502 lines
20 KiB
Python
502 lines
20 KiB
Python
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"""Mock models for testing without external dependencies"""
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import asyncio
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import logging
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import torch
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from typing import Dict, List, Optional, Tuple, Any
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from sentiment_engine.schemas.processed import SentimentScores, EmotionScores
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from sentiment_engine.schemas.processed import EventClassification, EventType
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logger = logging.getLogger(__name__)
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class MockSentimentModel:
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"""Mock sentiment model for testing without external dependencies"""
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def __init__(self, device: str = "cpu"):
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self.device = device
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def __call__(self, **inputs):
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"""Mock forward pass"""
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batch_size = inputs["input_ids"].shape[0]
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# Return mock logits: [batch_size, 3] for negative, neutral, positive
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logits = torch.randn(batch_size, 3, device=self.device)
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return type('Outputs', (), {'logits': logits})()
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class MockEmotionModel:
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"""Mock emotion model for testing"""
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def __init__(self, device: str = "cpu"):
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self.device = device
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def __call__(self, **inputs):
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"""Mock forward pass"""
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batch_size = inputs["input_ids"].shape[0]
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# Return mock logits: [batch_size, 6] for 6 emotions
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logits = torch.randn(batch_size, 6, device=self.device)
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return type('Outputs', (), {'logits': logits})()
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class MockTokenizer:
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"""Mock tokenizer for testing"""
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def __init__(self):
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self.vocab_size = 30522
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def __call__(self, text, return_tensors="pt", truncation=True, max_length=512, padding=True):
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"""Mock tokenization"""
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if isinstance(text, list):
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batch_size = len(text)
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else:
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batch_size = 1
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text = [text]
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# Create mock input_ids and attention_mask
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seq_len = min(max(len(t.split()) for t in text) + 2, 512)
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input_ids = torch.randint(1, 1000, (batch_size, 512))
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attention_mask = torch.ones_like(input_ids)
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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask
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}
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@classmethod
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def from_pretrained(cls, model_name: str):
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return MockTokenizer()
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def save_pretrained(self, path: str):
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pass
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class MockModel:
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def __init__(self, device="cpu"):
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self.device = device
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def to(self, device):
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self.device = device
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return self
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def eval(self):
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return self
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def __call__(self, **inputs):
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batch_size = inputs["input_ids"].shape[0]
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logits = torch.randn(batch_size, 3) # 3 classes: neg, neu, pos
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return type('Outputs', (), {'logits': logits})()
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class MockSentimentEmotionAnalyzer:
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"""Mock sentiment/emotion analyzer for testing"""
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def __init__(self, device: str = "cpu"):
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self.device = device
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self._tokenizer = None
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self._model = None
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self._emotion_model = None
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self._emotion_tokenizer = None
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self._labels = ["negative", "neutral", "positive"]
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self._emotion_labels = ["joy", "fear", "anger", "greed", "sadness", "neutral"]
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async def initialize(self) -> None:
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"""Mock initialization"""
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pass
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async def analyze(
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self,
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text: str,
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asset_mentions: List[Dict]
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) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""Mock sentiment/emotion analysis"""
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from sentiment_engine.schemas.processed import SentimentScores, EmotionScores
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sentiment_results = {}
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emotion_results = {}
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for mention in asset_mentions:
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asset_id = mention.get("asset_id")
