User Conversation Utilities¶
Various helpers regarding aggregated user,conversation-level attributes.
Example usage: user conversation attributes
-
class
convokit.user_convo_helpers.user_convo_attrs.UserConvoAttrs(attr_name, output_field=None, agg_fn=None, recompute=False)¶ Transformer that aggregates statistics per (user, convo). e.g., average wordcount of all utterances that user contributed per convo. Assumes that corpus.organize_user_convo_history has already been called.
- Parameters
attr_name – name of attribute to aggregate over. note that this attribute must already exist as an annotation to utterances in the corpus.
output_field – name of the aggregated attribute to output. defaults to attr_name.
agg_fn – function to aggregate utterance-level attribute with. defaults to returning a list.
recompute – if False, will not recompute the aggregate if output_field already exists for a user convo entry.
-
class
convokit.user_convo_helpers.user_convo_lifestage.UserConvoLifestage(lifestage_size, output_field='lifestage')¶ Transformer that, for each user in a conversation, computes the lifestage of the user in that conversation. For instance, if lifestages are 20 conversations long, then the first 20 conversations a user participates in will be in lifestage 0, and the second 20 will be in lifestage 1.
Assumes that corpus.organize_user_convo_history has already been called.
- Parameters
lifestage_size – size of the lifestage
output_field – name of user conversation attribute to output, defaults to “lifestage”
-
transform(corpus)¶ Modify the provided corpus. This is an abstract method that must be implemented by any Transformer subclass
- Parameters
corpus – the Corpus to transform
- Returns
modified version of the input Corpus. Note that unlike the scikit-learn equivalent,
transform()operates inplace on the Corpus (though for convenience and compatibility with scikit-learn, it also returns the modified Corpus).