Metadata-Version: 2.4
Name: Modeva
Version: 2.1.0
Summary: Integrated tool for model development and validation.
Author-email: admin@modeva.ai
License: proprietary
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# MoDeVa

**Mo**del **De**velopment and **Va**lidation — an integrated toolkit for
building and validating interpretable machine-learning models.

MoDeVa provides a single, consistent workflow for loading data, training a range
of interpretable and black-box models, and running a comprehensive test suite for
explanation, diagnostics, robustness, fairness and model comparison.

- **Documentation:** https://modeva.ai
- **License:** proprietary (offline signed-license activation; a free developer
  tier is available)

## Installation

```bash
pip install modeva
```

That's it — every dependency (numpy, scikit-learn, XGBoost, LightGBM, PyTorch,
SHAP, MLflow, …) is resolved and installed automatically. MoDeVa requires
**Python 3.12**.

### Verify your environment

If an import fails or a dependency looks off, run the built-in diagnostic. It
reads MoDeVa's declared dependencies, checks each against what's installed, and
prints the exact `pip install` line to fix any gap (it never installs anything
itself):

```python
import modeva
modeva.check_dependencies()
```

## Quickstart

```python
from modeva import DataSet, TestSuite
from modeva.models import MoXGBRegressor, MoLGBMRegressor

# 1. Load a dataset and make a train/test split
ds = DataSet()
ds.load(name="BikeSharing")
ds.set_random_split()

# 2. Train one or more models
model1 = MoXGBRegressor(name="XGB")
model1.fit(ds.train_x, ds.train_y)

model2 = MoLGBMRegressor(name="LGBM", max_depth=2, verbose=-1)
model2.fit(ds.train_x, ds.train_y)

# 3. Explain and diagnose
ts = TestSuite(ds, model1)
results = ts.explain_pdp("hr")
results.plot()

# 4. Compare models
tsc = TestSuite(ds, models=[model1, model2])
results = tsc.compare_slicing_accuracy(features="hr", method="quantile",
                                       bins=10, metric="MSE")
results.plot()
```

See the [documentation](https://modeva.ai) for the full user guide,
API reference and notebook examples.

## License activation

MoDeVa uses offline, signed-license activation — there is no license server and
no machine binding. A free **developer** tier is available; enterprise tiers
unlock the full capability set. See the documentation for how to obtain and
activate a license key.
