mirror of
https://github.com/wassname/Unsupervised-Elicitation.git
synced 2026-09-10 11:50:22 +08:00
fix fmt
This commit is contained in:
wassname
+84
-80
@@ -54,12 +54,12 @@ class Config:
|
||||
decay_rate: float = 0.99
|
||||
beta: float = 2.0
|
||||
num_seed: int = 8
|
||||
max_iters: int = 2500000 # should be at least dataset size X 2
|
||||
max_iters: int = 2500 # should be at least dataset size X 2
|
||||
n_shots: int = 6 # Number of in-context examples
|
||||
model_id: str = "meta-llama/llama-3.1-8b-instruct" # Logprobs supported
|
||||
provider_whitelist: Tuple[str] = None # None to let OpenRouter choose
|
||||
out_dir: Path = Path("../outputs/icm") # Directory to save outputs
|
||||
log_interval: int = 20 # Log progress every N iterations
|
||||
log_interval: int = 100 # Log progress every N iterations
|
||||
|
||||
C = Config(
|
||||
model_id="qwen/qwen3-235b-a22b-2507", # $0.2 0.6
|
||||
@@ -136,7 +136,7 @@ async def predict_label(example_uid, current_demos, config=C, verbose=False, all
|
||||
fewshot = []
|
||||
for demo in relevant_demos:
|
||||
label_str = "A" if demo['label'] == 1 else "B"
|
||||
fewshot.append(f"\n\n## Candidate:\n{demo['prompt']}\n## Set: {label_str}")
|
||||
fewshot.append(f"\n\n## Candidate:\n{demo['prompt']}\n## Set:\n{label_str}")
|
||||
|
||||
# Use all_demos if provided (for unlabeled examples), otherwise use current_demos
|
||||
demos_source = all_demos if all_demos is not None else current_demos
|
||||
@@ -319,88 +319,92 @@ async def run_icm(demonstrations, config=C):
|
||||
current_labeled = {k: v for k, v in demonstrations.items() if v['label'] is not None}
|
||||
old_energy, old_metrics = compute_energy(demonstrations, config)
|
||||
|
||||
for iter in range(config.max_iters):
|
||||
# Weighted sampling for inconsistent groups
|
||||
groups = {}
|
||||
all_uids = list(demonstrations.keys())
|
||||
for uid in all_uids:
|
||||
cid = demonstrations[uid]['consistency_id']
|
||||
if cid not in groups:
|
||||
groups[cid] = []
|
||||
groups[cid].append(uid)
|
||||
|
||||
weights = [0.1 for _ in all_uids] # Base low
|
||||
|
||||
for cid, group_uids in groups.items():
|
||||
labeled_labels = [demonstrations[uid]['label'] for uid in group_uids if demonstrations[uid]['label'] is not None]
|
||||
num_labeled = len(labeled_labels)
|
||||
num_unlabeled = len(group_uids) - num_labeled
|
||||
try:
|
||||
for iter in range(config.max_iters):
|
||||
# Weighted sampling for inconsistent groups
|
||||
groups = {}
|
||||
all_uids = list(demonstrations.keys())
|
||||
for uid in all_uids:
|
||||
cid = demonstrations[uid]['consistency_id']
|
||||
if cid not in groups:
|
||||
groups[cid] = []
|
||||
groups[cid].append(uid)
|
||||
|
||||
inconsistency = 0
|
||||
if num_labeled > 0:
|
||||
unique_labels = set(labeled_labels)
|
||||
inconsistency = 1 if len(unique_labels) > 1 else 0
|
||||
weights = [0.1 for _ in all_uids] # Base low
|
||||
|
||||
if num_unlabeled > 0:
|
||||
weight_factor = (0.5 + 0.5 * inconsistency) * (1 + num_unlabeled / len(group_uids))
|
||||
for uid in group_uids:
|
||||
if demonstrations[uid]['label'] is None: # Unlabeled
|
||||
idx = all_uids.index(uid)
|
||||
weights[idx] = weight_factor
|
||||
else:
|
||||
# Fully labeled groups with inconsistency get higher weight for re-prediction
|
||||
if inconsistency:
|
||||
for cid, group_uids in groups.items():
|
||||
labeled_labels = [demonstrations[uid]['label'] for uid in group_uids if demonstrations[uid]['label'] is not None]
|
||||
num_labeled = len(labeled_labels)
|
||||
num_unlabeled = len(group_uids) - num_labeled
|
||||
|
||||
inconsistency = 0
|
||||
if num_labeled > 0:
|
||||
unique_labels = set(labeled_labels)
|
||||
inconsistency = 1 if len(unique_labels) > 1 else 0
|
||||
|
||||
if num_unlabeled > 0:
|
||||
weight_factor = (0.5 + 0.5 * inconsistency) * (1 + num_unlabeled / len(group_uids))
|
||||
for uid in group_uids:
|
||||
idx = all_uids.index(uid)
|
||||
weights[idx] = 0.5 + random.uniform(0, 0.2) # Higher than base, lower than unlabeled; +rand for exploration/flipping
|
||||
if demonstrations[uid]['label'] is None: # Unlabeled
|
||||
idx = all_uids.index(uid)
|
||||
weights[idx] = weight_factor
|
||||
else:
|
||||
for uid in group_uids:
|
||||
idx = all_uids.index(uid)
|
||||
weights[idx] = 0.1
|
||||
# TODO: Weight by potential energy delta: for candidates, temp-assign/predict label, compute delta U vs current, prioritize high positive delta (improves over score; low complexity, med value).
