Selecting a ML-Matcher

Once you have created different concrete ML matchers, then you have to choose one of them for matching purposes. There are many different criteria by which one can decide to choose a matcher such as akaike information criterion, bayesian information criterion, k-fold cross validation, etc. Currently py_entitymatching supports k-fold cross validation and other approaches are left for future work.

Conceptually, the command to select a matcher would take in the following inputs:

  • List of ML matchers.

  • Training data (feature vector).

  • A column of labels that correspond to the feature vectors in the training data.

  • Number of folds.

And it would produce the following output:

  • Selected matcher.

  • Statistics such as mean accuracy of all input matchers.

In py_entitymatching, select_matcher command addresses the above needs. An example of using select_matcher is shown below:

>>> dt = em.DTMatcher()
>>> rf = em.RFMatcher()
>>> result = em.select_matcher(matchers=[dt, rf], table=train, exclude_attrs=['_id', 'ltable_id', 'rtable_id'], target_attr='gold_labels', k=5)

In the above the output, result is a dictionary containing three keys: (1) selected_matcher, (2) cv_stats, and (3) drill_down_cv_stats. selected_matcher is the selected ML-based matcher, cv_stats is a Dataframe which includes the average cross validation scores for each matcher and for each metric, and ‘drill_down_cv_stats’ is a dictionary where each key is a metric that includes the cross validation statistics for each fold.

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