duplicate_checker.py aktualisiert
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@@ -1,169 +1,107 @@
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import os
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duplicate_checker.py (v2.0 - mit Blocking-Strategie)
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import re
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import logging
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import logging
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import pandas as pd
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import pandas as pd
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import numpy as np
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from thefuzz import fuzz
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import recordlinkage
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from config import Config
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from rapidfuzz import fuzz
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from helpers import normalize_company_name, simple_normalize_url
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from google_sheet_handler import GoogleSheetHandler
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from google_sheet_handler import GoogleSheetHandler
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--- Konfiguration ---
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# --- Konfiguration ---
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CRM_SHEET_NAME = "CRM_Accounts"
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CRM_SHEET_NAME = "CRM_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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# Threshold gesenkt und konfigurierbar im Code
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SCORE_THRESHOLD = 80
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SCORE_THRESHOLD = 0.75
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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WEIGHTS = {
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def calculate_similarity(record1, record2):
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'domain': 0.5,
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"""Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
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'name': 0.4,
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total_score = 0
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'city': 0.1,
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if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
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}
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total_score += 100
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# Relativer Log-Ordner
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if record1['normalized_name'] and record2['normalized_name']:
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LOG_DIR = 'log'
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name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
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LOG_FILENAME = 'duplicate_check.log'
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total_score += name_similarity * 0.7
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if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
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# --- Logging Setup ---
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if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
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if not os.path.exists(LOG_DIR):
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total_score += 20
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try:
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return round(total_score)
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os.makedirs(LOG_DIR)
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except Exception as e:
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print(f"Warnung: Konnte Log-Ordner nicht anlegen: {e}")
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log_path = os.path.join(LOG_DIR, LOG_FILENAME)
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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formatter = logging.Formatter('%(asctime)s - %(levelname)-8s - %(name)s - %(message)s')
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# Console handler
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.INFO)
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console_handler.setFormatter(formatter)
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logger.addHandler(console_handler)
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# File handler
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try:
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file_handler = logging.FileHandler(log_path, mode='a', encoding='utf-8')
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file_handler.setLevel(logging.DEBUG)
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file_handler.setFormatter(formatter)
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logger.addHandler(file_handler)
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logger.info(f"Logging auch in Datei: {log_path}")
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except Exception as e:
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logger.warning(f"Konnte keine Log-Datei schreiben: {e}")
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# --- Hilfsfunktionen ---
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def normalize_company_name(name: str) -> str:
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s = str(name).casefold()
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for src, dst in [('ä','ae'), ('ö','oe'), ('ü','ue'), ('ß','ss')]:
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s = s.replace(src, dst)
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s = re.sub(r'[^a-z0-9\s]', ' ', s)
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stops = ['gmbh','ag','kg','ug','ohg','holding','group','international']
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tokens = [t for t in s.split() if t and t not in stops]
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return ' '.join(tokens)
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def normalize_domain(url: str) -> str:
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s = str(url).casefold().strip()
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s = re.sub(r'^https?://', '', s)
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s = s.split('/')[0]
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if s.startswith('www.'):
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s = s[4:]
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return s
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def main():
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def main():
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logger.info("Starte den Duplikats-Check mit Fallback-Blocking...")
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"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
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# GoogleSheetHandler initialisieren
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logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...")
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try:
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try:
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sheet_handler = GoogleSheetHandler()
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sheet_handler = GoogleSheetHandler()
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logger.info("GoogleSheetHandler initialisiert")
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except Exception as e:
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except Exception as e:
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logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
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logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
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return
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return
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# Daten laden
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logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if crm_df is None or crm_df.empty:
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if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
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logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.")
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logger.critical("CRM- oder Matching-Daten leer. Abbruch.")
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return
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return
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logger.info(f"{len(crm_df)} CRM-Zeilen, {len(match_df)} Matching-Zeilen geladen")
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# Normalisierung
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logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
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for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
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matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name)
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if matching_df is None or matching_df.empty:
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df['norm_domain'] = df['CRM Website'].fillna('').apply(normalize_domain)
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logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
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df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip())
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return
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df['name_token'] = df['norm_name'].apply(lambda x: x.split()[0] if x else np.nan)
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# Leere Werte als NaN markieren
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df['norm_domain'].replace('', np.nan, inplace=True)
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df['city'].replace('', np.nan, inplace=True)
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logger.debug(f"{label}-Normalisierung: norm_domain={df.iloc[0]['norm_domain']}, name_token={df.iloc[0]['name_token']}")
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# Blocking: Domain und Name-Token
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logging.info("Normalisiere Daten für den Vergleich...")
