pSeven Core is an integrated toolkit for design space exploration, optimization, and predictive modeling.

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License: Other/Proprietary License (# End-User License Agreement By installing this Software You confirm that You are the Authorized Us...)

Author: pSeven SAS

Maintainer: pSeven SAS

Tags adaptive design, adaptive doe, approximation, approximation model, approximation modeling, approximator, box-behnken, correlation analysis, design exploration, design of experiments, design optimization, design space, difference approximation, dimension reduction, doe, engineering, engineering optimization, feature importance, feature scoring, feature selection, fmi, fmu, fractional factorial, gaussian processes, gradient optimization, kendall, latin hypercube sampling, low discrepancy sequence, mixed integer, mixture of approximators, modeling, multiple fidelity, mutual information, optimization, optimizer, orthogonal array, parametric study, pearson, predictive modeling, principal component analysis, regression, regression tree, response surface, robust optimization, screening index, screening indices, sensitivity analysis, shap, shapley additive explanations, sobol index, sobol indices, spearman, surrogate based optimization, taguchi index, taguchi indices, tensor approximation, variable fidelity

Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, >=2.7

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Project description

pSeven Core is an integrated toolkit for predictive modeling, data analysis and optimization. It provides a variety of proprietary and classical algorithms for local and global optimization, approximation, dimension reduction, design of experiments, and sensitivity analysis. See the homepage and documentation for full details.

Requirements

  • Python 3.6 or newer.
  • NumPy 1.11.2 or newer.
  • Or Python 2.7 with NumPy 1.6.0.

NumPy is not required during installation, though you will not be able to run pSeven Core until you install NumPy.

Additionally recommended:

While the above are not required, they are widely used in pSeven Core examples and guides.

Optional:

  • SHAP - implements a game theoretic approach to explain model output.

SHAP is required only by some pSeven Core approximation models and only if you are going to use the SHAP evaluation feature for that certain kind of models.

Windows requirements

pSeven Core is tested on Windows 10, 64-bit desktop editions. Newer versions and corresponding server editions are also supported but not regularly tested.

Linux requirements

pSeven Core works on any Linux x86_64 with:

  • Linux kernel 2.6.18 or newer.
  • GNU C Library (glibc) 2.5 or newer.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for dmitry_vetrov from gravatar.com dmitry_vetrov Avatar for pseven from gravatar.com pseven

Unverified details

These details have not been verified by PyPI
Project links
GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Meta

License: Other/Proprietary License (# End-User License Agreement By installing this Software You confirm that You are the Authorized Us...)

Author: pSeven SAS

Maintainer: pSeven SAS

Tags adaptive design, adaptive doe, approximation, approximation model, approximation modeling, approximator, box-behnken, correlation analysis, design exploration, design of experiments, design optimization, design space, difference approximation, dimension reduction, doe, engineering, engineering optimization, feature importance, feature scoring, feature selection, fmi, fmu, fractional factorial, gaussian processes, gradient optimization, kendall, latin hypercube sampling, low discrepancy sequence, mixed integer, mixture of approximators, modeling, multiple fidelity, mutual information, optimization, optimizer, orthogonal array, parametric study, pearson, predictive modeling, principal component analysis, regression, regression tree, response surface, robust optimization, screening index, screening indices, sensitivity analysis, shap, shapley additive explanations, sobol index, sobol indices, spearman, surrogate based optimization, taguchi index, taguchi indices, tensor approximation, variable fidelity

Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, >=2.7

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This version

2024.4.26

2024.3.21

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