_pipeline` cleared. + """ + pipeline = self.preprocessing_pipeline + if pipeline is None or pipeline == "default": + return self + + if pipeline == "tabarena_default": + from tabarena.benchmark.preprocessing import ( + TabArenaModelAgnosticPreprocessing, + TabArenaModelSpecificPreprocessing, + ) + + resolved = copy.deepcopy(self) + resolved.preprocessing_pipeline = None + resolved.method_kwargs["fit_kwargs"]["feature_generator_cls"] = TabArenaModelAgnosticPreprocessing + resolved.method_kwargs["fit_kwargs"]["feature_generator_kwargs"] = {} + resolved.method_kwargs["model_hyperparameters"] = TabArenaModelSpecificPreprocessing.add_to_hyperparameters( + resolved.method_kwargs["model_hyperparameters"] + ) + return resolved + + if pipeline.startswith("FSBench__"): + # Logic for the (experimental) feature selection benchmark. + from tabarena.benchmark.feature_selection_methods.feature_selection_benchmark_utils import ( + apply_fs_bench_preprocessing, + ) + + resolved = apply_fs_bench_preprocessing(preprocessing_name=pipeline, experiment=self) + resolved.preprocessing_pipeline = None + return resolved + + raise ValueError(f"Preprocessing pipeline name '{pipeline}' not recognized.") + + def set_resources( + self, + *, + num_cpus: int | None = None, + num_gpus: int = 0, + memory_limit: int | None = None, + ) -> None: + """Bake compute resources into this experiment's ``fit_kwargs``. + + Useful for attaching resources to a manually-built Experiment (the + benchmark builder does the equivalent at construction time). ``num_cpus`` + and ``memory_limit`` may be left ``None`` to request run-time + auto-detection of the node's resources (see ``_autodetect_resources``). + """ + fit_kwargs = self.method_kwargs.setdefault("fit_kwargs", {}) + fit_kwargs["num_cpus"] = num_cpus + fit_kwargs["num_gpus"] = num_gpus + fit_kwargs["memory_limit"] = memory_limit + + @staticmethod + def _autodetect_resources(method_kwargs: dict) -> dict: + """Return ``method_kwargs`` with any ``None`` compute resources auto-detected. + + A serialized experiment can carry ``num_cpus=None`` / ``memory_limit=None`` + to mean "detect on whatever node I run on", preserving per-node + auto-detection while keeping the experiment self-contained. Returns the + input unchanged when there is nothing to detect (no copy); otherwise a + deep-copied ``method_kwargs`` with the detected values filled in. + """ + fit_kwargs = method_kwargs.get("fit_kwargs") or {} + detect_cpus = fit_kwargs.get("num_cpus", 0) is None + detect_memory = fit_kwargs.get("memory_limit", 0) is None + if not (detect_cpus or detect_memory): + return method_kwargs + + from tabarena.utils.resources import detect_memory_limit_gb, detect_num_cpus + + method_kwargs = copy.deepcopy(method_kwargs) + fit_kwargs = method_kwargs["fit_kwargs"] + if detect_cpus: + fit_kwargs["num_cpus"] = detect_num_cpus() + print(f"num_cpus not provided, using detected number of CPUs: {fit_kwargs['num_cpus']}") + if detect_memory: + fit_kwargs["memory_limit"] = detect_memory_limit_gb() + print(f"memory_limit not provided, using detected memory size: {fit_kwargs['memory_limit']} GB") + return method_kwargs def load_validation_split_metadata( self, @@ -411,6 +506,8 @@ def __init__( method_kwargs: dict = None, experiment_kwargs: dict = None, time_limit_with_preprocessing: bool = False, + preprocessing_pipeline: str | None = None, + dynamic_tabarena_validation_protocol: bool = False, ): if method_kwargs is None: method_kwargs = {} @@ -442,6 +539,8 @@ def __init__( }, experiment_cls=self._experiment_cls, experiment_kwargs=experiment_kwargs, + preprocessing_pipeline=preprocessing_pipeline, + dynamic_tabarena_validation_protocol=dynamic_tabarena_validation_protocol, ) def _to_yaml_dict(self, locals: dict) -> dict: @@ -542,6 +641,8 @@ def __init__( experiment_kwargs: dict = None, time_limit_with_preprocessing: bool = False, extra_model_hyperparameters: dict = None, + preprocessing_pipeline: str | None = None, + dynamic_tabarena_validation_protocol: bool = False, ): if method_kwargs is None: method_kwargs = {} @@ -591,6 +692,8 @@ def __init__( method_kwargs=method_kwargs, experiment_kwargs=experiment_kwargs, time_limit_with_preprocessing=time_limit_with_preprocessing, + preprocessing_pipeline=preprocessing_pipeline, + dynamic_tabarena_validation_protocol=dynamic_tabarena_validation_protocol, ) @@ -646,11 +749,19 @@ def _to_yaml_format(cls, experiment: Experiment) -> dict: class YamlExperimentSerializer: @classmethod - def from_yaml(cls, path: str, context=None) -> list[Experiment]: + def from_yaml(cls, path: str, context=None, config_index: list[int] | None = None) -> list[Experiment]: + """Load experiments from a YAML file. + + If `config_index` is given, only the experiments at those indices + (into the file's `methods` list) are parsed and returned; otherwise all + experiments are returned. + """ yaml_out = cls.load_yaml(path=path) experiments = [] - for experiment in yaml_out: + for m_i, experiment in enumerate(yaml_out): + if (config_index is not None) and (m_i not in config_index): + continue experiments.append( YamlSingleExperimentSerializer.parse_method( experiment, context=context diff --git a/tabarena/tabarena/benchmark/experiment/experiment_runner_api.py b/tabarena/tabarena/benchmark/experiment/experiment_runner_api.py index 31047d345..1543e49c1 100644 --- a/tabarena/tabarena/benchmark/experiment/experiment_runner_api.py +++ b/tabarena/tabarena/benchmark/experiment/experiment_runner_api.py @@ -177,7 +177,6 @@ def run_experiments_new( raise_on_failure: bool = True, debug_mode: bool = False, failure_on_non_finite_metric_error: bool = False, - dynamic_tabarena_validation_protocol: bool = False, ) -> list[dict]: """Run model experiments for a set of tasks. @@ -300,9 +299,10 @@ def run_experiments_new( the cache file produced for the run. This is useful to ensure that such errors resulting from models running into overflows are not ignored silently. Moreover, if `raise_on_failure` is also True, the exception will be raised. - dynamic_tabarena_validation_protocol: bool, default False - If True, the validation split is dynamically configured based on the task - type and dataset type. + + Each experiment carries its own `dynamic_tabarena_validation_protocol` flag + (see `Experiment`): when True, that experiment's validation split is + dynamically configured based on the task type and dataset type at run time. Returns: ------- @@ -365,7 +365,7 @@ def run_experiments_new( for me_index, model_experiment in enumerate(model_experiments, start=1): cur_experiment_idx += 1 - cache_task_key = task_id_or_object if isinstance(task_id_or_object, int) else task_id_or_object.task_id + cache_task_key = task_id_or_object if isinstance(task_id_or_object, int) else task_id_or_object.slug print( f"Starting Model {me_index}/{len(model_experiments)}..." f"\n\t" @@ -420,11 +420,14 @@ def run_experiments_new( print(f"Using eval metric: {eval_metric_name}") # EXPERIMENTAL: update validation split metadata logic - if (task is not None) and dynamic_tabarena_validation_protocol: + from contextlib import nullcontext + + text_cache_cm = nullcontext() + if (task is not None) and model_experiment.dynamic_tabarena_validation_protocol: from tabarena.benchmark.experiment.experiment_constructor import ( AGModelBagExperiment, ) - from tabarena.benchmark.task.user_task import TabArenaOpenMLSupervisedTask + from tabarena.benchmark.task.openml import TabArenaOpenMLSupervisedTask if not isinstance(task.task, TabArenaOpenMLSupervisedTask): raise ValueError( @@ -445,39 +448,31 @@ def run_experiments_new( **task.get_validation_split_kwargs(), ) - # FIXME: move this somewhere else and allow to enable/disable this to not use cache. - # Load text cache into memory for the current task - from tabarena.benchmark.preprocessing.text_feature_generators import ( - SemanticTextFeatureGenerator, - ) + # Load this task's semantic-text embedding cache for the duration of the + # fit (slug-keyed + encoder-versioned; restored afterwards). Default + # ``require``: a text task with no cache fails fast — warm it first via the + # prefetch/download path or pre-generation. + from tabarena.benchmark.preprocessing.text_cache import use_text_cache_for_task - cache_path = SemanticTextFeatureGenerator.get_text_cache_dir(task_id_str=str(task.task_id)) - if cache_path.exists(): - print("[LOADING TEXT CACHE] Loading text embedding cache into memory...") - SemanticTextFeatureGenerator._embedding_look_up = ( - SemanticTextFeatureGenerator.load_embedding_cache(path=cache_path) - ) - SemanticTextFeatureGenerator.only_load_from_cache = True - elif task._has_text: - raise FileNotFoundError( - f"Text cache not found for task {task.task_id} at expected location {cache_path}. " - f"This is required when `dynamic_tabarena_validation_protocol` is set to 'force'." - ) + text_cache_cm = use_text_cache_for_task( + task_id_or_object, has_text=task._has_text, mode="require" + ) try: - out = model_experiment.run( - task=task, - fold=fold, - cacher=cacher, - ignore_cache=cache_mode == "ignore", - debug_mode=debug_mode, - repeat=repeat, - # TODO: remove task_name as required parameter in .run() - # - also unclear how this is used in only cache case, - # where we don't have the task object. - task_name=tabarena_task_name, # used in eval as name later. - eval_metric_name=eval_metric_name, - ) + with text_cache_cm: + out = model_experiment.run( + task=task, + fold=fold, + cacher=cacher, + ignore_cache=cache_mode == "ignore", + debug_mode=debug_mode, + repeat=repeat, + # TODO: remove task_name as required parameter in .run() + # - also unclear how this is used in only cache case, + # where we don't have the task object. + task_name=tabarena_task_name, # used in eval as name later. + eval_metric_name=eval_metric_name, + ) except Exception as exc: if raise_on_failure: raise diff --git a/tabarena/tabarena/benchmark/models/wrapper/validation_utils.py b/tabarena/tabarena/benchmark/models/wrapper/validation_utils.py index 48157d5b4..0d96cac5f 100644 --- a/tabarena/tabarena/benchmark/models/wrapper/validation_utils.py +++ b/tabarena/tabarena/benchmark/models/wrapper/validation_utils.py @@ -6,12 +6,12 @@ import pandas as pd from loguru import logger -from tabarena.benchmark.task.user_task import ( +from tabarena.benchmark.task.metadata import ( GroupLabelTypes, ) if TYPE_CHECKING: - from tabarena.benchmark.task.user_task import ( + from tabarena.benchmark.task.metadata import ( SplitTimeHorizonTypes, SplitTimeHorizonUnitTypes, ) @@ -374,7 +374,7 @@ def get_num_group_instances(self, X: pd.DataFrame) -> int: """Compute the number of rows that represent how much (multi-instance) samples the data has. This is used to determine which splits to use. """ - from tabarena.benchmark.task.user_task import TabArenaTaskMetadataMixin + from tabarena.benchmark.task.openml import TabArenaTaskMetadataMixin return TabArenaTaskMetadataMixin.get_num_instance_groups( X=X, diff --git a/tabarena/tabarena/benchmark/preprocessing/model_agnostic_default_preprocessing.py b/tabarena/tabarena/benchmark/preprocessing/model_agnostic_default_preprocessing.py index 660bbd5c2..294facfc6 100644 --- a/tabarena/tabarena/benchmark/preprocessing/model_agnostic_default_preprocessing.py +++ b/tabarena/tabarena/benchmark/preprocessing/model_agnostic_default_preprocessing.py @@ -31,7 +31,7 @@ SemanticTextFeatureGenerator, StatisticalTextFeatureGenerator, ) -from tabarena.benchmark.task.user_task import GroupLabelTypes +from tabarena.benchmark.task.metadata import GroupLabelTypes if TYPE_CHECKING: from autogluon.common.features.feature_metadata import FeatureMetadata diff --git a/tabarena/tabarena/benchmark/preprocessing/text_cache.py b/tabarena/tabarena/benchmark/preprocessing/text_cache.py new file mode 100644 index 000000000..a70abf66d --- /dev/null +++ b/tabarena/tabarena/benchmark/preprocessing/text_cache.py @@ -0,0 +1,230 @@ +"""Canonical location, keying, and (re)use of the semantic-text embedding cache. + +Computing semantic text embeddings (via :class:`SemanticTextFeatureGenerator`) is expensive, so +they are precomputed once per task and cached to a parquet file (text value -> embedding). This +module owns the *contract* for that cache so generation, loading at fit time, and the +download/prefetch path all agree: + +* **Location** — under the TabArena cache root (``get_tabarena_cache_root()/text_cache/``), + consistent with results/artifacts, with a read-time fallback to the legacy OpenML-root location. +* **Versioning** — embeddings are tied to a specific encoder (model + truncation dim), so caches + live under an ```` subfolder. Changing the encoder + changes the subfolder, so a stale cache can never be silently loaded. +* **Key** — the readable :attr:`UserTask.slug` for data-foundry / user tasks (matching the result + caches and committed metadata), or the integer OpenML task id otherwise. + +Embeddings are model-agnostic across tasks (a text value embeds the same regardless of task), but +they are cached per task so a benchmark only ships/loads the caches for the tasks it runs. +""" + +from __future__ import annotations + +from contextlib import contextmanager +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING, Literal + +if TYPE_CHECKING: + import numpy as np + + +@dataclass(frozen=True) +class SemanticEmbeddingSpec: + """Identity + config of the semantic-text embedding, and the cache version tag it derives. + + The embeddings depend on more than the model + truncation dim (the ``query`` prompt, L2 + normalization, the fp16 dtype, the text preprocessing in :func:`sanitize_text`, and the pinned + ``sentence-transformers`` behavior). To keep the cache id *forward-safe*, the id is + + ``-d-r`` + + where ``model`` / ``truncate_dim`` flip the id automatically, and ``revision`` is the explicit + knob to bump for *any other* change that alters the produced embeddings — so a cache made by an + older encoder can never be silently loaded against a newer one. This spec is the single source + of truth: the encoder is built from it (see ``TabArenaDefaultTextEncoder.get_default_encoder``) + and the cache path / bundled artifact filename are keyed by :attr:`id`. + """ + + model: str + """Hugging Face model id of the sentence-transformer encoder.""" + truncate_dim: int + """Matryoshka truncation dimension applied to the embeddings.""" + revision: int + """Bump for embedding-affecting changes NOT captured by ``model``/``truncate_dim`` (prompt, + normalization, dtype, text preprocessing, an ST upgrade that changes outputs, ...).""" + description: str + """Human-readable note describing this embedding configuration (for maintainers).""" + + @property + def id(self) -> str: + """Forward-safe cache id, e.g. ``Qwen3-Embedding-8B-d32-r1``.""" + return f"{self.model.rsplit('/', 1)[-1]}-d{self.truncate_dim}-r{self.revision}" + + +#: The active semantic-text embedding. Change ``model``/``truncate_dim`` and/or bump ``revision`` +#: (per its docstring) whenever the produced embeddings change; this re-keys the cache so stale +#: caches are ignored rather than mis-loaded. +SEMANTIC_EMBEDDING = SemanticEmbeddingSpec( + model="Qwen/Qwen3-Embedding-8B", + truncate_dim=32, + revision=1, + description=( + "Qwen3-Embedding-8B with Matryoshka truncation to 32 dims; encoded with prompt_name='query', " + "L2-normalized, float16. Produced/consumed by SemanticTextFeatureGenerator." + ), +) + +TextCacheMode = Literal["require", "auto", "off"] +"""How the loader treats a (text) task's cache: ``require`` = must be present (raise if missing), +``auto`` = use it if present else compute on the fly, ``off`` = ignore the cache (always compute).""" + + +def embedding_id() -> str: + """Forward-safe id of the active embedding; the cache version tag (see :class:`SemanticEmbeddingSpec`).""" + return SEMANTIC_EMBEDDING.id + + +def text_cache_root() -> Path: + """Directory holding the embedding caches for the current encoder (created on demand).""" + from tabarena.loaders import get_tabarena_cache_root + + root = get_tabarena_cache_root() / "text_cache" / embedding_id() + root.mkdir(parents=True, exist_ok=True) + return root + + +def text_cache_key(task_id_or_object) -> str: + """Cache key for a task: the UserTask slug, or the integer OpenML task id as a string.""" + if isinstance(task_id_or_object, int): + return str(task_id_or_object) + slug = getattr(task_id_or_object, "slug", None) + if slug is not None: + return str(slug) + return str(task_id_or_object) + + +def text_cache_path(task_key: str) -> Path: + """Canonical (write) path for ``task_key`` under the current encoder's cache dir.""" + return text_cache_root() / f"{task_key}_cache.parquet" + + +def _legacy_text_cache_path(task_key: str) -> Path | None: + """Pre-refactor location: ``/tabarena_text_cache/text_cache/_cache.parquet``. + + Un-versioned and under the OpenML cache root. Returns ``None`` if OpenML is unavailable. + """ + try: + import openml + except ImportError: + return None + base = (openml.config._root_cache_directory / "tabarena_text_cache").expanduser().resolve() / "text_cache" + return base / f"{task_key}_cache.parquet" + + +def resolve_existing_cache_path(task_key: str) -> Path | None: + """Return the path to an existing cache for ``task_key`` (canonical first, then legacy), or None.""" + canonical = text_cache_path(task_key) + if canonical.exists(): + return canonical + legacy = _legacy_text_cache_path(task_key) + if legacy is not None and legacy.exists(): + return legacy + return canonical if canonical.exists() else None + + +def has_text_cache(task_key: str) -> bool: + """Whether an embedding cache (canonical or legacy) exists for ``task_key``.""" + return resolve_existing_cache_path(task_key) is not None + + +def load_text_cache(path: str | Path) -> dict[str, np.ndarray]: + """Load an embedding cache parquet into a ``{text_value: embedding}`` dict.""" + from tabarena.benchmark.preprocessing.text_feature_generators import SemanticTextFeatureGenerator + + return SemanticTextFeatureGenerator.load_embedding_cache(path=path) + + +def save_text_cache(cache: dict[str, np.ndarray], path: str | Path) -> None: + """Write a ``{text_value: embedding}`` dict to a parquet at ``path``.""" + from tabarena.benchmark.preprocessing.text_feature_generators import SemanticTextFeatureGenerator + + Path(path).parent.mkdir(parents=True, exist_ok=True) + SemanticTextFeatureGenerator.save_embedding_cache(cache=cache, path=path) + + +def generate_text_cache(task_id_or_object, *, ignore_cache: bool = False) -> Path: + """Compute + cache the semantic embeddings for one task (maintainer / pre-generation path). + + Runs the model-agnostic preprocessing with only semantic-text features enabled over the task's + feature matrix, then writes the embedding cache to :func:`text_cache_path`. Skips computation if + a cache already exists (unless ``ignore_cache``). + """ + from tabarena.benchmark.preprocessing.model_agnostic_default_preprocessing import ( + TabArenaModelAgnosticPreprocessing, + ) + from tabarena.benchmark.preprocessing.text_feature_generators import SemanticTextFeatureGenerator + from tabarena.benchmark.task.openml import OpenMLTaskWrapper + from tabarena.benchmark.task.user_task import UserTask + + task_key = text_cache_key(task_id_or_object) + out_path = text_cache_path(task_key) + if (not ignore_cache) and out_path.exists(): + print(f"Text cache already exists for {task_key} at {out_path}; skipping generation.") + return out_path + + if isinstance(task_id_or_object, UserTask): + oml_task = task_id_or_object.load_local_openml_task() + else: + oml_task = task_id_or_object # already an OpenML task object + task = OpenMLTaskWrapper(task=oml_task) + print(f"Loaded {task_key}: {len(task.X)} rows x {len(task.X.columns)} columns.") + + preprocessing = TabArenaModelAgnosticPreprocessing( + enable_sematic_text_features=True, + enable_raw_text_features=False, + enable_text_special_features=False, + enable_statistical_text_features=False, + enable_text_ngram_features=False, + enable_datetime_features=False, + verbosity=4, + ) + preprocessing.fit_transform(X=task.X) + + save_text_cache(cache=SemanticTextFeatureGenerator._embedding_look_up, path=out_path) + SemanticTextFeatureGenerator._embedding_look_up.clear() + print(f"Text cache generated and saved to: {out_path}") + return out_path + + +@contextmanager +def use_text_cache_for_task(task_id_or_object, *, has_text: bool, mode: TextCacheMode = "require"): + """Load a task's embedding cache into the generator for the duration of a fit, then restore. + + On enter (when ``mode != "off"`` and the task ``has_text``): load the cache into the shared + ``SemanticTextFeatureGenerator._embedding_look_up`` and set ``only_load_from_cache=True`` so any + unseen text raises rather than silently recomputing. If the cache is missing, ``require`` raises + while ``auto`` falls back to on-the-fly computation. On exit, the generator's prior class state + is always restored, so per-task caches never leak across tasks in a long-lived process. + """ + from tabarena.benchmark.preprocessing.text_feature_generators import SemanticTextFeatureGenerator + + prev_lookup = SemanticTextFeatureGenerator._embedding_look_up + prev_only_load = SemanticTextFeatureGenerator.only_load_from_cache + try: + if mode != "off" and has_text: + task_key = text_cache_key(task_id_or_object) + cache_path = resolve_existing_cache_path(task_key) + if cache_path is not None: + print(f"[TEXT CACHE] Loading embeddings for {task_key} from {cache_path}") + SemanticTextFeatureGenerator._embedding_look_up = load_text_cache(cache_path) + SemanticTextFeatureGenerator.only_load_from_cache = True + elif mode == "require": + raise FileNotFoundError( + f"Text cache not found for task {task_key!r} (encoder {embedding_id()!r}). " + f"Expected at {text_cache_path(task_key)}. Pre-generate or download it " + f"(text_cache mode='require'); use mode='auto' to compute on the fly instead." + ) + yield + finally: + SemanticTextFeatureGenerator._embedding_look_up = prev_lookup + SemanticTextFeatureGenerator.only_load_from_cache = prev_only_load diff --git a/tabarena/tabarena/benchmark/preprocessing/text_feature_generators.py b/tabarena/tabarena/benchmark/preprocessing/text_feature_generators.py index 49e24b9c8..0b9005db3 100644 --- a/tabarena/tabarena/benchmark/preprocessing/text_feature_generators.py +++ b/tabarena/tabarena/benchmark/preprocessing/text_feature_generators.py @@ -52,9 +52,11 @@ def get_default_encoder(): import torch from sentence_transformers import SentenceTransformer + from tabarena.benchmark.preprocessing.text_cache import SEMANTIC_EMBEDDING + return SentenceTransformer( - "Qwen/Qwen3-Embedding-8B", - truncate_dim=32, # minimal MRL dimension for Qwen3-Embedding + SEMANTIC_EMBEDDING.model, + truncate_dim=SEMANTIC_EMBEDDING.truncate_dim, # minimal MRL dimension for Qwen3-Embedding model_kwargs={"dtype": torch.float16, "attn_implementation": "sdpa"}, processor_kwargs={"padding_side": "left"}, ) @@ -162,10 +164,12 @@ def get_cache_data_for_dataset(*, X: pd.DataFrame, seen_texts: set[str]) -> dict class SemanticTextFeatureGenerator(AbstractFeatureGenerator): - """Create semantic text embeddings using a pre-trained sentencetransformer model. + """Create semantic text embeddings using a pre-trained sentence-transformer model. - Uses ``Qwen/Qwen3-Embedding-0.6B`` with Matryoshka Representation Learning - (MRL) to produce compact ``_MRL_DIM``-dimensional embeddings per text value. + Uses the encoder declared by + :data:`~tabarena.benchmark.preprocessing.text_cache.SEMANTIC_EMBEDDING` + (``Qwen/Qwen3-Embedding-8B`` with Matryoshka truncation to 32 dims) to produce a compact + embedding per text value. """ _embedding_look_up: dict[str, np.ndarray] = {} @@ -282,12 +286,14 @@ def load_embedding_cache(path: str | Path) -> dict[str, np.ndarray]: @staticmethod def get_text_cache_dir(task_id_str: str) -> Path: - import openml + """Deprecated: prefer :func:`tabarena.benchmark.preprocessing.text_cache.text_cache_path`. + + Kept for back-compat; returns the canonical (encoder-versioned, TabArena-cache-root) path. + """ + from tabarena.benchmark.preprocessing.text_cache import text_cache_path - base_path = (openml.config._root_cache_directory / "tabarena_text_cache").expanduser().resolve() / "text_cache" - Path(base_path).mkdir(parents=True, exist_ok=True) + return text_cache_path(task_id_str) - return base_path / f"{task_id_str}_cache.parquet" class StatisticalTextFeatureGenerator(AbstractFeatureGenerator): """Generate a statistical embedding of text features using skrub. diff --git a/tabarena/tabarena/benchmark/task/data_foundry/__init__.py b/tabarena/tabarena/benchmark/task/data_foundry/__init__.py new file mode 100644 index 000000000..1c68758a0 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/data_foundry/__init__.py @@ -0,0 +1,54 @@ +"""Adapter for converting Data Foundry collections into TabArena UserTasks. + +Primary entry points: + +* :class:`DataFoundryAdapter` — drives a :class:`data_foundry.collections.DatasetCollection` + (online ``HuggingFaceSource`` or offline ``LocalWarehouseSource``) and converts every + container into a TabArena :class:`UserTask`, returning the joined task-metadata DataFrame. +* :func:`convert_curated_container_to_user_task` — single-container conversion, useful when + iterating containers yourself. +* :func:`prepare_collection_for_tabarena` / :func:`get_metadata_for_benchmark_suite` — + download (if needed) + convert + persist a task-metadata CSV under the OpenML cache, + for downstream slurm jobs to discover. +* :func:`load_reference_metadata` / :func:`materialize_task` / + :func:`generate_reference_metadata` — load a + reference metadata table without downloading, then materialize only the tasks that + survive filtering (see :class:`tabarena.benchmark.task.metadata.BeyondArenaMetadataBundle`). + +The vocabulary "data_foundry_uri" is preserved in the metadata DataFrame and CSV for +backward compatibility with existing downstream code (e.g. ``tabflow_slurm``). +""" + +from __future__ import annotations + +from tabarena.benchmark.task.data_foundry.adapter import ( + DEFAULT_EVAL_METRICS, + DataFoundryAdapter, + convert_curated_container_to_user_task, +) +from tabarena.benchmark.task.data_foundry.beyond_arena import ( + generate_reference_metadata, + get_beyond_arena_collection, + load_reference_metadata, + materialize_task, + reference_metadata_package_path, +) +from tabarena.benchmark.task.data_foundry.metadata_cache import ( + get_metadata_for_benchmark_suite, + get_path_to_metadata_cache, + prepare_collection_for_tabarena, +) + +__all__ = [ + "DEFAULT_EVAL_METRICS", + "DataFoundryAdapter", + "convert_curated_container_to_user_task", + "generate_reference_metadata", + "get_beyond_arena_collection", + "get_metadata_for_benchmark_suite", + "get_path_to_metadata_cache", + "load_reference_metadata", + "materialize_task", + "prepare_collection_for_tabarena", + "reference_metadata_package_path", +] diff --git a/tabarena/tabarena/benchmark/task/data_foundry/adapter.py b/tabarena/tabarena/benchmark/task/data_foundry/adapter.py new file mode 100644 index 000000000..b29d4368e --- /dev/null +++ b/tabarena/tabarena/benchmark/task/data_foundry/adapter.py @@ -0,0 +1,269 @@ +"""Convert Data Foundry curated containers into TabArena UserTasks. + +This module is the TabArena-side bridge for the ``data_foundry`` package. It takes +:class:`data_foundry.curation_container.CuratedContainer` objects (typically obtained +by iterating a :class:`data_foundry.collections.DatasetCollection`) and produces +TabArena :class:`UserTask` instances together with a metadata DataFrame describing them. + +Caching, downloading, and source-resolution (Hugging Face vs. local warehouse) are +delegated to ``data_foundry``: the collection's :class:`~data_foundry.collections.DataSource` +handles all of that. This adapter only owns the TabArena-specific conversion logic: + +* problem-type / target-dtype validation, +* eval-metric fallback against TabArena's supported metric set per problem type, +* assembling the per-task metadata into a single DataFrame keyed by ``data_foundry_uri``. +""" + +from __future__ import annotations + +from copy import deepcopy +from typing import TYPE_CHECKING + +import pandas as pd +from loguru import logger +from tqdm import tqdm + +from tabarena.benchmark.task.user_task import UserTask + +if TYPE_CHECKING: + from data_foundry.collections import DatasetCollection + from data_foundry.curation_container import CuratedContainer + +DEFAULT_EVAL_METRICS: dict[str, list[str]] = { + "binary_classification": ["roc_auc"], + "multiclass_classification": ["log_loss"], + "regression": ["root_mean_squared_error"], +} +"""Allowed TabArena eval metrics per Data Foundry ``problem_type``. + +If a curated container's ``objective_metric_name`` is not in the allowed list for its +problem type, the first metric in the list is used as a fallback. Override by passing +``evaluation_metrics=...