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duplicate_checker.py aktualisiert

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Floke
2025-08-01 12:44:38 +00:00
parent 533796b65f
commit 8a7426dfb9
1 changed files with 68 additions and 44 deletions
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duplicate_checker.py
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@@ -1,4 +1,4 @@
# duplicate_checker.py (v2.6 - Final Optimized Brute-Force)
# duplicate_checker.py (v2.0 - Enhanced Transparency)
import logging
import pandas as pd
@@ -6,53 +6,68 @@ 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
# --- Konfiguration ---
CRM_SHEET_NAME = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt
SCORE_THRESHOLD = 80
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."""
def calculate_similarity_with_details(record1, record2):
"""
Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen
und gibt die Details für die Begründung zurück.
"""
scores = {'name': 0, 'location': 0, 'domain': 0}
# Domain-Match (höchste Priorität)
if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
# 1. Website-Domain (stärkstes Signal)
if record1['normalized_domain'] and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2['normalized_domain']:
scores['domain'] = 100
# Namensähnlichkeit (hohe Gewichtung)
if record1.get('normalized_name') and record2.get('normalized_name'):
# token_set_ratio ist gut bei unterschiedlicher Wortreihenfolge und Zusatzwörtern
scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
# 2. Firmenname (Fuzzy-Signal)
if record1['normalized_name'] and record2['normalized_name']:
name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
scores['name'] = round(name_similarity * 0.7)
# Standort-Bonus
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'):
# 3. Standort (Bestätigungs-Signal)
if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
scores['location'] = 20
total_score = sum(scores.values())
return {'total': total_score, 'details': scores}
# Erstelle den Begründungstext
reasons = []
if scores['domain'] > 0: reasons.append(f"Domain({scores['domain']})")
if scores['name'] > 0: reasons.append(f"Name({scores['name']})")
if scores['location'] > 0: reasons.append(f"Ort({scores['location']})")
reason_text = " + ".join(reasons) if reasons else "Keine Übereinstimmung"
return round(total_score), reason_text
def main():
start_time = time.time()
logging.info("Starte den Duplikats-Check (v2.6 - Final Optimized Brute-Force)...")
"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
logging.info("Starte den Duplikats-Check (v2.0 mit erweiterter Ausgabe)...")
try:
sheet_handler = GoogleSheetHandler()
except Exception as e:
logging.critical(f"FEHLER bei Initialisierung: {e}")
logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
return
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
if crm_df is None or crm_df.empty:
logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.")
return
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
if matching_df is None or matching_df.empty:
logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
return
# Kopie für die finale Ausgabe sichern
original_matching_df = matching_df.copy()
logging.info("Normalisiere Daten für den Vergleich...")
@@ -61,49 +76,58 @@ def main():
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_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x and x.split() else None)
logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
crm_index = {}
# Konvertiere in Records für schnelleren Zugriff
crm_records = crm_df.to_dict('records')
matching_records = matching_df.to_dict('records')
logging.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...")
for record in crm_records:
key = record['block_key']
if key:
if key not in crm_index:
crm_index[key] = []
crm_index[key].append(record)
logging.info("Starte Matching-Prozess...")
results = []
for i, match_record in enumerate(matching_records):
best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
for match_record in matching_df.to_dict('records'):
best_score = -1
best_match_name = ""
best_reason = ""
logging.info(f"Prüfe {i + 1}/{len(matching_records)}: {match_record.get('CRM Name', 'N/A')}...")
logging.info(f"Prüfe: {match_record.get('CRM Name', 'N/A')}...")
block_key = match_record.get('block_key')
candidates = crm_index.get(block_key, [])
# Brute-Force-Vergleich: Jede Zeile wird mit jeder CRM-Zeile verglichen
for crm_record in crm_records:
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')
for crm_row in candidates:
score, reason = calculate_similarity_with_details(match_record, crm_row)
if score > best_score:
best_score = score
best_match_name = crm_row.get('CRM Name', 'N/A')
best_reason = reason
results.append({
'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
'Score (Gesamt)': best_score_info['total'],
'Score (Name)': best_score_info['details']['name'],
'Bonus (Standort)': best_score_info['details']['location'],
'Bonus (Domain)': best_score_info['details']['domain']
'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "",
'Ähnlichkeits-Score': best_score,
'Matching-Grund': best_reason
})
logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
result_df = pd.DataFrame(results)
# Füge die neuen Spalten zu den Originaldaten hinzu
output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
if success:
logging.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.")
logging.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
else:
logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
end_time = time.time()
logging.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
if __name__ == "__main__":
main()