476 lines
17 KiB
Python
476 lines
17 KiB
Python
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#!/usr/bin/env python3
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"""
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LoRA Fine-Tuning Pipeline for Crypto Sentiment & Emotion
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- PEFT/LoRA: trains only ~0.1% params (r=8, alpha=16)
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- Data augmentation from 145 labeled samples
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- Human-in-the-loop verification UI (CLI)
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- Disk-space conscious: streaming, no giant checkpoints
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- CPU-friendly, single-file deployment
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"""
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import os
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import json
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import random
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import tempfile
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import shutil
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from pathlib import Path
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from datetime import datetime
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from typing import List, Dict, Any, Tuple, Optional
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from dataclasses import dataclass, asdict
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import hashlib
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# ============================================================
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# DEPENDENCIES (lazy import for fast startup)
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# ============================================================
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try:
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
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from peft import LoraConfig, get_peft_model, TaskType, PeftModel
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from datasets import Dataset
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TORCH_AVAILABLE = True
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except ImportError:
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TORCH_AVAILABLE = False
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print("⚠️ torch/peft not installed. Run: pip install torch transformers peft datasets accelerate")
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# ============================================================
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# CONFIGURATION (tunable via env vars)
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# ============================================================
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@dataclass
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class LoRAConfig:
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# Model
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base_model: str = "ProsusAI/finbert"
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output_dir: str = "./models/lora-finbert-crypto"
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# LoRA hyperparams (small for disk efficiency)
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r: int = 8
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lora_alpha: int = 16
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lora_dropout: float = 0.1
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target_modules: Tuple[str, ...] = ("query", "value", "key", "dense")
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# Training
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num_epochs: int = 3
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batch_size: int = 8
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grad_accum: int = 4
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learning_rate: float = 2e-4
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max_length: int = 128
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warmup_ratio: float = 0.1
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weight_decay: float = 0.01
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# Disk management
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save_steps: int = 50
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save_total_limit: int = 1 # ONLY keep best
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max_checkpoint_gb: float = 0.5
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# Data
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aug_factor: int = 5 # augment each sample 5x
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min_confidence: float = 0.8 # for auto-labeling
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# Human verification
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verify_batch: int = 20
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verify_interval: int = 100 # auto-label batches between human checks
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CFG = LoRAConfig()
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# ============================================================
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# CRYPTO VOCABULARY FOR AUGMENTATION
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# ============================================================
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CRYPTO_TEMPLATES = {
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"bullish": [
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"{asset} surges to new ATH at ${price}",
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"{asset} breaks resistance at ${price} with volume spike",
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"Institutional inflows drive {asset} to ${price}",
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"Whale accumulation pushes {asset} above ${price}",
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"{asset} golden cross confirmed on daily, targeting ${price}",
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"ETF approval sends {asset} to ${price}",
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"Major exchange lists {asset}, price jumps to ${price}",
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"Bullish structure forming on {asset}, next stop ${price}",
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"On-chain metrics flash buy signal for {asset}",
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"{asset} reclaims ${price} as support, bullish retest",
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],
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"bearish": [
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"{asset} crashes {pct}% after support breaks at ${price}",
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"SEC lawsuit sends {asset} plummeting to ${price}",
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"Massive liquidation cascade wipes {asset} longs at ${price}",
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"Whale dumps {asset}, price crashes to ${price}",
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"{asset} breaks key support at ${price}, bearish continuation",
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"Regulatory fears drive {asset} down {pct}%",
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"Exchange delists {asset}, panic selling to ${price}",
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"Rug pull suspected as {asset} drops {pct}% in minutes",
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"Bearish divergence on {asset} RSI, targeting ${price}",
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"Macro risk-off sends {asset} below ${price}",
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],
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"neutral": [
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"{asset} consolidates at ${price} in tight range",
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"{asset} trades sideways at ${price} awaiting catalyst",
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"Low volatility on {asset} at ${price}, volume drying up",
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"Market in wait-and-see mode for {asset} at ${price}",
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"{asset} forms doji at ${price}, direction unclear",
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"Summer lull keeps {asset} range-bound at ${price}",
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"No clear direction for {asset} at ${price}",
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"{asset} choppy between ${p_low} and ${p_high}",
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"Accumulation phase for {asset} around ${price}",
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"{asset} at ${price} with no fresh news",
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],
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}
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ASSETS = ["BTC", "ETH", "SOL", "AVAX", "MATIC", "DOT", "LINK", "ARB", "OP", "NEAR", "FET", "STX", "ZIL", "XTZ", "ENJ", "ETC", "TRX", "LTC", "DASH", "ONG", "ONE", "ALGO", "DOGE", "XLM", "ATOM", "KSM", "APT", "SUI", "NEAR", "ICP", "QNT", "INJ", "TIA", "SEI"]
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CRYPTO_SLANG = [
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"HODL", "moon", "rug", "ape", "degen", "FOMO", "FUD", "NGMI", "WAGMI",
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"diamond hands", "paper hands", "buy the dip", "to the moon", "rekt",
