fantabeto

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Author	SHA1	Message	Date
ramseshk	b0fab62a87	feat: add 10 new ML models for auction optimization (Phases 1-6) 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	2026-08-11 17:56:03 +08:00
ramseshk	916278a640	Add RL auction agent (Double DQN) and Set Transformer for team valuation - rl_auction_agent.py: Custom Gym-free auction environment with N opponents, numpy-only Q-network with manual backprop, Double DQN policy with target network, full training loop with epsilon decay and periodic evaluation, and baseline comparison vs greedy and MILP strategies. - set_transformer.py: Team-level valuation model treating roster as an unordered set. Two modes: full PyTorch Set Transformer with ISAB/PMA when torch is available, or sklearn Bag-of-Players fallback using per-role aggregates, pairwise cosine similarities, and position entropy.	2026-08-11 17:27:26 +08:00

Author

SHA1

Message

Date

ramseshk

b0fab62a87

feat: add 10 new ML models for auction optimization (Phases 1-6)

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

2026-08-11 17:56:03 +08:00

ramseshk

916278a640

Add RL auction agent (Double DQN) and Set Transformer for team valuation

- rl_auction_agent.py: Custom Gym-free auction environment with N opponents,
  numpy-only Q-network with manual backprop, Double DQN policy with target
  network, full training loop with epsilon decay and periodic evaluation,
  and baseline comparison vs greedy and MILP strategies.
- set_transformer.py: Team-level valuation model treating roster as an
  unordered set. Two modes: full PyTorch Set Transformer with ISAB/PMA
  when torch is available, or sklearn Bag-of-Players fallback using
  per-role aggregates, pairwise cosine similarities, and position entropy.

2026-08-11 17:27:26 +08:00

2 Commits