processor/matching.py

import json
from constants.constants import *

from tqdm import tqdm
from transliterate import translit, detect_language
import pandas as pd
from rapidfuzz import fuzz, process
import numpy as np
from math import isnan
from preprocess.utils.common.utils import *


def normalize_name(name):
    """
    Нормализует строку: если обнаруживается русский язык, транслитерирует её в латиницу,
    приводит к нижнему регистру.
    """
    try:
        if detect_language(name) == 'ru':
            return translit(name, 'ru', reversed=True).lower()
    except Exception:
        pass
    return name.lower()


def normalize_name_ex(name):
    name = normalize_name(name)
    for nnk in NORMALIZED_NAMES_ALTERNATIVES_DICT:
        word = find_full_word(name, NORMALIZED_NAMES_ALTERNATIVES_DICT[nnk])
        if word:
            name = name.replace(word, nnk)
    return name


def compare_names(name1, name2, scorer=fuzz.ratio, score_cutoff=50):
    print("Scoring: " + name1 + " vs " + name2)
    words1 = name1.split(" ")
    words2 = name2.split(" ")

    score = 0
    for w1 in words1:
        for w2 in words2:
            r = scorer(w1, w2)
            print("\t " + w1 + " - " + w2 + " ; " + str(r))
            if r >= score_cutoff:
                score = score + r

    print("Score result: " + str(score / (100*len(words1))))
    return score / (100*len(words1))

def compare_name_with_list(name, names_list, scorer=fuzz.ratio, score_cutoff=50):
    result = []
    index = 0
    for name2 in names_list:
        result.append((name2, compare_names(name, name2, scorer, score_cutoff), index))
        index = index + 1
    return result


def prepare_groups_with_ids(items_df):
    """
    Предварительная группировка данных из items по (new_brand, type, volume, new_type_wine, sour)
    с учетом нормализованного названия.

    Добавляем столбец 'norm_name', чтобы нормализовать значение name один раз заранее.

    :param items_df: DataFrame с колонками 'new_brand', 'type', 'name', 'id', 'volume', 'new_type_wine', 'sour'.
    :return: Словарь {(new_brand, type, volume, new_type_wine, sour): [(id, name, norm_name, volume, new_type_wine, sour)]}.
    """
    items_df = items_df.copy()
    items_df['norm_name'] = items_df['name'].apply(normalize_name_ex)

    grouped = items_df.groupby(['new_brand', 'type', 'volume', 'new_type_wine', 'sour']).apply(
        lambda x: list(zip(x['id'], x['name'], x['fullname'], x['norm_name'], x['volume'], x['new_type_wine'], x['sour'], x['year']))
    ).to_dict()

    #print(grouped)

    return grouped

def prepare_groups_by_alternative_keys(items_df):
    """
    Группировка данных из items по (new_type_wine, new_type, volume, sour) с сохранением id, new_brand,
    оригинального и нормализованного имени.

    :param items_df: DataFrame с колонками 'new_brand', 'new_type_wine', 'new_type', 'volume', 'name', 'id', 'sour'.
    :return: Словарь {(new_type_wine, new_type, volume, sour): [(id, new_brand, name, norm_name, volume, new_type_wine, sour)]}.
    """
    items_df = items_df.copy()
    items_df['norm_name'] = items_df['name'].apply(normalize_name_ex)

    #grouped = items_df.groupby(['new_type_wine', 'new_type', 'volume' ''', 'sour''''']).apply(
    grouped = items_df.groupby(['new_type_wine', 'new_type', 'volume']).apply(
        lambda x: list(zip(x['id'], x['new_brand'], x['name'], x['fullname'], x['norm_name'], x['volume'], x['new_type_wine'], x['sour'], x['year']))
    ).to_dict()
    return grouped

def parse_year(year):
    if not year:
        return False
    elif isinstance(year, str):
        return int(year)
    elif isinstance(year, (int, float)) and not isnan(year):
        return int(year)