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"bag holder", "whale", "accumulate", "distribute", "support", "resistance",
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"breakout", "breakdown", "flip", "fakeout", "liquidity", "slippage",
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"MEV", "arbitrage", "yield farm", "staking", "validator", "node",
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]
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# ============================================================
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# DATA LOADING & AUGMENTATION
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# ============================================================
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def load_labeled_data(path: str) -> List[Dict]:
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"""Load labeled JSONL files"""
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data = []
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for line in open(path):
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try:
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item = json.loads(line.strip())
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# Normalize to our format
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labels = item.get("labels", {})
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data.append({
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"text": item["text"],
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"sentiment": labels.get("sentiment", "Neutral"),
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"event_type": labels.get("event_type", "unknown"),
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"entities": labels.get("entities", []),
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})
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except Exception as e:
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print(f" Skip malformed line: {e}")
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return data
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def augment_sample(sample: Dict, factor: int = 5) -> List[Dict]:
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"""Generate augmented variations using templates + slang injection"""
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augmented = [sample] # Keep original
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sentiment = sample["sentiment"].lower()
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asset = random.choice(ASSETS)
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price = random.randint(100, 100000)
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pct = random.randint(5, 50)
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p_low = price - random.randint(10, 500)
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p_high = price + random.randint(10, 500)
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templates = CRYPTO_TEMPLATES.get(sentiment, CRYPTO_TEMPLATES["neutral"])
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for _ in range(factor - 1):
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template = random.choice(templates)
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new_text = template.format(
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asset=asset, price=price, pct=pct, p_low=p_low, p_high=p_high
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)
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# Inject random slang
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if random.random() < 0.3:
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slang = random.choice(CRYPTO_SLANG)
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new_text = f"{new_text} {slang}!"
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augmented.append({
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"text": new_text,
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"sentiment": sample["sentiment"],
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"event_type": sample.get("event_type", "unknown"),
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"entities": [asset],
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})
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return augmented
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def build_dataset() -> List[Dict]:
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"""Build training dataset from labeled files + augmentation"""
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print("📦 Loading labeled data...")
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all_data = []
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for fname in ["labeled_verified.jsonl", "labeled_expanded.jsonl", "labeled_large.jsonl"]:
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path = f"data/{fname}"
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if os.path.exists(path):
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loaded = load_labeled_data(path)
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print(f" {fname}: {len(loaded)} samples")
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all_data.extend(loaded)
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# Deduplicate by text hash
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seen = set()
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unique = []
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for d in all_data:
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h = hashlib.md5(d["text"].encode()).hexdigest()[:16]
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if h not in seen:
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seen.add(h)
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unique.append(d)
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print(f" Unique samples: {len(unique)}")
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# Augment
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print(f"🔄 Augmenting x{CFG.aug_factor}...")
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augmented = []
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for sample in unique:
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augmented.extend(augment_sample(sample, CFG.aug_factor))
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print(f" Total after augmentation: {len(augmented)}")
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return augmented
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# ============================================================
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# HUMAN-IN-THE-LOOP VERIFICATION
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# ============================================================
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@dataclass
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class VerificationRecord:
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sample_id: str
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text: str
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predicted: str
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confidence: float
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human_label: Optional[str] = None
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verified_at: Optional[str] = None
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verified_by: str = "cli"
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class HumanVerifier:
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"""CLI-based human verification with persistent state"""
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def __init__(self, verify_file: str = "data/verification_log.jsonl"):
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self.verify_file = Path(verify_file)
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self.records: List[VerificationRecord] = []
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self._load_existing()
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def _load_existing(self):
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if self.verify_file.exists():
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for line in open(self.verify_file):
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try:
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self.records.append(VerificationRecord(**json.loads(line)))
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except:
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pass
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print(f"📋 Loaded {len(self.records)} existing verifications")
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def _save_record(self, record: VerificationRecord):
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with open(self.verify_file, "a") as f:
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f.write(json.dumps(asdict(record)) + "\n")
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def verify_batch(self, samples: List[Dict], predictions: List[Tuple[str, float]]) -> List[Dict]:
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"""Interactive verification of a batch"""
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print(f"\n{'='*60}")
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print(f"👁 HUMAN VERIFICATION — Batch of {len(samples)}")
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print(f"{'='*60}")
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verified = []
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for i, (sample, (pred, conf)) in enumerate(zip(samples, predictions)):
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print(f"\n--- Sample {i+1}/{len(samples)} (conf: {conf:.2%}) ---")
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print(f"Text: {sample['text'][:200]}...")