`` to the adapter or converter. +""" + + +class DataFoundryAdapter: + """Convert every container in a Data Foundry collection into a TabArena UserTask. + + The adapter is a thin driver around a :class:`DatasetCollection`: it walks the + collection (downloading via its ``source`` when needed) and converts each + :class:`CuratedContainer` into a local OpenML task via :class:`UserTask`. The + resulting ``UserTask`` is persisted in the OpenML cache (see + :meth:`UserTask.save_local_openml_task`) and one metadata row per task is collected + into a DataFrame. + + Example: + >>> from data_foundry.collections import BEYOND_ARENA + >>> adapter = DataFoundryAdapter(collection=BEYOND_ARENA) + >>> metadata_df = adapter.to_tabarena_user_tasks() + + Offline use with a pre-populated local warehouse:: + + from data_foundry.collections import DatasetCollection, LocalWarehouseSource + + collection = DatasetCollection.from_relative_paths( + name="beyond_iid_benchmark_2026", + description="...", + relative_paths=["musk/", "mercedes_benz_.../", ...], + source=LocalWarehouseSource(base_dir=Path("/.../local-data-warehouse")), + ) + adapter = DataFoundryAdapter(collection=collection) + metadata_df = adapter.to_tabarena_user_tasks() + """ + + def __init__( + self, + *, + collection: DatasetCollection, + evaluation_metrics: dict[str, list[str]] | None = None, + ) -> None: + """Initialize the adapter. + + Args: + collection: The Data Foundry collection to convert. Must have a + :class:`~data_foundry.collections.DataSource` configured (either a + ``HuggingFaceSource`` for online access, a ``LocalWarehouseSource`` + pointing at a pre-populated directory, or any custom source). + evaluation_metrics: Mapping from Data Foundry ``problem_type`` to the list of + eval metrics TabArena is willing to use for that problem type. If a + container's ``objective_metric_name`` is not in the allowed list, the + first metric in the list is used as a fallback. ``None`` uses + :data:`DEFAULT_EVAL_METRICS`. + """ + if collection.source is None: + raise ValueError( + f"Collection {collection.name!r} has no `source` configured. " + "Wire a HuggingFaceSource (online), LocalWarehouseSource (offline), " + "or other DataSource subclass before passing the collection to the adapter.", + ) + self.collection = collection + self.evaluation_metrics = deepcopy(DEFAULT_EVAL_METRICS) if evaluation_metrics is None else evaluation_metrics + + def to_tabarena_user_tasks( + self, + *, + cache_dir: str | None = None, + force_download: bool = False, + show_sample: bool = False, + ) -> pd.DataFrame: + """Convert every container in the collection into a TabArena UserTask. + + Args: + cache_dir: Optional override for the data_foundry cache directory used by the + collection's :class:`DataSource`. Forwarded as-is to + :meth:`DatasetCollection.iter_containers`. + force_download: When ``True``, bypass any cached copy and re-fetch every + container from its source. Forwarded to ``iter_containers``. + show_sample: When ``True``, prints a 5-row sample of the resulting metadata + DataFrame for quick visual inspection. + + Returns: + A DataFrame with one row per ``(task, split)`` combination, containing all + fields from :class:`TabArenaTaskMetadata`/:class:`SplitMetadata` plus a + ``data_foundry_uri`` column with the relative path used to identify the + container in the source collection. + """ + rows: list[pd.DataFrame] = [] + + iterator = self.collection.iter_containers( + cache_dir=cache_dir, + force_download=force_download, + ) + for entry, container in tqdm( + zip(self.collection.entries, iterator, strict=True), + total=len(self.collection), + desc=f"Converting {self.collection.name} containers to TabArena UserTasks", + ): + user_task = convert_curated_container_to_user_task( + container=container, + evaluation_metrics=self.evaluation_metrics, + ) + oml_task = user_task.load_local_openml_task() + + task_metadata = oml_task.compute_metadata( + tabarena_task_name=user_task.tabarena_task_name, + task_id_str=user_task.task_id_str, + ) + task_metadata.data_foundry_uri = entry.relative_path.as_posix() + # Warehouse-level metadata only available from the Data Foundry container. + task_metadata.domain = container.dataset_metadata.domain_str + task_metadata.dataset_year = container.dataset_metadata.dataset_year + task_metadata.source = container.dataset_metadata.dataset_source + rows.append(task_metadata.to_dataframe()) + + metadata_df = pd.concat(rows, ignore_index=True) if rows else pd.DataFrame() + + if show_sample and not metadata_df.empty: + with pd.option_context( + "display.max_rows", + None, + "display.max_columns", + None, + "display.width", + None, + "display.max_colwidth", + None, + ): + print(metadata_df.sample(min(5, len(metadata_df)), random_state=42)) + + return metadata_df + + +def convert_curated_container_to_user_task( # noqa: C901 — linear problem-type validation + *, + container: CuratedContainer, + evaluation_metrics: dict[str, list[str]] | None = None, +) -> UserTask: + """Convert a single Data Foundry curated container into a TabArena UserTask. + + Validates the target column matches the declared ``problem_type``, resolves the + eval metric against the allowed list (falling back to the first allowed metric + when the container's metric is not allowed), and persists the resulting + ``UserTask`` to the OpenML cache so it can be reloaded later via + :meth:`UserTask.load_local_openml_task`. + + Args: + container: An already-loaded :class:`CuratedContainer` (e.g. yielded by + :meth:`DatasetCollection.iter_containers` or returned by + :meth:`DatasetCollection.get_dataset`). + evaluation_metrics: Mapping from Data Foundry ``problem_type`` to the list of + eval metrics TabArena is willing to use. ``None`` skips fallback and uses + whatever the container declares. + + Returns: + The persisted TabArena :class:`UserTask`. + """ + target_name = container.task_metadata.target_column_name + y: pd.Series = container.dataset[target_name] + df_problem_type = container.task_metadata.problem_type + unique_name = container.dataset_metadata.unique_name + + if df_problem_type == "regression": + problem_type = "regression" + if not pd.api.types.is_numeric_dtype(y): + raise ValueError( + f"Target column {target_name!r} is not numeric for regression problem. ({unique_name})", + ) + elif df_problem_type == "binary_classification": + problem_type = "classification" + if not isinstance(y.dtype, pd.CategoricalDtype): + raise ValueError( + f"Target column {target_name!r} is not categorical for classification problem. ({unique_name})", + ) + if y.nunique() != 2: + raise ValueError( + f"Target column {target_name!r} has {y.nunique()} classes, " + f"but expected 2 for binary classification problem. ({unique_name})", + ) + elif df_problem_type == "multiclass_classification": + problem_type = "classification" + if not isinstance(y.dtype, pd.CategoricalDtype): + raise ValueError( + f"Target column {target_name!r} is not categorical for classification problem. ({unique_name})", + ) + if y.nunique() < 3: + raise ValueError( + f"Target column {target_name!r} has {y.nunique()} classes, " + f"but expected at least 3 for multiclass classification " + f"problem. ({unique_name})", + ) + else: + raise ValueError(f"Unknown problem type {df_problem_type!r}") + + # Eval-metric fallback against TabArena's allowed set. + eval_metric = container.task_metadata.objective_metric_name + if evaluation_metrics is not None: + allowed = evaluation_metrics[df_problem_type] + if eval_metric not in allowed: + fallback = allowed[0] + logger.debug( + f"Objective metric {eval_metric!r} not allowed for problem type " + f"{problem_type!r}. Falling back to {fallback!r}.", + ) + eval_metric = fallback + + user_task = UserTask(task_name=container.unique_name) + oml_task = user_task.create_local_openml_task( + dataset=container.dataset, + target_feature=target_name, + problem_type=problem_type, + splits=container.experiment_metadata.splits, + eval_metric=eval_metric, + stratify_on=container.task_metadata.stratify_on, + group_on=container.task_metadata.group_on, + time_on=container.task_metadata.time_on, + group_time_on=container.task_metadata.group_time_on, + dataset_name=container.dataset_metadata.unique_name, + group_labels=container.task_metadata.group_labels, + split_time_horizon=container.experiment_metadata.time_horizon, + split_time_horizon_unit=container.experiment_metadata.time_horizon_unit, + ) + user_task.save_local_openml_task(oml_task) + + # The container ships its semantic-text embedding cache as an extra artifact; copy it into the + # canonical tabarena cache so the fit-time loader finds it (no-op when the container has none). + from tabarena.benchmark.task.data_foundry.text_cache import import_text_cache_from_container + + import_text_cache_from_container(container, user_task.slug) + + return user_task diff --git a/tabarena/tabarena/benchmark/task/data_foundry/beyond_arena.py b/tabarena/tabarena/benchmark/task/data_foundry/beyond_arena.py new file mode 100644 index 000000000..cf34d8f70 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/data_foundry/beyond_arena.py @@ -0,0 +1,216 @@ +"""End-to-end glue for running a Data Foundry collection (e.g. ``BeyondArena``). + +This module turns "the user picked a collection + some filters" into "runnable +TabArena tasks", while downloading as little as possible: + +* :func:`load_reference_metadata` returns a task-metadata DataFrame *without* + downloading any dataset. It prefers a git-committed reference CSV (shipped as + package data) so a user can filter by name / dtype / size before fetching + anything. If no committed CSV exists it regenerates one by downloading the + whole collection (and caches the result). +* :func:`materialize_task` ensures a *single* task's local OpenML pickle exists, + downloading + converting its container only when it is not already on disk. + This is what makes "download only the datasets that survived the filter" work. + +Task ids are portable by construction: :attr:`UserTask.task_id` is a deterministic +hash of the (collection-unique) task name, and standardized tasks serialize their +``task_id_str`` *without* a cache path (see :meth:`UserTask.task_id_str`), so the +same reference CSV resolves correctly on any machine via the ambient OpenML cache. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import TYPE_CHECKING + +import pandas as pd + +from tabarena.benchmark.task.data_foundry.adapter import ( + DEFAULT_EVAL_METRICS, + DataFoundryAdapter, + convert_curated_container_to_user_task, +) +from tabarena.benchmark.task.data_foundry.metadata_cache import get_path_to_metadata_cache + +if TYPE_CHECKING: + from data_foundry.collections import DatasetCollection + + +def get_beyond_arena_collection() -> DatasetCollection: + """Return the official ``BeyondArena`` Data Foundry collection. + + Imported lazily so ``tabarena`` works without the optional ``data-foundry`` + dependency installed. + """ + from data_foundry.collections import BEYOND_ARENA + + return BEYOND_ARENA + + +def reference_metadata_package_path(collection_name: str) -> Path: + """Path to the git-committed reference-metadata CSV for ``collection_name``. + + Shipped as package data in the unified sources data directory + (``benchmark/task/metadata/sources/data/``, shared with the built-in suites). + The file may not exist — callers fall back to regeneration when it is missing. + """ + from tabarena.benchmark.task.metadata.sources.base import committed_metadata_path + + return committed_metadata_path(collection_name) + + +def _generated_metadata_cache_path(collection_name: str) -> Path: + """Path of the regenerated metadata CSV inside the OpenML metadata cache.""" + return get_path_to_metadata_cache() / f"{collection_name}_tasks_metadata.csv" + + +def load_reference_metadata( + *, + collection: DatasetCollection | None = None, + cache_dir: str | None = None, + force_regenerate: bool = False, +) -> pd.DataFrame: + """Return the reference task-metadata DataFrame for ``collection``. + + Resolution order (unless ``force_regenerate``): + + 1. The git-committed CSV (package data) — no dataset downloads. + 2. A previously regenerated CSV in the OpenML metadata cache. + 3. Regenerate by downloading + converting the whole collection, caching the + result for next time. + + Args: + collection: Collection to describe. Defaults to ``BeyondArena``. + cache_dir: Optional data_foundry cache override used only when regenerating. + force_regenerate: Skip both caches and rebuild from the source collection. + + Returns: + One row per ``(task, split)`` with all :class:`TabArenaTaskMetadata` + columns plus ``data_foundry_uri``. ``task_id_str`` uses the portable + sentinel cache path (see :func:`localize_task_id_str`). + """ + if collection is None: + collection = get_beyond_arena_collection() + + if not force_regenerate: + committed = reference_metadata_package_path(collection.name) + if committed.exists(): + print(f"Loading committed {collection.name} reference metadata from {committed}.") + return pd.read_csv(committed) + + cached = _generated_metadata_cache_path(collection.name) + if cached.exists(): + print(f"Loading regenerated {collection.name} reference metadata from {cached}.") + return pd.read_csv(cached) + + out_path = generate_reference_metadata( + collection=collection, + out_path=_generated_metadata_cache_path(collection.name), + cache_dir=cache_dir, + force_download=force_regenerate, + ) + return pd.read_csv(out_path) + + +def generate_reference_metadata( + *, + collection: DatasetCollection | None = None, + out_path: str | Path | None = None, + cache_dir: str | None = None, + force_download: bool = False, + evaluation_metrics: dict[str, list[str]] | None = None, +) -> Path: + """Build the reference-metadata CSV for a collection (maintainer tool). + + Downloads + converts *every* container in the collection (creating each local + OpenML task pickle as a side effect), then writes the metadata CSV. The CSV is + machine-independent and clean to commit: standardized ``UserTask`` ids carry no + cache path (see :meth:`UserTask.task_id_str`). + + Commit the returned file to ``data_foundry/data/_tasks_metadata.csv`` + to enable filter-before-download for everyone. + + Args: + collection: Collection to materialize. Defaults to ``BeyondArena``. + out_path: Where to write the CSV. Defaults to the OpenML metadata cache. + cache_dir: Optional data_foundry cache override (where containers download). + force_download: Re-fetch every container from its source. + evaluation_metrics: Override the allowed eval metrics per problem type. + + Returns: + The path the CSV was written to. + """ + if collection is None: + collection = get_beyond_arena_collection() + + adapter = DataFoundryAdapter(collection=collection, evaluation_metrics=evaluation_metrics) + metadata_df = adapter.to_tabarena_user_tasks(cache_dir=cache_dir, force_download=force_download) + + out_path = Path(out_path) if out_path is not None else _generated_metadata_cache_path(collection.name) + out_path.parent.mkdir(parents=True, exist_ok=True) + print(f"Writing {len(metadata_df)} reference-metadata rows to {out_path}.") + metadata_df.to_csv(out_path, index=False) + return out_path + + +def materialize_task( + *, + collection: DatasetCollection, + task_id_str: str, + data_foundry_uri: str, + evaluation_metrics: dict[str, list[str]] | None = None, + cache_dir: str | None = None, + force_download: bool = False, +) -> str: + """Ensure one task's local OpenML pickle exists; return its local ``task_id_str``. + + If the pickle already exists on disk (matched by the stable task name) and + ``force_download`` is ``False``, this is a no-op that just returns the id — + *no* dataset is downloaded. Otherwise the container is fetched via the + collection's source (cached by data_foundry) and converted into a local + OpenML task. + + Args: + collection: The source collection (used to resolve + download the entry). + task_id_str: The ``UserTask`` id from the reference metadata. Only its + task-name segment is used; the pickle is resolved against the ambient + OpenML cache. + data_foundry_uri: The collection-entry relative path identifying the + container to download (``/[versions/]``). + evaluation_metrics: Allowed eval metrics per problem type. Defaults to + :data:`DEFAULT_EVAL_METRICS`. + cache_dir: Optional data_foundry cache override. + force_download: Re-fetch + reconvert even if the pickle already exists. + + Returns: + The ``task_id_str`` whose resolved pickle path now exists on disk. + """ + from tabarena.benchmark.task.user_task import UserTask + + if not data_foundry_uri or pd.isna(data_foundry_uri): + raise ValueError( + f"Cannot materialize task {task_id_str!r}: missing `data_foundry_uri`. " + "Reference metadata for a Data Foundry collection must carry it.", + ) + + task = UserTask.from_task_id_str(task_id_str) + if (not force_download) and task.openml_task_path.exists(): + # Dataset already local (no re-download). Still ensure its text cache was imported from the + # (already-downloaded) container — handles tasks materialized before text-cache import. + from tabarena.benchmark.task.data_foundry.text_cache import ensure_text_cache_for_task + + ensure_text_cache_for_task( + collection=collection, data_foundry_uri=data_foundry_uri, task_key=task.slug, cache_dir=cache_dir + ) + return task.task_id_str + + if evaluation_metrics is None: + evaluation_metrics = DEFAULT_EVAL_METRICS + + uuid = Path(data_foundry_uri).name + container = collection.get_dataset(uuid, cache_dir=cache_dir, force_download=force_download) + user_task = convert_curated_container_to_user_task( + container=container, + evaluation_metrics=evaluation_metrics, + ) + return user_task.task_id_str diff --git a/tabarena/tabarena/benchmark/task/data_foundry/metadata_cache.py b/tabarena/tabarena/benchmark/task/data_foundry/metadata_cache.py new file mode 100644 index 000000000..bb63d219a --- /dev/null +++ b/tabarena/tabarena/benchmark/task/data_foundry/metadata_cache.py @@ -0,0 +1,136 @@ +"""Persist + retrieve the per-collection TabArena task-metadata CSV. + +Downstream consumers (e.g. ``tabflow_slurm``) need a stable on-disk manifest that +lists which TabArena tasks exist for a given benchmark suite so workers can iterate +them without re-running the conversion. The contract is: + +* The manifest is written to ``/tabarena_metadata_cache/_tasks_metadata.csv``. +* Columns mirror :meth:`TabArenaTaskMetadata.to_dataframe` and include a + ``data_foundry_uri`` column with the collection-entry relative path. + +This module keeps that contract intact while delegating download + conversion to +:class:`DataFoundryAdapter` and the underlying ``data_foundry`` collection. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import TYPE_CHECKING + +import openml + +from tabarena.benchmark.task.data_foundry.adapter import DataFoundryAdapter + +if TYPE_CHECKING: + import pandas as pd + from data_foundry.collections import DatasetCollection + + +def _init_openml_cache(openml_cache: str | Path) -> None: + print(f"Setting OpenML cache directory to: {openml_cache}") + openml.config.set_root_cache_directory(root_cache_directory=str(openml_cache)) + + +def get_path_to_metadata_cache() -> Path: + """Return the directory where benchmark-suite metadata CSVs are stored. + + Resolves to ``/tabarena_metadata_cache``. + The directory is not created here — callers materialize it on write. + """ + return openml.config._root_cache_directory / "tabarena_metadata_cache" + + +def prepare_collection_for_tabarena( + *, + collection: DatasetCollection, + benchmark_suite_name: str | None = None, + cache_dir: str | None = None, + openml_cache: str | Path | None = None, + force_download: bool = False, + evaluation_metrics: dict[str, list[str]] | None = None, + show_sample: bool = True, +) -> Path: + """Materialize TabArena UserTasks for ``collection`` and save the metadata CSV. + + Convenience wrapper that: + + 1. Optionally points OpenML's root cache at ``openml_cache`` (so the UserTask + pickles + the metadata CSV land in a controlled location, e.g. a shared + filesystem). + 2. Runs :class:`DataFoundryAdapter` over the collection — downloading via its + source if needed (HuggingFace) or just resolving local paths + (LocalWarehouseSource). + 3. Writes the resulting DataFrame to + ``/tabarena_metadata_cache/_tasks_metadata.csv``. + + Args: + collection: The :class:`DatasetCollection` to process. + benchmark_suite_name: Identifier used to name the metadata CSV. Defaults to + ``collection.name``. Pass an explicit value when one source collection + backs multiple named suites. + cache_dir: Optional override for the data_foundry cache (e.g. when + downloading from HF). Forwarded to ``DatasetCollection.iter_containers``. + openml_cache: If given, sets OpenML's root cache directory before any + artifact is written. Affects where ``UserTask`` pickles and the + metadata CSV land. + force_download: When ``True``, re-fetch every container from its source. + evaluation_metrics: Override TabArena's allowed-eval-metric set. See + :data:`adapter.DEFAULT_EVAL_METRICS`. + show_sample: When ``True``, prints a 5-row sample of the metadata DataFrame. + + Returns: + The path to the metadata CSV that was written. + """ + if openml_cache is not None: + _init_openml_cache(openml_cache=openml_cache) + + suite_name = benchmark_suite_name if benchmark_suite_name is not None else collection.name + print(f"Preparing data foundry collection {collection.name!r} for TabArena suite {suite_name!r}...") + + adapter = DataFoundryAdapter( + collection=collection, + evaluation_metrics=evaluation_metrics, + ) + metadata_df: pd.DataFrame = adapter.to_tabarena_user_tasks( + cache_dir=cache_dir, + force_download=force_download, + show_sample=show_sample, + ) + + path_to_metadata = get_path_to_metadata_cache() / f"{suite_name}_tasks_metadata.csv" + path_to_metadata.parent.mkdir(parents=True, exist_ok=True) + print(f"Saving metadata to {path_to_metadata}") + metadata_df.to_csv(path_to_metadata, index=False) + return path_to_metadata + + +def get_metadata_for_benchmark_suite( + benchmark_suite_name: str, + *, + openml_cache: str | Path | None = None, +) -> Path: + """Return the metadata CSV path for ``benchmark_suite_name``. + + Args: + benchmark_suite_name: The suite name used when the manifest was written + (see :func:`prepare_collection_for_tabarena`). + openml_cache: If given, sets OpenML's root cache directory before + resolving the path. Use the same value here as during preparation. + + Returns: + Path to the existing ``_tasks_metadata.csv``. + + Raises: + FileNotFoundError: If no manifest has been written for ``benchmark_suite_name``. + """ + if openml_cache is not None: + _init_openml_cache(openml_cache=openml_cache) + + path_to_metadata = get_path_to_metadata_cache() / f"{benchmark_suite_name}_tasks_metadata.csv" + if not path_to_metadata.exists(): + raise FileNotFoundError( + f"Metadata file {path_to_metadata} does not exist. " + f"Call prepare_collection_for_tabarena(...) for suite " + f"{benchmark_suite_name!r} first.", + ) + return path_to_metadata diff --git a/tabarena/tabarena/benchmark/task/data_foundry/text_cache.py b/tabarena/tabarena/benchmark/task/data_foundry/text_cache.py new file mode 100644 index 000000000..a8d28a3a6 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/data_foundry/text_cache.py @@ -0,0 +1,89 @@ +"""Import the semantic-text embedding cache that ships *inside* a Data Foundry container. + +The BeyondArena HF dataset (e.g. ``TabArena/BeyondArena``) ships, inside each dataset container +directory (``//``), a ``tabarena_text_cache.parquet`` *extra artifact* alongside the +dataset files. data_foundry downloads the whole container folder in one ``snapshot_download``, so the +text cache arrives on disk together with the dataset — there is no separate text-cache download. + +This module copies that bundled parquet into the canonical, slug-keyed tabarena cache +(:func:`~tabarena.benchmark.preprocessing.text_cache.text_cache_path`) so the fit-time loader finds +it like any other cache. The copy is triggered when a container is converted to a TabArena task +(materialization); :func:`ensure_text_cache_for_task` re-derives it from the already-downloaded +container for tasks materialized before the cache was imported. +""" + +from __future__ import annotations + +import shutil +from pathlib import Path +from typing import TYPE_CHECKING + +from loguru import logger + +if TYPE_CHECKING: + from data_foundry.collections import DatasetCollection + +#: Prefix of the text-cache extra artifact shipped inside each Data Foundry container. The encoder +#: ```` is appended (the container can't use a subfolder — extra artifacts are bare +#: files and the snapshot download glob is non-recursive), so the filename carries the same +#: encoder-versioning the tabarena local cache encodes as a subfolder. +CONTAINER_TEXT_CACHE_PREFIX = "tabarena_text_cache" + + +def container_text_cache_filename(embedding_id: str | None = None) -> str: + """Filename of the text-cache artifact in a container for the given (or current) encoder.""" + if embedding_id is None: + from tabarena.benchmark.preprocessing.text_cache import embedding_id as current_embedding_id + + embedding_id = current_embedding_id() + return f"{CONTAINER_TEXT_CACHE_PREFIX}_{embedding_id}.parquet" + + +def import_text_cache_from_container(container, task_key: str) -> Path | None: + """Copy a container's bundled text cache into the canonical tabarena location. + + ``container`` is a (downloaded) ``data_foundry.curation_container.CuratedContainer`` — its + ``loaded_from_path`` points at the on-disk container dir. Looks for the encoder-versioned + artifact (``tabarena_text_cache_.parquet``). Returns the destination path, or + ``None`` if the container ships no cache for the current encoder. Existing destinations are kept. + """ + from tabarena.benchmark.preprocessing.text_cache import text_cache_path + + filename = container_text_cache_filename() + if not container.has_extra_file(filename): + logger.debug(f"[text-cache] {task_key}: container ships no '{filename}'; skipping.") + return None + dst = text_cache_path(task_key) + if dst.exists(): + logger.debug(f"[text-cache] {task_key}: already present at {dst}; skipping import.") + return dst + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(container.extra_file_path(filename), dst) + logger.info(f"[text-cache] {task_key}: imported embeddings from container -> {dst}") + return dst + + +def ensure_text_cache_for_task( + *, + collection: