| Name | klp-commons JSON |
| Version |
0.0.69
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| home_page | |
| Summary | Modulo Commons del ecosistema Kloop. Contiene los modulos de uso común para los paquetes |
| upload_time | 2023-07-04 18:26:05 |
| maintainer | |
| docs_url | None |
| author | |
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| license | |
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No requirements were recorded.
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## Commons
Este repositorio de código se crea para implementar la microservicio `Commons` de la infraestructura de Klopp.
A continuación se proporciona una descripción de la estructura de los archivos y directorios más importantes:
## Template
- `setup.py`
- [`Notebook`]
- `test`
- `requirements.txt`
- Blibliotecas necesarias para reproducir el entorno
## Estructura del proyecto
```
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── docs <- A default Sphinx project; see sphinx-doc.org for details
├── models <- Trained and serialized models, model predictions, or model summaries
├── experiments
│ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ │ └── mlflow <- Metretrics and model management
│ ├── references <- Data dictionaries, manuals, and all other explanatory materials.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
├── setup.py <- Run this project
├── pipeline <- Source pipeline for load, preprocessing, training and test
│ ├── __init__.py <- Makes src a Python module
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
├── categorization <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ ├── categorization.py <- class and method run() for app running
│ ├── classifier.py <- Class for model ML
│ ├── consumer.py <- class for Kafka consumer
│ ├── controller_dynamo_db.py <- class for management CRUD
│ ├── controller_ml_fow.py <- Class for management models
│ ├── controller_posgrest_db.py <- class for managemen CRUD
│ ├── producer.py <- class for Kafka producer
│ ├── nicknames.py <- Class
│ ├── merchantnames.py <- class
│ └── logs <- folder for logs files
└── tox.ini <- tox file with settings for running tox;(automate and standardize testing)
```
## Reproducir proyectos
## Software necesario
El proyecto se desarrollo con los siguientes requisitos a primer nivel :
Python 3.10.4
Se recomienda a nivel de desarrollo utilizar un entorno virtual administrado por conda.
`conda create -n categorization python=3.10.4`
Use sólo pip como gestor de paquetería después de crear en entorno virtual con conda.
Los requisitos de las bibliotecas necesarias se pueden pasar a pip a través del archivo `requiremets.txt`
pip install -r requirements.txt
Ver pagína de [python](https://requirements-txt.readthedocs.io/en/latest/#:~:text=txt%20installing%20them%20using%20pip.&text=The%20installation%20process%20include%20only,That's%20it.&text=Customize%20it%20the%20way%20you,allow%20or%20disallow%20automated%20requirements)
Otra opcíon es utilizar un docker oficial de python con la versión cómo 3.10 como mínima. Esta es sólo si utilizas Linux o Windows como sistema operativo, existe problemas de compatibilidad para MacBooks M1
[Docker Hub de Python](https://hub.docker.com/_/python)
- Para el entorno local se utiliza [Jupyer Notebook] como entorno de experimentación
- Para administrar los modelos de ML se utiliza [MLFlow]() con Posgrestdb
- Como gestor de bases de datos relacional se utiliza PosgrestDB
- Para almacenar información no estructurada se utiliza DynamoDB
- Para versionamiento de los dataset se utiliza [DVC]
- Para autoformatting se utilizan los paquetes [`Back`](), [Flake8]() y [autopep8] ()
- Para pruebas unitarias se utiliza el paquete estándar de python `unittest`
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"description": "## Commons \n\nEste repositorio de c\u00f3digo se crea para implementar la microservicio `Commons` de la infraestructura de Klopp.\n\nA continuaci\u00f3n se proporciona una descripci\u00f3n de la estructura de los archivos y directorios m\u00e1s importantes:\n\n## Template\n\n- `setup.py`\n- [`Notebook`]\n- `test`\n- `requirements.txt`\n - Blibliotecas necesarias para reproducir el entorno\n\n## Estructura del proyecto\n\n```\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 Makefile <- Makefile with commands like `make data` or `make train`\n\u251c\u2500\u2500 README.md <- The top-level README for developers using this project.