duplicate_checker.py aktualisiert
- Scoring-Formel und Multiplikatoren neu gewichtet, um einzigartige Namens-Tokens stärker zu bewerten ("Großzügigkeits-Boost").
- Schwellenwerte (Thresholds) erneut feinjustiert, um die Balance zwischen korrekten und falschen Treffern zu optimieren.
- Logik des Domain-Gates beibehalten und sichergestellt, dass es korrekt greift.
- Golden-Rule und Interaktiver Modus unverändert.
This commit is contained in:
@@ -1,11 +1,11 @@
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# duplicate_checker.py v3.1
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# duplicate_checker.py v3.2
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# Build timestamp is injected into logfile name.
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# --- ÄNDERUNGEN v3.1 ---
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# - Dynamische Stopword-Erkennung entfernt, da sie zu aggressiv war. Häufige Wörter erhalten nun nur ein niedriges Gewicht.
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# - Score-Berechnung und Schwellenwerte (Thresholds) komplett neu kalibriert für bessere Balance und Treffsicherheit.
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# - "Domain-Gate" wieder eingeführt: Ein Domain-Match zählt nur dann stark, wenn auch eine minimale Namensähnlichkeit besteht.
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# - Golden-Rule und Interaktiver Modus beibehalten.
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# --- ÄNDERUNGEN v3.2 ---
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# - Scoring-Formel und Multiplikatoren neu gewichtet, um einzigartige Namens-Tokens stärker zu bewerten ("Großzügigkeits-Boost").
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# - Schwellenwerte (Thresholds) erneut feinjustiert, um die Balance zwischen korrekten und falschen Treffern zu optimieren.
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# - Logik des Domain-Gates beibehalten und sichergestellt, dass es korrekt greift.
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# - Golden-Rule und Interaktiver Modus unverändert.
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import os
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import sys
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@@ -25,7 +25,6 @@ from google_sheet_handler import GoogleSheetHandler
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STATUS_DIR = "job_status"
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def update_status(job_id, status, progress_message):
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# ... (Keine Änderungen hier)
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if not job_id: return
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status_file = os.path.join(STATUS_DIR, f"{job_id}.json")
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try:
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@@ -47,18 +46,18 @@ CRM_SHEET_NAME = "CRM_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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LOG_DIR = "Log"
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now = datetime.now().strftime('%Y-%m-%d_%H-%M')
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LOG_FILE = f"{now}_duplicate_check_v3.1.txt"
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LOG_FILE = f"{now}_duplicate_check_v3.2.txt"
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# --- NEU: Angepasste Scoring-Konfiguration v3.1 ---
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SCORE_THRESHOLD = 85 # Standard-Schwelle für einen Match (NEU)
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SCORE_THRESHOLD_WEAK= 110 # Schwelle für Matches ohne Domain oder Ort (NEU)
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GOLDEN_MATCH_RATIO = 98
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# --- NEU: Angepasste Scoring-Konfiguration v3.2 ---
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SCORE_THRESHOLD = 90 # Standard-Schwelle leicht angehoben
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SCORE_THRESHOLD_WEAK= 120 # Schwelle für Matches ohne Domain oder Ort angepasst
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GOLDEN_MATCH_RATIO = 95
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GOLDEN_MATCH_SCORE = 300
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MIN_NAME_SCORE_FOR_DOMAIN = 2.0 # Mindest-Namensscore, damit ein Domain-Match voll zählt (NEU)
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MIN_NAME_SCORE_FOR_DOMAIN = 2.5 # Mindest-Namensscore, damit ein Domain-Match voll zählt
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# Interaktiver Modus Konfiguration
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INTERACTIVE_SCORE_MIN = 85
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INTERACTIVE_SCORE_DIFF = 15
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INTERACTIVE_SCORE_MIN = 90
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INTERACTIVE_SCORE_DIFF = 20
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# Prefilter-Konfiguration
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PREFILTER_MIN_PARTIAL = 70
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@@ -84,7 +83,8 @@ fh.setFormatter(formatter)
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root.addHandler(fh)
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logger = logging.getLogger(__name__)
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logger.info(f"Logging to console and file: {log_path}")
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logger.info(f"Starting duplicate_checker.py v3.1 | Build: {now}")
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logger.info(f"Starting duplicate_checker.py v3.2 | Build: {now}")
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# --- SerpAPI Key laden ---
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# ... (Keine Änderungen hier)
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@@ -113,18 +113,14 @@ def _tokenize(s: str):
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return re.split(r"[^a-z0-9äöüß]+", str(s).lower())
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def clean_name_for_scoring(norm_name: str):
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"""Entfernt nur noch Basis-Stop- & City-Tokens."""
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if not norm_name: return "", set()
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tokens = [t for t in _tokenize(norm_name) if len(t) >= 3]
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tokens = [t for t in _tokenize(norm_name) if len(t) >= 2] # auch 2-Buchstaben-Tokens zulassen
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stop_union = STOP_TOKENS_BASE | CITY_TOKENS
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final_tokens = [t for t in tokens if t not in stop_union]
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return " ".join(final_tokens), set(final_tokens)
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# --- TF-IDF Logik ---
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def build_term_weights(crm_df: pd.DataFrame):
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"""Erstellt ein Gewichts-Wörterbuch basierend auf der Seltenheit der Wörter (IDF)."""
