Source code for dedupe.convenience

# -*- coding: utf-8 -*-
from __future__ import annotations

import collections
import itertools
import random
import sys
import warnings
from typing import Iterator, Tuple

import numpy

import dedupe
from dedupe._typing import (
from dedupe.canonical import getCanonicalRep
from dedupe.core import unique

IndicesIterator = Iterator[Tuple[int, int]]

def randomPairs(n_records: int, sample_size: int) -> IndicesIterator:
    Return random combinations of indices for a square matrix of size n
    records. For a discussion of how this works see

    n: int = n_records * (n_records - 1) // 2

    if not sample_size:
        return iter([])
    elif sample_size >= n:
        random_pairs = numpy.arange(n)
            random_pairs = numpy.array(
                random.sample(range(n), sample_size), dtype=numpy.uint
        except OverflowError:
            return randomPairsWithReplacement(n_records, sample_size)

    b: int = 1 - 2 * n_records

    i = (-b - 2 * numpy.sqrt(2 * (n - random_pairs) + 0.25)) // 2
    i = i.astype(numpy.uint)

    j = random_pairs + i * (b + i + 2) // 2 + 1
    j = j.astype(numpy.uint)

    return zip(i, j)

def randomPairsMatch(
    n_records_A: int, n_records_B: int, sample_size: int
) -> IndicesIterator:
    Return random combinations of indices for record list A and B
    n: int = n_records_A * n_records_B

    if not sample_size:
        return iter([])
    elif sample_size >= n:
        random_pairs = numpy.arange(n)
        random_pairs = numpy.array(random.sample(range(n), sample_size))

    i, j = numpy.unravel_index(random_pairs, (n_records_A, n_records_B))

    return zip(i, j)

def randomPairsWithReplacement(n_records: int, sample_size: int) -> IndicesIterator:
    # If the population is very large relative to the sample
    # size than we'll get very few duplicates by chance
    warnings.warn("The same record pair may appear more than once in the sample")

        random_indices = numpy.random.randint(n_records, size=sample_size * 2)
    except (OverflowError, ValueError):
        max_int: int = numpy.iinfo("int").max
            "Asked to sample pairs from %d records, will only sample pairs from first %d records"
            % (n_records, max_int)

        random_indices = numpy.random.randint(max_int, size=sample_size * 2)

    random_indices = random_indices.reshape((-1, 2))

    return ((p.item(), q.item()) for p, q in random_indices)

def _print(*args) -> None:
    print(*args, file=sys.stderr)

LabeledPair = Tuple[RecordDictPair, Literal["match", "distinct", "unsure"]]

def _mark_pair(deduper: dedupe.api.ActiveMatching, labeled_pair: LabeledPair) -> None:
    record_pair, label = labeled_pair
    examples: TrainingData = {"distinct": [], "match": []}
    if label == "unsure":
        # See for reasoning
        # label is either "match" or "distinct"

[docs]def console_label(deduper: dedupe.api.ActiveMatching) -> None: # pragma: no cover """ Train a matcher instance (Dedupe, RecordLink, or Gazetteer) from the command line. Example .. code:: python > deduper = dedupe.Dedupe(variables) > deduper.prepare_training(data) > dedupe.console_label(deduper) """ finished = False use_previous = False fields = unique(var.field for var in deduper.data_model.primary_variables) buffer_len = 1 # Max number of previous operations unlabeled: list[RecordDictPair] = [] labeled: list[LabeledPair] = [] n_match = len(deduper.training_pairs["match"]) n_distinct = len(deduper.training_pairs["match"]) while not finished: if use_previous: record_pair, label = labeled.pop(0) if label == "match": n_match -= 1 elif label == "distinct": n_distinct -= 1 use_previous = False else: try: if not unlabeled: unlabeled = deduper.uncertain_pairs() record_pair = unlabeled.pop() except IndexError: break for record in record_pair: for field in fields: line = "%s : %s" % (field, record[field]) _print(line) _print() _print(f"{n_match}/10 positive, {n_distinct}/10 negative") _print("Do these records refer to the same thing?") valid_response = False user_input = "" while not valid_response: if labeled: _print("(y)es / (n)o / (u)nsure / (f)inished / (p)revious") valid_responses = {"y", "n", "u", "f", "p"} else: _print("(y)es / (n)o / (u)nsure / (f)inished") valid_responses = {"y", "n", "u", "f"} user_input = input() if user_input in valid_responses: valid_response = True if user_input == "y": labeled.insert(0, (record_pair, "match")) n_match += 1 elif user_input == "n": labeled.insert(0, (record_pair, "distinct")) n_distinct += 1 elif user_input == "u": labeled.insert(0, (record_pair, "unsure")) elif user_input == "f": _print("Finished labeling") finished = True elif user_input == "p": use_previous = True unlabeled.append(record_pair) while len(labeled) > buffer_len: _mark_pair(deduper, labeled.pop()) for labeled_pair in labeled: _mark_pair(deduper, labeled_pair)
[docs]def training_data_dedupe( data: Data, common_key: str, training_size: int = 50000 ) -> TrainingData: # pragma: nocover """ Construct training data for consumption by the func:`mark_pairs` method from an already deduplicated dataset. Args: data: Dictionary of records where the keys are record_ids and the values are dictionaries with the keys being field names common_key: The name of the record field that uniquely identifies a match training_size: the rough limit of the number of training examples, defaults to 50000 .. note:: Every match must be identified by the sharing of a common key. This function assumes that if two records do not share a common key then they are distinct records. """ identified_records: dict[str, list[RecordID]] identified_records = collections.defaultdict(list) matched_pairs: set[tuple[RecordID, RecordID]] = set() distinct_pairs: set[tuple[RecordID, RecordID]] = set() unique_record_ids: set[RecordID] = set() # a list of record_ids associated with each common_key for record_id, record in data.items(): unique_record_ids.add(record_id) identified_records[record[common_key]].append(record_id) # all combinations of matched_pairs from each common_key group for record_ids in identified_records.values(): if len(record_ids) > 1: matched_pairs.update(itertools.combinations(sorted(record_ids), 2)) # calculate indices using dedupe.core.randomPairs to avoid # the memory cost of enumerating all possible pairs unique_record_ids_l = list(unique_record_ids) pair_indices = randomPairs(len(unique_record_ids), training_size) distinct_pairs = set() for i, j in pair_indices: distinct_pairs.add((unique_record_ids_l[i], unique_record_ids_l[j])) distinct_pairs -= matched_pairs matched_records = [(data[key_1], data[key_2]) for key_1, key_2 in matched_pairs] distinct_records = [(data[key_1], data[key_2]) for key_1, key_2 in distinct_pairs] training_pairs: TrainingData training_pairs = {"match": matched_records, "distinct": distinct_records} return training_pairs
[docs]def canonicalize(record_cluster: list[RecordDict]) -> RecordDict: # pragma: nocover """ Constructs a canonical representation of a duplicate cluster by finding canonical values for each field Args: record_cluster: A list of records within a duplicate cluster, where the records are dictionaries with field names as keys and field values as values """ return getCanonicalRep(record_cluster)