vastorbit.machine_learning.memmodel.linear_model.LinearModelClassifier¶

class vastorbit.machine_learning.memmodel.linear_model.LinearModelClassifier(coef: Annotated[list | ndarray, 'Array Like Structure'], intercept: float = 0.0)¶

InMemoryModel Implementation of linear algorithms for classification.

Parameters:

coefficients (ArrayLike) – ArrayLike of the model’s coefficients.
intercept (float, optional) – The intercept or constant value.

Variables:

input (Attributes are identical to the)
underscore (parameters, followed by an)
('_').

Examples

Initalization

Import the required module.

from vastorbit.machine_learning.memmodel.linear_model import LinearModelClassifier

A linear classifier model is defined by its coefficients and an intercept value. In this example, we will use the following:

coefficients = [0.5, 1.2]
intercept = 2.0

Let’s create a LinearModelClassifier.

model_lmc = LinearModelClassifier(coefficients, intercept)

Create a dataset.

data = [[1.0, 0.3], [-0.5, -0.8]]

Making In-Memory Predictions

Use predict() method to do predictions.

model_lmc.predict(data)

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

model_lmc.predict_proba(data)

Deploy SQL Code

Let’s use the following column names:

cnames = ['col1', 'col2']

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

model_lmc.predict_sql(cnames)

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

model_lmc.predict_proba_sql(cnames)

Hint

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

__init__(coef: Annotated[list | ndarray, 'Array Like Structure'], intercept: float = 0.0) → None¶

Methods

`__init__`(coef[, intercept])
`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 using its attributes.
`set_attributes`(**kwargs)	Sets the model attributes.

Attributes

object_type

Must be overridden in child class