feat(sentiment): complete pipeline overhaul with ONNX priority + LoRA retraining
- Added 30 new sources (5 RSS + 25 Telegram) for previously ZERO-coverage assets - Fixed model loading priority: ONNX > LoRA v2 > PyTorch > Mock - ONNX FinBERT (pre-trained on 1.2M financial docs) now PRIMARY - best for real-world text - LoRA v2 models trained on 518 carefully labeled samples (balanced Bearish/Bullish/Neutral) - Emotion LoRA v2 trained with weighted loss (greed/fear 2x, joy 1.5x) - 30 new sources: STX, FET, XTZ, ENJ, ETC, TRX, ONG, DASH, LTC, ZIL, NEAR, APT, SUI, ICP - Early stopping (patience=3) on both LoRA trainings - Human-in-the-loop verification CLI tool created - Disk-conscious: save_total_limit=1, adapters 6-8MB each Pipeline now correctly classifies: - BTC breaks 100k → +0.54 Bullish ✅ - Major hack → -0.23 Bearish ✅ - HODL → +0.91 Bullish ✅ - Rug pull → -0.30 Bearish ✅ - SEC sues → -0.30 Bearish ✅ - ETF approval → +0.32 Bullish ✅ - Whale accumulation → +0.31 Bullish ✅ Models: ONNX FinBERT (PRIORITY 1) + LoRA v2 adapters (6-8MB each) Training data: 518 carefully labeled samples (190 real + 328 synthetic) Early stopping (patience=3) on both FinBERT and DistilRoBERTa LoRA Emotion LoRA v2: weighted loss (greed/fear 2x, joy 1.5x) + early stopping
This commit is contained in:
Codex
476
sentiment_engine/tests/unit/test_performance_benchmarks.py
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476
sentiment_engine/tests/unit/test_performance_benchmarks.py
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"""
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Performance benchmark tests for critical components.
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"""
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import pytest
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import asyncio
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import time
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import numpy as np
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from unittest.mock import AsyncMock, MagicMock, patch
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from sentiment_engine.nlp.pipeline import NLPProcessingPipeline
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from sentiment_engine.nlp.entity_extraction import EntityExtractor, AssetMapper
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from sentiment_engine.nlp.sentiment_emotion import SentimentEmotionAnalyzer
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from sentiment_engine.nlp.event_classification import EventClassifier
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from sentiment_engine.nlp.temporal import TemporalAnchorer
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from sentiment_engine.nlp.credibility import CredibilityScorer
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from sentiment_engine.signal.processor import FearGreedProcessor
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from sentiment_engine.signal.velocity import VelocityCalculator
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from sentiment_engine.signal.decay import DecayEngine
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from sentiment_engine.signal.fusion import MultiSourceFusion
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from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention
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from sentiment_engine.schemas.processed import ProcessedItem, SentimentScores, EmotionScores
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class TestPipelinePerformance:
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"""Performance benchmarks for NLP pipeline"""
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@pytest.fixture
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def pipeline(self):
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return NLPProcessingPipeline()
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@pytest.mark.asyncio
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async def test_pipeline_initialization_time(self, pipeline):
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"""Initialization should be fast"""
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start = time.time()
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await pipeline.initialize()
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elapsed = time.time() - start
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assert elapsed < 10 # 10 seconds max
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@pytest.mark.asyncio
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async def test_single_process_latency(self, pipeline):
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"""Single process should be under 2 seconds"""
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await pipeline.initialize()
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payload = NormalizedPayload(
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source_id="benchmark",
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source_type=SourceType.NEWS,
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source_credibility_base=0.8,
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ingest_ts=1700000000.0,
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publish_ts=1700000000.0,
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content_length=200,
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raw_text="Bitcoin surges to $108k as institutional inflows surge. BlackRock IBIT sees record $1.2B daily inflow. BTC and ETH both hit new all-time highs.",
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metadata={}
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)
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# Warm up
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await pipeline.process(payload)
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# Measure
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latencies = []
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for _ in range(10):
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start = time.time()
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await pipeline.process(payload)
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latencies.append((time.time() - start) * 1000)
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avg_latency = sum(latencies) / len(latencies)
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p95_latency = sorted(latencies)[int(len(latencies) * 0.95)]
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print(f"Avg latency: {avg_latency:.1f}ms, P95: {p95_latency:.1f}ms")
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assert avg_latency < 3000 # 3 seconds average
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assert p95_latency < 5000 # 5 seconds P95
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@pytest.mark.asyncio
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async def test_batch_throughput(self, pipeline):
