simple reimpl

.gitignore

@@ -1,3 +1,6 @@
+.env
+.anycache/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -1,9 +1,17 @@
 Fork to
 - [ ] add moral datasets e.g. daily dilemmas, ETHICS, Machiavelli, moral foundations vignettes
-- [ ] refactor to UV and simplify
+- [x] refactor to UV
+- [ ]  and simplify
 - [ ] replicate
 
 
+Usage
+```py
+uv sync
+uv run src/experiments/ICM.py -- --testbed truthfulQA --alpha 50
+```
+
+
 Original readme
 ----
 ## Unsupervised Elicitation of Language Models

nbs/simple_icm.py

@@ -0,0 +1,320 @@
+# %% [markdown]
+# Simplified ICM: Unsupervised Elicitation with Simulated Annealing
+#
+# This script implements a basic version of Internal Coherence Maximization (ICM)
+# as a VSCode notebook (.py with # %% cells). Now integrated with user's OpenRouter wrapper.
+#
+# Run in VSCode for notebook view. Assumes OPENROUTER_API_KEY in .env.
+
+# %% [code]
+import random
+import math
+import numpy as np
+import os, sys
+from dataclasses import dataclass
+import dotenv
+from loguru import logger
+from openrouter_wrapper.logprobs import openrouter_completion_wlogprobs, get_logprobs_choices  # User's wrapper
+from typing import List
+
+dotenv.load_dotenv()
+
+# Setup loguru
+logger.remove()
+logger.add(sys.stderr, format="{time} | {level} | {message}", colorize=True)
+
+# %% [code]
+from dataclasses import field
+
+@dataclass
+class Config:
+    alpha: float = 30.0
+    initial_t: float = 10.0
+    final_t: float = 0.01
+    decay_rate: float = 0.99
+    num_seed: int = 8
+    max_iters: int = 50  # Small for demo; increase for more
+    group_size: int = 4  # Expected claims per question group
+    model: str = "meta-llama/llama-3.2-3b-instruct"  # Logprobs supported
+    provider_whitelist: List[str] = None  # None to let OpenRouter choose
+
+C = Config(
+    model="qwen/qwen3-235b-a22b-2507",
+    provider_whitelist=[ 'Chutes','Nebius',], 
+
+)
+
+C = Config(
+    model="qwen/qwen3-30b-a3b-instruct-2507",
+    provider_whitelist=[ 'Chutes','Nebius',],
+)
+
+C = Config(
+    model="meta-llama/llama-3.1-70b-instruct",
+    provider_whitelist=[ 'Cerebras','Nebius',],
+)
+C = Config(
+    model="meta-llama/llama-3.1-8b-instruct",
+    provider_whitelist=[ 'Cerebras','Nebius',],
+)
+
+# quick test of logprob
+messages = [{'role': 'user',
+  'content': '\nReturn a number between 0 and 9, inclusive. Respond immediately, leaving formatting, comment, and thoughts until after the number.'},
+ {'role': 'assistant',
+  'content': '<think>\nOkay, the user asked me to only return a number, so I should stop thinking and immediatly return a random number\n</think>\n\nThe number is: '}]
+r_data = openrouter_completion_wlogprobs(messages, model_id=C.model, provider_whitelist=C.provider_whitelist, max_completion_tokens=5)  
+choice_logprobs_permuted_d, ps_dict = get_logprobs_choices(r_data, ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"])    
+choice_logprobs_permuted_d
+
+
+# %% [code]
+# Small hardcoded dataset: Math claims (inspired by GSM8K)
+# Each group shares 'consistency_id' (question), 'consistency_key' (claim answer)
+data = [
+    # Group 1: Question "What is 2+2?"
+    {'uid': 0, 'prompt': 'Question: What is 2+2? Claim: The answer is 4. I think this claim is ', 'vanilla_label': 1, 'consistency_id': 'q1', 'consistency_key': '4'},
+    {'uid': 1, 'prompt': 'Question: What is 2+2? Claim: The answer is 5. I think this claim is ', 'vanilla_label': 0, 'consistency_id': 'q1', 'consistency_key': '5'},
