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
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sentiment_engine/tests/unit/test_mock_models_comprehensive.py
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sentiment_engine/tests/unit/test_mock_models_comprehensive.py
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"""
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Comprehensive tests for mock models and test utilities.
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"""
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import pytest
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import asyncio
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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.utils.mock_models import (
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MockTokenizer, MockSentimentModel, MockEmotionModel,
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MockEventModel, MockNERModel, create_mock_pipeline
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)
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from sentiment_engine.schemas.processed import SentimentScores, EmotionScores, EventClassification, EventType
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from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention
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class TestMockTokenizer:
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"""Tests for MockTokenizer"""
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def test_single_text(self):
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"""Should tokenize single text"""
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tokenizer = MockTokenizer()
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result = tokenizer("test text")
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assert "input_ids" in result
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assert "attention_mask" in result
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assert "token_type_ids" in result
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def test_batch_text(self):
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"""Should tokenize batch of texts"""
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tokenizer = MockTokenizer()
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result = tokenizer(["text1", "text2", "text3"])
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assert result["input_ids"].shape[0] == 3
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def test_truncation(self):
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"""Should respect truncation"""
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tokenizer = MockTokenizer()
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long_text = "word " * 1000
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result = tokenizer(long_text, max_length=128, truncation=True)
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assert result["input_ids"].shape[1] <= 128
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def test_padding(self):
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"""Should pad to max_length"""
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tokenizer = MockTokenizer()
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result = tokenizer("short", max_length=128, padding=True)
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assert result["input_ids"].shape[1] == 128
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def test_return_tensors_pt(self):
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"""Should return PyTorch tensors when requested"""
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import torch
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tokenizer = MockTokenizer()
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result = tokenizer("test", return_tensors="pt")
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assert isinstance(result["input_ids"], torch.Tensor)
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def test_return_tensors_np(self):
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"""Should return numpy arrays when requested"""
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tokenizer = MockTokenizer()
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result = tokenizer("test", return_tensors="np")
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assert isinstance(result["input_ids"], np.ndarray)
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def test_from_pretrained(self):
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"""from_pretrained should return new instance"""
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tokenizer = MockTokenizer.from_pretrained("test-model")
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assert isinstance(tokenizer, MockTokenizer)
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class TestMockSentimentModel:
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"""Tests for MockSentimentModel"""
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def test_returns_logits(self):
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"""Should return logits"""
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model = MockSentimentModel()
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result = model(input_ids=np.ones((2, 10)), attention_mask=np.ones((2, 10)))
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assert hasattr(result, 'logits')
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assert result.logits.shape == (2, 3)
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def test_to_device(self):
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"""to() should return self"""
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model = MockSentimentModel()
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result = model.to("cuda")
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assert result is model
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def test_eval_mode(self):
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"""eval() should return self"""
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model = MockSentimentModel()
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result = model.eval()
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assert result is model
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class TestMockEmotionModel:
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"""Tests for MockEmotionModel"""
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def test_returns_logits(self):
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"""Should return logits for 6 emotions"""
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model = MockEmotionModel()
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result = model(input_ids=np.ones((1, 10)), attention_mask=np.ones((1, 10)))
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assert hasattr(result, 'logits')
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assert result.logits.shape == (1, 6)
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class TestMockEventModel:
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"""Tests for MockEventModel"""
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def test_returns_logits(self):
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"""Should return logits for 12 events"""
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model = MockEventModel()
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result = model(input_ids=np.ones((1, 10)), attention_mask=np.ones((1, 10)))
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assert hasattr(result, 'logits')
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assert result.logits.shape == (1, 12)
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class TestMockNERModel:
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"""Tests for MockNERModel"""
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def test_extract_entities(self):
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"""Should extract entities"""
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model = MockNERModel()
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entities = model.extract_entities("Bitcoin and Ethereum surge")
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assert isinstance(entities, list)
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assert len(entities) >= 0
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class TestMockPipeline:
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"""Tests for create_mock_pipeline"""
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def test_creates_full_pipeline(self):
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"""Should create complete mock pipeline"""
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pipeline = create_mock_pipeline()
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assert hasattr(pipeline, 'entity_extractor')
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assert hasattr(pipeline, 'sentiment_analyzer')
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assert hasattr(pipeline, 'event_classifier')
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assert hasattr(pipeline, 'temporal_anchorer')
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assert hasattr(pipeline, 'credibility_scorer')
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@pytest.mark.asyncio
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async def test_mock_pipeline_process(self):
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"""Mock pipeline should process payloads"""
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pipeline = create_mock_pipeline()
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payload = NormalizedPayload(
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source_id="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!",
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metadata={}
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)
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result = await pipeline.process(payload)
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assert hasattr(result, 'entities')
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assert hasattr(result, 'sentiment_per_asset')
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assert hasattr(result, 'events')
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class TestMockModelIntegration:
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"""Integration tests for mock models"""
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@pytest.mark.asyncio
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async def test_mock_tokenizer_with_sentiment_model(self):
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"""Mock tokenizer should work with sentiment model"""
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tokenizer = MockTokenizer()
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model = MockSentimentModel()
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text = "Bitcoin surges!"
