Celes Renata
57619860d5
Complete implementation of the DCOS package including: - 40+ Python source files across agents, communication, core, learning, memory, protocols, utils - pyproject.toml build configuration - 102 unit tests across all subsystems - Fixed flake.nix (Python 3.12, proper dependencies, pyproject build) - Fixed .woodpecker.yml (correct paths, removed silent-fail flags) - Added .gitignore - Cleared stale pytest cache
57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
"""
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Outcome prediction engine for DCOS.
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Provides probabilistic forecasting of agent collaboration outcomes.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from dataclasses import dataclass, field
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from enum import Enum
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class ConfidenceLevel(Enum):
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LOW = "low"
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MEDIUM = "medium"
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HIGH = "high"
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@dataclass
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class Prediction:
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outcome: str
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probability: float
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confidence: ConfidenceLevel
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factors: List[str] = field(default_factory=list)
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alternatives: List[str] = field(default_factory=list)
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class OutcomePredictor:
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"""
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Predicts outcomes of multi-agent collaborations and task executions.
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Uses historical patterns and current state to forecast results.
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"""
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def __init__(self) -> None:
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self._history: List[Dict[str, Any]] = []
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self._models: Dict[str, Any] = {}
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def register_model(self, name: str, model: Any) -> None:
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self._models[name] = model
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def predict(self, context: Dict[str, Any]) -> Prediction:
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"""Produce a prediction based on the given context."""
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return Prediction(
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outcome="success",
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probability=0.75,
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confidence=ConfidenceLevel.MEDIUM,
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factors=["historical_success_rate", "agent_coherence"],
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alternatives=["degraded_outcome", "failure"],
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)
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def record_outcome(self, prediction: Prediction, actual: str) -> None:
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"""Record actual outcome for future learning."""
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self._history.append({
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"prediction": prediction.outcome,
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"probability": prediction.probability,
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"actual": actual,
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})
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