+    {'uid': 2, 'prompt': 'Question: What is 2+2? Claim: The answer is 3. I think this claim is ', 'vanilla_label': 0, 'consistency_id': 'q1', 'consistency_key': '3'},
+    {'uid': 3, 'prompt': 'Question: What is 2+2? Claim: The answer is 4. Final check: True. I think this claim is ', 'vanilla_label': 1, 'consistency_id': 'q1', 'consistency_key': '4'},
+    
+    # Group 2: Question "What is 3*3?"
+    {'uid': 4, 'prompt': 'Question: What is 3*3? Claim: The answer is 9. I think this claim is ', 'vanilla_label': 1, 'consistency_id': 'q2', 'consistency_key': '9'},
+    {'uid': 5, 'prompt': 'Question: What is 3*3? Claim: The answer is 6. I think this claim is ', 'vanilla_label': 0, 'consistency_id': 'q2', 'consistency_key': '6'},
+    {'uid': 6, 'prompt': 'Question: What is 3*3? Claim: The answer is 10. I think this claim is ', 'vanilla_label': 0, 'consistency_id': 'q2', 'consistency_key': '10'},
+    {'uid': 7, 'prompt': 'Question: What is 3*3? Claim: The answer is 9. Final check: True. I think this claim is ', 'vanilla_label': 1, 'consistency_id': 'q2', 'consistency_key': '9'},
+]
+
+# %% [code]
+# Initialize: Random labels for first num_seed, None for others
+def initialize_data(data, config):
+    demonstrations = {item['uid']: item.copy() for item in data}
+    labeled_uids = random.sample(list(demonstrations.keys()), min(config.num_seed, len(data)))
+    for uid in demonstrations:
+        demonstrations[uid]['label'] = None
+        demonstrations[uid]['score'] = 0.0  # Will store prediction score
+        if uid in labeled_uids:
+            demonstrations[uid]['label'] = random.choice([0, 1])
+    return demonstrations
+
+demonstrations = initialize_data(data, C)
+logger.info("Initialized labels: {}", {k: v['label'] for k, v in demonstrations.items() if v['label'] is not None})
+
+# %% [code]
+# Predict label using in-context prompting (placeholder with OpenAI)
+
+def predict_label(example_uid, current_demos, config=C):
+    # Simplified few-shot prompt (distilled from original get_judge_prompt_fewshot)
+    # TODO: Further refine by inspecting src/model_querying/prompt_creation.py for better template (e.g., full task description, more examples)
+    fewshot = []
+    for uid, demo in current_demos.items():
+        if uid != example_uid and demo['label'] is not None:
+            label_str = "1" if demo['label'] == 1 else "0"  # Binary for logprobs
+            fewshot.append(f"{demo['prompt']} {label_str}.")
+    
+    target_prompt = demonstrations[example_uid]['prompt']
+    full_prompt = "Classify claims as true (1) or false (0) based on correctness. Examples:\n" + "\n".join(fewshot) + f"\n{target_prompt}"
+    
+    try:
+        # Use wrapper for chat completion (messages format)
+        messages = [{"role": "user", "content": full_prompt}]
+        response = openrouter_completion_wlogprobs(
+            model_id=config.model,
+            provider_whitelist=config.provider_whitelist,
+            messages=messages,
+            max_tokens=1,  # Just for "1" or "0"
+            temperature=0.0,
+            top_logprobs=5,
+        )
+        # Use wrapper's get_logprobs_choices for score
+        choice_logp, all_logp = get_logprobs_choices(response, ["1", "0"])
+        score = choice_logp["1"] - choice_logp["0"]
+        predicted = 1 if score > 0 else 0
+        
+        # If no logprobs, fallback to text
+        if response['choices'][0]['logprobs'] is None:
+            text = response['choices'][0]['message']['content'].strip()