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span = mention.get("span", (0, 0))
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# Simple heuristic based on text content
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text_lower = text.lower() if isinstance(text, str) else ""
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# Simple keyword-based sentiment
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positive_words = ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"]
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negative_words = ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"]
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pos_count = sum(1 for kw in ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"] if kw in text_lower)
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neg_count = sum(1 for kw in ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"] if kw in text_lower)
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polarity = (pos_count - neg_count) * 0.3
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polarity = max(-1.0, min(1.0, polarity))
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confidence = min(0.9, 0.3 + abs(polarity) * 0.5)
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sentiment_results[asset_id] = type('SentimentScores', (), {
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'polarity': polarity,
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'confidence': confidence,
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'positive_prob': max(0, polarity),
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'negative_prob': max(0, -polarity),
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'neutral_prob': 1 - abs(polarity)
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})()
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# Simple emotions
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emotion_results[asset_id] = type('EmotionScores', (), {
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'joy': 0.5 if polarity > 0 else 0.1,
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'fear': 0.5 if polarity < 0 else 0.1,
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'anger': 0.1,
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'greed': 0.5 if polarity > 0.2 else 0.1,
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'sadness': 0.5 if polarity < -0.2 else 0.1,
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'intensity': 0.5
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})()
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return sentiment_results, emotion_results
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async def initialize(self) -> None:
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"""Mock initialization"""
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pass
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async def analyze(
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self,
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text: str,
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asset_mentions: List[Dict]
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) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""Mock sentiment/emotion analysis"""
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from sentiment_engine.schemas.processed import SentimentScores, EmotionScores
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sentiment_results = {}
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emotion_results = {}
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for mention in asset_mentions:
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asset_id = mention.get("asset_id")
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span = mention.get("span", (0, 0))
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# Simple heuristic based on text content
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text_lower = text.lower() if isinstance(text, str) else ""
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# Simple keyword-based sentiment
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positive_words = ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"]
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negative_words = ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"]
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pos_count = sum(1 for kw in ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"] if kw in text_lower)
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neg_count = sum(1 for kw in ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"] if kw in text_lower)
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polarity = (pos_count - neg_count) * 0.3
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polarity = max(-1.0, min(1.0, polarity))
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confidence = min(0.9, 0.3 + abs(polarity) * 0.5)
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# Create mock sentiment scores
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sentiment_scores = type('SentimentScores', (), {
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'polarity': polarity,
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'confidence': confidence,
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'positive_prob': max(0, polarity),
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'negative_prob': max(0, -polarity),
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'neutral_prob': 1 - abs(polarity)
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})()
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# Simple emotions
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emotion_scores = type('EmotionScores', (), {
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'joy': 0.5 if polarity > 0 else 0.1,
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'fear': 0.5 if polarity < 0 else 0.1,
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'anger': 0.1,
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'greed': 0.5 if polarity > 0.2 else 0.1,
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'sadness': 0.5 if polarity < -0.2 else 0.1,
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'intensity': 0.5
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})()
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sentiment_results[asset_id] = sentiment_scores
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emotion_results[asset_id] = emotion_scores
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return sentiment_results, emotion_results
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async def initialize(self) -> None:
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"""Mock initialization"""
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pass
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async def analyze(
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self,
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text: str,
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asset_mentions: List[Dict]
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) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""Mock sentiment/emotion analysis"""
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from sentiment_engine.schemas.processed import SentimentScores, EmotionScores
