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import json
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Literal
DecisionLabel = Literal["do_now", "do_later", "remind", "no_action"]
MissingFieldStrategy = Literal["unknown", "synthetic"]
_USER_STATE_FLAG_TO_TOKEN = {
"user_asleep": "asleep",
"user_in_rush": "in_rush",
"user_injured_or_disabled": "injured_or_disabled",
"user_nearby": "nearby",
}
_ENV_FLAG_DIRECT = {
"adverse_weather": "adverse_weather",
"guests_present": "guests_present",
}
_VALID_DECISIONS = {"do_now", "do_later", "remind", "no_action"}
@dataclass
class BuilderConfig:
language: Literal["en", "es"] = "en"
missing_field_strategy: MissingFieldStrategy = "unknown"
include_compact_task_fields: bool = True
@dataclass
class BuiltTrainingSample:
sample_id: str
user_id: int
user_external_id: str
label_action: DecisionLabel
action_input: dict[str, Any]
context_input: dict[str, Any]
structured_task_features: dict[str, Any]
preference_snapshot: list[dict[str, Any]] = field(default_factory=list)
source_metadata: dict[str, Any] = field(default_factory=dict)
data_provenance: dict[str, str] = field(default_factory=dict)
def to_dict(self) -> dict[str, Any]:
return asdict(self)
class TrainingDataBuilder:
"""
Builds model-ready samples from mapped TAACO JSONL files.
Designed for MVP bootstrap training where feedback events are still sparse.
"""
def __init__(self, config: BuilderConfig | None = None):
self.config = config or BuilderConfig()
def build_from_taaco_mapped(
self,
*,
input_path: Path,
) -> list[BuiltTrainingSample]:
records = self._load_jsonl(input_path)
persona_map = self._build_persona_id_map(records)
out: list[BuiltTrainingSample] = []
for row in records:
sample = self._build_sample(row=row, persona_id_map=persona_map)
if sample is not None:
out.append(sample)
return out
def write_jsonl(self, samples: list[BuiltTrainingSample], output_path: Path) -> None:
output_path.parent.mkdir(parents=True, exist_ok=True)
with output_path.open("w", encoding="utf-8") as f:
for sample in samples:
f.write(json.dumps(sample.to_dict(), ensure_ascii=False) + "\n")
def summarize(self, samples: list[BuiltTrainingSample]) -> dict[str, Any]:
label_counts: dict[str, int] = {}
user_counts: dict[str, int] = {}
with_time = 0
with_weekday = 0
with_available_objects = 0
for sample in samples:
label_counts[sample.label_action] = label_counts.get(sample.label_action, 0) + 1
user_counts[sample.user_external_id] = (
user_counts.get(sample.user_external_id, 0) + 1
)
if sample.context_input.get("time_of_day", "unknown") != "unknown":
with_time += 1
if sample.context_input.get("weekday", "unknown") != "unknown":
with_weekday += 1
if sample.context_input.get("available_objects"):
with_available_objects += 1
total = len(samples)
return {
"num_samples": total,
"labels": label_counts,
"users": user_counts,
"coverage": {
"time_of_day_known": with_time,
"weekday_known": with_weekday,
"available_objects_nonempty": with_available_objects,
},
"config": asdict(self.config),
}
def _build_sample(
self,
*,
row: dict[str, Any],
persona_id_map: dict[str, int],
) -> BuiltTrainingSample | None:
persona = str(row.get("source_persona", "unknown_persona"))
user_id = persona_id_map[persona]
task_input = self._select_task_input(row)
if task_input is None:
return None
label = self._select_label(row)
if label is None:
return None
action_text = str(task_input.get("action", "")).strip()
activity = str(task_input.get("activity", "")).strip() or None
locations = self._as_list(task_input.get("locations"))
objects = self._as_list(task_input.get("objects"))
conditions = self._as_list(task_input.get("conditions"))
context_flags = task_input.get("context_flags") or {}
context_input, provenance = self._build_context_input(
locations=locations,
objects=objects,
conditions=conditions,
context_flags=context_flags,
user_busy=bool(task_input.get("user_busy", False)),
quiet_hours=bool(task_input.get("quiet_hours", False)),
)
sample_id = f"{persona}:{task_input.get('task_id', '')}:{row.get('source_preference_index', '')}"
structured_task = {
"kind": task_input.get("kind"),
"urgency": task_input.get("urgency"),
"sensitivity": task_input.get("sensitivity"),
"user_busy": bool(task_input.get("user_busy", False)),
"quiet_hours": bool(task_input.get("quiet_hours", False)),
}
if self.config.include_compact_task_fields:
structured_task["conditions"] = conditions
structured_task["context_flags"] = context_flags
return BuiltTrainingSample(
sample_id=sample_id,
user_id=user_id,
user_external_id=persona,
label_action=label,
action_input={
"action_text": action_text,
"activity": activity,
},
context_input=context_input,
structured_task_features=structured_task,
preference_snapshot=[],
source_metadata={
"source_example_index": row.get("source_example_index"),
"source_preference_index": row.get("source_preference_index"),
"expected_action_taaco": self._extract_expected_action_taaco(row),
"mapping_trace": row.get("mapping_trace", []),
},
data_provenance=provenance,
)
def _build_context_input(
self,
*,
locations: list[str],
