Extracting Feature Vectors

Once you have created a set of features, you use them to convert labeled sample to feature vectors. In py_entitymatching, you can use extract_feature_vecs to convert labeled sample to feature vectors using the features created (see section Creating Features for Matching).

An example of using extract_feature_vecs is shown below:

>>> H = em.extract_feature_vecs(G, feature_table=match_f, attrs_before=['title'], attrs_after=['gold_labels'])

Conceptually, the command takes the labeled data (G), applies the feature functions (in match_f) to each tuple in G to create a Dataframe, adds the attrs_before and attrs_after columns, updates the metadata and returns the resulting Dataframe.

If there is one (or several columns) in labeled data that contains the labels, then those need to be explicitly specified in attrs_after, if you want them them to copy over.

Please refer to the API reference of extract_feature_vecs() for more details.