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duplicate_checker.py aktualisiert

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Floke
2025-08-05 14:36:25 +00:00
parent 012e091ed0
commit 7adba270ed
1 changed files with 43 additions and 35 deletions
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78
duplicate_checker.py
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@@ -16,10 +16,8 @@ WEIGHTS = {
}
# --- Logging Setup ---
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(levelname)-8s - %(message)s'
)
LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s'
logging.basicConfig(level=logging.DEBUG, format=LOG_FORMAT)
logger = logging.getLogger(__name__)
# --- Hilfsfunktionen ---
@@ -43,20 +41,37 @@ def normalize_domain(url: str) -> str:
def main():
logger.info("Starte Duplikat-Check mit ausführlichem Logging...")
sheet_handler = GoogleSheetHandler()
crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
logger.error("Leere Daten in CRM oder Matching Tab. Abbruch.")
logger.info("Starte den Duplikats-Check (v2.0 mit Blocking und Maximum Logging)...")
# GoogleSheetHandler initialisieren
try:
sheet_handler = GoogleSheetHandler()
logger.info("GoogleSheetHandler initialisiert")
except Exception as e:
logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
return
# CRM-Daten laden
logger.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:
logger.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Abbruch.")
return
logger.info(f"{len(crm_df)} Zeilen aus '{CRM_SHEET_NAME}' geladen")
# Matching-Daten laden
logger.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
if match_df is None or match_df.empty:
logger.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Abbruch.")
return
logger.info(f"{len(match_df)} Zeilen aus '{MATCHING_SHEET_NAME}' geladen")
# Normalisierung
for df, name in [(crm_df, 'CRM'), (match_df, 'Matching')]:
for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name)
df['norm_domain'] = df['CRM Website'].fillna('').apply(normalize_domain)
df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip())
logger.debug(f"{name}-Daten nach Normalisierung. Erste Zeile: {df.iloc[0].to_dict()}")
logger.debug(f"{label}-Daten nach Normalisierung. Erste Zeile: {df.iloc[0].to_dict()}")
# Blocking per Domain
indexer = recordlinkage.Index()
@@ -64,13 +79,13 @@ def main():
candidate_pairs = indexer.index(crm_df, match_df)
logger.info(f"Blocking abgeschlossen: {len(candidate_pairs)} Kandidatenpaare gefunden")
# Vergleichsregeln
# Vergleichsregeln definieren
compare = recordlinkage.Compare()
compare.exact('norm_domain', 'norm_domain', label='domain')
compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim')
compare.exact('city', 'city', label='city')
features = compare.compute(candidate_pairs, crm_df, match_df)
logger.debug(f"Feature-DataFrame Vorschau:\n{features.head()}" )
logger.debug(f"Feature-DataFrame Vorschau:\n{features.head()}")
# Score berechnen
features['score'] = (
@@ -80,36 +95,29 @@ def main():
)
logger.info("Scores berechnet")
# Best Match pro neuer Zeile mit Logging der Kandidaten
# Best Match pro neuer Zeile mit detailliertem Logging
results = []
crm_idx_col = []
match_idx_col = []
for match_idx, group in features.reset_index().groupby('level_1'):
crm_idx_col.append(match_idx)
# sortiere Kandidaten nach Score
sorted_group = group.sort_values('score', ascending=False)
logger.debug(f"Matching-Index {match_idx}: untersuchte Kandidaten:\n{sorted_group[['level_0','score','domain','name_sim','city']]}" )
top = sorted_group.iloc[0]
if top['score'] >= SCORE_THRESHOLD:
results.append((top['level_0'], match_idx, top['score']))
logger.info(f"Zeile {match_idx}: Match mit CRM-Index {top['level_0']} Score {top['score']:.2f}")
logger.info(f"--- Prüfe: Zeile {match_idx} ---")
df_block = group.sort_values('score', ascending=False)
logger.debug(f" Kandidaten für Zeile {match_idx}:\n{df_block[['level_0','score','domain','name_sim','city']].to_string(index=False)}")
top = df_block.iloc[0]
crm_idx = top['level_0'] if top['score'] >= SCORE_THRESHOLD else None
if crm_idx is not None:
logger.info(f" --> Match: CRM-Index {crm_idx} mit Score {top['score']:.2f}")
else:
results.append((None, match_idx, top['score']))
logger.info(f"Zeile {match_idx}: Kein ausreichender Score (top {top['score']:.2f})")
logger.info(f" --> Kein Match (höchster Score {top['score']:.2f})")
results.append((crm_idx, match_idx, top['score']))
# Ausgabe DataFrame
# Ausgabe DataFrame zusammenstellen
crm_df = crm_df.reset_index()
match_df = match_df.reset_index()
output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
output['Matched CRM Name'] = ''
output['Matched CRM Website'] = ''
output['Matched CRM Ort'] = ''
output['Matched CRM Land'] = ''
output['Score'] = 0.0
output[['Matched CRM Name','Matched CRM Website','Matched CRM Ort','Matched CRM Land','Score']] = ''
for crm_idx, match_idx, score in results:
if crm_idx is not None:
row_crm = crm_df.loc[crm_df['index'] == crm_idx].iloc[0]
row_crm = crm_df.loc[crm_df['index']==crm_idx].iloc[0]
output.at[match_idx, 'Matched CRM Name'] = row_crm['CRM Name']
output.at[match_idx, 'Matched CRM Website'] = row_crm['CRM Website']
output.at[match_idx, 'Matched CRM Ort'] = row_crm['CRM Ort']
@@ -120,7 +128,7 @@ def main():
data = [output.columns.tolist()] + output.values.tolist()
success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
if success:
logger.info("Erfolgreich geschrieben ins Google Sheet")
logger.info(f"Erfolgreich geschrieben: {len([r for r in results if r[0] is not None])} Matches")
else:
logger.error("Fehler beim Schreiben ins Google Sheet.")