verticapy.machine_learning.vertica.preprocessing.MinMaxScaler.deployInverseSQL¶

MinMaxScaler.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¶

str: the SQL code needed to deploy the inverse model.

Examples¶

We import verticapy:

import verticapy as vp

For this example, we will use a dummy dataset.

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

Let’s import the model:

from verticapy.machine_learning.vertica import Scaler

Then we can create the model:

model = Scaler(method = "zscore")

We can now fit the model:

model.fit(data)


=======
details
=======
column_name|  avg   |std_dev 
-----------+--------+--------
  values   | 1.02080| 0.01879

To get the Model Vertica Inverse SQL, use below:

model.deployInverseSQL()
Out[48]: 'REVERSE_NORMALIZE("values" USING PARAMETERS model_name = \'"public"."_verticapy_tmp_scaler_v_demo_b6d1baa455a411ef880f0242ac120002_"\', match_by_pos = \'true\')'

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.