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sentiment_results = {}
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emotion_results = {}
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for mention in asset_mentions:
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asset_id = mention.get("asset_id")
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span = mention.get("span", (0, 0))
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# Simple heuristic based on text content
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text_lower = text.lower() if isinstance(text, str) else ""
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# Simple keyword-based sentiment
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positive_words = ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"]
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negative_words = ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"]
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pos_count = sum(1 for kw in ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"] if kw in text_lower)
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neg_count = sum(1 for kw in ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"] if kw in text_lower)
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polarity = (pos_count - neg_count) * 0.3
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polarity = max(-1.0, min(1.0, polarity))
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confidence = min(0.9, 0.3 + abs(polarity) * 0.5)
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# Create mock sentiment scores
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sentiment_results[asset_id] = type('SentimentScores', (), {
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'polarity': polarity,
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'confidence': confidence,
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'positive_prob': max(0, polarity),
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'negative_prob': max(0, -polarity),
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'neutral_prob': 1 - abs(polarity)
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})()
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# Simple emotions
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emotion_results[asset_id] = type('EmotionScores', (), {
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'joy': 0.5 if polarity > 0 else 0.1,
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'fear': 0.5 if polarity < 0 else 0.1,
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'anger': 0.1,
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'greed': 0.5 if polarity > 0.2 else 0.1,
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'sadness': 0.5 if polarity < -0.2 else 0.1,
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'intensity': 0.5
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})()
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return sentiment_results, emotion_results
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async def initialize(self) -> None:
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"""Mock initialization"""
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pass
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async def analyze(
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self,
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text: str,
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asset_mentions: List[Dict]
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) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""Mock sentiment/emotion analysis"""
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from sentiment_engine.schemas.processed import SentimentScores, EmotionScores
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sentiment_results = {}
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emotion_results = {}
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for mention in asset_mentions:
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asset_id = mention.get("asset_id")
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span = mention.get("span", (0, 0))
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# Simple heuristic based on text content
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text_lower = text.lower() if isinstance(text, str) else ""
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# Simple keyword-based sentiment
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positive_words = ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"]
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negative_words = ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"]
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pos_count = sum(1 for kw in ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"] if kw in text_lower)
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neg_count = sum(1 for kw in ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"] if kw in text_lower)
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polarity = (pos_count - neg_count) * 0.3
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polarity = max(-1.0, min(1.0, polarity))
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confidence = min(0.9, 0.3 + abs(polarity) * 0.5)
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# Create mock sentiment scores
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sentiment_results[asset_id] = type('SentimentScores', (), {
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'polarity': polarity,
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'confidence': confidence,
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'positive_prob': max(0, polarity),
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'negative_prob': max(0, -polarity),
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'neutral_prob': 1 - abs(polarity)
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})()
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# Simple emotions
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emotion_results[asset_id] = type('EmotionScores', (), {
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'joy': 0.5 if polarity > 0 else 0.1,
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'fear': 0.5 if polarity < 0 else 0.1,
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'anger': 0.1,
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'greed': 0.5 if polarity > 0.2 else 0.1,
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'sadness': 0.5 if polarity < -0.2 else 0.1,
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'intensity': 0.5
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})()
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return sentiment_results, emotion_results
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async def initialize(self) -> None:
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"""Mock initialization"""
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pass
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async def analyze(
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self,
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text: str,
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asset_mentions: List[Dict]
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) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""Mock sentiment/emotion analysis"""
