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e415506 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 | """Metric tracking RL environment."""
from __future__ import annotations
from dataclasses import dataclass
from uuid import uuid4
from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import State
try:
from ..analysis_tools import AnalysisContext, SharedAnalysisToolkit, available_analysis_methods
from ..evaluation import EvaluationConfig
from ..models import (
MetricTrackerRlAction,
MetricTrackerRlObservation,
MetricSubmissionRow,
SyntheticAnomalyGenerator,
)
from ..tasks import DEFAULT_TASK_ID, available_task_specs, get_task_spec
from .data_generator import (
EpisodeConfig,
EpisodeData,
MetricDataGenerator,
available_synthetic_generator_methods,
)
except ImportError:
from analysis_tools import AnalysisContext, SharedAnalysisToolkit, available_analysis_methods
from models import (
MetricTrackerRlAction,
MetricTrackerRlObservation,
MetricSubmissionRow,
SyntheticAnomalyGenerator,
)
from tasks import DEFAULT_TASK_ID, available_task_specs, get_task_spec
from server.data_generator import (
EpisodeConfig,
EpisodeData,
MetricDataGenerator,
available_synthetic_generator_methods,
)
from evaluation import EvaluationConfig
@dataclass(frozen=True)
class RewardConfig:
"""Compatibility wrapper around the evaluator configuration."""
evaluation: EvaluationConfig = EvaluationConfig()
class MetricTrackerRlEnvironment(Environment):
"""Iterative multi-anomaly benchmark with safe analysis methods."""
SUPPORTS_CONCURRENT_SESSIONS: bool = True
def __init__(
self,
generator: MetricDataGenerator | None = None,
reward_config: RewardConfig | None = None,
) -> None:
initial_task = get_task_spec(DEFAULT_TASK_ID)
self._generator = generator or MetricDataGenerator()
self._reward_config = reward_config or RewardConfig()
self._state = State(episode_id=str(uuid4()), step_count=0)
self._episode: EpisodeData | None = None
self._completed = False
self._debug_mode = False
self._active_task = initial_task
self._next_task_id = initial_task.task_id
self._next_reset_config = initial_task.build_episode_config()
self._last_analysis_result: dict | None = None
self._expose_applied_generators = False
def configure_next_reset(
self,
*,
task_id: str | None = None,
seed: int | None = None,
scenario_family: str | None = None,
difficulty: str | None = None,
anomaly_density: str | None = None,
anomaly_count: int | None = None,
anomalies: list[dict] | list[SyntheticAnomalyGenerator] | None = None,
) -> None:
"""Update the configuration used for the next reset."""
base_task = get_task_spec(task_id or self._next_task_id)
base_config = base_task.build_episode_config() if task_id else self._next_reset_config
anomaly_generators = tuple(
item if isinstance(item, SyntheticAnomalyGenerator) else SyntheticAnomalyGenerator(**item)
for item in (anomalies or [])
)
self._next_task_id = base_task.task_id
self._next_reset_config = EpisodeConfig(
seed=base_config.seed if seed is None else seed,
scenario_family=base_config.scenario_family if scenario_family is None else scenario_family,
difficulty=base_config.difficulty if difficulty is None else difficulty,
anomaly_density=base_config.anomaly_density if anomaly_density is None else anomaly_density,
anomaly_count=base_config.anomaly_count if anomaly_count is None else anomaly_count,
anomaly_generators=anomaly_generators or base_config.anomaly_generators,
).normalized()
def set_debug_mode(self, enabled: bool) -> None:
"""Enable or disable debug-only environment views."""
self._debug_mode = bool(enabled)
def export_debug_snapshot(self) -> dict:
"""Return a developer-only debug snapshot for the active episode."""
if not self._debug_mode:
raise RuntimeError("Debug mode is disabled.")
if self._episode is None:
return {}
return {
"config": self._episode.config.__dict__,
"expected_payload": [row.model_dump() for row in self._episode.expected_rows],
"anomaly_schedule": self._episode.anomaly_schedule,
"applied_synthetic_generators": [
row.model_dump() for row in self._episode.applied_synthetic_generators
],
}
def reset(
self,
task_id: str | None = None,
seed: int | None = None,
scenario_family: str | None = None,
difficulty: str | None = None,
anomaly_density: str | None = None,
anomaly_count: int | None = None,
anomalies: list[dict] | list[SyntheticAnomalyGenerator] | None = None,
) -> MetricTrackerRlObservation:
"""Generate a fresh dataset and hidden target payload."""