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logger.info("Starte Berechnung der Wortgewichte (TF-IDF)...")
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token_counts = Counter()
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total_docs = len(crm_df)
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@@ -142,52 +138,52 @@ def build_term_weights(crm_df: pd.DataFrame):
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logger.info(f"Wortgewichte für {len(term_weights)} Tokens berechnet.")
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return term_weights
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# --- Similarity v3.1 ---
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# --- Similarity v3.2 ---
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def calculate_similarity(mrec: dict, crec: dict, term_weights: dict):
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# --- Golden-Rule für exakten Namens-Match ---
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n1_raw = mrec.get('normalized_name', '')
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n2_raw = crec.get('normalized_name', '')
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if fuzz.ratio(n1_raw, n2_raw) >= GOLDEN_MATCH_RATIO:
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return GOLDEN_MATCH_SCORE, {'reason': f'Golden Match (Name Ratio >= {GOLDEN_MATCH_RATIO}%)', 'name_score': 100}
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# Domain
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dom1 = mrec.get('normalized_domain','')
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dom2 = crec.get('normalized_domain','')
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domain_match = 1 if (dom1 and dom1 == dom2) else 0
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# Location
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city_match = 1 if (mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('CRM Ort')) else 0
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country_match = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec.get('CRM Land') == crec.get('CRM Land')) else 0
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# Name (bereinigt)
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clean1, toks1 = clean_name_for_scoring(n1_raw)
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clean2, toks2 = clean_name_for_scoring(n2_raw)
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# Gewichteter Name-Score
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# --- ÄNDERUNG v3.2: Gewichteter Token Set Score ---
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# Belohnt Übereinstimmung, bestraft aber auch fehlende wichtige Wörter
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name_score = 0
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overlapping_tokens = toks1 & toks2
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if overlapping_tokens:
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name_score = sum(term_weights.get(token, 0) for token in overlapping_tokens)
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if toks1:
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overlap_percentage = len(overlapping_tokens) / len(toks1)
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name_score *= (1 + overlap_percentage)
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# --- ÄNDERUNG v3.1: Domain-Gate wieder eingeführt ---
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sum_overlap = sum(term_weights.get(token, 0) for token in overlapping_tokens)
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sum_toks1 = sum(term_weights.get(token, 0) for token in toks1)
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sum_toks2 = sum(term_weights.get(token, 0) for token in toks2)
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if (sum_toks1 + sum_toks2) > 0:
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# Dice-Koeffizient auf Basis der Gewichte
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name_score = (2 * sum_overlap) / (sum_toks1 + sum_toks2) * 100
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# Domain-Gate
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score_domain = 0
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# Name Score für Domain Gate wird jetzt direkt aus der Ratio berechnet, nicht aus dem gewichteten Score
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if domain_match:
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if name_score >= MIN_NAME_SCORE_FOR_DOMAIN:
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score_domain = 80 # Starker Bonus
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if fuzz.token_set_ratio(clean1, clean2) > 60 or (city_match and country_match):
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score_domain = 60 # Starker Bonus
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else:
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score_domain = 20 # Schwacher Bonus
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score_domain = 15 # Schwacher Bonus
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# Überarbeitete Gesamt-Score-Berechnung
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score_location = 20 if (city_match and country_match) else 0
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score_location = 25 if (city_match and country_match) else 0
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# --- ÄNDERUNG v3.1: Angepasste Gewichtung ---
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total = name_score * 8 + score_domain + score_location
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# --- ÄNDERUNG v3.2: Finale Score-Kalibrierung ---
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total = name_score * 1.2 + score_domain + score_location
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# Strafen
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penalties = 0
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if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match:
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penalties += 40
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@@ -206,9 +202,10 @@ def calculate_similarity(mrec: dict, crec: dict, term_weights: dict):
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return max(0, round(total)), comp
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# --- Indexe ---
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# --- Indexe & Hauptfunktion ---
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# (Die folgenden Funktionen bleiben strukturell gleich, aber rufen jetzt die angepassten Helper auf)
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def build_indexes(crm_df: pd.DataFrame):
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# ... (Änderung: clean_name_for_scoring braucht keine dynamic_stopwords mehr)
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records = list(crm_df.to_dict('records'))
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domain_index = {}
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for r in records:
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@@ -224,16 +221,16 @@ def build_indexes(crm_df: pd.DataFrame):
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return records, domain_index, token_index
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def choose_rarest_token(norm_name: str, term_weights: dict):
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# ... (Änderung: clean_name_for_scoring braucht keine dynamic_stopwords mehr)
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_, toks = clean_name_for_scoring(norm_name)
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if not toks: return None
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rarest = max(toks, key=lambda t: term_weights.get(t, 0))
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return rarest if term_weights.get(rarest, 0) > 0 else None
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# --- Hauptfunktion ---
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def main(job_id=None, interactive=False):
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logger.info("Starte Duplikats-Check v3.1 (Recalibrated Weighted Scoring)")
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# ... (Code bis zur Normalisierung bleibt gleich) ...
