From 920ae7ce38cf745059d4aa94690a82674377c3ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fahri=20Can=20Se=C3=A7er?= Date: Sun, 24 May 2026 02:58:53 +0300 Subject: [PATCH] gg --- .gitignore | 3 + ai-engine/models/basketball_v25/__init__.py | 356 ++++++++ .../models/basketball_v25/feature_cols.json | 101 +++ .../basketball_v25/lgb_basketball_v25_ml.txt | 655 +++++++++++++++ .../lgb_basketball_v25_spread.txt | 790 ++++++++++++++++++ .../lgb_basketball_v25_total.txt | 202 +++++ .../basketball_v25/xgb_basketball_v25_ml.json | 1 + .../xgb_basketball_v25_spread.json | 1 + .../xgb_basketball_v25_total.json | 1 + .../models/calibration/dc_calibrator.pkl | Bin 0 -> 549 bytes ai-engine/models/calibration/dc_metrics.json | 8 + .../models/calibration/ht_ft_calibrator.pkl | Bin 0 -> 517 bytes .../models/calibration/ht_ft_metrics.json | 8 + .../models/calibration/ou15_calibrator.pkl | Bin 0 -> 485 bytes .../models/calibration/ou15_metrics.json | 8 + .../models/calibration/ou25_calibrator.pkl | Bin 0 -> 485 bytes .../models/calibration/ou25_metrics.json | 8 + ai-engine/models/v27/v27_btts_lgb.pkl | Bin 0 -> 464646 bytes ai-engine/models/v27/v27_btts_xgb.pkl | Bin 0 -> 850463 bytes ai-engine/models/v27/v27_catboost.pkl | Bin 0 -> 1527993 bytes ai-engine/models/v27/v27_feature_cols.json | 46 + .../models/v27/v27_feature_importance.csv | 45 + ai-engine/models/v27/v27_lightgbm.pkl | Bin 0 -> 1891047 bytes ai-engine/models/v27/v27_metadata.json | 18 + ai-engine/models/v27/v27_ms_cb.pkl | Bin 0 -> 2355009 bytes ai-engine/models/v27/v27_ms_lgb.pkl | Bin 0 -> 3741609 bytes ai-engine/models/v27/v27_ms_xgb.pkl | Bin 0 -> 4167198 bytes ai-engine/models/v27/v27_ou25_cb.pkl | Bin 0 -> 1065255 bytes ai-engine/models/v27/v27_ou25_lgb.pkl | Bin 0 -> 730823 bytes ai-engine/models/v27/v27_ou25_xgb.pkl | Bin 0 -> 954628 bytes ai-engine/models/v27/v27_xgboost.pkl | Bin 0 -> 3363276 bytes 31 files changed, 2251 insertions(+) create mode 100644 ai-engine/models/basketball_v25/__init__.py create mode 100644 ai-engine/models/basketball_v25/feature_cols.json create mode 100644 ai-engine/models/basketball_v25/lgb_basketball_v25_ml.txt create mode 100644 ai-engine/models/basketball_v25/lgb_basketball_v25_spread.txt create mode 100644 ai-engine/models/basketball_v25/lgb_basketball_v25_total.txt create mode 100644 ai-engine/models/basketball_v25/xgb_basketball_v25_ml.json create mode 100644 ai-engine/models/basketball_v25/xgb_basketball_v25_spread.json create mode 100644 ai-engine/models/basketball_v25/xgb_basketball_v25_total.json create mode 100644 ai-engine/models/calibration/dc_calibrator.pkl create mode 100644 ai-engine/models/calibration/dc_metrics.json create mode 100644 ai-engine/models/calibration/ht_ft_calibrator.pkl create mode 100644 ai-engine/models/calibration/ht_ft_metrics.json create mode 100644 ai-engine/models/calibration/ou15_calibrator.pkl create mode 100644 ai-engine/models/calibration/ou15_metrics.json create mode 100644 ai-engine/models/calibration/ou25_calibrator.pkl create mode 100644 ai-engine/models/calibration/ou25_metrics.json create mode 100644 ai-engine/models/v27/v27_btts_lgb.pkl create mode 100644 ai-engine/models/v27/v27_btts_xgb.pkl create mode 100644 ai-engine/models/v27/v27_catboost.pkl create mode 100644 ai-engine/models/v27/v27_feature_cols.json create mode 100644 ai-engine/models/v27/v27_feature_importance.csv create mode 100644 ai-engine/models/v27/v27_lightgbm.pkl create mode 100644 ai-engine/models/v27/v27_metadata.json create mode 