if any(value is not None for value in (task_id, seed, scenario_family, difficulty, anomaly_density, anomaly_count)) or anomalies is not None:
self.configure_next_reset(
task_id=task_id,
seed=seed,
scenario_family=scenario_family,
difficulty=difficulty,
anomaly_density=anomaly_density,
anomaly_count=anomaly_count,
anomalies=anomalies,
)
self._state = State(episode_id=str(uuid4()), step_count=0)
self._active_task = get_task_spec(self._next_task_id)
self._episode = self._generator.generate_episode(self._next_reset_config)
self._completed = False
self._last_analysis_result = None
self._expose_applied_generators = anomalies is not None
return self._build_observation(
status="ready",
message=self._active_task.objective,
reward=0.0,
done=False,
)
def step(self, action: MetricTrackerRlAction) -> MetricTrackerRlObservation: # type: ignore[override]
"""Evaluate a submitted payload and return deterministic feedback."""
if self._episode is None:
return self.reset()
if self._completed:
return self._build_observation(
status="completed",
message="Dataset already solved. Call reset() to create a new dataset.",
reward=1.0,
done=True,
submitted_rows=action.classifications,
)
if action.analysis_method:
self._state.step_count += 1
analysis_result = self._run_analysis(action.analysis_method, action.analysis_args)
self._last_analysis_result = analysis_result
return self._build_observation(
status="analyzed",
message=f"Ran analysis method `{action.analysis_method}`.",
reward=0.0,
done=False,
analysis_result=analysis_result,
)
submitted_rows = action.classifications
generated_rows: list[MetricSubmissionRow] = []
if action.payload_generators:
generator_result = self._run_analysis(
"payload_generator",
{"generator_methods": [item.model_dump() for item in action.payload_generators]},
)
self._last_analysis_result = generator_result
generated_rows = [
MetricSubmissionRow(**row)
for row in generator_result["result"]["generated_rows"]
]
submitted_rows = generated_rows
self._state.step_count += 1
result = self._active_task.grade_submission(
submitted_rows,
self._episode.expected_rows,
config=self._reward_config.evaluation,
include_debug_expected=self._debug_mode,
)
self._completed = result.is_perfect
reward = result.reward_breakdown.total_score
message = self._submission_message(result)
return self._build_observation(
status="evaluated" if result.is_perfect else "in_progress",
message=message,
reward=reward,
done=result.is_perfect,
submitted_rows=result.preview.normalized_rows,
reward_breakdown=result.reward_breakdown,
submission_preview=result.preview,
issues=result.issues,
correct_row_count=result.matched_rows,
analysis_result=self._last_analysis_result,
generated_rows=generated_rows,
)
@property
def state(self) -> State:
"""Return current episode state."""
return self._state
def _build_observation(
self,
*,
status: str,
message: str,
reward: float,
done: bool,
submitted_rows=None,
reward_breakdown=None,
submission_preview=None,
issues=None,
correct_row_count: int = 0,
analysis_result=None,
generated_rows=None,
) -> MetricTrackerRlObservation:
assert self._episode is not None
metadata = {
"step": self._state.step_count,
"current_state": self.state.model_dump(),
"task_id": self._active_task.task_id,
"objective": self._active_task.objective,
"grader_name": self._active_task.grader_name,
"seed": self._episode.config.seed,
"scenario_family": self._episode.config.scenario_family,
"difficulty": self._episode.config.difficulty,
"anomaly_density": self._episode.config.anomaly_density,
"anomaly_count": self._episode.config.anomaly_count,
}
return MetricTrackerRlObservation(
task_id=self._active_task.task_id,
status=status,
message=message,
instruction=self._active_task.instruction,
conversion_metric_definitions=list(self._generator.config.conversion_definitions),
available_synthetic_generator_methods=available_synthetic_generator_methods(),
applied_synthetic_generators=(
self._episode.applied_synthetic_generators
if self._debug_mode or self._expose_applied_generators
else []
),
available_methods=available_analysis_methods(),
available_tasks=available_task_specs(),
daily_metrics=[],
hourly_metrics=[],
analysis_result=analysis_result,
generated_rows=generated_rows or [],
submitted_rows=submitted_rows or [],
submission_preview=submission_preview,
submission_issues=issues or [],
reward_breakdown=reward_breakdown,
expected_row_count=len(self._episode.expected_rows),
correct_row_count=correct_row_count,
reward=reward,
done=done,
config=metadata,
debug=(
{
"task_id": self._active_task.task_id,
"expected_payload": [row.model_dump() for row in self._episode.expected_rows],
