From 5117625e0d22510bd6877cae79231e90c5d6954e Mon Sep 17 00:00:00 2001 From: Yitang Gao Date: Wed, 8 Oct 2025 16:52:35 +0800 Subject: [PATCH] Add Suna synthetic dataset generator and wire CLI support. --- dataset_examples/suna_synthetic.csv | 2001 +++++++++++++++++ dataset_examples/suna_synthetic_metadata.json | 56 + seeker/src/query_processor/query_processor.py | 41 +- seeker/src/scripts/run_suna_discovery.py | 151 ++ seeker/src/suna/README_SUNA.md | 85 + seeker/src/suna/__init__.py | 35 + seeker/src/suna/config.py | 185 ++ seeker/src/suna/discovery.py | 128 ++ seeker/src/suna/encoders.py | 124 + seeker/src/suna/pipelines.py | 52 + seeker/src/suna/results.py | 57 + seeker/src/suna/scoring.py | 113 + seeker/src/suna/service.py | 110 + seeker/src/suna/sketches.py | 107 + seeker/src/suna/synthetic.py | 169 ++ 15 files changed, 3413 insertions(+), 1 deletion(-) create mode 100644 dataset_examples/suna_synthetic.csv create mode 100644 dataset_examples/suna_synthetic_metadata.json create mode 100644 seeker/src/scripts/run_suna_discovery.py create mode 100644 seeker/src/suna/README_SUNA.md create mode 100644 seeker/src/suna/__init__.py create mode 100644 seeker/src/suna/config.py create mode 100644 seeker/src/suna/discovery.py create mode 100644 seeker/src/suna/encoders.py create mode 100644 seeker/src/suna/pipelines.py create mode 100644 seeker/src/suna/results.py create mode 100644 seeker/src/suna/scoring.py create mode 100644 seeker/src/suna/service.py create mode 100644 seeker/src/suna/sketches.py create mode 100644 seeker/src/suna/synthetic.py diff --git a/dataset_examples/suna_synthetic.csv b/dataset_examples/suna_synthetic.csv new file mode 100644 index 0000000..6945638 --- /dev/null +++ b/dataset_examples/suna_synthetic.csv @@ -0,0 +1,2001 @@ +treatment,outcome,confounder_1,confounder_2,instrument,proxy,seasonal_component,linear_trend,noise 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+0,-2.951588870803337,-1.2560152925928039,-0.6773869828259044,1.2442832471810967,-1.0501967959428804,-0.0031431590602380684,0.9994997498749374,0.01832953833425093 +0,-0.1615703850956659,-0.8672076840515678,0.173838834603124,-0.1284308881662851,-0.6125621973628377,-2.4492935982947064e-16,1.0,0.9788217743380844 diff --git a/dataset_examples/suna_synthetic_metadata.json b/dataset_examples/suna_synthetic_metadata.json new file mode 100644 index 0000000..03a26ca --- /dev/null +++ b/dataset_examples/suna_synthetic_metadata.json @@ -0,0 +1,56 @@ +{ + "dataset_name": "suna_synthetic", + "description": "Synthetic dataset generated for Suna confounder discovery demos. It contains known confounders that influence both treatment assignment and outcomes, an instrumental variable, and nuisance features used to test bootstrap selection heuristics.", + "columns": [ + "treatment", + "outcome", + "confounder_1", + "confounder_2", + "instrument", + "proxy", + "seasonal_component", + "linear_trend", + "noise" + ], + "row_count": 2000, + "generator": { + "n_samples": 2000, + "seed": 7, + "treatment_intercept": -0.3, + "treatment_beta_conf1": 1.2, + "treatment_beta_conf2": 0.9, + "treatment_beta_instrument": 0.6, + "outcome_base": 0.5, + "ate": 1.5, + "outcome_beta_conf1": 2.0, + "outcome_beta_conf2": 1.3, + "outcome_beta_instrument": 0.2, + "noise_std": 1.0 + }, + "checksum": { + "sha256_first100": "cb3029805c08577248ff8147146469f67e10dd413e2b2ef20a8acee95a7dc2bd", + "note": "Hash computed from the first 100 rows to provide a lightweight fingerprint." + }, + "tags": [ + "synthetic", + "causal", + "suna" + ], + "source": "Programmatically generated by seeker.src.suna.synthetic", + "suna": { + "treatment_column": "treatment", + "outcome_column": "outcome", + "candidate_covariates": [ + "confounder_1", + "confounder_2", + "instrument", + "proxy", + "seasonal_component", + "linear_trend" + ], + "tau": 0.05, + "n_boot": 100, + "default_method": "bcd", + "random_state": 7 + } +} \ No newline at end of file diff --git a/seeker/src/query_processor/query_processor.py b/seeker/src/query_processor/query_processor.py index c5328aa..b2bc1e6 100644 --- a/seeker/src/query_processor/query_processor.py +++ b/seeker/src/query_processor/query_processor.py @@ -1,13 +1,17 @@ from seeker.src.seeker_service_modules import DataSeeker +from seeker.src.suna.service import execute_from_cli, SunaDiscoveryResult class QueryProcessorClass: def __init__(self,dataset_models, query, search_in_metadata): self.service_modules = { "semantic": self.semantic_search, "vector": self.vector_search, "cause_and_consequences": self.cause_and_consequences_search, - "query_by_example": self.query_by_example_search + "query_by_example": self.query_by_example_search, } + self.service_modules.update({ + "suna_discover": self.suna_discover_search + }) self.dataset_models = dataset_models self.search_query = query self.search_in_metadata = search_in_metadata @@ -42,3 +46,38 @@ def query_by_example_search(self, *args): search_query = args[0] if args else self.search_query seeker = DataSeeker(search_query) print(seeker.query_by_example_search(self.dataset_models, search_query)) + + def suna_discover_search(self, *args): + try: + result: SunaDiscoveryResult = execute_from_cli( + self.dataset_models, + args, + search_query="suna_discover", + ) + except ValueError as exc: + print(f"Suna discovery error: {exc}") + return + + findings_df = result.to_dataframe() + if findings_df.empty: + print( + f"Suna discovery completed for dataset '{result.dataset_name}' " + "with no confounders selected." + ) + else: + display_cols = [ + "confounder", + "mi_drop", + "bootstrap_quantile", + "bootstrap_mean", + ] + print("Suna discovery findings:") + print(findings_df[display_cols].to_string(index=False)) + if result.ate is not None: + print( + f"Estimated ATE of treatment '{result.treatment}' on outcome " + f"'{result.outcome}': {result.ate:.4f} " + f"(std err: {result.ate_std_err:.4f})" + if result.ate_std_err is not None + else f"{result.ate:.4f}" + ) diff --git a/seeker/src/scripts/run_suna_discovery.py b/seeker/src/scripts/run_suna_discovery.py new file mode 100644 index 0000000..13c16c3 --- /dev/null +++ b/seeker/src/scripts/run_suna_discovery.py @@ -0,0 +1,151 @@ +import argparse +import json +from pathlib import Path + +from seeker.src.metadata_dataset_separation.data_import import DataLoader +from seeker.src.suna.service import SunaConfounderDiscoveryService +from seeker.src.suna.synthetic import ( + SYNTHETIC_DATASET_NAME, + SyntheticDatasetSpec, + ensure_synthetic_dataset, +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run Suna confounder discovery on a dataset loaded by SEEKER.", + ) + parser.add_argument( + "--data-dir", + default="dataset_examples", + help="Directory containing CSV datasets and metadata JSON files.", + ) + parser.add_argument( + "--dataset", + required=True, + help="Dataset name (without .csv) to run discovery on.", + ) + parser.add_argument( + "--tau", + type=float, + default=None, + help="Override quantile threshold tau (default from metadata).", + ) + parser.add_argument( + "--n-samples", + type=int, + default=None, + help="Override number of bootstrap samples.", + ) + parser.add_argument( + "--disable-cache", + action="store_true", + help="Disable local sketch caching for this run.", + ) + parser.add_argument( + "--generate-synthetic", + action="store_true", + help=( + "Regenerate the synthetic Suna demo dataset before running discovery. " + "Only applies when --dataset is set to 'suna_synthetic'." + ), + ) + parser.add_argument( + "--synthetic-samples", + type=int, + default=None, + help=( + "Override the number of rows used when generating the synthetic " + "dataset (requires --dataset suna_synthetic)." + ), + ) + parser.add_argument( + "--synthetic-seed", + type=int, + default=None, + help=( + "Override the RNG seed used for the synthetic dataset generation " + "(requires --dataset suna_synthetic)." + ), + ) + return parser.parse_args() + + +def build_overrides(args: argparse.Namespace): + overrides = {} + if args.tau is not None: + overrides["bootstrap.tau"] = args.tau + if args.n_samples is not None: + overrides["bootstrap.n_samples"] = args.n_samples + if args.disable_cache: + overrides["sketch.enable_cache"] = False + return overrides + + +def main(): + args = parse_args() + data_loader = DataLoader() + + if args.dataset == SYNTHETIC_DATASET_NAME: + spec_kwargs = {} + if args.synthetic_samples is not None: + spec_kwargs["n_samples"] = args.synthetic_samples + if args.synthetic_seed is not None: + spec_kwargs["seed"] = args.synthetic_seed + spec = SyntheticDatasetSpec(**spec_kwargs) if spec_kwargs else None + ensure_synthetic_dataset( + args.data_dir, + spec=spec, + force=args.generate_synthetic, + ) + + dataset_models = data_loader.upload_multiple(args.data_dir, include_metadata=True) + if args.dataset not in dataset_models: + raise ValueError(f"Dataset '{args.dataset}' not found in {args.data_dir}.") + + overrides = build_overrides(args) + dataset_model = dataset_models[args.dataset] + service = SunaConfounderDiscoveryService.from_dataset( + dataset_model, + overrides=overrides, + ) + result = service.discover() + + df = result.to_dataframe() + if df.empty: + print("No confounders selected for the supplied configuration.") + else: + print(df.to_string(index=False)) + if result.ate is not None: + print( + f"\nEstimated ATE ({result.treatment} -> {result.outcome}): " + f"{result.ate:.4f}" + + ( + f" ± {result.ate_std_err:.4f}" + if result.ate_std_err is not None + else "" + ) + ) + + report_path = Path("dist") / "suna" / f"{args.dataset}_latest.json" + report_path.parent.mkdir(parents=True, exist_ok=True) + report_path.write_text( + json.dumps( + { + "dataset": result.dataset_name, + "treatment": result.treatment, + "outcome": result.outcome, + "method": result.method, + "findings": df.to_dict(orient="records"), + "ate": result.ate, + "ate_std_err": result.ate_std_err, + "metadata": result.metadata, + }, + indent=2, + ) + ) + print(f"\nDiscovery report saved to {report_path}") + + +if __name__ == "__main__": + main() diff --git a/seeker/src/suna/README_SUNA.md b/seeker/src/suna/README_SUNA.md new file mode 100644 index 0000000..d294731 --- /dev/null +++ b/seeker/src/suna/README_SUNA.md @@ -0,0 +1,85 @@ +# Suna Integration Overview + +This package brings the Suna confounder discovery algorithm into SEEKER’s service architecture. It mirrors the layout of other features (e.g., `qgram_index`) and exposes a service API for the interpreter, scripts, and notebooks. + +## Module Layout +- `__init__.py` — Re-exports configs, services, and result dataclasses for convenient imports. +- `config.py` — Dataclasses (`SunaConfig`, `BootstrapConfig`, `SketchConfig`) plus CLI parsing helpers. Supports metadata-driven defaults with override hooks. +- `encoders.py` — Lightweight categorical encoder utilities and helpers for selecting candidate covariates. +- `sketches.py` — Sketch preparation and caching helpers. Currently materializes single-table sketches but keeps cache management aligned with the paper’s design. +- `scoring.py` — Bivariate causal discovery scoring (bootstrap MI deltas) and stubs for optional subspace methods. +- `discovery.py` — Iterative Algorithm 1 loop that selects confounders based on bootstrap quantiles and MI-drop heuristics; produces typed results. +- `pipelines.py` — High-level orchestration that wires preprocessing, discovery, and caching for a `DatasetModel`. +- `results.py` — Dataclasses describing findings, bootstrap summaries, and overall discovery results. +- `service.py` — SEEKER-facing service + `SunaDataSeeker` adapter used by the interpreter and scripts. + +## Usage + +### Interpreter Command +Add the operation to a query plan: +``` +suna_discover:dataset=:treatment=:outcome=:candidate_covariates=Z1,Z2 +``` +- `dataset` (required): key from the loaded dataset