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.
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 This tutorial demonstrates how to create classification models for the
weatherAUSdataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models. -
Data Preprocessing Example I - New York City Airbnb This tutorial demonstrates how to use the
feature_engineeringmodule and all the main features of each class. For this, theAB_NYC_2019dataset is used. -
Data Preprocessing Example II - New York City Airbnb This tutorial also demonstrates how to use the
feature_engineeringmodule, but it covers the automated data preprossesing class and all of its features. For this, theAB_NYC_2019dataset is used. -
Gradient Boosting Example - Lending Club This tutorial demonstrates how to create classification models for the
Lending Clubdataset using the Gradient Boosting libraries and theInsolverGBMWrapperclass. -
Transforms Inference Example This tutorial demonstrates how to load
InsolverTransformtransforms from a file using theload_transformsfunction. -
InsolverDataFrame and InsolverTransform Example This tutorial demonstrates main features of the
InsolverDataFrameclass and theInsolverTransformclass. -
Regression Example - FreeMLP This tutorial demonstrates how to create regression models for the
freMPL-Rdataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models. -
Regression Example - US Accidents This tutorial demonstrates how to create regression models for the
US Traffic Accidentdataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models. -
Report Example This tutorial demonstrates how to create a HTML report with different models using the
Reportclass.
Documentation:
Available here
Supported libraries:
| GLM | Boosting models | Serving (REST-API) | Model interpretation |
|---|---|---|---|
| - sklearn - h2o |
- XGBoost - LightGBM - CatBoost |
- Flask - FastAPI - Django |
- shap plots |
Run tests:
python -m pytest
tests with coverage:
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
[email protected] +79263790123