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"""Batch processing should achieve high throughput"""
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await pipeline.initialize()
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payloads = [
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NormalizedPayload(
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source_id=f"bench_{i}",
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source_type=SourceType.NEWS,
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source_credibility_base=0.8,
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ingest_ts=1700000000.0,
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publish_ts=1700000000.0,
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content_length=100,
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raw_text=f"Bitcoin news item {i} with some content for processing.",
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metadata={}
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)
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for i in range(50)
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]
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start = time.time()
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results = await pipeline.process_batch(payloads)
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elapsed = time.time() - start
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throughput = len(results) / elapsed
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print(f"Throughput: {throughput:.1f} items/sec")
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assert len(results) == 50
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assert throughput > 5 # At least 5 items/sec
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class TestEntityExtractorPerformance:
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"""Performance benchmarks for EntityExtractor"""
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@pytest.fixture
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def extractor(self):
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return EntityExtractor(AssetMapper())
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@pytest.mark.asyncio
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async def test_extraction_latency(self, extractor):
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"""Entity extraction should be fast"""
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await extractor.initialize()
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texts = [
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"BTC and ETH surge as Bitcoin hits new high. Vitalik says ETH to $10k.",
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"Major hack on exchange. SEC sues Kraken. Whale moves 10000 BTC.",
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"Ethereum Dencun upgrade live. PEPE and BONK listed on Coinbase."
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] * 20 # 60 texts
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start = time.time()
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for text in texts:
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await extractor.extract_all(text)
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elapsed = time.time() - start
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throughput = len(texts) / elapsed
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print(f"Entity extraction: {throughput:.1f} texts/sec")
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assert throughput > 20 # At least 20 texts/sec
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class TestSentimentAnalyzerPerformance:
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"""Performance benchmarks for SentimentEmotionAnalyzer"""
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@pytest.fixture
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def analyzer(self):
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return SentimentEmotionAnalyzer()
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@pytest.mark.asyncio
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async def test_sentiment_latency(self, analyzer):
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"""Sentiment analysis should be fast"""
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await analyzer.initialize()
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texts = [
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"Bitcoin surges to new all-time high!",
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"Market crashes as panic selling ensues.",
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"SEC approves Bitcoin ETF.",
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"Ethereum upgrade goes live.",
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"Whale moves 10000 BTC."
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] * 50 # 250 texts
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asset_mentions = [{"asset_id": "BTC", "span": (0, 3)}] * len(texts)
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start = time.time()
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for text, mentions in zip(texts, asset_mentions):
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await analyzer.analyze(text, [mentions])
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elapsed = time.time() - start
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throughput = len(texts) / elapsed
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print(f"Sentiment analysis: {throughput:.1f} texts/sec")
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assert throughput > 10 # At least 10 texts/sec
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class TestEventClassifierPerformance:
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"""Performance benchmarks for EventClassifier"""
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@pytest.fixture
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def classifier(self):
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return EventClassifier()
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@pytest.mark.asyncio
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async def test_classification_latency(self, classifier):
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"""Event classification should be fast"""
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await classifier.initialize()
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texts = [
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"Bitcoin surges to $100k!",
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"Major hack on exchange!",
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"SEC sues exchange!",
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"Ethereum upgrade live!",
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"Coinbase lists new token!"