models. +- `treatment`, `outcome`: required if metadata lacks `suna.treatment_column` / `suna.outcome_column`. +- Optional overrides include `method`, `candidate_covariates`, `tau`, `bootstrap.n_samples`, `sketch.enable_cache=false`, etc. +Results print in the terminal and, when available, render via `SearchResultsVisualizer`. + +### Script +Execute the helper script to run discovery offline: +```bash +python -m seeker.src.scripts.run_suna_discovery \ + --dataset my_dataset \ + --tau 0.1 \ + --n-samples 200 +``` +The script reads metadata from `dataset_examples/.json`, stores discovery reports under `dist/suna/`, and echoes findings to stdout. + +## Metadata Schema +Annotate datasets with an optional `suna` block: +```json +{ + "suna": { + "treatment_column": "T", + "outcome_column": "O", + "candidate_covariates": ["Z1", "Z2"], + "join_keys": [["user_id"]], + "tau": 0.05, + "n_boot": 100, + "default_method": "bcd" + } +} +``` +Overrides supplied via CLI/interpreter take precedence. + +### Synthetic Demo Dataset + +The Suna prototype relied on synthetic data with known confounders. The SEEKER +integration now ships an equivalent generator in `seeker.src.suna.synthetic`. +Run discovery against the synthetic asset the same way you would target any +other dataset: + +```bash +python -m seeker.src.scripts.run_suna_discovery \ + --dataset suna_synthetic \ + --generate-synthetic \ + --tau 0.05 +``` + +- The first invocation materializes `dataset_examples/suna_synthetic.csv` and + `dataset_examples/suna_synthetic_metadata.json`. Subsequent runs reuse the + files unless `--generate-synthetic` is specified. +- Use `--synthetic-samples ` and `--synthetic-seed ` to customise the + number of rows or the RNG seed while still producing metadata that matches the + SEEKER ingestion contract. +- Columns cover treatment, outcome, two true confounders, an instrumental + variable, a proxy, seasonal and trend terms, plus noise—mirroring the + structure of the prototype experiments. +- Metadata for the synthetic dataset already declares the treatment, outcome, + candidate covariates, and bootstrap defaults under the `suna` block so the CLI + requires no extra overrides. + +## Follow-Up Ideas +- Expand `sketches.py` to build semi-ring sketches across relational joins once SEEKER exposes join graphs. +- Replace the MI-drop heuristic with a proper conditional MI estimator from the Suna prototype. +- Wire optional extensions (`subgroup`, `mprp`, `shap`) once corresponding extras are packaged. diff --git a/seeker/src/suna/__init__.py b/seeker/src/suna/__init__.py new file mode 100644 index 0000000..a5d2233 --- /dev/null +++ b/seeker/src/suna/__init__.py @@ -0,0 +1,35 @@ +""" +Suna confounder discovery package. + +This package refactors the standalone Suna prototype into SEEKER’s +service-oriented architecture. It exposes configuration factories, +discovery pipelines, and service bindings used by the interpreter. +""" + +from .config import SunaConfig, BootstrapConfig, SketchConfig +from .service import ( + SunaConfounderDiscoveryService, + SunaDataSeeker, + OptionalDependencyUnavailable, +) +from .results import SunaDiscoveryResult, ConfounderFinding, BootstrapSummary +from .synthetic import ( + SYNTHETIC_DATASET_NAME, + SyntheticDatasetSpec, + ensure_synthetic_dataset, +) + +__all__ = [ + "SunaConfig", + "BootstrapConfig", + "SketchConfig", + "SunaConfounderDiscoveryService", + "SunaDataSeeker", + "OptionalDependencyUnavailable", + "SunaDiscoveryResult", + "ConfounderFinding", + "BootstrapSummary", + "SYNTHETIC_DATASET_NAME", + "SyntheticDatasetSpec", + "ensure_synthetic_dataset", +] diff --git a/seeker/src/suna/config.py b/seeker/src/suna/config.py new file mode 100644 index 0000000..ed7f38b --- /dev/null +++ b/seeker/src/suna/config.py @@ -0,0 +1,185 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, replace +from pathlib import Path +from typing import Dict, Iterable, List, Optional + +from seeker.src.index_creation.dataset_model import DatasetModel + + +DEFAULT_CACHE_ROOT = Path("dist") / "suna" + + +@dataclass(frozen=True) +class BootstrapConfig: + """Configuration for bootstrap sampling in the BCD loop.""" + + n_samples: int = 100 + tau: float = 0.05 # quantile level used in Algorithm 1 + random_state: Optional[int] = 0 + + +@dataclass(frozen=True) +class SketchConfig: + """Configuration for sketch caching and storage.""" + + cache_dir: Path = DEFAULT_CACHE_ROOT + enable_cache: bool = True + overwrite: bool = False + + def resolve(self, dataset_id: str) -> Path: + """Return the concrete cache directory for a dataset.""" + return self.cache_dir / dataset_id + + +@dataclass(frozen=True) +class SunaConfig: + """Top-level configuration for the Suna discovery pipeline.""" + + dataset_name: str + treatment: str + outcome: str + method: str = "bcd" + candidate_covariates: Optional[List[str]] = None + join_keys: Optional[List[List[str]]] = None + mi_drop_threshold: float = 0.0 + device: str = "cpu" + bootstrap: BootstrapConfig = field(default_factory=BootstrapConfig) + sketch: SketchConfig = field(default_factory=SketchConfig) + random_state: Optional[int] = 0 + + def with_overrides(self, overrides: Dict[str, object]) -> "SunaConfig": + """Return a new config with simple attribute overrides.""" + update_kwargs = {} + nested_overrides: Dict[str, Dict[str, object]] = {} + + for key, value in overrides.items(): + if key.startswith("bootstrap."): + nested_overrides.setdefault("bootstrap", {})[ + key.split(".", 1)[1] + ] = value + elif key.startswith("sketch."): + nested_overrides.setdefault("sketch", {})[ + key.split(".", 1)[1] + ] = value + else: + update_kwargs[key] = value + + config = replace(self, **update_kwargs) if update_kwargs else self + + if "bootstrap" in nested_overrides: + config = replace( + config, + bootstrap=replace(config.bootstrap, **nested_overrides["bootstrap"]), + ) + if "sketch" in nested_overrides: + sketch_updates = nested_overrides["sketch"] + if "cache_dir" in sketch_updates: + cache_dir = Path(sketch_updates["cache_dir"]) + sketch_updates = {**sketch_updates, "cache_dir": cache_dir} + config = replace(config, sketch=replace(config.sketch, **sketch_updates)) + return config + + @staticmethod + def _metadata_block(metadata: Optional[Dict]) -> Dict: + if not metadata: + return {} + return metadata.get("suna", {}) + + @classmethod + def from_metadata( + cls, + dataset_model: DatasetModel, + *, + overrides: Optional[Dict[str, object]] = None, + ) -> "SunaConfig": + overrides = overrides or {} + metadata = dataset_model.metadata or {} + suna_block = cls._metadata_block(metadata) + + treatment = suna_block.get("treatment_column") + outcome = suna_block.get("outcome_column") + if not treatment: + treatment = overrides.get("treatment") + if not outcome: + outcome = overrides.get("outcome") + if not treatment or not outcome: + raise ValueError( + "Dataset metadata must declare `suna.treatment_column` and " + "`suna.outcome_column`, or they must be provided via overrides " + "(treatment=, outcome=)." + ) + + bootstrap_block = suna_block.get("bootstrap", {}) + bootstrap = BootstrapConfig( + n_samples=bootstrap_block.get("n_samples", suna_block.get("n_boot", 100)), + tau=suna_block.get("tau", 0.05), + random_state=bootstrap_block.get("random_state", 0), + ) + + sketch_block = suna_block.get("sketch", {}) + cache_dir = Path(sketch_block.get("cache_dir", DEFAULT_CACHE_ROOT)) + sketch = SketchConfig( + cache_dir=cache_dir, + enable_cache=sketch_block.get("enable_cache", True), + overwrite=sketch_block.get("overwrite", False), + ) + + dataset_name = getattr(dataset_model, "dataset_name", None) + if dataset_name is None: + dataset_name = getattr(dataset_model, "name", "unknown_dataset") + + config = cls( + dataset_name=dataset_name, + treatment=treatment, + outcome=outcome, + method=suna_block.get("default_method", "bcd"), + candidate_covariates=suna_block.get("candidate_covariates"), + join_keys=suna_block.get("join_keys"), + mi_drop_threshold=suna_block.get("mi_drop_threshold", 0.0), + device=suna_block.get("device", "cpu"), + bootstrap=bootstrap, + sketch=sketch, + random_state=suna_block.get("random_state", overrides.get("random_state", 0)), + ) + + if overrides: + config = config.with_overrides(overrides) + return config + + +def parse_cli_overrides(params: Iterable[str]) -> Dict[str, object]: + """ + Convert colon-separated CLI parameters into a dictionary. + + Example: + ["dataset=foo", "treatment=T", "bootstrap.n_samples=50"] + """ + config_overrides: Dict[str, object] = {} + for param in params: + if "=" not in param: + continue + key, value = param.split("=", 1) + key = key.strip() + value = value.strip() + if key == "candidate_covariates": + config_overrides[key] = [part.strip() for part in value.split(",") if part.strip()] + elif key == "join_keys": + groups = [] + for segment in value.split(";"): + cols = [col.strip() for col in segment.split(",") if col.strip()] + if cols: + groups.append(cols) + config_overrides[key] = groups + elif value.lower() in {"true", "false"}: + config_overrides[key] = value.lower() == "true" + else: + try: + numeric = float(value) + if numeric.is_integer(): + config_overrides[key] = int(numeric) + else: + config_overrides[key] = numeric + except ValueError: + config_overrides[key] = value + return config_overrides diff --git a/seeker/src/suna/discovery.py b/seeker/src/suna/discovery.py new file mode 100644 index 0000000..e4dcf50 --- /dev/null +++ b/seeker/src/suna/discovery.py @@ -0,0 +1,128 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Sequence + +import numpy as np +import pandas as pd +from sklearn.linear_model import LinearRegression + +from .config import BootstrapConfig, SunaConfig +from .results import BootstrapSummary, ConfounderFinding, SunaDiscoveryResult +from .scoring import BCDScore, score_bcd +from .sketches import SketchArtifacts + + +def _linear_regression_score(df: pd.DataFrame, features: Sequence[str], target: str) -> LinearRegression: + model = LinearRegression() + model.fit(df[list(features)], df[target]) + return model + + +@dataclass +class IterativeConfounderDiscovery: + config: SunaConfig + bootstrap: BootstrapConfig = field(default_factory=BootstrapConfig) + + def run(self, artifacts: SketchArtifacts) -> SunaDiscoveryResult: + df = artifacts.dataframe.copy() + findings: List[ConfounderFinding] = [] + selected: List[str] = [] + remaining = [c for c in artifacts.candidate_columns if c not in (artifacts.treatment_column, artifacts.outcome_column)] + + metadata: Dict[str, object] = { + "initial_candidate_count": len(remaining), + "bootstrap_n": self.bootstrap.n_samples, + "bootstrap_tau": self.bootstrap.tau, + } + + base_features = [artifacts.treatment_column] + ate = None + ate_std_err = None + + while remaining: + candidate_scores: List[BCDScore] = [] + for candidate in remaining: + score = score_bcd( + df, + artifacts.treatment_column, + artifacts.outcome_column, + candidate, + self.bootstrap, + ) + candidate_scores.append(score) + + admissible = [ + s + for s in candidate_scores + if s.quantile_value > self.config.mi_drop_threshold + ] + if not admissible: + break + + base_model = _linear_regression_score( + df, base_features + selected, artifacts.outcome_column + ) + base_r2 = base_model.score( + df[base_features + selected], df[artifacts.outcome_column] + ) + + def mi_drop_for(score: BCDScore) -> float: + augmented_features = base_features + selected + [score.candidate] + augmented_model = _linear_regression_score( + df, augmented_features, artifacts.outcome_column + ) + return augmented_model.score(df[augmented_features], df[artifacts.outcome_column]) - base_r2 + + candidate_drops = [(score, mi_drop_for(score)) for score in admissible] + candidate_drops.sort(key=lambda item: item[1], reverse=True) + + best_score, mi_drop = candidate_drops[0] + if mi_drop <= self.config.mi_drop_threshold: + break + + selected.append(best_score.candidate) + remaining = [c for c in remaining if c != best_score.candidate] + + findings.append( + ConfounderFinding( + name=best_score.candidate, + mi_drop=mi_drop, + bootstrap=BootstrapSummary( + quantile_value=best_score.quantile_value, + mean_delta=best_score.mean_delta, + samples=best_score.delta_samples.tolist(), + ), + r2_treat_on_candidate=best_score.r2_treat_on_candidate, + r2_candidate_on_treat=best_score.r2_candidate_on_treat, + ) + ) + + final_features = base_features + selected + if final_features: + final_model = _linear_regression_score( + df, final_features, artifacts.outcome_column + ) + ate = float(final_model.coef_[final_features.index(artifacts.treatment_column)]) + # Standard error estimate via residual variance + design = df[final_features].to_numpy() + if len(df) > len(final_features): + preds = final_model.predict(df[final_features]) + residuals = df[artifacts.outcome_column] - preds + residual_var = np.var(residuals, ddof=len(final_features)) + XtX_inv = np.linalg.pinv(design.T @ design) + treat_index = final_features.index(artifacts.treatment_column) + ate_std_err = float( + np.sqrt(max(residual_var, 0) * XtX_inv[treat_index, treat_index]) + ) + + return SunaDiscoveryResult( + dataset_name=self.config.dataset_name, + treatment=artifacts.treatment_column, + outcome=artifacts.outcome_column, + method=self.config.method, + findings=findings, + ate=ate, + ate_std_err=ate_std_err, + metadata=metadata, + ) diff --git a/seeker/src/suna/encoders.py b/seeker/src/suna/encoders.py new file mode 100644 index 0000000..147509d --- /dev/null +++ b/seeker/src/suna/encoders.py @@ -0,0 +1,124 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Iterable, List, Optional, Sequence, Tuple + +import pandas as pd +from sklearn.preprocessing import OneHotEncoder + + +def infer_categorical_columns( + df: pd.DataFrame, + *, + excludes: Optional[Sequence[str]] = None, + max_unique: int = 30, +) -> List[str]: + """Heuristic selection of categorical columns.""" + excludes = set(excludes or []) + categorical_cols = [] + for col in df.columns: + if col in excludes: + continue + series = df[col] + if pd.api.types.is_bool_dtype(series): + categorical_cols.append(col) + elif pd.api.types.is_categorical_dtype(series): + categorical_cols.append(col) + elif pd.api.types.is_object_dtype(series): + categorical_cols.append(col) + elif series.nunique(dropna=True) <= max_unique: + categorical_cols.append(col) + return categorical_cols + + +def select_numeric_candidates( + df: pd.DataFrame, excludes: Iterable[str] +) -> List[str]: + """Return numeric columns that are not part of the excluded set.""" + excludes = set(excludes) + candidates = [] + for col in df.columns: + if col in excludes: + continue + if pd.api.types.is_numeric_dtype(df[col]): + candidates.append(col) + return candidates + + +@dataclass +class EncodingArtifacts: + encoded_frame: pd.DataFrame + numeric_columns: List[str] + categorical_columns: List[str] + feature_names: List[str] + + +class CategoricalEncoder: + """ + Thin wrapper around scikit-learn encoders to keep preprocessing local. + """ + + def __init__(self, handle_unknown: str = "ignore"): + self.handle_unknown = handle_unknown + self.encoder: Optional[OneHotEncoder] = None + self._categorical_columns: List[str] = [] + + def fit( + self, + df: pd.DataFrame, + *, + categorical_columns: Optional[Sequence[str]] = None, + ) -> None: + if categorical_columns is None: + categorical_columns = infer_categorical_columns(df) + + self._categorical_columns = list(categorical_columns) + if not self._categorical_columns: + self.encoder = None + return + + self.encoder = OneHotEncoder( + handle_unknown=self.handle_unknown, + sparse_output=False, + dtype=float, + ) + self.encoder.fit(df[self._categorical_columns]) + + def transform(self, df: pd.DataFrame) -> EncodingArtifacts: + if self.encoder is None or not self._categorical_columns: + numeric_cols = [col for col in df.columns if pd.api.types.is_numeric_dtype(df[col])] + return EncodingArtifacts( + encoded_frame=df.copy(), + numeric_columns=numeric_cols, + categorical_columns=[], + feature_names=list(df.columns), + ) + + encoded = self.encoder.transform(df[self._categorical_columns]) + encoded_df = pd.DataFrame( + encoded, + columns=self.encoder.get_feature_names_out(self._categorical_columns), + index=df.index, + ) + numeric_columns = [ + col + for col in df.columns + if col not in self._categorical_columns + and pd.api.types.is_numeric_dtype(df[col]) + ] + result = pd.concat([df[numeric_columns], encoded_df], axis=1) + return EncodingArtifacts( + encoded_frame=result, + numeric_columns=numeric_columns, + categorical_columns=self._categorical_columns, + feature_names=list(result.columns), + ) + + def fit_transform( + self, + df: pd.DataFrame, + *, + categorical_columns: Optional[Sequence[str]] = None, + ) -> EncodingArtifacts: + self.fit(df, categorical_columns=categorical_columns) + return self.transform(df) diff --git a/seeker/src/suna/pipelines.py b/seeker/src/suna/pipelines.py new file mode 100644 index 0000000..269204b --- /dev/null +++ b/seeker/src/suna/pipelines.