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Rückgang zur stabilen Version

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Floke
2025-08-01 13:27:15 +00:00
parent e48e44ea21
commit 05ecb01228
1 changed files with 55 additions and 74 deletions
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duplicate_checker.py
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@@ -1,4 +1,4 @@
# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring)
# duplicate_checker.py (v2.0 - mit Blocking-Strategie)
import logging
import pandas as pd
@@ -6,70 +6,48 @@ from thefuzz import fuzz
from config import Config
from helpers import normalize_company_name, simple_normalize_url
from google_sheet_handler import GoogleSheetHandler
from collections import defaultdict
# --- Konfiguration ---
CRM_SHEET_NAME = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD = 85 # Etwas höherer Schwellenwert für bessere Präzision
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."""
scores = {'name': 0, 'location': 0, 'domain': 0}
if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
scores['domain'] = 100
# Höhere Gewichtung für den Namen, da die Website oft fehlt
if record1.get('normalized_name') and record2.get('normalized_name'):
scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
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
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 calculate_similarity(record1, record2):
"""Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
total_score = 0
if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
total_score += 100
if record1['normalized_name'] and record2['normalized_name']:
name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
total_score += name_similarity * 0.7
if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
total_score += 20
return round(total_score)
def main():
logging.info("Starte den Duplikats-Check (v2.2 mit Multi-Key Blocking)...")
"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...")
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
original_matching_df = matching_df.copy()
if matching_df is None or matching_df.empty:
logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
return
logging.info("Normalisiere Daten für den Vergleich...")
for df in [crm_df, matching_df]:
@@ -77,55 +55,58 @@ 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_keys'] = df['normalized_name'].apply(create_blocking_keys)
# Blocking Key: Das erste Wort des normalisierten Namens
df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
# --- NEUE, SCHNELLE BLOCKING-STRATEGIE ---
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)
crm_index = {}
for index, row in crm_df.iterrows():
key = row['block_key']
if key:
if key not in crm_index:
crm_index[key] = []
crm_index[key].append(row)
logging.info("Starte Matching-Prozess...")
results = []
total_matches = len(matching_df)
for match_record in matching_df.to_dict('records'):
best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
for index, match_row in matching_df.iterrows():
best_score = 0
best_match_name = ""
logging.info(f"Prüfe: {match_record['CRM Name']}...")
logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['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
# Finde den Block von Kandidaten
block_key = match_row['block_key']
candidates = crm_index.get(block_key, [])
if not candidate_pool:
logging.debug(" -> Keine Kandidaten im Index gefunden.")
# Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch
for crm_row in candidates:
score = calculate_similarity(match_row, crm_row)
if score > best_score:
best_score = score
best_match_name = crm_row['CRM Name']
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['CRM Name']
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']
})
if best_score >= SCORE_THRESHOLD:
results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score})
else:
# Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen
results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score})
logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
result_df = pd.DataFrame(results)
output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
# Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen
output_df = matching_df[['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land']].copy()
output_df = pd.concat([output_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.")