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] * 50 # 250 texts
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assets = [["BTC"]] * len(texts)
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start = time.time()
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for text, asset_list in zip(texts, assets):
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await classifier.classify(text, asset_list)
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elapsed = time.time() - start
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throughput = len(texts) / elapsed
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print(f"Event classification: {throughput:.1f} texts/sec")
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assert throughput > 20 # At least 20 texts/sec
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class TestSignalProcessingPerformance:
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"""Performance benchmarks for Signal Processing"""
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def test_fear_greed_computation(self):
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"""Fear/Greed computation should be fast"""
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processor = FearGreedProcessor()
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items = [
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ProcessedItem(
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payload_id=f"item_{i}",
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source_id="source",
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source_type="news",
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ingest_ts=1700000000.0,
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publish_ts=1700000000.0,
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entities=[],
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sentiment_per_asset={
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"BTC": SentimentScores(
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polarity=0.5, confidence=0.8,
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positive_prob=0.7, negative_prob=0.1, neutral_prob=0.2
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)
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},
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emotions_per_asset={},
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events=[],
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temporal=None,
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credibility=None,
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processed_ts=1700000000.0,
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processing_latency_ms=100,
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model_versions={}
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)
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for i in range(1000)
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]
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start = time.time()
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result = processor.compute(items)
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elapsed = time.time() - start
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print(f"Fear/Greed: {1000/elapsed:.1f} items/sec")
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assert elapsed < 0.1 # < 100ms for 1000 items
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def test_velocity_computation(self):
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"""Velocity computation should be fast"""
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calculator = VelocityCalculator()
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now = 1700000000.0
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items = [
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{"asset_id": "BTC", "publish_ts": now - i*60, "sentiment_polarity": 0.5 + i*0.01}
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for i in range(1000)
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]
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start = time.time()
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for _ in range(100):
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calculator.compute_velocity("BTC", items)
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elapsed = time.time() - start
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throughput = 100 / elapsed
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print(f"Velocity: {throughput:.1f} computations/sec")
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assert throughput > 100 # At least 100/sec
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def test_decay_computation(self):
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"""Decay computation should be fast"""
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engine = DecayEngine()
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now = 1700000000.0
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timestamps = [now - i*60 for i in range(10000)]
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start = time.time()
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for ts in timestamps:
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engine.compute_decay(ts, now, halflife_minutes=60)
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elapsed = time.time() - start
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throughput = 10000 / elapsed
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print(f"Decay: {throughput:.1f} computations/sec")
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assert throughput > 10000 # At least 10k/sec
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def test_fusion_computation(self):
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"""Fusion computation should be fast"""
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fusion = MultiSourceFusion()
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scores = {f"source_{i}": 0.5 for i in range(100)}
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weights = {f"source_{i}": 1.0 for i in range(100)}
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start = time.time()
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for _ in range(1000):
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fusion.fuse(scores, weights)
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elapsed = time.time() - start
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throughput = 1000 / elapsed
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print(f"Fusion: {throughput:.1f} fusions/sec")
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assert throughput > 1000 # At least 1000/sec
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class TestONNXInferencePerformance:
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"""Performance benchmarks for ONNX inference"""
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@pytest.mark.asyncio
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async def test_onnx_finbert_inference(self):
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"""ONNX FinBERT inference should be fast"""
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import onnxruntime as ort
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session = ort.InferenceSession(
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"models/onnx/finbert/model.onnx",
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providers=['CPUExecutionProvider']
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)
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input_ids = np.ones((1, 128), dtype=np.int64)
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attention_mask = np.ones((1, 128), dtype=np.int64)
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token_type_ids = np.zeros((1, 128), dtype=np.int64)
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# Warm up
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for _ in range(10):
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session.run(None, {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids
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})
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start = time.time()
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for _ in range(100):
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session.run(None, {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids
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})
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elapsed = time.time() - start
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throughput = 100 / elapsed
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print(f"ONNX FinBERT: {throughput:.1f} inferences/sec")
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assert throughput > 50 # At least 50/sec
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@pytest.mark.asyncio
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async def test_onnx_bert_events_inference(self):
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"""ONNX BERT Events inference should be fast"""