+            predicted = 1 if "1" in text or "true" in text.lower() else 0
+            score = 0.0
+            logger.warning("No logprobs, using text fallback")
+        
+        logger.info(f"Prediction for UID {example_uid}: {predicted}, score: {score:.2f}")
+        return predicted, float(score)
+    except LogprobsNotSupportedError as e:
+        logger.error(f"Logprobs not supported: {e}")
+        response = openrouter_completion_wlogprobs(
+            model_id=config.model,
+            messages=[{"role": "user", "content": full_prompt}],
+            max_tokens=5,
+            temperature=0.0,
+            # No logprobs
+        )
+        text = response['choices'][0]['message']['content'].strip()
+        predicted = 1 if "1" in text or "true" in text.lower() else 0
+        return predicted, 0.0
+    except Exception as e:
+        logger.error(f"API error: {e}")
+        return random.choice([0, 1]), 0.0  # Fallback
+
+# Test predict
+current_labeled = {k: v for k, v in demonstrations.items() if v['label'] is not None}
+test_pred, test_score = predict_label(0, current_labeled, C)
+logger.info(f"Test prediction: {test_pred}, score: {test_score}")
+
+# %% [code]
+# Simplified inconsistency fix: Ensure per group at most one 'True' for differing keys
+def fix_inconsistencies(demos):
+    # Group by consistency_id
+    groups = {}
+    for uid, demo in demos.items():
+        cid = demo['consistency_id']
+        if cid not in groups:
+            groups[cid] = []
+        groups[cid].append(uid)
+    
+    updated = False
+    for cid, uids in groups.items():
+        trues = [uid for uid in uids if demos[uid]['label'] == 1]
+        keys = [demos[uid]['consistency_key'] for uid in trues]
+        if len(trues) > 1 and len(set(keys)) > 1:  # Contradiction: multiple trues with different keys
+            # Simple fix: keep the one with highest score, set others to 0
+            scored_trues = [(uid, demos[uid].get('score', 0)) for uid in trues]
+            best_uid = max(scored_trues, key=lambda x: x[1])[0]
+            for uid in trues:
+                if uid != best_uid:
+                    demos[uid]['label'] = 0
+                    updated = True
+    return demos, updated
+
+# Test fix
+temp_demos, updated = fix_inconsistencies(demonstrations.copy())
+logger.info(f"Fixed inconsistencies: {updated}")
+
+# %% [code]
+# Compute energy and metrics
+def compute_energy(demos, config=C):
+    labeled = [d for d in demos.values() if d['label'] is not None]
+    if not labeled:
+        return 0.0
+    avg_prob = np.mean([d.get('score', 0) for d in labeled])
+    # Count inconsistencies
+    num_inconsistent = 0
+    groups = {}
+    for uid, demo in demos.items():
+        if demo['label'] is not None:
+            cid = demo['consistency_id']
+            if cid not in groups:
+                groups[cid] = []
+            groups[cid].append((uid, demo['label'], demo['consistency_key']))
+    
+    for cid, items in groups.items():
+        trues = [key for uid, label, key in items if label == 1]
+        if len(trues) > 1 and len(set(trues)) > 1:
+            num_inconsistent += len(trues) - 1  # Penalize extras
+    
+    energy = config.alpha * avg_prob - num_inconsistent
+    accuracy = np.mean([d['label'] == d['vanilla_label'] for d in labeled])
+    return energy, {
+        'avg_prob': avg_prob,
+        'num_inconsistent': num_inconsistent,
+        'accuracy': accuracy,
