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Solve Applicable_Fee_IDs questions in the dabstep dataset. Use this skill whenever a question asks which fee IDs apply to a merchant, a specific account_type/aci combination, a specific day/month/year, or when reverse-looking up which merchants are affected by a given fee ID.
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基于 SOC 职业分类
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| name | Applicable_Fee_IDs |
| description | Solve Applicable_Fee_IDs questions in the dabstep dataset. Use this skill whenever a question asks which fee IDs apply to a merchant, a specific account_type/aci combination, a specific day/month/year, or when reverse-looking up which merchants are affected by a given fee ID. |
The #1 failure mode is computing the correct answer and then entering a verification loop that exhausts all turns. Once you have the sorted list of applicable fee IDs, format and print the final answer, then submit it. Do NOT spend additional turns verifying individual fees or checking why specific fees don't apply.
fees.json: 1000 fee rules, each with filtering fields (card_scheme, account_type, capture_delay, monthly_fraud_level, monthly_volume, merchant_category_code, is_credit, aci, intracountry) and formula fields (fixed_amount, rate)merchant_data.json: 30 merchants with account_type, capture_delay (string: "1", "2", "7", "immediate", "manual"), acquirer (list), merchant_category_codepayments.csv: Transaction-level data with merchant, year, day_of_year, card_scheme, is_credit, aci, issuing_country, acquirer_country, eur_amount, has_fraudulent_disputemanual.md: Domain definitions — read first if neededA fee rule applies when all specified fields match.
null AND empty list [] both mean the rule applies to all values of that field.
def field_matches_list(rule_list, value):
return not rule_list or value in rule_list
def field_matches_scalar(rule_val, value):
return rule_val is None or rule_val == value
Merchant data stores raw strings; fee rules use categorical strings:
def map_capture_delay(raw):
if raw in ('immediate', 'manual'):
return raw
n = int(raw) # merchant_data has "1", "2", "7"
if n < 3: return '<3'
if n <= 5: return '3-5'
return '>5'
Computed per transaction: issuing_country == acquirer_country
In fee rules: 1.0 = True (domestic), 0.0 = False (international), null = all.
'<100k' → monthly_vol_eur < 100_000
'100k-1m' → 100_000 ≤ vol < 1_000_000
'1m-5m' → 1_000_000 ≤ vol < 5_000_000
'>5m' → vol ≥ 5_000_000
'<7.2%' → fraud_pct < 7.2
'7.2%-7.7%' → 7.2 ≤ pct < 7.7
'7.7%-8.3%' → 7.7 ≤ pct < 8.3
'>8.3%' → pct ≥ 8.3
Fraud rate = sum(eur_amount where has_fraudulent_dispute==True) / sum(eur_amount) × 100 for the merchant in that natural calendar month.
def matches_monthly_volume(rule_vol, monthly_vol):
if rule_vol is None: return True
if rule_vol == '<100k': return monthly_vol < 100_000
if rule_vol == '100k-1m': return 100_000 <= monthly_vol < 1_000_000
if rule_vol == '1m-5m': return 1_000_000 <= monthly_vol < 5_000_000
return monthly_vol >= 5_000_000 # '>5m'
def matches_monthly_fraud(rule_fraud, fraud_pct):
if rule_fraud is None: return True
if rule_fraud == '<7.2%': return fraud_pct < 7.2
if rule_fraud == '7.2%-7.7%': return 7.2 <= fraud_pct < 7.7
if rule_fraud == '7.7%-8.3%': return 7.7 <= fraud_pct < 8.3
return fraud_pct >= 8.3 # '>8.3%'
def fee_applies(fee, card_scheme, is_credit, aci, intracountry,
account_type, capture_delay_mapped, mcc,
monthly_vol, monthly_fraud_pct):
if not field_matches_scalar(fee['card_scheme'], card_scheme): return False
if not field_matches_list(fee['account_type'], account_type): return False
if not field_matches_scalar(fee['capture_delay'], capture_delay_mapped): return False
if not field_matches_list(fee['merchant_category_code'], mcc): return False
if not field_matches_scalar(fee['is_credit'], is_credit): return False
if not field_matches_list(fee['aci'], aci): return False
if fee['intracountry'] is not None and bool(fee['intracountry']) != intracountry: return False
if not matches_monthly_volume(fee['monthly_volume'], monthly_vol): return False
if not matches_monthly_fraud(fee['monthly_fraud_level'], monthly_fraud_pct): return False
return True
"What fee IDs apply to account_type = X and aci = Y?"
