vastorbit.machine_learning.vast.decomposition.SVD.deployInverseSQL¶

SVD.deployInverseSQL(key_columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, exclude_columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None) → str¶

Returns the SQL code needed to deploy the inverse model.

Parameters:

key_columns (SQLColumns, optional) – Predictors used during the algorithm computation which will be deployed with the principal components.
exclude_columns (SQLColumns, optional) – Columns to exclude from the prediction.
X (SQLColumns, optional) – list of the columns used to deploy the inverse model. If empty, the model predictors are used.

Returns:

the SQL code needed to deploy the inverse model.

Return type:

str

Examples

We import vastorbit:

import vastorbit as vo

For this example, we will use a dummy dataset.

data = vo.VastFrame(
    {
        "values": [1, 1.01, 1.02, 1.05, 1.024],
    }
)

Let’s import the model:

from vastorbit.machine_learning.vast import Scaler

Then we can create the model:

model = Scaler(method = "zscore")

We can now fit the model:

model.fit(data)

To get the Model VAST Inverse SQL, use below:

model.deployInverseSQL()

Important

For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.