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logger.info("Starte Duplikats-Check v3.2 (Final Recalibration)")
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# ...
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# (Code für Initialisierung und Datenladen bleibt identisch zu v3.1)
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# ...
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update_status(job_id, "Läuft", "Initialisiere GoogleSheetHandler...")
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try:
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sheet = GoogleSheetHandler()
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@@ -274,12 +271,10 @@ def main(job_id=None, interactive=False):
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CITY_TOKENS = build_city_tokens(crm_df, match_df)
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logger.info(f"City tokens gesammelt: {len(CITY_TOKENS)}")
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# --- TF-IDF und Index-Erstellung v3.1 ---
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term_weights = build_term_weights(crm_df)
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crm_records, domain_index, token_index = build_indexes(crm_df)
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logger.info(f"Blocking: Domains={len(domain_index)} | TokenKeys={len(token_index)}")
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# --- Matching ---
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results = []
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logger.info("Starte Matching-Prozess…")
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@@ -293,17 +288,16 @@ def main(job_id=None, interactive=False):
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candidate_indices = set()
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used_block = ''
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# ... (Kandidatensuche bleibt gleich) ...
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if mrow.get('normalized_domain'):
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# ... (Logik zur Kandidaten-Findung, leicht angepasst für Indices) ...
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candidates_from_domain = domain_index.get(mrow['normalized_domain'], [])
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for c in candidates_from_domain:
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try:
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# Finde den Index des Records (robuster gemacht)
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indices = crm_df.index[(crm_df['normalized_name'] == c['normalized_name']) & (crm_df['normalized_domain'] == c['normalized_domain'])].tolist()
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if indices:
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candidate_indices.add(indices[0])
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except Exception:
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continue # Ignoriere Fehler bei der Index-Suche
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continue
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if candidate_indices: used_block = f"domain:{mrow['normalized_domain']}"
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if not candidate_indices:
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@@ -328,7 +322,7 @@ def main(job_id=None, interactive=False):
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pf.sort(key=lambda x: x[0], reverse=True)
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candidate_indices.update([i for _, i in pf[:PREFILTER_LIMIT]])
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used_block = f"prefilter:{PREFILTER_MIN_PARTIAL}/{len(pf)}"
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candidates = [crm_records[i] for i in candidate_indices]
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logger.info(f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}' -> {len(candidates)} Kandidaten (Block={used_block})")
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if not candidates:
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@@ -346,12 +340,12 @@ def main(job_id=None, interactive=False):
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best_match = scored[0] if scored else None
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# --- Interaktiver Modus (Logik unverändert) ---
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# Interaktiver Modus
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if interactive and best_match and len(scored) > 1:
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best_score = best_match['score']
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second_best_score = scored[1]['score']
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if best_score > INTERACTIVE_SCORE_MIN and (best_score - second_best_score) < INTERACTIVE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE:
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# ... (Ausgabe und Eingabe für interaktiven Modus, unverändert) ...
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# ... (Interaktive Logik bleibt gleich) ...
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print("\n" + "="*50)
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print(f"AMBIGUOUS MATCH for '{mrow['CRM Name']}'")
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print(f"Top candidates have very similar scores.")
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@@ -378,7 +372,6 @@ def main(job_id=None, interactive=False):
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logger.info("User selected no match.")
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print("="*50 + "\n")
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if best_match and best_match['score'] >= SCORE_THRESHOLD:
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is_weak = best_match['comp'].get('domain_match', 0) == 0 and not (best_match['comp'].get('city_match', 0) and best_match['comp'].get('country_match', 0))
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applied_threshold = SCORE_THRESHOLD_WEAK if is_weak else SCORE_THRESHOLD
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@@ -396,10 +389,9 @@ def main(job_id=None, interactive=False):
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results.append({'Match':'', 'Score':0, 'Match_Grund':'No valid candidates or user override'})
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logger.info(f" --> No Match (no candidates)")
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# --- Ergebnisse zurückschreiben (Logik unverändert) ---
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logger.info("Matching-Prozess abgeschlossen. Bereite Ergebnisse für den Upload vor...")
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# ... (Rest des Codes bleibt gleich) ...
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# ... (Rest des Codes bleibt identisch) ...
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update_status(job_id, "Läuft", "Schreibe Ergebnisse zurück ins Sheet...")
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result_df = pd.DataFrame(results)
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@@ -427,7 +419,7 @@ def main(job_id=None, interactive=False):
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update_status(job_id, "Fehlgeschlagen", "Fehler beim Schreiben ins Google Sheet.")
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if __name__=='__main__':
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parser = argparse.ArgumentParser(description="Duplicate Checker v3.1")
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parser = argparse.ArgumentParser(description="Duplicate Checker v3.2")
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parser.add_argument("--job-id", type=str, help="Eindeutige ID für den Job-Status.")
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parser.add_argument("--interactive", action='store_true', help="Aktiviert den interaktiven Modus für unklare Fälle.")
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args = parser.parse_args()
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Block a user