100644 ai-engine/models/v27/v27_ms_cb.pkl create mode 100644 ai-engine/models/v27/v27_ms_lgb.pkl create mode 100644 ai-engine/models/v27/v27_ms_xgb.pkl create mode 100644 ai-engine/models/v27/v27_ou25_cb.pkl create mode 100644 ai-engine/models/v27/v27_ou25_lgb.pkl create mode 100644 ai-engine/models/v27/v27_ou25_xgb.pkl create mode 100644 ai-engine/models/v27/v27_xgboost.pkl diff --git a/.gitignore b/.gitignore index ecb19f9..60268af 100644 --- a/.gitignore +++ b/.gitignore @@ -48,6 +48,9 @@ public/uploads/ ai-engine/models/* !ai-engine/models/*.py !ai-engine/models/v25/ +!ai-engine/models/v27/ +!ai-engine/models/basketball_v25/ +!ai-engine/models/calibration/ models/* !models/*.py colab_export/ diff --git a/ai-engine/models/basketball_v25/__init__.py b/ai-engine/models/basketball_v25/__init__.py new file mode 100644 index 0000000..16f0290 --- /dev/null +++ b/ai-engine/models/basketball_v25/__init__.py @@ -0,0 +1,356 @@ +""" +Basketball V25 Predictor Package +================================= +Loads trained XGBoost + LightGBM models for basketball market predictions: + - ML (Money Line — home / away win) + - Total (Over/Under total points) + - Spread (ATS home cover / away cover) + +Model files live in this directory: + xgb_basketball_v25_{market}.json — XGBoost (primary) + lgb_basketball_v25_{market}.txt — LightGBM (ensemble) + feature_cols.json — ordered feature list +""" + +from __future__ import annotations + +import json +import os +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + +# ── Constants ───────────────────────────────────────────────────────────────── +_DIR = os.path.dirname(os.path.abspath(__file__)) +_MARKETS = ("ml", "total", "spread") + + +# ── Output dataclass ────────────────────────────────────────────────────────── + +@dataclass +class BasketballMatchPrediction: + """Complete basketball match prediction output.""" + + match_id: str + home_team_name: str + away_team_name: str + league_name: str = "" + + # Money Line + ml_home_prob: float = 0.50 + ml_away_prob: float = 0.50 + ml_pick: str = "" + ml_confidence: float = 0.0 + + # Total (Over/Under) + total_line: float = 0.0 + total_over_prob: float = 0.50 + total_under_prob: float = 0.50 + total_pick: str = "" + total_confidence: float = 0.0 + + # Spread (ATS) + spread_home_line: float = 0.0 + spread_home_prob: float = 0.50 + spread_away_prob: float = 0.50 + spread_pick: str = "" + spread_confidence: float = 0.0 + + # Meta + model_version: str = "basketball_v25" + risk_level: str = "MEDIUM" + analysis_details: Dict[str, Any] = field(default_factory=dict) + market_board: Dict[str, Any] = field(default_factory=dict) + + def to_dict(self) -> Dict[str, Any]: + return { + "match_id": self.match_id, + "home_team": self.home_team_name, + "away_team": self.away_team_name, + "league": self.league_name, + "model": self.model_version, + "risk_level": self.risk_level, + "ml": { + "home_prob": round(self.ml_home_prob * 100, 1), + "away_prob": round(self.ml_away_prob * 100, 1), + "pick": self.ml_pick, + "confidence": round(self.ml_confidence, 1), + }, + "total": { + "line": self.total_line, + "over_prob": round(self.total_over_prob * 100, 1), + "under_prob": round(self.total_under_prob * 100, 1), + "pick": self.total_pick, + "confidence": round(self.total_confidence, 1), + }, + "spread": { + "home_line": self.spread_home_line, + "home_prob": round(self.spread_home_prob * 100, 1), + "away_prob": round(self.spread_away_prob * 100, 1), + "pick": self.spread_pick, + "confidence": round(self.spread_confidence, 