# Insolver
[](https://pypi.org/project/insolver/)
[](https://insolver.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/MindSetLib/Insolver/actions)
[](https://codecov.io/github/MindSetLib/Insolver)
[](https://github.com/psf/black)
[](https://pepy.tech/project/insolver)
<!--- [](https://github.com/MindSetLib/Insolver/actions) --->
Insolver is a low-code machine learning library, originally created for the insurance industry, but can be used in any other. You can find a more detailed overview [here](https://insolver.readthedocs.io/en/latest/source/overview.html).
## Installation:
Insolver can be installed via pip from PyPI. There are several installation options available:
| Description | Command |
|--------------------------------------------|-------------------------------|
| Regular installation | `pip install insolver` |
| Installation with all heavy requirements | `pip install insolver[full]` |
| Installation with development requirements | `pip install insolver[dev]` |
### Insolver is already installed in the easy access cloud via the GitHub login. Try https://mset.space with a familiar notebook-style environment.
## Examples:
- [Binary Classification Example - Rain in Australia Prediction](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Binary%20Classification%20Example%20-%20Rain%20in%20Australia%20Prediction.ipynb)
This tutorial demonstrates how to create **classification models** for the [`weatherAUS`](https://www.kaggle.com/jsphyg/weather-dataset-rattle-package) dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.
- [Data Preprocessing Example I - New York City Airbnb](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Data%20Preprocessing%20Example%20I%20-%20New%20York%20City%20Airbnb.ipynb)
This tutorial demonstrates how to use the [`feature_engineering`](https://github.com/MindSetLib/Insolver/tree/master/insolver/feature_engineering) module and all the **main features of each class**. For this, the [`AB_NYC_2019`](https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data) dataset is used.
- [Data Preprocessing Example II - New York City Airbnb](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Data%20Preprocessing%20Example%20II%20-%20New%20York%20City%20Airbnb.ipynb)
This tutorial also demonstrates how to use the [`feature_engineering`](https://github.com/MindSetLib/Insolver/tree/master/insolver/feature_engineering) module, but it covers the **automated data preprossesing** class and all of its features. For this, the [`AB_NYC_2019`](https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data) dataset is used.
- [Gradient Boosting Example - Lending Club](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Gradient%20Boosting%20Example%20-%20Lending%20Club.ipynb)
This tutorial demonstrates how to create **classification models** for the [`Lending Club`](https://www.kaggle.com/wordsforthewise/lending-club) dataset using the **Gradient Boosting libraries** and the `InsolverGBMWrapper` class.
- [Transforms Inference Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Transforms%20Inference%20Example.ipynb)
This tutorial demonstrates how to load `InsolverTransform` transforms from a file using the `load_transforms` function.
- [InsolverDataFrame and InsolverTransform Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/InsolverDataFrame%20and%20InsolverTransform%20Example.ipynb)
This tutorial demonstrates main features of the `InsolverDataFrame` class and the `InsolverTransform` class.
- [Regression Example - FreeMLP](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)
This tutorial demonstrates how to create **regression models** for the `freMPL-R` dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.
- [Regression Example - US Accidents](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)
This tutorial demonstrates how to create **regression models** for the [`US Traffic Accident`](https://smoosavi.org/datasets/us_accidents) dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.
- [Report Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Report%20Example.ipynb)
This tutorial demonstrates how to create a **HTML report** with different models using the `Report` class.
## Documentation:
Available [here](https://insolver.readthedocs.io/)
## Supported libraries:
| GLM | Boosting models | Serving (REST-API) | Model interpretation |
|---------------------|-------------------------------------------|------------------------------------|----------------------|
| - sklearn<br/>- h2o | - XGBoost<br/> - LightGBM<br/> - CatBoost | - Flask<br/>- FastAPI<br/>- Django | - shap plots |
### Run tests:
```shell
python -m pytest
```
tests with coverage:
```shell
python -m pytest --cov=insolver; coverage html; xdg-open htmlcov/index.html
```
## Contributing to Insolver:
Please, feel free to open an issue or/and suggest PR, if you find any bugs or any enhancements.
