duplicate_checker.py aktualisiert
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@@ -1,4 +1,4 @@
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# duplicate_checker.py (v2.0 - mit Blocking-Strategie)
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# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring)
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import logging
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import pandas as pd
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@@ -6,48 +6,70 @@ from thefuzz import fuzz
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from config import Config
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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 collections import defaultdict
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# --- Konfiguration ---
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CRM_SHEET_NAME = "CRM_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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SCORE_THRESHOLD = 80
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SCORE_THRESHOLD = 85 # Etwas höherer Schwellenwert für bessere Präzision
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def calculate_similarity(record1, record2):
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"""Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
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total_score = 0
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if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
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total_score += 100
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if record1['normalized_name'] and record2['normalized_name']:
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name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
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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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if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
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total_score += 20
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return round(total_score)
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def calculate_similarity_details(record1, record2):
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"""Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
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scores = {'name': 0, 'location': 0, 'domain': 0}
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if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
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scores['domain'] = 100
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# Höhere Gewichtung für den Namen, da die Website oft fehlt
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if record1.get('normalized_name') and record2.get('normalized_name'):
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scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
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if record1.get('CRM Ort') and record1['CRM Ort'] == record2.get('CRM Ort'):
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if record1.get('CRM Land') and record1['CRM Land'] == record2.get('CRM Land'):
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scores['location'] = 20
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total_score = sum(scores.values())
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return {'total': total_score, 'details': scores}
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def create_blocking_keys(name):
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"""Erstellt mehrere Blocking Keys für einen Namen, um die Sensitivität zu erhöhen."""
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if not name:
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return []
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words = name.split()
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keys = set()
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# 1. Erstes Wort
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if len(words) > 0:
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keys.add(words[0])
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# 2. Zweites Wort (falls vorhanden)
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if len(words) > 1:
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keys.add(words[1])
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# 3. Erste 4 Buchstaben des ersten Wortes
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if len(words) > 0 and len(words[0]) >= 4:
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keys.add(words[0][:4])
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return list(keys)
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def main():
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"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
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logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...")
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logging.info("Starte den Duplikats-Check (v2.2 mit Multi-Key Blocking)...")
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try:
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sheet_handler = GoogleSheetHandler()
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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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logging.critical(f"FEHLER bei Initialisierung: {e}")
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return
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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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if crm_df is None or crm_df.empty:
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logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.")
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return
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if crm_df is None or crm_df.empty: return
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logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
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matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if matching_df is None or matching_df.empty:
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logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
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return
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if matching_df is None or matching_df.empty: return
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original_matching_df = matching_df.copy()
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logging.info("Normalisiere Daten für den Vergleich...")
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for df in [crm_df, matching_df]:
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@@ -55,58 +77,55 @@ def main():
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df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
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df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
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df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
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# Blocking Key: Das erste Wort des normalisierten Namens
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df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
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df['block_keys'] = df['normalized_name'].apply(create_blocking_keys)
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# --- NEUE, SCHNELLE BLOCKING-STRATEGIE ---
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logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
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crm_index = {}
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for index, row in crm_df.iterrows():
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key = row['block_key']
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if key:
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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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crm_index = defaultdict(list)
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for record in crm_df.to_dict('records'):
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for key in record['block_keys']:
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crm_index[key].append(record)
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logging.info("Starte Matching-Prozess...")
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results = []
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total_matches = len(matching_df)
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for index, match_row in matching_df.iterrows():
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best_score = 0
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for match_record in matching_df.to_dict('records'):
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best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
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best_match_name = ""
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logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...")
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logging.info(f"Prüfe: {match_record['CRM Name']}...")
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# Finde den Block von Kandidaten
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block_key = match_row['block_key']
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candidates = crm_index.get(block_key, [])
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candidate_pool = {}
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for key in match_record['block_keys']:
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for crm_record in crm_index.get(key, []):
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candidate_pool[crm_record['CRM Name']] = crm_record
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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 not candidate_pool:
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logging.debug(" -> Keine Kandidaten im Index gefunden.")
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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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# 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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for crm_record in candidate_pool.values():
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score_info = calculate_similarity_details(match_record, crm_record)
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if score_info['total'] > best_score_info['total']:
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best_score_info = score_info
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best_match_name = crm_record['CRM Name']
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results.append({
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'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
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'Score (Gesamt)': best_score_info['total'],
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'Score (Name)': best_score_info['details']['name'],
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'Bonus (Standort)': best_score_info['details']['location'],
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'Bonus (Domain)': best_score_info['details']['domain']
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})
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logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
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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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output_df = pd.concat([original_matching_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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logging.info(f"Ergebnisse erfolgreich in '{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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