duplicate_checker.py aktualisiert
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@@ -2,6 +2,7 @@ import os
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import re
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import logging
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import pandas as pd
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import numpy as np
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import recordlinkage
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from rapidfuzz import fuzz
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from google_sheet_handler import GoogleSheetHandler
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@@ -64,7 +65,8 @@ def normalize_domain(url: str) -> str:
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def main():
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logger.info("Starte den Duplikats-Check (v2.0 mit Kandidaten-Logging)...")
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logger.info("Starte den Duplikats-Check (v2.0 mit korrekten Missing-Werten)...")
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# Initialize GoogleSheetHandler
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try:
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sheet_handler = GoogleSheetHandler()
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logger.info("GoogleSheetHandler initialisiert")
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@@ -72,7 +74,7 @@ def main():
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logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
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return
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# Daten laden
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# Load data
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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 or match_df is None or match_df.empty:
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@@ -80,73 +82,73 @@ def main():
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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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# Normalize fields
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for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
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df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name)
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df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name)
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df['norm_domain'] = df['CRM Website'].fillna('').apply(normalize_domain)
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df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip())
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logger.debug(f"{label}-Daten normalisiert: Beispiel: {df.iloc[0][['norm_name','norm_domain','city']].to_dict()}")
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df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip())
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# Replace empty strings with NaN so they aren't considered matches
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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}-Daten normalisiert: Beispiel: {{'norm_name': df.iloc[0]['norm_name'], 'norm_domain': df.iloc[0]['norm_domain'], 'city': df.iloc[0]['city']}}")
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# Blocking
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# Blocking per Domain
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indexer = recordlinkage.Index()
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indexer.block('norm_domain')
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candidate_pairs = indexer.index(crm_df, match_df)
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logger.info(f"Blocking abgeschlossen: {len(candidate_pairs)} Kandidatenpaare")
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# Compare
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# Vergleichsregeln definieren
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compare = recordlinkage.Compare()
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compare.exact('norm_domain', 'norm_domain', label='domain')
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compare.exact('norm_domain', 'norm_domain', label='domain', missing_value=0)
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compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim')
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compare.exact('city', 'city', label='city')
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compare.exact('city', 'city', label='city', missing_value=0)
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features = compare.compute(candidate_pairs, crm_df, match_df)
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logger.debug(f"Features berechnet: {features.head()}\n...")
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# Score
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features['score'] = (WEIGHTS['domain']*features['domain'] +
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WEIGHTS['name']*features['name_sim'] +
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WEIGHTS['city']*features['city'])
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# Score berechnen
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features['score'] = (
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WEIGHTS['domain'] * features['domain'] +
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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
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# Detailed per-match logging
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results = []
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crm_df_idx = crm_df.reset_index()
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crm_idx_map = crm_df.reset_index()
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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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# Enrich with CRM fields
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df_block['CRM Name'] = df_block['level_0'].map(crm_df_idx.set_index('index')['CRM Name'])
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df_block['CRM Website'] = df_block['level_0'].map(crm_df_idx.set_index('index')['CRM Website'])
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df_block['CRM Ort'] = df_block['level_0'].map(crm_df_idx.set_index('index')['CRM Ort'])
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# Log top candidates
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logger.debug("Kandidaten (CRM_Index, Score, Domain, Name_sim, City, CRM Name):")
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# Enrich with CRM info
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df_block['CRM Name'] = df_block['level_0'].map(crm_idx_map.set_index('index')['CRM Name'])
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df_block['CRM Website'] = df_block['level_0'].map(crm_idx_map.set_index('index')['CRM Website'])
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df_block['CRM Ort'] = df_block['level_0'].map(crm_idx_map.set_index('index')['CRM Ort'])
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logger.debug("Kandidaten (Index, Score, Domain, Name_sim, City, CRM Name):")
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for _, row in df_block.iterrows():
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logger.debug(f" [{int(row['level_0'])}] score={row['score']:.3f} dom={row['domain']} name_sim={row['name_sim']:.3f} city={row['city']} => {row['CRM Name']}")
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logger.debug(f" [{int(row['level_0'])}] score={row['score']:.3f} dom={row['domain']} name={row['name_sim']:.3f} 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: CRM-Index {int(crm_idx)} ({top['CRM Name']}) mit Score {top['score']:.2f}")
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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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# Prepare output
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match_df_idx = match_df.reset_index()
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output = match_df_idx[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
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output['Matched CRM Name'] = ''
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output['Matched CRM Website'] = ''
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output['Matched CRM Ort'] = ''
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output['Matched CRM Land'] = ''
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output['Score'] = 0.0
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match_idx_map = match_df.reset_index()
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output = match_idx_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_df_idx[crm_df_idx['index']==crm_idx].iloc[0]
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output.at[match_idx, 'Matched CRM Name'] = crm_row['CRM Name']
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crm_row = crm_idx_map[crm_idx_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, '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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# Write back
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# Zurückschreiben ins Google Sheet
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data = [output.columns.tolist()] + output.values.tolist()
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
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if success:
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