Insolver is low-code machine learning library, initially created for the insurance industry.
Project description
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
weatherAUS
dataset: 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_engineering
module and all the main features of each class. For this, theAB_NYC_2019
dataset is used. -
Data Preprocessing Example II - New York City Airbnb This tutorial also demonstrates how to use the
feature_engineering
module, but it covers the automated data preprossesing class and all of its features. For this, theAB_NYC_2019
dataset is used. -
Gradient Boosting Example - Lending Club This tutorial demonstrates how to create classification models for the
Lending Club
dataset using the Gradient Boosting libraries and theInsolverGBMWrapper
class. -
Transforms Inference Example This tutorial demonstrates how to load
InsolverTransform
transforms from a file using theload_transforms
function. -
InsolverDataFrame and InsolverTransform Example This tutorial demonstrates main features of the
InsolverDataFrame
class and theInsolverTransform
class. -
Regression Example - FreeMLP 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 This tutorial demonstrates how to create regression models for the
US Traffic Accident
dataset: 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
Report
class.
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
frank@mind-set.ru +79263790123
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