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91e7690 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | from __future__ import annotations
import numpy as np
import pandas as pd
NULL_DISGUISES = ["NULL", "N/A", "UNKNOWN", "-", "", "0", "none"]
def generate_dataset(task_id: int, seed: int) -> tuple[dict[str, pd.DataFrame], dict]:
"""
Returns:
tables_dict: {table_name: DataFrame}
gold_faults: dict
"""
rng = np.random.default_rng(seed)
if task_id == 1:
return _task1(rng, seed)
if task_id == 2:
return _task2(rng)
if task_id == 3:
return _task3(rng)
if task_id == 4:
return _task4(rng)
raise ValueError(f"Unknown task_id {task_id}")
def _task1(rng: np.random.Generator, seed: int) -> tuple[dict[str, pd.DataFrame], dict]:
n = 200
df = pd.DataFrame(
{
"customer_id": range(1001, 1001 + n),
"email": [f"user{i}@example.com" for i in range(n)],
"name": [f"Name {i}" for i in range(n)],
"signup_date": pd.date_range("2023-01-01", periods=n, freq="D").astype(str),
"country": rng.choice(["US", "UK", "IN", "DE", "FR"], n).tolist(),
}
)
real_null_cid = int(rng.integers(3, 7))
null_cid_idx = rng.choice(n, real_null_cid, replace=False)
df.loc[null_cid_idx, "customer_id"] = None
real_null_email = int(rng.integers(8, 15))
null_email_idx = rng.choice(n, real_null_email, replace=False)
df.loc[null_email_idx, "email"] = None
disguised_null_email = int(rng.integers(4, 9))
avail = [i for i in range(n) if i not in set(null_email_idx.tolist())]
dis_idx = rng.choice(avail, disguised_null_email, replace=False)
df.loc[dis_idx, "email"] = rng.choice(NULL_DISGUISES, disguised_null_email).tolist()
dup_count = int(rng.integers(10, 19))
dup_src = rng.choice(n, dup_count, replace=True)
dups = df.iloc[dup_src].copy()
df = pd.concat([df, dups], ignore_index=True)
near_dup_count = int(rng.integers(5, 9))
near_src = rng.choice(n, near_dup_count, replace=False)
near_dups = df.iloc[near_src].copy()
near_dups["country"] = rng.choice(["US", "UK", "IN", "DE", "FR"], near_dup_count).tolist()
df = pd.concat([df, near_dups], ignore_index=True)
df = df.sample(frac=1, random_state=seed).reset_index(drop=True)
gold = {
"null_customer_id": real_null_cid,
"null_email_real": real_null_email,
"null_email_disguised": disguised_null_email,
"null_email_total": real_null_email + disguised_null_email,
"exact_duplicate_rows": dup_count,
"near_duplicate_rows": near_dup_count,
}
return {"customers": df}, gold
def _task2(rng: np.random.Generator) -> tuple[dict[str, pd.DataFrame], dict]:
n = 300
amounts_float = (rng.random(n) * 500 + 5).round(2)
dates = pd.date_range("2023-01-01", periods=n, freq="h")[:n]
df = pd.DataFrame(
{
"order_id": range(5001, 5001 + n),
"customer_id": rng.integers(1001, 1201, n).tolist(),
"amount": [f"${a}" for a in amounts_float],
"order_date": [d.strftime("%b %d %Y") for d in dates],
"status": rng.choice(["pending", "shipped", "delivered", "cancelled"], n).tolist(),
"quantity": rng.integers(1, 20, n).tolist(),
}
)
neg_qty = int(rng.integers(5, 11))
neg_idx = rng.choice(n, neg_qty, replace=False)
df.loc[neg_idx, "quantity"] = rng.integers(-10, 0, neg_qty).tolist()
bad_amt = int(rng.integers(3, 8))
bad_idx = rng.choice([i for i in range(n) if i not in set(neg_idx.tolist())], bad_amt, replace=False)
df.loc[bad_idx, "amount"] = rng.choice(["N/A", "#ERR", "TBD", "--"], bad_amt).tolist()
gold = {
"amount_type_violation": True,
"date_format_violation": True,
"negative_quantity_rows": neg_qty,
"unparseable_amount_rows": bad_amt,
