duplicate-recognition 0.0.1

This Project uses the calculation of similarities scores of a set of entities in an edge list. To allow for versatile usage, it uses dependency injection to implement it into any application.

Duplicate-Recognition

This Project uses the calculation of similarities scores of a set of entities in an edge list. To allow for versatile usage, it uses dependency injection to implement it into any application.

Usage

You need to implement all the read/write methods, to keep the project versatile.

For an example on how to use it, see the example, or the following code block:

"""
This is an example implementation of the DuplicateRecognition class.
It won't work. It's just to show, how it could be used.
"""

import logging
import os
from collections import defaultdict
from typing import Dict, Set
from typing import Generator, Tuple, Any
from itertools import chain, islice

from mysql.connector import connect

from duplicate_recognition import DuplicateRecognition, Algorithm, Comparison

logging.basicConfig(level=logging.DEBUG)


def chunks(iterable, size=1000):
    # https://stackoverflow.com/a/24527424
    iterator = iter(iterable)
    for first in iterator:
        yield chain([first], islice(iterator, size - 1))


class Entity(DuplicateRecognition):
    ID_COLUMN: str = "id"
    F_SCORES: Dict[str, float] = defaultdict(lambda: 0, {
        "id": DuplicateRecognition.F_SCORE_FOR_EXACT_MATCH,
        "company": 1,
        "postal_code": 1,
        "country": 0.5,
    })
    MATCHING_ALGORITHM: Dict[str, Algorithm] = defaultdict(lambda: Algorithm.EQUALITY, {
        "id": Algorithm.EQUALITY,
        "company": Algorithm.PHONETIC_DISTANCE,
        "postal_code": Algorithm.EQUALITY,
        "country": Algorithm.COUNTRY,
    })
    THRESHOLDS: Dict[str, float] = defaultdict(lambda: 0, {
        "country": 1,
    })
    NEGATIVE_FIELDS: Set[str] = {"country"}

    def __init__(self):
        self.connection = connect(
            host=os.getenv("MYSQL_HOST"),
            port=os.getenv("MYSQL_PORT"),
            user=os.getenv("MYSQL_USER"),
            password=os.getenv("MYSQL_PASSWORD"),
            database="foo",
        )
        super().__init__()

    def get_relevant_entities(self) -> Generator[Dict[str, Any], None, None]:
        cursor = self.connection.cursor(dictionary=True)

        cursor.execute("""
            SELECT DISTINCT * FROM entity    
            ORDER BY entity.id ASC
            """)
        return cursor

    def get_refresh_pairs(self) -> Generator[Tuple[int, int], None, None]:
        cursor = self.connection.cursor(buffered=True)
        cursor.execute("""
            SELECT entity_edge_list.a, entity_edge_list.b
            FROM entity_edge_list
            
            INNER JOIN entity
                ON entity.id = entity_edge_list.a OR entity.id = entity_edge_list.b
            
            WHERE entity.change_date > entity_edge_list.change_date
            ORDER BY entity_edge_list.a, entity_edge_list.b ASC
            """)
        return cursor

    def get_compared(self) -> Generator[int, None, None]:
        cursor = self.connection.cursor(buffered=True)
        cursor.execute("SELECT DISTINCT a FROM entity_edge_list")
        for row in cursor:
            yield row[0]

    def get_uncompared(self) -> Generator[int, None, None]:
        cursor = self.connection.cursor(buffered=True)

        cursor.execute("""
            SELECT DISTINCT entity.id
            FROM entity
            LEFT JOIN entity_edge_list
                ON entity.id = entity_edge_list.a
        
            WHERE entity_edge_list.a IS NULL
            ORDER BY entity.id ASC
            """)
        for row in cursor:
            yield row[0]

    def write_comparisons(self, comparisons: Generator[Comparison, None, None]):
        cursor = self.connection.cursor()

        query = f"""
        INSERT INTO aussteller_dubletten (a, b, score, count, f_score_sum, change_date) VALUE (%s, %s, %s, %s, %s, NOW())
        ON DUPLICATE KEY UPDATE score=VALUES(score), count=VALUES(count), f_score_sum=VALUES(f_score_sum), change_date=NOW();
        """

        # execute in batches of 1000
        for chunk in chunks(comparisons, size=1000):
            cursor.executemany(query, [
                (c.entity[self.ID_COLUMN], c.other_entity[self.ID_COLUMN], c.score, c.count, c.f_score_sum)
                for c in chunk
            ])
        self.connection.commit()


if __name__ == "__main__":
    Entity().execute(limit=None)