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print(f"Model: {pred} ({conf:.2%})")
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# Quick keys: b=bullish, r=bearish, n=neutral, s=skip, q=quit
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while True:
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choice = input("Label [B]ullish/[R]earish/[N]eutral/[S]kip/[Q]uit: ").strip().lower()
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if choice in ('b', 'bullish'):
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human = "Bullish"
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break
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elif choice in ('r', 'bearish'):
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human = "Bearish"
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break
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elif choice in ('n', 'neutral'):
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human = "Neutral"
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break
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elif choice in ('s', 'skip'):
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human = None
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break
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elif choice in ('q', 'quit'):
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print("🛑 Quitting verification")
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return verified
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else:
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print(" Invalid. Use B/R/N/S/Q")
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if human:
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record = VerificationRecord(
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sample_id=hashlib.md5(sample["text"].encode()).hexdigest()[:12],
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text=sample["text"],
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predicted=pred,
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confidence=conf,
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human_label=human,
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verified_at=datetime.now().isoformat(),
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)
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self.records.append(record)
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self._save_record(record)
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verified.append({**sample, "sentiment": human})
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print(f" ✅ Saved: {human}")
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else:
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print(f" ⏭ Skipped")
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return verified
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# ============================================================
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# LORA TRAINING
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# ============================================================
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def train_lora(train_data: List[Dict], val_data: List[Dict]) -> str:
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"""Train LoRA adapter, return path to best model"""
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if not TORCH_AVAILABLE:
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print("❌ torch/peft not available. Cannot train.")
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return ""
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print(f"\n🚀 Starting LoRA training on {len(train_data)} samples...")
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# Label mapping
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label2id = {"Bearish": 0, "Bullish": 1, "Neutral": 2}
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id2label = {v: k for k, v in label2id.items()}
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# Prepare datasets
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def to_dataset(data):
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texts = [d["text"] for d in data]
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labels = [label2id.get(d["sentiment"], 2) for d in data]
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return Dataset.from_dict({"text": texts, "label": labels})
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train_ds = to_dataset(train_data)
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val_ds = to_dataset(val_data)
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# Tokenizer
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tokenizer = AutoTokenizer.from_pretrained(CFG.base_model)
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def tokenize(batch):
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return tokenizer(batch["text"], truncation=True, max_length=CFG.max_length, padding="max_length")
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train_ds = train_ds.map(tokenize, batched=True)
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val_ds = val_ds.map(tokenize, batched=True)
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train_ds.set_format("torch", columns=["input_ids", "attention_mask", "label"])
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val_ds.set_format("torch", columns=["input_ids", "attention_mask", "label"])
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# Model + LoRA
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model = AutoModelForSequenceClassification.from_pretrained(
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CFG.base_model, num_labels=3, id2label=id2label, label2id=label2id
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)
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lora_config = LoraConfig(
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r=CFG.r, lora_alpha=CFG.lora_alpha, lora_dropout=CFG.lora_dropout,
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target_modules=list(CFG.target_modules), bias="none", task_type=TaskType.SEQ_CLS
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)
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model = get_peft_model(model, lora_config)
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model.print_trainable_parameters()
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# Training args (disk-conscious)
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output_dir = CFG.output_dir
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os.makedirs(output_dir, exist_ok=True)
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training_args = TrainingArguments(
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output_dir=output_dir,
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num_train_epochs=CFG.num_epochs,
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per_device_train_batch_size=CFG.batch_size,
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per_device_eval_batch_size=CFG.batch_size * 2,
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gradient_accumulation_steps=CFG.grad_accum,
|
||
|
|
learning_rate=CFG.learning_rate,
|
||
|
|
warmup_ratio=CFG.warmup_ratio,
|
||
|
|
weight_decay=CFG.weight_decay,
|
||
|
|
max_grad_norm=1.0,
|
||
|
|
eval_strategy="steps",
|
||
|
|
eval_steps=CFG.save_steps,
|
||
|
|
save_strategy="steps",
|
||
|
|
save_steps=CFG.save_steps,
|
||
|
|
save_total_limit=CFG.save_total_limit,
|
||
|
|
load_best_model_at_end=True,
|
||
|
|
metric_for_best_model="f1_macro",
|
||
|
|
greater_is_better=True,
|
||
|
|
fp16=False, # CPU
|
||
|
|
dataloader_num_workers=0,
|
||
|
|
logging_steps=10,
|
||
|
|
remove_unused_columns=False,
|
||
|
|
report_to="none",
|
||
|
|
seed=42,
|
||
|
|
)
|
||
|
|
|
||
|
|
def compute_metrics(eval_pred):
|
||
|
|
from sklearn.metrics import f1_score, accuracy_score
|
||
|
|
logits, labels = eval_pred
|
||
|
|
preds = logits.argmax(-1)
|
||
|
|
return {
|
||
|
|
"f1_macro": f1_score(labels, preds, average="macro"),
|
||
|
|
"accuracy": accuracy_score(labels, preds),
|
||
|
|
}
|
||
|
|
|
||
|
|
trainer = Trainer(
|
||
|
|
model=model,
|
||
|
|
args=training_args,
|
||
|
|
train_dataset=train_ds,
|
||
|
|
eval_dataset=val_ds,
|
||
|
|
tokenizer=tokenizer,
|
||
|
|
compute_metrics=compute_metrics,
|
||
|
|
)
|
||
|
|
|
||
|
|
print("🏋️ Training...")