DatasetCollection, + data_foundry_uri: str, + task_key: str, + cache_dir: str | None = None, + force_download: bool = False, +) -> Path | None: + """Ensure ``task_key``'s text cache is in the canonical location, importing it from the container. + + No-op (returns the existing path) if the cache is already present. Otherwise resolves the task's + container — using the local Data Foundry download if available, else fetching it from the source + (the same ``snapshot_download`` that brings the dataset) — and copies its bundled text cache. + """ + from tabarena.benchmark.preprocessing.text_cache import resolve_existing_cache_path + + if not force_download: + existing = resolve_existing_cache_path(task_key) + if existing is not None: + return existing + + uuid = Path(data_foundry_uri).name + container = collection.get_dataset(uuid, cache_dir=cache_dir, force_download=force_download) + return import_text_cache_from_container(container, task_key) diff --git a/tabarena/tabarena/benchmark/task/metadata/__init__.py b/tabarena/tabarena/benchmark/task/metadata/__init__.py new file mode 100644 index 000000000..9cc8bc3dc --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/__init__.py @@ -0,0 +1,47 @@ +"""TabArena task metadata: schema (data classes) and bundle (selection/filtering).""" + +from __future__ import annotations + +from tabarena.benchmark.task.metadata.bundles import ( + BeyondArenaLiteMetadataBundle, + BeyondArenaMetadataBundle, + TabArenaMetadataBundle, + TabArenaV0pt1LiteMetadataBundle, + TabArenaV0pt1MetadataBundle, +) +from tabarena.benchmark.task.metadata.schema import ( + GroupLabelTypes, + SplitIndex, + SplitMetadata, + SplitTimeHorizonTypes, + SplitTimeHorizonUnitTypes, + TabArenaTaskMetadata, +) +from tabarena.benchmark.task.metadata.sources import ( + DataFoundryTaskMetadataSource, + InMemoryTaskMetadataSource, + OpenMLTaskMetadataSource, + TabArenaV0pt1TaskMetadataSource, + TaskMetadataSource, + resolve_source, +) + +__all__ = [ + "BeyondArenaLiteMetadataBundle", + "BeyondArenaMetadataBundle", + "DataFoundryTaskMetadataSource", + "GroupLabelTypes", + "InMemoryTaskMetadataSource", + "OpenMLTaskMetadataSource", + "SplitIndex", + "SplitMetadata", + "SplitTimeHorizonTypes", + "SplitTimeHorizonUnitTypes", + "TabArenaMetadataBundle", + "TabArenaTaskMetadata", + "TabArenaV0pt1LiteMetadataBundle", + "TabArenaV0pt1MetadataBundle", + "TabArenaV0pt1TaskMetadataSource", + "TaskMetadataSource", + "resolve_source", +] diff --git a/tabarena/tabarena/benchmark/task/metadata/bundles/__init__.py b/tabarena/tabarena/benchmark/task/metadata/bundles/__init__.py new file mode 100644 index 000000000..2f22b000e --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/bundles/__init__.py @@ -0,0 +1,26 @@ +"""Task-selection bundles: the base class plus shipped benchmark-suite presets. + +A bundle defines *which* tasks (datasets x splits) to run and how to filter them. +``base`` holds the generic :class:`TabArenaMetadataBundle`; the other modules are +thin presets pinned to a specific benchmark suite. +""" + +from __future__ import annotations + +from tabarena.benchmark.task.metadata.bundles.base import TabArenaMetadataBundle +from tabarena.benchmark.task.metadata.bundles.beyond_arena import ( + BeyondArenaLiteMetadataBundle, + BeyondArenaMetadataBundle, +) +from tabarena.benchmark.task.metadata.bundles.tabarena_v0pt1 import ( + TabArenaV0pt1LiteMetadataBundle, + TabArenaV0pt1MetadataBundle, +) + +__all__ = [ + "BeyondArenaLiteMetadataBundle", + "BeyondArenaMetadataBundle", + "TabArenaMetadataBundle", + "TabArenaV0pt1LiteMetadataBundle", + "TabArenaV0pt1MetadataBundle", +] diff --git a/tabarena/tabarena/benchmark/task/metadata/bundles/base.py b/tabarena/tabarena/benchmark/task/metadata/bundles/base.py new file mode 100644 index 000000000..16624ac18 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/bundles/base.py @@ -0,0 +1,182 @@ +"""Task (dataset x split) selection and metadata loading/filtering.""" + +from __future__ import annotations + +import re +from dataclasses import dataclass, field +from pathlib import Path +from typing import Literal + +import pandas as pd + +from tabarena.benchmark.task.metadata.schema import SplitMetadata, TabArenaTaskMetadata +from tabarena.benchmark.task.metadata.sources import TaskMetadataSource, resolve_source + + +@dataclass +class TabArenaMetadataBundle: + """Encapsulates the source of task metadata and any filters applied on top of it. + + Can be used to define which tasks (datasets x splits) to run in a benchmark. + """ + + task_metadata: ( + TaskMetadataSource + | Literal["TabArena-v0.1", "BeyondArena"] + | pd.DataFrame + | list[TabArenaTaskMetadata] + | str + | Path + ) + """Where the tasks come from, resolved via + :func:`~tabarena.benchmark.task.metadata.sources.resolve_source`. + + Accepts a :class:`TaskMetadataSource` instance (e.g. + ``DataFoundryTaskMetadataSource(my_collection)``), a registered suite literal + (``"TabArena-v0.1"``, ``"BeyondArena"``), a pandas DataFrame, a list of + TabArenaTaskMetadata, or a str/Path to a CSV file. DataFrames/CSVs are parsed + per row via :meth:`TabArenaTaskMetadata.from_row`. + """ + problem_types_to_run: list[str] = field( + default_factory=lambda: [ + "binary", + "multiclass", + "regression", + ] + ) + """Problem types to run in the benchmark. Adjust as needed to run only + specific problem types. + Options: "binary", "regression", "multiclass". + + Can be understood as a filter on top of the TabArenaTaskMetadata. + """ + split_indices_to_run: list[str] | Literal["lite"] | None = None + """Split indices to run in the benchmark. Adjust as needed to run only specific + splits. If None, we run all splits. If "lite", we run only the first split.""" + required_dtypes_to_run: list[str] | None = None + """Adjust as needed to run only datasets with at least one column of data types. + Options: "numeric", "categorical", "text", "datetime". + If None, we do not require any data types. + """ + forbidden_dtypes_to_run: list[str] | None = None + """Adjust as needed to run only datasets without any columns of data types. + Options: "numeric", "categorical", "text", "datetime". + If None, we do not forbid any data types. + """ + n_train_samples_to_run: tuple[int | None, int | None] | None = None + """Tuple of lower and upper limit for the number of training samples of datasets run in the benchmark. + Adjust as needed to run only datasets with a certain number of training samples. + If None, we run all datasets. + Lower limit is exclusive, upper limit is inclusive. For example, (0, 1000) runs only datasets with less + than 1000 training samples. If a tuple value is None, there is no limit in that direction. + """ + dataset_names_to_run: list[str] | None = None + """List of dataset names to run in the benchmark. Adjust as needed to run only specific datasets. + If None, we run all datasets. Matches against `dataset_name` of the task metadata. + """ + materialize: bool = True + """Whether to make the surviving (filtered) tasks runnable via the source's + :meth:`~tabarena.benchmark.task.metadata.sources.base.TaskMetadataSource.materialize` + hook. For sources backed by a remote collection (e.g. Data Foundry) this + downloads + converts only the tasks that survived filtering; for already-local + sources it is a no-op. Set False to inspect / filter metadata without any + downloads. + """ + + def load_task_metadata(self) -> list[TabArenaTaskMetadata]: + """Load, filter, and (optionally) materialize the task metadata to run.""" + source = resolve_source(self.task_metadata) + task_metadata = source.load() + + # Unify format to be unrolled (one entry per split). + task_metadata = [single_ttm for ttm in task_metadata for single_ttm in ttm.unroll_splits()] + + filter_steps = [ + ("problem types", self._filter_by_problem_types), + ("splits", self._filter_by_split_indices), + ("dataset names", self._filter_by_dataset_names), + ("dtypes", self._filter_by_dtypes), + ("dataset size", self._filter_by_train_samples), + ] + + filter_history: list[tuple[str, int]] = [("Starting", len(task_metadata))] + for label, filter_fn in filter_steps: + task_metadata = filter_fn(task_metadata) + filter_history.append((f"Filter to {label}", len(task_metadata))) + + if self.materialize: + source.materialize(task_metadata) + + self._sanity_check_task_ids(task_metadata) + self._print_filter_history(filter_history) + return task_metadata + + def _filter_by_problem_types(self, task_metadata: list[TabArenaTaskMetadata]) -> list[TabArenaTaskMetadata]: + return [ttm for ttm in task_metadata if ttm.problem_type in self.problem_types_to_run] + + def _filter_by_split_indices(self, task_metadata: list[TabArenaTaskMetadata]) -> list[TabArenaTaskMetadata]: + if self.split_indices_to_run is None: + return task_metadata + + if self.split_indices_to_run == "lite": + split_indices_to_run = [SplitMetadata.get_split_index(repeat_i=0, fold_i=0)] + else: + split_indices_to_run = self.split_indices_to_run + + split_index_pattern = re.compile(r"^r\d+f\d+$") + for split_index in split_indices_to_run: + assert split_index_pattern.match(split_index), ( + f"Invalid SplitIndex format: {split_index!r}, expected 'r{{int}}f{{int}}'" + ) + + return [ttm for ttm in task_metadata if ttm.split_index in split_indices_to_run] + + def _filter_by_dataset_names(self, task_metadata: list[TabArenaTaskMetadata]) -> list[TabArenaTaskMetadata]: + if self.dataset_names_to_run is None: + return task_metadata + + requested = set(self.dataset_names_to_run) + available = {ttm.dataset_name for ttm in task_metadata} + missing = requested - available + if missing: + raise ValueError( + f"Requested dataset names not found in task metadata: {sorted(missing)}. " + f"Available dataset names: {sorted(available)}" + ) + return [ttm for ttm in task_metadata if ttm.dataset_name in requested] + + def _filter_by_dtypes(self, task_metadata: list[TabArenaTaskMetadata]) -> list[TabArenaTaskMetadata]: + if (self.forbidden_dtypes_to_run is None) and (self.required_dtypes_to_run is None): + return task_metadata + return [ + ttm + for ttm in task_metadata + if ttm.has_supported_dtypes( + required_dtypes=self.required_dtypes_to_run, + forbidden_dtypes=self.forbidden_dtypes_to_run, + ) + ] + + def _filter_by_train_samples(self, task_metadata: list[TabArenaTaskMetadata]) -> list[TabArenaTaskMetadata]: + if self.n_train_samples_to_run is None: + return task_metadata + + lb, ub = self.n_train_samples_to_run + lb = lb if lb is not None else 0 + ub = ub if ub is not None else float("inf") + return [ttm for ttm in task_metadata if lb < ttm.splits_metadata[ttm.split_index].num_instances_train <= ub] + + @staticmethod + def _sanity_check_task_ids(task_metadata: list[TabArenaTaskMetadata]) -> None: + for ttm in task_metadata: + if ttm.task_id_str is None: + raise ValueError(f"Task metadata for task {ttm.tabarena_task_name} does not have a task_id_str!") + + @staticmethod + def _print_filter_history(filter_history: list[tuple[str, int]]) -> None: + lines = [ + f"Found {filter_history[-1][1]} tasks to run.", + "\tTask Filter History:", + *(f"\t({i}) {label}: {count}." for i, (label, count) in enumerate(filter_history, start=1)), + ] + print("\n".join(lines)) \ No newline at end of file diff --git a/tabarena/tabarena/benchmark/task/metadata/bundles/beyond_arena.py b/tabarena/tabarena/benchmark/task/metadata/bundles/beyond_arena.py new file mode 100644 index 000000000..b77ddbfcc --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/bundles/beyond_arena.py @@ -0,0 +1,40 @@ +"""Preset metadata bundles for the Data Foundry ``BeyondArena`` collection. + +Thin :class:`~tabarena.benchmark.task.metadata.bundles.base.TabArenaMetadataBundle` +subclasses that default the source to the ``"BeyondArena"`` suite literal (resolved +to a :class:`~tabarena.benchmark.task.metadata.sources.data_foundry.DataFoundryTaskMetadataSource` +over the official collection). + +All the loading / download behavior lives on the (general-purpose) source, so: + +* the same filters as any bundle apply (``problem_types_to_run``, + ``dataset_names_to_run``, dtype / size filters), and ``materialize=False`` skips + all downloads (inspect / filter only); +* passing ``task_metadata=`` makes the bundle use your + own metadata instead (it is *not* special-cased here — the base resolves it); +* to run a *different* collection, use the source directly:: + + TabArenaMetadataBundle(task_metadata=DataFoundryTaskMetadataSource(my_collection)) + +Requires the optional ``data-foundry`` dependency (``tabarena[data-foundry]``). +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from tabarena.benchmark.task.metadata.bundles.base import TabArenaMetadataBundle + + +@dataclass +class BeyondArenaMetadataBundle(TabArenaMetadataBundle): + """Tasks sourced from the official Data Foundry ``BeyondArena`` collection.""" + + task_metadata: str = "BeyondArena" + + +@dataclass +class BeyondArenaLiteMetadataBundle(BeyondArenaMetadataBundle): + """BeyondArena Lite: the first split (``r0f0``) of each dataset.""" + + split_indices_to_run: str = "lite" diff --git a/tabarena/tabarena/benchmark/task/metadata/bundles/tabarena_v0pt1.py b/tabarena/tabarena/benchmark/task/metadata/bundles/tabarena_v0pt1.py new file mode 100644 index 000000000..73dfbb5b5 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/bundles/tabarena_v0pt1.py @@ -0,0 +1,26 @@ +"""Preset metadata bundles for the TabArena v0.1 benchmark. + +Thin :class:`~tabarena.benchmark.task.metadata.bundles.base.TabArenaMetadataBundle` +subclasses that default the source to the ``"TabArena-v0.1"`` suite literal (resolved +to :class:`~tabarena.benchmark.task.metadata.sources.tabarena_v0pt1.TabArenaV0pt1TaskMetadataSource`). +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from tabarena.benchmark.task.metadata.bundles.base import TabArenaMetadataBundle + + +@dataclass +class TabArenaV0pt1MetadataBundle(TabArenaMetadataBundle): + """Metadata for full TabArena v0.1 benchmark: 51 datasets, 816 tasks.""" + + task_metadata: str = "TabArena-v0.1" + + +@dataclass +class TabArenaV0pt1LiteMetadataBundle(TabArenaV0pt1MetadataBundle): + """TabArena v0.1 Lite (first split of each dataset): 51 datasets, 51 tasks.""" + + split_indices_to_run: str = "lite" diff --git a/tabarena/tabarena/benchmark/task/metadata/schema.py b/tabarena/tabarena/benchmark/task/metadata/schema.py new file mode 100644 index 000000000..12921b883 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/schema.py @@ -0,0 +1,333 @@ +from __future__ import annotations + +from dataclasses import MISSING, asdict, dataclass, fields, replace +from enum import StrEnum +from typing import Annotated, Literal + +import pandas as pd + +SplitIndex = Annotated[str, "format: r{int}f{int}"] + +SplitTimeHorizonTypes = str | int | float +SplitTimeHorizonUnitTypes = Literal["steps", "days", "weeks", "months", "years"] | str + + +class GroupLabelTypes(StrEnum): + PER_SAMPLE = "per_sample" + PER_GROUP = "per_group" + + +def derive_task_type(*, time_on: str | None, group_on: str | list[str] | None) -> str: + """Classify a task's split regime from its split columns. + + Returns ``"temporal"`` if ``time_on`` is set, ``"grouped"`` if ``group_on`` is set, + else ``"random"`` (IID). Mirrors the Data Foundry warehouse ``task_type``. + """ + if time_on is not None: + return "temporal" + if group_on is not None: + return "grouped" + return "random" + + +@dataclass +class TabArenaTaskMetadata: + """Metadata about the task to run. + + This metadata has different use cases for 1 or N elements in the splits_metadata. + """ + + dataset_name: str + """Simple name of the dataset used for the task.""" + + problem_type: str + """The problem type of the task (e.g., 'binary', 'multiclass', 'regression').""" + is_classification: bool + """Whether the task is a classification task.""" + + target_name: str + """The name of the target variable in the dataset.""" + eval_metric: str + """The evaluation metric used for the task.""" + + """The number of splits in the task.""" + splits_metadata: dict[SplitIndex, SplitMetadata] + """Mapping of split index to Metadata about the splits in the task.""" + split_time_horizon: SplitTimeHorizonTypes | None + """The time horizon used for temporal splitting. This can be a number (e.g., 5).""" + split_time_horizon_unit: SplitTimeHorizonUnitTypes | None + """The unit of the time horizon used for temporal splitting. This can be a string (e.g., 'days') or one of + the predefined literals.""" + + stratify_on: str | None + """The name of the column used for stratification during splitting.""" + time_on: str | None + """The name of the column used for temporal splitting.""" + group_on: str | list[str] | None + """The name of the column used for grouping during splitting.""" + group_time_on: str | None + """The name of the column that contains the time information for""" + group_labels: GroupLabelTypes | None + """Whether the group_on column(s) contain labels for each sample, or for each group.""" + + multiclass_min_n_classes_over_splits: int | None + """The minimum number of classes across splits for classification tasks, + None for regression tasks.""" + multiclass_max_n_classes_over_splits: int | None + """The maximum number of classes across splits for classification tasks, + None for regression tasks.""" + class_consistency_over_splits: bool | None + """Whether the number of classes is consistent across splits for classification tasks, + None for regression tasks.""" + + num_instances: int + """The total number of instances in the dataset.""" + num_features: int + """The total number of features (excluding the target) in the dataset.""" + num_classes: int + """The total number of classes in the dataset. -1 for regression tasks.""" + num_instance_groups: int + """The total number of unique groups of data in the dataset. For normal IID data, + this is equal to num_instances. For non-IID grouped data, this is equal to the number + of groups in the data. + """ + + tabarena_task_name: str | None + """The name of the task used for TabArena. This is used for better identification + and can be set by the user.""" + task_id_str: str | None + """The task ID string used for the task. This functions as the unique identifier + of the source of the metadat. This can either be an OpenML task ID, or it is the + identifier of a local task (see `UserTask.task_id_str`). + """ + + # -- Feature dtype flags (added later; default to None for backward compat) -- + has_datetime: bool | None = None + """Whether the dataset contains datetime feature columns (incl. period dtypes).""" + has_text: bool | None = None + """Whether the dataset contains text (string) feature columns.""" + has_categorical: bool | None = None + """Whether the dataset contains categorical feature columns.""" + has_numerical: bool | None = None + """Whether the dataset contains numerical feature columns.""" + has_binary: bool | None = None + """Whether the dataset contains any feature column with exactly two distinct + non-null values.""" + has_high_cardinality_categorical: bool | None = None + """Whether the dataset contains a categorical (category dtype) feature column + with more than 50 unique values.""" + + data_foundry_uri: str | None = None + """For tasks sourced from a Data Foundry collection, the collection-entry relative + path (``/[versions/]``) that identifies the source container. + Used to (re)download and materialize the task on demand. ``None`` for tasks that do + not originate from Data Foundry.""" + + # -- Warehouse-level metadata (added later; default to None for backward compat) -- + # These align the in-task metadata with the columns previously merged in from a + # separate warehouse_metadata.csv (see BeyondArenaContext). Dataset-derived fields + # are computed at creation when the dataset is available (data_foundry / OpenML + # task path); they remain None when only tabular metadata is known (e.g. v0.1). + task_type: str | None = None + """The split regime of the task: ``"random"`` (IID), ``"temporal"``, or + ``"grouped"``. Derived from ``time_on`` / ``group_on`` (see :func:`derive_task_type`).""" + num_text_cols: int | None = None + """Number of text (string-dtype) feature columns.""" + num_high_cardinality_cats: int | None = None + """Number of categorical (category-dtype) feature columns with more than 50 unique values.""" + num_cols_after_preprocessing: int | None = None + """Estimated feature count after preprocessing: numerical_non_binary + + categorical_non_binary + num_text * 32 + num_binary + num_datetime * 10 + (matches the Data Foundry warehouse computation).""" + missing_value_fraction: float | None = None + """Fraction of missing values across all feature cells (excluding target/group cols).""" + domain: str | None = None + """Application domain of the dataset (e.g. ``"medical & healthcare"``).""" + dataset_year: str | None = None + """Year the dataset originates from.""" + source: str | None = None + """Origin of the dataset (e.g. ``"Kaggle"``, ``"OpenML"``, ``"UCI"``).""" + + @property + def n_splits(self): + """Get the number of splits in the task.""" + return len(self.splits_metadata) + + @property + def split_indices(self) -> list[SplitIndex]: + """Get a list of all split indices in the task.""" + return list(self.splits_metadata.keys()) + + @property + def split_index(self) -> SplitIndex: + """Get the split index for the task. + This is only supported for tasks with one split. + """ + if self.n_splits != 1: + raise ValueError( + f"Cannot get split index for task with {self.n_splits} splits. " + "This is only supported for tasks with exactly one split." + ) + return self.split_indices[0] + + def has_supported_dtypes(self, *, required_dtypes: list[str] | None, forbidden_dtypes: list[str] | None) -> bool: + """Check if the dataset contains only allowed dtypes based on the feature dtype flags.""" + flag_by_dtype = { + "datetime": self.has_datetime, + "text": self.has_text, + "categorical": self.has_categorical, + "numerical": self.has_numerical, + "binary": self.has_binary, + "high_cardinality_categorical": self.has_high_cardinality_categorical, + } + + if required_dtypes is not None: + for dtype in required_dtypes: + if not flag_by_dtype.get(dtype): + return False + + if forbidden_dtypes is not None: + for dtype in forbidden_dtypes: + if flag_by_dtype.get(dtype): + return False + + return True + + def to_dict(self, *, exclude_splits_metadata: bool = False) -> dict: + """Convert the task metadata to a dictionary for better visualization.""" + res = asdict(self) + if exclude_splits_metadata: + res.pop("splits_metadata") + return res + + def to_dataframe(self, *, add_old_minimal_metadata: bool = False) -> pd.DataFrame: + """Transform metadata to a DataFrame. + + If add_old_minimal_metadata is True, also add old minimal metadata for backward + compatibility with old eval code. That is, we add the columns: "tid", "name", + "dataset", "n_samples_train_per_fold", "n_samples_test_per_fold". + """ + rows = [] + static_metadata = self.to_dict(exclude_splits_metadata=True) + for split_metadata in self.splits_metadata.values(): + rows.append( + { + **static_metadata, + **split_metadata.to_dict(), + } + ) + + df = pd.DataFrame(rows) + + # TODO: move somewhere else / get rid of this? + if add_old_minimal_metadata: + # Add old minimal metadata for backward compatibility with old eval code + # Integer task id: the UserTask hash id (``UserTask||...``) for local + # tasks, or the plain OpenML integer task id otherwise (handles str or int). + task_id_str = str(self.task_id_str) + df["tid"] = int(task_id_str.split("|")[1]) if task_id_str.startswith("UserTask|") else int(task_id_str) + df["name"] = df["tabarena_task_name"] + df["dataset"] = df["tabarena_task_name"] + df["n_samples_train_per_fold"] = df["num_instances_train"] + df["n_samples_test_per_fold"] = df["num_instances_test"] + + return df + + @staticmethod + def from_row(row: pd.Series) -> TabArenaTaskMetadata: + """Reconstruct TabArenaTaskMetadata from a single dataframe row.""" + row_dict = row.to_dict() + + # Identify TabArenaTaskMetadata fields (excluding splits_metadata). + # Fields with defaults are optional for backward compatibility with + # older serialized metadata that may not contain newer columns. + all_task_fields = {f.name for f in fields(TabArenaTaskMetadata) if f.name != "splits_metadata"} + required_task_fields = { + f.name + for f in fields(TabArenaTaskMetadata) + if f.name != "splits_metadata" and f.default is MISSING and f.default_factory is MISSING + } + if not all(name in row_dict for name in required_task_fields): + raise ValueError( + "Metadata row is missing required TabArenaTaskMetadata fields: " + f"{required_task_fields - row_dict.keys()}" + ) + task_kwargs = {key: row_dict[key] for key in all_task_fields if key in row_dict} + + # Identify SplitMetadata fields + split_field_names = {f.name for f in fields(SplitMetadata)} + if not all(name in row_dict for name in split_field_names): + raise ValueError( + f"Metadata row is missing required SplitMetadata fields: {split_field_names - row_dict.keys()}" + ) + split_kwargs = {key: row_dict[key] for key in split_field_names if key in row_dict} + # Reconstruct SplitMetadata + split_metadata = SplitMetadata(**split_kwargs) + + # --- Construct final object --- + return TabArenaTaskMetadata( + **task_kwargs, + splits_metadata={split_metadata.split_index: split_metadata}, + ) + + def unroll_splits(self) -> list[TabArenaTaskMetadata]: + """Unroll the TabArenaTaskMetadata into a list of TabArenaTaskMetadata instances + each containing exactly one split in `splits_metadata`. + """ + return [ + replace( + self, + splits_metadata={split_idx: split_meta}, + ) + for split_idx, split_meta in self.splits_metadata.items() + ] + + +@dataclass +class SplitMetadata: + """Metadata about the splits in the task.""" + + repeat: int + """The repeat index of the split. + + All splits with the same repeat index have the same data points, + but split into folds differently. + """ + fold: int + """The fold index of the split.""" + num_instances_train: int + """The number of training instances in the split.""" + num_instances_test: int + """The number of test instances in the split.""" + num_instance_groups_train: int + """The number unique groups of data in the train split. + For normal IID data, this is always equal to `num_instances_train`. + For non-IID grouped data, this is equal to the number of groups in the data. + """ + num_instance_groups_test: int + """The number unique groups of data in the test split.""" + num_classes_train: int + """-1 for regression tasks, and maximal number of unique classes in the training + set for classification tasks.""" + num_classes_test: int + """Classes in test data""" + num_features_train: int + """The number of features (excluding the target) in the training set of the split.""" + num_features_test: int + """The number of features (excluding the target) in the test set of the split.""" + + @staticmethod + def get_split_index(*, repeat_i: int, fold_i: int) -> SplitIndex: + """Get the split index for the given repeat and fold.""" + return f"r{repeat_i}f{fold_i}" + + @property + def split_index(self) -> SplitIndex: + """Get the split index for this split metadata.""" + return self.get_split_index(repeat_i=self.repeat, fold_i=self.fold) + + def to_dict(self) -> dict[str, int | str]: + """Convert the split metadata to a dictionary.""" + res = asdict(self) + res["split_index"] = self.split_index + return res diff --git a/tabarena/tabarena/benchmark/task/metadata/sources/__init__.py b/tabarena/tabarena/benchmark/task/metadata/sources/__init__.py new file mode 100644 index 000000000..dc03abae3 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/sources/__init__.py @@ -0,0 +1,74 @@ +"""Task metadata sources + the single ``resolve_source`` entry point. + +A :class:`TaskMetadataSource` knows how to load (and optionally materialize) task +metadata. :func:`resolve_source` maps whatever a bundle was configured with — a +source instance, a registered suite literal, or already-available metadata +(DataFrame / CSV path / list) — onto a concrete source. This is the *one* place +that knows about all built-in loaders, so bundles never special-case a suite. +""" + +from __future__ import annotations + +from collections.abc import Callable +from pathlib import Path + +import pandas as pd + +from tabarena.benchmark.task.metadata.schema import TabArenaTaskMetadata +from tabarena.benchmark.task.metadata.sources.base import ( + InMemoryTaskMetadataSource, + TaskMetadataSource, +) +from tabarena.benchmark.task.metadata.sources.data_foundry import DataFoundryTaskMetadataSource +from tabarena.benchmark.task.metadata.sources.openml import OpenMLTaskMetadataSource +from tabarena.benchmark.task.metadata.sources.tabarena_v0pt1 import ( + TABARENA_V0PT1_NAME, + TabArenaV0pt1TaskMetadataSource, + load_tabarena_v0_1_task_metadata, +) + + +def _beyond_arena_source() -> TaskMetadataSource: + """Build the default source for the official ``BeyondArena`` collection.""" + from tabarena.benchmark.task.data_foundry import get_beyond_arena_collection + + return DataFoundryTaskMetadataSource(get_beyond_arena_collection()) + + +# Registered suite literals -> factory. Factories are lazy so optional deps +# (e.g. data-foundry for "BeyondArena") are only imported when actually used. +_SOURCE_REGISTRY: dict[str, Callable[[], TaskMetadataSource]] = { + TABARENA_V0PT1_NAME: TabArenaV0pt1TaskMetadataSource, # "TabArena-v0.1" + "BeyondArena": _beyond_arena_source, +} + + +def resolve_source( + task_metadata: TaskMetadataSource | pd.DataFrame | list[TabArenaTaskMetadata] | str | Path, +) -> TaskMetadataSource: + """Resolve a bundle's ``task_metadata`` into a :class:`TaskMetadataSource`. + + Resolution order: + + 1. A :class:`TaskMetadataSource` instance is returned unchanged. + 2. A string matching a registered suite literal (e.g. ``"TabArena-v0.1"``, + ``"BeyondArena"``) builds that suite's source. + 3. Anything else — a DataFrame, a ``list[TabArenaTaskMetadata]``, or a str/Path + to a CSV file — is wrapped in an :class:`InMemoryTaskMetadataSource`. + """ + if isinstance(task_metadata, TaskMetadataSource): + return task_metadata + if isinstance(task_metadata, str) and task_metadata in _SOURCE_REGISTRY: + return _SOURCE_REGISTRY[task_metadata]() + return InMemoryTaskMetadataSource(task_metadata) + + +__all__ = [ + "DataFoundryTaskMetadataSource", + "InMemoryTaskMetadataSource", + "OpenMLTaskMetadataSource", + "TabArenaV0pt1TaskMetadataSource", + "TaskMetadataSource", + "load_tabarena_v0_1_task_metadata", + "resolve_source", +] diff --git a/tabarena/tabarena/benchmark/task/metadata/sources/base.py b/tabarena/tabarena/benchmark/task/metadata/sources/base.py new file mode 100644 index 000000000..e7b8b9a0d --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/sources/base.py @@ -0,0 +1,92 @@ +"""The :class:`TaskMetadataSource` strategy and the in-memory source. + +A *source* answers two questions for a bundle, decoupled from any filtering: + +* :meth:`TaskMetadataSource.load` — *where does the task metadata come from?