gbnnetwork


Namegbnnetwork JSON
Version 1.0.0 PyPI version JSON
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SummaryImplementation of Bayesian network.
upload_time2023-02-09 20:39:43
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authorGuillermo
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keywords python probability bayes
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# BNNetwork Library
Esta librería permite crear redes Bayesianas a partir de la probabilidad condicional de cada nodo.

 ## Instalar
`pip install gbnnetwork`

## Uso
Para crear una Red de Inferencia, se deben de seguir los siguientes pasos:
1. Crear una instancia de la clase BNNetwork `network = BNetwork()`
2. Agregar variables a la red `network.add_variable("A")`
3. Asignar los padres de cada variable (en caso la variable no tenga padres este paso se puede omitir) `network.set_parents_to_variable("A", ["B", "E"])`
4. Agregar las probabilidades a las variables para definir la red totalmente. **Toda la red debe de estar definida si se desea responder cualquier consulta** `network.add_probability("A|BE", 0.001)`
5. Hacer una query `p = network.inference({'B': False}, {'A': False})`

## Funciones De Utilidad
1. Obtener la representación compacta `network.compact_string()` (string)
2. Obtener los factores de la red `network.factor_string()` (string)
3. Saber si la red esta totalmente definida `network.validate_defined_state()` (boolean)

## Ejemplo
Tomando como referencia el siguiente documento: https://people.cs.pitt.edu/~milos/courses/cs2740/Lectures/class19.pdf

La red que se propone tiene la siguiente topología:
![Example Network](example_network.png)

Se procede de la siguiente manera:

```python
from gbnnetwork import BNetwork
network = BNetwork()    # Paso 1

# Paso 2
network.add_variable("B")
network.add_variable("E")
network.add_variable("A")
network.add_variable("J")
network.add_variable("M")

# Paso 3
network.set_parents_to_variable("A", ["B", "E"])
network.set_parents_to_variable("J", ["A"])
network.set_parents_to_variable("M", ["A"])

# Paso 4
network.add_probability("B", 0.001)
network.add_probability("E", 0.002)
network.add_probability("A|BE", 0.95)
network.add_probability("A|-BE", 0.29)
network.add_probability("A|B-E", 0.94)
network.add_probability("A|-B-E", 0.001)
network.add_probability("M|A", 0.7)
network.add_probability("M|-A", 0.01)
network.add_probability("J|A", 0.9)
network.add_probability("J|-A", 0.05)

# Hacer la query P(B=True|J=True,M=True)
p = network.inference({'B': True}, {'J': True, 'M': True})  # 0.2841718353643929 = 28%

```

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    "description": "\n# BNNetwork Library\nEsta librer\u00eda permite crear redes Bayesianas a partir de la probabilidad condicional de cada nodo.\n\n ## Instalar\n`pip install gbnnetwork`\n\n## Uso\nPara crear una Red de Inferencia, se deben de seguir los siguientes pasos:\n1. Crear una instancia de la clase BNNetwork `network = BNetwork()`\n2. Agregar variables a la red `network.add_variable(\"A\")`\n3. Asignar los padres de cada variable (en caso la variable no tenga padres este paso se puede omitir) `network.set_parents_to_variable(\"A\", [\"B\", \"E\"])`\n4. Agregar las probabilidades a las variables para definir la red totalmente. **Toda la red debe de estar definida si se desea responder cualquier consulta** `network.add_probability(\"A|BE\", 0.001)`\n5. Hacer una query `p = network.inference({'B': False}, {'A': False})`\n\n## Funciones De Utilidad\n1. Obtener la representaci\u00f3n compacta `network.compact_string()` (string)\n2. Obtener los factores de la red `network.factor_string()` (string)\n3. Saber si la red esta totalmente definida `network.validate_defined_state()` (boolean)\n\n## Ejemplo\nTomando como referencia el siguiente documento: https://people.cs.pitt.edu/~milos/courses/cs2740/Lectures/class19.pdf\n\nLa red que se propone tiene la siguiente topolog\u00eda:\n![Example Network](example_network.png)\n\nSe procede de la siguiente manera:\n\n```python\nfrom gbnnetwork import BNetwork\nnetwork = BNetwork()    # Paso 1\n\n# Paso 2\nnetwork.add_variable(\"B\")\nnetwork.add_variable(\"E\")\nnetwork.add_variable(\"A\")\nnetwork.add_variable(\"J\")\nnetwork.add_variable(\"M\")\n\n# Paso 3\nnetwork.set_parents_to_variable(\"A\", [\"B\", \"E\"])\nnetwork.set_parents_to_variable(\"J\", [\"A\"])\nnetwork.set_parents_to_variable(\"M\", [\"A\"])\n\n# Paso 4\nnetwork.add_probability(\"B\", 0.001)\nnetwork.add_probability(\"E\", 0.002)\nnetwork.add_probability(\"A|BE\", 0.95)\nnetwork.add_probability(\"A|-BE\", 0.29)\nnetwork.add_probability(\"A|B-E\", 0.94)\nnetwork.add_probability(\"A|-B-E\", 0.001)\nnetwork.add_probability(\"M|A\", 0.7)\nnetwork.add_probability(\"M|-A\", 0.01)\nnetwork.add_probability(\"J|A\", 0.9)\nnetwork.add_probability(\"J|-A\", 0.05)\n\n# Hacer la query P(B=True|J=True,M=True)\np = network.inference({'B': True}, {'J': True, 'M': True})  # 0.2841718353643929 = 28%\n\n```\n",
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Guillermo
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