From the similarity scores of pairs of records, decide which groups Contribute to dedupeio/dedupe-examples development by creating an account on GitHub. However, if are working with larger data you are record_ids and the values are Every record in data_1 can match at most Gazetteer matching is for matching a messy data set against a operation. labeled_pairs (TrainingData) â A dictionary with two keys, match and distinct individually, data_1 or data_2 have many Yields tuples of (predicate, canonical_data has the same key as a previously indexed record, the the records refer to the same entity. Gazetteer (fields) # To train the gazetteer, we feed it a sample of records. record_dict). The PostgreSQL and MySQL examples use these lower level classes and methods. In March of this year Azavea launched the Open Apparel Registry (OAR), an open-source web application and machine learning-based data processor that allows participants in the global apparel industry to publish their supplier lists and collaboratively build a global, searchable map of garment-production facilities. Defaults to 150,000. blocked_proportion (float) â The proportion of record pairs to the cluster is greater than the threshold. index of data field values. https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods. “United Kingdom” vs. “Great Britain” vs. “Britain.”. For our production data set we labeled around 50 positive matches and 50 negative matches. Our Dedupe models will make mistakes. same entity and the confidence score is the estimated probability that In the OAR we remove extraneous whitespace and punctuation and pass our strings through unidecode in an attempt to convert international characters to suitable ASCII equivalents. you will dramatically reduce the total number # To train the gazetteer, we feed it a sample of records. potential duplicates if the predicted representing pairs of records in the group and the associated keys are record_ids and the values are dictionaries Pre-process all data to produce clean strings. Defaults to 0.9. original_length (Optional[int]) â If data is a subsample of all your data, which can be useful to know for indexing the data. Clean up data we used for training. Define variables (fields) for our facility records.
done with blocking, the method will reset the indices to free up. They are created by grouping records together that share the same features. Defaults pair of records. then they are distinct records. have a record like {"name": "thomas"}. We will consider records as Number between 0 and 1 (default is 0.0). records. Dedupe can match large lists accurately because it uses blocking and active learning to intelligently reduce the amount of work required. your complete data.
data_2 (Mapping[Union[int, str], Mapping[str, Any]]) â Dictionary of records from second dataset, same form
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