* + (a built-in suite, a DataFrame/CSV the user already has, a Data Foundry + collection, ...). Returns ``list[TabArenaTaskMetadata]`` without applying any + selection filters. +* :meth:`TaskMetadataSource.materialize` — *how do we make the (already-filtered) + tasks runnable?* The default is a no-op; sources backed by a remote collection + override it to download + convert only the tasks that survived filtering. + +This keeps :class:`~tabarena.benchmark.task.metadata.bundles.base.TabArenaMetadataBundle` +free of any per-suite special-casing: the bundle owns filtering, the source owns +loading + materialization. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from pathlib import Path + +import pandas as pd + +from tabarena.benchmark.task.metadata.schema import TabArenaTaskMetadata + + +def committed_metadata_dir() -> Path: + """Directory holding the git-committed reference-metadata CSVs (one per source). + + All sources (built-in suites and Data Foundry collections) share this single + location: ``benchmark/task/metadata/sources/data/``. + """ + return Path(__file__).parent / "data" + + +def committed_metadata_path(name: str) -> Path: + """Path to the committed ``_tasks_metadata.csv`` reference file. + + ``name`` is the suite literal / collection name, e.g. ``"TabArena-v0.1"`` or + ``"BeyondArena"``. + """ + return committed_metadata_dir() / f"{name}_tasks_metadata.csv" + + +class TaskMetadataSource(ABC): + """Produces (and optionally materializes) task metadata for a bundle.""" + + @abstractmethod + def load(self) -> list[TabArenaTaskMetadata]: + """Return the (unfiltered) task metadata this source describes. + + Implementations must not download dataset contents here when it can be + avoided — loading should be cheap so bundles can filter before any + expensive materialization (see :meth:`materialize`). + """ + + def materialize(self, task_metadata: list[TabArenaTaskMetadata]) -> None: + """Ensure the given (already-filtered) tasks are runnable, in place. + + Called by the bundle after filtering, only when the bundle's + ``materialize`` flag is set. The default does nothing — metadata that is + already local (in-memory frames, built-in suites) needs no preparation. + Sources backed by a remote collection override this to fetch + convert + the tasks and update each ``task_id_str`` accordingly. + """ + return + + +class InMemoryTaskMetadataSource(TaskMetadataSource): + """Wraps task metadata the caller already has: a list, DataFrame, or CSV path. + + A DataFrame (or CSV loaded into one) is parsed row-by-row via + :meth:`TabArenaTaskMetadata.from_row`. A list is passed through as-is. There + is nothing to materialize — see :meth:`TaskMetadataSource.materialize`. + """ + + def __init__(self, data: pd.DataFrame | list[TabArenaTaskMetadata] | str | Path) -> None: + """Store the already-available metadata (list, DataFrame, or CSV path).""" + self.data = data + + def load(self) -> list[TabArenaTaskMetadata]: + """Parse the wrapped data into a list of TabArenaTaskMetadata.""" + data = self.data + if isinstance(data, (str, Path)): + print(f"Loading task metadata from {data}...") + data = pd.read_csv(data, index_col=False) + if isinstance(data, pd.DataFrame): + data = [TabArenaTaskMetadata.from_row(row) for _, row in data.iterrows()] + data = list(data) + assert all(isinstance(x, TabArenaTaskMetadata) for x in data) + return data diff --git a/tabarena/tabarena/benchmark/task/metadata/sources/data/BeyondArena_tasks_metadata.csv b/tabarena/tabarena/benchmark/task/metadata/sources/data/BeyondArena_tasks_metadata.csv new file mode 100644 index 000000000..1b12042f2 --- 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+blood_tests_drink_prediction,regression,False,drinks,root_mean_squared_error,,,,,,,,,,,345,5,-1,345,blood_tests_drink_prediction-aeec267edab7,UserTask|534613218|blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,False,False,False,True,False,False,blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,random,0,0,5,0.0,medical & healthcare,1996,UCI,18,2,230,115,230,115,-1,-1,5,5,r18f2 +blood_tests_drink_prediction,regression,False,drinks,root_mean_squared_error,,,,,,,,,,,345,5,-1,345,blood_tests_drink_prediction-aeec267edab7,UserTask|534613218|blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,False,False,False,True,False,False,blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,random,0,0,5,0.0,medical & healthcare,1996,UCI,19,0,230,115,230,115,-1,-1,5,5,r19f0 +blood_tests_drink_prediction,regression,False,drinks,root_mean_squared_error,,,,,,,,,,,345,5,-1,345,blood_tests_drink_prediction-aeec267edab7,UserTask|534613218|blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,False,False,False,True,False,False,blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,random,0,0,5,0.0,medical & healthcare,1996,UCI,19,1,230,115,230,115,-1,-1,5,5,r19f1 +blood_tests_drink_prediction,regression,False,drinks,root_mean_squared_error,,,,,,,,,,,345,5,-1,345,blood_tests_drink_prediction-aeec267edab7,UserTask|534613218|blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,False,False,False,True,False,False,blood_tests_drink_prediction/019d736a-5d45-743a-b7c5-11da6b494fb7,random,0,0,5,0.0,medical & healthcare,1996,UCI,19,2,230,115,230,115,-1,-1,5,5,r19f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,0,0,168,84,168,84,-1,-1,13,13,r0f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,0,1,168,84,168,84,-1,-1,13,13,r0f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,0,2,168,84,168,84,-1,-1,13,13,r0f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,1,0,168,84,168,84,-1,-1,13,13,r1f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,1,1,168,84,168,84,-1,-1,13,13,r1f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,1,2,168,84,168,84,-1,-1,13,13,r1f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,2,0,168,84,168,84,-1,-1,13,13,r2f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,2,1,168,84,168,84,-1,-1,13,13,r2f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,2,2,168,84,168,84,-1,-1,13,13,r2f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,3,0,168,84,168,84,-1,-1,13,13,r3f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,3,1,168,84,168,84,-1,-1,13,13,r3f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,3,2,168,84,168,84,-1,-1,13,13,r3f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,4,0,168,84,168,84,-1,-1,13,13,r4f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,4,1,168,84,168,84,-1,-1,13,13,r4f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,4,2,168,84,168,84,-1,-1,13,13,r4f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,5,0,168,84,168,84,-1,-1,13,13,r5f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,5,1,168,84,168,84,-1,-1,13,13,r5f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,5,2,168,84,168,84,-1,-1,13,13,r5f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,6,0,168,84,168,84,-1,-1,13,13,r6f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,6,1,168,84,168,84,-1,-1,13,13,r6f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,6,2,168,84,168,84,-1,-1,13,13,r6f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,7,0,168,84,168,84,-1,-1,13,13,r7f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,7,1,168,84,168,84,-1,-1,13,13,r7f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,7,2,168,84,168,84,-1,-1,13,13,r7f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,8,0,168,84,168,84,-1,-1,13,13,r8f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,8,1,168,84,168,84,-1,-1,13,13,r8f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,8,2,168,84,168,84,-1,-1,13,13,r8f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,9,0,168,84,168,84,-1,-1,13,13,r9f0 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,9,1,168,84,168,84,-1,-1,13,13,r9f1 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,9,2,168,84,168,84,-1,-1,13,13,r9f2 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+body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,10,2,168,84,168,84,-1,-1,13,13,r10f2 +body_density_prediction,regression,False,Density,root_mean_squared_error,,,,,,,,,,,252,13,-1,252,body_density_prediction-19548b7220c1,UserTask|8512801702|body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,False,False,False,True,False,False,body_density_prediction/019d736a-6e2b-77a5-ae25-1222ef4fe4e9,random,0,0,13,0.0,medical & healthcare,1985,Kaggle,11,0,168,84,168,84,-1,-1,13,13,r11f0 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+forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,9,0,344,173,344,173,-1,-1,12,12,r9f0 +forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,9,1,345,172,345,172,-1,-1,12,12,r9f1 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+forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,11,0,344,173,344,173,-1,-1,12,12,r11f0 +forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,11,1,345,172,345,172,-1,-1,12,12,r11f1 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+forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,18,1,345,172,345,172,-1,-1,12,12,r18f1 +forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,18,2,345,172,345,172,-1,-1,12,12,r18f2 +forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,19,0,344,173,344,173,-1,-1,12,12,r19f0 +forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,19,1,345,172,345,172,-1,-1,12,12,r19f1 +forest_fires,regression,False,area,root_mean_squared_error,,,,,,,,,,,517,12,-1,517,forest_fires-7dc3af8601af,UserTask|4718142135|forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,False,False,True,True,False,False,forest_fires/019d736b-2610-7f4f-ae46-3f6a4c98962e,random,0,0,12,0.0,environmental science & climate,2008,UCI,19,2,345,172,345,172,-1,-1,12,12,r19f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,0,0,212,107,212,107,2,2,38,38,r0f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,0,1,213,106,213,106,2,2,38,38,r0f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,0,2,213,106,213,106,2,2,38,38,r0f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,1,0,212,107,212,107,2,2,38,38,r1f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,1,1,213,106,213,106,2,2,38,38,r1f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,1,2,213,106,213,106,2,2,38,38,r1f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,2,0,212,107,212,107,2,2,38,38,r2f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,2,1,213,106,213,106,2,2,38,38,r2f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,2,2,213,106,213,106,2,2,38,38,r2f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,3,0,212,107,212,107,2,2,38,38,r3f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,3,1,213,106,213,106,2,2,38,38,r3f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,3,2,213,106,213,106,2,2,38,38,r3f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,4,0,212,107,212,107,2,2,38,38,r4f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,4,1,213,106,213,106,2,2,38,38,r4f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,4,2,213,106,213,106,2,2,38,38,r4f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,5,0,212,107,212,107,2,2,38,38,r5f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,5,1,213,106,213,106,2,2,38,38,r5f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,5,2,213,106,213,106,2,2,38,38,r5f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,6,0,212,107,212,107,2,2,38,38,r6f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,6,1,213,106,213,106,2,2,38,38,r6f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,6,2,213,106,213,106,2,2,38,38,r6f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,7,0,212,107,212,107,2,2,38,38,r7f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,7,1,213,106,213,106,2,2,38,38,r7f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,7,2,213,106,213,106,2,2,38,38,r7f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,8,0,212,107,212,107,2,2,38,38,r8f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,8,1,213,106,213,106,2,2,38,38,r8f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,8,2,213,106,213,106,2,2,38,38,r8f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,9,0,212,107,212,107,2,2,38,38,r9f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,9,1,213,106,213,106,2,2,38,38,r9f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,9,2,213,106,213,106,2,2,38,38,r9f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,10,0,212,107,212,107,2,2,38,38,r10f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,10,1,213,106,213,106,2,2,38,38,r10f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,10,2,213,106,213,106,2,2,38,38,r10f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,11,0,212,107,212,107,2,2,38,38,r11f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,11,1,213,106,213,106,2,2,38,38,r11f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,11,2,213,106,213,106,2,2,38,38,r11f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,12,0,212,107,212,107,2,2,38,38,r12f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,12,1,213,106,213,106,2,2,38,38,r12f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,12,2,213,106,213,106,2,2,38,38,r12f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,13,0,212,107,212,107,2,2,38,38,r13f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,13,1,213,106,213,106,2,2,38,38,r13f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,13,2,213,106,213,106,2,2,38,38,r13f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,14,0,212,107,212,107,2,2,38,38,r14f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,14,1,213,106,213,106,2,2,38,38,r14f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,14,2,213,106,213,106,2,2,38,38,r14f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,15,0,212,107,212,107,2,2,38,38,r15f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,15,1,213,106,213,106,2,2,38,38,r15f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,15,2,213,106,213,106,2,2,38,38,r15f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,16,0,212,107,212,107,2,2,38,38,r16f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,16,1,213,106,213,106,2,2,38,38,r16f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,16,2,213,106,213,106,2,2,38,38,r16f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,17,0,212,107,212,107,2,2,38,38,r17f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,17,1,213,106,213,106,2,2,38,38,r17f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,17,2,213,106,213,106,2,2,38,38,r17f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,18,0,212,107,212,107,2,2,38,38,r18f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,18,1,213,106,213,106,2,2,38,38,r18f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,18,2,213,106,213,106,2,2,38,38,r18f2 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,19,0,212,107,212,107,2,2,38,38,r19f0 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,19,1,213,106,213,106,2,2,38,38,r19f1 +gallstone_disease,binary,True,Gallstone Status,roc_auc,,,Gallstone Status,,,,,2,2,True,319,38,2,319,gallstone_disease-63982cfe3e7c,UserTask|2644296913|gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,False,False,True,True,True,False,gallstone_disease/019d736b-3795-7523-ac59-9fbe676ebc70,random,0,0,38,0.0,medical & healthcare,2023,UCI,19,2,213,106,213,106,2,2,38,38,r19f2 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,0,0,202,101,202,101,2,2,13,13,r0f0 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,0,1,202,101,202,101,2,2,13,13,r0f1 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,0,2,202,101,202,101,2,2,13,13,r0f2 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,1,0,202,101,202,101,2,2,13,13,r1f0 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,1,1,202,101,202,101,2,2,13,13,r1f1 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,1,2,202,101,202,101,2,2,13,13,r1f2 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,2,0,202,101,202,101,2,2,13,13,r2f0 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,2,1,202,101,202,101,2,2,13,13,r2f1 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,2,2,202,101,202,101,2,2,13,13,r2f2 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,3,0,202,101,202,101,2,2,13,13,r3f0 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,3,1,202,101,202,101,2,2,13,13,r3f1 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,3,2,202,101,202,101,2,2,13,13,r3f2 +heart_disease_cleveland,binary,True,heart_disease_diagnosis,roc_auc,,,heart_disease_diagnosis,,,,,2,2,True,303,13,2,303,heart_disease_cleveland-1e633334e0ef,UserTask|7083857072|heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,False,False,True,True,True,False,heart_disease_cleveland/019d736b-4900-714b-8dfc-be588ce802c3,random,0,0,13,0.0015232292460015233,medical & healthcare,1989,UCI,4,0,202,101,202,101,2,2,13,13,r4f0 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+kickstarter,binary,True,state,roc_auc,1,years,state,created_at,,,,2,2,True,187118,15,2,187118,kickstarter-fc5123d788a6,UserTask|4116239403|kickstarter/019db497-6b35-71e0-9c6b-f4a7286f8f6f,True,True,True,True,True,True,kickstarter/019db497-6b35-71e0-9c6b-f4a7286f8f6f,temporal,5,1,206,0.05605268689632567,business & marketing,2025,Other,2,0,166292,20826,166292,20826,2,2,15,15,r2f0 +sf_permit_time,regression,False,DaysToIssue,root_mean_squared_error,1,years,,Filed Date,,,,,,,116954,37,-1,116954,sf_permit_time-a3faf4c318ef,UserTask|1371025716|sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,True,True,True,True,True,False,sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,temporal,14,0,480,0.25284438464834175,business & marketing,2025,GOV Website,0,0,60784,10636,60784,10636,-1,-1,37,37,r0f0 +sf_permit_time,regression,False,DaysToIssue,root_mean_squared_error,1,years,,Filed Date,,,,,,,116954,37,-1,116954,sf_permit_time-a3faf4c318ef,UserTask|1371025716|sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,True,True,True,True,True,False,sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,temporal,14,0,480,0.25284438464834175,business & marketing,2025,GOV Website,1,0,71420,9915,71420,9915,-1,-1,37,37,r1f0 +sf_permit_time,regression,False,DaysToIssue,root_mean_squared_error,1,years,,Filed Date,,,,,,,116954,37,-1,116954,sf_permit_time-a3faf4c318ef,UserTask|1371025716|sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,True,True,True,True,True,False,sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,temporal,14,0,480,0.25284438464834175,business & marketing,2025,GOV Website,2,0,81335,9449,81335,9449,-1,-1,37,37,r2f0 +sf_permit_time,regression,False,DaysToIssue,root_mean_squared_error,1,years,,Filed Date,,,,,,,116954,37,-1,116954,sf_permit_time-a3faf4c318ef,UserTask|1371025716|sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,True,True,True,True,True,False,sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,temporal,14,0,480,0.25284438464834175,business & marketing,2025,GOV Website,3,0,90784,9063,90784,9063,-1,-1,37,37,r3f0 +sf_permit_time,regression,False,DaysToIssue,root_mean_squared_error,1,years,,Filed Date,,,,,,,116954,37,-1,116954,sf_permit_time-a3faf4c318ef,UserTask|1371025716|sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,True,True,True,True,True,False,sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,temporal,14,0,480,0.25284438464834175,business & marketing,2025,GOV Website,4,0,99847,8532,99847,8532,-1,-1,37,37,r4f0 +sf_permit_time,regression,False,DaysToIssue,root_mean_squared_error,1,years,,Filed Date,,,,,,,116954,37,-1,116954,sf_permit_time-a3faf4c318ef,UserTask|1371025716|sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,True,True,True,True,True,False,sf_permit_time/019d738d-80c6-71a6-99f0-e3e0955ce2d0,temporal,14,0,480,0.25284438464834175,business & marketing,2025,GOV Website,5,0,108379,8575,108379,8575,-1,-1,37,37,r5f0 +mice_protein_trisomy_discriminant,multiclass,True,class,log_loss,,,class,,MouseID,,per_group,8,8,True,1080,77,8,72,mice_protein_trisomy_discriminant-14c7a0eb99a0,UserTask|1273313252|mice_protein_trisomy_discriminant/019d738d-99a8-7850-b911-f244ac4ad485,False,False,False,True,False,False,mice_protein_trisomy_discriminant/019d738d-99a8-7850-b911-f244ac4ad485,grouped,0,0,76,0.017007797270955166,biology & life sciences,2015,UCI,0,0,720,360,48,24,8,8,77,77,r0f0 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+micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,4,1,376,195,142,71,20,20,1083,1083,r4f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,4,2,392,179,142,71,20,20,1083,1083,r4f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,5,0,373,198,142,71,20,20,1083,1083,r5f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,5,1,381,190,142,71,20,20,1083,1083,r5f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,5,2,388,183,142,71,20,20,1083,1083,r5f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,6,0,393,178,142,71,20,20,1083,1083,r6f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,6,1,366,205,142,71,20,20,1083,1083,r6f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,6,2,383,188,142,71,20,20,1083,1083,r6f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,7,0,383,188,142,71,20,20,1083,1083,r7f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,7,1,378,193,142,71,20,20,1083,1083,r7f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,7,2,381,190,142,71,20,20,1083,1083,r7f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,8,0,369,202,142,71,20,20,1083,1083,r8f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,8,1,382,189,142,71,20,20,1083,1083,r8f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,8,2,391,180,142,71,20,20,1083,1083,r8f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,9,0,394,177,142,71,20,20,1083,1083,r9f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,9,1,375,196,142,71,20,20,1083,1083,r9f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,9,2,373,198,142,71,20,20,1083,1083,r9f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,10,0,382,189,142,71,20,20,1083,1083,r10f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,10,1,379,192,142,71,20,20,1083,1083,r10f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,10,2,381,190,142,71,20,20,1083,1083,r10f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,11,0,377,194,142,71,20,20,1083,1083,r11f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,11,1,378,193,142,71,20,20,1083,1083,r11f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,11,2,387,184,142,71,20,20,1083,1083,r11f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,12,0,384,187,142,71,20,20,1083,1083,r12f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,12,1,367,204,142,71,20,20,1083,1083,r12f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,12,2,391,180,142,71,20,20,1083,1083,r12f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,13,0,381,190,142,71,20,20,1083,1083,r13f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,13,1,380,191,142,71,20,20,1083,1083,r13f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,13,2,381,190,142,71,20,20,1083,1083,r13f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,14,0,375,196,142,71,20,20,1083,1083,r14f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,14,1,384,187,142,71,20,20,1083,1083,r14f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,14,2,383,188,142,71,20,20,1083,1083,r14f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,15,0,380,191,142,71,20,20,1083,1083,r15f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,15,1,381,190,142,71,20,20,1083,1083,r15f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,15,2,381,190,142,71,20,20,1083,1083,r15f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,16,0,389,182,142,71,20,20,1083,1083,r16f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,16,1,381,190,142,71,20,20,1083,1083,r16f1 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,16,2,372,199,142,71,20,20,1083,1083,r16f2 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,17,0,392,179,142,71,20,20,1083,1083,r17f0 +micro_mass,multiclass,True,Species,log_loss,,,Species,,Strain,,per_group,20,20,True,571,1083,20,213,micro_mass-5deb16d4fa0e,UserTask|5248194439|micro_mass/019d738d-c82e-796a-9343-e30919f324d5,False,False,False,True,True,False,micro_mass/019d738d-c82e-796a-9343-e30919f324d5,grouped,0,0,1082,0.0,biology & life sciences,2013,UCI,17,1,371,200,142,71,20,20,1083,1083,r17f1 