sentiment-engine/sentiment_engine/tests/unit/test_performance_benchmarks.py

"""
Performance benchmark tests for critical components.
"""

import pytest
import asyncio
import time
import numpy as np
from unittest.mock import AsyncMock, MagicMock, patch

from sentiment_engine.nlp.pipeline import NLPProcessingPipeline
from sentiment_engine.nlp.entity_extraction import EntityExtractor, AssetMapper
from sentiment_engine.nlp.sentiment_emotion import SentimentEmotionAnalyzer
from sentiment_engine.nlp.event_classification import EventClassifier
from sentiment_engine.nlp.temporal import TemporalAnchorer
from sentiment_engine.nlp.credibility import CredibilityScorer
from sentiment_engine.signal.processor import FearGreedProcessor
from sentiment_engine.signal.velocity import VelocityCalculator
from sentiment_engine.signal.decay import DecayEngine
from sentiment_engine.signal.fusion import MultiSourceFusion
from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention
from sentiment_engine.schemas.processed import ProcessedItem, SentimentScores, EmotionScores


class TestPipelinePerformance:
    """Performance benchmarks for NLP pipeline"""

    @pytest.fixture
    def pipeline(self):
        return NLPProcessingPipeline()

    @pytest.mark.asyncio
    async def test_pipeline_initialization_time(self, pipeline):
        """Initialization should be fast"""
        start = time.time()
        await pipeline.initialize()
        elapsed = time.time() - start
        
        assert elapsed < 10  # 10 seconds max

    @pytest.mark.asyncio
    async def test_single_process_latency(self, pipeline):
        """Single process should be under 2 seconds"""
        await pipeline.initialize()
        
        payload = NormalizedPayload(
            source_id="benchmark",
            source_type=SourceType.NEWS,
            source_credibility_base=0.8,
            ingest_ts=1700000000.0,
            publish_ts=1700000000.0,
            content_length=200,
            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.",
            metadata={}
        )
        
        # Warm up
        await pipeline.process(payload)
        
        # Measure
        latencies = []
        for _ in range(10):
            start = time.time()
            await pipeline.process(payload)
            latencies.append((time.time() - start) * 1000)
        
        avg_latency = sum(latencies) / len(latencies)
        p95_latency = sorted(latencies)[int(len(latencies) * 0.95)]
        
        print(f"Avg latency: {avg_latency:.1f}ms, P95: {p95_latency:.1f}ms")
        assert avg_latency < 3000  # 3 seconds average
        assert p95_latency < 5000  # 5 seconds P95

    @pytest.mark.asyncio
    async def test_batch_throughput(self, pipeline):
        """Batch processing should achieve high throughput"""
        await pipeline.initialize()
        
        payloads = [
            NormalizedPayload(
                source_id=f"bench_{i}",
                source_type=SourceType.NEWS,
                source_credibility_base=0.8,
                ingest_ts=1700000000.0,
                publish_ts=1700000000.0,
                content_length=100,
                raw_text=f"Bitcoin news item {i} with some content for processing.",
                metadata={}
            )
            for i in range(50)
        ]
        
        start = time.time()
        results = await pipeline.process_batch(payloads)
        elapsed = time.time() - start
        
        throughput = len(results) / elapsed
        print(f"Throughput: {throughput:.1f} items/sec")
        
        assert len(results) == 50
        assert throughput > 5  # At least 5 items/sec


class TestEntityExtractorPerformance:
    """Performance benchmarks for EntityExtractor"""

    @pytest.fixture
    def extractor(self):
        return EntityExtractor(AssetMapper())

    @pytest.mark.asyncio
    async def test_extraction_latency(self, extractor):
        """Entity extraction should be fast"""
        await extractor.initialize()
        
        texts = [
            "BTC and ETH surge as Bitcoin hits new high. Vitalik says ETH to $10k.",
            "Major hack on exchange. SEC sues Kraken. Whale moves 10000 BTC.",
            "Ethereum Dencun upgrade live. PEPE and BONK listed on Coinbase."
        ] * 20  # 60 texts
        
        start = time.time()
        for text in texts:
            await extractor.extract_all(text)
        elapsed = time.time() - start
        
        throughput = len(texts) / elapsed
        print(f"Entity extraction: {throughput:.1f} texts/sec")
        
        assert throughput > 20  # At least 20 texts/sec


class TestSentimentAnalyzerPerformance:
    """Performance benchmarks for SentimentEmotionAnalyzer"""

    @pytest.fixture
    def analyzer(self):
        return SentimentEmotionAnalyzer()

