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f8768df 8947dc2 f8768df 8947dc2 f8768df c4aee46 f8768df c4aee46 f8768df | 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 | """Public API for deterministic financial-news relevance screening."""
from __future__ import annotations
import hashlib
import os
from collections.abc import Iterable, Sequence
from datetime import datetime
from functools import lru_cache
from pathlib import Path
from typing import Literal
from ._core.accelerated.indicator import AcceleratedRelevanceIndicator
from ._core.accelerated.compact import (
ArrowCompactFacts,
ColumnArticleBatch,
CompactRelevanceIndicator,
)
from ._core.models import Article, ExposureFamily, RelevanceDecision
from .backends import available_backends, create_backend
from .customization import (
DefaultRanker,
DefaultTaxonomy,
Ranker,
Taxonomy,
)
from .models import (
BackendInfo,
Prediction,
PythonScreeningBatch,
RankedPrediction,
)
TimestampLike = datetime | str
def _workers(value: int | None) -> int:
if value is None:
return max(1, min(10, os.cpu_count() or 1))
if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
raise ValueError("workers must be a positive integer or None")
return value
def _article_id(text: str, timestamp: TimestampLike) -> str:
payload = f"{timestamp}\0{text}".encode("utf-8")
return f"article-{hashlib.sha256(payload).hexdigest()[:20]}"
def _optional_length(
name: str,
values: Sequence[object] | None,
expected: int,
) -> None:
if values is not None and len(values) != expected:
raise ValueError(f"{name} length {len(values)} does not match {expected}")
class RelevanceEngine:
"""Exact rich prediction plus explicitly separate compact screening paths."""
__slots__ = (
"_backend",
"_indicator",
"_ranker",
"_screening_indicator",
"_taxonomy",
"_workers",
)
def __init__(
self,
taxonomy: Taxonomy | None = None,
*,
ranker: Ranker | None = None,
backend: Literal["cpu", "cuda"] = "cpu",
workers: int | None = None,
) -> None:
resolved_taxonomy = taxonomy or DefaultTaxonomy()
if not isinstance(resolved_taxonomy, Taxonomy):
raise TypeError("taxonomy must be a Taxonomy or None")
resolved_ranker = ranker or DefaultRanker()
if not isinstance(resolved_ranker, Ranker):
raise TypeError("ranker must inherit Ranker")
self._taxonomy = resolved_taxonomy
self._ranker = resolved_ranker
self._workers = _workers(workers)
self._backend = create_backend(backend)
self._indicator = AcceleratedRelevanceIndicator(
config=resolved_taxonomy.config,
catalogs=resolved_taxonomy.catalogs,
)
self._screening_indicator = CompactRelevanceIndicator(
config=resolved_taxonomy.config,
catalogs=resolved_taxonomy.catalogs,
)
@classmethod
def from_yaml(
cls,
path: str | Path,
*,
ranker: Ranker | None = None,
backend: Literal["cpu", "cuda"] = "cpu",
workers: int | None = None,
) -> "RelevanceEngine":
return cls(
Taxonomy.from_yaml(path),
ranker=ranker,
backend=backend,
workers=workers,
)
@property
def taxonomy(self) -> Taxonomy:
return self._taxonomy
@property
def ranker(self) -> Ranker:
return self._ranker
@property
def backend(self) -> str:
return self._backend.name
@property
def backend_info(self) -> BackendInfo:
return self._backend.describe(self._screening_indicator)
@property
def workers(self) -> int:
return self._workers
def predict(
self,
text: str,
*,
timestamp: TimestampLike,
article_id: str | None = None,
source: str = "unknown",
) -> Prediction:
"""Build one rich decision with evidence and explanation provenance."""
article = Article(
article_id=(
_article_id(text, timestamp) if article_id is None else article_id
),
timestamp=timestamp,
text=text,
source=source,
)
return self._indicator.predict(article)
def predict_batch(
self,
texts: Sequence[str],
*,
timestamps: Sequence[TimestampLike],
article_ids: Sequence[str] | None = None,
sources: Sequence[str] | None = None,
workers: int | None = None,
) -> tuple[Prediction, ...]:
"""Build rich decisions; use `screen_batch()` for throughput work."""
