Evaluating the Matching Output

Once you have predicted matches using ML-based matcher, then you would have to evaluate the matches. py_entitymatching supports eval_matches command for that purpose.

An example of using eval_matches command is shown below:

>>> H = em.extract_feat_vecs(G, feat_table=match_f, attrs_after='gold_labels')
>>> dt = em.DTMatcher()
>>> dt.fit(table=H, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'], target_attr='gold_labels')
>>> pred_table = dt.predict(table=H,  exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'],  append=True, target_attr='predicted_labels')
>>> eval_summary = em.eval_matches(pred_table, 'gold_labels', 'predicted_labels')

In the above, eval_summary is a dictionary containing accuracy numbers (such as precision, recall, F1, etc) and the list of false positives/negatives.

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