ramseshk
b0fab62a87
Phase 1 - Quick Wins: - QuantileEnsemble: P10/P50/P90 predictions for risk-aware bidding - MinutesSurvivalModel: Weibull AFT for minutes distribution modeling Phase 2 - Adaptive Auction: - BanditAuctionSolver: Thompson Sampling for live auction bids - OpponentBidModel: Predict competitor bids via LightGBM - BudgetOptimizer: Bayesian optimization for role-level allocation Phase 3 - Deep Learning: - RLAuctionPolicy: Double DQN agent for auction strategy - SetTransformer: Team composition valuation via set-based ML Phase 4 - Probabilistic: - BayesianPlayerModel: Hierarchical pooling for rookie uncertainty - ConformalPredictor: Calibrated prediction intervals Phase 5 - Chemistry & Form: - PlayerChemistryGAT: Graph attention network for player synergies - PlayerFormModel: Hawkes process for form momentum Phase 6 - Causal: - TransferCausalModel: Causal forest for transfer effects - AuctionEffectAnalyzer: Bid adjustment from causal analysis 81 tests passing
15 lines
648 B
Python
15 lines
648 B
Python
"""Optimization modules for auction and lineup selection."""
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from .auction_solver import AuctionSolver, AuctionConfig, PlayerValuation
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from .lineup_solver import LineupSolver, LineupConstraints, PlayerScore, MCTSNode
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from .opponent_model import OpponentModel
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from .transfer_analyzer import TransferAnalyzer
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# Phase 2: Bandit, opponent bidding, budget optimization
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from .bandit_auction import BanditAuctionSolver
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from .opponent_bidding_model import OpponentBidModel
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from .budget_optimizer import BudgetOptimizer
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# Phase 3: Reinforcement learning auction agent
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from .rl_auction_agent import AuctionEnv, RLAuctionPolicy, RLAuctionTrainer, QNetwork
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