    @pytest.mark.asyncio
    async def test_sentiment_latency(self, analyzer):
        """Sentiment analysis should be fast"""
        await analyzer.initialize()
        
        texts = [
            "Bitcoin surges to new all-time high!",
            "Market crashes as panic selling ensues.",
            "SEC approves Bitcoin ETF.",
            "Ethereum upgrade goes live.",
            "Whale moves 10000 BTC."
        ] * 50  # 250 texts
        
        asset_mentions = [{"asset_id": "BTC", "span": (0, 3)}] * len(texts)
        
        start = time.time()
        for text, mentions in zip(texts, asset_mentions):
            await analyzer.analyze(text, [mentions])
        elapsed = time.time() - start
        
        throughput = len(texts) / elapsed
        print(f"Sentiment analysis: {throughput:.1f} texts/sec")
        
        assert throughput > 10  # At least 10 texts/sec


class TestEventClassifierPerformance:
    """Performance benchmarks for EventClassifier"""

    @pytest.fixture
    def classifier(self):
        return EventClassifier()

    @pytest.mark.asyncio
    async def test_classification_latency(self, classifier):
        """Event classification should be fast"""
        await classifier.initialize()
        
        texts = [
            "Bitcoin surges to $100k!",
            "Major hack on exchange!",
            "SEC sues exchange!",
            "Ethereum upgrade live!",
            "Coinbase lists new token!"
        ] * 50  # 250 texts
        
        assets = [["BTC"]] * len(texts)
        
        start = time.time()
        for text, asset_list in zip(texts, assets):
            await classifier.classify(text, asset_list)
        elapsed = time.time() - start
        
        throughput = len(texts) / elapsed
        print(f"Event classification: {throughput:.1f} texts/sec")
        
        assert throughput > 20  # At least 20 texts/sec


class TestSignalProcessingPerformance:
    """Performance benchmarks for Signal Processing"""

    def test_fear_greed_computation(self):
        """Fear/Greed computation should be fast"""
        processor = FearGreedProcessor()
        
        items = [
            ProcessedItem(
                payload_id=f"item_{i}",
                source_id="source",
                source_type="news",
                ingest_ts=1700000000.0,
                publish_ts=1700000000.0,
                entities=[],
                sentiment_per_asset={
                    "BTC": SentimentScores(
                        polarity=0.5, confidence=0.8,
                        positive_prob=0.7, negative_prob=0.1, neutral_prob=0.2
                    )
                },
                emotions_per_asset={},
                events=[],
                temporal=None,
                credibility=None,
                processed_ts=1700000000.0,
                processing_latency_ms=100,
                model_versions={}
            )
            for i in range(1000)
        ]
        
        start = time.time()
        result = processor.compute(items)
        elapsed = time.time() - start
        
        print(f"Fear/Greed: {1000/elapsed:.1f} items/sec")
        assert elapsed < 0.1  # < 100ms for 1000 items

    def test_velocity_computation(self):
        """Velocity computation should be fast"""
        calculator = VelocityCalculator()
        
        now = 1700000000.0
        items = [
            {"asset_id": "BTC", "publish_ts": now - i*60, "sentiment_polarity": 0.5 + i*0.01}
            for i in range(1000)
        ]
        
        start = time.time()
        for _ in range(100):
            calculator.compute_velocity("BTC", items)
        elapsed = time.time() - start
        
        throughput = 100 / elapsed
        print(f"Velocity: {throughput:.1f} computations/sec")
        assert throughput > 100  # At least 100/sec

    def test_decay_computation(self):
        """Decay computation should be fast"""
        engine = DecayEngine()
        
        now = 1700000000.0
        timestamps = [now - i*60 for i in range(10000)]
        
        start = time.time()
        for ts in timestamps:
            engine.compute_decay(ts, now, halflife_minutes=60)
        elapsed = time.time() - start
        
        throughput = 10000 / elapsed
        print(f"Decay: {throughput:.1f} computations/sec")
        assert throughput > 10000  # At least 10k/sec

    def test_fusion_computation(self):
        """Fusion computation should be fast"""
        fusion = MultiSourceFusion()
        
        scores = {f"source_{i}": 0.5 for i in range(100)}
        weights = {f"source_{i}": 1.0 for i in range(100)}
        
        start = time.time()
        for _ in range(1000):
            fusion.fuse(scores, weights)
        elapsed = time.time() - start
        
        throughput = 1000 / elapsed
        print(f"Fusion: {throughput:.1f} fusions/sec")
        assert throughput > 1000  # At least 1000/sec


class TestONNXInferencePerformance:
    """Performance benchmarks for ONNX inference"""

    @pytest.mark.asyncio
    async def test_onnx_finbert_inference(self):
        """ONNX FinBERT inference should be fast"""
        import onnxruntime as ort
        
        session = ort.InferenceSession(
            "models/onnx/finbert/model.onnx",
            providers=['CPUExecutionProvider']
        )
        
        input_ids = np.ones((1, 128), dtype=np.int64)
        attention_mask = np.ones((1, 128), dtype=np.int64)
        token_type_ids = np.zeros((1, 128), dtype=np.int64)
        
