duplicate_checker_old.py aktualisiert
This commit is contained in:
@@ -1,12 +1,12 @@
|
||||
# duplicate_checker.py v5.0
|
||||
# duplicate_checker.py v4.0
|
||||
# Build timestamp is injected into logfile name.
|
||||
|
||||
# --- FEATURES v5.0 ---
|
||||
# - NEU: Mehrstufiges Entscheidungsmodell für höhere Präzision und "Großzügigkeit".
|
||||
# - Stufe 1: "Golden Match" für exakte Treffer.
|
||||
# - Stufe 2: "Kernidentitäts-Bonus & Tie-Breaker" zur korrekten Zuordnung von Konzerngesellschaften.
|
||||
# - Stufe 3: Neu kalibrierter, gewichteter Score für alle anderen Fälle.
|
||||
# - Intelligenter Tie-Breaker, der nur bei wirklich guten und engen Kandidaten greift.
|
||||
# --- FEATURES v4.0 ---
|
||||
# - NEU: "Kernidentitäts-Bonus": Ein hoher Bonus wird vergeben, wenn das seltenste (wichtigste) Token übereinstimmt.
|
||||
# Dies fördert das "großzügige Matchen" auf Basis der Kernmarke (z.B. "ANDRITZ AG" vs. "ANDRITZ HYDRO").
|
||||
# - NEU: Intelligenter "Shortest Name Tie-Breaker": Wird nur noch bei sehr hohen und sehr ähnlichen Scores angewendet.
|
||||
# - Finale Kalibrierung der Score-Berechnung und Schwellenwerte für optimale Balance.
|
||||
# - Golden-Rule für exakte Matches und Interaktiver Modus beibehalten.
|
||||
|
||||
import os
|
||||
import sys
|
||||
@@ -30,41 +30,49 @@ def update_status(job_id, status, progress_message):
|
||||
status_file = os.path.join(STATUS_DIR, f"{job_id}.json")
|
||||
try:
|
||||
try:
|
||||
with open(status_file, 'r') as f: data = json.load(f)
|
||||
except FileNotFoundError: data = {}
|
||||
with open(status_file, 'r') as f:
|
||||
data = json.load(f)
|
||||
except FileNotFoundError:
|
||||
data = {}
|
||||
|
||||
data.update({"status": status, "progress": progress_message})
|
||||
with open(status_file, 'w') as f: json.dump(data, f)
|
||||
|
||||
with open(status_file, 'w') as f:
|
||||
json.dump(data, f)
|
||||
except Exception as e:
|
||||
logging.error(f"Konnte Statusdatei für Job {job_id} nicht schreiben: {e}")
|
||||
|
||||
# --- Konfiguration v5.0 ---
|
||||
# --- Konfiguration ---
|
||||
CRM_SHEET_NAME = "CRM_Accounts"
|
||||
MATCHING_SHEET_NAME = "Matching_Accounts"
|
||||
LOG_DIR = "Log"
|
||||
now = datetime.now().strftime('%Y-%m-%d_%H-%M')
|
||||
LOG_FILE = f"{now}_duplicate_check_v5.0.txt"
|
||||
LOG_FILE = f"{now}_duplicate_check_v4.0.txt"
|
||||
|
||||
# Scoring-Konfiguration
|
||||
SCORE_THRESHOLD = 110 # Standard-Schwelle
|
||||
SCORE_THRESHOLD_WEAK= 140 # Schwelle für Matches ohne Domain oder Ort
|
||||
# --- Scoring-Konfiguration v4.0 ---
|
||||
SCORE_THRESHOLD = 100 # Standard-Schwelle
|
||||
SCORE_THRESHOLD_WEAK= 130 # Schwelle für Matches ohne Domain oder Ort
|
||||
GOLDEN_MATCH_RATIO = 97
|
||||
GOLDEN_MATCH_SCORE = 300
|
||||
CORE_IDENTITY_BONUS = 50 # Bonus für die Übereinstimmung des wichtigsten Tokens
