| Name | insolver JSON |
| Version |
0.4.29
JSON |
| download |
| home_page | http://insolver.io/ |
| Summary | Insolver is low-code machine learning library, initially created for the insurance industry. |
| upload_time | 2024-10-03 10:40:14 |
| maintainer | None |
| docs_url | None |
| author | Mindset Team |
| requires_python | <3.12,>=3.8 |
| license | MIT |
| keywords |
insurance
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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)
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 feature engineering requirements | `pip install insolver[feature_engineering]` |
| Installation with feature monitoring requirements | `pip install insolver[feature_monitoring]` |
| Installation with interpretation requirements | `pip install insolver[interpretation]` |
| Installation with serving requirements | `pip install insolver[serving]` |
| Installation with report requirements | `pip install insolver[report]` |
| Installation with all requirements | `pip install insolver[all]` |
### 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 --cov-report 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
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"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\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. 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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 --cov-report 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\n",
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