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duplicate_checker.py aktualisiert

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Floke
2025-08-04 05:29:32 +00:00
parent 7c9ee2f799
commit c0db46d2a8
1 changed files with 51 additions and 41 deletions
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duplicate_checker.py
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@@ -1,4 +1,4 @@
# duplicate_checker.py (v3.0 - Back to Basics: Optimized Brute-Force)
# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring)
import logging
import pandas as pd
@@ -6,34 +6,26 @@ from thefuzz import fuzz
from config import Config
from helpers import normalize_company_name, simple_normalize_url
from google_sheet_handler import GoogleSheetHandler
import time
from collections import defaultdict
# --- Konfiguration ---
CRM_SHEET_NAME = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt
# WICHTIG: Logging Setup für detaillierte Ausgaben
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-8s - %(name)s - %(message)s')
logger = logging.getLogger(__name__)
SCORE_THRESHOLD = 85 # Etwas höherer Schwellenwert für bessere Präzision
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def calculate_similarity_details(record1, record2):
"""
Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück.
"""
"""Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
scores = {'name': 0, 'location': 0, 'domain': 0}
# Domain-Match (höchste Priorität, 100 Punkte)
if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
scores['domain'] = 100
# Namensähnlichkeit (hohe 85% Gewichtung)
# Höhere Gewichtung für den Namen, da die Website oft fehlt
if record1.get('normalized_name') and record2.get('normalized_name'):
# token_set_ratio ist robust gegen zusätzliche Wörter wie "Holding" oder "Gruppe"
scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
# Standort-Bonus (20 Punkte)
if record1.get('CRM Ort') and record1['CRM Ort'] == record2.get('CRM Ort'):
if record1.get('CRM Land') and record1['CRM Land'] == record2.get('CRM Land'):
scores['location'] = 20
@@ -41,59 +33,80 @@ def calculate_similarity_details(record1, record2):
total_score = sum(scores.values())
return {'total': total_score, 'details': scores}
def create_blocking_keys(name):
"""Erstellt mehrere Blocking Keys für einen Namen, um die Sensitivität zu erhöhen."""
if not name:
return []
words = name.split()
keys = set()
# 1. Erstes Wort
if len(words) > 0:
keys.add(words[0])
# 2. Zweites Wort (falls vorhanden)
if len(words) > 1:
keys.add(words[1])
# 3. Erste 4 Buchstaben des ersten Wortes
if len(words) > 0 and len(words[0]) >= 4:
keys.add(words[0][:4])
return list(keys)
def main():
start_time = time.time()
logger.info("Starte den Duplikats-Check (v3.0 - Back to Basics)...")
logging.info("Starte den Duplikats-Check (v2.2 mit Multi-Key Blocking)...")
# ... (Initialisierung und Laden der Daten bleibt gleich) ...
try:
sheet_handler = GoogleSheetHandler()
except Exception as e:
logger.critical(f"FEHLER bei Initialisierung: {e}")
logging.critical(f"FEHLER bei Initialisierung: {e}")
return
logger.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
if crm_df is None or crm_df.empty: return
logger.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
if matching_df is None or matching_df.empty: return
original_matching_df = matching_df.copy()
logger.info("Normalisiere Daten für den Vergleich...")
logging.info("Normalisiere Daten für den Vergleich...")
for df in [crm_df, matching_df]:
df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
df['block_keys'] = df['normalized_name'].apply(create_blocking_keys)
crm_records = crm_df.to_dict('records')
matching_records = matching_df.to_dict('records')
logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
crm_index = defaultdict(list)
for record in crm_df.to_dict('records'):
for key in record['block_keys']:
crm_index[key].append(record)
logger.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...")
logging.info("Starte Matching-Prozess...")
results = []
for i, match_record in enumerate(matching_records):
best_score_info = {'total': -1, 'details': {'name': 0, 'location': 0, 'domain': 0}}
for match_record in matching_df.to_dict('records'):
best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
best_match_name = ""
logger.info(f"--- Prüfe {i + 1}/{len(matching_records)}: '{match_record.get('CRM Name', 'N/A')}' ---")
# BRUTE-FORCE: Vergleiche mit jedem einzelnen CRM-Eintrag
for crm_record in crm_records:
score_info = calculate_similarity_details(match_record, crm_record)
# Logge jeden interessanten Vergleich (Score > 60)
if score_info['total'] > 60:
logger.debug(f" - Kandidat: '{crm_record.get('CRM Name', 'N/A')}' -> Score: {score_info['total']} (Details: {score_info['details']})")
logging.info(f"Prüfe: {match_record['CRM Name']}...")
candidate_pool = {}
for key in match_record['block_keys']:
for crm_record in crm_index.get(key, []):
candidate_pool[crm_record['CRM Name']] = crm_record
if not candidate_pool:
logging.debug(" -> Keine Kandidaten im Index gefunden.")
for crm_record in candidate_pool.values():
score_info = calculate_similarity_details(match_record, crm_record)
if score_info['total'] > best_score_info['total']:
best_score_info = score_info
best_match_name = crm_record.get('CRM Name', 'N/A')
logger.info(f" --> Bester Treffer: '{best_match_name}' mit Score {best_score_info['total']}")
best_match_name = crm_record['CRM Name']
results.append({
'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
@@ -116,8 +129,5 @@ def main():
else:
logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
end_time = time.time()
logger.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
if __name__ == "__main__":
main()