nvtabular.ops.ColumnSimilarity

class nvtabular.ops.ColumnSimilarity(left_features, right_features=None, metric='tfidf', on_device=True)

Bases: Operator

Calculates the similarity between two columns using tf-idf, cosine or inner product as the distance metric. For each row, this calculates the distance between the two columns by looking up features for those columns in a sparse matrix, and then computing the distance between the rows of the feature matrices.

Example usage:

# Read in the 'document_categories' file from the kaggle outbrains dataset and convert
# to a sparse matrix
df = cudf.read_csv("document_categories.csv.zip")
categories = cupyx.scipy.sparse.coo_matrix((cupy.ones(len(df)),
                                           (df.document_id.values, df.category_id.values))
# compute a new column 'document_id_document_id_promo_sim' between the document_id and
# document_id_promo columns on tfidf distance on the categories matrix we just loaded up
sim_features = [["document_id", "document_id_promo"]] >> ColumnSimilarity(categories,
                                                        metric='tfidf', on_device=False)
workflow = nvt.Workflow(sim_features)

Parameters:

• left_features (csr_matrix) – Sparse feature matrix for the left column

• right_features (csr_matrix, optional) – Sparse feature matrix for the right column in each pair. If not given will use the same feature matrix as for the left (for example when calculating document-document distances)

• on_device (bool) – Whether to compute on the GPU or CPU. Computing on the GPU will be faster, but requires that the left_features/right_features sparse matrices fit into GPU memory.

__init__(left_features, right_features=None, metric='tfidf', on_device=True)

Methods

__init__(left_features[, right_features, ...])
column_mapping(col_selector)
compute_column_schema(col_name, input_schema)
compute_input_schema(root_schema, ...)	Given the schemas coming from upstream sources and a column selector for the input columns, returns a set of schemas for the input columns this operator will use
compute_output_schema(input_schema, col_selector)	Given a set of schemas and a column selector for the input columns, returns a set of schemas for the transformed columns this operator will produce
compute_selector(input_schema, selector, ...)
create_node(selector)
export(path, input_schema, output_schema, ...)	Export the class object as a config and all related files to the user defined path.
load_artifacts([artifact_path])	Load artifacts from disk required for operator function.
output_column_names(col_selector)	Given a set of columns names returns the names of the transformed columns this operator will produce
save_artifacts([artifact_path])	Save artifacts required to be reload operator state from disk
transform(col_selector, df)	Transform the dataframe by applying this operator to the set of input columns
validate_schemas(parents_schema, ...[, ...])	Provides a hook method that sub-classes can override to implement schema validation logic.

Attributes

dependencies	Defines an optional list of column dependencies for this operator.
dynamic_dtypes
export_name	Provides a clear common english identifier for this operator.
is_subgraph
label
output_dtype
output_properties
output_tags
supported_formats
supports	Returns what kind of data representation this operator supports

transform(col_selector: ColumnSelector, df: DataFrame) → DataFrame

Transform the dataframe by applying this operator to the set of input columns

Parameters:

• col_selector (ColumnSelector) – The columns to apply this operator to

• transformable (Transformable) – A pandas or cudf dataframe that this operator will work on

Returns:

Returns a transformed dataframe or dictarray for this operator

Return type:

Transformable

compute_selector(input_schema: Schema, selector: ColumnSelector, parents_selector: ColumnSelector, dependencies_selector: ColumnSelector) → ColumnSelector

column_mapping(col_selector)

property output_tags

property output_dtype