|
||||
CORE_IDENTITY_BONUS = 60 # NEU: Bonus für die Übereinstimmung des wichtigsten Tokens
|
||||
|
||||
# Tie-Breaker & Interaktiver Modus
|
||||
TRIGGER_SCORE_MIN = 150 # Mindestscore für Tie-Breaker / Interaktiv
|
||||
TIE_SCORE_DIFF = 25
|
||||
# Tie-Breaker & Interaktiver Modus Konfiguration
|
||||
TRIGGER_SCORE_MIN = 150 # NEU: Mindestscore für Tie-Breaker / Interaktiv
|
||||
TIE_SCORE_DIFF = 20
|
||||
|
||||
# Prefilter
|
||||
# Prefilter-Konfiguration
|
||||
PREFILTER_MIN_PARTIAL = 70
|
||||
PREFILTER_LIMIT = 30
|
||||
|
||||
# --- Logging Setup ---
|
||||
if not os.path.exists(LOG_DIR): os.makedirs(LOG_DIR, exist_ok=True)
|
||||
# ... (Keine Änderungen hier)
|
||||
if not os.path.exists(LOG_DIR):
|
||||
os.makedirs(LOG_DIR, exist_ok=True)
|
||||
log_path = os.path.join(LOG_DIR, LOG_FILE)
|
||||
root = logging.getLogger()
|
||||
root.setLevel(logging.DEBUG)
|
||||
for h in list(root.handlers): root.removeHandler(h)
|
||||
for h in list(root.handlers):
|
||||
root.removeHandler(h)
|
||||
formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
|
||||
ch = logging.StreamHandler(sys.stdout)
|
||||
ch.setLevel(logging.INFO)
|
||||
@@ -76,13 +84,15 @@ fh.setFormatter(formatter)
|
||||
root.addHandler(fh)
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.info(f"Logging to console and file: {log_path}")
|
||||
logger.info(f"Starting duplicate_checker.py v5.0 | Build: {now}")
|
||||
logger.info(f"Starting duplicate_checker.py v4.0 | Build: {now}")
|
||||
|
||||
# --- API Keys ---
|
||||
# --- SerpAPI Key laden ---
|
||||
# ... (Keine Änderungen hier)
|
||||
try:
|
||||
Config.load_api_keys()
|
||||
serp_key = Config.API_KEYS.get('serpapi')
|
||||
if not serp_key: logger.warning("SerpAPI Key nicht gefunden; Serp-Fallback deaktiviert.")
|
||||
if not serp_key:
|
||||
logger.warning("SerpAPI Key nicht gefunden; Serp-Fallback deaktiviert.")
|
||||
except Exception as e:
|
||||
logger.warning(f"Fehler beim Laden API-Keys: {e}")
|
||||
serp_key = None
|
||||
@@ -113,25 +123,34 @@ def build_term_weights(crm_df: pd.DataFrame):
|
||||
logger.info("Starte Berechnung der Wortgewichte (TF-IDF)...")
|
||||
token_counts = Counter()
|
||||
total_docs = len(crm_df)
|
||||
|
||||
for name in crm_df['normalized_name']:
|
||||
_, tokens = clean_name_for_scoring(name)
|
||||
for token in set(tokens): token_counts[token] += 1
|
||||
term_weights = {token: math.log(total_docs / (count + 1)) for token, count in token_counts.items()}
|
||||
for token in set(tokens):
|
||||
token_counts[token] += 1
|
||||
|
||||
term_weights = {}
|
||||
for token, count in token_counts.items():
|
||||
idf = math.log(total_docs / (count + 1))
|
||||
term_weights[token] = idf
|
||||
|
||||
logger.info(f"Wortgewichte für {len(term_weights)} Tokens berechnet.")