|
||
|
|
trainer.train()
|
||
|
|
|
||
|
|
# Save best
|
||
|
|
best_path = os.path.join(output_dir, "best")
|
||
|
|
trainer.save_model(best_path)
|
||
|
|
tokenizer.save_pretrained(best_path)
|
||
|
|
|
||
|
|
# Cleanup old checkpoints (keep only best)
|
||
|
|
for d in os.listdir(output_dir):
|
||
|
|
if d.startswith("checkpoint-"):
|
||
|
|
shutil.rmtree(os.path.join(output_dir, d), ignore_errors=True)
|
||
|
|
|
||
|
|
print(f"✅ Best model saved to: {best_path}")
|
||
|
|
return best_path
|
||
|
|
|
||
|
|
# ============================================================
|
||
|
|
# MAIN PIPELINE
|
||
|
|
# ============================================================
|
||
|
|
|
||
|
|
def run_pipeline(mode: str = "train"):
|
||
|
|
"""Main entry point"""
|
||
|
|
print("="*60)
|
||
|
|
print("LoRA CRYPTO SENTIMENT PIPELINE")
|
||
|
|
print("="*60)
|
||
|
|
print(f"Mode: {mode}")
|
||
|
|
print(f"Config: r={CFG.r}, alpha={CFG.lora_alpha}, epochs={CFG.num_epochs}")
|
||
|
|
print(f"Disk limit: {CFG.max_checkpoint_gb}GB checkpoints")
|
||
|
|
|
||
|
|
# Build dataset
|
||
|
|
all_data = build_dataset()
|
||
|
|
random.shuffle(all_data)
|
||
|
|
|
||
|
|
# Split train/val
|
||
|
|
split = int(0.9 * len(all_data))
|
||
|
|
train_data = all_data[:split]
|
||
|
|
val_data = all_data[split:]
|
||
|
|
print(f"Train: {len(train_data)} | Val: {len(val_data)}")
|
||
|
|
|
||
|
|
if mode == "train":
|
||
|
|
# Train
|
||
|
|
best_path = train_lora(train_data, val_data)
|
||
|
|
print(f"\n🎉 Training complete! Best model: {best_path}")
|
||
|
|
|
||
|
|
elif mode == "verify":
|
||
|
|
# Human verification mode - run model on new data, verify
|
||
|
|
print("🔮 Loading model for verification...")
|
||
|
|
# This would load the trained LoRA and run on new sources
|
||
|
|
print("Run: python lora_pipeline.py verify --model-path <path>")
|
||
|
|
|
||
|
|
elif mode == "augment-only":
|
||
|
|
# Just show augmented samples
|
||
|
|
for d in train_data[:5]:
|
||
|
|
print(f"\n{d['sentiment']}: {d['text'][:100]}...")
|
||
|
|
|
||
|
|
print("\n✅ Pipeline complete")
|
||
|
|
|
||
|
|
# ============================================================
|
||
|
|
# CLI
|
||
|
|
# ============================================================
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
import argparse
|
||
|
|
parser = argparse.ArgumentParser(description="LoRA Crypto Sentiment Pipeline")
|
||
|
|
parser.add_argument("mode", choices=["train", "verify", "augment-only"], default="train")
|
||
|
|
parser.add_argument("--epochs", type=int, default=3)
|
||
|
|
parser.add_argument("--r", type=int, default=8)
|
||
|
|
parser.add_argument("--batch", type=int, default=8)
|
||
|
|
parser.add_argument("--lr", type=float, default=2e-4)
|
||
|
|
parser.add_argument("--max-len", type=int, default=128)
|
||
|
|
parser.add_argument("--aug", type=int, default=5)
|
||
|
|
parser.add_argument("--output", type=str, default="./models/lora-finbert-crypto")
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
# Override config
|
||
|
|
CFG.num_epochs = args.epochs
|
||
|
|
CFG.r = args.r
|
||
|
|
CFG.batch_size = args.batch
|
||
|
|
CFG.learning_rate = args.lr
|
||
|
|
CFG.max_length = args.max_len
|
||
|
|
CFG.aug_factor = args.aug
|
||
|
|
CFG.output_dir = args.output
|
||
|
|
|
||
|
|
run_pipeline(args.mode)
|