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+telemonitoring_parkinsons_biomedical_voice_measurements,regression,False,total_UPDRS,root_mean_squared_error,,,,,subject#,test_time,per_sample,,,,502,20,-1,502,telemonitoring_parkinsons_biomedical_voi-c608f1a3d898,UserTask|6101375254|telemonitoring_parkinsons_biomedical_voice_measurements/019d7391-4b65-7d0e-a950-684a098c3df9,False,False,False,True,True,False,telemonitoring_parkinsons_biomedical_voice_measurements/019d7391-4b65-7d0e-a950-684a098c3df9,grouped,0,0,19,0.0,medical & healthcare,2007,UCI,19,0,336,166,336,166,-1,-1,20,20,r19f0 +telemonitoring_parkinsons_biomedical_voice_measurements,regression,False,total_UPDRS,root_mean_squared_error,,,,,subject#,test_time,per_sample,,,,502,20,-1,502,telemonitoring_parkinsons_biomedical_voi-c608f1a3d898,UserTask|6101375254|telemonitoring_parkinsons_biomedical_voice_measurements/019d7391-4b65-7d0e-a950-684a098c3df9,False,False,False,True,True,False,telemonitoring_parkinsons_biomedical_voice_measurements/019d7391-4b65-7d0e-a950-684a098c3df9,grouped,0,0,19,0.0,medical & healthcare,2007,UCI,19,1,339,163,339,163,-1,-1,20,20,r19f1 +telemonitoring_parkinsons_biomedical_voice_measurements,regression,False,total_UPDRS,root_mean_squared_error,,,,,subject#,test_time,per_sample,,,,502,20,-1,502,telemonitoring_parkinsons_biomedical_voi-c608f1a3d898,UserTask|6101375254|telemonitoring_parkinsons_biomedical_voice_measurements/019d7391-4b65-7d0e-a950-684a098c3df9,False,False,False,True,True,False,telemonitoring_parkinsons_biomedical_voice_measurements/019d7391-4b65-7d0e-a950-684a098c3df9,grouped,0,0,19,0.0,medical & healthcare,2007,UCI,19,2,329,173,329,173,-1,-1,20,20,r19f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,0,0,250,120,250,120,3,3,9,9,r0f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,0,1,256,114,256,114,3,3,9,9,r0f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,0,2,234,136,234,136,3,3,9,9,r0f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,1,0,252,118,252,118,3,3,9,9,r1f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,1,1,240,130,240,130,3,3,9,9,r1f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,1,2,248,122,248,122,3,3,9,9,r1f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,2,0,246,124,246,124,3,3,9,9,r2f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,2,1,243,127,243,127,3,3,9,9,r2f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,2,2,251,119,251,119,3,3,9,9,r2f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,3,0,249,121,249,121,3,3,9,9,r3f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,3,1,252,118,252,118,3,3,9,9,r3f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,3,2,239,131,239,131,3,3,9,9,r3f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,4,0,242,128,242,128,3,3,9,9,r4f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,4,1,246,124,246,124,3,3,9,9,r4f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,4,2,252,118,252,118,3,3,9,9,r4f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,5,0,248,122,248,122,3,3,9,9,r5f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,5,1,247,123,247,123,3,3,9,9,r5f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,5,2,245,125,245,125,3,3,9,9,r5f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,6,0,244,126,244,126,3,3,9,9,r6f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,6,1,246,124,246,124,3,3,9,9,r6f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,6,2,250,120,250,120,3,3,9,9,r6f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,7,0,246,124,246,124,3,3,9,9,r7f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,7,1,247,123,247,123,3,3,9,9,r7f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,7,2,247,123,247,123,3,3,9,9,r7f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,8,0,250,120,250,120,3,3,9,9,r8f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,8,1,258,112,258,112,3,3,9,9,r8f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,8,2,232,138,232,138,3,3,9,9,r8f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,9,0,248,122,248,122,3,3,9,9,r9f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,9,1,242,128,242,128,3,3,9,9,r9f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,9,2,250,120,250,120,3,3,9,9,r9f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,10,0,252,118,252,118,3,3,9,9,r10f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,10,1,238,132,238,132,3,3,9,9,r10f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,10,2,250,120,250,120,3,3,9,9,r10f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,11,0,246,124,246,124,3,3,9,9,r11f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,11,1,252,118,252,118,3,3,9,9,r11f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,11,2,242,128,242,128,3,3,9,9,r11f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,12,0,240,130,240,130,3,3,9,9,r12f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,12,1,256,114,256,114,3,3,9,9,r12f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,12,2,244,126,244,126,3,3,9,9,r12f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,13,0,242,128,242,128,3,3,9,9,r13f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,13,1,251,119,251,119,3,3,9,9,r13f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,13,2,247,123,247,123,3,3,9,9,r13f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,14,0,252,118,252,118,3,3,9,9,r14f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,14,1,248,122,248,122,3,3,9,9,r14f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,14,2,240,130,240,130,3,3,9,9,r14f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,15,0,244,126,244,126,3,3,9,9,r15f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,15,1,252,118,252,118,3,3,9,9,r15f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,15,2,244,126,244,126,3,3,9,9,r15f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,16,0,249,121,249,121,3,3,9,9,r16f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,16,1,247,123,247,123,3,3,9,9,r16f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,16,2,244,126,244,126,3,3,9,9,r16f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,17,0,238,132,238,132,3,3,9,9,r17f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,17,1,251,119,251,119,3,3,9,9,r17f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,17,2,251,119,251,119,3,3,9,9,r17f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,18,0,241,129,241,129,3,3,9,9,r18f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,18,1,254,116,254,116,3,3,9,9,r18f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,18,2,245,125,245,125,3,3,9,9,r18f2 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,19,0,245,125,245,125,3,3,9,9,r19f0 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,19,1,247,123,247,123,3,3,9,9,r19f1 +dementia_prediction,multiclass,True,CDR,log_loss,,,CDR,,Subject ID,,per_sample,3,3,True,370,9,3,370,dementia_prediction-d8ce15bd8d63,UserTask|4916766226|dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,False,False,False,True,True,False,dementia_prediction/019d7391-61e5-7cca-9a66-e85718233be9,grouped,0,0,8,0.007094594594594595,medical & healthcare,2010,Other,19,2,248,122,248,122,3,3,9,9,r19f2 +covertype,multiclass,True,Cover_Type,log_loss,,,Cover_Type,,Wilderness_Area,,per_sample,3,3,True,512625,14,3,512625,covertype-1ed1c3d23864,UserTask|8449509019|covertype/019d7391-8e33-757b-b38a-79ec188fa001,False,False,True,True,False,False,covertype/019d7391-8e33-757b-b38a-79ec188fa001,grouped,0,0,13,0.0,environmental science & climate,1998,UCI,0,0,286899,225726,286899,225726,3,3,14,14,r0f0 +covertype,multiclass,True,Cover_Type,log_loss,,,Cover_Type,,Wilderness_Area,,per_sample,3,3,True,512625,14,3,512625,covertype-1ed1c3d23864,UserTask|8449509019|covertype/019d7391-8e33-757b-b38a-79ec188fa001,False,False,True,True,False,False,covertype/019d7391-8e33-757b-b38a-79ec188fa001,grouped,0,0,13,0.0,environmental science & climate,1998,UCI,0,1,482741,29884,482741,29884,3,3,14,14,r0f1 +covertype,multiclass,True,Cover_Type,log_loss,,,Cover_Type,,Wilderness_Area,,per_sample,3,3,True,512625,14,3,512625,covertype-1ed1c3d23864,UserTask|8449509019|covertype/019d7391-8e33-757b-b38a-79ec188fa001,False,False,True,True,False,False,covertype/019d7391-8e33-757b-b38a-79ec188fa001,grouped,0,0,13,0.0,environmental science & climate,1998,UCI,0,2,255610,257015,255610,257015,3,3,14,14,r0f2 +sepsis_prediction_1m,binary,True,SepsisLabel,roc_auc,,,SepsisLabel,,Patient_ID,Hour,per_sample,2,2,True,1228686,43,2,1228686,sepsis_prediction_1m-77a236a7d8eb,UserTask|5654342513|sepsis_prediction_1m/019d7391-f36e-72f8-89d7-f7ca71725034,False,False,False,True,True,False,sepsis_prediction/versions/019d7391-f36e-72f8-89d7-f7ca71725034,grouped,0,0,42,0.6672615918065935,medical & healthcare,2019,Other,0,0,970175,258511,970175,258511,2,2,43,43,r0f0 +cardiotocography,multiclass,True,NSP,log_loss,,,NSP,,patient_id,,per_sample,3,3,True,2126,23,3,2126,cardiotocography-172ef52f9a1b,UserTask|5346935632|cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,False,False,False,True,True,False,cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,grouped,0,0,22,0.0,medical & healthcare,2010,UCI,0,0,1254,872,1254,872,3,3,23,23,r0f0 +cardiotocography,multiclass,True,NSP,log_loss,,,NSP,,patient_id,,per_sample,3,3,True,2126,23,3,2126,cardiotocography-172ef52f9a1b,UserTask|5346935632|cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,False,False,False,True,True,False,cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,grouped,0,0,22,0.0,medical & healthcare,2010,UCI,0,1,1544,582,1544,582,3,3,23,23,r0f1 +cardiotocography,multiclass,True,NSP,log_loss,,,NSP,,patient_id,,per_sample,3,3,True,2126,23,3,2126,cardiotocography-172ef52f9a1b,UserTask|5346935632|cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,False,False,False,True,True,False,cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,grouped,0,0,22,0.0,medical & healthcare,2010,UCI,0,2,1454,672,1454,672,3,3,23,23,r0f2 +cardiotocography,multiclass,True,NSP,log_loss,,,NSP,,patient_id,,per_sample,3,3,True,2126,23,3,2126,cardiotocography-172ef52f9a1b,UserTask|5346935632|cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,False,False,False,True,True,False,cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,grouped,0,0,22,0.0,medical & healthcare,2010,UCI,1,0,1448,678,1448,678,3,3,23,23,r1f0 +cardiotocography,multiclass,True,NSP,log_loss,,,NSP,,patient_id,,per_sample,3,3,True,2126,23,3,2126,cardiotocography-172ef52f9a1b,UserTask|5346935632|cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,False,False,False,True,True,False,cardiotocography/019d7392-12c2-7a87-991c-13a7246555d4,grouped,0,0,22,0.0,medical & healthcare,2010,UCI,1,1,1398,728,1398,728,3,3,23,23,r1f1 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manufacturing,2021,Kaggle,0,0,968996,327320,968996,327320,-1,-1,110,110,r0f0 diff --git a/tabarena/tabarena/benchmark/task/metadata/sources/data/README.md b/tabarena/tabarena/benchmark/task/metadata/sources/data/README.md new file mode 100644 index 000000000..e15200b9e --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/sources/data/README.md @@ -0,0 +1,79 @@ +# Committed reference task-metadata CSVs + +This directory holds the **git-committed reference metadata** for every task +metadata source, one CSV per source named `_tasks_metadata.csv`: + +| File | Source | `name` | +| --- | --- | --- | +| `tabarena-v0.1_tasks_metadata.csv` | `TabArenaV0pt1TaskMetadataSource` | `tabarena-v0.1` | +| `BeyondArena_tasks_metadata.csv` | `DataFoundryTaskMetadataSource` (official collection) | `BeyondArena` | +| `_tasks_metadata.csv` | `DataFoundryTaskMetadataSource` (any collection) | `` | + +Each CSV is one row per `(task, split)` with every `TabArenaTaskMetadata` column +(the `TabArenaTaskMetadata.to_dataframe()` format), plus `data_foundry_uri` for +Data Foundry collections. The files carry **no dataset contents**, only metadata, +so they are small and safe to commit. They let users filter tasks (by name, dtype, +size, problem type) **before downloading any data**, and let the sources skip +rebuilding / re-deriving the metadata at runtime. + +Each CSV also carries **warehouse-level metadata** (`task_type`, `domain`, +`dataset_year`, `source`, `num_text_cols`, `num_high_cardinality_cats`, +`num_cols_after_preprocessing`, `missing_value_fraction`) so the table is +self-contained for filtering / plotting — no separate `warehouse_metadata.csv` +merge is needed. Dataset-derived stats (text / cardinality / cols-after-preprocessing +/ missing fraction) are computed at creation when the dataset is available +(Data Foundry collections); for v0.1 only the tabular fields (`task_type`, `domain`, +`dataset_year`, `source`) are populated and the dataset-derived stats stay empty. + +`task_id_str` is stored in its portable form (no machine-specific cache path); it +resolves against the local cache at load time. + +If a CSV is missing, the corresponding source regenerates the metadata on the fly +(see below) — committing the CSV is purely an optimization (and, for Data Foundry, +enables filter-before-download). + +## Regenerating + +The committed CSVs are produced by maintainer scripts under `scripts/`: + +### TabArena v0.1 + +Pure local transform of the curated v0.1 metadata (no downloads): + +```bash +python scripts/generate_tabarena_v0pt1_metadata.py +``` + +Equivalently, in code: + +```python +from tabarena.benchmark.task.metadata.sources.tabarena_v0pt1 import ( + generate_tabarena_v0_1_reference_metadata, +) + +generate_tabarena_v0_1_reference_metadata() # writes tabarena-v0.1_tasks_metadata.csv +``` + +### Data Foundry collections (e.g. BeyondArena) + +Downloads + converts the whole collection (large, one-off): + +```bash +python scripts/generate_beyond_arena_metadata.py +``` + +For an arbitrary collection: + +```python +from tabarena.benchmark.task.data_foundry import ( + generate_reference_metadata, + reference_metadata_package_path, +) + +generate_reference_metadata( + collection=my_collection, + out_path=reference_metadata_package_path(my_collection.name), +) +``` + +Then commit the resulting CSV. diff --git a/tabarena/tabarena/benchmark/task/metadata/sources/data/TabArena-v0.1_tasks_metadata.csv b/tabarena/tabarena/benchmark/task/metadata/sources/data/TabArena-v0.1_tasks_metadata.csv new file mode 100644 index 000000000..969bd2108 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/sources/data/TabArena-v0.1_tasks_metadata.csv @@ -0,0 +1,817 @@ +dataset_name,problem_type,is_classification,target_name,eval_metric,split_time_horizon,split_time_horizon_unit,stratify_on,time_on,group_on,group_time_on,group_labels,multiclass_min_n_classes_over_splits,multiclass_max_n_classes_over_splits,class_consistency_over_splits,num_instances,num_features,num_classes,num_instance_groups,tabarena_task_name,task_id_str,has_datetime,has_text,has_categorical,has_numerical,has_binary,has_high_cardinality_categorical,data_foundry_uri,task_type,num_text_cols,num_high_cardinality_cats,num_cols_after_preprocessing,missing_value_fraction,domain,dataset_year,source,repeat,fold,num_instances_train,num_instances_test,num_instance_groups_train,num_instance_groups_test,num_classes_train,num_classes_test,num_features_train,num_features_test,split_index 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& marketing,2023,Kaggle,6,1,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r6f1 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,6,2,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r6f2 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,7,0,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r7f0 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,7,1,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r7f1 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,7,2,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r7f2 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,8,0,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r8f0 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,8,1,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r8f1 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,8,2,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r8f2 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,9,0,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r9f0 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,9,1,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r9f1 +Fitness_Club,binary,True,attended,roc_auc,,,attended,,,,,2.0,2.0,True,1500.0,7.0,2.0,1500.0,Fitness_Club,363671,,,,,,,,random,,,,,business & marketing,2023,Kaggle,9,2,1000.0,500.0,1000.0,500.0,2.0,2.0,7.0,7.0,r9f2 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,0,0,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r0f0 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,0,1,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r0f1 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,0,2,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r0f2 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,1,0,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r1f0 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,1,1,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r1f1 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,1,2,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r1f2 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,2,0,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r2f0 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,2,1,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r2f1 +Food_Delivery_Time,regression,False,Time_taken(min),rmse,,,,,,,,,,True,45451.0,10.0,,45451.0,Food_Delivery_Time,363672,,,,,,,,random,,,,,business & marketing,2023,Kaggle,2,2,30300.666666666668,15150.333333333334,30300.666666666668,15150.333333333334,,,10.0,10.0,r2f2 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,0,0,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r0f0 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,0,1,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r0f1 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,0,2,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r0f2 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,1,0,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r1f0 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,1,1,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r1f1 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,1,2,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r1f2 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,2,0,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r2f0 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,2,1,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r2f1 +GiveMeSomeCredit,binary,True,FinancialDistressNextTwoYears,roc_auc,,,FinancialDistressNextTwoYears,,,,,2.0,2.0,True,150000.0,11.0,2.0,150000.0,GiveMeSomeCredit,363673,,,,,,,,random,,,,,finance,2011,Kaggle,2,2,100000.0,50000.0,100000.0,50000.0,2.0,2.0,11.0,11.0,r2f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,0,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r0f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,0,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r0f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,0,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r0f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,1,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r1f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,1,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r1f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,1,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r1f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,2,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r2f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,2,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r2f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,2,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r2f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,3,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r3f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,3,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r3f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,3,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r3f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,4,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r4f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,4,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r4f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,4,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r4f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,5,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r5f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,5,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r5f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,5,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r5f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,6,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r6f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,6,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r6f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,6,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r6f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,7,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r7f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,7,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r7f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,7,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r7f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,8,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r8f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,8,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r8f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,8,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r8f2 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,9,0,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r9f0 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,9,1,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r9f1 +hazelnut-spread-contaminant-detection,binary,True,Contaminated,roc_auc,,,Contaminated,,,,,2.0,2.0,True,2400.0,31.0,2.0,2400.0,hazelnut-spread-contaminant-detection,363674,,,,,,,,random,,,,,biology & life sciences,2020,OpenML,9,2,1600.0,800.0,1600.0,800.0,2.0,2.0,31.0,31.0,r9f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,0,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r0f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,0,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r0f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,0,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r0f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,1,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r1f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,1,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r1f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,1,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r1f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,2,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r2f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,2,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r2f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,2,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r2f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,3,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r3f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,3,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r3f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,3,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r3f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,4,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r4f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,4,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r4f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,4,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r4f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,5,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r5f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,5,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r5f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,5,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r5f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,6,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r6f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,6,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r6f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,6,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r6f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,7,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r7f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,7,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r7f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,7,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r7f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,8,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r8f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,8,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r8f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,8,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r8f2 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,9,0,892.0,446.0,892.0,446.0,,,7.0,7.0,r9f0 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,9,1,892.0,446.0,892.0,446.0,,,7.0,7.0,r9f1 +healthcare_insurance_expenses,regression,False,charges,rmse,,,,,,,,,,True,1338.0,7.0,,1338.0,healthcare_insurance_expenses,363675,,,,,,,,random,,,,,medical & healthcare,2023,Kaggle,9,2,892.0,446.0,892.0,446.0,,,7.0,7.0,r9f2 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,0,0,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r0f0 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,0,1,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r0f1 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,0,2,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r0f2 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,1,0,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r1f0 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,1,1,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r1f1 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,1,2,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r1f2 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,2,0,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r2f0 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,2,1,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r2f1 +heloc,binary,True,RiskPerformance,roc_auc,,,RiskPerformance,,,,,2.0,2.0,True,10459.0,24.0,2.0,10459.0,heloc,363676,,,,,,,,random,,,,,finance,2021,Kaggle,2,2,6972.666666666667,3486.3333333333335,6972.666666666667,3486.3333333333335,2.0,2.0,24.0,24.0,r2f2 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,0,0,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r0f0 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,0,1,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r0f1 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,0,2,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r0f2 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,1,0,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r1f0 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,1,1,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r1f1 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,1,2,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r1f2 