|
||||
return term_weights
|
||||
|
||||
# --- Similarity v5.0 ---
|
||||
def calculate_similarity(mrec: dict, crec: dict, term_weights: dict, rarest_token_mrec: str):
|
||||
# --- Similarity v4.0 ---
|
||||
def calculate_similarity(mrec: dict, crec: dict, term_weights: dict):
|
||||
|
||||
n1_raw = mrec.get('normalized_name', '')
|
||||
n2_raw = crec.get('normalized_name', '')
|
||||
if fuzz.ratio(n1_raw, n2_raw) >= GOLDEN_MATCH_RATIO:
|
||||
return GOLDEN_MATCH_SCORE, {'reason': f'Golden Match (Ratio >= {GOLDEN_MATCH_RATIO}%)'}
|
||||
return GOLDEN_MATCH_SCORE, {'reason': f'Golden Match (Ratio >= {GOLDEN_MATCH_RATIO}%)', 'name_score': 100}
|
||||
|
||||
dom1, dom2 = mrec.get('normalized_domain',''), crec.get('normalized_domain','')
|
||||
dom1 = mrec.get('normalized_domain','')
|
||||
dom2 = crec.get('normalized_domain','')
|
||||
domain_match = 1 if (dom1 and dom1 == dom2) else 0
|
||||
|
||||
city_match = 1 if mrec.get('CRM Ort') and crec.get('CRM Ort') == mrec.get('CRM Ort') else 0
|
||||
country_match = 1 if mrec.get('CRM Land') and crec.get('CRM Land') == mrec.get('CRM Land') else 0
|
||||
city_match = 1 if (mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('CRM Ort')) else 0
|
||||
country_match = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec.get('CRM Land') == crec.get('CRM Land')) else 0
|
||||
|
||||
clean1, toks1 = clean_name_for_scoring(n1_raw)
|
||||
clean2, toks2 = clean_name_for_scoring(n2_raw)
|
||||
@@ -139,29 +158,46 @@ def calculate_similarity(mrec: dict, crec: dict, term_weights: dict, rarest_toke
|
||||
name_score = 0
|
||||
overlapping_tokens = toks1 & toks2
|
||||
if overlapping_tokens:
|
||||
name_score = sum(term_weights.get(t, 0) for t in overlapping_tokens)
|
||||
if toks1: name_score *= (1 + len(overlapping_tokens) / len(toks1))
|
||||
name_score = sum(term_weights.get(token, 0) for token in overlapping_tokens)
|
||||
if toks1:
|
||||
overlap_percentage = len(overlapping_tokens) / len(toks1)
|
||||
name_score *= (1 + overlap_percentage)
|
||||
|
||||
core_identity_bonus = CORE_IDENTITY_BONUS if rarest_token_mrec and rarest_token_mrec in toks2 else 0
|
||||
# --- NEU v4.0: Kernidentitäts-Bonus ---
|
||||
core_identity_bonus = 0
|
||||
rarest_token_mrec = choose_rarest_token(n1_raw, term_weights)
|
||||
if rarest_token_mrec and rarest_token_mrec in toks2:
|
||||
core_identity_bonus = CORE_IDENTITY_BONUS
|
||||
|
||||
# Domain-Gate
|
||||
score_domain = 0
|
||||
if domain_match:
|
||||
if name_score > 3.0 or (city_match and country_match): score_domain = 75
|
||||
else: score_domain = 20
|
||||
if name_score > 2.0 or (city_match and country_match):
|
||||
score_domain = 70
|
||||
else:
|
||||
score_domain = 20
|
||||
|
||||
score_location = 25 if (city_match and country_match) else 0
|
||||
|
||||
# Finale Score-Kalibrierung v4.0
|
||||
total = name_score * 10 + score_domain + score_location + core_identity_bonus
|
||||
|
||||
penalties = 0
|
||||
if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match: penalties += 40
|
||||
if mrec.get('CRM Ort') and crec.get('CRM Ort') and not city_match: penalties += 30
|
||||
if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match:
|
||||
penalties += 40
|
||||
if mrec.get('CRM Ort') and crec.get('CRM Ort') and not city_match:
|
||||
penalties += 30
|
||||
total -= penalties
|
||||
|
||||
comp = {
|
||||
'name_score': round(name_score,1), 'domain': domain_match, 'location': int(city_match and country_match),
|
||||
'core_bonus': core_identity_bonus, 'penalties': penalties, 'tokens': list(overlapping_tokens)
|
||||
'name_score': round(name_score,1),
|
||||
'domain_match': domain_match,
|
||||
'location_match': int(city_match and country_match),
|
||||
'core_bonus': core_identity_bonus,
|
||||
'penalties': penalties,
|
||||
'overlapping_tokens': list(overlapping_tokens)
|
||||
}
|
||||
|
||||
return max(0, round(total)), comp
|
||||
|
||||
# --- Indexe & Hauptfunktion ---
|
||||
@@ -171,23 +207,28 @@ def build_indexes(crm_df: pd.DataFrame):
|
||||
for r in records:
|
||||
d = r.get('normalized_domain')
|
||||
if d: domain_index.setdefault(d, []).append(r)
