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# Dataset Factory
# ===============
# Generates deterministic dirty + expected DataFrames for each task.
# Uses numpy default_rng(seed) — every (task_id, seed) pair is identical.
# Task 4 is the novel one: also exposes generate_drift_batch() which the
# environment calls every DRIFT_EVERY steps to inject fresh dirty rows
# mid-episode, simulating a live streaming pipeline under data drift.
# """
# import pandas as pd
# import numpy as np
# from typing import Tuple, Dict
# def make_task(task_id: str, seed: int) -> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame]]:
# if task_id == "task1":
# return _task1(seed)
# elif task_id == "task2":
# return _task2(seed)
# elif task_id == "task3":
# return _task3(seed)
# elif task_id == "task4_data_drift":
# return _task4(seed)
# raise ValueError(f"Unknown task_id: {task_id!r}.")
# # ── Task 1 ────────────────────────────────────────────────────────────────────
# def _task1(seed: int):
# rng = np.random.default_rng(seed)
# n = 50
# ids = list(range(1, n + 1))
# names = [f"Customer_{i:03d}" for i in range(n)]
# ages = rng.integers(18, 75, size=n).tolist()
# sals = np.round(rng.uniform(30_000, 120_000, size=n), 2).tolist()
# cities = rng.choice(["Mumbai","Delhi","Bangalore","Chennai","Pune"], size=n).tolist()
# null_age = set(rng.choice(n, size=10, replace=False).tolist())
# null_sal = set(rng.choice(n, size=8, replace=False).tolist())
# markers = ["", "N/A", "null", "missing", "NaN"]
# age_d = [str(ages[i]) if i not in null_age else str(rng.choice(markers)) for i in range(n)]
# sal_d = [sals[i] if i not in null_sal else None for i in range(n)]
# dirty = pd.DataFrame({"id": ids, "name": names, "age": age_d, "salary": sal_d, "city": cities})
# age_fill = int(np.median([ages[i] for i in range(n) if i not in null_age]))
# sal_fill = round(float(np.mean([sals[i] for i in range(n) if i not in null_sal])), 2)
# expected = pd.DataFrame({
# "id": ids, "name": names,
# "age": pd.array([ages[i] if i not in null_age else age_fill for i in range(n)], dtype="int64"),
# "salary": pd.array([round(sals[i],2) if i not in null_sal else sal_fill for i in range(n)], dtype="float64"),
# "city": cities,
# })
# return {"main": dirty}, {"main": expected}
# # ── Task 2 ────────────────────────────────────────────────────────────────────
# def _task2(seed: int):
# rng = np.random.default_rng(seed)
# nu = 170
# ids = list(range(1, nu + 1))
# cids = rng.integers(1001, 1200, size=nu).tolist()
# amts = np.round(rng.uniform(10, 5_000, size=nu), 2).tolist()
# null_a = set(rng.choice(nu, size=12, replace=False).tolist())
# stats = rng.choice(["completed","pending","cancelled","refunded"], size=nu).tolist()
# cats = rng.choice(["Electronics","Clothing","Food","Books","Sports"], size=nu).tolist()
# CV = {
# "USA":["USA","usa","U.S.A","United States","US"],
# "UK":["UK","uk","U.K.","United Kingdom"],
# "INDIA":["India","india","INDIA","IN"],
# "GERMANY":["Germany","germany","DE","GERMANY"],
# "FRANCE":["France","france","FR","FRANCE"],
# }
# ckeys = list(CV.keys())
# cc = rng.choice(ckeys, size=nu).tolist()
# cd = [str(rng.choice(CV[c])) for c in cc]
# dr = pd.date_range("2023-01-01","2024-12-31", periods=nu)
# diso = dr.strftime("%Y-%m-%d").tolist()
# dd = [pd.Timestamp(d).strftime("%d/%m/%Y") if rng.random()<0.35 else d for d in diso]
# ad = [amts[i] if i not in null_a else None for i in range(nu)]
# base = pd.DataFrame({"order_id":ids,"customer_id":cids,"country":cd,