objects: list[str],
conditions: list[str],
context_flags: dict[str, Any],
user_busy: bool,
quiet_hours: bool,
) -> tuple[dict[str, Any], dict[str, str]]:
true_flags = {k for k, v in context_flags.items() if bool(v)}
user_state: list[str] = []
for flag, token in _USER_STATE_FLAG_TO_TOKEN.items():
if flag in true_flags:
user_state.append(token)
if user_busy:
user_state.append("busy")
environment_flags: list[str] = []
for flag, token in _ENV_FLAG_DIRECT.items():
if flag in true_flags:
environment_flags.append(token)
if quiet_hours:
environment_flags.append("quiet_hours")
if "weekend" in true_flags:
environment_flags.append("weekend")
location_current = locations[0] if locations else None
time_of_day = "unknown"
weekday = "unknown"
available_objects: list[str] = []
provenance = {
"location_current": "derived_from_locations[0]",
"objects_nearby": "direct_from_task.objects",
"raw_conditions": "direct_from_task.conditions",
"user_state": "derived_from_context_flags_and_user_busy",
"environment_flags": "derived_from_context_flags_and_quiet_hours",
}
if self.config.missing_field_strategy == "synthetic":
available_objects = sorted(set(objects))
provenance["available_objects"] = "synthetic_from_objects_nearby"
if "early_morning" in true_flags:
time_of_day = "morning"
provenance["time_of_day"] = "synthetic_from_context_flags.early_morning"
elif "user_asleep" in true_flags:
time_of_day = "night"
provenance["time_of_day"] = "synthetic_from_context_flags.user_asleep"
else:
provenance["time_of_day"] = "unknown_no_signal"
if "weekend" in true_flags:
weekday = "saturday"
provenance["weekday"] = "synthetic_from_context_flags.weekend"
else:
provenance["weekday"] = "unknown_no_signal"
else:
provenance["available_objects"] = "unknown_default"
provenance["time_of_day"] = "unknown_default"
provenance["weekday"] = "unknown_default"
return (
{
"location_current": location_current,
"objects_nearby": objects,
"available_objects": available_objects,
"raw_conditions": conditions,
"time_of_day": time_of_day,
"weekday": weekday,
"user_state": sorted(set(user_state)),
"environment_flags": sorted(set(environment_flags)),
},
provenance,
)
def _select_task_input(self, row: dict[str, Any]) -> dict[str, Any] | None:
# Preferred format: all_personas_mapped_both.jsonl
if self.config.language == "en" and isinstance(row.get("task_input_en"), dict):
return row["task_input_en"]
if self.config.language == "es" and isinstance(row.get("task_input_es"), dict):
return row["task_input_es"]
# Compact format: all_personas_mapped_en/es.jsonl
compact = row.get("task_input")
source = row.get("source") or {}
if isinstance(compact, dict):
if "action" not in compact and isinstance(source, dict):
# Recover dropped fields from source payload.
compact = dict(compact)
compact["action"] = source.get("action", "")
compact["activity"] = source.get("activity", "")
compact["objects"] = self._as_list(source.get("object"))
compact["locations"] = self._as_list(source.get("location"))
compact["conditions"] = self._as_list(source.get("conditions"))
compact["explanations"] = source.get("explanations", [])
compact["explanations_opposing"] = source.get("explanations_opposing", [])
compact["context_flags"] = row.get("context_flags", {})
return compact
return None
def _select_label(self, row: dict[str, Any]) -> DecisionLabel | None:
expected = row.get("expected_action")
if isinstance(expected, str):
return self._normalize_label(expected)
if isinstance(expected, dict):
if self.config.language == "en":
return self._normalize_label(expected.get("en"))
if self.config.language == "es":
# Training label remains canonical English token.
return self._normalize_label(expected.get("taaco"))
return None
def _extract_expected_action_taaco(self, row: dict[str, Any]) -> str | None:
expected = row.get("expected_action")
if isinstance(expected, str):
return expected
if isinstance(expected, dict):
taaco = expected.get("taaco")
return str(taaco) if taaco is not None else None
alt = row.get("expected_action_taaco")
return str(alt) if alt is not None else None
def _build_persona_id_map(self, rows: list[dict[str, Any]]) -> dict[str, int]:
personas = sorted({str(r.get("source_persona", "unknown_persona")) for r in rows})
return {persona: i + 1 for i, persona in enumerate(personas)}
def _normalize_label(self, label: Any) -> DecisionLabel | None:
if label is None:
return None
text = str(label).strip().lower().replace(" ", "_")
if text in _VALID_DECISIONS:
return text # type: ignore[return-value]
return None
def _as_list(self, value: Any) -> list[str]:
if value is None:
return []
if isinstance(value, list):
return [str(x) for x in value]
return [str(value)]
def _load_jsonl(self, path: Path) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
with path.open("r", encoding="utf-8") as f:
for raw in f:
line = raw.strip()
if not line:
continue
rows.append(json.loads(line))
return rows
__all__ = [
"BuilderConfig",
"BuiltTrainingSample",
"DecisionLabel",
"MissingFieldStrategy",
"TrainingDataBuilder",
]
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