[](https://www.gnu.org/licenses/gpl-3.0)
[](https://www.python.org/dev/peps/pep-0008/)
[](https://python-poetry.org/)
# Scikit-transformers : Scikit-learn + Custom transformers
## About
**scikit-transformers** is a very usefull package to enable and provide custom transformers such as ```LogColumnTransformer```, ```BoolColumnTransformers``` and others fancy transformers.
It was created to provide a simple way to use custom transformers in ```scikit-learn``` pipelines, and allow to use them in a ```scikit-learn ```model, using ```GridSearchCV``` for testing and tuning hyperparameters.
The starting point was to provide a simple ```LogColumnTransformer```, which is a simple wrapper around the numpy log function, making possible to use a skew threshold to apply the log transformation only on columns with a skew superior to a given threshold.
With ```scikit-transformers```, it is now possible to use this ```LogColumnTransformer``` in transformer in a ```GridSearchCV``` using a skew threshold as hyperparameter to find what columns are good to log or not.
```LogColumnTransformer``` is one of the many transformers implemented in ```scikit-transformers```.
## Installation
Using regular pip and venv tools :
```bash
python3 -m venv .venv
source .venv/bin/activate
pip install scikit-transformers
```
## Usage
For a very basic usage :
```python
import pandas as pd
from sktransf.trasnformer import LogColumnTransformer
df = pd.DataFrame(
{ "a": range(10),
"b": range(10)
}
)
logger = LogColumnTransformer()
logger.fit_transform(df)
df_transf = logger.transform(df)
```
Using common transformers :
```python
import pandas as pd
from sktransf.transformer import LogColumnTransformer, BoolColumnTransformer
from sktransf.selector import DropUniqueColumnSelector
df = pd.DataFrame(
{ "a": range(10),
"b": range(10)
}
)
df_bool = BoolColumnTransformer().fit_transform(df)
df_unique = DropUniqueColumnTransformer().fit_transform(df)
df_logged = LogColumnTransformer().fit_transform(df)
```
Using a pipeline with a scikit-learn model :
```python
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sktransf.transformer import LogColumnTransformer, BoolColumnTransformer
from sktransf.selector import DropUniqueColumnSelector
pipe = Pipeline([
('bool', BoolColumnTransformer()),
('unique', DropUniqueColumnTransformer()),
('log', LogColumnTransformer()),
('model', LinearRegression())
])
X = pd.DataFrame(
{ "a": range(10),
"b": range(10)
}
)
y = range(10)
pipe.fit(X, y)
y_pred = pipe.predict(X)
```
## Documentation
For more specific information, please refer to the notebooks:
* Transformers :
* [LogColumnTransformer notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/transformer/LogColumnTransformer.ipynb)
* [BoolColumnTransformer notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/transformer/BoolColumnTransformer.ipynb)
* Selectors :
* [DropUniqueColumnSelector notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/selector/DropUniqueColumnSelector.ipynb)
* [DropSkuColumnSelector notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/selector/DropSkuColumnSelector.ipynb)
* Pipelines :
* [Pipelines notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/Pipelines.ipynb)
A complete documentation is be available on the [github page](https://alexandregazagnes.github.io/scikit-transformers/).
## Changelog, Releases and Roadmap
Please refer to the [changelog](https://alexandregazagnes.github.io/scikit-transformers/CHANGELOG/) page for more information.
## Contributing
Pull requests are welcome.
For major changes, please open an issue first to discuss what you would like to change.
For more information, please refer to the [contributing](https://alexandregazagnes.github.io/scikit-transformers/CONTRIBUTING/) page.
## License
[GPLv3](https://raw.githubusercontent.com/AlexandreGazagnes/scikit-transformers/main/LICENSE)
Raw data
{
"_id": null,
"home_page": "https://alexandregazagnes.github.io/scikit-transformers/",
"name": "scikit-transformers",
"maintainer": "AlexandreGazagnes",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "alex@gazagnes.net",
"keywords": "python,machine learning,sklearn,transformers,scikit-learn,tools,data,pandas",
"author": "AlexandreGazagnes",
"author_email": "alex@gazagnes.net",
"download_url": "https://files.pythonhosted.org/packages/dd/01/b95d328f3dfcd3313590a21ac6842780c7739c0ae3b1b8e148e23b18f110/scikit_transformers-0.3.1.tar.gz",
"platform": null,
"description": "\n[](https://www.gnu.org/licenses/gpl-3.0)\n\n\n[](https://www.python.org/dev/peps/pep-0008/)\n[](https://python-poetry.org/)\n\n\n\n\n\n\n\n# Scikit-transformers : Scikit-learn + Custom transformers\n\n\n## About\n\n**scikit-transformers** is a very usefull package to enable and provide custom transformers such as ```LogColumnTransformer```, ```BoolColumnTransformers``` and others fancy transformers.\n\nIt was created to provide a simple way to use custom transformers in ```scikit-learn``` pipelines, and allow to use them in a ```scikit-learn ```model, using ```GridSearchCV``` for testing and tuning hyperparameters.\n\nThe starting point was to provide a simple ```LogColumnTransformer```, which is a simple wrapper around the numpy log function, making possible to use a skew threshold to apply the log transformation only on columns with a skew superior to a given threshold.\n\nWith ```scikit-transformers```, it is now possible to use this ```LogColumnTransformer``` in transformer in a ```GridSearchCV``` using a skew threshold as hyperparameter to find what columns are good to log or not.