# "amount":ad,"order_date":dd,"status":stats,"product_category":cats})
# dups = base.iloc[rng.choice(nu, size=30, replace=True)].copy()
# dirty = (pd.concat([base, dups], ignore_index=True)
# .sample(frac=1, random_state=int(seed)).reset_index(drop=True))
# af = round(float(np.mean([amts[i] for i in range(nu) if i not in null_a])), 2)
# expected = pd.DataFrame({
# "order_id":ids,"customer_id":cids,"country":cc,
# "amount":pd.array([round(amts[i],2) if i not in null_a else af for i in range(nu)], dtype="float64"),
# "order_date":pd.to_datetime(diso),"status":stats,"product_category":cats,
# })
# return {"main": dirty}, {"main": expected}
# # ── Task 3 ────────────────────────────────────────────────────────────────────
# def _task3(seed: int):
# rng = np.random.default_rng(seed)
# nc, no = 100, 300
# cids = list(range(1001, 1001+nc))
# cname = [f"Customer_{i:03d}" for i in range(nc)]
# ctry = rng.choice(["USA","UK","India","Germany"], size=nc).tolist()
# ages = rng.integers(18, 70, size=nc).tolist()
# nai = set(rng.choice(nc, size=8, replace=False).tolist())
# ages_d = [str(ages[i]) if i not in nai else "N/A" for i in range(nc)]
# cust_dirty = pd.DataFrame({"customer_id":cids,"name":cname,"country":ctry,"age":ages_d})
# af = int(np.median([ages[i] for i in range(nc) if i not in nai]))
# cust_clean = pd.DataFrame({
# "customer_id":cids,"name":cname,"country":ctry,
# "age":pd.array([ages[i] if i not in nai else af for i in range(nc)], dtype="int64"),
# })
# oids = list(range(1, no+1))
# ocid = rng.choice(cids, size=no).tolist()
# amts = np.round(rng.uniform(10, 2_000, size=no), 2)
# for idx in rng.choice(no, size=20, replace=False):
# amts[idx] = float(rng.choice([0.01, -5.0, 50_000.0, 99_999.0]))
# dates = pd.date_range("2023-01-01","2024-12-31", periods=no).strftime("%Y-%m-%d").tolist()
# orders_dirty = pd.DataFrame({"order_id":oids,"customer_id":ocid,
# "amount":amts.tolist(),"order_date":dates})
# merged = pd.merge(orders_dirty, cust_clean, on="customer_id", how="inner")
# Q1, Q3 = merged["amount"].quantile(0.25), merged["amount"].quantile(0.75)
# IQR = Q3 - Q1
# mc = merged[(merged["amount"]>=Q1-1.5*IQR) & (merged["amount"]<=Q3+1.5*IQR)].copy().reset_index(drop=True)
# mc["order_year"] = pd.to_datetime(mc["order_date"]).dt.year
# return ({"orders": orders_dirty, "customers": cust_dirty}, {"main": mc})
# # ── Task 4: Data Drift (Expert) ───────────────────────────────────────────────
# def _task4(seed: int):
# """
# Live streaming transactions — 120 initial dirty rows.
# Env injects fresh dirty rows every DRIFT_EVERY=5 steps via generate_drift_batch().
# Agent must keep cleaning as new dirty data continuously arrives.
# Columns: txn_id, customer_id, amount, category, region, event_ts
# Dirty issues: nulls, wrong dtypes (amount as str), outliers, mixed timestamp formats.
# """
# rng = np.random.default_rng(seed)
# n = 120
# txn_ids = [f"TXN_INIT_{i:04d}" for i in range(n)]
# cids = rng.integers(1, 501, size=n).tolist()
# cats_c = rng.choice(["Electronics","Clothing","Food","Books","Sports","Toys"], size=n).tolist()
# regs_c = rng.choice(["North","South","East","West","Central"], size=n).tolist()
# amts_t = np.round(rng.uniform(10, 3000, size=n), 2).tolist()
# amts_d = []
# for i in range(n):
# r = rng.random()
# if r < 0.15: amts_d.append(None)
# elif r < 0.22: amts_d.append(str(round(amts_t[i], 2)))
# elif r < 0.27: amts_d.append(float(-rng.uniform(100, 5000)))
# elif r < 0.31: amts_d.append(float(rng.uniform(80000, 250000)))
# else: amts_d.append(amts_t[i])
