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Data Janitor Env — 5-Minute Quickstart
======================================
Run from the repo root:
python examples/quickstart.py
No server, no Docker, no API key needed. Pure in-process execution.
"""
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
from gym_env import DataJanitorGymEnv
# ── 1. Create environment (in-process, no server needed) ────────────────────
env = DataJanitorGymEnv(task_id="fix_basics", mode="text")
obs, info = env.reset()
print("=" * 60)
print("TASK: Fix the Basics")
print("=" * 60)
print(f"Initial quality score : {info['quality_score']:.2%}")
print(f"Issues detected : {len(info['issues'])}")
print()
# ── 2. Manual cleaning sequence ─────────────────────────────────────────────
cleaning_steps = [
env.action_from_dict("drop_duplicates", params={"subset": ["employee_id"]}),
env.action_from_dict("convert_type", column="age", params={"target_type": "int"}),
env.action_from_dict("normalize_text", column="email", params={"operation": "trim"}),
env.action_from_dict("normalize_text", column="email", params={"operation": "lower"}),
env.action_from_dict("normalize_text", column="salary", params={"operation": "regex_replace",
"pattern": r"[$,]",
"replacement": ""}),
env.action_from_dict("convert_type", column="salary", params={"target_type": "float"}),
env.action_from_dict("map_values", column="department", params={"mapping": {
"Ops": "Operations", "Engg": "Engineering", "OPERATIONS": "Operations",
"FINANCE": "Finance", "MARKETING": "Marketing", "Mktg": "Marketing",
"engineering": "Engineering", "finance": "Finance",
"hr": "HR", "marketing": "Marketing", "sales": "Sales",
}}),
env.action_from_dict("submit"),
]
for i, action in enumerate(cleaning_steps, 1):
import json
cmd = json.loads(action)["command"]
obs, reward, terminated, truncated, info = env.step(action)
print(f"Step {i:2d}: {cmd:20s} quality={info['quality_score']:.2%} d={reward:+.4f}")
if terminated:
print(f"\n✓ Final score: {info['quality_score']:.4f}")
break
env.close()
# ── 3. Dict (numerical) mode — for classical RL ──────────────────────────────
print("\n" + "=" * 60)
print("NUMERICAL MODE DEMO (for classical RL)")
print("=" * 60)
env2 = DataJanitorGymEnv(task_id="fix_basics", mode="dict")
vec_obs, info = env2.reset()
print(f"Observation vector shape : {vec_obs.shape}")
print(f"Observation values : {vec_obs}")
print(f"Action space : {env2.action_space}")
print(f"Action table size : {env2.get_action_count()} pre-defined actions")
print()
# Run through the optimal action sequence (action indices 1..10)
for action_idx in range(1, env2.get_action_count()):
vec_obs, reward, terminated, truncated, info = env2.step(action_idx)
cmd, col, _ = env2.action_table[action_idx]
print(f" Action {action_idx:2d}: {cmd}({col or ''}) -> quality={info['quality_score']:.2%} d={reward:+.4f}")
if terminated:
print(f"\n✓ Final score: {info['quality_score']:.4f}")
break
env2.close()
print("\nDone! See examples/train_rl_agent.py to train a policy.")
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