SVD¶

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SVD(name: str,
    n_components: int = 0,
    method: str = "lapack")

Creates a SVD (Singular Value Decomposition) object using the Vertica SVD function.

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the database.
n_components	int	✓	The number of components to keep in the model. If this value is not provided, all components are kept. The maximum number of components is the number of non-zero singular values returned by the internal call to SVD. This number is less than or equal to SVD (number of columns, number of rows).
method	str	✓	The method to use to calculate PCA. method : Lapack definition.

After the object is created, all parameters become attributes. Additional attributes will be created when fitting the model:

Name	Type	Description
singular_values_	tablesample	The singular values.
explained_variance_	tablesample	The singular values explained variance.
input_relation	str	Training relation.
X	list	List of the predictors.

Name	Description
deploySQL	Returns the SQL code needed to deploy the model.
deployInverseSQL	Returns the SQL code needed to deploy the inverse model (SVD ** -1).
drop	Drops the model from the Vertica DB.
fit	Trains the model.
get_attr	Returns the model attribute.
get_params	Returns the model Parameters.
inverse_transform	Applies the inverse model on a vDataFrame.
plot	Draws a decomposition scatter plot.
plot_circle	Draws a decomposition circle.
plot_scree	Draws a decomposition scree plot.
score	Returns the decomposition Score on a dataset for each trasformed column.
set_params	Sets the parameters of the model.
to_memmodel	Converts a specified Vertica model to a memModel model.
to_python	Returns the Python code needed to deploy the model without using built-in Vertica functions.
to_sql	Returns the SQL code needed to deploy the model without using Vertica built-in functions.
transform	Applies the model on a vDataFrame.

In [37]:

from verticapy.learn.decomposition import SVD
model = SVD(name = "public.svd_iris")
display(model)

<SVD>