No transaction data needed. Filter fees.json directly:
import json
with open('fees.json') as f: fees = json.load(f)
result = [
fee['ID'] for fee in fees
if field_matches_list(fee['account_type'], 'F') # target account_type
and field_matches_list(fee['aci'], 'A') # target aci
]
print(', '.join(str(x) for x in sorted(result)))
"For the Nth of the year 2023, what Fee IDs apply to Merchant X?"
import pandas as pd, json
with open('merchant_data.json') as f: merchants = json.load(f)
with open('fees.json') as f: fees = json.load(f)
payments = pd.read_csv('payments.csv')
# Merchant properties
merch = next(m for m in merchants if m['merchant'] == 'MerchantName')
account_type = merch['account_type']
mcc = merch['merchant_category_code']
capture_delay = map_capture_delay(merch['capture_delay'])
# Add month column (works for 2023 non-leap year)
merch_payments = payments[(payments['merchant'] == merch['merchant']) & (payments['year'] == 2023)].copy()
merch_payments['month'] = (pd.to_datetime('2023-01-01') +
pd.to_timedelta(merch_payments['day_of_year'] - 1, unit='D')).dt.month
# Target day and its calendar month
target_day = 200
target_month = merch_payments[merch_payments['day_of_year'] == target_day]['month'].iloc[0]
# Monthly stats for that calendar month
month_txns = merch_payments[merch_payments['month'] == target_month]
monthly_vol = month_txns['eur_amount'].sum()
fraud_vol = month_txns[month_txns['has_fraudulent_dispute'] == True]['eur_amount'].sum()
monthly_fraud_pct = (fraud_vol / monthly_vol * 100) if monthly_vol > 0 else 0.0
# Unique profiles from the specific day
day_txns = merch_payments[merch_payments['day_of_year'] == target_day].copy()
day_txns['intracountry'] = day_txns['issuing_country'] == day_txns['acquirer_country']
unique_profiles = day_txns[['card_scheme', 'is_credit', 'aci', 'intracountry']].drop_duplicates()
# Find applicable fees
applicable_ids = set()
for _, row in unique_profiles.iterrows():
for fee in fees:
if fee_applies(fee, row['card_scheme'], row['is_credit'], row['aci'],
row['intracountry'], account_type, capture_delay, mcc,
monthly_vol, monthly_fraud_pct):
applicable_ids.add(fee['ID'])
# Print and submit immediately
result = ', '.join(str(x) for x in sorted(applicable_ids))
print(result)
Key: Use actual unique (card_scheme, is_credit, aci, intracountry) combinations from real transactions — NOT Cartesian products.
"What were the applicable Fee IDs for Merchant X in 2023?"
Iterate over each calendar month, compute per-month stats, collect union of applicable fees:
# (Load data and merchant properties same as Type 2)
merch_payments['month'] = (pd.to_datetime('2023-01-01') +
pd.to_timedelta(merch_payments['day_of_year'] - 1, unit='D')).dt.month
all_applicable = set()
for month_num in range(1, 13):
month_txns = merch_payments[merch_payments['month'] == month_num]
if len(month_txns) == 0:
continue
monthly_vol = month_txns['eur_amount'].sum()
fraud_vol = month_txns[month_txns['has_fraudulent_dispute'] == True]['eur_amount'].sum()
monthly_fraud_pct = (fraud_vol / monthly_vol * 100) if monthly_vol > 0 else 0.0
month_txns = month_txns.copy()
month_txns['intracountry'] = month_txns['issuing_country'] == month_txns['acquirer_country']
unique_profiles = month_txns[['card_scheme', 'is_credit', 'aci', 'intracountry']].drop_duplicates()
for _, row in unique_profiles.iterrows():
for fee in fees:
if fee_applies(fee, row['card_scheme'], row['is_credit'], row['aci'],
row['intracountry'], account_type, capture_delay, mcc,
monthly_vol, monthly_fraud_pct):
all_applicable.add(fee['ID'])
print(', '.join(str(x) for x in sorted(all_applicable)))
"What were the applicable Fee IDs for Merchant X in October 2023?"
Same as Type 3a but filter for one month only. Parse month name to number:
month_map = {'January':1,'February':2,'March':3,'April':4,'May':5,'June':6,
'July':7,'August':8,'September':9,'October':10,'November':11,'December':12}
target_month = month_map['October'] # from question
# (Same as Type 3a loop but only for target_month)
"In 2023, which merchants were affected by Fee with ID 709?"
target_fee = next(f for f in fees if f['ID'] == target_fee_id)
filtered = payments.copy()
filtered['intracountry'] = filtered['issuing_country'] == filtered['acquirer_country']
if target_fee['card_scheme'] is not None:
filtered = filtered[filtered['card_scheme'] == target_fee['card_scheme']]
if target_fee['is_credit'] is not None:
filtered = filtered[filtered['is_credit'] == target_fee['is_credit']]
if target_fee['aci']:
filtered = filtered[filtered['aci'].isin(target_fee['aci'])]
if target_fee['intracountry'] is not None:
filtered = filtered[filtered['intracountry'] == bool(target_fee['intracountry'])]
merch_lookup = {m['merchant']: m for m in merchants}
affected = set()
for merch_name in filtered['merchant'].unique():
merch = merch_lookup.get(merch_name)
if not merch: continue
if not field_matches_list(target_fee['account_type'], merch['account_type']): continue
if not field_matches_list(target_fee['merchant_category_code'], merch['merchant_category_code']): continue
if not field_matches_scalar(target_fee['capture_delay'], map_capture_delay(merch['capture_delay'])): continue
affected.add(merch_name)
print(', '.join(sorted(affected)))
3, 4, 5, 7, 8, 9, 11[] as "no match" — empty lists mean "applies to all", same as null<3; "7" → >51.0/0.0, convert with bool() before comparing