\n\u251c\u2500\u2500 docs <- A default Sphinx project; see sphinx-doc.org for details\n\u251c\u2500\u2500 models <- Trained and serialized models, model predictions, or model summaries\n\u251c\u2500\u2500 experiments \n\u2502 \u251c\u2500\u2500 notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),\n\u2502 \u2502 \u2514\u2500\u2500 mlflow <- Metretrics and model management \n\u2502 \u251c\u2500\u2500 references <- Data dictionaries, manuals, and all other explanatory materials.\n\u2502 \u251c\u2500\u2500 processed <- The final, canonical data sets for modeling. \n\u2502 \u2514\u2500\u2500 data \n\u2502 \u251c\u2500\u2500 external <- Data from third party sources.\n\u2502 \u251c\u2500\u2500 interim <- Intermediate data that has been transformed.\n\u2502 \u251c\u2500\u2500 processed <- The final, canonical data sets for modeling.\n\u2502 \u2514\u2500\u2500 raw <- The original, immutable data dump.\n\u251c\u2500\u2500 requirements.txt <- The requirements file for reproducing the analysis environment, e.g.\n\u2502 generated with `pip freeze > requirements.txt`\n\u251c\u2500\u2500 setup.py <- Run this project \n\u251c\u2500\u2500 pipeline <- Source pipeline for load, preprocessing, training and test \n\u2502 \u251c\u2500\u2500 __init__.py <- Makes src a Python module\n\u2502 \u251c\u2500\u2500 data <- Scripts to download or generate data\n\u2502 \u2502 \u2514\u2500\u2500 make_dataset.py\n\u2502 \u251c\u2500\u2500 features <- Scripts to turn raw data into features for modeling\n\u2502 \u2502 \u2514\u2500\u2500 build_features.py\n\u2502 \u251c\u2500\u2500 models <- Scripts to train models and then use trained models to make\n\u2502 \u2502 \u2502 predictions\n\u2502 \u2502 \u251c\u2500\u2500 predict_model.py\n\u2502 \u2502 \u2514\u2500\u2500 train_model.py\n\u2502 \u2514\u2500\u2500 visualization <- Scripts to create exploratory and results oriented visualizations\n\u2502 \u2514\u2500\u2500 visualize.py\n\u251c\u2500\u2500 categorization <- Source code for use in this project.\n\u2502 \u251c\u2500\u2500 __init__.py <- Makes src a Python module\n\u2502 \u251c\u2500\u2500 categorization.py <- class and method run() for app running \n\u2502 \u251c\u2500\u2500 classifier.py <- Class for model ML\n\u2502 \u251c\u2500\u2500 consumer.py <- class for Kafka consumer \n\u2502 \u251c\u2500\u2500 controller_dynamo_db.py <- class for management CRUD \n\u2502 \u251c\u2500\u2500 controller_ml_fow.py <- Class for management models\n\u2502 \u251c\u2500\u2500 controller_posgrest_db.py <- class for managemen CRUD \n\u2502 \u251c\u2500\u2500 producer.py <- class for Kafka producer\n\u2502 \u251c\u2500\u2500 nicknames.py <- Class \n\u2502 \u251c\u2500\u2500 merchantnames.py <- class \n\u2502 \u2514\u2500\u2500 logs <- folder for logs files \n\u2514\u2500\u2500 tox.ini <- tox file with settings for running tox;(automate and standardize testing)\n```\n\n\n## Reproducir proyectos \n\n## Software necesario\n\nEl proyecto se desarrollo con los siguientes requisitos a primer nivel :\n\nPython 3.10.4\n\nSe recomienda a nivel de desarrollo utilizar un entorno virtual administrado por conda.\n\n`conda create -n categorization python=3.10.4` \n\nUse s\u00f3lo pip como gestor de paqueter\u00eda despu\u00e9s de crear en entorno virtual con conda.\nLos requisitos de las bibliotecas necesarias se pueden pasar a pip a trav\u00e9s del archivo `requiremets.txt`\n\npip install -r requirements.txt\n\nVer pag\u00edna de [python](https://requirements-txt.readthedocs.io/en/latest/#:~:text=txt%20installing%20them%20using%20pip.&text=The%20installation%20process%20include%20only,That's%20it.&text=Customize%20it%20the%20way%20you,allow%20or%20disallow%20automated%20requirements)\n\n\nOtra opc\u00edon es utilizar un docker oficial de python con la versi\u00f3n c\u00f3mo 3.10 como m\u00ednima. Esta es s\u00f3lo si utilizas Linux o Windows como sistema operativo, existe problemas de compatibilidad para MacBooks M1\n\n\n[Docker Hub de Python](https://hub.docker.com/_/python)\n\n- Para el entorno local se utiliza [Jupyer Notebook] como entorno de experimentaci\u00f3n\n- Para administrar los modelos de ML se utiliza [MLFlow]() con Posgrestdb\n- Como gestor de bases de datos relacional se utiliza PosgrestDB\n- Para almacenar informaci\u00f3n no estructurada se utiliza DynamoDB\n- Para versionamiento de los dataset se utiliza [DVC]\n- Para autoformatting se utilizan los paquetes [`Back`](), [Flake8]() y [autopep8] () \n- Para pruebas unitarias se utiliza el paquete est\u00e1ndar de python `unittest` \n",
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