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DataQA Environment Models
-------------------------
Action/Observation/State types for the Data Quality Assurance environment.
The agent receives a dataset with planted quality issues and must identify them.
Grading is based on F1 score (precision × recall) of correctly identified issues.
"""
from __future__ import annotations
from typing import List, Optional
from openenv.core.env_server.interfaces import Action, Observation, State
class DataQAAction(Action):
"""
Agent submits identified issues AND optional proposed fixes.
Two-phase action space:
Phase 1 (Identify): List issues in format "row:<N>,col:<name>,issue:<type>"
Phase 2 (Fix): List fixes in format "row:<N>,col:<name>,fix:<proposed_value>"
The agent can submit both in the same step or across multiple steps.
Combined reward = 0.6 * identify_score + 0.4 * fix_score
Supported issue types:
missing_value, wrong_type, duplicate_row, out_of_range,
format_violation, inconsistent_value, statistical_outlier,
referential_integrity
"""
issues: List[str]
fixes: List[str] = []
# Include task_id so step() can reconstruct context in stateless HTTP mode
task_id: str = "easy"
class DataQAObservation(Observation):
"""
What the agent sees: a dataset, its schema/rules, and feedback.
"""
# The dataset as CSV text
dataset_csv: str = ""
# Schema description (column names, expected types, constraints)
schema_description: str = ""
# Validation rules in plain text
validation_rules: str = ""
# Task description
task_description: str = ""
# Feedback from previous step (empty on reset)
feedback: str = ""
# Current task ID
task_id: str = ""
# Number of planted issues (hint for the agent)
num_issues_hint: int = 0
# Max allowed steps for this task
max_steps: int = 3
class DataQAState(State):
"""Tracks episode progress."""
task_id: str = ""
current_step: int = 0
max_steps: int = 3
best_score: float = 0.0
total_planted_issues: int = 0
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