semvec/pypi-packages huggingface dataset:
sync-pre-commit-deps 0.0.3
Sync pre-commit hook dependencies based on other installed hooks
License: MIT
Requires Python: >=3.9

sync-pre-commit-deps
====================

Sync pre-commit hook dependencies based on other installed hooks

## install (standalone)

```bash
pip install sync-pre-commit-deps
```

## install as a pre-commit hook (recommended)

See [pre-commit](https://github.com/pre-commit/pre-commit) for instructions

Sample `.pre-commit-config.yaml`:

```yaml
-   repo: https://github.com/pre-commit/sync-pre-commit-deps
    rev: v0.0.3
    hooks:
    -   id: sync-pre-commit-deps
```

## cli

```console
$ sync-pre-commit-deps path/to/.pre-commit-config.yaml
```

## what it does

Ensures tools which declare `flake8`, `black`, or `mypy` as additional dependencies will have those versions synced with the `flake8`, `black`, or `mypy` versions in the rest of the config. For example, `flake8` under `yesqa` is updated from `5.0.0` to `6.0.0`.

```diff
 repos:
 -   repo: https://github.com/PyCQA/flake8
     rev: 6.0.0
     hooks:
     -   id: flake8
 -   repo: https://github.com/asottile/yesqa
     rev: v1.5.0
     hooks:
     -   id: yesqa
         additional_dependencies:
-        -   flake8==5.0.0
+        -   flake8==6.0.0
```
autogam 0.1.0
Automate the Creation of Generalized Additive Models (GAMs)
License: MIT
Requires Python: >=3.12

# autogam

Automate the Creation of Generalized Additive Models (GAMs)

## Installation

```bash
$ pip install autogam
```

## Usage

### Example 1: Fitting AutoGAM on a Random Dataset

```python
import numpy as np
import pandas as pd
from autogam.autogam import AutoGAM

# Generate a random dataset
df_random = pd.DataFrame({
    'x1': np.random.uniform(0, 10, 100),
    'x2': np.random.uniform(0, 10, 100),
    'y': np.random.uniform(0, 10, 100)
})

# Fit the model using AutoGAM
ag_random = AutoGAM(df_random, 'y')

# Display the model summary
ag_random.summary()

# Display the performance metrics
ag_random.print()
```

### Example 2: Fitting AutoGAM on the `df_autos` Dataset

```python
import pandas as pd
from statsmodels.gam.tests.test_penalized import df_autos
from autogam.autogam import AutoGAM

# Use the `df_autos` dataset from statsmodels
ag_autos = AutoGAM(df_autos, 'city_mpg')

# Display the model summary
ag_autos.summary()

# Display the performance metrics
ag_autos.print()
```

## License

`autogam` was created by Chitu Okoli. It is licensed under the terms of the MIT license.

## Credits

`autogam` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).