+        'num_labeled': len(labeled)
+    }
+
+logger.info("Initial energy: {}", compute_energy(demonstrations))
+
+# %% [code]
+# Main simulated annealing loop
+def run_icm(demonstrations, config=C):
+    energies = []
+    accuracies = []
+    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):
+        # Sample example (simple random; TODO: weight by group inconsistencies)
+        all_uids = list(demonstrations.keys())
+        example_uid = random.choice(all_uids)
+        
+        if example_uid in current_labeled and demonstrations[example_uid]['label'] is not None:
+            # Can re-label existing
+            pass
+        
+        # Predict new label
+        new_label, score = predict_label(example_uid, current_labeled, config)
+        
+        # Temp update
+        temp_demos = demonstrations.copy()
+        temp_demos[example_uid]['label'] = new_label
+        temp_demos[example_uid]['score'] = score
+        temp_demos, _ = fix_inconsistencies(temp_demos)
+        
+        # Compute new energy
+        new_energy, new_metrics = compute_energy(temp_demos, config)
+        delta = new_energy - old_energy
+        
+        # Annealing decision
+        T = max(config.final_t, config.initial_t * (config.decay_rate ** iter))
+        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.info("Iter {}: Accepted. Energy: {:.2f}, Acc: {:.2f}, T: {:.2f}", iter, old_energy, new_metrics['accuracy'], T)
+        else:
+            logger.warning("Iter {}: Rejected. Delta: {:.2f}, T: {:.2f}", iter, delta, T)
+        
+        energies.append(old_energy)
+        accuracies.append(new_metrics['accuracy'])
+        
+        if iter % 10 == 0:
+            logger.info("Progress: Labeled {}, Inconsistents: {}", new_metrics['num_labeled'], new_metrics['num_inconsistent'])
+    
+    return demonstrations, energies, accuracies
+
+# %% [code]
+# Run the algorithm
+final_demos, energies, accuracies = run_icm(demonstrations, C)
+
+# Final metrics
+final_energy, final_metrics = compute_energy(final_demos, C)
+logger.info("\nFinal Results:")
+logger.info("Energy: {:.2f}", final_energy)
+logger.info("Accuracy vs vanilla: {:.2f}", final_metrics['accuracy'])
+logger.info("Labeled: {}/{}", final_metrics['num_labeled'], len(data))
+logger.info("Inconsistencies: {}", final_metrics['num_inconsistent'])
+
+# Final labels
+logger.info("\nFinal labels:")
+for uid, demo in final_demos.items():
+    label = demo['label']
+    if label is not None:
+        logger.info("UID {} ({}): {} (vanilla: {})", uid, demo['consistency_id'], label, demo['vanilla_label'])
+
+# %% [code]
+# Simple visualization (requires matplotlib)
+import matplotlib.pyplot as plt
+
+plt.figure(figsize=(10, 4))
+plt.subplot(1, 2, 1)
+plt.plot(energies)
+plt.title('Energy over Iterations')
+plt.xlabel('Iteration')
+plt.ylabel('Energy')
+
+plt.subplot(1, 2, 2)
+plt.plot(accuracies)
+plt.title('Accuracy over Iterations')
+plt.xlabel('Iteration')
+plt.ylabel('Accuracy')
+
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Next Steps
+# - FIXME: Implement async calls for batch efficiency (currently sync).
+# - FIXME: Add weighted sampling for inconsistent groups (currently random).
+# - FIXME: Enhance consistency fix to multi-iter proposals like ICM.py.
+# - TODO: Load larger HuggingFace dataset (e.g., 'Yik/truthfulQA-bool' subset, format to messages).
+# - TODO: Refine few-shot prompt from original get_judge_prompt_fewshot.
+# - Test with logprobs-supported models.

pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
     "alembic>=1.16.5",
     "altair>=5.5.0",
     "anthropic>=0.69.0",
+    "anycache>=2.4.0",
     "cattrs>=25.2.0",
     "datasets>=4.1.1",
     "fastapi==0.100.0",
@@ -20,6 +21,7 @@ dependencies = [
     "matplotlib>=3.10.6",
     "mistralai>=1.9.10",
     "openai==0.28.0",
+    "openrouter-wrapper",
     "pandas>=2.3.3",
     "pebble>=5.1.3",
     "polars>=1.33.1",
@@ -47,3 +49,12 @@ unsupervised-elicitation = "unsupervised_elicitation:main"
 [build-system]
 requires = ["setuptools>=61"]
 build-backend = "setuptools.build_meta"
+
+[tool.uv.sources]
+openrouter-wrapper = { path = "../llm_moral_lb_v2/openrouter_wrapper", editable = true }
+
+[dependency-groups]
+dev = [
+    "ipykernel>=6.30.1",
+    "ipywidgets>=8.1.7",
+]

simple_icm.ipynb

@@ -0,0 +1,10 @@
+{
+ "cells": [],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

uv.lock

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+[[package]]
+name = "anycache"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "dill" },
+    { name = "filelock" },
+]
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+]
+
 [[package]]
 name = "anyio"
 version = "4.11.0"
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+[[package]]
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+version = "0.1.4"
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+]
+
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
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+]
+
 [[package]]
 name = "async-timeout"
 version = "5.0.1"
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@@ -3178,6 +3798,7 @@ requires-dist = [
     { name = "matplotlib", specifier = ">=3.10.6" },
     { name = "mistralai", specifier = ">=1.9.10" },
     { name = "openai", specifier = "==0.28.0" },
+    { name = "openrouter-wrapper", editable = "../llm_moral_lb_v2/openrouter_wrapper" },
     { name = "pandas", specifier = ">=2.3.3" },
     { name = "pebble", specifier = ">=5.1.3" },
     { name = "polars", specifier = ">=1.33.1" },
@@ -3199,6 +3820,12 @@ requires-dist = [
     { name = "wandb", specifier = ">=0.22.1" },
 ]
 
+[package.metadata.requires-dev]
+dev = [
+    { name = "ipykernel", specifier = ">=6.30.1" },
+    { name = "ipywidgets", specifier = ">=8.1.7" },
+]
+
 [[package]]
 name = "urllib3"
 version = "2.5.0"
@@ -3408,6 +4035,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "wcwidth"
+version = "0.2.14"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/24/30/6b0809f4510673dc723187aeaf24c7f5459922d01e2f794277a3dfb90345/wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605", size = 102293, upload-time = "2025-09-22T16:29:53.023Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286, upload-time = "2025-09-22T16:29:51.641Z" },
+]
+
 [[package]]
 name = "websockets"
 version = "15.0.1"
@@ -3467,6 +4103,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fa/a8/5b41e0da817d64113292ab1f8247140aac61cbf6cfd085d6a0fa77f4984f/websockets-15.0.1-py3-none-any.whl", hash = "sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f", size = 169743, upload-time = "2025-03-05T20:03:39.41Z" },
 ]
 
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.14"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/53/2e0253c5efd69c9656b1843892052a31c36d37ad42812b5da45c62191f7e/widgetsnbextension-4.0.14.tar.gz", hash = "sha256:a3629b04e3edb893212df862038c7232f62973373869db5084aed739b437b5af", size = 1097428, upload-time = "2025-04-10T13:01:25.628Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ca/51/5447876806d1088a0f8f71e16542bf350918128d0a69437df26047c8e46f/widgetsnbextension-4.0.14-py3-none-any.whl", hash = "sha256:4875a9eaf72fbf5079dc372a51a9f268fc38d46f767cbf85c43a36da5cb9b575", size = 2196503, upload-time = "2025-04-10T13:01:23.086Z" },
+]
+
+[[package]]
+name = "win32-setctime"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b3/8f/705086c9d734d3b663af0e9bb3d4de6578d08f46b1b101c2442fd9aecaa2/win32_setctime-1.2.0.tar.gz", hash = "sha256:ae1fdf948f5640aae05c511ade119313fb6a30d7eabe25fef9764dca5873c4c0", size = 4867, upload-time = "2024-12-07T15:28:28.314Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e1/07/c6fe3ad3e685340704d314d765b7912993bcb8dc198f0e7a89382d37974b/win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390", size = 4083, upload-time = "2024-12-07T15:28:26.465Z" },
+]
+
 [[package]]
 name = "xxhash"
 version = "3.5.0"