    return False

def order_by_best_year(matched_items, year):
    best_matched_items = []
    max_year_matched_items = []
    other_matched_items = []
    max_year = 0

    year = parse_year(year)

    for mi in matched_items:
        # Если в оригинале указан год, то ищем точное совпадение, иначе сортируем по году в обратном порядке
        try:
            if isinstance(mi['year'], (int, float, str)):
                mi_year = int(mi['year'])
            else:
                mi_year = False

            if year and mi_year and (mi_year == year):
                best_matched_items.append(mi['item_id'])
            elif mi_year:
                if mi_year > max_year:
                    max_year_matched_items = [mi]
                    max_year = mi_year
                elif mi_year == max_year:
                    max_year_matched_items.append(mi)
                else:
                    other_matched_items.append(mi['item_id'])
            else:
                other_matched_items.append(mi['item_id'])
        except Exception as ex:
            print("Error processing best year for item " + str(mi["item_id"]) + " value " + str(mi['year']) + ": " + str(ex))

    if len(best_matched_items) > 0:
        for m in matched_items:
            if not m['item_id'] in best_matched_items:
                m['score'] = m['score']*0.8

    return matched_items


def new_find_matches_with_ids(products_df, items_groups, items_df, name_threshold=85, include_alternatives=True):
    """
    Поиск совпадений с сохранением id найденных итемов, используя заранее подготовленные
    нормализованные группы.

    Производится два прохода:
    - Первый: поиск по группам (brand, type, volume, new_type_wine, sour);
    - Второй: для продуктов без совпадения ищем по альтернативным группам (new_type_wine, new_type, volume, sour),
      исключая итемы с исходным брендом.

    Сравнение производится по столбцу norm_name, а для вывода используется оригинальное name.

    :param products_df: DataFrame с колонками 'id', 'brand', 'type', 'name', 'volume', 'new_type_wine', 'sour', 'new_type'.
    :param items_groups: Словарь, сформированный функцией prepare_groups_with_ids.
    :param items_df: DataFrame итемов с колонками 'id', 'new_brand', 'new_type_wine', 'new_type', 'volume', 'name', 'sour'.
    :param name_threshold: Порог сходства для fuzzy matching.
    :return: DataFrame с добавленными столбцами 'matched_items' (список совпадений) и 'alternative' (альтернативные совпадения).
    """
    results = []
    no_match_products = []  # Список для хранения продуктов без совпадения в исходной группе

    if name_threshold < 50:
        name_threshold = 50

    # Первый проход: поиск по группам (brand, type, volume, new_type_wine, sour)
    for idx, product in tqdm(products_df.iterrows(), total=len(products_df)):
        product_brand = product['brand']
        product_type = product['type']
        product_name = product['name']
        product_volume = product['volume']
        product_type_wine = product['new_type_wine']
        product_sour = product['sour']

        key = (product_brand, product_type, product_volume, product_type_wine, product_sour)
        #print("Name: " + product_name)
        #print("Key: " + str(key))
        #print("Groups: " + str(items_groups))
        items_data = items_groups.get(key, [])
        if items_data:
            # Распаковываем: id, оригинальное имя, нормализованное имя, volume, new_type_wine, sour
            #print("Data: " + str(items_data))
            items_ids, items_names, items_full_names, items_norm_names, items_volumes, item_type_wine, items_sour, items_year = zip(*items_data)
        else:
            #print("Data: No")
            items_ids, items_names,items_full_names, items_norm_names, items_volumes, item_type_wine, items_sour, items_year = ([], [], [], [], [],[], [], [])

        norm_product_name = normalize_name_ex(product_name)
        matches = process.extract(
            norm_product_name, list(items_norm_names), scorer=fuzz.ratio, score_cutoff=name_threshold, limit=20
        )

        matched_items = [
            {
                'item_id': items_ids[idx_candidate],
                'brand': product_brand,
                'item_name': items_full_names[idx_candidate],
                #'item_name': items_names[idx_candidate],
                'score': score,
                'volume': items_volumes[idx_candidate],
                'color': item_type_wine[idx_candidate],
                'sour': items_sour[idx_candidate],
                'year': items_year[idx_candidate],
            }
            for match, score, idx_candidate in matches
        ]

