trustcv — Trustworthy Cross-Validation Toolkit

Website & Docs: https://ki-smile.github.io/trustcv/

📖 Visit the project website for interactive demos, tutorials, and the full documentation.

TrustCV is a framework-agnostic toolkit for reliable cross-validation in safety-critical and regulated settings. It builds on familiar scikit-learn idioms, but adds:

• Carefully designed IID, grouped, temporal, and spatial cross-validation splitters.
• Automatic data leakage and class balance checks.
• Clinical/industrial metrics with confidence intervals.
• Simple reporting utilities that support regulatory documentation.

Status: v1.1.2 – Conservative check statuses, corrected confidence intervals, calibrated leakage diagnostics, and an actionable CV advisor.

Why TrustCV?

Standard cross-validation is easy to misuse:

• Train/test splits can accidentally leak information (e.g., shared patients, timestamps, engineered features).
• Imbalanced datasets can give overly optimistic metrics if not stratified or monitored.
• For clinical and industrial applications, we often need meaningful metrics and reproducible reports, not just accuracy.

TrustCV addresses these issues by:

• Providing well-tested IID splitters with clear semantics.
• Running leakage and balance checks alongside your CV.
• Exposing clinical metrics and simple reporting utilities for audits and regulatory files.

What's in v1.1.0

This release includes 29 cross-validation methods across four categories:

• IID splitters (9 methods): HoldOut, KFold, StratifiedKFold, RepeatedKFold, LeaveOneOut, LeavePOut, BootstrapValidation, MonteCarloCV, NestedCV

• Grouped splitters (6 methods): GroupKFold, StratifiedGroupKFold, LeaveOneGroupOut, RepeatedGroupKFold, NestedGroupedCV, HierarchicalGroupKFold

• Temporal splitters (8 methods): TimeSeriesSplit, BlockedTimeSeriesSplit, RollingWindowCV, ExpandingWindowCV, PurgedKFold, CombinatorialPurgedKFold, PurgedGroupTimeSeriesSplit, NestedTemporalCV

• Spatial splitters (4 methods): SpatialBlockCV, BufferedSpatialCV, SpatiotemporalBlockCV, EnvironmentalHealthCV

• Framework-agnostic runner & split_kwargs: UniversalCVRunner + CVResults for consistent, reusable CV loops across scikit-learn, PyTorch, TensorFlow, MONAI, and JAX with support for split-specific arguments (split_kwargs).

• Metric Feasibility Diagnostics System: check_fold_metric_feasibility() diagnostic warnings and tables when group-exclusive splitters (e.g., LOGO) have single-class or small validation folds.

• High-level validator & integrity checks: TrustCVValidator with automatic leakage checks, near-duplicate detection, hierarchical group leakage, auto-computed spatial thresholds, and LeakageDetectionCallback.

• Clinical/medical metrics: ClinicalMetrics with confidence intervals (sensitivity, specificity, PPV/NPV, etc.).

• Regulatory documentation support: RegulatoryReport for generating documentation that maps to FDA/CE MDR requirements.

Quick Start

Installation

pip install trustcv

Ask the advisor for a runnable splitter first, then keep all preprocessing inside the model pipeline:

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from trustcv import TrustCV, recommend_cv

X, y = load_breast_cancer(return_X_y=True)
recommendation = recommend_cv(X, y)
print(recommendation.category, recommendation.method, type(recommendation.splitter).__name__)

model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=2000))
result = TrustCV(
    method=recommendation.method,
    n_splits=5,
    random_state=42,
    declare_independent_samples=True,
).validate(model=model, X=X, y=y, cv=recommendation.splitter)
print(result.summary())

Real output from v1.1.0:

iid stratified_kfold StratifiedKFold
=== Trustworthy Cross-Validation Results ===

Performance Metrics (mean +/- std) (method: corrected_t):
  accuracy: 0.974 +/- 0.019 [95% CI (corrected_t): 0.939-1.008]
  roc_auc: 0.995 +/- 0.006 [95% CI (corrected_t): 0.984-1.006]
  sensitivity: 0.992 +/- 0.013 [95% CI (corrected_t): 0.968-1.015]
  specificity: 0.944 +/- 0.059 [95% CI (corrected_t): 0.834-1.053]
  precision: 0.968 +/- 0.032 [95% CI (corrected_t): 0.908-1.029]
  recall: 0.992 +/- 0.013 [95% CI (corrected_t): 0.968-1.015]
  f1: 0.979 +/- 0.014 [95% CI (corrected_t): 0.953-1.006]