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,2,0,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r2f0 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,2,1,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r2f1 +hiva_agnostic,multiclass,True,CompoundActivity,log_loss,,,CompoundActivity,,,,,3.0,3.0,True,3845.0,1618.0,3.0,3845.0,hiva_agnostic,363677,,,,,,,,random,,,,,chemistry & material science,2007,Other,2,2,2563.3333333333335,1281.6666666666667,2563.3333333333335,1281.6666666666667,3.0,3.0,1618.0,1618.0,r2f2 +houses,regression,False,LnMedianHouseValue,rmse,,,,,,,,,,True,20640.0,9.0,,20640.0,houses,363678,,,,,,,,random,,,,,business & marketing,1990,Other,0,0,13760.0,6880.0,13760.0,6880.0,,,9.0,9.0,r0f0 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+MIC,multiclass,True,LET_IS,log_loss,,,LET_IS,,,,,8.0,8.0,True,1699.0,112.0,8.0,1699.0,MIC,363711,,,,,,,,random,,,,,medical & healthcare,2020,UCI,9,0,1132.6666666666667,566.3333333333334,1132.6666666666667,566.3333333333334,8.0,8.0,112.0,112.0,r9f0 +MIC,multiclass,True,LET_IS,log_loss,,,LET_IS,,,,,8.0,8.0,True,1699.0,112.0,8.0,1699.0,MIC,363711,,,,,,,,random,,,,,medical & healthcare,2020,UCI,9,1,1132.6666666666667,566.3333333333334,1132.6666666666667,566.3333333333334,8.0,8.0,112.0,112.0,r9f1 +MIC,multiclass,True,LET_IS,log_loss,,,LET_IS,,,,,8.0,8.0,True,1699.0,112.0,8.0,1699.0,MIC,363711,,,,,,,,random,,,,,medical & healthcare,2020,UCI,9,2,1132.6666666666667,566.3333333333334,1132.6666666666667,566.3333333333334,8.0,8.0,112.0,112.0,r9f2 +NATICUSdroid,binary,True,Malware,roc_auc,,,Malware,,,,,2.0,2.0,True,7491.0,87.0,2.0,7491.0,NATICUSdroid,363689,,,,,,,,random,,,,,technology & internet,2021,UCI,0,0,4994.0,2497.0,4994.0,2497.0,2.0,2.0,87.0,87.0,r0f0 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+NATICUSdroid,binary,True,Malware,roc_auc,,,Malware,,,,,2.0,2.0,True,7491.0,87.0,2.0,7491.0,NATICUSdroid,363689,,,,,,,,random,,,,,technology & internet,2021,UCI,1,2,4994.0,2497.0,4994.0,2497.0,2.0,2.0,87.0,87.0,r1f2 +NATICUSdroid,binary,True,Malware,roc_auc,,,Malware,,,,,2.0,2.0,True,7491.0,87.0,2.0,7491.0,NATICUSdroid,363689,,,,,,,,random,,,,,technology & internet,2021,UCI,2,0,4994.0,2497.0,4994.0,2497.0,2.0,2.0,87.0,87.0,r2f0 +NATICUSdroid,binary,True,Malware,roc_auc,,,Malware,,,,,2.0,2.0,True,7491.0,87.0,2.0,7491.0,NATICUSdroid,363689,,,,,,,,random,,,,,technology & internet,2021,UCI,2,1,4994.0,2497.0,4994.0,2497.0,2.0,2.0,87.0,87.0,r2f1 +NATICUSdroid,binary,True,Malware,roc_auc,,,Malware,,,,,2.0,2.0,True,7491.0,87.0,2.0,7491.0,NATICUSdroid,363689,,,,,,,,random,,,,,technology & internet,2021,UCI,2,2,4994.0,2497.0,4994.0,2497.0,2.0,2.0,87.0,87.0,r2f2 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,0,0,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r0f0 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,0,1,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r0f1 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,0,2,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r0f2 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,1,0,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r1f0 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,1,1,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r1f1 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,1,2,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r1f2 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,2,0,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r2f0 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,2,1,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r2f1 +online_shoppers_intention,binary,True,Revenue,roc_auc,,,Revenue,,,,,2.0,2.0,True,12330.0,18.0,2.0,12330.0,online_shoppers_intention,363691,,,,,,,,random,,,,,business & marketing,2017,UCI,2,2,8220.0,4110.0,8220.0,4110.0,2.0,2.0,18.0,18.0,r2f2 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,0,0,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r0f0 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,0,1,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r0f1 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,0,2,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r0f2 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,1,0,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r1f0 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,1,1,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r1f1 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,1,2,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r1f2 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,2,0,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r2f0 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,2,1,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r2f1 +physiochemical_protein,regression,False,ResidualSize,rmse,,,,,,,,,,True,45730.0,10.0,,45730.0,physiochemical_protein,363693,,,,,,,,random,,,,,chemistry & material science,2013,UCI,2,2,30486.666666666668,15243.333333333334,30486.666666666668,15243.333333333334,,,10.0,10.0,r2f2 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,0,0,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r0f0 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,0,1,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r0f1 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,0,2,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r0f2 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,1,0,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r1f0 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,1,1,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r1f1 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,1,2,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r1f2 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,2,0,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r2f0 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,2,1,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r2f1 +polish_companies_bankruptcy,binary,True,company_bankrupt,roc_auc,,,company_bankrupt,,,,,2.0,2.0,True,5910.0,65.0,2.0,5910.0,polish_companies_bankruptcy,363694,,,,,,,,random,,,,,finance,2010,UCI,2,2,3940.0,1970.0,3940.0,1970.0,2.0,2.0,65.0,65.0,r2f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,0,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r0f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,0,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r0f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,0,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r0f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,1,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r1f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,1,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r1f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,1,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r1f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,2,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r2f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,2,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r2f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,2,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r2f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,3,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r3f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,3,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r3f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,3,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r3f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,4,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r4f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,4,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r4f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,4,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r4f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,5,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r5f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,5,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r5f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,5,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r5f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,6,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r6f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,6,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r6f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,6,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r6f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,7,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r7f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,7,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r7f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,7,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r7f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,8,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r8f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,8,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r8f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,8,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r8f2 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,9,0,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r9f0 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,9,1,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r9f1 +qsar-biodeg,binary,True,Biodegradable,roc_auc,,,Biodegradable,,,,,2.0,2.0,True,1054.0,42.0,2.0,1054.0,qsar-biodeg,363696,,,,,,,,random,,,,,biology & life sciences,2013,UCI,9,2,702.6666666666666,351.3333333333333,702.6666666666666,351.3333333333333,2.0,2.0,42.0,42.0,r9f2 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,0,0,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r0f0 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,0,1,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r0f1 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,0,2,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r0f2 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,1,0,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r1f0 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,1,1,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r1f1 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,1,2,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r1f2 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,2,0,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r2f0 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,2,1,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r2f1 +QSAR-TID-11,regression,False,MEDIAN_PXC50,rmse,,,,,,,,,,True,5742.0,1025.0,,5742.0,QSAR-TID-11,363697,,,,,,,,random,,,,,chemistry & material science,2015,OpenML,2,2,3828.0,1914.0,3828.0,1914.0,,,1025.0,1025.0,r2f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,0,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r0f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,0,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r0f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,0,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r0f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,1,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r1f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,1,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r1f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,1,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r1f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,2,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r2f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,2,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r2f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,2,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r2f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,3,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r3f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,3,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r3f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,3,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r3f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,4,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r4f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,4,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r4f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,4,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r4f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,5,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r5f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,5,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r5f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,5,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r5f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,6,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r6f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,6,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r6f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,6,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r6f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,7,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r7f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,7,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r7f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,7,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r7f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,8,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r8f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,8,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r8f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,8,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r8f2 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,9,0,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r9f0 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,9,1,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r9f1 +QSAR_fish_toxicity,regression,False,LC50,rmse,,,,,,,,,,True,907.0,7.0,,907.0,QSAR_fish_toxicity,363698,,,,,,,,random,,,,,biology & life sciences,2015,UCI,9,2,604.6666666666666,302.3333333333333,604.6666666666666,302.3333333333333,,,7.0,7.0,r9f2 +SDSS17,multiclass,True,ObjectType,log_loss,,,ObjectType,,,,,3.0,3.0,True,78053.0,12.0,3.0,78053.0,SDSS17,363699,,,,,,,,random,,,,,physics & astronomy,2022,Kaggle,0,0,52035.333333333336,26017.666666666668,52035.333333333336,26017.666666666668,3.0,3.0,12.0,12.0,r0f0 +SDSS17,multiclass,True,ObjectType,log_loss,,,ObjectType,,,,,3.0,3.0,True,78053.0,12.0,3.0,78053.0,SDSS17,363699,,,,,,,,random,,,,,physics & astronomy,2022,Kaggle,0,1,52035.333333333336,26017.666666666668,52035.333333333336,26017.666666666668,3.0,3.0,12.0,12.0,r0f1 +SDSS17,multiclass,True,ObjectType,log_loss,,,ObjectType,,,,,3.0,3.0,True,78053.0,12.0,3.0,78053.0,SDSS17,363699,,,,,,,,random,,,,,physics & astronomy,2022,Kaggle,0,2,52035.333333333336,26017.666666666668,52035.333333333336,26017.666666666668,3.0,3.0,12.0,12.0,r0f2 +SDSS17,multiclass,True,ObjectType,log_loss,,,ObjectType,,,,,3.0,3.0,True,78053.0,12.0,3.0,78053.0,SDSS17,363699,,,,,,,,random,,,,,physics & astronomy,2022,Kaggle,1,0,52035.333333333336,26017.666666666668,52035.333333333336,26017.666666666668,3.0,3.0,12.0,12.0,r1f0 +SDSS17,multiclass,True,ObjectType,log_loss,,,ObjectType,,,,,3.0,3.0,True,78053.0,12.0,3.0,78053.0,SDSS17,363699,,,,,,,,random,,,,,physics & astronomy,2022,Kaggle,1,1,52035.333333333336,26017.666666666668,52035.333333333336,26017.666666666668,3.0,3.0,12.0,12.0,r1f1 +SDSS17,multiclass,True,ObjectType,log_loss,,,ObjectType,,,,,3.0,3.0,True,78053.0,12.0,3.0,78053.0,SDSS17,363699,,,,,,,,random,,,,,physics & 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+seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,0,0,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r0f0 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,0,1,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r0f1 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,0,2,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r0f2 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,1,0,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r1f0 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,1,1,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r1f1 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,1,2,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r1f2 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,2,0,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r2f0 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,2,1,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r2f1 +seismic-bumps,binary,True,HighEnergySeismicBump,roc_auc,,,HighEnergySeismicBump,,,,,2.0,2.0,True,2584.0,16.0,2.0,2584.0,seismic-bumps,363700,,,,,,,,random,,,,,environmental science & climate,2013,UCI,2,2,1722.6666666666667,861.3333333333334,1722.6666666666667,861.3333333333334,2.0,2.0,16.0,16.0,r2f2 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,0,0,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r0f0 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,0,1,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r0f1 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,0,2,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r0f2 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,1,0,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r1f0 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,1,1,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r1f1 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,1,2,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r1f2 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,2,0,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r2f0 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,2,1,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r2f1 +splice,multiclass,True,SiteType,log_loss,,,SiteType,,,,,3.0,3.0,True,3190.0,61.0,3.0,3190.0,splice,363702,,,,,,,,random,,,,,biology & life sciences,1991,UCI,2,2,2126.6666666666665,1063.3333333333333,2126.6666666666665,1063.3333333333333,3.0,3.0,61.0,61.0,r2f2 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,0,0,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r0f0 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,0,1,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r0f1 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,0,2,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r0f2 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,1,0,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r1f0 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,1,1,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r1f1 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,1,2,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r1f2 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,2,0,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r2f0 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,2,1,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r2f1 +students_dropout_and_academic_success,multiclass,True,AcademicOutcome,log_loss,,,AcademicOutcome,,,,,3.0,3.0,True,4424.0,37.0,3.0,4424.0,students_dropout_and_academic_success,363704,,,,,,,,random,,,,,education,2021,UCI,2,2,2949.3333333333335,1474.6666666666667,2949.3333333333335,1474.6666666666667,3.0,3.0,37.0,37.0,r2f2 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,0,0,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r0f0 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,0,1,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r0f1 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,0,2,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r0f2 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,1,0,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r1f0 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,1,1,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r1f1 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,1,2,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r1f2 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,2,0,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r2f0 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,2,1,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r2f1 +superconductivity,regression,False,critical_temp,rmse,,,,,,,,,,True,21263.0,82.0,,21263.0,superconductivity,363705,,,,,,,,random,,,,,physics & astronomy,2018,UCI,2,2,14175.333333333334,7087.666666666667,14175.333333333334,7087.666666666667,,,82.0,82.0,r2f2 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,0,0,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r0f0 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,0,1,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r0f1 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,0,2,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r0f2 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,1,0,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r1f0 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,1,1,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r1f1 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,1,2,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r1f2 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,2,0,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r2f0 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,2,1,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r2f1 +taiwanese_bankruptcy_prediction,binary,True,Bankrupt,roc_auc,,,Bankrupt,,,,,2.0,2.0,True,6819.0,95.0,2.0,6819.0,taiwanese_bankruptcy_prediction,363706,,,,,,,,random,,,,,finance,2009,UCI,2,2,4546.0,2273.0,4546.0,2273.0,2.0,2.0,95.0,95.0,r2f2 +website_phishing,multiclass,True,WebsiteType,log_loss,,,WebsiteType,,,,,3.0,3.0,True,1353.0,10.0,3.0,1353.0,website_phishing,363707,,,,,,,,random,,,,,technology & internet,2014,UCI,0,0,902.0,451.0,902.0,451.0,3.0,3.0,10.0,10.0,r0f0 +website_phishing,multiclass,True,WebsiteType,log_loss,,,WebsiteType,,,,,3.0,3.0,True,1353.0,10.0,3.0,1353.0,website_phishing,363707,,,,,,,,random,,,,,technology & internet,2014,UCI,0,1,902.0,451.0,902.0,451.0,3.0,3.0,10.0,10.0,r0f1 +website_phishing,multiclass,True,WebsiteType,log_loss,,,WebsiteType,,,,,3.0,3.0,True,1353.0,10.0,3.0,1353.0,website_phishing,363707,,,,,,,,random,,,,,technology & internet,2014,UCI,0,2,902.0,451.0,902.0,451.0,3.0,3.0,10.0,10.0,r0f2 +website_phishing,multiclass,True,WebsiteType,log_loss,,,WebsiteType,,,,,3.0,3.0,True,1353.0,10.0,3.0,1353.0,website_phishing,363707,,,,,,,,random,,,,,technology & internet,2014,UCI,1,0,902.0,451.0,902.0,451.0,3.0,3.0,10.0,10.0,r1f0 +website_phishing,multiclass,True,WebsiteType,log_loss,,,WebsiteType,,,,,3.0,3.0,True,1353.0,10.0,3.0,1353.0,website_phishing,363707,,,,,,,,random,,,,,technology & internet,2014,UCI,1,1,902.0,451.0,902.0,451.0,3.0,3.0,10.0,10.0,r1f1 +website_phishing,multiclass,True,WebsiteType,log_loss,,,WebsiteType,,,,,3.0,3.0,True,1353.0,10.0,3.0,1353.0,website_phishing,363707,,,,,,,,random,,,,,technology & internet,2014,UCI,1,2,902.0,451.0,902.0,451.0,3.0,3.0,10.0,10.0,r1f2 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b/tabarena/tabarena/benchmark/task/metadata/sources/data_foundry.py new file mode 100644 index 000000000..e84fd533c --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/sources/data_foundry.py @@ -0,0 +1,113 @@ +"""Task metadata source backed by *any* Data Foundry collection. + +This is general-purpose: ``BeyondArena`` is just one collection. Point it at any +:class:`data_foundry.collections.DatasetCollection` to get the same +"filter the reference metadata, then download only what survived" workflow. + +Requires the optional ``data-foundry`` dependency (``tabarena[data-foundry]``); +all data_foundry imports are deferred to call time so importing this module never +forces that dependency. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from tabarena.benchmark.task.metadata.schema import TabArenaTaskMetadata +from tabarena.benchmark.task.metadata.sources.base import TaskMetadataSource + +if TYPE_CHECKING: + from data_foundry.collections import DatasetCollection + + +class DataFoundryTaskMetadataSource(TaskMetadataSource): + """Load + materialize tasks from a Data Foundry collection. + + :meth:`load` returns the collection's *reference* metadata without downloading + any dataset (preferring a committed CSV, else regenerating once and caching), + so a bundle can filter first. :meth:`materialize` then downloads + converts + only the tasks that survived filtering. + """ + + def __init__( + self, + collection: DatasetCollection, + *, + cache_dir: str | None = None, + force_download: bool = False, + force_regenerate: bool = False, + evaluation_metrics: dict[str, list[str]] | None = None, + ) -> None: + """Initialize the source. + + Args: + collection: The Data Foundry collection to load tasks from. + cache_dir: Optional override for the data_foundry download cache. + force_download: Re-fetch + reconvert each container during + materialization even if its local task pickle already exists. + force_regenerate: Ignore the committed / cached reference CSV and + rebuild it by downloading the whole collection. + evaluation_metrics: Override the allowed eval metrics per problem type + (see :data:`tabarena.benchmark.task.data_foundry.DEFAULT_EVAL_METRICS`). + """ + self.collection = collection + self.cache_dir = cache_dir + self.force_download = force_download + self.force_regenerate = force_regenerate + self.evaluation_metrics = evaluation_metrics + + def load(self) -> list[TabArenaTaskMetadata]: + """Load the collection's reference metadata (no dataset downloads).""" + from tabarena.benchmark.task.data_foundry import load_reference_metadata + + metadata_df = load_reference_metadata( + collection=self.collection, + cache_dir=self.cache_dir, + force_regenerate=self.force_regenerate, + ) + return [TabArenaTaskMetadata.from_row(row) for _, row in metadata_df.iterrows()] + + def materialize(self, task_metadata: list[TabArenaTaskMetadata]) -> None: + """Download + convert each task that carries a ``data_foundry_uri``, in place. + + Tasks without a ``data_foundry_uri`` are skipped (e.g. custom tasks mixed + in via a hand-edited frame) — they are assumed to be locally available. + """ + to_materialize = [ + ttm for ttm in task_metadata if isinstance(ttm.data_foundry_uri, str) and ttm.data_foundry_uri + ] + if not to_materialize: + return + + from contextlib import suppress + + from tqdm import tqdm + + from tabarena.benchmark.task.data_foundry import materialize_task + + # Silence huggingface_hub's per-file "Fetching N files" bars — the single progress bar below + # (showing the current dataset) is the clean view; restore HF bars afterwards. + restore_hf_bars = None + with suppress(ImportError): + from huggingface_hub.utils import disable_progress_bars, enable_progress_bars + + disable_progress_bars() + restore_hf_bars = enable_progress_bars + + print(f"Materializing {len(to_materialize)} {self.collection.name} task(s) (datasets + text caches)...") + bar = tqdm(to_materialize, desc=f"Materializing {self.collection.name}", unit="task") + try: + for ttm in bar: + bar.set_postfix_str(ttm.tabarena_task_name or ttm.dataset_name or "") + ttm.task_id_str = materialize_task( + collection=self.collection, + task_id_str=ttm.task_id_str, + data_foundry_uri=ttm.data_foundry_uri, + evaluation_metrics=self.evaluation_metrics, + cache_dir=self.cache_dir, + force_download=self.force_download, + ) + finally: + bar.close() + if restore_hf_bars is not None: + restore_hf_bars() diff --git a/tabarena/tabarena/benchmark/task/metadata/sources/openml.py b/tabarena/tabarena/benchmark/task/metadata/sources/openml.py new file mode 100644 index 000000000..8f760446a --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/sources/openml.py @@ -0,0 +1,73 @@ +"""Base source for OpenML-task-backed metadata (integer OpenML task ids). + +Sources whose tasks are identified by integer OpenML task ids (e.g. TabArena v0.1) +can inherit :meth:`OpenMLTaskMetadataSource.materialize` to pre-cache each task's +dataset + splits into the OpenML cache. This warms a (shared) cache up front so +SLURM workers don't each re-download — replacing the old manual download step. +``load`` stays abstract: subclasses decide where the metadata table comes from. + +Cache location: tasks are cached into OpenML's root cache directory. By default +that is whatever the ambient ``openml.config`` is set to — in the SLURM pipeline +this is configured by ``PathSetup.ensure_runtime_dirs`` (which honors the run's +``openml_cache``) before ``load_task_metadata`` runs, and otherwise falls back to +``~/.cache/openml``. Pass ``openml_cache_dir`` to override it explicitly (mirrors +the old pre-download ``--directory`` flag). +""" + +from __future__ import annotations + +from pathlib import Path + +from tabarena.benchmark.task.metadata.schema import TabArenaTaskMetadata +from tabarena.benchmark.task.metadata.sources.base import TaskMetadataSource + + +class OpenMLTaskMetadataSource(TaskMetadataSource): + """Source whose tasks are OpenML tasks; ``materialize`` pre-caches them.""" + + def __init__(self, *, openml_cache_dir: str | Path | None = None) -> None: + """Initialize the source. + + Args: + openml_cache_dir: Optional OpenML root cache directory to cache tasks + into. When set, it is applied globally via + ``openml.config.set_root_cache_directory`` before downloading + (mirroring the old pre-download step). When ``None``, the ambient + OpenML cache is used — in the SLURM pipeline this is configured by + ``PathSetup.ensure_runtime_dirs`` (else ``~/.cache/openml``). + """ + self.openml_cache_dir = openml_cache_dir + + def materialize(self, task_metadata: list[TabArenaTaskMetadata]) -> None: + """Download each OpenML task's dataset + splits into the OpenML cache, in place. + + Tasks whose ``task_id_str`` is not an integer OpenML id are skipped. Task ids + are de-duplicated (splits of the same task share one id), so each dataset is + fetched once. + """ + task_ids: list[int] = [] + for ttm in task_metadata: + try: + task_ids.append(int(ttm.task_id_str)) + except (TypeError, ValueError): + continue # not an OpenML integer task id (e.g. a local UserTask) — skip + unique_task_ids = list(dict.fromkeys(task_ids)) + if not unique_task_ids: + return + + import openml + from tqdm import tqdm + + if self.openml_cache_dir is not None: + openml.config.set_root_cache_directory(root_cache_directory=str(self.openml_cache_dir)) + + cache_dir = openml.config.get_cache_directory() + print(f"Caching {len(unique_task_ids)} OpenML task(s) into OpenML cache: {cache_dir}") + + for task_id in tqdm(unique_task_ids, desc="Caching OpenML tasks"): + openml.tasks.get_task( + task_id, + download_data=True, + download_qualities=True, + download_splits=True, + ) diff --git a/tabarena/tabarena/benchmark/task/metadata/sources/tabarena_v0pt1.py b/tabarena/tabarena/benchmark/task/metadata/sources/tabarena_v0pt1.py new file mode 100644 index 000000000..595943c28 --- /dev/null +++ b/tabarena/tabarena/benchmark/task/metadata/sources/tabarena_v0pt1.py @@ -0,0 +1,173 @@ +"""Task metadata source for the built-in TabArena v0.1 benchmark suite. + +Aligned with the Data Foundry sources: the source loads a *committed* reference CSV +(per-task x split, the :meth:`TabArenaTaskMetadata.to_dataframe` format) shipped as +package data, instead of rebuilding it from the curated metadata at runtime. + +:func:`load_tabarena_v0_1_task_metadata` (the on-the-fly rebuild) is kept for +backward compatibility and is what :func:`generate_tabarena_v0_1_reference_metadata` +(used by a maintainer script) runs to (re)produce the committed CSV. +""" + +from __future__ import annotations + +from pathlib import Path + +import pandas as pd + +from tabarena.benchmark.task.metadata.schema import SplitMetadata, TabArenaTaskMetadata, derive_task_type +from tabarena.benchmark.task.metadata.sources.base import committed_metadata_path as _committed_metadata_path +from tabarena.benchmark.task.metadata.sources.openml import OpenMLTaskMetadataSource + +#: Surface suite name used for the committed reference CSV and the source registry. +TABARENA_V0PT1_NAME = "TabArena-v0.1" + + +def committed_metadata_path() -> Path: + """Path to the git-committed v0.1 reference-metadata CSV (package data).""" + return _committed_metadata_path(TABARENA_V0PT1_NAME) + + +def load_tabarena_v0_1_task_metadata(curated_metadata: pd.DataFrame) -> list[TabArenaTaskMetadata]: + """Convert curated TabArena v0.1 metadata into the current TabArenaTaskMetadata format. + + Produces one entry per task with splits unrolled. This is the backward-compatible + rebuild; prefer the committed CSV via :class:`TabArenaV0pt1TaskMetadataSource`. It is + also what :func:`generate_tabarena_v0_1_reference_metadata` runs to (re)build the CSV. + + Args: + curated_metadata: The curated v0.1 metadata table (one row per task), e.g. the + output of ``tabarena.nips2025_utils.fetch_metadata.load_curated_task_metadata``. + Must expose the columns accessed below (``num_classes``, ``num_instances``, + ``tabarena_num_repeats``, ``num_folds``, ``problem_type``, ``task_id``, ...). + """ + print("Converting TabArena v0.1 curated metadata to the new TabArenaTaskMetadata format...") + + metric_map = { + "binary": "roc_auc", + "multiclass": "log_loss", + "regression": "rmse", + } + + task_metadata: list[TabArenaTaskMetadata] = [] + for row in curated_metadata.itertuples(): + num_classes = row.num_classes + num_instances = row.num_instances + num_features = row.num_features + + n_repeats = row.tabarena_num_repeats + n_folds = row.num_folds + + eval_metric = metric_map[row.problem_type] + + # Warehouse-level metadata, present in the curated table but optional for + # other curated sources — fall back to None when a column is absent. + domain = getattr(row, "domain", None) + year = getattr(row, "year", None) + data_source = getattr(row, "data_source", None) + dataset_year = None if year is None or pd.isna(year) else str(year) + + for repeat_i in range(n_repeats): + for fold_i in range(n_folds): + split_index = SplitMetadata.get_split_index(repeat_i=repeat_i, fold_i=fold_i) + splits_metadata = { + split_index: SplitMetadata( + repeat=repeat_i, + fold=fold_i, + num_instances_train=num_instances * 2 / 3, + num_instances_test=num_instances * 1 / 3, + num_instance_groups_train=num_instances * 2 / 3, + num_instance_groups_test=num_instances * 1 / 3, + num_classes_train=num_classes, + num_classes_test=num_classes, + num_features_train=num_features, + num_features_test=num_features, + ) + } + + task_metadata.append( + TabArenaTaskMetadata( + task_id_str=row.task_id, + dataset_name=row.dataset_name, + tabarena_task_name=row.dataset_name, + problem_type=row.problem_type, + is_classification=row.is_classification, + target_name=row.target_feature, + stratify_on=row.target_feature if row.is_classification else None, + split_time_horizon=None, + split_time_horizon_unit=None, + time_on=None, + group_on=None, + group_time_on=None, + group_labels=None, + multiclass_max_n_classes_over_splits=num_classes, + multiclass_min_n_classes_over_splits=num_classes, + class_consistency_over_splits=True, + num_instances=num_instances, + num_features=num_features, + num_instance_groups=num_instances, + num_classes=num_classes, + splits_metadata=splits_metadata, + eval_metric=eval_metric, + # Warehouse-level metadata available from the curated table. + # v0.1 is IID, and dataset-derived dtype stats (text / cardinality / + # cols-after-preprocessing / missing fraction) are not in the curated + # table, so they remain None (no dataset is loaded here). + task_type=derive_task_type(time_on=None, group_on=None), + domain=domain, + dataset_year=dataset_year, + source=data_source, + ) + ) + return task_metadata + + +def generate_tabarena_v0_1_reference_metadata( + out_path: str | Path | None = None, + *, + curated_metadata: pd.DataFrame | None = None, +) -> Path: + """Write the committed v0.1 reference CSV by running the rebuild (maintainer tool). + + Args: + out_path: Where to write the CSV. Defaults to :func:`committed_metadata_path`. + curated_metadata: Curated v0.1 metadata to convert. Defaults to the built-in + ``tabarena.nips2025_utils.fetch_metadata.load_curated_task_metadata()``. + + Returns: + The path the CSV was written to. + """ + if curated_metadata is None: + from tabarena.nips2025_utils.fetch_metadata import load_curated_task_metadata + + curated_metadata = load_curated_task_metadata() + + task_metadata = load_tabarena_v0_1_task_metadata(curated_metadata) + metadata_df = pd.concat([ttm.to_dataframe() for ttm in task_metadata], ignore_index=True) + out_path = Path(out_path) if out_path is not None else committed_metadata_path() + out_path.parent.mkdir(parents=True, exist_ok=True) + print(f"Writing {len(metadata_df)} reference-metadata rows to {out_path}.") + metadata_df.to_csv(out_path, index=False) + return out_path + + +class TabArenaV0pt1TaskMetadataSource(OpenMLTaskMetadataSource): + """Source for the built-in TabArena v0.1 curated task metadata. + + Loads the committed reference CSV (package data); falls back to rebuilding from + the curated metadata if that file is missing. ``materialize`` (inherited from + :class:`OpenMLTaskMetadataSource`) pre-caches each task's OpenML dataset + splits, + since every v0.1 task is identified by an integer OpenML task id. + """ + + def load(self) -> list[TabArenaTaskMetadata]: + """Load v0.1 task metadata from the committed CSV (or rebuild as a fallback).""" + path = committed_metadata_path() + if path.exists(): + print(f"Loading committed TabArena v0.1 reference metadata from {path}.") + metadata_df = pd.read_csv(path) + return [TabArenaTaskMetadata.from_row(row) for _, row in metadata_df.iterrows()] + + from tabarena.nips2025_utils.fetch_metadata import load_curated_task_metadata + + return load_tabarena_v0_1_task_metadata(load_curated_task_metadata()) diff --git a/tabarena/tabarena/benchmark/task/openml/__init__.py b/tabarena/tabarena/benchmark/task/openml/__init__.py index 54ec21fa9..bee5a10bd 100644 --- a/tabarena/tabarena/benchmark/task/openml/__init__.py +++ b/tabarena/tabarena/benchmark/task/openml/__init__.py @@ -1 +1,18 @@ -from .task_wrapper import OpenMLTaskWrapper, OpenMLS3TaskWrapper +from __future__ import annotations + +from .metadata_mixin import ( + TabArenaOpenMLClassificationTask, + TabArenaOpenMLRegressionTask, + TabArenaOpenMLSupervisedTask, + TabArenaTaskMetadataMixin, +) +from .task_wrapper import OpenMLS3TaskWrapper, OpenMLTaskWrapper + +__all__ = [ + "OpenMLS3TaskWrapper", + "OpenMLTaskWrapper", + "TabArenaOpenMLClassificationTask", + "TabArenaOpenMLRegressionTask", + "TabArenaOpenMLSupervisedTask", + "TabArenaTaskMetadataMixin", +] diff --git a/tabarena/tabarena/benchmark/task/openml/metadata_mixin.py b/tabarena/tabarena/benchmark/task/openml/metadata_mixin.py new file mode 100644 index 000000000..7cafc513f --- /dev/null +++ b/tabarena/tabarena/benchmark/task/openml/metadata_mixin.py @@ -0,0 +1,304 @@ +from __future__ import annotations + +import pandas as pd +from openml.tasks import ( + OpenMLClassificationTask, + OpenMLRegressionTask, +) + +from tabarena.benchmark.task.metadata import ( + GroupLabelTypes, + SplitMetadata, + SplitTimeHorizonTypes, + SplitTimeHorizonUnitTypes, + TabArenaTaskMetadata, +) +from tabarena.benchmark.task.metadata.schema import derive_task_type + + +def _detect_binary_columns(feature_df: pd.DataFrame, *, sample_size: int = 10_000) -> set[str]: + """Return columns of `feature_df` with exactly 2 distinct non-null values. + + Two-stage scan to avoid a full nunique() pass on every column for wide/large + frames. We first check an evenly-spaced sample: nunique on a subset is a + lower bound on the full nunique, so any column with >2 uniques in the sample + cannot be binary and is skipped. Surviving candidates are verified on the + full column. + """ + n = len(feature_df) + if n <= sample_size: + return {c for c in feature_df.columns if feature_df[c].nunique(dropna=True) == 2} + + sample = feature_df.iloc[:: max(1, n // sample_size)] + candidates = [c for c in feature_df.columns if sample[c].nunique(dropna=True) <= 2] + return {c for c in candidates if feature_df[c].nunique(dropna=True) == 2} + + +class TabArenaTaskMetadataMixin: + """A mixin class to add TabArena-specific metadata to OpenML tasks.""" + + _task_metadata: TabArenaTaskMetadata | None = None + + # TODO: move split metadata to the split object itself and create a TabArena split object + def __init__( + self, + *, + stratify_on: str | None = None, + time_on: str | None = None, + group_on: str | list[str] | None = None, + group_time_on: str | None = None, + group_labels: GroupLabelTypes | None = None, + split_time_horizon: SplitTimeHorizonTypes | None = None, + split_time_horizon_unit: SplitTimeHorizonUnitTypes | None = None, + **kwargs, + ) -> None: + """Checkout Data Foundry's PredictiveMLTaskMetadata for more information.""" + super().__init__(**kwargs) + self.stratify_on = stratify_on + self.group_on = group_on + self.time_on = time_on + self.group_time_on = group_time_on + self.group_labels = group_labels + self._task_metadata = None + self.split_time_horizon = split_time_horizon + self.split_time_horizon_unit = split_time_horizon_unit + + @staticmethod + def get_num_instance_groups( + *, + X: pd.DataFrame, + group_on: str | list[str] | None, + group_labels: GroupLabelTypes | None, + ) -> int: + """Compute the number of instance groups in data based on the group_on.""" + if (group_on is None) or (group_labels == GroupLabelTypes.PER_SAMPLE): + return len(X) + + group_on = group_on if isinstance(group_on, list) else [group_on] + return X.groupby(group_on, dropna=False, observed=True).ngroups + + def _get_dataset_stats( + self, *, oml_dataset: pd.DataFrame, is_classification: bool, target_name: str + ) -> tuple[int, int, int, int]: + num_instance = len(oml_dataset) + num_features = oml_dataset.shape[1] - 1 # -1 for target + num_classes = -1 + if is_classification: + num_classes = max( + int(oml_dataset[target_name].nunique()), + int(oml_dataset[target_name].nunique()), + ) + + num_instance_groups = num_instance + + # Resolve instance groups + if self.group_on is not None: + num_instance_groups = self.get_num_instance_groups( + X=oml_dataset, + group_on=self.group_on, + group_labels=self.group_labels, + ) + + return ( + num_instance, + num_features, + num_classes, + num_instance_groups, + ) + + def compute_metadata( + self: TabArenaOpenMLSupervisedTask, + tabarena_task_name: str | None = None, + task_id_str: str | None = None, + ) -> TabArenaTaskMetadata: + """Get the metadata for the tasks.""" + oml_dataset_object = self.get_dataset() + oml_dataset, *_ = oml_dataset_object.get_data() + dataset_name = oml_dataset_object.name + eval_metric = self.evaluation_measure + is_classification = self.task_type_id.value == 1 + target_name = self.target_name + + task_problem_type = None + num_classes_list = [] + + # Get overall stats of the dataset + ( + full_num_instance, + full_num_features, + full_num_classes, + full_num_instance_groups, + ) = self._get_dataset_stats( + oml_dataset=oml_dataset, + is_classification=is_classification, + target_name=target_name, + ) + + splits_metadata = {} + for repeat_i, splits in self.split.split.items(): + for fold_i, samples_for_split in splits.items(): + assert len(samples_for_split) == 1, "Only one sample per split is supported so far!." + train_idx, test_idx = samples_for_split[0] + + ( + train_num_instance, + train_num_features, + train_num_classes, + train_num_instance_groups, + ) = self._get_dataset_stats( + oml_dataset=oml_dataset.iloc[train_idx], + is_classification=is_classification, + target_name=target_name, + ) + ( + test_num_instance, + test_num_features, + test_num_classes, + test_num_instance_groups, + ) = self._get_dataset_stats( + oml_dataset=oml_dataset.iloc[test_idx], + is_classification=is_classification, + target_name=target_name, + ) + + # Resolve problem type + max_num_classes = max(train_num_classes, test_num_classes) + if max_num_classes == -1: + split_problem_type = "regression" + elif max_num_classes == 2: + split_problem_type = "binary" + num_classes_list.append(max_num_classes) + else: + split_problem_type = "multiclass" + num_classes_list.append(max_num_classes) + if task_problem_type is None: + task_problem_type = split_problem_type + else: + assert task_problem_type == split_problem_type, "All splits must have the same problem type." + s_index = SplitMetadata.get_split_index(repeat_i=repeat_i, fold_i=fold_i) + splits_metadata[s_index] = SplitMetadata( + repeat=repeat_i, + fold=fold_i, + num_instances_train=train_num_instance, + num_instances_test=test_num_instance, + num_instance_groups_train=train_num_instance_groups, + num_instance_groups_test=test_num_instance_groups, + num_classes_train=train_num_classes, + num_classes_test=test_num_classes, + num_features_train=train_num_features, + num_features_test=test_num_features, + ) + + if len(num_classes_list) == 0: + min_n_classes = None + max_n_classes = None + class_consistency_over_splits = None + else: + min_n_classes = min(num_classes_list) + max_n_classes = max(num_classes_list) + class_consistency_over_splits = min_n_classes == max_n_classes + + # Detect feature dtype flags (exclude target column) + excluded_columns = {target_name} + if self.group_on is not None: + if isinstance(self.group_on, list): + excluded_columns.update(self.group_on) + else: + excluded_columns.add(self.group_on) + feature_df = oml_dataset.drop(columns=excluded_columns) + + # FIXME: make this less strict? + if len(feature_df.select_dtypes(include=["object"]).columns) > 0: + raise ValueError( + "Object dtype columns are not supported. Please convert them to string dtype or categorical dtype!" + ) + + # Independent dtype flags + binary_cols = _detect_binary_columns(feature_df) + numerical_cols = feature_df.select_dtypes(include=["number"], exclude=["bool"]).columns + categorical_cols = feature_df.select_dtypes(include=["category", "bool"]).columns + datetime_cols = list(feature_df.select_dtypes(include=["datetime", "datetimetz"]).columns) + datetime_cols += [c for c in feature_df.columns if isinstance(feature_df[c].dtype, pd.PeriodDtype)] + text_cols = feature_df.select_dtypes(include=["string"]).columns + + has_numerical = sum(c not in binary_cols for c in numerical_cols) > 0 + has_datetime = sum(c not in binary_cols for c in datetime_cols) > 0 + has_text = sum(c not in binary_cols for c in text_cols) > 0 + has_binary = len(binary_cols) > 0 + + non_binary_categorical_cols = [c for c in categorical_cols if c not in binary_cols] + has_categorical = len(non_binary_categorical_cols) > 0 + has_high_cardinality_categorical = any( + feature_df[c].nunique(dropna=True) > 50 + for c in non_binary_categorical_cols + ) + + # Warehouse-level counts (consistent with the Data Foundry warehouse computation, + # see data-foundry simple_metadata_exploration). Text/datetime counts are raw + # (binary-inclusive); the after-preprocessing estimate expands text/datetime. + category_only_cols = feature_df.select_dtypes(include=["category"]).columns + num_text_cols = len(text_cols) + num_datetime_cols = len(datetime_cols) + num_high_cardinality_cats = int( + sum(feature_df[c].nunique(dropna=True) > 50 for c in category_only_cols) + ) + num_cols_after_preprocessing = ( + sum(c not in binary_cols for c in numerical_cols) + + sum(c not in binary_cols for c in categorical_cols) + + num_text_cols * 32 + + len(binary_cols) + + num_datetime_cols * 10 + ) + missing_value_fraction = ( + float(feature_df.isna().to_numpy().sum() / feature_df.size) if feature_df.size else 0.0 + ) + + self._task_metadata = TabArenaTaskMetadata( + dataset_name=dataset_name, + eval_metric=eval_metric, + splits_metadata=splits_metadata, + is_classification=is_classification, + problem_type=task_problem_type, + multiclass_min_n_classes_over_splits=min_n_classes, + multiclass_max_n_classes_over_splits=max_n_classes, + class_consistency_over_splits=class_consistency_over_splits, + target_name=target_name, + stratify_on=self.stratify_on, + group_on=self.group_on, + time_on=self.time_on, + group_time_on=self.group_time_on, + group_labels=self.group_labels, + tabarena_task_name=tabarena_task_name, + task_id_str=task_id_str, + num_instances=full_num_instance, + num_features=full_num_features, + num_classes=full_num_classes, + num_instance_groups=full_num_instance_groups, + split_time_horizon=self.split_time_horizon, + split_time_horizon_unit=self.split_time_horizon_unit, + has_datetime=has_datetime, + has_text=has_text, + has_categorical=has_categorical, + has_numerical=has_numerical, + has_binary=has_binary, + has_high_cardinality_categorical=has_high_cardinality_categorical, + task_type=derive_task_type(time_on=self.time_on, group_on=self.group_on), + num_text_cols=num_text_cols, + num_high_cardinality_cats=num_high_cardinality_cats, + num_cols_after_preprocessing=num_cols_after_preprocessing, + missing_value_fraction=missing_value_fraction, + ) + + return self._task_metadata + + +class TabArenaOpenMLClassificationTask(TabArenaTaskMetadataMixin, OpenMLClassificationTask): + """A local OpenMLClassificationTask with additional metadata for TabArena.""" + + +class TabArenaOpenMLRegressionTask(TabArenaTaskMetadataMixin, OpenMLRegressionTask): + """A local OpenMLRegressionTask with additional metadata for TabArena.""" + + +# For typing +TabArenaOpenMLSupervisedTask = TabArenaOpenMLClassificationTask | TabArenaOpenMLRegressionTask diff --git a/tabarena/tabarena/benchmark/task/openml/task_wrapper.py b/tabarena/tabarena/benchmark/task/openml/task_wrapper.py index 4b96def48..f8f6c111f 100644 --- a/tabarena/tabarena/benchmark/task/openml/task_wrapper.py +++ b/tabarena/tabarena/benchmark/task/openml/task_wrapper.py @@ -240,7 +240,7 @@ def get_validation_split_kwargs(self) -> dict: """ from openml.tasks import OpenMLSupervisedTask - from tabarena.benchmark.task.user_task import TabArenaOpenMLSupervisedTask + from tabarena.benchmark.task.openml.metadata_mixin import TabArenaOpenMLSupervisedTask oml_task: TabArenaOpenMLSupervisedTask | OpenMLSupervisedTask = self.task diff --git a/tabarena/tabarena/benchmark/task/user_task.py b/tabarena/tabarena/benchmark/task/user_task.py index d116cfaaa..22c0fbd87 100644 --- a/tabarena/tabarena/benchmark/task/user_task.py +++ b/tabarena/tabarena/benchmark/task/user_task.py @@ -5,16 +5,15 @@ from collections import OrderedDict from collections.abc import Iterable from copy import deepcopy -from dataclasses import MISSING, asdict, dataclass, fields, replace -from enum import StrEnum from pathlib import Path -from typing import Annotated, Literal +from typing import TYPE_CHECKING, Literal import numpy as np import openml import openml._api_calls import openml.utils import pandas as pd +from loguru import logger from openml.datasets.dataset import OpenMLDataset from openml.datasets.functions import ( _expand_parameter, @@ -22,555 +21,36 @@ attributes_arff_from_df, ) from openml.tasks import ( - OpenMLClassificationTask, - OpenMLRegressionTask, OpenMLSupervisedTask, TaskType, ) -SplitIndex = Annotated[str, "format: r{int}f{int}"] - -SplitTimeHorizonTypes = str | int | float -SplitTimeHorizonUnitTypes = Literal["steps", "days", "weeks", "months", "years"] | str - - -class GroupLabelTypes(StrEnum): - PER_SAMPLE = "per_sample" - PER_GROUP = "per_group" - - -@dataclass -class TabArenaTaskMetadata: - """Metadata about the task to run. - - This metadata has different use cases for 1 or N elements in the splits_metadata. - """ - - dataset_name: str - """Simple name of the dataset used for the task.""" - - problem_type: str - """The problem type of the task (e.g., 'binary', 'multiclass', 'regression').""" - is_classification: bool - """Whether the task is a classification task.""" - - target_name: str - """The name of the target variable in the dataset.""" - eval_metric: str - """The evaluation metric used for the task.""" - - """The number of splits in the task.""" - splits_metadata: dict[SplitIndex, SplitMetadata] - """Mapping of split index to Metadata about the splits in the task.""" - split_time_horizon: SplitTimeHorizonTypes | None - """The time horizon used for temporal splitting. This can be a number (e.g., 5).""" - split_time_horizon_unit: SplitTimeHorizonUnitTypes | None - """The unit of the time horizon used for temporal splitting. This can be a string (e.g., 'days') or one of - the predefined literals.""" - - stratify_on: str | None - """The name of the column used for stratification during splitting.""" - time_on: str | None - """The name of the column used for temporal splitting.""" - group_on: str | list[str] | None - """The name of the column used for grouping during splitting.""" - group_time_on: str | None - """The name of the column that contains the time information for""" - group_labels: GroupLabelTypes | None - """Whether the group_on column(s) contain labels for each sample, or for each group.""" - - multiclass_min_n_classes_over_splits: int | None - """The minimum number of classes across splits for classification tasks, - None for regression tasks.""" - multiclass_max_n_classes_over_splits: int | None - """The maximum number of classes across splits for classification tasks, - None for regression tasks.""" - class_consistency_over_splits: bool | None - """Whether the number of classes is consistent across splits for classification tasks, - None for regression tasks.""" - - num_instances: int - """The total number of instances in the dataset.""" - num_features: int - """The total number of features (excluding the target) in the dataset.""" - num_classes: int - """The total number of classes in the dataset. -1 for regression tasks.""" - num_instance_groups: int - """The total number of unique groups of data in the dataset. For normal IID data, - this is equal to num_instances. For non-IID grouped data, this is equal to the number - of groups in the data. - """ - - tabarena_task_name: str | None - """The name of the task used for TabArena. This is used for better identification - and can be set by the user.""" - task_id_str: str | None - """The task ID string used for the task. This functions as the unique identifier - of the source of the metadat. This can either be an OpenML task ID, or it is the - identifier of a local task (see `UserTask.task_id_str`). - """ - - # -- Feature dtype flags (added later; default to None for backward compat) -- - has_datetime: bool | None = None - """Whether the dataset contains datetime feature columns (incl. period dtypes).""" - has_text: bool | None = None - """Whether the dataset contains text (string) feature columns.""" - has_categorical: bool | None = None - """Whether the dataset contains categorical feature columns.""" - has_numerical: bool | None = None - """Whether the dataset contains numerical feature columns.""" - has_binary: bool | None = None - """Whether the dataset contains any feature column with exactly two distinct - non-null values.""" - has_high_cardinality_categorical: bool | None = None - """Whether the dataset contains a categorical (category dtype) feature column - with more than 50 unique values.""" - - @property - def n_splits(self): - """Get the number of splits