Add Logging

duplicate_checker.py

@@ -1,119 +1,148 @@
-# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring)
+# duplicate_checker.py (v2.0 - Maximum Logging)
 
 import logging
 import pandas as pd
 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, create_log_filename
 from google_sheet_handler import GoogleSheetHandler
-from collections import defaultdict
+import time
 
 # --- 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')
+# --- VOLLSTÄNDIGES LOGGING SETUP ---
+LOG_LEVEL = logging.DEBUG if Config.DEBUG else logging.INFO
+LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s'
 
-def calculate_similarity_details(record1, record2):
-    """Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
+root_logger = logging.getLogger()
+root_logger.setLevel(LOG_LEVEL)
+
+for handler in root_logger.handlers[:]:
+    root_logger.removeHandler(handler)
+
+stream_handler = logging.StreamHandler()
+stream_handler.setFormatter(logging.Formatter(LOG_FORMAT))
+root_logger.addHandler(stream_handler)
+
+log_file_path = create_log_filename("duplicate_check_v2_0")
+if log_file_path:
+    file_handler = logging.FileHandler(log_file_path, mode='a', encoding='utf-8')
+    file_handler.setFormatter(logging.Formatter(LOG_FORMAT))
+    root_logger.addHandler(file_handler)
+
+logger = logging.getLogger(__name__)
+
+def calculate_similarity_with_details(record1, record2):
+    """
+    Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen
+    und gibt die Details für die Begründung 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'):
+    if record1['normalized_domain'] and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2['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['normalized_name'] and record2['normalized_name']:
+        name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
+        scores['name'] = round(name_similarity * 0.7)
 
-    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'):
+    if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
+        if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
             scores['location'] = 20
-
+            
     total_score = sum(scores.values())
-    return {'total': total_score, 'details': scores}
+    
+    reasons = []
+    if scores['domain'] > 0: reasons.append(f"Domain({scores['domain']})")
+    if scores['name'] > 0: reasons.append(f"Name({scores['name']})")
+    if scores['location'] > 0: reasons.append(f"Ort({scores['location']})")
+    reason_text = " + ".join(reasons) if reasons else "Keine Übereinstimmung"
 
-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)
+    return round(total_score), reason_text
 
 def main():
-    logging.info("Starte den Duplikats-Check (v2.2 mit Multi-Key Blocking)...")
+    """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
+    start_time = time.time()
+    logger.info("Starte den Duplikats-Check (v2.0 mit Blocking und Maximum Logging)...")
+    logger.info(f"Logdatei: {log_file_path}")
     
     try:
         sheet_handler = GoogleSheetHandler()
     except Exception as e:
-        logging.critical(f"FEHLER bei Initialisierung: {e}")
+        logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
         return
 
-    logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
+    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: return
+    if crm_df is None or crm_df.empty:
+        return
 
-    logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
+    logger.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
+    if matching_df is None or matching_df.empty:
+        return
     original_matching_df = matching_df.copy()
 
-    logging.info("Normalisiere Daten für den Vergleich...")
+    logger.info("Normalisiere Daten für den Vergleich...")
     for df in [crm_df, matching_df]:
         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['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)
+        df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x and x.split() else None)
 
-    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']:
+    logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
+    crm_index = {}
+    crm_records = crm_df.to_dict('records')
+    for record in crm_records:
+        key = record['block_key']
+        if key:
+            if key not in crm_index:
+                crm_index[key] = []
             crm_index[key].append(record)
-    
-    logging.info("Starte Matching-Prozess...")
+
+    logger.info("Starte Matching-Prozess...")
     results = []
     
     for match_record in matching_df.to_dict('records'):
-        best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
+        best_score = -1
         best_match_name = ""
+        best_reason = ""
         
-        logging.info(f"Prüfe: {match_record['CRM Name']}...")
+        logger.info(f"--- Prüfe: '{match_record.get('CRM Name', 'N/A')}' ---")
+        logger.debug(f"  [Normalisiert: '{match_record.get('normalized_name')}', Domain: '{match_record.get('normalized_domain')}', Key: '{match_record.get('block_key')}']")
 
-        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
+        block_key = match_record.get('block_key')
+        candidates = crm_index.get(block_key, [])
         
-        if not candidate_pool:
-            logging.debug("  -> Keine Kandidaten im Index gefunden.")
+        if not candidates:
+            logger.debug("  -> Keine Kandidaten im Index gefunden. Überspringe Vergleich.")
+            results.append({
+                'Potenzieller Treffer im CRM': "", 'Ähnlichkeits-Score': 0, 'Matching-Grund': "Keine Kandidaten"
+            })
+            continue
+
+        logger.debug(f"  -> Vergleiche mit {len(candidates)} Kandidaten aus Block '{block_key}'.")
         
-        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']
+        for crm_row in candidates:
+            score, reason = calculate_similarity_with_details(match_record, crm_row)
+            
+            if score > 0: # Logge jeden Vergleich, der einen Score > 0 hat
+                logger.debug(f"    - Kandidat: '{crm_row.get('CRM Name', 'N/A')}' -> Score: {score} (Grund: {reason})")
+            
+            if score > best_score:
+                best_score = score
+                best_match_name = crm_row.get('CRM Name', 'N/A')
+                best_reason = reason
+                logger.info(f"  --> Neuer bester Treffer: '{best_match_name}' mit Score {best_score}")
         
         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']
+            'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "",
+            'Ähnlichkeits-Score': best_score,
+            'Matching-Grund': best_reason
         })
 
     logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
@@ -129,5 +158,9 @@ def main():
     else:
         logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
 
+    end_time = time.time()
+    logger.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
+    logger.info(f"===== Skript beendet =====")
+
 if __name__ == "__main__":
     main()