verticapy.machine_learning.vertica.feature_extraction.text.TfidfVectorizer.transform¶

TfidfVectorizer.transform(vdf: Annotated[str | vDataFrame, ''], index: str, x: str, pivot: bool = False) → vDataFrame¶

Transforms input data to tf-idf representation.

Parameters¶

vdf: SQLRelation: Object used to run the prediction. You can also specify a customized relation, but you must enclose it with an alias. For example, (SELECT 1) x is valid, whereas (SELECT 1) and “SELECT 1” are invalid.
index: str: Column name of the document id.
x: str: Column name which contains the text.
pivot: str: If set to True, the final table will be pivoted to have one row per document, resulting in a sparse matrix. It’s important to note that when dealing with a large dictionary, the pivot operation can be resource-intensive. In such cases, it might be more efficient to set pivot to False, filter the output as needed, and then manually perform the pivot operation.