in the task.""" - return len(self.splits_metadata) - - @property - def split_indices(self) -> list[SplitIndex]: - """Get a list of all split indices in the task.""" - return list(self.splits_metadata.keys()) - - @property - def split_index(self) -> SplitIndex: - """Get the split index for the task. - This is only supported for tasks with one split. - """ - if self.n_splits != 1: - raise ValueError( - f"Cannot get split index for task with {self.n_splits} splits. " - "This is only supported for tasks with exactly one split." - ) - return self.split_indices[0] - - def has_supported_dtypes(self, *, required_dtypes: list[str] | None, forbidden_dtypes: list[str] | None) -> bool: - """Check if the dataset contains only allowed dtypes based on the feature dtype flags.""" - flag_by_dtype = { - "datetime": self.has_datetime, - "text": self.has_text, - "categorical": self.has_categorical, - "numerical": self.has_numerical, - "binary": self.has_binary, - "high_cardinality_categorical": self.has_high_cardinality_categorical, - } - - if required_dtypes is not None: - for dtype in required_dtypes: - if not flag_by_dtype.get(dtype): - return False - - if forbidden_dtypes is not None: - for dtype in forbidden_dtypes: - if flag_by_dtype.get(dtype): - return False - - return True - - def to_dict(self, *, exclude_splits_metadata: bool = False) -> dict: - """Convert the task metadata to a dictionary for better visualization.""" - res = asdict(self) - if exclude_splits_metadata: - res.pop("splits_metadata") - return res - - def to_dataframe(self, *, add_old_minimal_metadata: bool = False) -> pd.DataFrame: - """Transform metadata to a DataFrame. - - If add_old_minimal_metadata is True, also add old minimal metadata for backward - compatibility with old eval code. That is, we add the columns: "tid", "name", "task_type", "dataset", - "n_samples_train_per_fold", "n_samples_test_per_fold". - """ - rows = [] - static_metadata = self.to_dict(exclude_splits_metadata=True) - for split_metadata in self.splits_metadata.values(): - rows.append( - { - **static_metadata, - **split_metadata.to_dict(), - } - ) - - df = pd.DataFrame(rows) - - # TODO: move somewhere else / get rid of this? - if add_old_minimal_metadata: - # Add old minimal metadata for backward compatibility with old eval code - df["tid"] = int(self.task_id_str.split("|")[1]) # FIXME: avoid hacky way - df["name"] = df["tabarena_task_name"] - df["task_type"] = "classification" - df.loc[~df["is_classification"], "task_type"] = "regression" - df["dataset"] = df["tabarena_task_name"] - df["n_samples_train_per_fold"] = df["num_instances_train"] - df["n_samples_test_per_fold"] = df["num_instances_test"] - - return df - - @staticmethod - def from_row(row: pd.Series) -> TabArenaTaskMetadata: - """Reconstruct TabArenaTaskMetadata from a single dataframe row.""" - row_dict = row.to_dict() - - # Identify TabArenaTaskMetadata fields (excluding splits_metadata). - # Fields with defaults are optional for backward compatibility with - # older serialized metadata that may not contain newer columns. - all_task_fields = {f.name for f in fields(TabArenaTaskMetadata) if f.name != "splits_metadata"} - required_task_fields = { - f.name - for f in fields(TabArenaTaskMetadata) - if f.name != "splits_metadata" and f.default is MISSING and f.default_factory is MISSING - } - if not all(name in row_dict for name in required_task_fields): - raise ValueError( - "Metadata row is missing required TabArenaTaskMetadata fields: " - f"{required_task_fields - row_dict.keys()}" - ) - task_kwargs = {key: row_dict[key] for key in all_task_fields if key in row_dict} - - # Identify SplitMetadata fields - split_field_names = {f.name for f in fields(SplitMetadata)} - if not all(name in row_dict for name in split_field_names): - raise ValueError( - f"Metadata row is missing required SplitMetadata fields: {split_field_names - row_dict.keys()}" - ) - split_kwargs = {key: row_dict[key] for key in split_field_names if key in row_dict} - # Reconstruct SplitMetadata - split_metadata = SplitMetadata(**split_kwargs) - - # --- Construct final object --- - return TabArenaTaskMetadata( - **task_kwargs, - splits_metadata={split_metadata.split_index: split_metadata}, - ) - - def unroll_splits(self) -> list[TabArenaTaskMetadata]: - """Unroll the TabArenaTaskMetadata into a list of TabArenaTaskMetadata instances - each containing exactly one split in `splits_metadata`. - """ - return [ - replace( - self, - splits_metadata={split_idx: split_meta}, - ) - for split_idx, split_meta in self.splits_metadata.items() - ] - - -@dataclass -class SplitMetadata: - """Metadata about the splits in the task.""" - - repeat: int - """The repeat index of the split. - - All splits with the same repeat index have the same data points, - but split into folds differently. - """ - fold: int - """The fold index of the split.""" - num_instances_train: int - """The number of training instances in the split.""" - num_instances_test: int - """The number of test instances in the split.""" - num_instance_groups_train: int - """The number unique groups of data in the train split. - For normal IID data, this is always equal to `num_instances_train`. - For non-IID grouped data, this is equal to the number of groups in the data. - """ - num_instance_groups_test: int - """The number unique groups of data in the test split.""" - num_classes_train: int - """-1 for regression tasks, and maximal number of unique classes in the training - set for classification tasks.""" - num_classes_test: int - """Classes in test data""" - num_features_train: int - """The number of features (excluding the target) in the training set of the split.""" - num_features_test: int - """The number of features (excluding the target) in the test set of the split.""" +from tabarena.benchmark.task.openml import ( + TabArenaOpenMLClassificationTask, + TabArenaOpenMLRegressionTask, + TabArenaOpenMLSupervisedTask, +) - @staticmethod - def get_split_index(*, repeat_i: int, fold_i: int) -> SplitIndex: - """Get the split index for the given repeat and fold.""" - return f"r{repeat_i}f{fold_i}" +if TYPE_CHECKING: + from tabarena.benchmark.task.metadata import ( + GroupLabelTypes, + SplitTimeHorizonTypes, + SplitTimeHorizonUnitTypes, + ) - @property - def split_index(self) -> SplitIndex: - """Get the split index for this split metadata.""" - return self.get_split_index(repeat_i=self.repeat, fold_i=self.fold) - def to_dict(self) -> dict[str, int | str]: - """Convert the split metadata to a dictionary.""" - res = asdict(self) - res["split_index"] = self.split_index - return res +#: Number of hex chars of the task-name hash appended to a slug for uniqueness. +_SLUG_HASH_LEN = 12 -def _detect_binary_columns(feature_df: pd.DataFrame, *, sample_size: int = 10_000) -> set[str]: - """Return columns of `feature_df` with exactly 2 distinct non-null values. +def _slugify(name: str, *, max_len: int = 40) -> str: + """Make ``name`` filesystem/display safe: chars outside ``[A-Za-z0-9._-]`` -> ``_``. - Two-stage scan to avoid a full nunique() pass on every column for wide/large - frames. We first check an evenly-spaced sample: nunique on a subset is a - lower bound on the full nunique, so any column with >2 uniques in the sample - cannot be binary and is skipped. Surviving candidates are verified on the - full column. + Trimmed of leading/trailing ``_.-`` and length-capped; falls back to ``"task"`` + if nothing readable remains. """ - n = len(feature_df) - if n <= sample_size: - return {c for c in feature_df.columns if feature_df[c].nunique(dropna=True) == 2} - - sample = feature_df.iloc[:: max(1, n // sample_size)] - candidates = [c for c in feature_df.columns if sample[c].nunique(dropna=True) <= 2] - return {c for c in candidates if feature_df[c].nunique(dropna=True) == 2} - - -class TabArenaTaskMetadataMixin: - """A mixin class to add TabArena-specific metadata to OpenML tasks.""" - - _task_metadata: TabArenaTaskMetadata | None = None - - # TODO: move split metadata to the split object itself and create a TabArena split object - def __init__( - self, - *, - stratify_on: str | None = None, - time_on: str | None = None, - group_on: str | list[str] | None = None, - group_time_on: str | None = None, - group_labels: GroupLabelTypes | None = None, - split_time_horizon: SplitTimeHorizonTypes | None = None, - split_time_horizon_unit: SplitTimeHorizonUnitTypes | None = None, - **kwargs, - ) -> None: - """Checkout Data Foundry's PredictiveMLTaskMetadata for more information.""" - super().__init__(**kwargs) - self.stratify_on = stratify_on - self.group_on = group_on - self.time_on = time_on - self.group_time_on = group_time_on - self.group_labels = group_labels - self._task_metadata = None - self.split_time_horizon = split_time_horizon - self.split_time_horizon_unit = split_time_horizon_unit - - @staticmethod - def get_num_instance_groups( - *, - X: pd.DataFrame, - group_on: str | list[str] | None, - group_labels: GroupLabelTypes | None, - ) -> int: - """Compute the number of instance groups in data based on the group_on.""" - if (group_on is None) or (group_labels == GroupLabelTypes.PER_SAMPLE): - return len(X) - - group_on = group_on if isinstance(group_on, list) else [group_on] - return X.groupby(group_on, dropna=False, observed=True).ngroups - - def _get_dataset_stats( - self, *, oml_dataset: pd.DataFrame, is_classification: bool, target_name: str - ) -> tuple[int, int, int, int]: - num_instance = len(oml_dataset) - num_features = oml_dataset.shape[1] - 1 # -1 for target - num_classes = -1 - if is_classification: - num_classes = max( - int(oml_dataset[target_name].nunique()), - int(oml_dataset[target_name].nunique()), - ) - - num_instance_groups = num_instance - - # Resolve instance groups - if self.group_on is not None: - num_instance_groups = self.get_num_instance_groups( - X=oml_dataset, - group_on=self.group_on, - group_labels=self.group_labels, - ) - - return ( - num_instance, - num_features, - num_classes, - num_instance_groups, - ) - - def compute_metadata( - self: TabArenaOpenMLSupervisedTask, - tabarena_task_name: str | None = None, - task_id_str: str | None = None, - ) -> TabArenaTaskMetadata: - """Get the metadata for the tasks.""" - oml_dataset_object = self.get_dataset() - oml_dataset, *_ = oml_dataset_object.get_data() - dataset_name = oml_dataset_object.name - eval_metric = self.evaluation_measure - is_classification = self.task_type_id.value == 1 - target_name = self.target_name - - task_problem_type = None - num_classes_list = [] - - # Get overall stats of the dataset - ( - full_num_instance, - full_num_features, - full_num_classes, - full_num_instance_groups, - ) = self._get_dataset_stats( - oml_dataset=oml_dataset, - is_classification=is_classification, - target_name=target_name, - ) - - splits_metadata = {} - for repeat_i, splits in self.split.split.items(): - for fold_i, samples_for_split in splits.items(): - assert len(samples_for_split) == 1, "Only one sample per split is supported so far!." - train_idx, test_idx = samples_for_split[0] - - ( - train_num_instance, - train_num_features, - train_num_classes, - train_num_instance_groups, - ) = self._get_dataset_stats( - oml_dataset=oml_dataset.iloc[train_idx], - is_classification=is_classification, - target_name=target_name, - ) - ( - test_num_instance, - test_num_features, - test_num_classes, - test_num_instance_groups, - ) = self._get_dataset_stats( - oml_dataset=oml_dataset.iloc[test_idx], - is_classification=is_classification, - target_name=target_name, - ) - - # Resolve problem type - max_num_classes = max(train_num_classes, test_num_classes) - if max_num_classes == -1: - split_problem_type = "regression" - elif max_num_classes == 2: - split_problem_type = "binary" - num_classes_list.append(max_num_classes) - else: - split_problem_type = "multiclass" - num_classes_list.append(max_num_classes) - if task_problem_type is None: - task_problem_type = split_problem_type - else: - assert task_problem_type == split_problem_type, "All splits must have the same problem type." - s_index = SplitMetadata.get_split_index(repeat_i=repeat_i, fold_i=fold_i) - splits_metadata[s_index] = SplitMetadata( - repeat=repeat_i, - fold=fold_i, - num_instances_train=train_num_instance, - num_instances_test=test_num_instance, - num_instance_groups_train=train_num_instance_groups, - num_instance_groups_test=test_num_instance_groups, - num_classes_train=train_num_classes, - num_classes_test=test_num_classes, - num_features_train=train_num_features, - num_features_test=test_num_features, - ) - - if len(num_classes_list) == 0: - min_n_classes = None - max_n_classes = None - class_consistency_over_splits = None - else: - min_n_classes = min(num_classes_list) - max_n_classes = max(num_classes_list) - class_consistency_over_splits = min_n_classes == max_n_classes - - # Detect feature dtype flags (exclude target column) - excluded_columns = {target_name} - if self.group_on is not None: - if isinstance(self.group_on, list): - excluded_columns.update(self.group_on) - else: - excluded_columns.add(self.group_on) - feature_df = oml_dataset.drop(columns=excluded_columns) - - # FIXME: make this less strict? - if len(feature_df.select_dtypes(include=["object"]).columns) > 0: - raise ValueError( - "Object dtype columns are not supported. Please convert them to string dtype or categorical dtype!" - ) - - # Independent dtype flags - binary_cols = _detect_binary_columns(feature_df) - numerical_cols = feature_df.select_dtypes(include=["number"], exclude=["bool"]).columns - categorical_cols = feature_df.select_dtypes(include=["category", "bool"]).columns - datetime_cols = list(feature_df.select_dtypes(include=["datetime", "datetimetz"]).columns) - datetime_cols += [c for c in feature_df.columns if isinstance(feature_df[c].dtype, pd.PeriodDtype)] - text_cols = feature_df.select_dtypes(include=["string"]).columns - - has_numerical = sum(c not in binary_cols for c in numerical_cols) > 0 - has_datetime = sum(c not in binary_cols for c in datetime_cols) > 0 - has_text = sum(c not in binary_cols for c in text_cols) > 0 - has_binary = len(binary_cols) > 0 - - non_binary_categorical_cols = [c for c in categorical_cols if c not in binary_cols] - has_categorical = len(non_binary_categorical_cols) > 0 - has_high_cardinality_categorical = any( - feature_df[c].nunique(dropna=True) > 50 - for c in non_binary_categorical_cols - ) - - self._task_metadata = TabArenaTaskMetadata( - dataset_name=dataset_name, - eval_metric=eval_metric, - splits_metadata=splits_metadata, - is_classification=is_classification, - problem_type=task_problem_type, - multiclass_min_n_classes_over_splits=min_n_classes, - multiclass_max_n_classes_over_splits=max_n_classes, - class_consistency_over_splits=class_consistency_over_splits, - target_name=target_name, - stratify_on=self.stratify_on, - group_on=self.group_on, - time_on=self.time_on, - group_time_on=self.group_time_on, - group_labels=self.group_labels, - tabarena_task_name=tabarena_task_name, - task_id_str=task_id_str, - num_instances=full_num_instance, - num_features=full_num_features, - num_classes=full_num_classes, - num_instance_groups=full_num_instance_groups, - split_time_horizon=self.split_time_horizon, - split_time_horizon_unit=self.split_time_horizon_unit, - has_datetime=has_datetime, - has_text=has_text, - has_categorical=has_categorical, - has_numerical=has_numerical, - has_binary=has_binary, - has_high_cardinality_categorical=has_high_cardinality_categorical, - ) - - return self._task_metadata - - -class TabArenaOpenMLClassificationTask(TabArenaTaskMetadataMixin, OpenMLClassificationTask): - """A local OpenMLClassificationTask with additional metadata for TabArena.""" - - -class TabArenaOpenMLRegressionTask(TabArenaTaskMetadataMixin, OpenMLRegressionTask): - """A local OpenMLRegressionTask with additional metadata for TabArena.""" - - -# For typing -TabArenaOpenMLSupervisedTask = TabArenaOpenMLClassificationTask | TabArenaOpenMLRegressionTask + safe = "".join(c if (c.isalnum() or c in "._-") else "_" for c in name) + return safe.strip("_.-")[:max_len] or "task" # Patch Functions for OpenML Dataset @@ -610,28 +90,66 @@ def task_cache_path(self) -> Path: @staticmethod def from_task_id_str(task_id_str: str) -> UserTask: - """Create a UserTask from a task ID string.""" + """Reconstruct a UserTask from its ``task_id_str``. + + Two forms are accepted: + + * ``UserTask|{task_id}|{task_name}`` — the standardized, portable form. + The cache directory is resolved from the ambient OpenML config at load + time (``/tabarena_tasks``), so the same id works on any + machine. This is what data_foundry / BeyondArena tasks use. + * ``UserTask|{task_id}|{task_name}|{task_cache_path}`` — the explicit-path + fallback, for a task stored at a custom location. The embedded path is + honored as-is. + + ``{task_id}`` is informational only; it is recomputed from ``{task_name}``. + """ parts = task_id_str.split("|") - if (len(parts)) != 4 or (parts[0] != "UserTask"): + if (parts[0] != "UserTask") or (len(parts) not in (3, 4)): raise ValueError(f"Invalid task ID string: {task_id_str}") task_name = parts[2] - task_cache_path = Path(parts[3]) + task_cache_path = Path(parts[3]) if len(parts) == 4 else None return UserTask(task_name=task_name, task_cache_path=task_cache_path) @property def task_id_str(self) -> str: - """Task ID used for the task.""" + """Portable identifier for this task. + + Standardized tasks (no explicit ``task_cache_path``) serialize *without* a + path, so the id is machine-independent and resolves against the ambient + OpenML cache on load. Tasks created with a custom ``task_cache_path`` + include it so they remain self-describing. + """ + if self._task_cache_path is None: + return f"UserTask|{self.task_id}|{self.task_name}" return f"UserTask|{self.task_id}|{self.task_name}|{self.task_cache_path}" + @property + def slug(self) -> str: + """Human-readable, filesystem-safe, unique identifier for the task. + + Format ``{readable}-{shorthash}`` where ``readable`` is the first + ``/``-segment of the task name (the dataset name for data_foundry tasks, + e.g. ``blood_transfusion/`` -> ``blood_transfusion``) and + ``shorthash`` is the first :data:`_SLUG_HASH_LEN` hex chars of the task + name's SHA-256. The hash is always present and derived from the full + (unique) task name, so the slug stays unique and collision-safe even when + the readable parts coincide, and is reproducible from ``task_name`` alone. + """ + readable = _slugify(self.task_name.split("/")[0]) + return f"{readable}-{self._task_name_hash[:_SLUG_HASH_LEN]}" + @property def tabarena_task_name(self) -> str: - """Task/Dataset Name used for the task/dataset.""" - return f"Task-{self.task_id}" + """Task/Dataset name used for the task (and as the results ``dataset`` key).""" + return self.slug @property def task_id(self) -> int: - """Generate a unique task ID based on the task name and a UUID. - This is used to identify the task, for example, when caching results. + """Generate a unique integer task ID based on the task name. + + Used where an integer id is structurally required (the OpenML task object). + Human-facing references / caches use :attr:`slug` instead. """ return int(self._task_name_hash, 16) % 10**10 @@ -748,7 +266,7 @@ def create_local_openml_task( raise NotImplementedError(f"Task type {task_type:d} not supported.") dataset_name = self.get_dataset_name(dataset_name=dataset_name) - print(f"Creating local OpenML task {self.task_id} with dataset '{dataset_name}'...") + logger.debug(f"Creating local OpenML task {self.task_id} with dataset '{dataset_name}'...") local_dataset = openml_create_datasets_without_arff_dump( name=dataset_name, data=dataset, @@ -852,11 +370,11 @@ def _validate_splits(*, splits: dict[int, dict[int, tuple[list, list]]], n_sampl @property def openml_task_path(self) -> Path: - return self.task_cache_path / f"{self.task_id}.pkl" + return self.task_cache_path / f"{self.slug}.pkl" def save_local_openml_task(self, task: OpenMLSupervisedTask) -> None: """Safe the OpenML task to be usable by loading from disk later.""" - print(f"Saving local task {self.task_name} to: {self.task_cache_path}") + logger.debug(f"Saving local task {self.task_name} to: {self.task_cache_path}") self.task_cache_path.mkdir(parents=True, exist_ok=True) # Remove monkey patch to avoid pickle issues. diff --git a/tabarena/tabarena/evaluation/__init__.py b/tabarena/tabarena/evaluation/__init__.py index e69de29bb..93169bd34 100644 --- a/tabarena/tabarena/evaluation/__init__.py +++ b/tabarena/tabarena/evaluation/__init__.py @@ -0,0 +1,31 @@ +"""Evaluation runners for benchmark results. + +Two flavors share the post-process/cache/subset-loop skeleton (in ``_eval_common``): + +* :func:`~tabarena.evaluation.benchmark_eval.run_eval` — TabArena v0.1, compared against the + published paper baselines. +* :func:`~tabarena.evaluation.beyond_arena_eval.run_beyond_arena_eval` — BeyondArena (data-foundry), + combining one or more runs and filtering by data-foundry subset predicates. + +Only light dataclasses/functions are imported at module load here; the heavy result-processing / +comparison imports are deferred to inside the runners (keeps ``import tabarena.evaluation`` cheap +and avoids the historical import cycle through ``nips2025_utils``). +""" + +from __future__ import annotations + +from tabarena.evaluation.benchmark_eval import EvalMethod, TabArenaEvalConfig, run_eval +from tabarena.evaluation.beyond_arena_eval import ( + BenchmarkRun, + BeyondArenaEvalConfig, + run_beyond_arena_eval, +) + +__all__ = [ + "BenchmarkRun", + "BeyondArenaEvalConfig", + "EvalMethod", + "TabArenaEvalConfig", + "run_beyond_arena_eval", + "run_eval", +] diff --git a/tabarena/tabarena/evaluation/_eval_common.py b/tabarena/tabarena/evaluation/_eval_common.py new file mode 100644 index 000000000..e9539b001 --- /dev/null +++ b/tabarena/tabarena/evaluation/_eval_common.py @@ -0,0 +1,150 @@ +"""Shared building blocks for the TabArena-v0.1 and BeyondArena evaluation runners. + +Both runners follow the same skeleton — init caches/env, post-process each method's raw +artifacts into the cache (or load it), then run a per-subset compare/leaderboard loop. The +pieces that are identical across flavors live here so the two runners stay thin and consistent; +the *comparison* step differs (v0.1 compares against paper baselines via ``compare_on_tabarena``; +BeyondArena uses ``compare`` + data-foundry subset predicates) and is kept in each runner. + +Heavy imports (``end_to_end*``, model registry) are deferred to call time so importing +``tabarena.evaluation`` stays cheap and free of the historical import cycle. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING + +from tabarena.loaders import set_tabarena_cache_root + +if TYPE_CHECKING: + import pandas as pd + + from tabarena.nips2025_utils.end_to_end import EndToEndResults + + +@dataclass +class MethodArtifact: + """One method's raw-result location + cache identity, for post-processing. + + A flat, runner-agnostic spec: the v0.1 runner builds one per ``EvalMethod`` (all sharing the + config's ``path_raw``/``benchmark_name``); the BeyondArena runner builds one per (run, model), + so a single method name can appear under multiple ``artifact_name``s. + """ + + ag_name: str + """The model's AutoGluon name — both the raw-folder prefix and the cache method name.""" + path_raw: Path + """Directory holding the raw ``results.pkl`` artifacts (the run's ``/data``).""" + artifact_name: str + """Cache artifact name (upper cache dir); distinct values disambiguate the same method + across different runs.""" + result_suffix: str | None = None + """Optional suffix appended to the method name in the leaderboard (baked in at post-process).""" + only_load_cache: bool = False + """If True, skip raw->cache post-processing and load the existing cache instead.""" + + +def init_caches(tabarena_cache_path: str | None = None, openml_cache_path: str | None = None) -> None: + """Point TabArena/OpenML at the configured caches (resolved lazily, so order-independent).""" + if tabarena_cache_path is not None: + set_tabarena_cache_root(tabarena_cache_path) + print("Set TabArena cache root to:", tabarena_cache_path) + if openml_cache_path is not None: + import openml + + openml.config.set_root_cache_directory(str(Path(openml_cache_path).expanduser())) + print("Set OpenML cache root to:", openml_cache_path) + + +def init_aux_metric_env(aux_metric_map: dict[str, str] | None) -> None: + """Publish/clear the auxiliary problem_type->metric map consumed during post-processing. + + When set, an ``aux_metric_error`` column is added to per-config and HPO/ensemble result rows. + Passing ``None`` clears the env var (auxiliary metric disabled). + """ + import json + import os + + from tabarena.utils.aux_metric import AUX_METRIC_ENV_VAR + + if aux_metric_map is None: + os.environ.pop(AUX_METRIC_ENV_VAR, None) + return + os.environ[AUX_METRIC_ENV_VAR] = json.dumps(aux_metric_map) + print(f"Set {AUX_METRIC_ENV_VAR} to: {os.environ[AUX_METRIC_ENV_VAR]}") + + +def resolve_ag_name(name: str, ag_name_override: str | None = None) -> str: + """Resolve a model-registry name (e.g. ``"TabPFN-3"``) to its AutoGluon name. + + ``ag_name_override`` short-circuits the registry lookup (for custom methods not registered in + ``tabarena.models.utils.get_configs_generator_from_name``). + """ + if ag_name_override is not None: + return ag_name_override + from tabarena.models.utils import get_configs_generator_from_name + + return get_configs_generator_from_name(name).model_cls.ag_name + + +def post_process_to_results( + method_artifacts: list[MethodArtifact], + *, + task_metadata: pd.DataFrame | None = None, + num_cpus: int | None = None, +) -> EndToEndResults: + """Post-process each method's raw artifacts into the cache, then re-load all from the cache. + + Two phases, matching the canonical eval flow: + + 1. **Cache:** for each non-``only_load_cache`` method, ``EndToEndSingle.from_path_raw_to_results`` + processes the raw ``results.pkl`` files into per-task results and writes them to the cache + (keyed by ``(artifact_name, ag_name)``). ``task_metadata`` is forwarded so custom (e.g. + BeyondArena) task sets match correctly; pass ``None`` to infer it from the results. + 2. **Load:** every method is (re-)loaded from the cache via ``EndToEndResults.from_cache`` — in + all cases, including the ones just cached — so the in-memory results always come from the + same code path (the cache), not from the transient post-processing return value. + """ + from tabarena.nips2025_utils.end_to_end import EndToEndResults + from tabarena.nips2025_utils.end_to_end_single import EndToEndSingle + + # Phase 1: post-process raw -> cache for each method (skip the cache-only ones). + for ma in method_artifacts: + if ma.only_load_cache: + continue + print(f"Post-processing raw results for ag_name={ma.ag_name} (artifact={ma.artifact_name})...") + EndToEndSingle.from_path_raw_to_results( + path_raw=ma.path_raw, + name_prefix_raw=ma.ag_name, + name_suffix=ma.result_suffix, + method=ma.ag_name, + artifact_name=ma.artifact_name, + task_metadata=task_metadata, + num_cpus=num_cpus, + ) + + # Phase 2: re-load every method from the cache (one (ag_name, artifact_name) per artifact). + return EndToEndResults.from_cache(methods=[(ma.ag_name, ma.artifact_name) for ma in method_artifacts]) + + +def subset_label(subset: list[str]) -> str: + """Filesystem-friendly label for a subset spec (``[]`` -> ``"full"``).""" + return "_".join(sorted(subset)) if subset else "full" + + +def save_leaderboard( + leaderboard: pd.DataFrame, + figure_output_dir: str | Path, + label: str, + *, + subdir: str = "leaderboards", +) -> Path: + """Write a subset's leaderboard CSV under ``figure_output_dir//