# def _ts(rng):
# base = (f"2024-{rng.integers(1,13):02d}-{rng.integers(1,29):02d} "
# f"{rng.integers(0,24):02d}:{rng.integers(0,60):02d}:00")
# r = rng.random()
# if r < 0.20: return base.split(" ")[0].replace("-", "/")
# if r < 0.30:
# p = base.split("-"); return f"{p[2][:2]}/{p[1]}/{p[0]}"
# return base
# ts_d = [_ts(rng) for _ in range(n)]
# cats_d = [None if rng.random()<0.15 else cats_c[i] for i in range(n)]
# regs_d = [None if rng.random()<0.10 else regs_c[i] for i in range(n)]
# dirty = pd.DataFrame({
# "txn_id":txn_ids, "customer_id":cids,
# "amount":amts_d, "category":cats_d, "region":regs_d, "event_ts":ts_d,
# })
# # Expected: cleaned initial batch (outliers dropped, nulls filled, ts parsed)
# good_amts = [x for x in amts_t if 0 < x <= 10000]
# amt_fill = round(float(np.mean(good_amts)), 2)
# amts_e = []
# for a in amts_d:
# if a is None: amts_e.append(amt_fill)
# elif isinstance(a, str): amts_e.append(float(a))
# elif isinstance(a, float) and (a<0 or a>10000): amts_e.append(None)
# else: amts_e.append(round(a, 2))
# exp_df = pd.DataFrame({
# "txn_id":txn_ids, "customer_id":cids,
# "amount":pd.to_numeric(amts_e, errors="coerce"),
# "category":cats_c, "region":regs_c,
# "event_ts":pd.to_datetime(ts_d, errors="coerce"),
# }).dropna(subset=["amount"]).reset_index(drop=True)
# return {"stream": dirty}, {"stream": exp_df}
# # ── Drift Batch Generator ─────────────────────────────────────────────────────
# def generate_drift_batch(seed: int, batch_num: int, n_rows: int = 7) -> pd.DataFrame:
# """
# Generate a fresh batch of dirty rows injected mid-episode into task4.
# Called by DataCleanEnvironment.step() every DRIFT_EVERY steps.
# Fully deterministic: (seed, batch_num) always → same batch.
# Each batch introduces different dirty patterns so the agent faces novel problems.
# """
# rng = np.random.default_rng(seed * 1000 + batch_num)
# txn_ids = [f"TXN_DRIFT_{batch_num:03d}_{i:02d}" for i in range(n_rows)]
# cids = rng.integers(1, 501, size=n_rows).tolist()
# cats = rng.choice(["Electronics","Clothing","Food","Books","Sports","Toys"], size=n_rows).tolist()
# regs = rng.choice(["North","South","East","West","Central"], size=n_rows).tolist()
# amts = []
# for _ in range(n_rows):
# r = rng.random()
# base = round(float(rng.uniform(10, 3000)), 2)
# if r < 0.20: amts.append(None)
# elif r < 0.30: amts.append(str(base))
# elif r < 0.38: amts.append(float(-rng.uniform(100, 5000)))
# elif r < 0.44: amts.append(float(rng.uniform(80000, 250000)))
# else: amts.append(base)
# ts = []
# for _ in range(n_rows):
# base = (f"2024-{rng.integers(1,13):02d}-{rng.integers(1,29):02d} "
# f"{rng.integers(0,24):02d}:{rng.integers(0,60):02d}:00")
# r = rng.random()
# if r < 0.20: ts.append(base.split(" ")[0].replace("-", "/"))
# elif r < 0.35: p = base.split("-"); ts.append(f"{p[2][:2]}/{p[1]}/{p[0]}")
# else: ts.append(base)
# for i in range(n_rows):
# if rng.random() < 0.18: cats[i] = None
# if rng.random() < 0.12: regs[i] = None
# return pd.DataFrame({
# "txn_id":txn_ids, "customer_id":cids,
# "amount":amts, "category":cats, "region":regs, "event_ts":ts,
# })
import pandas as pd
import numpy as np
from typing import Tuple, Dict
def make_task(task_id: str, seed: int) -> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame]]:
if task_id == "task1": return _task1(seed)
elif task_id == "task2": return _task2(seed)
elif task_id == "task3": return _task3(seed)
elif task_id == "task4_data_drift": return _task4(seed)
raise ValueError(f"Unknown task_id: {task_id!r}.")