|
||||
|
||||
token_index = {}
|
||||
for idx, r in enumerate(records):
|
||||
_, toks = clean_name_for_scoring(r.get('normalized_name',''))
|
||||
for t in set(toks): token_index.setdefault(t, []).append(idx)
|
||||
for t in set(toks):
|
||||
token_index.setdefault(t, []).append(idx)
|
||||
|
||||
return records, domain_index, token_index
|
||||
|
||||
def choose_rarest_token(norm_name: str, term_weights: dict):
|
||||
_, toks = clean_name_for_scoring(norm_name)
|
||||
if not toks: return None
|
||||
return max(toks, key=lambda t: term_weights.get(t, 0))
|
||||
rarest = max(toks, key=lambda t: term_weights.get(t, 0))
|
||||
return rarest if term_weights.get(rarest, 0) > 0 else None
|
||||
|
||||
def main(job_id=None, interactive=False):
|
||||
logger.info("Starte Duplikats-Check v5.0 (Hybrid Model & Core Identity)")
|
||||
# ... (Initialisierung und Datenladen bleibt identisch)
|
||||
logger.info("Starte Duplikats-Check v4.0 (Core Identity Bonus)")
|
||||
# ... (Code für Initialisierung und Datenladen bleibt identisch) ...
|
||||
update_status(job_id, "Läuft", "Initialisiere GoogleSheetHandler...")
|
||||
try:
|
||||
sheet = GoogleSheetHandler()
|
||||
logger.info("GoogleSheetHandler initialisiert")
|
||||
except Exception as e:
|
||||
logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
|
||||
update_status(job_id, "Fehlgeschlagen", f"Init GoogleSheetHandler fehlgeschlagen: {e}")
|
||||
@@ -197,21 +238,31 @@ def main(job_id=None, interactive=False):
|
||||
crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
|
||||
match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
|
||||
total = len(match_df) if match_df is not None else 0
|
||||
logger.info(f"{0 if crm_df is None else len(crm_df)} CRM-Datensätze | {total} Matching-Datensätze")
|
||||
if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
|
||||
logger.critical("Leere Daten in einem der Sheets. Abbruch.")
|
||||
update_status(job_id, "Fehlgeschlagen", "Leere Daten in einem der Sheets.")
|
||||
return
|
||||
logger.info(f"{len(crm_df)} CRM-Datensätze | {total} Matching-Datensätze")
|
||||
|
||||
update_status(job_id, "Läuft", "Normalisiere Daten...")
|
||||
for df in [crm_df, match_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()
|
||||
crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
|
||||
crm_df['normalized_domain'] = crm_df['CRM Website'].astype(str).apply(simple_normalize_url)
|
||||
crm_df['CRM Ort'] = crm_df['CRM Ort'].astype(str).str.lower().str.strip()
|
||||
crm_df['CRM Land'] = crm_df['CRM Land'].astype(str).str.lower().str.strip()
|
||||
|
||||
match_df['normalized_name'] = match_df['CRM Name'].astype(str).apply(normalize_company_name)
|
||||
match_df['normalized_domain'] = match_df['CRM Website'].astype(str).apply(simple_normalize_url)
|
||||
match_df['CRM Ort'] = match_df['CRM Ort'].astype(str).str.lower().str.strip()
|
||||
match_df['CRM Land'] = match_df['CRM Land'].astype(str).str.lower().str.strip()
|
||||
|
||||
def build_city_tokens(crm_df, match_df):
|
||||
cities = set()
|
||||
for s in pd.concat([crm_df['CRM Ort'], match_df['CRM Ort']], ignore_index=True).dropna().unique():
|
||||
for t in _tokenize(s):
|
||||
if len(t) >= 3: cities.add(t)
|
||||
return cities
|
||||
global CITY_TOKENS
|
||||
CITY_TOKENS = {t for s in pd.concat([crm_df['CRM Ort'], match_df['CRM Ort']]).dropna().unique() for t in _tokenize(s) if len(t) >= 3}
|
||||
CITY_TOKENS = build_city_tokens(crm_df, match_df)
|
||||
logger.info(f"City tokens gesammelt: {len(CITY_TOKENS)}")
|
||||
|
||||
term_weights = build_term_weights(crm_df)
|
||||
@@ -225,82 +276,115 @@ def main(job_id=None, interactive=False):
|
||||
processed = idx + 1
|
||||
progress_message = f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}'"
|
||||
logger.info(progress_message)
|
||||
if processed % 10 == 0 or processed == total: update_status(job_id, "Läuft", progress_message)
|
||||
if processed % 5 == 0 or processed == total:
|
||||
update_status(job_id, "Läuft", progress_message)
|
||||
|
||||
candidate_indices = set()
|
||||
# ... (Kandidatensuche bleibt gleich)
|
||||
used_block = ''