        if matched_items:
            matched_items = order_by_best_year(matched_items, product['year'])
            matched_items = matched_items[:5]
        else:
            no_match_products.append((idx, product))

        results.append({
            'product_id': product['id'],
            #"matched_top_id": top_matched_id,
            'matched_items': matched_items,
            #"alternative_top_id": "",
            #'alternative': []  # Заполняется во втором проходе
        })

    if include_alternatives:
        # Подготовка альтернативной группировки по (new_type_wine, new_type, volume, sour)
        groups_by_alternative_keys = prepare_groups_by_alternative_keys(items_df)

        # Второй проход: для продуктов без совпадений ищем по альтернативным группам
        for idx, product in tqdm(no_match_products):
            #print("Product: " + str(product))
            product_brand = product['brand']
            product_type_wine = product['new_type_wine']
            product_type = product['new_type']
            product_volume = product['volume']
            product_name = product['name']
            product_sour = product['sour']

            #alt_key = (product_type_wine, product_type, product_volume, product_sour)
            alt_key = (product_type_wine, product_type, product_volume)

            #print("AltName: " + str(product))
            #print("AltKey: " + str(alt_key))
            #print("AltGroups: " + str(groups_by_alternative_keys))
            #print("AltGroups Keys: " + str(groups_by_alternative_keys.keys()))
            type_items = groups_by_alternative_keys.get(alt_key, [])
            #print("AltGroups2: " + str(type_items))
            # Фильтруем, исключая итемы с исходным брендом
            filtered_items = [item for item in type_items if item[1] != product_brand]
            if filtered_items:
                #print("AltData: " + str(filtered_items))
                alt_ids, alt_brands, alt_names, alt_full_names, alt_norm_names, alt_volumes, alt_type_wine, alt_sour, alt_year = zip(*filtered_items)
            else:
                #print("AltData: No")
                alt_ids, alt_brands, alt_names, alt_full_names, alt_norm_names, alt_volumes, alt_type_wine, alt_sour, alt_year = ([], [], [], [], [], [], [],[], [])

            norm_product_name = normalize_name_ex(product_name)
            #print("norm_product_name: " + str(norm_product_name))
            #print("alt_norm_names: " + str(alt_norm_names))
            alt_matches = process.extract(
                norm_product_name, list(alt_norm_names), scorer=fuzz.ratio, score_cutoff=50
            )


            #alt_matches = compare_name_with_list(
            #    norm_product_name, list(alt_norm_names), scorer=fuzz.ratio, score_cutoff=70
            #)

            #print("alt_matches: " + str(alt_matches))
            alt_matched_items = [
                {
                    'item_id': alt_ids[idx_candidate],
                    'brand': alt_brands[idx_candidate],
                    #'item_name': alt_names[idx_candidate],
                    'item_name': alt_full_names[idx_candidate],
                    'score': score / 2,
                    'volume': alt_volumes[idx_candidate],
                    'color': alt_type_wine[idx_candidate],
                    'sour': alt_sour[idx_candidate],
                    'year': alt_year[idx_candidate],
                }
                for match, score, idx_candidate in alt_matches
            ]

            alt_matched_items = order_by_best_year(alt_matched_items, product['year'])
            alt_matched_items = alt_matched_items[:5]

            results[idx]['matched_items'].extend(alt_matched_items)

    for r in results:
        r['matched_items'] = json.dumps(r['matched_items'], ensure_ascii=False)

    #if alt_matched_items:
        #    results[idx]['alternative_top_id'] = alt_matched_items[0]["item_id"]

        #results[idx]['alternative'] = alt_matched_items

    results_df = pd.DataFrame(results)
    merged_df = products_df.merge(results_df, left_on='id', right_on='product_id').drop(columns=['product_id'])
    return merged_df