vastorbit.machine_learning.memmodel.naive_bayes.NaiveBayes

class vastorbit.machine_learning.memmodel.naive_bayes.NaiveBayes(attributes: list[dict], prior: Annotated[list | ndarray, 'Array Like Structure'], classes: Annotated[list | ndarray, 'Array Like Structure'])

InMemoryModel implementation of the NaiveBayes algorithm.

Parameters:

• attributes (list) –

  List of the model’s attributes. Each feature must be represented by a dictionary, which differs based on the distribution.

  • For ‘gaussian’:

    Key “type” must have ‘gaussian’ as value. Each of the model’s classes must include a dictionary with two keys:

    sigma_sq:

    Square root of the standard deviation.

    mu:

    Average.

    Example:

    {
        'type': 'gaussian',
        'C': {
            'mu': 63.9878308300395,
            'sigma_sq': 7281.87598377196
        },
        'Q': {
            'mu': 13.0217386792453,
            'sigma_sq': 211.626862330204
        },
        'S': {
            'mu': 27.6928120412844,
            'sigma_sq': 1428.57067393938,
        },
    }
    

  • For ‘multinomial’:

    Key “type” must have ‘multinomial’ as value. Each of the model’s classes must be represented by a key with its probability as the value.

    Example:

    {
        'type': 'multinomial',
        'C': 0.771666666666667,
        'Q': 0.910714285714286,
        'S': 0.878216123499142,
    }
    

  • For ‘bernoulli’:

    Key “type” must have ‘bernoulli’ as value. Each of the model’s classes must be represented by a key with its probability as the value.

    Example:

    {
        'type': 'bernoulli',
        'C': 0.537254901960784,
        'Q': 0.277777777777778,
        'S': 0.324942791762014,
    }
    

  • For ‘categorical’:

    Key “type” must have ‘categorical’ as value. Each of the model’s classes must include a dictionary with all the feature categories.

    Example:

    {
        'type': 'categorical',
        'C': {
            'female': 0.407843137254902,
            'male': 0.592156862745098
        },
        'Q': {
            'female': 0.416666666666667,
            'male': 0.583333333333333,
        },
        'S': {
            'female': 0.311212814645309,
            'male': 0.688787185354691,
        },
    }
    

  prior: ArrayLike

  The model’s classes probabilities.

  classes: ArrayLike

  The model’s classes.

• note:: (..) – memmodel() are defined entirely by their attributes. For example, prior probabilities, classes and input feature attributes specific to the type of distribution, defines a NaiveBayes model.

Variables:

• input (Attributes are identical to the)

• underscore (parameters, followed by an)

• ('_').

Examples

Initalization

Import the required module.

from vastorbit.machine_learning.memmodel.naive_bayes import NaiveBayes

Here we will be using attributes of model trained on well known titanic dataset.

It tries to predict the port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton), using age (continous), pclass (discrete), survived (boolean) and sex (categorical) as input features.

Let’s define attributes representing

each input feature:

attributes = [
    {
        "type": "gaussian",
        "C": {"mu": 63.9878308300395, "sigma_sq": 7281.87598377196},
        "Q": {"mu": 13.0217386792453, "sigma_sq": 211.626862330204},
        "S": {"mu": 27.6928120412844, "sigma_sq": 1428.57067393938},
    },
    {
        "type": "multinomial",
        "C": 0.771666666666667,
        "Q": 0.910714285714286,
        "S": 0.878216123499142,
    },
    {
        "type": "bernoulli",
        "C": 0.771666666666667,
        "Q": 0.910714285714286,
        "S": 0.878216123499142,
    },
    {
        "type": "categorical",
        "C": {
            "female": 0.407843137254902,
            "male": 0.592156862745098,
        },
        "Q": {
            "female": 0.416666666666667,
            "male": 0.583333333333333,
        },
        "S": {
            "female": 0.406666666666667,
            "male": 0.593333333333333,
        },
    },
]

We also need to provide class names and their prior probabilities.

prior = [0.8, 0.1, 0.1]
classes = ["C", "Q", "S"]

Let’s create a NaiveBayes model.

model_nb = NaiveBayes(attributes, prior, classes)

Create a dataset.

data = [
    [40.0, 1, True, "male"],
    [60.0, 3, True, "male"],
    [15.0, 2, False, "female"],
]

Making In-Memory Predictions

Use predict() method to do predictions.

model_nb.predict(data)

Use predict_proba() method to calculate the predicted probabilities for each class.

model_nb.predict_proba(data)

Deploy SQL Code

Let’s use the following column names:

cnames = ["age", "pclass", "survived", "sex"]

Use predict_sql() method to get the SQL code needed to deploy the model using its attributes.

model_nb.predict_sql(cnames)

Use predict_proba_sql() method to get the SQL code needed to deploy the model that computes predicted probabilities.

model_nb.predict_proba_sql(cnames)

Hint

This object can be pickled and used in any in-memory environment, just like scikit-learn models.

__init__(attributes: list[dict], prior: Annotated[list | ndarray, 'Array Like Structure'], classes: Annotated[list | ndarray, 'Array Like Structure']) → None

Methods

__init__(attributes, prior, classes)
get_attributes()	Returns the model attributes.
predict(X)	Predicts using the input matrix.
predict_proba(X)	Computes the model's probabilites using the input matrix.
predict_proba_sql(X)	Returns the SQL code needed to deploy the model probabilities using its attributes.
predict_sql(X)	Returns the SQL code needed to deploy the model.
set_attributes(**kwargs)	Sets the model attributes.

Attributes

object_type

Must be overridden in child class