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import onnxruntime as ort
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session = ort.InferenceSession(
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"models/onnx/bert-base-event/model.onnx",
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providers=['CPUExecutionProvider']
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)
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input_ids = np.ones((1, 128), dtype=np.int64)
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attention_mask = np.ones((1, 128), dtype=np.int64)
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token_type_ids = np.zeros((1, 128), dtype=np.int64)
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# Warm up
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for _ in range(10):
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session.run(None, {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids
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})
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start = time.time()
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for _ in range(100):
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session.run(None, {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids
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})
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elapsed = time.time() - start
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throughput = 100 / elapsed
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print(f"ONNX BERT Events: {throughput:.1f} inferences/sec")
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assert throughput > 50 # At least 50/sec
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@pytest.mark.asyncio
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async def test_onnx_emotion_inference(self):
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"""ONNX Emotion inference should be fast"""
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import onnxruntime as ort
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session = ort.InferenceSession(
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"models/onnx/distilroberta-emotion/model.onnx",
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providers=['CPUExecutionProvider']
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)
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input_ids = np.ones((1, 128), dtype=np.int64)
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attention_mask = np.ones((1, 128), dtype=np.int64)
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# Warm up
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for _ in range(10):
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session.run(None, {
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"input_ids": input_ids,
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"attention_mask": attention_mask
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})
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start = time.time()
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for _ in range(100):
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session.run(None, {
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"input_ids": input_ids,
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"attention_mask": attention_mask
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})
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elapsed = time.time() - start
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throughput = 100 / elapsed
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print(f"ONNX Emotion: {throughput:.1f} inferences/sec")
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assert throughput > 100 # At least 100/sec
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class TestMemoryUsage:
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"""Memory usage tests"""
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@pytest.mark.asyncio
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async def test_pipeline_memory_stable(self):
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"""Pipeline memory should not grow unbounded"""
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import psutil
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import os
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pipeline = NLPProcessingPipeline()
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await pipeline.initialize()
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process = psutil.Process(os.getpid())
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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payload = NormalizedPayload(
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source_id="mem_test",
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source_type=SourceType.NEWS,
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source_credibility_base=0.8,
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ingest_ts=1700000000.0,
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publish_ts=1700000000.0,
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content_length=100,
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raw_text="Bitcoin surges to new high!",
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metadata={}
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)
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# Process many items
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for i in range(100):
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payload.raw_text = f"Bitcoin news item {i}"
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await pipeline.process(payload)
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final_memory = process.memory_info().rss / 1024 / 1024 # MB
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memory_growth = final_memory - initial_memory
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print(f"Memory growth: {memory_growth:.1f} MB")
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assert memory_growth < 500 # Less than 500MB growth
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class TestConcurrency:
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"""Concurrency tests"""
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@pytest.mark.asyncio
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async def test_pipeline_concurrent_requests(self):
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"""Pipeline should handle concurrent requests"""
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pipeline = NLPProcessingPipeline()
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await pipeline.initialize()
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payload = NormalizedPayload(
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source_id="concurrent",
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source_type=SourceType.NEWS,
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source_credibility_base=0.8,
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ingest_ts=1700000000.0,
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publish_ts=1700000000.0,
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content_length=100,
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raw_text="Bitcoin surges to new high!",
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metadata={}
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)
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# Run 20 concurrent requests
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tasks = [pipeline.process(NormalizedPayload(
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source_id=f"concurrent_{i}",
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source_type=SourceType.NEWS,
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source_credibility_base=0.8,
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ingest_ts=1700000000.0,
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publish_ts=1700000000.0,
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content_length=100,
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raw_text=f"Bitcoin news {i}",
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metadata={}
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)) for i in range(20)]
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start = time.time()
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results = await asyncio.gather(*tasks)
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elapsed = time.time() - start
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assert len(results) == 20
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# Should be faster than sequential
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assert elapsed < 30 # Under 30 seconds for 20 concurrent
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if __name__ == "__main__":
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pytest.main([__file__, "-v", "-s"])
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Reference in New Issue
Block a user