|
||||
|
||||
# ... (Kandidatensuche bleibt gleich) ...
|
||||
if mrow.get('normalized_domain'):
|
||||
candidates_from_domain = domain_index.get(mrow['normalized_domain'], [])
|
||||
for c in candidates_from_domain:
|
||||
try:
|
||||
indices = crm_df.index[(crm_df['normalized_name'] == c['normalized_name']) & (crm_df['normalized_domain'] == c['normalized_domain'])].tolist()
|
||||
if indices: candidate_indices.add(indices[0])
|
||||
except Exception: continue
|
||||
|
||||
rarest_token_mrec = choose_rarest_token(mrow.get('normalized_name',''), term_weights)
|
||||
if not candidate_indices and rarest_token_mrec:
|
||||
candidate_indices.update(token_index.get(rarest_token_mrec, []))
|
||||
|
||||
if not candidate_indices:
|
||||
pf = sorted([(fuzz.partial_ratio(clean_name_for_scoring(mrow.get('normalized_name',''))[0], clean_name_for_scoring(r.get('normalized_name',''))[0]), i) for i, r in enumerate(crm_records)], key=lambda x: x[0], reverse=True)
|
||||
candidate_indices.update([i for score, i in pf if score >= PREFILTER_MIN_PARTIAL][:PREFILTER_LIMIT])
|
||||
if indices:
|
||||
candidate_indices.add(indices[0])
|
||||
except Exception:
|
||||
continue
|
||||
if candidate_indices: used_block = f"domain:{mrow['normalized_domain']}"
|
||||
|
||||
if not candidate_indices:
|
||||
rtok = choose_rarest_token(mrow.get('normalized_name',''), term_weights)
|
||||
if rtok:
|
||||
indices_from_token = token_index.get(rtok, [])
|
||||
candidate_indices.update(indices_from_token)
|
||||
used_block = f"token:{rtok}"
|
||||
|
||||
if not candidate_indices:
|
||||
pf = []
|
||||
n1 = mrow.get('normalized_name','')
|
||||
clean1, _ = clean_name_for_scoring(n1)
|
||||
if clean1:
|
||||
for i, r in enumerate(crm_records):
|
||||
n2 = r.get('normalized_name','')
|
||||
clean2, _ = clean_name_for_scoring(n2)
|
||||
if not clean2: continue
|
||||
pr = fuzz.partial_ratio(clean1, clean2)
|
||||
if pr >= PREFILTER_MIN_PARTIAL:
|
||||
pf.append((pr, i))
|
||||
pf.sort(key=lambda x: x[0], reverse=True)
|
||||
candidate_indices.update([i for _, i in pf[:PREFILTER_LIMIT]])
|
||||
used_block = f"prefilter:{PREFILTER_MIN_PARTIAL}/{len(pf)}"
|
||||
|
||||
candidates = [crm_records[i] for i in candidate_indices]
|
||||
logger.info(f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}' -> {len(candidates)} Kandidaten (Block={used_block})")
|
||||
if not candidates:
|
||||
results.append({'Match':'', 'Score':0, 'Match_Grund':'keine Kandidaten'})
|
||||
continue
|
||||
|
||||
scored = sorted([{'score': s, 'comp': c, 'record': r} for r in candidates for s, c in [calculate_similarity(mrow, r, term_weights, rarest_token_mrec)]], key=lambda x: x['score'], reverse=True)
|
||||
scored = []
|
||||
for cr in candidates:
|
||||
score, comp = calculate_similarity(mrow, cr, term_weights)
|
||||
scored.append({'name': cr.get('CRM Name',''), 'score': score, 'comp': comp, 'record': cr})