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inputs = tokenizer(text, return_tensors="np")
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result = model(**inputs)
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assert result.logits.shape == (1, 3)
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@pytest.mark.asyncio
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async def test_mock_pipeline_end_to_end(self):
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"""Full mock pipeline should work end-to-end"""
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pipeline = create_mock_pipeline()
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payload = NormalizedPayload(
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source_id="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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result = await pipeline.process(payload)
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assert hasattr(result, 'entities')
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assert hasattr(result, 'sentiment_per_asset')
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assert hasattr(result, 'emotions_per_asset')
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assert hasattr(result, 'events')
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assert hasattr(result, 'temporal')
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assert hasattr(result, 'credibility')
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assert isinstance(result.processing_latency_ms, float)
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class TestMockModelEdgeCases:
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"""Edge case tests for mock models"""
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def test_mock_tokenizer_empty_text(self):
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"""Should handle empty text"""
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tokenizer = MockTokenizer()
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result = tokenizer("")
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assert "input_ids" in result
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def test_mock_tokenizer_very_long(self):
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"""Should handle very long text"""
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tokenizer = MockTokenizer()
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long_text = "word " * 10000
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result = tokenizer(long_text, truncation=True, max_length=512)
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assert result["input_ids"].shape[1] == 512
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def test_mock_model_batch_size(self):
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"""Should handle various batch sizes"""
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model = MockSentimentModel()
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for batch_size in [1, 2, 4, 8, 16, 32]:
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inputs = {
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"input_ids": np.ones((batch_size, 128)),
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"attention_mask": np.ones((batch_size, 128))
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}
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result = model(**inputs)
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assert result.logits.shape == (batch_size, 3)
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def test_mock_model_different_devices(self):
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"""Should work on different devices"""
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model = MockSentimentModel()
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for device in ["cpu", "cuda"]:
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model.to(device)
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result = model(input_ids=np.ones((1, 10)), attention_mask=np.ones((1, 10)))
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assert result.logits.shape == (1, 3)
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class TestMockModelCompatibility:
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"""Tests for compatibility with real model interfaces"""
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def test_tokenizer_interface(self):
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"""MockTokenizer should match HF tokenizer interface"""
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tokenizer = MockTokenizer()
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# Should have required methods
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assert hasattr(tokenizer, '__call__')
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assert hasattr(tokenizer, 'from_pretrained')
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assert hasattr(tokenizer, 'save_pretrained')
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def test_model_interface(self):
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"""MockSentimentModel should match HF model interface"""
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model = MockSentimentModel()
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assert hasattr(model, 'to')
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assert hasattr(model, 'eval')
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assert hasattr(model, '__call__')
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def test_output_structure(self):
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"""Output should match HF model output structure"""
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model = MockSentimentModel()
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result = model(input_ids=np.ones((1, 10)), attention_mask=np.ones((1, 10)))
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# Should have logits attribute
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assert hasattr(result, 'logits')
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# Logits should be 2D: (batch, num_labels)
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assert len(result.logits.shape) == 2
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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