count = len(texts)
if len(timestamps) != count:
raise ValueError("texts and timestamps must have equal lengths")
_optional_length("article_ids", article_ids, count)
_optional_length("sources", sources, count)
articles = tuple(
Article(
article_id=(
article_ids[index] if article_ids is not None else f"row-{index}"
),
timestamp=timestamps[index],
text=text,
source=sources[index] if sources is not None else "unknown",
)
for index, text in enumerate(texts)
)
return self._indicator.predict_batch(
articles,
workers=_workers(workers) if workers is not None else self._workers,
)
def screen_batch(
self,
texts: Sequence[str],
*,
timestamps: Sequence[TimestampLike],
article_ids: Sequence[str] | None = None,
sources: Sequence[str] | None = None,
workers: int | None = None,
) -> PythonScreeningBatch:
"""Run compact list screening with implicit zero-observation rows."""
count = len(texts)
if len(timestamps) != count:
raise ValueError("texts and timestamps must have equal lengths")
_optional_length("article_ids", article_ids, count)
_optional_length("sources", sources, count)
ids = (
article_ids
if article_ids is not None
else tuple(f"row-{index}" for index in range(count))
)
batch = ColumnArticleBatch(
article_ids=ids,
timestamps=timestamps,
texts=texts,
sources=sources,
)
return self._backend.screen_batch(
self._screening_indicator,
batch,
workers=_workers(workers) if workers is not None else self._workers,
)
def screen_arrow(
self,
data,
*,
text_column: str = "text",
timestamp_column: str = "timestamp",
workers: int | None = None,
row_count: int | None = None,
) -> ArrowCompactFacts:
"""Run the sparse Arrow path without materializing per-row Python objects."""
if row_count is not None and (
isinstance(row_count, bool)
or not isinstance(row_count, int)
or row_count < 0
):
raise ValueError("row_count must be a non-negative integer or None")
discovered = getattr(data, "num_rows", None)
if discovered is not None:
discovered = int(discovered)
if row_count is None:
row_count = discovered
elif row_count != discovered:
raise ValueError(
f"row_count {row_count} does not match Arrow rows {discovered}"
)
return self._backend.screen_arrow(
self._screening_indicator,
self._taxonomy,
data,
text_column=text_column,
timestamp_column=timestamp_column,
workers=_workers(workers) if workers is not None else self._workers,
row_count=row_count,
)
def explain(
self,
prediction: RelevanceDecision,
*,
format: Literal["text", "mermaid"] = "text",
detail: Literal["summary", "details"] = "summary",
) -> str:
"""Format already-recorded evidence; this does not rerun prediction."""
if not isinstance(prediction, RelevanceDecision):
raise TypeError("prediction must be a rich Prediction")
if detail not in {"summary", "details"}:
raise ValueError("detail must be 'summary' or 'details'")
return self._indicator.explain(
prediction,
output_format=format,
detail=detail,
)
def rank(
self,
predictions: Iterable[Prediction],
*,
exposure_family: ExposureFamily | str,
decision_bucket: datetime | str,
limit: int | None = None,
) -> tuple[RankedPrediction, ...]:
return self._ranker.rank(
predictions,
config=self._taxonomy.config,
exposure_family=exposure_family,
decision_bucket=decision_bucket,
limit=limit,
)
@lru_cache(maxsize=1)
def _default_engine() -> RelevanceEngine:
return RelevanceEngine()
def predict(
text: str,
*,
timestamp: TimestampLike,
article_id: str | None = None,
source: str = "unknown",
) -> Prediction:
return _default_engine().predict(
text,
timestamp=timestamp,
article_id=article_id,
source=source,
)
def predict_batch(
texts: Sequence[str],
*,
timestamps: Sequence[TimestampLike],
article_ids: Sequence[str] | None = None,
sources: Sequence[str] | None = None,
workers: int | None = None,
) -> tuple[Prediction, ...]:
return _default_engine().predict_batch(
texts,
timestamps=timestamps,
article_ids=article_ids,
sources=sources,
workers=workers,
)
def screen_batch(
texts: Sequence[str],
*,
timestamps: Sequence[TimestampLike],
article_ids: Sequence[str] | None = None,
sources: Sequence[str] | None = None,
workers: int | None = None,
) -> PythonScreeningBatch:
return _default_engine().screen_batch(
texts,
timestamps=timestamps,
article_ids=article_ids,
sources=sources,
workers=workers,
)
def screen_arrow(
data,
*,
text_column: str = "text",
timestamp_column: str = "timestamp",
workers: int | None = None,
row_count: int | None = None,
) -> ArrowCompactFacts:
return _default_engine().screen_arrow(
data,
text_column=text_column,
timestamp_column=timestamp_column,
workers=workers,
row_count=row_count,
)
__all__ = [
"RelevanceEngine",
"available_backends",
"predict",
"predict_batch",
"screen_arrow",
"screen_batch",
]
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