|
||||
scored.sort(key=lambda x: x['score'], reverse=True)
|
||||
|
||||
for cand in scored[:5]:
|
||||
logger.debug(f" Kandidat: {cand['name']} | Score={cand['score']} | Comp={cand['comp']}")
|
||||
|
||||
for cand in scored[:5]: logger.debug(f" Kandidat: {cand['record']['CRM Name']} | Score={cand['score']} | Comp={cand['comp']}")
|
||||
best_match = scored[0] if scored else None
|
||||
|
||||
# --- Stufenmodell-Logik v5.0 ---
|
||||
if best_match:
|
||||
# Stufe 1 ist bereits in calculate_similarity behandelt (score=300)
|
||||
|
||||
# Stufe 2: Intelligenter Tie-Breaker für Konzern-Logik
|
||||
# --- Intelligenter Tie-Breaker v4.0 ---
|
||||
if best_match and len(scored) > 1:
|
||||
best_score = best_match['score']
|
||||
if len(scored) > 1 and best_score >= TRIGGER_SCORE_MIN and (best_score - scored[1]['score']) < TIE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE:
|
||||
if interactive: # Stufe 4: Manuelle Klärung
|
||||
# ... (Interaktive Logik wie gehabt)
|
||||
pass
|
||||
else: # Stufe 2 Automatik
|
||||
logger.info(f" Tie-Breaker-Situation erkannt. Scores: {best_score} vs {scored[1]['score']}")
|
||||
tie_candidates = [c for c in scored if (best_score - c['score']) < TIE_SCORE_DIFF]
|
||||
original_best = best_match
|
||||
best_match_by_length = min(tie_candidates, key=lambda x: len(x['record']['normalized_name']))
|
||||
if best_match_by_length['record']['CRM Name'] != original_best['record']['CRM Name']:
|
||||
logger.info(f" Tie-Breaker angewendet: '{original_best['record']['CRM Name']}' -> '{best_match_by_length['record']['CRM Name']}' (kürzer).")
|
||||
best_match = best_match_by_length
|
||||
second_best_score = scored[1]['score']
|
||||
if best_score >= TRIGGER_SCORE_MIN and (best_score - second_best_score) < TIE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE:
|
||||
logger.info(f" Tie-Breaker-Situation erkannt für '{mrow['CRM Name']}'. Scores: {best_score} vs {second_best_score}")
|
||||
tie_candidates = [c for c in scored if (best_score - c['score']) < TIE_SCORE_DIFF]
|
||||
best_match_by_length = min(tie_candidates, key=lambda x: len(x['name']))
|
||||
if best_match_by_length['name'] != best_match['name']:
|
||||
logger.info(f" Tie-Breaker angewendet: '{best_match['name']}' ({best_score}) -> '{best_match_by_length['name']}' ({best_match_by_length['score']}) wegen kürzerem Namen.")
|
||||
best_match = best_match_by_length
|
||||
|
||||
# Finale Entscheidung und Logging
|
||||
# Interaktiver Modus
|
||||
if interactive and best_match and len(scored) > 1:
|
||||
best_score = best_match['score']
|
||||
second_best_score = scored[1]['score']
|
||||
if best_score > INTERACTIVE_SCORE_MIN and (best_score - second_best_score) < INTERACTIVE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE:
|
||||
# ... (Interaktive Logik bleibt gleich) ...
|
||||
print("\n" + "="*50)