"anomaly_schedule": self._episode.anomaly_schedule,
"reward_breakdown": reward_breakdown.model_dump() if reward_breakdown else None,
"issues": [item.model_dump() for item in (issues or [])],
}
if self._debug_mode
else None
),
)
def _run_analysis(self, method_name: str, arguments: dict) -> dict:
toolkit = SharedAnalysisToolkit(
AnalysisContext(
daily_metrics=self._episode.daily_metrics,
hourly_metrics=self._episode.hourly_metrics,
conversion_definitions=list(self._generator.config.conversion_definitions),
instruction=self._active_task.instruction,
config={
"task_id": self._active_task.task_id,
"objective": self._active_task.objective,
"grader_name": self._active_task.grader_name,
**self._episode.config.__dict__,
},
)
)
if method_name == "task_overview":
result = toolkit.task_overview()
elif method_name == "list_dates":
result = toolkit.list_dates()
elif method_name == "list_entities":
result = toolkit.list_entities()
elif method_name == "rows_for_date":
result = toolkit.rows_for_date(arguments["date"])
elif method_name == "hourly_rows_for_date":
result = toolkit.hourly_rows_for_date(arguments["date"])
elif method_name == "compare_rate_to_median":
result = toolkit.compare_rate_to_median(arguments["date"], arguments["entity_name"])
elif method_name == "compare_count_to_median":
result = toolkit.compare_count_to_median(arguments["date"], arguments["entity_name"])
elif method_name == "detect_funnel_break":
result = toolkit.detect_funnel_break(arguments["date"])
elif method_name == "check_impossible_counts":
result = toolkit.check_impossible_counts(arguments["date"])
elif method_name == "list_suspicious_dates":
result = toolkit.list_suspicious_dates(limit=arguments.get("limit", 10))
elif method_name == "preview_submission":
result = toolkit.preview_submission(arguments.get("rows", []))
elif method_name == "show_raw_data":
result = toolkit.show_raw_data(limit=arguments.get("limit", 5))
elif method_name == "get_metric_median":
result = toolkit.get_metric_median_multi(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
)
elif method_name == "get_metric_std_dev_from_median":
result = toolkit.get_metric_std_dev_from_median_multi(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
)
elif method_name == "get_rows_with_abs_diff_from_median_gt":
result = toolkit.get_rows_with_abs_diff_from_median_gt_multi(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
threshold=float(arguments["threshold"]),
)
elif method_name == "get_median_filter_rows":
result = toolkit.get_median_filter_rows_multi(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "get_rate_drop_from_median_rows":
result = toolkit.get_rate_drop_from_median_rows(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "get_rate_spike_from_median_rows":
result = toolkit.get_rate_spike_from_median_rows(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "get_absolute_drop_in_event_count_rows":
result = toolkit.get_absolute_drop_in_event_count_rows(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "get_absolute_spike_in_event_count_rows":
result = toolkit.get_absolute_spike_in_event_count_rows(
metric_name=arguments.get("metric_name"),
metric_names=arguments.get("metric_names", []),
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "get_funnel_break_rows":
result = toolkit.get_funnel_break_rows(
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "get_hourly_traffic_mix_shift_rows":
result = toolkit.get_hourly_traffic_mix_shift_rows(
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "get_instrumentation_data_quality_issue_rows":
result = toolkit.get_instrumentation_data_quality_issue_rows(
threshold_multiplier=float(arguments["threshold_multiplier"]),
)
elif method_name == "payload_generator":
result = toolkit.payload_generator(arguments.get("generator_methods", []))
else:
raise ValueError(f"Unsupported analysis method: {method_name}")
return {
"method": method_name,
"arguments": arguments,
"result": result,
}
@staticmethod
def _submission_message(result) -> str:
if result.is_perfect:
return "Submission is fully correct."
extra_issues = [issue for issue in result.issues if issue.issue_type == "extra_row"]
missing_count = result.reward_breakdown.missing_rows
if not extra_issues and missing_count > 0:
return (
"All submitted rows are anomalies, but a few are missing. "
f"Missing value count: {missing_count}."
)
if extra_issues:
first = extra_issues[0]
return f"Specific row is not an anomaly: {first.row_key}."
return (
f"Matched {result.reward_breakdown.matched_rows}/"
f"{result.reward_breakdown.expected_rows} expected rows. Review the feedback."
)
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