\n\n```LogColumnTransformer``` is one of the many transformers implemented in ```scikit-transformers```.\n\n\n\n## Installation\n\nUsing regular pip and venv tools :\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\npip install scikit-transformers\n```\n\n\n## Usage\n\nFor a very basic usage :\n```python\nimport pandas as pd\n\nfrom sktransf.trasnformer import LogColumnTransformer\n\ndf = pd.DataFrame(\n { \"a\": range(10),\n \"b\": range(10)\n }\n)\n\nlogger = LogColumnTransformer()\nlogger.fit_transform(df)\ndf_transf = logger.transform(df)\n```\n\nUsing common transformers : \n\n```python\nimport pandas as pd\n\nfrom sktransf.transformer import LogColumnTransformer, BoolColumnTransformer\nfrom sktransf.selector import DropUniqueColumnSelector\n\ndf = pd.DataFrame(\n { \"a\": range(10),\n \"b\": range(10)\n }\n)\n\ndf_bool = BoolColumnTransformer().fit_transform(df)\ndf_unique = DropUniqueColumnTransformer().fit_transform(df)\ndf_logged = LogColumnTransformer().fit_transform(df)\n```\n\nUsing a pipeline with a scikit-learn model : \n\n```python\nimport pandas as pd\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import LinearRegression\n\nfrom sktransf.transformer import LogColumnTransformer, BoolColumnTransformer\nfrom sktransf.selector import DropUniqueColumnSelector\n\npipe = Pipeline([\n ('bool', BoolColumnTransformer()),\n ('unique', DropUniqueColumnTransformer()),\n ('log', LogColumnTransformer()),\n ('model', LinearRegression())\n])\n\nX = pd.DataFrame(\n { \"a\": range(10),\n \"b\": range(10)\n }\n)\n\ny = range(10)\n\npipe.fit(X, y)\n\ny_pred = pipe.predict(X)\n```\n\n\n## Documentation\n\nFor more specific information, please refer to the notebooks: \n\n* Transformers : \n * [LogColumnTransformer notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/transformer/LogColumnTransformer.ipynb)\n * [BoolColumnTransformer notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/transformer/BoolColumnTransformer.ipynb)\n* Selectors : \n * [DropUniqueColumnSelector notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/selector/DropUniqueColumnSelector.ipynb)\n * [DropSkuColumnSelector notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/selector/DropSkuColumnSelector.ipynb)\n* Pipelines :\n * [Pipelines notebook](https://github.com/AlexandreGazagnes/scikit-transformers/blob/main/docs/notebooks/Pipelines.ipynb)\n\n\nA complete documentation is be available on the [github page](https://alexandregazagnes.github.io/scikit-transformers/).\n\n\n## Changelog, Releases and Roadmap\n\nPlease refer to the [changelog](https://alexandregazagnes.github.io/scikit-transformers/CHANGELOG/) page for more information.\n\n\n## Contributing\n\nPull requests are welcome.\n\nFor major changes, please open an issue first to discuss what you would like to change.\n\nFor more information, please refer to the [contributing](https://alexandregazagnes.github.io/scikit-transformers/CONTRIBUTING/) page.\n\n\n## License\n\n[GPLv3](https://raw.githubusercontent.com/AlexandreGazagnes/scikit-transformers/main/LICENSE)\n",
"bugtrack_url": null,
"license": "GPL-3.0",
"summary": "scikit-transformers is a very usefull package to enable and provide custom transformers such as LogColumnTransformer, BoolColumnTransformers and others fancy transformers.",
"version": "0.3.1",
"project_urls": {
"Changelog": "https://alexandregazagnes.github.io/scikit-transformers/CHANGELOG/",
"Code": "https://github.com/AlexandreGazagnes/scikit-transformers/tree/main",
"Documentation": "https://alexandregazagnes.github.io/scikit-transformers/",
"Homepage": "https://alexandregazagnes.github.io/scikit-transformers/",
"Issues": "https://github.com/AlexandreGazagnes/scikit-transformers/issues",
"Repository": "https://github.com/AlexandreGazagnes/scikit-transformers/tree/main"
},
"split_keywords": [
"python",
"machine learning",
"sklearn",
"transformers",
"scikit-learn",
"tools",
"data",
"pandas"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "71fbaf1077afa931cc6a69e3a48e1e8a094eafe9bc5e7e9efe381f7a7bb1ef6e",
"md5": "be4cdb53b097488ccd0b0a00d8f32143",
"sha256": "750a47393836b2a74ffb2febf1fbbe947339c124d058d30085ad842a72db786a"
},
"downloads": -1,
"filename": "scikit_transformers-0.3.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "be4cdb53b097488ccd0b0a00d8f32143",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 26090,
"upload_time": "2024-02-09T23:42:50",
"upload_time_iso_8601": "2024-02-09T23:42:50.680501Z",
"url": "https://files.pythonhosted.org/packages/71/fb/af1077afa931cc6a69e3a48e1e8a094eafe9bc5e7e9efe381f7a7bb1ef6e/scikit_transformers-0.3.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "dd01b95d328f3dfcd3313590a21ac6842780c7739c0ae3b1b8e148e23b18f110",
"md5": "ea985016a40b9af0db50e592a6bc259d",
"sha256": "5c1578daf6c0a93f0f015a7db4ecb675f2a59b3e0ed243f53fc6ee23eb030138"
},
"downloads": -1,
"filename": "scikit_transformers-0.3.1.tar.gz",
"has_sig": false,
"md5_digest": "ea985016a40b9af0db50e592a6bc259d",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 22632,
"upload_time": "2024-02-09T23:42:52",
"upload_time_iso_8601": "2024-02-09T23:42:52.550266Z",
"url": "https://files.pythonhosted.org/packages/dd/01/b95d328f3dfcd3313590a21ac6842780c7739c0ae3b1b8e148e23b18f110/scikit_transformers-0.3.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-02-09 23:42:52",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "AlexandreGazagnes",
"github_project": "scikit-transformers",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "scikit-transformers"
}
JSON