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from sentiment_engine.schemas.processed import SentimentScores, EmotionScores
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sentiment_results = {}
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emotion_results = {}
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for mention in asset_mentions:
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asset_id = mention.get("asset_id")
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span = mention.get("span", (0, 0))
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# Simple heuristic based on text content
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text_lower = text.lower() if isinstance(text, str) else ""
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# Simple keyword-based sentiment
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positive_words = ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"]
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negative_words = ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"]
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pos_count = sum(1 for kw in ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"] if kw in text_lower)
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neg_count = sum(1 for kw in ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"] if kw in text_lower)
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polarity = (pos_count - neg_count) * 0.3
|
||
|
|
polarity = max(-1.0, min(1.0, polarity))
|
||
|
|
|
||
|
|
confidence = min(0.9, 0.3 + abs(polarity) * 0.5)
|
||
|
|
|
||
|
|
# Create mock sentiment scores
|
||
|
|
sentiment_results[asset_id] = type('SentimentScores', (), {
|
||
|
|
'polarity': polarity,
|
||
|
|
'confidence': confidence,
|
||
|
|
'positive_prob': max(0, polarity),
|
||
|
|
'negative_prob': max(0, -polarity),
|
||
|
|
'neutral_prob': 1 - abs(polarity)
|
||
|
|
})()
|
||
|
|
|
||
|
|
# Simple emotions
|
||
|
|
emotion_results[asset_id] = type('EmotionScores', (), {
|
||
|
|
'joy': 0.5 if polarity > 0 else 0.1,
|
||
|
|
'fear': 0.5 if polarity < 0 else 0.1,
|
||
|
|
'anger': 0.1,
|
||
|
|
'greed': 0.5 if polarity > 0.2 else 0.1,
|
||
|
|
'sadness': 0.5 if polarity < -0.2 else 0.1,
|
||
|
|
'intensity': 0.5
|
||
|
|
})()
|
||
|
|
|
||
|
|
return sentiment_results, emotion_results
|
||
|
|
|
||
|
|
async def initialize(self) -> None:
|
||
|
|
"""Mock initialization"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
async def analyze(
|
||
|
|
self,
|
||
|
|
text: str,
|
||
|
|
asset_mentions: List[Dict]
|
||
|
|
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
|
||
|
|
"""Mock sentiment/emotion analysis"""
|
||
|
|
from sentiment_engine.schemas.processed import SentimentScores, EmotionScores
|
||
|
|
|
||
|
|
sentiment_results = {}
|
||
|
|
emotion_results = {}
|
||
|
|
|
||
|
|
for mention in asset_mentions:
|
||
|
|
asset_id = mention.get("asset_id")
|
||
|
|
span = mention.get("span", (0, 0))
|
||
|
|
|
||
|
|
# Simple heuristic based on text content
|
||
|
|
text_lower = text.lower() if isinstance(text, str) else ""
|
||
|
|
|
||
|
|
# Simple keyword-based sentiment
|
||
|
|
positive_words = ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"]
|
||
|
|
negative_words = ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"]
|
||
|
|
|
||
|
|
pos_count = sum(1 for kw in ["rally", "surge", "pump", "moon", "bullish", "profit", "gain", "win", "success", "breakthrough"] if kw in text_lower)
|
||
|
|
neg_count = sum(1 for kw in ["crash", "dump", "panic", "fear", "scared", "worried", "risk", "danger", "collapse", "liquidation"] if kw in text_lower)
|
||
|
|
|
||
|
|
polarity = (pos_count - neg_count) * 0.3
|
||
|
|
polarity = max(-1.0, min(1.0, polarity))
|
||
|
|
|
||
|
|
confidence = min(0.9, 0.3 + abs(polarity) * 0.5)
|
||
|
|
|
||
|
|
# Create mock sentiment scores
|
||
|
|
sentiment_results[asset_id] = type('SentimentScores', (), {
|
||
|
|
'polarity': polarity,
|
||
|
|
'confidence': confidence,
|
||
|
|
'positive_prob': max(0, polarity),
|
||
|
|
'negative_prob': max(0, -polarity),
|
||
|
|
'neutral_prob': 1 - abs(polarity)
|
||
|
|
})()
|
||
|
|
|
||
|
|
# Simple emotions
|
||
|
|
emotion_results[asset_id] = type('EmotionScores', (), {
|
||
|
|
'joy': 0.5 if polarity > 0 else 0.1,
|
||
|
|
'fear': 0.5 if polarity < 0 else 0.1,
|
||
|
|
'anger': 0.1,
|
||
|
|
'greed': 0.5 if polarity > 0.2 else 0.1,
|
||
|
|
'sadness': 0.5 if polarity < -0.2 else 0.1,
|
||
|
|
'intensity': 0.5
|
||
|
|
})()
|
||
|
|
|
||
|
|
return sentiment_results, emotion_results
|
||
|
|
|
||
|
|
async def initialize(self) -> None:
|
||
|
|
"""Mock initialization"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def create_mock_event_classifier():
|
||
|
|
"""Create mock event classifier"""
|
||
|
|
classifier = type('MockEventClassifier', (), {
|
||
|
|
'EVENT_KEYWORDS': {
|
||
|
|
'listing': ["listing", "listed", "debut", "launch", "goes live", "trading starts"],
|
||
|
|
'hack': ["hack", "hacked", "exploit", "exploited", "breach", "stolen", "theft"],
|
||
|
|
'regulatory': ["sec", "cftc", "regulation", "regulatory", "compliance"],
|
||
|
|
},
|
||
|
|
'EVENT_TYPES': ["listing", "hack", "regulatory", "delisting", "governance",
|
||
|
|
"upgrade", "partnership", "earnings", "macro", "liquidation", "whale", "manipulation"]
|
||
|
|
})()
|
||
|
|
return classifier
|
||
|
|
|
||
|
|
|
||
|
|
def create_mock_asset_mapper():
|
||
|
|
"""Create mock asset mapper"""
|
||
|
|
mapper = type('MockAssetMapper', (), {
|
||
|
|
'aliases': {"VITALIK": "ETH", "CZ": "BNB", "ELON": "DOGE", "SAYLOR": "BTC"},
|
||
|
|
'known_entities': {
|
||
|
|
"BTC": {"name": "Bitcoin", "type": "crypto", "contracts": []},
|
||
|
|
"ETH": {"name": "Ethereum", "type": "crypto", "contracts": ["0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2"]},
|
||
|
|
"SOL": {"name": "Solana", "type": "crypto", "contracts": ["So11111111111111111111111111111111111111112"]},
|
||
|
|
}
|
||
|
|
})()
|
||
|
|
return mapper
|
||
|
|
|
||
|
|
|
||
|
|
def create_mock_entity_extractor():
|
||
|
|
"""Create mock entity extractor"""
|
||
|
|
from sentiment_engine.nlp.entity_extraction import EntityExtractor, AssetMapper
|
||
|
|
|
||
|
|
asset_mapper = type('MockAssetMapper', (), {
|
||
|
|
'aliases': {"VITALIK": "ETH", "CZ": "BNB", "ELON": "DOGE", "SAYLOR": "BTC"},
|
||
|
|
'known_entities': {
|
||
|
|
"BTC": {"name": "Bitcoin", "type": "crypto", "contracts": []},
|
||
|
|
"ETH": {"name": "Ethereum", "type": "crypto", "contracts": ["0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2"]},
|
||
|
|
"SOL": {"name": "Solana", "type": "crypto", "contracts": ["So11111111111111111111111111111111111111112"]},
|
||
|
|
}
|
||
|
|
})()
|
||
|
|
|
||
|
|
from sentiment_engine.nlp.entity_extraction import EntityExtractor, AssetMapper
|
||
|
|
extractor = EntityExtractor(asset_mapper)
|
||
|
|
# Override initialize to not load spaCy
|
||
|
|
extractor.initialize = lambda: None
|
||
|
|
return extractor
|
||
|
|
|
||
|
|
|
||
|
|
# Export all mocks
|
||
|
|
__all__ = [
|
||
|
|
"MockSentimentModel",
|
||
|
|
"MockEmotionModel",
|
||
|
|
"MockTokenizer",
|
||
|
|
"MockModel",
|
||
|
|
"MockTokenizer",
|
||
|
|
"MockSentimentEmotionAnalyzer",
|
||
|
|
"MockModel",
|
||
|
|
"MockAssetMapper",
|
||
|
|
"create_mock_sentiment_analyzer",
|
||
|
|
"create_mock_event_classifier",
|
||
|
|
"create_mock_asset_mapper",
|
||
|
|
"create_mock_entity_extractor",
|
||
|
|
]
|