|
||||
# ...
|
||||
|
||||
if best_match and best_match['score'] >= SCORE_THRESHOLD:
|
||||
is_weak = best_match['comp'].get('domain', 0) == 0 and not best_match['comp'].get('location', 0)
|
||||
is_weak = best_match['comp'].get('domain_match', 0) == 0 and not (best_match['comp'].get('location_match', 0))
|
||||
applied_threshold = SCORE_THRESHOLD_WEAK if is_weak else SCORE_THRESHOLD
|
||||
|
||||
if best_match['score'] >= applied_threshold:
|
||||
results.append({'Match': best_match['record']['CRM Name'], 'Score': best_match['score'], 'Match_Grund': str(best_match['comp'])})
|
||||
logger.info(f" --> Match: '{best_match['record']['CRM Name']}' ({best_match['score']})")
|
||||
results.append({'Match': best_match['name'], 'Score': best_match['score'], 'Match_Grund': str(best_match['comp'])})
|
||||
logger.info(f" --> Match: '{best_match['name']}' ({best_match['score']}) | TH={applied_threshold}{' (weak)' if is_weak else ''}")
|
||||
else:
|
||||
results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below WEAK TH | {str(best_match['comp'])}"})
|
||||
logger.info(f" --> No Match (below weak TH): '{best_match['record']['CRM Name']}' ({best_match['score']})")
|
||||
results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below WEAK threshold | {str(best_match['comp'])}"})
|
||||
logger.info(f" --> No Match (below weak TH): '{best_match['name']}' ({best_match['score']}) | TH={applied_threshold}")
|
||||
elif best_match:
|
||||
results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below TH | {str(best_match['comp'])}"})
|
||||
logger.info(f" --> No Match (below TH): '{best_match['record']['CRM Name']}' ({best_match['score']})")
|
||||
results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below threshold | {str(best_match['comp'])}"})
|
||||
logger.info(f" --> No Match (below TH): '{best_match['name']}' ({best_match['score']})")
|
||||
else:
|
||||
results.append({'Match':'', 'Score':0, 'Match_Grund':'No valid candidates'})
|
||||
results.append({'Match':'', 'Score':0, 'Match_Grund':'No valid candidates or user override'})
|
||||
logger.info(f" --> No Match (no candidates)")
|
||||
|
||||
# --- Ergebnisse zurückschreiben ---
|
||||
logger.info("Matching-Prozess abgeschlossen. Schreibe Ergebnisse...")
|
||||
# --- Ergebnisse zurückschreiben (Logik unverändert) ---
|
||||
logger.info("Matching-Prozess abgeschlossen. Bereite Ergebnisse für den Upload vor...")
|
||||
# ... (Rest des Codes bleibt identisch) ...
|
||||
update_status(job_id, "Läuft", "Schreibe Ergebnisse zurück ins Sheet...")
|
||||
result_df = pd.DataFrame(results)
|
||||
final_df = pd.concat([match_df.reset_index(drop=True), result_df.reset_index(drop=True)], axis=1)
|
||||
cols_to_drop_from_match = ['Match', 'Score', 'Match_Grund']
|
||||
match_df_clean = match_df.drop(columns=[col for col in cols_to_drop_from_match if col in match_df.columns], errors='ignore')
|
||||
final_df = pd.concat([match_df_clean.reset_index(drop=True), result_df.reset_index(drop=True)], axis=1)
|
||||
cols_to_drop = ['normalized_name', 'normalized_domain']
|
||||
final_df = final_df.drop(columns=[col for col in cols_to_drop if col in final_df.columns], errors='ignore')
|
||||
upload_df = final_df.astype(str).replace({'nan': '', 'None': ''})
|
||||
data_to_write = [upload_df.columns.tolist()] + upload_df.values.tolist()
|
||||
|
||||
logger.info(f"Versuche, {len(data_to_write) - 1} Ergebniszeilen in das Sheet '{MATCHING_SHEET_NAME}' zu schreiben...")
|
||||
ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
|
||||
if ok:
|
||||
logger.info("Ergebnisse erfolgreich in das Google Sheet geschrieben.")
|
||||
@@ -310,10 +394,11 @@ def main(job_id=None, interactive=False):
|
||||
update_status(job_id, "Fehlgeschlagen", "Fehler beim Schreiben ins Google Sheet.")
|
||||
|
||||
if __name__=='__main__':
|
||||
parser = argparse.ArgumentParser(description="Duplicate Checker v5.0")
|
||||
parser = argparse.ArgumentParser(description="Duplicate Checker v4.0")
|
||||
parser.add_argument("--job-id", type=str, help="Eindeutige ID für den Job-Status.")
|
||||
parser.add_argument("--interactive", action='store_true', help="Aktiviert den interaktiven Modus für unklare Fälle.")
|
||||
args = parser.parse_args()
|
||||
|
||||
Config.load_api_keys()
|
||||
|
||||
main(job_id=args.job_id, interactive=args.interactive)
|
||||
Reference in New Issue
Block a user