}
return {"orders": df}, gold
def _task3(rng: np.random.Generator) -> tuple[dict[str, pd.DataFrame], dict]:
def make_txn(n: int, rg: np.random.Generator, mean_amt: float, cats: list[str], id_start: int) -> pd.DataFrame:
return pd.DataFrame(
{
"txn_id": range(id_start, id_start + n),
"user_id": rg.integers(2001, 2501, n).tolist(),
"amount": rg.normal(mean_amt, 15, n).round(2).tolist(),
"category": rg.choice(cats, n).tolist(),
"ts": pd.date_range("2024-01-01", periods=n, freq="h")[:n].astype(str).tolist(),
}
)
base_cats = ["food", "travel", "retail", "health", "utilities"]
new_cats = ["crypto", "NFT"]
baseline = make_txn(500, rng, mean_amt=50.0, cats=base_cats, id_start=10001)
current_rng = np.random.default_rng(int(rng.integers(9999)))
current = make_txn(500, current_rng, mean_amt=78.0, cats=base_cats + new_cats, id_start=10501)
new_uid_count = int(0.15 * 500)
new_uid_idx = current_rng.choice(500, new_uid_count, replace=False)
current.loc[new_uid_idx, "user_id"] = current_rng.integers(3000, 3500, new_uid_count).tolist()
gold = {
"amount_mean_shift": True,
"baseline_mean": 50.0,
"current_mean": float(current["amount"].mean()),
"new_categories": new_cats,
"referential_drift_pct": new_uid_count / 500,
}
return {"transactions_baseline": baseline, "transactions_current": current}, gold
def _task4(rng: np.random.Generator) -> tuple[dict[str, pd.DataFrame], dict]:
nc = 200
customers = pd.DataFrame(
{
"customer_id": range(1, nc + 1),
"name": [f"Customer {i}" for i in range(nc)],
"tier": rng.choice(["bronze", "silver", "gold"], nc).tolist(),
}
)
no = 500
orphan_count = int(rng.integers(15, 22))
valid_cids = list(range(1, nc + 1))
order_cids = rng.choice(valid_cids, no - orphan_count).tolist()
orphan_cids = rng.integers(9000, 9999, orphan_count).tolist()
all_cids = order_cids + orphan_cids
rng.shuffle(all_cids)
order_dates = pd.date_range("2024-01-01", periods=no, freq="h")[:no]
ship_dates = [d + pd.Timedelta(days=int(rng.integers(1, 10))) for d in order_dates]
temp_viol = int(rng.integers(10, 16))
temp_idx = rng.choice(no, temp_viol, replace=False)
for i in temp_idx:
ship_dates[i] = order_dates[i] - pd.Timedelta(days=int(rng.integers(1, 5)))
orders = pd.DataFrame(
{
"order_id": range(1, no + 1),
"customer_id": all_cids,
"order_date": order_dates.astype(str).tolist(),
"ship_date": [str(d) for d in ship_dates],
"order_total": (rng.random(no) * 400 + 20).round(2).tolist(),
}
)
nl = 1500
li_order_ids = rng.choice(range(1, no + 1), nl).tolist()
li_prices = (rng.random(nl) * 100 + 5).round(2)
li_qtys = rng.integers(1, 6, nl)
line_items = pd.DataFrame(
{
"line_id": range(1, nl + 1),
"order_id": li_order_ids,
"product": rng.choice(["Widget A", "Widget B", "Widget C", "Widget D"], nl).tolist(),
"price": li_prices.tolist(),
"quantity": li_qtys.tolist(),
"subtotal": (li_prices * li_qtys).round(2).tolist(),
}
)
agg_mismatch = int(rng.integers(5, 9))
mismatch_order_ids = rng.choice(range(1, no + 1), agg_mismatch, replace=False)
for oid in mismatch_order_ids:
idx = orders[orders["order_id"] == oid].index
if len(idx):
orders.loc[idx[0], "order_total"] = round(float(orders.loc[idx[0], "order_total"]) * rng.uniform(1.3, 2.0), 2)
gold = {
"orphaned_order_count": orphan_count,
"temporal_violation_count": temp_viol,
"aggregate_mismatch_count": agg_mismatch,
"total_orders": no,
}
return {"customers": customers, "orders": orders, "line_items": line_items}, gold
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