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Optional, Sequence + +import pandas as pd + +from seeker.src.index_creation.dataset_model import DatasetModel + +from .config import SunaConfig +from .discovery import IterativeConfounderDiscovery +from .results import SunaDiscoveryResult +from .sketches import SketchPreprocessor + + +def _dataset_identifier(dataset_model: DatasetModel) -> str: + dataset_id = getattr(dataset_model, "id", None) + if dataset_id is None: + dataset_id = getattr(dataset_model, "dataset_id", None) + if dataset_id is None: + dataset_id = getattr(dataset_model, "dataset_name", None) + if dataset_id is None: + dataset_id = getattr(dataset_model, "name", None) + return str(dataset_id) + + +@dataclass +class SunaDiscoveryPipeline: + config: SunaConfig + preprocessor: SketchPreprocessor = field(init=False) + + def __post_init__(self) -> None: + self.preprocessor = SketchPreprocessor(self.config.sketch) + + def execute(self, dataset_model: DatasetModel) -> SunaDiscoveryResult: + dataset: pd.DataFrame = dataset_model.dataset + dataset_id = _dataset_identifier(dataset_model) + + artifacts = self.preprocessor.prepare( + dataset, + dataset_id=dataset_id, + treatment=self.config.treatment, + outcome=self.config.outcome, + candidate_covariates=self.config.candidate_covariates, + join_keys=self.config.join_keys, + ) + + discovery = IterativeConfounderDiscovery( + config=self.config, bootstrap=self.config.bootstrap + ) + result = discovery.run(artifacts) + return result diff --git a/seeker/src/suna/results.py b/seeker/src/suna/results.py new file mode 100644 index 0000000..c5a392b --- /dev/null +++ b/seeker/src/suna/results.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Dict, Iterable, List, Optional + +import pandas as pd + + +@dataclass +class BootstrapSummary: + quantile_value: float + mean_delta: float + samples: List[float] + + def to_dict(self) -> Dict[str, object]: + return { + "quantile": self.quantile_value, + "mean": self.mean_delta, + "samples": self.samples, + } + + +@dataclass +class ConfounderFinding: + name: str + mi_drop: float + bootstrap: BootstrapSummary + r2_treat_on_candidate: float + r2_candidate_on_treat: float + + def to_row(self, dataset_name: str) -> Dict[str, object]: + return { + "dataset_name": dataset_name, + "confounder": self.name, + "mi_drop": self.mi_drop, + "bootstrap_quantile": self.bootstrap.quantile_value, + "bootstrap_mean": self.bootstrap.mean_delta, + "r2_treat_on_candidate": self.r2_treat_on_candidate, + "r2_candidate_on_treat": self.r2_candidate_on_treat, + } + + +@dataclass +class SunaDiscoveryResult: + dataset_name: str + treatment: str + outcome: str + method: str + findings: List[ConfounderFinding] = field(default_factory=list) + ate: Optional[float] = None + ate_std_err: Optional[float] = None + metadata: Dict[str, object] = field(default_factory=dict) + warnings: List[str] = field(default_factory=list) + + def to_dataframe(self) -> pd.DataFrame: + rows = [finding.to_row(self.dataset_name) for finding in self.findings] + return pd.DataFrame(rows) diff --git a/seeker/src/suna/scoring.py b/seeker/src/suna/scoring.py new file mode 100644 index 0000000..b36f07d --- /dev/null +++ b/seeker/src/suna/scoring.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Callable, Dict, Iterable, List, Optional, Sequence + +import numpy as np +import pandas as pd +from sklearn.feature_selection import mutual_info_regression +from sklearn.linear_model import LinearRegression + +from .config import BootstrapConfig + + +@dataclass +class BCDScore: + candidate: str + delta_samples: np.ndarray + quantile_value: float + mean_delta: float + r2_treat_on_candidate: float + r2_candidate_on_treat: float + + +def _compute_residual(y: np.ndarray, X: np.ndarray) -> np.ndarray: + model = LinearRegression() + model.fit(X, y) + return y - model.predict(X), model + + +def _safe_mutual_info(X: np.ndarray, y: np.ndarray, random_state: Optional[int]) -> float: + try: + return float(mutual_info_regression(X, y, random_state=random_state)[0]) + except ValueError: + return 0.0 + + +def _bootstrap_indices(n_samples: int, n_bootstrap: int, rng: np.random.Generator): + for _ in range(n_bootstrap): + yield rng.integers(0, n_samples, size=n_samples) + + +def score_bcd( + df: pd.DataFrame, + treatment: str, + outcome: str, + candidate: str, + bootstrap: BootstrapConfig, +) -> BCDScore: + """ + Compute bootstrap BCD scores using the simplified single-table statistics. + """ + series_candidate = df[candidate].to_numpy() + series_treatment = df[treatment].to_numpy() + + if np.var(series_candidate) < 1e-10 or np.var(series_treatment) < 1e-10: + zero_array = np.zeros(bootstrap.n_samples, dtype=float) + return BCDScore( + candidate=candidate, + delta_samples=zero_array, + quantile_value=0.0, + mean_delta=0.0, + r2_treat_on_candidate=0.0, + r2_candidate_on_treat=0.0, + ) + + X_candidate = series_candidate.reshape(-1, 1) + X_treatment = series_treatment.reshape(-1, 1) + + res_treat, model_z_on_t = _compute_residual(series_candidate, X_treatment) + res_candidate, model_t_on_z = _compute_residual(series_treatment, X_candidate) + + r2_treat_on_candidate = model_t_on_z.score(X_candidate, series_treatment) + r2_candidate_on_treat = model_z_on_t.score(X_treatment, series_candidate) + + rng = np.random.default_rng(bootstrap.random_state) + delta_samples = np.zeros(bootstrap.n_samples, dtype=float) + + for idx, sample_idx in enumerate( + _bootstrap_indices(len(df), bootstrap.n_samples, rng) + ): + res_t_sample = res_treat[sample_idx] + res_c_sample = res_candidate[sample_idx] + X_c_sample = X_candidate[sample_idx] + X_t_sample = X_treatment[sample_idx] + + p_plus = _safe_mutual_info(X_c_sample, res_c_sample, bootstrap.random_state) + p_minus = _safe_mutual_info(X_t_sample, res_t_sample, bootstrap.random_state) + delta_samples[idx] = p_minus - p_plus + + quantile_value = float(np.quantile(delta_samples, 1 - bootstrap.tau)) + mean_delta = float(np.mean(delta_samples)) + + return BCDScore( + candidate=candidate, + delta_samples=delta_samples, + quantile_value=quantile_value, + mean_delta=mean_delta, + r2_treat_on_candidate=r2_treat_on_candidate, + r2_candidate_on_treat=r2_candidate_on_treat, + ) + + +def unavailable_feature(*_args, **_kwargs): + raise ImportError( + "Optional dependency not installed. Install seeker[suna_subspace] to enable." + ) + + +score_subgroup = unavailable_feature +score_cmi = unavailable_feature +score_mprp = unavailable_feature +score_shap = unavailable_feature +forest_clusters = unavailable_feature diff --git a/seeker/src/suna/service.py b/seeker/src/suna/service.py new file mode 100644 index 0000000..8892aae --- /dev/null +++ b/seeker/src/suna/service.py @@ -0,0 +1,110 @@ +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Iterable, List, Optional + +from seeker.src.seeker_service_modules.search_module import DataSeeker +from seeker.src.data_visualization.search_visualizer import SearchResultsVisualizer +from seeker.src.index_creation.dataset_model import DatasetModel + +from .config import SunaConfig, parse_cli_overrides +from .pipelines import SunaDiscoveryPipeline +from .results import ConfounderFinding, SunaDiscoveryResult + + +class OptionalDependencyUnavailable(RuntimeError): + """Raised when a requested scoring routine requires missing extras.""" + + +@dataclass +class SunaConfounderDiscoveryService: + dataset_model: DatasetModel + base_config: SunaConfig + + @classmethod + def from_dataset( + cls, + dataset_model: DatasetModel, + *, + overrides: Optional[Dict[str, object]] = None, + ) -> "SunaConfounderDiscoveryService": + config = SunaConfig.from_metadata(dataset_model, overrides=overrides or {}) + return cls(dataset_model=dataset_model, base_config=config) + + def discover(self, *, config_overrides: Optional[Dict[str, object]] = None) -> SunaDiscoveryResult: + config = self.base_config + if config_overrides: + config = config.with_overrides(config_overrides) + pipeline = SunaDiscoveryPipeline(config) + return pipeline.execute(self.dataset_model) + + +def _to_visualizer_rows(result: SunaDiscoveryResult) -> List[Dict[str, object]]: + rows: List[Dict[str, object]] = [] + for finding in result.findings: + rows.append( + { + "dataset_name": result.dataset_name, + "score": finding.mi_drop, + "metadata": { + "bootstrap_quantile": finding.bootstrap.quantile_value, + "bootstrap_mean": finding.bootstrap.mean_delta, + "r2_treat_on_candidate": finding.r2_treat_on_candidate, + "r2_candidate_on_treat": finding.r2_candidate_on_treat, + "treatment": result.treatment, + "outcome": result.outcome, + "ate": result.ate, + "ate_std_err": result.ate_std_err, + }, + "top_words": None, + } + ) + return rows + + +class SunaDataSeeker(DataSeeker): + """DataSeeker adaptor that surfaces Suna discovery results.""" + + def __init__(self, search_query: str, dataset_models: Dict[str, DatasetModel]): + super().__init__(search_query) + self.dataset_models = dataset_models + + def discover( + self, + dataset_name: str, + overrides: Optional[Dict[str, object]] = None, + ) -> SunaDiscoveryResult: + if dataset_name not in self.dataset_models: + raise ValueError(f"Dataset '{dataset_name}' not found in loaded models.") + dataset_model = self.dataset_models[dataset_name] + service = SunaConfounderDiscoveryService.from_dataset( + dataset_model, overrides=overrides + ) + result = service.discover() + rows = _to_visualizer_rows(result) + if rows: + visualizer = SearchResultsVisualizer(rows, self.search_query) + visualizer.display() + else: + print( + f"Suna discovery finished for dataset '{dataset_name}' but no confounders " + "passed the selection thresholds." + ) + return result + + +def execute_from_cli( + dataset_models: Dict[str, DatasetModel], + args: Iterable[str], + *, + search_query: str = "", +) -> SunaDiscoveryResult: + params = parse_cli_overrides(args) + dataset_name = params.pop("dataset", None) + if not dataset_name: + raise ValueError("Suna discovery requires specifying dataset=.") + + overrides = params + seeker = SunaDataSeeker(search_query, dataset_models) + return seeker.discover(dataset_name, overrides=overrides) diff --git a/seeker/src/suna/sketches.py b/seeker/src/suna/sketches.py new file mode 100644 index 0000000..8c5d51c --- /dev/null +++ b/seeker/src/suna/sketches.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import pickle +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable, List, Optional, Sequence, Tuple + +import pandas as pd + +from .encoders import CategoricalEncoder, EncodingArtifacts, select_numeric_candidates +from .config import SketchConfig + + +@dataclass +class SketchArtifacts: + """Materialized artifacts produced by the sketch preprocessor.""" + + dataframe: pd.DataFrame + treatment_column: str + outcome_column: str + candidate_columns: List[str] + encoding: EncodingArtifacts + + def features_for(self, columns: Sequence[str]) -> pd.DataFrame: + return self.dataframe[list(columns)] + + +class SketchCacheManager: + """Utility to persist and restore sketch artifacts from disk.""" + + def __init__(self, sketch_config: SketchConfig, dataset_id: str): + self.sketch_config = sketch_config + self.dataset_id = dataset_id + self.cache_dir = self.sketch_config.resolve(dataset_id) + self.cache_dir.mkdir(parents=True, exist_ok=True) + + def _artifact_path(self) -> Path: + return self.cache_dir / "sketch.pkl" + + def load(self) -> Optional[SketchArtifacts]: + if not self.sketch_config.enable_cache: + return None + artifact_path = self._artifact_path() + if artifact_path.exists(): + with artifact_path.open("rb") as infile: + return pickle.load(infile) + return None + + def save(self, artifacts: SketchArtifacts) -> None: + if not self.sketch_config.enable_cache: + return + artifact_path = self._artifact_path() + if artifact_path.exists() and not self.sketch_config.overwrite: + return + with artifact_path.open("wb") as outfile: + pickle.dump(artifacts, outfile) + + +class SketchPreprocessor: + """ + Prepare dataset sketches. For the first SEEKER integration we keep an + in-memory representation but expose caching hooks for future expansion. + """ + + def __init__(self, sketch_config: SketchConfig): + self.sketch_config = sketch_config + self.encoder = CategoricalEncoder() + + def prepare( + self, + df: pd.DataFrame, + *, + dataset_id: str, + treatment: str, + outcome: str, + candidate_covariates: Optional[Sequence[str]] = None, + join_keys: Optional[Sequence[Sequence[str]]] = None, # placeholder for future + ) -> SketchArtifacts: + cache_manager = SketchCacheManager(self.sketch_config, dataset_id) + cached = cache_manager.load() + if cached and not self.sketch_config.overwrite: + return cached + + if candidate_covariates is None: + excludes = {treatment, outcome} + if join_keys: + excludes.update({jk for key in join_keys for jk in key}) + candidate_covariates = select_numeric_candidates(df, excludes) + + encoding = self.encoder.fit_transform(df) + encoded_df = encoding.encoded_frame + # Ensure treatment and outcome remain available (after encoding) + if treatment not in encoded_df.columns or outcome not in encoded_df.columns: + encoded_df = encoded_df.assign( + **{treatment: df[treatment], outcome: df[outcome]} + ) + + artifacts = SketchArtifacts( + dataframe=encoded_df, + treatment_column=treatment, + outcome_column=outcome, + candidate_columns=list(candidate_covariates), + encoding=encoding, + ) + + cache_manager.save(artifacts) + return artifacts diff --git a/seeker/src/suna/synthetic.py b/seeker/src/suna/synthetic.py new file mode 100644 index 0000000..914a2c1 --- /dev/null +++ b/seeker/src/suna/synthetic.py @@ -0,0 +1,169 @@ +""" +Utilities for generating a synthetic dataset tailored for the Suna pipeline. + +The original Suna prototype relied on synthetic data to demonstrate bootstrap +confounder discovery. This module recreates that experience inside SEEKER by +producing a reproducible dataset and metadata pair that plug directly into the +standard ingestion flow (`dataset_examples/` + `DataLoader`). +""" + +from __future__ import annotations + +import json +import math +from dataclasses import dataclass, asdict +from hashlib import sha256 +from pathlib import Path +from typing import Dict, Tuple + +import numpy as np +import pandas as pd + +SYNTHETIC_DATASET_NAME = "suna_synthetic" +CSV_SUFFIX = ".csv" +METADATA_SUFFIX = "_metadata.json" + + +@dataclass(frozen=True) +class SyntheticDatasetSpec: + """Configuration describing the synthetic data generating process.""" + + n_samples: int = 2000 + seed: int = 7 + treatment_intercept: float = -0.3 + treatment_beta_conf1: float = 1.2 + treatment_beta_conf2: float = 0.9 + treatment_beta_instrument: float = 0.6 + outcome_base: float = 0.5 + ate: float = 1.5 + outcome_beta_conf1: float = 2.0 + outcome_beta_conf2: float = 1.3 + outcome_beta_instrument: float = 0.2 + noise_std: float = 1.0 + + +def _sigmoid(x: np.ndarray) -> np.ndarray: + return 1.0 / (1.0 + np.exp(-x)) + + +def _generate_dataframe(spec: SyntheticDatasetSpec) -> pd.DataFrame: + rng = np.random.default_rng(spec.seed) + confounder_1 = rng.normal(0.0, 1.0, size=spec.n_samples) + confounder_2 = rng.normal(0.0, 1.0, size=spec.n_samples) + instrument = rng.normal(0.0, 1.0, size=spec.n_samples) + proxy = 0.7 * confounder_1 + 0.2 * rng.normal(0.0, 1.0, size=spec.n_samples) + seasonal = np.sin(np.linspace(0, 2 * math.pi, spec.n_samples)) + linear_trend = np.linspace(0.0, 1.0, spec.n_samples) + + logits = ( + spec.treatment_intercept + + spec.treatment_beta_conf1 * confounder_1 + + spec.treatment_beta_conf2 * confounder_2 + + spec.treatment_beta_instrument * instrument + ) + treatment_prob = _sigmoid(logits) + treatment = rng.binomial(1, treatment_prob) + + noise = rng.normal(0.0, spec.noise_std, size=spec.n_samples) + outcome = ( + spec.outcome_base + + spec.ate * treatment + + spec.outcome_beta_conf1 * confounder_1 + + spec.outcome_beta_conf2 * confounder_2 + + spec.outcome_beta_instrument * instrument + + 0.5 * proxy + + 0.3 * seasonal + + 0.2 * linear_trend + + noise + ) + + df = pd.DataFrame( + { + "treatment": treatment, + "outcome": outcome, + "confounder_1": confounder_1, + "confounder_2": confounder_2, + "instrument": instrument, + "proxy": proxy, + "seasonal_component": seasonal, + "linear_trend": linear_trend, + "noise": noise, + } + ) + return df + + +def _build_metadata(df: pd.DataFrame, spec: SyntheticDatasetSpec) -> Dict[str, object]: + csv_preview = df.head(100).to_csv(index=False).encode("utf-8") + checksum = sha256(csv_preview).hexdigest() + + metadata: Dict[str, object] = { + "dataset_name": SYNTHETIC_DATASET_NAME, + "description": ( + "Synthetic dataset generated for Suna confounder discovery demos. " + "It contains known confounders that influence both treatment " + "assignment and outcomes, an instrumental variable, and nuisance " + "features used to test bootstrap selection heuristics." + ), + "columns": list(df.columns), + "row_count": len(df), + "generator": asdict(spec), + "checksum": { + "sha256_first100": checksum, + "note": "Hash computed from the first 100 rows to provide a lightweight fingerprint.", + }, + "tags": ["synthetic", "causal", "suna"], + "source": "Programmatically generated by seeker.src.suna.synthetic", + "suna": { + "treatment_column": "treatment", + "outcome_column": "outcome", + "candidate_covariates": [ + "confounder_1", + "confounder_2", + "instrument", + "proxy", + "seasonal_component", + "linear_trend", + ], + "tau": 0.05, + "n_boot": 100, + "default_method": "bcd", + "random_state": spec.seed, + }, + } + return metadata + + +def ensure_synthetic_dataset( + data_dir: Path | str = Path("dataset_examples"), + *, + spec: SyntheticDatasetSpec | None = None, + force: bool = False, +) -> Tuple[Path, Path]: + """ + Guarantee that the synthetic dataset + metadata exist in ``data_dir``. + + Returns the paths to the CSV and metadata files. Files are regenerated + when they are missing or when ``force`` is set to ``True``. + """ + spec = spec or SyntheticDatasetSpec() + data_dir = Path(data_dir) + data_dir.mkdir(parents=True, exist_ok=True) + + csv_path = data_dir / f"{SYNTHETIC_DATASET_NAME}{CSV_SUFFIX}" + metadata_path = data_dir / f"{SYNTHETIC_DATASET_NAME}{METADATA_SUFFIX}" + + if not csv_path.exists() or not metadata_path.exists() or force: + df = _generate_dataframe(spec) + df.to_csv(csv_path, index=False) + metadata = _build_metadata(df, spec) + metadata_path.write_text(json.dumps(metadata, indent=2)) + + return csv_path, metadata_path + + +__all__ = [ + "SYNTHETIC_DATASET_NAME", + "SyntheticDatasetSpec", + "ensure_synthetic_dataset", +]