        # Warm up
        for _ in range(10):
            session.run(None, {
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "token_type_ids": token_type_ids
            })
        
        start = time.time()
        for _ in range(100):
            session.run(None, {
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "token_type_ids": token_type_ids
            })
        elapsed = time.time() - start
        
        throughput = 100 / elapsed
        print(f"ONNX FinBERT: {throughput:.1f} inferences/sec")
        assert throughput > 50  # At least 50/sec

    @pytest.mark.asyncio
    async def test_onnx_bert_events_inference(self):
        """ONNX BERT Events inference should be fast"""
        import onnxruntime as ort
        
        session = ort.InferenceSession(
            "models/onnx/bert-base-event/model.onnx",
            providers=['CPUExecutionProvider']
        )
        
        input_ids = np.ones((1, 128), dtype=np.int64)
        attention_mask = np.ones((1, 128), dtype=np.int64)
        token_type_ids = np.zeros((1, 128), dtype=np.int64)
        
        # Warm up
        for _ in range(10):
            session.run(None, {
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "token_type_ids": token_type_ids
            })
        
        start = time.time()
        for _ in range(100):
            session.run(None, {
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "token_type_ids": token_type_ids
            })
        elapsed = time.time() - start
        
        throughput = 100 / elapsed
        print(f"ONNX BERT Events: {throughput:.1f} inferences/sec")
        assert throughput > 50  # At least 50/sec

    @pytest.mark.asyncio
    async def test_onnx_emotion_inference(self):
        """ONNX Emotion inference should be fast"""
        import onnxruntime as ort
        
        session = ort.InferenceSession(
            "models/onnx/distilroberta-emotion/model.onnx",
            providers=['CPUExecutionProvider']
        )
        
        input_ids = np.ones((1, 128), dtype=np.int64)
        attention_mask = np.ones((1, 128), dtype=np.int64)
        
        # Warm up
        for _ in range(10):
            session.run(None, {
                "input_ids": input_ids,
                "attention_mask": attention_mask
            })
        
        start = time.time()
        for _ in range(100):
            session.run(None, {
                "input_ids": input_ids,
                "attention_mask": attention_mask
            })
        elapsed = time.time() - start
        
        throughput = 100 / elapsed
        print(f"ONNX Emotion: {throughput:.1f} inferences/sec")
        assert throughput > 100  # At least 100/sec


class TestMemoryUsage:
    """Memory usage tests"""

    @pytest.mark.asyncio
    async def test_pipeline_memory_stable(self):
        """Pipeline memory should not grow unbounded"""
        import psutil
        import os
        
        pipeline = NLPProcessingPipeline()
        await pipeline.initialize()
        
        process = psutil.Process(os.getpid())
        initial_memory = process.memory_info().rss / 1024 / 1024  # MB
        
        payload = NormalizedPayload(
            source_id="mem_test",
            source_type=SourceType.NEWS,
            source_credibility_base=0.8,
            ingest_ts=1700000000.0,
            publish_ts=1700000000.0,
            content_length=100,
            raw_text="Bitcoin surges to new high!",
            metadata={}
        )
        
        # Process many items
        for i in range(100):
            payload.raw_text = f"Bitcoin news item {i}"
            await pipeline.process(payload)
        
        final_memory = process.memory_info().rss / 1024 / 1024  # MB
        memory_growth = final_memory - initial_memory
        
        print(f"Memory growth: {memory_growth:.1f} MB")
        assert memory_growth < 500  # Less than 500MB growth


class TestConcurrency:
    """Concurrency tests"""

    @pytest.mark.asyncio
    async def test_pipeline_concurrent_requests(self):
        """Pipeline should handle concurrent requests"""
        pipeline = NLPProcessingPipeline()
        await pipeline.initialize()
        
        payload = NormalizedPayload(
            source_id="concurrent",
            source_type=SourceType.NEWS,
            source_credibility_base=0.8,
            ingest_ts=1700000000.0,
            publish_ts=1700000000.0,
            content_length=100,
            raw_text="Bitcoin surges to new high!",
            metadata={}
        )
        
        # Run 20 concurrent requests
        tasks = [pipeline.process(NormalizedPayload(
            source_id=f"concurrent_{i}",
            source_type=SourceType.NEWS,
            source_credibility_base=0.8,
            ingest_ts=1700000000.0,
            publish_ts=1700000000.0,
            content_length=100,
            raw_text=f"Bitcoin news {i}",
            metadata={}
        )) for i in range(20)]
        
        start = time.time()
        results = await asyncio.gather(*tasks)
        elapsed = time.time() - start
        
        assert len(results) == 20
        # Should be faster than sequential
        assert elapsed < 30  # Under 30 seconds for 20 concurrent


if __name__ == "__main__":
    pytest.main([__file__, "-v", "-s"])