Metadata-Version: 2.4
Name: ev-tabpfn
Version: 0.1.0
Summary: Portable TabPFN evaluation pipeline with baselines, artifacts, reports, CLI, Python API, and MCP tools.
Author: Hawk Franklin Research
License: Proprietary research package scaffold.
        
        This local package is created for internal evaluation and packaging work.
        Replace this placeholder with the final project license before publishing.
        
        
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: tabpfn
Requires-Dist: catboost
Requires-Dist: xgboost
Requires-Dist: lightgbm
Requires-Dist: autogluon.tabular
Requires-Dist: mcp
Dynamic: license-file

# ev-tabpfn

`ev-tabpfn` is a portable evaluator for TabPFN and tabular baselines. It packages the working Evaluate-TABPFN phase scripts into user-facing concepts:

- data loading
- model execution
- artifacts
- reporting
- batch orchestration
- MCP tools for agents

## Install Locally

```bash
pip install -e /home/prime/Documents/g3/tab-r1/package
```

## CLI

```bash
ev-tabpfn run-single --dataset data.csv --target label --task binary --output outputs/
ev-tabpfn run --config examples/batch_satya_recreation.json
ev-tabpfn aggregate --runs-root outputs/runs --results-dir outputs/results
ev-tabpfn validate --dataset data.csv --target label
ev-tabpfn summarize-run --run-dir outputs/runs/dataset/run_id
ev-tabpfn generate-report --run-dir outputs/runs/dataset/run_id
```

## Python API

```python
from ev_tabpfn import evaluate_dataset, evaluate_batch, aggregate_results, summarize_run

evaluate_dataset(
    dataset_path="data.csv",
    target_column="label",
    task="binary",
    output_root="outputs",
)

evaluate_batch(config_path="config.json")
aggregate_results(output_root="outputs")
summarize_run("outputs/runs/dataset/run_id")
```

## Output Contract

Each dataset run writes:

- `predictions/*.csv`
- `metrics/metrics_summary.csv`
- `metrics/metrics_summary.json`
- `metadata/dataset_metadata.json`
- `metadata/run_config.json`
- `metadata/model_status.json`
- `plots/*.png`
- `logs/*.log`

Batch runs additionally write:

- `batch_config.resolved.json`
- `batch_manifest.json`
- `summary/batch_summary.json`
- `logs/batch.log`
