Description:
insolver 0.4.28
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, the AB_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, the AB_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 the InsolverGBMWrapper class.
Transforms Inference Example
This tutorial demonstrates how to load InsolverTransform transforms from a file using the load_transforms function.
InsolverDataFrame and InsolverTransform Example
This tutorial demonstrates main features of the InsolverDataFrame class and the InsolverTransform 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
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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