def _task1(seed):
rng = np.random.default_rng(seed)
n = 50
ids = list(range(1, n+1))
names = [f"Customer_{i:03d}" for i in range(n)]
ages = rng.integers(18, 75, size=n).tolist()
sals = np.round(rng.uniform(30000, 120000, size=n), 2).tolist()
cities = rng.choice(["Mumbai","Delhi","Bangalore","Chennai","Pune"], size=n).tolist()
null_age = set(rng.choice(n, size=10, replace=False).tolist())
null_sal = set(rng.choice(n, size=8, replace=False).tolist())
markers = ["", "N/A", "null", "missing", "NaN"]
age_d = [str(ages[i]) if i not in null_age else str(rng.choice(markers)) for i in range(n)]
sal_d = [sals[i] if i not in null_sal else None for i in range(n)]
dirty = pd.DataFrame({"id":ids,"name":names,"age":age_d,"salary":sal_d,"city":cities})
age_fill = int(np.median([ages[i] for i in range(n) if i not in null_age]))
sal_fill = round(float(np.mean([sals[i] for i in range(n) if i not in null_sal])), 2)
expected = pd.DataFrame({
"id":ids,"name":names,
"age": pd.array([ages[i] if i not in null_age else age_fill for i in range(n)], dtype="int64"),
"salary": pd.array([round(sals[i],2) if i not in null_sal else sal_fill for i in range(n)], dtype="float64"),
"city":cities,
})
return {"main": dirty}, {"main": expected}
def _task2(seed):
rng = np.random.default_rng(seed)
nu = 170
ids = list(range(1, nu+1))
cids = rng.integers(1001, 1200, size=nu).tolist()
amts = np.round(rng.uniform(10, 5000, size=nu), 2).tolist()
null_a = set(rng.choice(nu, size=12, replace=False).tolist())
stats = rng.choice(["completed","pending","cancelled","refunded"], size=nu).tolist()
cats = rng.choice(["Electronics","Clothing","Food","Books","Sports"], size=nu).tolist()
CV = {"USA":["USA","usa","U.S.A","United States","US"],"UK":["UK","uk","U.K.","United Kingdom"],
"INDIA":["India","india","INDIA","IN"],"GERMANY":["Germany","germany","DE","GERMANY"],
"FRANCE":["France","france","FR","FRANCE"]}
ckeys = list(CV.keys())
cc = rng.choice(ckeys, size=nu).tolist()
cd = [str(rng.choice(CV[c])) for c in cc]
dr = pd.date_range("2023-01-01","2024-12-31", periods=nu)
diso = dr.strftime("%Y-%m-%d").tolist()
dd = [pd.Timestamp(d).strftime("%d/%m/%Y") if rng.random()<0.35 else d for d in diso]
ad = [amts[i] if i not in null_a else None for i in range(nu)]
base = pd.DataFrame({"order_id":ids,"customer_id":cids,"country":cd,"amount":ad,"order_date":dd,"status":stats,"product_category":cats})
dups = base.iloc[rng.choice(nu, size=30, replace=True)].copy()
dirty = (pd.concat([base, dups], ignore_index=True).sample(frac=1, random_state=int(seed)).reset_index(drop=True))
af = round(float(np.mean([amts[i] for i in range(nu) if i not in null_a])), 2)
expected = pd.DataFrame({"order_id":ids,"customer_id":cids,"country":cc,
"amount":pd.array([round(amts[i],2) if i not in null_a else af for i in range(nu)], dtype="float64"),
"order_date":pd.to_datetime(diso),"status":stats,"product_category":cats})
return {"main": dirty}, {"main": expected}
def _task3(seed):
rng = np.random.default_rng(seed)
nc, no = 100, 300
cids = list(range(1001, 1001+nc))
cname = [f"Customer_{i:03d}" for i in range(nc)]
ctry = rng.choice(["USA","UK","India","Germany"], size=nc).tolist()
ages = rng.integers(18, 70, size=nc).tolist()
nai = set(rng.choice(nc, size=8, replace=False).tolist())
ages_d = [str(ages[i]) if i not in nai else "N/A" for i in range(nc)]
cust_dirty = pd.DataFrame({"customer_id":cids,"name":cname,"country":ctry,"age":ages_d})
af = int(np.median([ages[i] for i in range(nc) if i not in nai]))
cust_clean = pd.DataFrame({"customer_id":cids,"name":cname,"country":ctry,
"age":pd.array([ages[i] if i not in nai else af for i in range(nc)], dtype="int64")})
oids = list(range(1, no+1))
ocid = rng.choice(cids, size=no).tolist()
amts = np.round(rng.uniform(10, 2000, size=no), 2)
for idx in rng.choice(no, size=20, replace=False):
