83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
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import torch
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler
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class LogisticProber:
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def __init__(self):
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self.scaler = StandardScaler()
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# `penalty="l2"` is the default and was deprecated in scikit-learn 1.8;
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# naming it explicitly stops working in 1.10.
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self.clf = LogisticRegression(
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C=1.0, class_weight="balanced", max_iter=1000, random_state=42
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)
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def fit_predict(self, X_train, y_train, X_test):
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"""Predictions on the test events, or None if either pool is too small.
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Returning the predictions rather than one score lets the caller report
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the probe with the same metrics as the zero-shot readout.
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"""
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if len(y_train) < 10 or len(X_test) < 10:
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return None
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self.clf.fit(self.scaler.fit_transform(X_train), y_train)
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return self.clf.predict(self.scaler.transform(X_test))
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def get_param_groups(named_parameters, weight_decay):
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"""Decay matrices only.
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Norm gains, biases, and embedding tables are 1-D; shrinking them toward
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zero costs capacity without the regularizing effect it has on weight
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matrices. Frozen parameters are dropped so they never reach the optimizer.
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"""
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decay, no_decay = [], []
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for _, parameter in named_parameters:
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if not parameter.requires_grad:
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continue
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(decay if parameter.ndim >= 2 else no_decay).append(parameter)
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return [
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{"params": decay, "weight_decay": weight_decay},
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{"params": no_decay, "weight_decay": 0.0},
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]
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def get_adamw_optimizer(params, lr, weight_decay):
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return torch.optim.AdamW(params, lr=lr, weight_decay=weight_decay)
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def get_cosine_warmup_scheduler(optimizer, total_steps, warmup_ratio=0.1, min_lr=None):
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total_steps = max(1, int(total_steps))
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warmup_steps = min(int(total_steps * warmup_ratio), total_steps - 1)
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decay_steps = max(1, total_steps - warmup_steps)
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max_lr = optimizer.param_groups[0]["lr"]
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min_lr = max_lr * 0.01 if min_lr is None else min_lr
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cosine = torch.optim.lr_scheduler.CosineAnnealingLR(
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optimizer, T_max=decay_steps, eta_min=min_lr
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)
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if warmup_steps == 0:
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return cosine
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warmup = torch.optim.lr_scheduler.LinearLR(
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optimizer, start_factor=0.01, end_factor=1.0, total_iters=warmup_steps
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)
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return torch.optim.lr_scheduler.SequentialLR(
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optimizer, schedulers=[warmup, cosine], milestones=[warmup_steps]
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)
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def get_optimizer_cfg(optimizer, scheduler=None):
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cfg = {"optimizer": optimizer}
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if scheduler:
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cfg["lr_scheduler"] = {
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"scheduler": scheduler,
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"interval": "step",
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"frequency": 1,
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}
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return cfg
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