Data Integrity Checks:
  Duplicate Samples: PASSED — No exact duplicate feature rows were found.
  Group Leakage: NOT_APPLICABLE — Samples were explicitly declared independent; no groups were supplied.
  Preprocessing Leakage: PASSED — Preprocessing steps are inside an sklearn Pipeline and are refit within each fold.
  External Leakage Detector: PASSED — The external leakage detector found no supported leakage pattern.
  Target Leakage Features: PASSED — No feature crossed the configured univariate target-leakage threshold.
  Permutation Sanity: NOT_CHECKED — Permutation sanity checking is disabled.
  Class Balance: INFO — Minority-to-majority class ratio is 0.594.
  Covariate Shift: INFO — No statistically significant covariate shift was detected.

Overall Status: PASSED
Leakage Check: PASSED

Integrity checks and status semantics

Every ValidationResult.checks mapping contains the following keys:

overall_status considers the first five leakage-relevant checks and the permutation check when enabled. It is FAILED for any FAILED/ERROR, PASSED only when all relevant checks are PASSED/NOT_APPLICABLE, and NOT_FULLY_VERIFIED otherwise.

leakage_check remains for compatibility. Use external_leakage_detected for non-inverted semantics. The legacy has_leakage key is deprecated for one release and is inverted: external_leakage_detected == (not has_leakage).

What trustcv can and cannot detect

TrustCV can verify exact duplicates, supplied-group separation, whether visible sklearn preprocessing is inside a Pipeline, selected leakage patterns in DataLeakageChecker, near-deterministic single-feature target encodings, class balance, covariate shift, and—when enabled—whether shuffled labels score above chance inside the CV loop.

TrustCV cannot infer missing patient IDs, timestamps, coordinates, or causal relationships. It cannot detect preprocessing or feature selection already applied to the full dataset before validate() receives X. The permutation check is not a general leakage detector: with a fixed pre-selected matrix its null scores may remain at chance while the observed score is inflated. A PASSED status therefore means every applicable supported check passed; it is not proof that all possible leakage is absent.

Confidence intervals

corrected_t is the default and uses the Nadeau–Bengio correction with actual mean train/test fold sizes. oof_bootstrap resamples pooled out-of-fold predictions and defaults to whole-group cluster resampling when groups are supplied; set ci_cluster=False only for row-level comparison. Non-partition splitters fall back to corrected_t with a warning. Legacy ci_method="bootstrap" remains available but warns because bootstrapping a handful of correlated fold scores produces intervals that are too narrow.

Run the interactive notebooks

Prefer to learn by running code? From the repo root, open the 14 notebooks in notebooks/:

• notebooks/01_IID_Methods_Showcase.ipynb – Quick tour of IID splitters and metrics.
• notebooks/02_Advanced_Workflow_UniversalRunner.ipynb – End-to-end UniversalCVRunner workflow with callbacks.
• notebooks/03_TrustCVValidator_Showcase.ipynb – TrustCVValidator examples with leakage/balance checks.
• notebooks/04_TrustCVValidator_IID_Comparison.ipynb – Side-by-side IID method comparison.
• notebooks/05_CrossValidation_Comparison.ipynb – Comprehensive CV methods comparison across categories.
• notebooks/06_TrustCV_DeepLearning_Showcase_with_Glossary.ipynb – Deep learning framework integration (PyTorch & TensorFlow).
• notebooks/07_trustcv_grouped_cv_medical.ipynb – Grouped cross-validation for patient/subject-level data.
• notebooks/08_TrustCV_Structured_Scenarios_S3.ipynb – Structured validation scenarios for medical AI pipeline.
• notebooks/09_RealData_UCI_HAR_Grouped_Leakage_NestedCV.ipynb – UCI HAR sensor dataset grouped leakage analysis & nested CV.
• notebooks/10_Advanced_Workflow_oob_clinical_metrics.ipynb – Out-of-fold clinical metrics aggregation & diagnostics.
• notebooks/11_Spatial_Synthetic_EnvironmentalHealth_TrustCV_SplitKwargs.ipynb – Spatial & environmental health splitters with split_kwargs.
• notebooks/12_PhysioNet2019_Sepsis_RealClinicalData_TrustCV_cloud_TEMPORAL.ipynb – PhysioNet 2019 Sepsis clinical benchmark & temporal validation.
• notebooks/13_LeakageChecker_Injection_Boundary_Validation.ipynb – Data leakage checker injection boundary validation.
• notebooks/14_LOGO_metric_feasibility_warning.ipynb – Leave-One-Group-Out fold metric feasibility diagnostic system.
• notebooks/15_Trust_Claims_v1.1.ipynb – End-to-end v1.1.0 trust-claims validation: correct semantics, leakage checks, CI, and advisor workflow.

