composition_based_feature_vector

v1.0.6

Tool for quickly creating a composition-based feature vector. For more information about how to use this package see README

Latest version published 1 year ago

License: MIT

PyPI

GitHub

Package Health Score

38 / 100

security
Security review needed
popularity
Limited
maintenance
Inactive
community
Limited

Popularity

Limited

GitHub Stars: 4
Forks: 0
Contributors: 4

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package composition_based_feature_vector, we found that it has been starred 4 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jun 27, 2023
Direct Vulnerabilities: 0
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0
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0
M
0
L
Indirect Vulnerabilities: 0
C
0
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0
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0
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License Risk: 0
H
0
M
0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 0
Open PR: 0
Last Release: 1 year ago
Last Commit: 1 year ago

Further analysis of the maintenance status of composition_based_feature_vector based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for composition_based_feature_vector is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 4
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3

Age: 3 years
Latest Release: 1 year ago
Dependencies: 4 Direct / 15 Total
Versions: 4
Maintainers: 1
Wheels: OS Independent

Readme

CBFV Package

Tool to quickly create a composition-based feature vectors from materials datafiles.

Installation

The source code is currently hosted on GitHub at: https://github.com/kaaiian/CBFV

Binary installers for the latest released version are available at the Python Package Index (PyPI)

# PyPI
pip install CBFV

Making the composition-based feature vector

The CBFV package assumes your data is stored in a pandas dataframe of the following structure:

formula	target
Tc1V1	248.539
Cu1Dy1	66.8444
Cd3N2	91.5034

To featurize this data, the generate_features function can be called as follows:

from CBFV import composition
X, y, formulae, skipped = composition.generate_features(df)

Extended Functionality

The featurization scheme can be adjusted by calling the the elem_prop parameter. The following featurization schemes are included within CBFV:

jarvis
magpie
mat2vec
oliynyk (default)
onehot
random_200

Duplicate formula handeling is controlled by the drop_duplicates parameter. It is set to False by default to preserve datapoints containing variation outside of their formula. For example, heat capacity measurements performed for the same material at different temperatures.

The extend_features parameter is used to specify whether columns outside of ['formula', 'target'] should be considered during featurization. It is set to False by default to exclude nuisance information from consideration. Setting extend_features=True would allow additional information (i.e. ['temperature', 'pressure']) to be preserved.

The sum_feat parameter specifies whether to calculate the sum features when generating the CBFVs for the chemical formulae. It is set to False by default.

Calling generate_features with these parameters can be implemented as follows:

formula	target	temp
Tc1V1	248.539	373
Tc1V1	66.8444	473
Cd3N2	91.5034	273

from CBFV import composition
X, y, formulae, skipped = composition.generate_features(df,
                                                        elem_prop='magpie',
                                                        drop_duplicates=False,
                                                        extend_features=True,
                                                        sum_feat=True)

composition_based_feature_vector dependencies

numpy pandas pytest tqdm

FAQs

What is composition_based_feature_vector?

Tool for quickly creating a composition-based feature vector. Visit Snyk Advisor to see a full health score report for composition_based_feature_vector, including popularity, security, maintenance & community analysis.

Is composition_based_feature_vector popular?

The python package composition_based_feature_vector receives a total of 81 weekly downloads. As such, composition_based_feature_vector popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is composition_based_feature_vector well maintained?

We found indications that composition_based_feature_vector is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is composition_based_feature_vector safe to use?

While scanning the latest version of composition_based_feature_vector, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 30 November-2024, at 04:54 (UTC).

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