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index_dom = recordlinkage.Index()
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for df in [crm_df, matching_df]:
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index_dom.block('norm_domain')
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df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
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pairs_dom = index_dom.index(crm_df, match_df)
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df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
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index_name = recordlinkage.Index()
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df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
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index_name.block('name_token')
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df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
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pairs_name = index_name.index(crm_df, match_df)
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# Blocking Key: Das erste Wort des normalisierten Namens
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# Union der Kandidatenpaare
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df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
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candidate_pairs = pairs_dom.append(pairs_name).drop_duplicates()
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logger.info(f"Blocking abgeschlossen: Dom-Paare={len(pairs_dom)}, Name-Paare={len(pairs_name)}, Gesamt={len(candidate_pairs)}")
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# Vergleichsregeln definieren
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# --- NEUE, SCHNELLE BLOCKING-STRATEGIE ---
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compare = recordlinkage.Compare()
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logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
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compare.exact('norm_domain', 'norm_domain', label='domain', missing_value=0)
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crm_index = {}
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compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim')
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for index, row in crm_df.iterrows():
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compare.exact('city', 'city', label='city', missing_value=0)
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key = row['block_key']
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features = compare.compute(candidate_pairs, crm_df, match_df)
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if key:
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logger.debug(f"Features berechnet: {features.head()}...")
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if key not in crm_index:
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crm_index[key] = []
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crm_index[key].append(row)
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# Score berechnen
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logging.info("Starte Matching-Prozess...")
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features['score'] = (
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results = []
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WEIGHTS['domain'] * features['domain'] +
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total_matches = len(matching_df)
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WEIGHTS['name'] * features['name_sim'] +
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WEIGHTS['city'] * features['city']
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)
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logger.info("Scores berechnet")
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# Per-Match Logging und Auswahl
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for index, match_row in matching_df.iterrows():
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results = []
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best_score = 0
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crm_map = crm_df.reset_index()
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best_match_name = ""
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for match_idx, group in features.reset_index().groupby('level_1'):
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logger.info(f"--- Prüfe Matching-Zeile {match_idx} ---")
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df_block = group.sort_values('score', ascending=False).copy()
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# CRM-Daten für Log
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df_block['CRM Name'] = df_block['level_0'].map(crm_map.set_index('index')['CRM Name'])
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# Log der Top-Kandidaten
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for _, row in df_block.head(5).iterrows():
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logger.debug(f"Candidate [{int(row['level_0'])}]: score={row['score']:.3f}, name_sim={row['name_sim']:.3f}, dom={row['domain']}, city={row['city']} => {row['CRM Name']}")
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top = df_block.iloc[0]
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crm_idx = top['level_0'] if top['score'] >= SCORE_THRESHOLD else None
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if crm_idx is not None:
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logger.info(f" --> Match: {int(crm_idx)} ({top['CRM Name']}) mit Score {top['score']:.2f}")
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else:
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logger.info(f" --> Kein Match (höchster Score {top['score']:.2f})")
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results.append((crm_idx, match_idx, top['score']))
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# Ausgabe zusammenstellen
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logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...")
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match_map = match_df.reset_index()
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output = match_map[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
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output[['Matched CRM Name','Matched CRM Website','Matched CRM Ort','Matched CRM Land','Score']] = ''
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for crm_idx, match_idx, score in results:
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if crm_idx is not None:
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crm_row = crm_map[crm_map['index']==crm_idx].iloc[0]
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output.at[match_idx, 'Matched CRM Name'] = crm_row['CRM Name']
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output.at[match_idx, 'Matched CRM Website'] = crm_row['CRM Website']
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output.at[match_idx, 'Matched CRM Ort'] = crm_row['CRM Ort']
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output.at[match_idx, 'Matched CRM Land'] = crm_row['CRM Land']
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output.at[match_idx, 'Score'] = round(score,3)
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# Zurückschreiben ins Google Sheet
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# Finde den Block von Kandidaten
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data = [output.columns.tolist()] + output.values.tolist()
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block_key = match_row['block_key']
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
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candidates = crm_index.get(block_key, [])
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if success:
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logger.info(f"Erfolgreich geschrieben: {len([r for r in results if r[0] is not None])} Matches")
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# Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch
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for crm_row in candidates:
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score = calculate_similarity(match_row, crm_row)
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if score > best_score:
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best_score = score
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best_match_name = crm_row['CRM Name']
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if best_score >= SCORE_THRESHOLD:
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results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score})
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else:
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else:
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logger.error("Fehler beim Schreiben ins Google Sheet.")
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# Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen
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results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score})
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if __name__ == '__main__':
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logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
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main()
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result_df = pd.DataFrame(results)
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# Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen
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output_df = matching_df[['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land']].copy()
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output_df = pd.concat([output_df.reset_index(drop=True), result_df], axis=1)
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data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
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if success:
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logging.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
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else:
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logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
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if name == "main":
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main()
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