Reports generated by the notebooks are saved in notebooks/reports/ (HTML/PDF).

How TrustCV relates to scikit-learn

Similarities:

• Uses familiar scikit-learn idioms: estimators with fit/predict, splitter objects with split(X, y).
• Works seamlessly with scikit-learn models, pipelines, and metrics.
• IID splitters follow scikit-learn semantics (e.g., KFold, StratifiedKFold).

Added value:

• Leakage and balance checks: DataLeakageChecker and BalanceChecker run alongside your CV.
• Clinical metrics: ClinicalMetrics computes sensitivity, specificity, PPV/NPV, ROC/PR metrics, and CIs.
• Structured results: CVResults and ValidationResult standardize fold-level outputs.
• Reporting: UniversalRegulatoryReport turns your evaluation into a reproducible HTML/JSON report.

Framework Support

Supported frameworks:

• scikit-learn (native)
• PyTorch (via adapter)
• TensorFlow/Keras (via adapter)
• MONAI (via adapter, for medical imaging)
• JAX/Flax (via adapter)
• XGBoost, LightGBM, CatBoost (via sklearn-compatible API)

Contributors

See AUTHORS.md for a full list of contributors and acknowledgments.

Lead Contributors

• Farhad Abtahi
• Abdelamir Karbalaie

Contributing

We welcome contributions!

• Code contributions
• Medical use case examples
• Documentation improvements
• Bug reports and feature requests

Please see:

• CONTRIBUTING.md
• CODE_OF_CONDUCT.md

3. Quickstart: IID CV with TrustCV – outline

See file: docs/quickstart_iid.md
(and a matching notebook: notebooks/Quickstart_IID_TrustCV.ipynb)

Repository Structure

trustcv/       # Python package (splitters, validators, metrics, core, CLI, bundled examples)
docs/          # Documentation & guides
notebooks/     # Jupyter tutorials
examples/      # Heavier framework examples (PyTorch, TensorFlow, JAX, MONAI)
tests/         # Unit & integration tests
website/       # Static site and visualizations

Development

pip install -e .[dev]
pytest tests/
cd docs && make html

Command Line

pip install trustcv ships a trustcv command with 7 runnable usage recipes:

trustcv --help               # Quick start
trustcv about                # Project info, links, citation
trustcv examples             # 7 numbered usage recipes
trustcv examples 1 --run     # Run a recipe
trustcv examples 3 --copy .  # Copy a recipe to edit

Citation

If you use trustcv in your research, please cite:

@software{trustcv2025,
  title = {trustcv: Trustworthy Cross-Validation Toolkit},
  author = {Abtahi, Farhad and Karbalaie, Abdolamir},
  year = {2025},
  url = {https://github.com/ki-smile/trustcv}
}

License

MIT License — see LICENSE.

Contact & Support

• GitHub Issues: https://github.com/ki-smile/trustcv/issues

⚠️ Disclaimer

This toolkit is for research and educational purposes. Always validate results with domain experts before clinical deployment.

Regulatory Note: TrustCV provides documentation templates and structured outputs that can support regulatory submissions, but regulatory compliance depends on the complete device lifecycle and cannot be guaranteed by any single tool. Always consult with regulatory affairs professionals for your specific submission requirements.

Advancing Medical AI Through Rigorous Validation