Floke/Brancheneinstufung2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add Logging

Browse Source
This commit is contained in:
Floke
2025-08-06 09:31:33 +00:00
parent 9193ab1a2f
commit b9a046bd0b
1 changed files with 88 additions and 70 deletions
Download Patch File Download Diff File Expand all files Collapse all files

142
duplicate_checker.py
View File

@@ -1,114 +1,132 @@
# duplicate_checker.py (v2.0 - mit Blocking-Strategie) import os
import logging import logging
import pandas as pd import pandas as pd
from thefuzz import fuzz from thefuzz import fuzz
from config import Config
from helpers import normalize_company_name, simple_normalize_url from helpers import normalize_company_name, simple_normalize_url
from google_sheet_handler import GoogleSheetHandler from google_sheet_handler import GoogleSheetHandler
# --- Konfiguration --- # --- Konfiguration ---
CRM_SHEET_NAME = "CRM_Accounts" CRM_SHEET_NAME = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD = 80 SCORE_THRESHOLD = 80 # Score ab hier gilt als Match
LOG_DIR = "Log"
LOG_FILE = "duplicate_check.log"
# --- Logging Setup ---
if not os.path.exists(LOG_DIR):
os.makedirs(LOG_DIR, exist_ok=True)
log_path = os.path.join(LOG_DIR, LOG_FILE)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# Console Handler: INFO+
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s"))
logger.addHandler(ch)
# File Handler: DEBUG+
fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
fh.setLevel(logging.DEBUG)
fh.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(name)s - %(message)s"))
logger.addHandler(fh)
logger.info(f"Logging in Datei: {log_path}")
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def calculate_similarity(record1, record2): def calculate_similarity(record1, record2):
"""Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen.""" """Berechnet gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
total_score = 0 total_score = 0
# Domain exact match
if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']: if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
total_score += 100 total_score += 100
if record1['normalized_name'] and record2['normalized_name']: # Name fuzzy
name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
total_score += name_similarity * 0.7 total_score += name_similarity * 0.7
if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']: # Ort+Land exact
if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']: if record1['CRM Ort'] == record2['CRM Ort'] and record1['CRM Land'] == record2['CRM Land']:
total_score += 20 total_score += 20
return round(total_score) return round(total_score)
def main():
"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...")
def main():
logger.info("Starte Duplikats-Check (v2.0 - mit Blocking & relevantem Kandidaten-Log)")
try: try:
sheet_handler = GoogleSheetHandler() sheet_handler = GoogleSheetHandler()
logger.info("GoogleSheetHandler initialisiert")
except Exception as e: except Exception as e:
logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}") logger.critical(f"FEHLER Init GoogleSheetHandler: {e}")
return return
logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...") # Daten laden
crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME) 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: if crm_df is None or crm_df.empty:
logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.") logger.critical(f"Keine Daten in '{CRM_SHEET_NAME}'")
return return
if match_df is None or match_df.empty:
logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...") logger.critical(f"Keine Daten in '{MATCHING_SHEET_NAME}'")
matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
if matching_df is None or matching_df.empty:
logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
return return
logger.info(f"{len(crm_df)} CRM-Zeilen, {len(match_df)} Matching-Zeilen geladen")
logging.info("Normalisiere Daten für den Vergleich...") # Normalisierung
for df in [crm_df, matching_df]: for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name) df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url) 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 Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip() df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
# Blocking Key: Das erste Wort des normalisierten Namens # Blocking Key
df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None) df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
logger.debug(f"{label}-Sample nach Norm: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
# --- NEUE, SCHNELLE BLOCKING-STRATEGIE --- # Blocking Index erstellen
logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
crm_index = {} crm_index = {}
for index, row in crm_df.iterrows(): for idx, row in crm_df.iterrows():
key = row['block_key'] key = row['block_key']
if key: if not key: continue
if key not in crm_index: crm_index.setdefault(key, []).append(row)
crm_index[key] = [] logger.info(f"Blocking-Index erstellt: {len(crm_index)} Keys")
crm_index[key].append(row)
logging.info("Starte Matching-Prozess...") # Matching
results = [] results = []
total_matches = len(matching_df) total = len(match_df)
for i, match_row in match_df.iterrows():
key = match_row['block_key']
candidates = crm_index.get(key, [])
logger.info(f"Prüfe {i+1}/{total}: {match_row['CRM Name']} (Key='{key}') -> {len(candidates)} Kandidaten")
for index, match_row in matching_df.iterrows(): if not candidates:
best_score = 0 results.append({'Potenzieller Treffer im CRM': '', 'Ähnlichkeits-Score': 0})
best_match_name = "" continue
logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...") # Scores für Kandidaten sammeln
scored = []
# Finde den Block von Kandidaten
block_key = match_row['block_key']
candidates = crm_index.get(block_key, [])
# Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch
for crm_row in candidates: for crm_row in candidates:
score = calculate_similarity(match_row, crm_row) score = calculate_similarity(match_row, crm_row)
if score > best_score: scored.append((crm_row['CRM Name'], score))
best_score = score # Top 3 loggen
best_match_name = crm_row['CRM Name'] top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3]
logger.debug(f" Top 3 Kandidaten: {top3}")
# Besten Treffer wählen
best_name, best_score = max(scored, key=lambda x: x[1])
if best_score >= SCORE_THRESHOLD: if best_score >= SCORE_THRESHOLD:
results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score}) results.append({'Potenzieller Treffer im CRM': best_name, 'Ähnlichkeits-Score': best_score})
logger.info(f" --> Match: '{best_name}' mit Score {best_score}")
else: else:
# Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen results.append({'Potenzieller Treffer im CRM': '', 'Ähnlichkeits-Score': best_score})
results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score}) logger.info(f" --> Kein Match (höchster Score {best_score})")
logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") # Ergebnisse zurückschreiben
result_df = pd.DataFrame(results) out_df = pd.DataFrame(results)
output = pd.concat([match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].reset_index(drop=True), out_df], axis=1)
# Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen data = [output.columns.tolist()] + output.values.tolist()
output_df = matching_df[['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land']].copy() ok = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
output_df = pd.concat([output_df.reset_index(drop=True), result_df], axis=1) if ok:
logger.info("Ergebnisse erfolgreich geschrieben")
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 das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
else: else:
logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.") logger.error("Fehler beim Schreiben ins Google Sheet")
if __name__ == "__main__": if __name__ == '__main__':
main() main()