## Demo
### Example of creating models using the Insolver
### Example of a model production service
### Example of an elyra pipeline built with the Insolver inside
## Contacts
frank@mind-set.ru
+79263790123
Raw data
{
"_id": null,
"home_page": "http://insolver.io/",
"name": "insolver",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "insurance,machine learning",
"author": "Mindset Team",
"author_email": "request@mind-set.ru",
"download_url": "https://files.pythonhosted.org/packages/4b/97/dad3aece16d69b4f19b280a8c1bbcf58f3cd88ff145fa624545b5ca6b060/insolver-0.4.22.tar.gz",
"platform": null,
"description": "# Insolver\n\n[](https://pypi.org/project/insolver/)\n[](https://insolver.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/MindSetLib/Insolver/actions)\n[](https://codecov.io/github/MindSetLib/Insolver)\n[](https://github.com/psf/black)\n[](https://pepy.tech/project/insolver)\n<!--- [](https://github.com/MindSetLib/Insolver/actions) --->\n\nInsolver is a low-code machine learning library, originally created for the insurance industry, but can be used in any other. You can find a more detailed overview [here](https://insolver.readthedocs.io/en/latest/source/overview.html).\n\n## Installation:\n\nInsolver can be installed via pip from PyPI. There are several installation options available:\n\n| Description | Command |\n|--------------------------------------------|-------------------------------|\n| Regular installation | `pip install insolver` |\n| Installation with all heavy requirements | `pip install insolver[full]` |\n| Installation with development requirements | `pip install insolver[dev]` |\n\n\n### Insolver is already installed in the easy access cloud via the GitHub login. Try https://mset.space with a familiar notebook-style environment.\n\n## Examples:\n\n- [Binary Classification Example - Rain in Australia Prediction](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Binary%20Classification%20Example%20-%20Rain%20in%20Australia%20Prediction.ipynb)\nThis tutorial demonstrates how to create **classification models** for the [`weatherAUS`](https://www.kaggle.com/jsphyg/weather-dataset-rattle-package) dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.\n\n- [Data Preprocessing Example I - New York City Airbnb](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Data%20Preprocessing%20Example%20I%20-%20New%20York%20City%20Airbnb.ipynb)\nThis tutorial demonstrates how to use the [`feature_engineering`](https://github.com/MindSetLib/Insolver/tree/master/insolver/feature_engineering) module and all the **main features of each class**. For this, the [`AB_NYC_2019`](https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data) dataset is used.\n\n- [Data Preprocessing Example II - New York City Airbnb](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Data%20Preprocessing%20Example%20II%20-%20New%20York%20City%20Airbnb.ipynb)\nThis tutorial also demonstrates how to use the [`feature_engineering`](https://github.com/MindSetLib/Insolver/tree/master/insolver/feature_engineering) module, but it covers the **automated data preprossesing** class and all of its features. For this, the [`AB_NYC_2019`](https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data) dataset is used.\n\n- [Gradient Boosting Example - Lending Club](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Gradient%20Boosting%20Example%20-%20Lending%20Club.ipynb)\nThis tutorial demonstrates how to create **classification models** for the [`Lending Club`](https://www.kaggle.com/wordsforthewise/lending-club) dataset using the **Gradient Boosting libraries** and the `InsolverGBMWrapper` class.\n\n- [Transforms Inference Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Transforms%20Inference%20Example.ipynb)\nThis tutorial demonstrates how to load `InsolverTransform` transforms from a file using the `load_transforms` function.\n\n- [InsolverDataFrame and InsolverTransform Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/InsolverDataFrame%20and%20InsolverTransform%20Example.ipynb)\nThis tutorial demonstrates main features of the `InsolverDataFrame` class and the `InsolverTransform` class.\n\n- [Regression Example - FreeMLP](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)\nThis tutorial demonstrates how to create **regression models** for the `freMPL-R` dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.\n\n- [Regression Example - US Accidents](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)\nThis tutorial demonstrates how to create **regression models** for the [`US Traffic Accident`](https://smoosavi.org/datasets/us_accidents) dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.\n\n- [Report Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Report%20Example.ipynb)\nThis tutorial demonstrates how to create a **HTML report** with different models using the `Report` class.\n\n## Documentation:\n\nAvailable [here](https://insolver.readthedocs.io/)\n\n## Supported libraries:\n\n| GLM | Boosting models | Serving (REST-API) | Model interpretation |\n|---------------------|-------------------------------------------|------------------------------------|----------------------|\n| - sklearn<br/>- h2o | - XGBoost<br/> - LightGBM<br/> - CatBoost | - Flask<br/>- FastAPI<br/>- Django | - shap plots |\n\n### Run tests:\n```shell\npython -m pytest\n```\n\ntests with coverage:\n```shell\npython -m pytest --cov=insolver; coverage html; xdg-open htmlcov/index.html\n```\n\n\n## Contributing to Insolver:\n\nPlease, feel free to open an issue or/and suggest PR, if you find any bugs or any enhancements.\n\n## Demo\n### Example of creating models using the Insolver\n\n\n### Example of a model production service\n\n\n### Example of an elyra pipeline built with the Insolver inside\n\n\n## Contacts\nfrank@mind-set.ru\n+79263790123\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Insolver is low-code machine learning library, initially created for the insurance industry.",
"version": "0.4.22",
"split_keywords": [
"insurance",
"machine learning"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "b92b69704bd960c49175dfd02710efe5",
"sha256": "89baf96a59b0a37b3a25318d518b7b52f7dbcda3d938f85a2135ce2ab4332cc7"
},
"downloads": -1,
"filename": "insolver-0.4.22-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b92b69704bd960c49175dfd02710efe5",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 485955,
"upload_time": "2022-12-05T07:05:56",
"upload_time_iso_8601": "2022-12-05T07:05:56.065780Z",
"url": "https://files.pythonhosted.org/packages/d0/0f/85a159796819b29ffd1ecf3caf76ee8914fd8f6a1fa133e7eec665799063/insolver-0.4.22-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "7fe8b80173a3955a8e90bd5b679e04cc",
"sha256": "833a82ace95aea3fa1d37e9ec291c22a33e8c3ffecd80cb5d4237173d9ccb8a4"
},
"downloads": -1,
"filename": "insolver-0.4.22.tar.gz",
"has_sig": false,
"md5_digest": "7fe8b80173a3955a8e90bd5b679e04cc",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 6744624,
"upload_time": "2022-12-05T07:05:58",
"upload_time_iso_8601": "2022-12-05T07:05:58.492083Z",
"url": "https://files.pythonhosted.org/packages/4b/97/dad3aece16d69b4f19b280a8c1bbcf58f3cd88ff145fa624545b5ca6b060/insolver-0.4.22.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-05 07:05:58",
"github": false,
"gitlab": false,
"bitbucket": false,
"lcname": "insolver"
}
JSON