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File size: 2,207 Bytes
11ac7be | 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 | from __future__ import annotations
import math
from dataclasses import dataclass
from random import Random
from typing import Dict, List, Optional
from .logistic import LogisticRegression
from .metrics import evaluate
@dataclass(frozen=True)
class TrainResult:
model: LogisticRegression
history: List[Dict[str, float]]
def _l2_norm(v: List[float]) -> float:
return math.sqrt(sum(x * x for x in v))
def train_logreg_sgd(
*,
seed: int,
xs_train: List[List[float]],
ys_train: List[int],
xs_val: List[List[float]],
ys_val: List[int],
epochs: int,
lr: float,
l2: float = 0.0,
grad_clip: Optional[float] = None,
loss_eps: float = 1e-12,
) -> TrainResult:
r = Random(seed)
n_features = len(xs_train[0]) if xs_train else 0
model = LogisticRegression.init(n_features)
history: List[Dict[str, float]] = []
idx = list(range(len(xs_train)))
for epoch in range(1, epochs + 1):
r.shuffle(idx)
for i in idx:
x = xs_train[i]
y = ys_train[i]
p = model.predict_proba_one(x)
g = (p - y)
grad_w = [g * xi + (l2 * wi) for xi, wi in zip(x, model.w)]
grad_b = g
if grad_clip is not None and grad_clip > 0:
norm = _l2_norm(grad_w)
if norm > grad_clip:
scale = grad_clip / (norm + 1e-12)
grad_w = [gw * scale for gw in grad_w]
grad_b *= scale
model.w = [wi - lr * gw for wi, gw in zip(model.w, grad_w)]
model.b = model.b - lr * grad_b
train_loss, train_acc = evaluate(model, xs_train, ys_train, eps=loss_eps)
val_loss, val_acc = evaluate(model, xs_val, ys_val, eps=loss_eps)
history.append(
{
"epoch": float(epoch),
"train_loss": float(train_loss),
"train_acc": float(train_acc),
"val_loss": float(val_loss),
"val_acc": float(val_acc),
}
)
if not math.isfinite(train_loss) or not math.isfinite(val_loss):
break
return TrainResult(model=model, history=history)
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