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Ensemble classifier combining DistilBERT, RoBERTa, and XLNet
with parallel execution and multiple voting strategies.
"""
import asyncio
import time
import logging
from dataclasses import dataclass
from typing import List, Dict, Optional
from concurrent.futures import ThreadPoolExecutor
import numpy as np
from .inference import get_classifier
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class TokenImportance:
token: str
score: float
@dataclass
class ModelPrediction:
model_name: str
label: str
confidence: float
scores: Dict[str, float]
tokens: List[Dict]
@dataclass
class EnsembleResult:
hard_voting_label: str
hard_voting_confidence: float
soft_voting_label: str
soft_voting_confidence: float
soft_voting_scores: Dict[str, float]
weighted_voting_label: str
weighted_voting_confidence: float
weighted_voting_scores: Dict[str, float]
individual_predictions: List[ModelPrediction]
merged_explanation: List[Dict]
execution_time_ms: float
warnings: Optional[List[str]] = None
class EnsembleClassifier:
"""Combines predictions from all three models using voting strategies."""
def __init__(self):
self.model_names = ['distilbert', 'roberta', 'xlnet']
self.models = {name: get_classifier(name) for name in self.model_names}
self.weights = {'distilbert': 0.859, 'roberta': 0.858, 'xlnet': 0.862}
self.executor = ThreadPoolExecutor(max_workers=3)
async def predict_ensemble(self, text: str, model_timeout: float = 10.0,
total_timeout: float = 15.0) -> EnsembleResult:
start_time = time.time()
warnings = []
loop = asyncio.get_event_loop()
tasks = [
loop.run_in_executor(
self.executor, self._predict_with_timeout, name, text, model_timeout)
for name in self.model_names
]
try:
results = await asyncio.wait_for(
asyncio.gather(*tasks, return_exceptions=True),
timeout=total_timeout
)
except asyncio.TimeoutError:
warnings.append("Ensemble prediction exceeded total timeout")
raise
valid_predictions = []
for name, result in zip(self.model_names, results):
if isinstance(result, Exception):
warnings.append(f"{name} failed: {str(result)}")
elif result is None:
warnings.append(f"{name} returned no result")
else:
valid_predictions.append(ModelPrediction(
model_name=name,
label=result['label'],
confidence=result['confidence'],
scores=result['scores'],
tokens=result['tokens']
))
if not valid_predictions:
raise RuntimeError("All models failed to process the request")
hard_label, hard_conf = self.hard_voting(valid_predictions)
soft_scores = self.soft_voting(valid_predictions)
soft_label = max(soft_scores.items(), key=lambda x: x[1])[0]
soft_conf = soft_scores[soft_label]
weighted_scores = self.weighted_voting(valid_predictions)
weighted_label = max(weighted_scores.items(), key=lambda x: x[1])[0]
weighted_conf = weighted_scores[weighted_label]
merged_tokens = self._merge_explanations(valid_predictions)
execution_time = (time.time() - start_time) * 1000
logger.info(
f"Ensemble completed in {execution_time:.2f}ms with {len(valid_predictions)}/{len(self.model_names)} models")
if warnings:
logger.warning(f"Ensemble warnings: {warnings}")
return EnsembleResult(
hard_voting_label=hard_label,
hard_voting_confidence=hard_conf,
soft_voting_label=soft_label,
soft_voting_confidence=soft_conf,
soft_voting_scores=soft_scores,
weighted_voting_label=weighted_label,
weighted_voting_confidence=weighted_conf,
weighted_voting_scores=weighted_scores,
individual_predictions=valid_predictions,
merged_explanation=merged_tokens,
execution_time_ms=round(execution_time, 2),
warnings=warnings if warnings else None
)
def _predict_with_timeout(self, model_name: str, text: str, timeout: float) -> Optional[Dict]:
try:
return self.models[model_name].predict(text)
except Exception as e:
logger.error(f"[ensemble] {model_name} prediction failed: {e}")
return None
def hard_voting(self, predictions: List[ModelPrediction]) -> tuple[str, float]:
votes = {}
for pred in predictions:
votes[pred.label] = votes.get(pred.label, 0) + 1
winning_label = max(votes.items(), key=lambda x: x[1])[0]
confidences = [
p.confidence for p in predictions if p.label == winning_label]
return winning_label, round(sum(confidences) / len(confidences), 4)
def soft_voting(self, predictions: List[ModelPrediction]) -> Dict[str, float]:
all_labels = set(
label for pred in predictions for label in pred.scores)
return {
label: round(sum(p.scores.get(label, 0.0)
for p in predictions) / len(predictions), 4)
for label in all_labels
}
def weighted_voting(self, predictions: List[ModelPrediction]) -> Dict[str, float]:
all_labels = set(
label for pred in predictions for label in pred.scores)
total_weight = sum(self.weights[p.model_name] for p in predictions)
return {
label: round(
sum(p.scores.get(label, 0.0) *
self.weights[p.model_name] for p in predictions) / total_weight,
4
)
for label in all_labels
}
def _merge_explanations(self, predictions: List[ModelPrediction]) -> List[Dict]:
token_scores: Dict[str, float] = {}
token_counts: Dict[str, int] = {}
for pred in predictions:
for td in pred.tokens:
token = td['token']
token_scores[token] = token_scores.get(
token, 0.0) + td['score']
token_counts[token] = token_counts.get(token, 0) + 1
merged = [
{'token': t, 'score': round(token_scores[t] / token_counts[t], 4)}
for t in token_scores
]
return sorted(merged, key=lambda x: x['score'], reverse=True)[:10]
_ensemble_classifier: Optional[EnsembleClassifier] = None
def get_ensemble_classifier() -> EnsembleClassifier:
global _ensemble_classifier
if _ensemble_classifier is None:
_ensemble_classifier = EnsembleClassifier()
return _ensemble_classifier
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