# Source code for dedupe.blocking

#!/usr/bin/python
# -*- coding: utf-8 -*-

from collections import defaultdict
import logging
import time

from typing import Generator, Tuple, Iterable, Dict, List, Union
from dedupe._typing import Record, RecordID, Data

import dedupe.predicates

logger = logging.getLogger(__name__)

Docs = Union[Iterable[str], Iterable[Iterable[str]]]

def index_list():
return defaultdict(list)

[docs]class Fingerprinter(object): '''Takes in a record and returns all blocks that record belongs to''' def __init__(self, predicates: Iterable[dedupe.predicates.Predicate]) -> None: self.predicates = predicates self.index_fields: Dict[str, Dict[str, List[dedupe.predicates.IndexPredicate]]] self.index_fields = defaultdict(index_list) ''' A dictionary of all the fingerprinter methods that use an index of data field values. The keys are the field names, which can be useful to know for indexing the data. ''' self.index_predicates = [] for full_predicate in predicates: for predicate in full_predicate: if hasattr(predicate, 'index'): self.index_fields[predicate.field][predicate.type].append( predicate) self.index_predicates.append(predicate)
[docs] def __call__(self, records: Iterable[Record], target: bool = False) -> Generator[Tuple[str, RecordID], None, None]: ''' Generate the predicates for records. Yields tuples of (predicate, record_id). Args: records: A sequence of tuples of (record_id, record_dict). Can often be created by data_dict.items(). target: Indicates whether the data should be treated as the target data. This effects the behavior of search predicates. If target is set to True, an search predicate will return the value itself. If target is set to False the search predicate will return all possible values within the specified search distance. Let's say we have a LevenshteinSearchPredicate with an associated distance of 1 on a "name" field; and we have a record like {"name": "thomas"}. If the target is set to True then the predicate will return "thomas". If target is set to False, then the blocker could return "thomas", "tomas", and "thoms". By using the target argument on one of your datasets, you will dramatically reduce the total number of comparisons without a loss of accuracy. .. code:: python > data = [(1, {'name' : 'bob'}), (2, {'name' : 'suzanne'})] > blocked_ids = deduper.fingerprinter(data) > print list(blocked_ids) [('foo:1', 1), ..., ('bar:1', 100)] ''' start_time = time.perf_counter() predicates = [(':' + str(i), predicate) for i, predicate in enumerate(self.predicates)] for i, record in enumerate(records): record_id, instance = record for pred_id, predicate in predicates: block_keys = predicate(instance, target=target) for block_key in block_keys: yield block_key + pred_id, record_id if i and i % 10000 == 0: logger.info('%(iteration)d, %(elapsed)f2 seconds', {'iteration': i, 'elapsed': time.perf_counter() - start_time})
[docs] def reset_indices(self) -> None: ''' Fingeprinter indicdes can take up a lot of memory. If you are done with blocking, the method will reset the indices to free up. If you need to block again, the data will need to be re-indexed. ''' for predicate in self.index_predicates: predicate.reset()
[docs] def index(self, docs: Docs, field: str) -> None: ''' Add docs to the indices used by fingerprinters. Some fingerprinter methods depend upon having an index of values that a field may have in the data. This method adds those values to the index. If you don't have any fingerprinter methods that use an index, this method will do nothing. Args: docs: an iterator of values from your data to index. While not required, it is recommended that docs be a unique set of of those values. Indexing can be an expensive operation. field: fieldname or key associated with the values you are indexing ''' indices = extractIndices(self.index_fields[field]) for doc in docs: if doc: for _, index, preprocess in indices: index.index(preprocess(doc)) for index_type, index, _ in indices: index.initSearch() for predicate in self.index_fields[field][index_type]: logger.debug("Canopy: %s", str(predicate)) predicate.index = index predicate.bust_cache()
[docs] def unindex(self, docs: Docs, field: str) -> None: '''Remove docs from indices used by fingerprinters Args: docs: an iterator of values from your data to remove. While not required, it is recommended that docs be a unique set of of those values. Indexing can be an expensive operation. field: fieldname or key associated with the values you are unindexing ''' indices = extractIndices(self.index_fields[field]) for doc in docs: if doc: for _, index, preprocess in indices: try: index.unindex(preprocess(doc)) except KeyError: pass for index_type, index, _ in indices: index._index.initSearch() for predicate in self.index_fields[field][index_type]: logger.debug("Canopy: %s", str(predicate)) predicate.index = index predicate.bust_cache()
def index_all(self, data: Data): for field in self.index_fields: unique_fields = {record[field] for record in data.values() if record[field]} self.index(unique_fields, field)
def extractIndices(index_fields): indices = [] for index_type, predicates in index_fields.items(): predicate = predicates[0] index = predicate.index preprocess = predicate.preprocess if predicate.index is None: index = predicate.initIndex() indices.append((index_type, index, preprocess)) return indices