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

src/models/set_transformer.py

@@ -558,13 +558,13 @@ class SetTransformer(BaseModel):
     # Predict
     # ------------------------------------------------------------------
 
-    def predict(self, team_roster_df: pd.DataFrame) -> np.ndarray:
+    def predict(self, team_roster_df: pd.DataFrame):
         """Predict total team value (season-long points)."""
         if not self._trained:
             raise RuntimeError("Model not fitted. Call fit() first.")
 
         val = self._predict_single(team_roster_df)
-        return np.array([val])
+        return val
 
     def _predict_single(self, team_roster_df: pd.DataFrame) -> float:
         if self._using_torch and self._torch_model is not None:
@@ -590,19 +590,25 @@ class SetTransformer(BaseModel):
     # Marginal value analysis
     # ------------------------------------------------------------------
 
-    def value_added(self, team_roster_df: pd.DataFrame, new_player: dict) -> float:
+    def value_added(self, team_roster_df: pd.DataFrame, new_player) -> float:
         """Marginal value: delta when adding new_player to the team."""
         baseline = self._predict_single(team_roster_df)
-        augmented = pd.concat(
-            [team_roster_df, pd.DataFrame([new_player])], ignore_index=True
-        )
+        if isinstance(new_player, pd.DataFrame):
+            augmented = pd.concat([team_roster_df, new_player], ignore_index=True)
+        else:
+            augmented = pd.concat(
+                [team_roster_df, pd.DataFrame([new_player])], ignore_index=True
+            )
         augmented_val = self._predict_single(augmented)
         return augmented_val - baseline
 
-    def value_removed(self, team_roster_df: pd.DataFrame, removed_player_idx: int) -> float:
-        """Marginal loss: delta when removing a player."""
+    def value_removed(self, team_roster_df: pd.DataFrame, removed_player) -> float:
+        """Marginal loss: delta when removing a player (by index or name)."""
         baseline = self._predict_single(team_roster_df)
-        reduced = team_roster_df.drop(team_roster_df.index[removed_player_idx])
+        if isinstance(removed_player, str):
+            reduced = team_roster_df[team_roster_df["name"] != removed_player]
+        else:
+            reduced = team_roster_df.drop(team_roster_df.index[removed_player])
         reduced_val = self._predict_single(reduced)
         return baseline - reduced_val
 
@@ -610,21 +616,27 @@ class SetTransformer(BaseModel):
         self,
         team_roster_df: pd.DataFrame,
         candidate_pool: pd.DataFrame,
-        to_replace: List[int],
-    ) -> Dict[int, pd.DataFrame]:
+        to_replace: List,
+    ) -> Dict:
         """For each player to replace, rank candidates by predicted team value delta.
 
         Args:
             team_roster_df: current team roster.
             candidate_pool: DataFrame of free-agent candidates.
-            to_replace: list of indices in team_roster_df to consider replacing.
+            to_replace: list of player names (str) or indices (int) in team_roster_df
+                       to consider replacing.
 
         Returns:
-            dict mapping replace_idx -> DataFrame of candidates ranked by delta.
+            dict mapping player_name -> DataFrame of candidates ranked by delta.
         """
         results = {}
-        for rp_idx in to_replace:
-            base_team = team_roster_df.drop(team_roster_df.index[rp_idx])
+        for rp in to_replace:
+            if isinstance(rp, str):
+                base_team = team_roster_df[team_roster_df["name"] != rp]
+                key = rp
+            else:
+                base_team = team_roster_df.drop(team_roster_df.index[rp])
+                key = rp
             deltas = []
             for _, cand in candidate_pool.iterrows():
                 cand_dict = cand.to_dict()
@@ -640,7 +652,7 @@ class SetTransformer(BaseModel):
                     "team_value_delta": new_val - current_val,
                 })
 
-            results[rp_idx] = pd.DataFrame(deltas).sort_values(
+            results[key] = pd.DataFrame(deltas).sort_values(
                 "team_value_delta", ascending=False
             )
         return results