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759bf41 b64175d 759bf41 9668975 759bf41 b64175d 759bf41 9668975 759bf41 9668975 759bf41 9668975 759bf41 9668975 759bf41 b42781c 759bf41 b64175d 9668975 759bf41 9668975 759bf41 9668975 759bf41 | 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 | import time
from collections.abc import Callable, Iterable
from functools import lru_cache
from typing import NamedTuple
import numpy as np
from datasets import load_dataset
from .config import DATASET_REPO, TOKEN, display
from .hub import resolve
from .predictors import Predictor, load
BATCH_SIZE = 8
Track = Callable[[Iterable], Iterable]
class Split(NamedTuple):
ids: list[str]
texts: list[str]
gold: list[str]
sha: str
class Report(NamedTuple):
repo: str
model_sha: str
dataset_sha: str
hits: int
total: int
ms_per_case: float
confusion: list[list]
cases: list[list]
@lru_cache(maxsize=2)
def at_sha(sha: str) -> Split:
data = load_dataset(DATASET_REPO, split="test", revision=sha, token=TOKEN)
names = data.features["label"].names
return Split(
ids=list(data["id"]),
texts=list(data["text"]),
gold=[names[i] for i in data["label"]],
sha=sha,
)
def test_split() -> Split:
return at_sha(resolve(DATASET_REPO, "main", repo_type="dataset"))
def confusion(labels: list[str], gold: list[str], predicted: list[str]) -> list[list]:
counts = dict.fromkeys(((want, got) for want in labels for got in labels), 0)
for pair in zip(gold, predicted):
counts[pair] += 1
return [
[f"actual {display(want).lower()}", *(counts[want, got] for got in labels)]
for want in labels
]
def cases(ids, texts, gold, predicted, probabilities: np.ndarray) -> list[list]:
rows = [
[
"✓" if want == got else "✗",
row_id,
display(want),
display(got),
round(float(confidence), 3),
text.replace("\n", " / "),
]
for row_id, text, want, got, confidence in zip(
ids, texts, gold, predicted, probabilities.max(axis=1)
)
]
return sorted(rows, key=lambda row: (row[0] == "✓", row[4]))
def batches(texts: list[str], size: int = BATCH_SIZE) -> list[list[str]]:
return [texts[start : start + size] for start in range(0, len(texts), size)]
def classify_all(
predictor: Predictor, texts: list[str], track: Track | None = None
) -> tuple[list[str], np.ndarray]:
chunks = batches(texts)
labels: list[str] = []
scored = []
for batch in track(chunks) if track else chunks:
labels, probabilities = predictor(batch)
scored.append(probabilities)
return labels, np.concatenate(scored)
def run(
repo: str,
revision: str,
predict: Predictor | None = None,
track: Track | None = None,
) -> Report:
predictor, model_sha = (predict, revision) if predict else load(repo, revision)
split = test_split()
started = time.perf_counter()
labels, probabilities = classify_all(predictor, split.texts, track)
elapsed = time.perf_counter() - started
predicted = [labels[int(row.argmax())] for row in probabilities]
return Report(
repo=repo,
model_sha=model_sha,
dataset_sha=split.sha,
hits=sum(want == got for want, got in zip(split.gold, predicted)),
total=len(split.gold),
ms_per_case=elapsed * 1000 / len(split.texts),
confusion=confusion(labels, split.gold, predicted),
cases=cases(split.ids, split.texts, split.gold, predicted, probabilities),
)
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