Data

Tools

Indexes

Applications

Experiments

Open Source Science

alquimodelia

Keras based model builder

https://github.com/alquimodelia/alquimodelia

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Keras based model builder

Basic Info
  • Host: GitHub
  • Owner: alquimodelia
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 320 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 7
Created about 2 years ago · Last pushed 7 months ago
Metadata Files
Readme Contributing License Code of conduct Citation Security

README.md

Alquimodelia

Alquimodelia is a Python package that provides a Keras-based forecast model builder.

Python Keras

It provides the arquitectures for CNN, LSTM, and Encoder Decoder, and even from imagery UNET. Any suggestions and tips are welcome. Use this to fastly have your forecast models ready to use!

Usage

To use Alquimodelia, follow these steps:

bash pip install alquimodelia

Since Alquimodelia is based on keras-core you can choose which backend to use, otherwise it will default to tensorflow. To change backend change the KERAS-BACKEND enviromental variable. Follow this.

To get an arquiteture you only need to have a simple configuration and call the module:

```python import alquimodelia

The input arguments

inputargs = { "Xtimeseries": 168, "Ytimeseries": 24, "nfeaturestrain": 18, "nfeatures_predict": 1, }

This is make a model with shapes:

# input_shape = (N, 168, 18)
# output_shape = (N, 24, 1)

forearch = alquimodelia.CNNArch(**input_args)

Now for Vanilla and Stacked CNN:

architectureargs = {} VanillaCNN = forearch.architecture(**architectureargs)

architectureargs = {"blockrepetition": 2} StackedCNN = forearch.architecture(**architecture_args)

Keras Models ready to use:

VanillaCNN.summary() StackedCNN.summary()

```

Contribution

Contributions to Alquimodelia are welcome! If you find any issues or have suggestions for improvement, please feel free to contribute. Make sure to update tests as appropriate and follow the contribution guidelines.

License

Alquimodelia is licensed under the MIT License, which allows you to use, modify, and distribute the package according to the terms of the license. For more details, please refer to the LICENSE file.

Owner

  • Name: alquimodelia
  • Login: alquimodelia
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 0.0.1
message: "If you use this software, please cite it as below."
authors:
- family-names: "Santos"
  given-names: "João"
  orcid: "https://orcid.org/0009-0007-5995-8060"
title: "Alquimodelia: Alquimodelia"
version: 0.0.1
date-released: 2023-10-09
url: "https://github.com/alquimodelia/alquimodelia"
repository-code: "https://github.com/alquimodelia/alquimodelia"
keywords:
  - python
  - machine learning
  - forecast
  - model builder
type: software
license: BSD-3-Clause
license-url: "https://github.com/alquimodelia/alquimodelia/blob/main/LICENSE"

GitHub Events

Total
  • Release event: 5
  • Delete event: 2
  • Push event: 17
  • Pull request event: 4
  • Create event: 8
Last Year
  • Release event: 5
  • Delete event: 2
  • Push event: 17
  • Pull request event: 4
  • Create event: 8

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 33
  • Total Committers: 1
  • Avg Commits per committer: 33.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 12
  • Committers: 1
  • Avg Commits per committer: 12.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
João Santos j****s@e****t 33
Committer Domains (Top 20 + Academic)
edp.pt: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 0
  • Total pull requests: 5
  • Average time to close issues: N/A
  • Average time to close pull requests: 23 days
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 4
  • Average time to close issues: N/A
  • Average time to close pull requests: 7 days
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • JotaFan (8)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 48 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
pypi.org: alquimodelia

Keras based model builder

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 48 Last month
Rankings
Dependent packages count: 10.3%
Average: 34.1%
Dependent repos count: 57.9%
Maintainers (1)
JotaFan
Last synced: 7 months ago