vastorbit.machine_learning.memmodel.naive_bayes.NaiveBayes.predict_proba_sql

NaiveBayes.predict_proba_sql(X: Annotated[list | ndarray, 'Array Like Structure']) → list[str]

Returns the SQL code needed to deploy the model probabilities using its attributes.

Parameters:

X (ArrayLike) – The names or values of the input predictors.

Returns:

SQL code.

Return type:

list

Examples

Import the required module.

from vastorbit.machine_learning.memmodel.naive_bayes import NaiveBayes

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 model.

model_nb = NaiveBayes(attributes, prior, classes)

Let’s use the following column names:

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

Get the SQL code needed to deploy the model.

model_nb.predict_proba_sql(cnames)

Note

Refer to NaiveBayes for more information about the different methods and usages.