| Name | sbr-model JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | Un regresor de Machine Learning que implementa un ensamblaje de Stacking con modelos de Boosting. |
| upload_time | 2025-09-18 11:32:18 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Pablo Eduardo Chavez Mercado
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
stacking
boosting
regressor
ensemble
xgboost
machine-learning
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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| coveralls test coverage |
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|
# sbr-model 📈
[](https://badge.fury.io/py/sbr-model)
[](https://opensource.org/licenses/MIT)
Un regresor de Machine Learning fácil de usar que implementa un ensamblaje de Stacking con modelos de Boosting (XGBoost, LightGBM, CatBoost).
## ¿Qué es sbr-model? 🤔
`sbr-model` es una librerÃa de alto nivel diseñada para simplificar el proceso de creación de modelos de ensamblaje robustos para tareas de **regresión**. En lugar de configurar manualmente la validación cruzada y el meta-modelo, `sbr-model` lo encapsula en una sola clase, compatible con scikit-learn.
El nombre **SBR** significa **S**tacking **B**oosting **R**egressor.
---
## CaracterÃsticas Principales ✨
* **Modelos Potentes:** Utiliza XGBoost, LightGBM y CatBoost como modelos base, tres de los algoritmos más potentes para datos tabulares.
* **Stacking Automatizado:** Gestiona automáticamente el proceso de validación cruzada para generar predicciones "out-of-fold" y entrenar un meta-modelo.
* **Fácil de Usar:** Interfaz simple inspirada en scikit-learn. Solo necesitas instanciar la clase y llamar a `.fit()` y `.predict()`.
* **Compatible:** Al heredar de `RegressorMixin`, se integra con el ecosistema de scikit-learn.
---
## Instalación 📦
Puedes instalar `sbr-model` directamente desde PyPI:
```bash
pip install sbr-model
import numpy as np
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import xgboost as xgb
# Importamos tu clase desde la librerÃa instalada
from sbr_model import StackingRegressor
# 1. Crear datos de ejemplo para un problema de regresión
# Esto nos permite probar el modelo sin necesidad de un archivo CSV
X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=42)
X_test, _ = make_regression(n_samples=500, n_features=20, n_informative=15, noise=0.1, random_state=2025)
# 2. Definir los "ingredientes": modelos base y meta-modelo
base_models = [
('xgb', xgb.XGBRegressor(random_state=42)),
# Aquà podrÃas añadir más modelos, como LGBMRegressor o CatBoostRegressor
]
meta_model = LinearRegression()
# 3. Instanciar y entrenar el modelo
sbr = StackingRegressor(base_models=base_models, meta_model=meta_model, n_folds=5)
sbr.fit(X, y)
# 4. Hacer predicciones
predictions = sbr.predict(X_test)
# 5. Ver los resultados
print("Primeras 10 predicciones generadas por sbr-model:")
print(predictions[:10])
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"description": "# sbr-model \ud83d\udcc8\r\n\r\n[](https://badge.fury.io/py/sbr-model)\r\n[](https://opensource.org/licenses/MIT)\r\n\r\nUn regresor de Machine Learning f\u00e1cil de usar que implementa un ensamblaje de Stacking con modelos de Boosting (XGBoost, LightGBM, CatBoost).\r\n\r\n## \u00bfQu\u00e9 es sbr-model? \ud83e\udd14\r\n\r\n`sbr-model` es una librer\u00eda de alto nivel dise\u00f1ada para simplificar el proceso de creaci\u00f3n de modelos de ensamblaje robustos para tareas de **regresi\u00f3n**. En lugar de configurar manualmente la validaci\u00f3n cruzada y el meta-modelo, `sbr-model` lo encapsula en una sola clase, compatible con scikit-learn.\r\n\r\nEl nombre **SBR** significa **S**tacking **B**oosting **R**egressor.\r\n\r\n---\r\n\r\n## Caracter\u00edsticas Principales \u2728\r\n\r\n* **Modelos Potentes:** Utiliza XGBoost, LightGBM y CatBoost como modelos base, tres de los algoritmos m\u00e1s potentes para datos tabulares.\r\n* **Stacking Automatizado:** Gestiona autom\u00e1ticamente el proceso de validaci\u00f3n cruzada para generar predicciones \"out-of-fold\" y entrenar un meta-modelo.\r\n* **F\u00e1cil de Usar:** Interfaz simple inspirada en scikit-learn. Solo necesitas instanciar la clase y llamar a `.fit()` y `.predict()`.\r\n* **Compatible:** Al heredar de `RegressorMixin`, se integra con el ecosistema de scikit-learn.\r\n\r\n---\r\n\r\n## Instalaci\u00f3n \ud83d\udce6\r\n\r\nPuedes instalar `sbr-model` directamente desde PyPI:\r\n\r\n```bash\r\npip install sbr-model\r\n\r\nimport numpy as np\r\nfrom sklearn.datasets import make_regression\r\nfrom sklearn.linear_model import LinearRegression\r\nimport xgboost as xgb\r\n\r\n# Importamos tu clase desde la librer\u00eda instalada\r\nfrom sbr_model import StackingRegressor\r\n\r\n# 1. Crear datos de ejemplo para un problema de regresi\u00f3n\r\n# Esto nos permite probar el modelo sin necesidad de un archivo CSV\r\nX, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=42)\r\nX_test, _ = make_regression(n_samples=500, n_features=20, n_informative=15, noise=0.1, random_state=2025)\r\n\r\n# 2. Definir los \"ingredientes\": modelos base y meta-modelo\r\nbase_models = [\r\n ('xgb', xgb.XGBRegressor(random_state=42)),\r\n # Aqu\u00ed podr\u00edas a\u00f1adir m\u00e1s modelos, como LGBMRegressor o CatBoostRegressor\r\n]\r\nmeta_model = LinearRegression()\r\n\r\n# 3. Instanciar y entrenar el modelo\r\nsbr = StackingRegressor(base_models=base_models, meta_model=meta_model, n_folds=5)\r\nsbr.fit(X, y)\r\n\r\n# 4. Hacer predicciones\r\npredictions = sbr.predict(X_test)\r\n\r\n# 5. Ver los resultados\r\nprint(\"Primeras 10 predicciones generadas por sbr-model:\")\r\nprint(predictions[:10])\r\n",
"bugtrack_url": null,
"license": "MIT License\r\n \r\n Copyright (c) 2025 Pablo Eduardo Chavez Mercado\r\n \r\n Permission is hereby granted, free of charge, to any person obtaining a copy\r\n of this software and associated documentation files (the \"Software\"), to deal\r\n in the Software without restriction, including without limitation the rights\r\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n copies of the Software, and to permit persons to whom the Software is\r\n furnished to do so, subject to the following conditions:\r\n \r\n The above copyright notice and this permission notice shall be included in all\r\n copies or substantial portions of the Software.\r\n \r\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\n SOFTWARE.",
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