|
||||
|
||||
weights = [max(w, 0.01) for w in weights]
|
||||
example_uid = random.choices(all_uids, weights=weights)[0]
|
||||
|
||||
# Predict new label
|
||||
if iter%100==0:
|
||||
verbose = 1
|
||||
elif iter%100==1:
|
||||
verbose = 2
|
||||
else:
|
||||
verbose = 0
|
||||
new_label, score = await predict_label(example_uid, current_labeled, config, verbose=verbose, all_demos=demonstrations)
|
||||
# Fully labeled groups with inconsistency get higher weight for re-prediction
|
||||
if inconsistency:
|
||||
for uid in group_uids:
|
||||
idx = all_uids.index(uid)
|
||||
weights[idx] = 0.5 + random.uniform(0, 0.2) # Higher than base, lower than unlabeled; +rand for exploration/flipping
|
||||
else:
|
||||
for uid in group_uids:
|
||||
idx = all_uids.index(uid)
|
||||
weights[idx] = 0.1
|
||||
# TODO: Weight by potential energy delta: for candidates, temp-assign/predict label, compute delta U vs current, prioritize high positive delta (improves over score; low complexity, med value).
|
||||
|
||||
weights = [max(w, 0.01) for w in weights]
|
||||
example_uid = random.choices(all_uids, weights=weights)[0]
|
||||
|
||||
# Predict new label
|
||||
if iter%100==0:
|
||||
verbose = 1
|
||||
elif iter%100==1:
|
||||
verbose = 2
|
||||
else:
|
||||
verbose = 0
|
||||
new_label, score = await predict_label(example_uid, current_labeled, config, verbose=verbose, all_demos=demonstrations)
|
||||
|
||||
# Update with new label and fix any inconsistencies ONLY if label changed
|
||||
temp_demos = deepcopy(demonstrations)
|
||||
temp_demos[example_uid]['label'] = new_label
|
||||
temp_demos[example_uid]['score'] = score
|
||||
|
||||
# Only fix inconsistencies if the label actually changed
|
||||
if demonstrations[example_uid]['label'] != new_label:
|
||||
temp_demos = await fix_inconsistencies_greedy(temp_demos, config)
|
||||
|
||||
# Compute new energy
|
||||
new_energy, new_metrics = compute_energy(temp_demos, config)
|
||||
delta = new_energy - old_energy
|
||||
temp_demos[example_uid]['deltaE'] = delta
|
||||
|
||||
# Annealing decision
|
||||
T = max(config.final_t, config.initial_t / (1 + config.beta * math.log(1 + iter)))
|
||||
accept_msg = f"Delta: {delta:.2f}, T: {T:.2f}, Acc: {new_metrics['accuracy']:.2f}"
|
||||
if delta > 0 or random.random() < math.exp(delta / T):
|
||||
demonstrations = temp_demos
|
||||
old_energy = new_energy
|
||||
current_labeled = {k: v for k, v in demonstrations.items() if v['label'] is not None}
|
||||
logger.debug("Iter {}: Accepted. Energy: {:.2f}. {}", iter, old_energy, accept_msg)
|
||||
else:
|
||||
logger.debug("Iter {}: Rejected. {}", iter, accept_msg)
|
||||
|
||||
energies.append(old_energy)
|
||||
accuracies.append(new_metrics['accuracy'])
|
||||
|
||||
if iter % C.log_interval == 0:
|
||||
logger.info(f"Progress: Labeled {new_metrics['num_labeled']}, Inconsistents: {new_metrics['num_inconsistent']}. Acc: {new_metrics['accuracy']:.2f}, Energy: {old_energy:.2f}. Cost so far: ${total_cost:.4f}")
|
||||
# Update with new label and fix any inconsistencies ONLY if label changed
|
||||
temp_demos = deepcopy(demonstrations)
|
||||
temp_demos[example_uid]['label'] = new_label
|
||||
temp_demos[example_uid]['score'] = score
|
||||
|
||||
# Only fix inconsistencies if the label actually changed
|
||||
if demonstrations[example_uid]['label'] != new_label:
|
||||
temp_demos = await fix_inconsistencies_greedy(temp_demos, config)
|
||||
|
||||
# Compute new energy
|
||||
new_energy, new_metrics = compute_energy(temp_demos, config)
|
||||
delta = new_energy - old_energy
|
||||
temp_demos[example_uid]['deltaE'] = delta
|
||||
|
||||
# Annealing decision
|
||||
T = max(config.final_t, config.initial_t / (1 + config.beta * math.log(1 + iter)))
|
||||
accept_msg = f"Delta: {delta:.2f}, T: {T:.2f}, Acc: {new_metrics['accuracy']:.2f}"
|
||||
if delta > 0 or random.random() < math.exp(delta / T):
|
||||
demonstrations = temp_demos
|
||||
old_energy = new_energy
|
||||
current_labeled = {k: v for k, v in demonstrations.items() if v['label'] is not None}
|
||||
logger.debug("Iter {}: Accepted. Energy: {:.2f}. {}", iter, old_energy, accept_msg)
|
||||
else:
|
||||
logger.debug("Iter {}: Rejected. {}", iter, accept_msg)
|
||||
|
||||
energies.append(old_energy)
|
||||
accuracies.append(new_metrics['accuracy'])
|
||||
|
||||
if iter % C.log_interval == 0:
|
||||
logger.info(f"Progress: Labeled {new_metrics['num_labeled']}, Inconsistents: {new_metrics['num_inconsistent']}. Acc: {new_metrics['accuracy']:.2f}, Energy: {old_energy:.2f}. Cost so far: ${total_cost:.4f}")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
logger.info("Stopping early.")
|
||||
|
||||
return demonstrations, energies, accuracies
|
||||
|
||||
|
||||
Reference in New Issue
Block a user