amts[idx] = float(rng.choice([0.01, -5.0, 50000.0, 99999.0]))
dates = pd.date_range("2023-01-01","2024-12-31", periods=no).strftime("%Y-%m-%d").tolist()
orders_dirty = pd.DataFrame({"order_id":oids,"customer_id":ocid,"amount":amts.tolist(),"order_date":dates})
merged = pd.merge(orders_dirty, cust_clean, on="customer_id", how="inner")
Q1, Q3 = merged["amount"].quantile(0.25), merged["amount"].quantile(0.75)
IQR = Q3 - Q1
mc = merged[(merged["amount"]>=Q1-1.5*IQR) & (merged["amount"]<=Q3+1.5*IQR)].copy().reset_index(drop=True)
mc["order_year"] = pd.to_datetime(mc["order_date"]).dt.year
return ({"orders": orders_dirty, "customers": cust_dirty}, {"main": mc})
def _task4(seed):
rng = np.random.default_rng(seed)
n = 120
txn_ids = [f"TXN_INIT_{i:04d}" for i in range(n)]
cids = rng.integers(1, 501, size=n).tolist()
cats_c = rng.choice(["Electronics","Clothing","Food","Books","Sports","Toys"], size=n).tolist()
regs_c = rng.choice(["North","South","East","West","Central"], size=n).tolist()
amts_t = np.round(rng.uniform(10, 3000, size=n), 2).tolist()
amts_d = []
for i in range(n):
r = rng.random()
if r < 0.15: amts_d.append(None)
elif r < 0.22: amts_d.append(str(round(amts_t[i], 2)))
elif r < 0.27: amts_d.append(float(-rng.uniform(100, 5000)))
elif r < 0.31: amts_d.append(float(rng.uniform(80000, 250000)))
else: amts_d.append(amts_t[i])
def _ts(rng):
base = (f"2024-{rng.integers(1,13):02d}-{rng.integers(1,29):02d} "
f"{rng.integers(0,24):02d}:{rng.integers(0,60):02d}:00")
r = rng.random()
if r < 0.20: return base.split(" ")[0].replace("-", "/")
if r < 0.30:
p = base.split("-"); return f"{p[2][:2]}/{p[1]}/{p[0]}"
return base
ts_d = [_ts(rng) for _ in range(n)]
cats_d = [None if rng.random()<0.15 else cats_c[i] for i in range(n)]
regs_d = [None if rng.random()<0.10 else regs_c[i] for i in range(n)]
dirty = pd.DataFrame({"txn_id":txn_ids,"customer_id":cids,"amount":amts_d,"category":cats_d,"region":regs_d,"event_ts":ts_d})
good_amts = [x for x in amts_t if 0 < x <= 10000]
amt_fill = round(float(np.mean(good_amts)), 2)
amts_e = []
for a in amts_d:
if a is None: amts_e.append(amt_fill)
elif isinstance(a, str): amts_e.append(float(a))
elif isinstance(a, float) and (a<0 or a>10000): amts_e.append(None)
else: amts_e.append(round(a, 2))
exp_df = pd.DataFrame({"txn_id":txn_ids,"customer_id":cids,
"amount":pd.to_numeric(amts_e, errors="coerce"),"category":cats_c,
"region":regs_c,"event_ts":pd.to_datetime(ts_d, errors="coerce")}).dropna(subset=["amount"]).reset_index(drop=True)
return {"stream": dirty}, {"stream": exp_df}
def generate_drift_batch(seed: int, batch_num: int, n_rows: int = 7) -> pd.DataFrame:
rng = np.random.default_rng(seed * 1000 + batch_num)
txn_ids = [f"TXN_DRIFT_{batch_num:03d}_{i:02d}" for i in range(n_rows)]
cids = rng.integers(1, 501, size=n_rows).tolist()
cats = rng.choice(["Electronics","Clothing","Food","Books","Sports","Toys"], size=n_rows).tolist()
regs = rng.choice(["North","South","East","West","Central"], size=n_rows).tolist()
amts = []
for _ in range(n_rows):
r = rng.random(); base = round(float(rng.uniform(10, 3000)), 2)
if r < 0.20: amts.append(None)
elif r < 0.30: amts.append(str(base))
elif r < 0.38: amts.append(float(-rng.uniform(100, 5000)))
elif r < 0.44: amts.append(float(rng.uniform(80000, 250000)))
else: amts.append(base)
ts = []
for _ in range(n_rows):
base = (f"2024-{rng.integers(1,13):02d}-{rng.integers(1,29):02d} "
f"{rng.integers(0,24):02d}:{rng.integers(0,60):02d}:00")
r = rng.random()
if r < 0.20: ts.append(base.split(" ")[0].replace("-", "/"))
elif r < 0.35:
p = base.split("-"); ts.append(f"{p[2][:2]}/{p[1]}/{p[0]}")
else: ts.append(base)
for i in range(n_rows):
if rng.random() < 0.18: cats[i] = None
if rng.random() < 0.12: regs[i] = None
return pd.DataFrame({"txn_id":txn_ids,"customer_id":cids,"amount":amts,"category":cats,"region":regs,"event_ts":ts}) |