1), + }, + "market_board": self.market_board, + "analysis_details": self.analysis_details, + } + + +# ── Predictor ───────────────────────────────────────────────────────────────── + +class BasketballV25Predictor: + """ + Ensemble basketball predictor using XGBoost + LightGBM models. + + Markets: + - ml → home/away win probability + - total → over/under total points + - spread → home/away ATS cover + """ + + def __init__(self) -> None: + self.feature_cols: List[str] = self._load_feature_cols() + self.models: Dict[str, Any] = {} + self._load_models() + print(f"✅ BasketballV25Predictor ready ({len(self.models)} models loaded)") + + # ── Setup ────────────────────────────────────────────────────────────── + + def _load_feature_cols(self) -> List[str]: + path = os.path.join(_DIR, "feature_cols.json") + try: + with open(path, "r") as f: + return json.load(f) + except Exception as e: + print(f"⚠️ [Basketball] Could not load feature_cols.json: {e}") + return [] + + def _load_models(self) -> None: + for market in _MARKETS: + xgb_path = os.path.join(_DIR, f"xgb_basketball_v25_{market}.json") + lgb_path = os.path.join(_DIR, f"lgb_basketball_v25_{market}.txt") + + xgb_model = self._try_load_xgb(xgb_path, market) + lgb_model = self._try_load_lgb(lgb_path, market) + + if xgb_model is not None or lgb_model is not None: + self.models[market] = {"xgb": xgb_model, "lgb": lgb_model} + + def _try_load_xgb(self, path: str, market: str) -> Optional[Any]: + if not os.path.exists(path): + return None + try: + import xgboost as xgb # type: ignore[import-not-found] + m = xgb.XGBClassifier() + m.load_model(path) + return m + except Exception as e: + print(f"⚠️ [Basketball] XGB {market} load failed: {e}") + return None + + def _try_load_lgb(self, path: str, market: str) -> Optional[Any]: + if not os.path.exists(path): + return None + try: + import lightgbm as lgb # type: ignore[import-not-found] + with open(path, "r", encoding="utf-8") as f: + model_str = f.read() + return lgb.Booster(model_str=model_str) + except Exception as e: + print(f"⚠️ [Basketball] LGB {market} load failed: {e}") + return None + + # ── Inference ────────────────────────────────────────────────────────── + + def _build_feature_row(self, odds_data: Dict[str, Any], **kwargs: Any) -> "Any": + """Build a single-row DataFrame aligned to training feature columns.""" + import pandas as pd # type: ignore[import-not-found] + + row: Dict[str, float] = {} + for col in self.feature_cols: + row[col] = float(kwargs.get(col) or odds_data.get(col) or 0.0) + + # Map common odds keys + row["ml_home_odds"] = float(odds_data.get("ml_h") or 0.0) + row["ml_away_odds"] = float(odds_data.get("ml_a") or 0.0) + row["total_line"] = float(odds_data.get("tot_line") or 0.0) + row["total_over_odds"] = float(odds_data.get("tot_o") or 0.0) + row["total_under_odds"] = float(odds_data.get("tot_u") or 0.0) + row["spread_home_line"] = float(odds_data.get("spread_home_line") or 0.0) + row["spread_home_odds"] = float(odds_data.get("spread_h") or 0.0) + row["spread_away_odds"] = float(odds_data.get("spread_a") or 0.0) + + # Implied probabilities + def _imp(odd: float) -> float: + return (1.0 / odd) if odd > 1.01 else 0.5 + + ml_h = row["ml_home_odds"] + ml_a = row["ml_away_odds"] + if ml_h > 1.01 and ml_a > 1.01: + raw = _imp(ml_h) + _imp(ml_a) + row["implied_home"] = _imp(ml_h) / raw + row["implied_away"] = _imp(ml_a) / raw + row["odds_overround"] = raw - 1.0 + + tot_o = row["total_over_odds"] + tot_u = row["total_under_odds"] + if tot_o > 1.01 and tot_u > 1.01: + raw = _imp(tot_o) + _imp(tot_u) + row["implied_total_over"] = _imp(tot_o) / raw + row["implied_total_under"] = _imp(tot_u) / raw + + sp_h = row["spread_home_odds"] + sp_a = row["spread_away_odds"] + if sp_h > 1.01 and sp_a > 1.01: + raw = _imp(sp_h) + _imp(sp_a) + row["implied_spread_home"] = _imp(sp_h) / raw + row["implied_spread_away"] = _imp(sp_a) / raw + + return pd.DataFrame([row]) + + def _ensemble_predict(self, market: str, df: "Any") -> List[float]: + """Return [p_class0, p_class1] from XGB+LGB ensemble.""" + models = self.models.get(market, {}) + xgb_model = models.get("xgb") + lgb_model = models.get("lgb") + + probs_list: List[List[float]] = [] + + if xgb_model is not None: + try: + p = xgb_model.predict_proba(df) + probs_list.append([float(p[0][0]), float(p[0][1])]) + except Exception as e: + print(f"⚠️ [Basketball] XGB {market} inference failed: {e}") + + if lgb_model is not None: + try: + p_raw = lgb_model.predict(df) + p1 = float(p_raw[0]) if len(p_raw.shape) == 1 else float(p_raw[0][1]) + probs_list.append([1.0 - p1, p1]) + except Exception as e: + print(f"⚠️ [Basketball] LGB {market} inference failed: {e}") + + if not probs_list: + return [0.5, 0.5] + + p0 = sum(p[0] for p in probs_list) / len(probs_list) + p1 = sum(p[1] for p in probs_list) / len(probs_list) + total = p0 + p1 or 1.0 + return [p0 / total, p1 / total] + + # ── Public API ───────────────────────────────────────────────────────── + + def predict( + self, + match_id: str, + home_team_id: str, + away_team_id: str, + home_team_name: str = "", + away_team_name: str = "", + league_id: str = "", + league_name: str = "", + odds_data: Optional[Dict[str, Any]] = None, + sidelined_data: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> BasketballMatchPrediction: + odds = odds_data or {} + prediction = BasketballMatchPrediction( + match_id=match_id, + home_team_name=home_team_name, + away_team_name=away_team_name, + league_name=league_name, + ) + + # Sidelined impact + home_sl = int((sidelined_data or {}).get("homeTeam", {}).get("totalSidelined", 0) or 0) + away_sl = int((sidelined_data or {}).get("awayTeam", {}).get("totalSidelined", 0) or 0) + kwargs.setdefault("home_sidelined_count", float(home_sl)) + kwargs.setdefault("away_sidelined_count", float(away_sl)) + kwargs.setdefault("sidelined_diff", float(home_sl - away_sl)) + kwargs.setdefault("missing_players_impact", float(home_sl + away_sl) / 10.0) + + if not self.models: + print("⚠️ [Basketball] No models loaded — returning neutral defaults") + return prediction + + try: + df = self._build_feature_row(odds, **kwargs) + + # ── ML ── + ml_probs = self._ensemble_predict("ml", df) + prediction.ml_home_prob = ml_probs[0] + prediction.ml_away_prob = ml_probs[1] + prediction.ml_pick = "1" if ml_probs[0] >= ml_probs[1] else "2" + prediction.ml_confidence = max(ml_probs) * 100.0 + + # ── Total ── + prediction.total_line = float(odds.get("tot_line") or 0.0) + tot_probs = self._ensemble_predict("total", df) + prediction.total_over_prob = tot_probs[1] + prediction.total_under_prob = tot_probs[0] + total_line = prediction.total_line + prediction.total_pick = ( + f"Over {total_line}" if tot_probs[1] >= tot_probs[0] else f"Under {total_line}" + ) + prediction.total_confidence = max(tot_probs) * 100.0 + + # ── Spread ── + prediction.spread_home_line = float(odds.get("spread_home_line") or 0.0) + sp_probs = self._ensemble_predict("spread", df) + prediction.spread_home_prob = sp_probs[0] + prediction.spread_away_prob = sp_probs[1] + home_line = prediction.spread_home_line + away_line = -home_line + prediction.spread_pick = ( + f"Home {home_line:+.1f}" if sp_probs[0] >= sp_probs[1] else f"Away {away_line:+.1f}" + ) + prediction.spread_confidence = max(sp_probs) * 100.0 + + # Market board summary + prediction.market_board = { + "ML": { + "1": f"{prediction.ml_home_prob * 100:.0f}%", + "2": f"{prediction.ml_away_prob * 100:.0f}%", + }, + "Totals": { + f"Over {total_line}": f"{prediction.total_over_prob * 100:.0f}%", + f"Under {total_line}": f"{prediction.total_under_prob * 100:.0f}%", + }, + "Spread": { + f"Home {home_line:+.1f}": f"{prediction.spread_home_prob * 100:.0f}%", + f"Away {away_line:+.1f}": f"{prediction.spread_away_prob * 100:.0f}%", + }, + } + + # Risk + top_conf = max(prediction.ml_confidence, prediction.total_confidence, prediction.spread_confidence) + prediction.risk_level = "LOW" if top_conf >= 65 else "MEDIUM" if top_conf >= 55 else "HIGH" + prediction.analysis_details = { + "model_version": "basketball_v25", + "markets_predicted": list(self.models.keys()), + "ensemble_size": {m: sum(1 for k in v.values() if v[k] is not None) for m, v in self.models.items()}, + } + + except Exception as e: + print(f"⚠️ [Basketball] Prediction failed for {match_id}: {e}") + + return prediction + + +# ── Singleton factory ────────────────────────────────────────────────────────── + +_predictor: Optional[BasketballV25Predictor] = None + + +def get_basketball_v25_predictor() -> BasketballV25Predictor: + """Return the singleton BasketballV25Predictor (lazy-loaded).""" + global _predictor + if _predictor is None: + _predictor = BasketballV25Predictor() + return _predictor + + +__all__ = [ + "BasketballMatchPrediction", + "BasketballV25Predictor", + "get_basketball_v25_predictor", +] diff --git a/ai-engine/models/basketball_v25/feature_cols.json b/ai-engine/models/basketball_v25/feature_cols.json new file mode 100644 index 0000000..e7d18de --- /dev/null +++ b/ai-engine/models/basketball_v25/feature_cols.json @@ -0,0 +1,101 @@ +[ + "home_overall_elo", + "away_overall_elo", + "elo_diff", + "home_home_elo", + "away_away_elo", + "home_form_elo", + "away_form_elo", + "home_form_score", + "away_form_score", + "form_score_diff", + "home_points_avg", + "away_points_avg", + "points_avg_diff", + "home_conceded_avg", + "away_conceded_avg", + "conceded_avg_diff", + "home_net_rating", + "away_net_rating", + "net_rating_diff", + "home_win_rate", + "away_win_rate", + "win_rate_diff", + "home_winning_streak", + "away_winning_streak", + "streak_diff", + "home_rest_days", + "away_rest_days", + "rest_diff", + "home_rebounds_avg", + "away_rebounds_avg", + "rebounds_diff", + "home_assists_avg", + "away_assists_avg", + "assists_diff", + "home_steals_avg", + "away_steals_avg", + "steals_diff", + "home_blocks_avg", + "away_blocks_avg", + "blocks_diff", + "home_turnovers_avg", + "away_turnovers_avg", + "turnovers_diff", + "home_fg_pct", + "away_fg_pct", + "fg_pct_diff", + "home_three_pt_pct", + "away_three_pt_pct", + "three_pt_pct_diff", + "home_ft_pct", + "away_ft_pct", + "ft_pct_diff", + "home_q1_avg", + "away_q1_avg", + "home_q4_avg", + "away_q4_avg", + "home_conc_rebounds_avg", + "away_conc_rebounds_avg", + "home_conc_assists_avg", + "away_conc_assists_avg", + "home_conc_turnovers_avg", + "away_conc_turnovers_avg", + "home_conc_fg_pct", + "away_conc_fg_pct", + "home_conc_three_pt_pct", + "away_conc_three_pt_pct", + "h2h_total_matches", + "h2h_home_win_rate", + "h2h_avg_points", + "h2h_avg_margin", + "h2h_over_total_rate", + "h2h_home_cover_rate", + "league_avg_points", + "league_home_win_rate", + "league_over_total_rate", + "league_home_cover_rate", + "ml_home_odds", + "ml_away_odds", + "implied_home", + "implied_away", + "total_line", + "total_over_odds", + "total_under_odds", + "implied_total_over", + "implied_total_under", + "spread_home_line", + "spread_home_odds", + "spread_away_odds", + "implied_spread_home", + "implied_spread_away", + "odds_overround", + "home_sidelined_count", + "away_sidelined_count", + "sidelined_diff", + "missing_players_impact", + "total_points_form", + "total_points_allowed_form", + "projected_total_delta_vs_line", + "projected_margin_vs_spread" +] \ No newline at end of file diff --git a/ai-engine/models/basketball_v25/lgb_basketball_v25_ml.txt b/ai-engine/models/basketball_v25/lgb_basketball_v25_ml.txt new file mode 100644 index 0000000..3732c98 --- /dev/null +++ b/ai-engine/models/basketball_v25/lgb_basketball_v25_ml.txt @@ -0,0 +1,655 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=98 +objective=binary sigmoid:1 +feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 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9.0104166666666696 4.4375000000000009 0.059692514253813854 39.812500000000007 0.045203152003977003 0.023949864400422651 0.52433570113128136 0.36314281963351264 0.78248216953695626 -13.09523809523809 0.51328944240770225 -16.645833333333325 -0.0072528547698737485 85.062500000000014 1.0937500000000002 0.19841107122821819 3.1550000000000007 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=12 2 6 9 5 7 16 8 -3 15 -9 17 14 -14 -1 -4 -2 -8 -16 -18 +right_child=1 4 3 -5 -6 -7 11 10 -10 -11 -12 -13 13 -15 18 -17 19 -19 -20 -21 +leaf_value=0.013957264641046819 -0.027472132914840498 -0.0020934000360734932 0.022613724189803789 0.050832305755293579 0.037916740044630914 0.040588735510979898 0.027960766295875104 -0.003405364332683979 -0.031143520168862283 -0.05384418391600191 0.040275812485981034 -0.044330514984326927 0.030259337595429918 -0.015671661984799638 0.062645279427693656 -0.020045453140862098 -0.066399946636320761 -0.01585144878027565 0.043445571662247148 -0.058570658366565144 +leaf_weight=7.1345093548297873 8.8107999712228793 87.334490463137627 6.9631915688514745 6.7626315653324118 9.8303922265768033 6.9817947149276725 5.6945501416921633 11.879387885332109 17.382111921906471 5.3163712173700324 11.272227853536604 5.8104494661092749 7.708228871226309 5.7933596074581146 6.5536312311887803 7.778242379426958 5.6191599071025831 5.7529874593019485 10.513165161013601 5.361001193523407 +leaf_count=31 39 362 29 28 40 29 24 49 72 24 46 25 33 25 29 33 24 25 47 25 +internal_value=0.00160475 -0.00342443 -0.0165911 0.00220167 0.002388 -0.000201998 -0.0301955 -0.00242923 -0.00691549 -0.0141945 0.0178624 -0.0109833 0.0294231 0.0105509 0.0399517 0.000104827 -0.0469487 0.00594283 0.0508182 -0.0625773 +internal_weight=246.253 208.55 63.8694 26.8204 144.68 134.85 37.0489 127.868 104.717 20.0578 23.1516 17.258 37.7029 13.5016 24.2013 14.7414 19.791 11.4475 17.0668 10.9802 +internal_count=1039 874 276 114 598 558 162 529 434 86 95 74 165 58 107 62 88 49 76 49 +is_linear=0 +shrinkage=0.04 + + +Tree=21 +num_leaves=21 +num_cat=0 +split_feature=77 85 42 26 25 51 29 88 90 36 49 14 9 18 61 43 13 59 90 77 +split_gain=20.02 9.2622 9.52085 12.2814 7.88967 7.82256 7.04841 6.6998 6.53054 7.87452 5.68149 5.30824 4.42523 4.07084 4.07033 4.04241 3.62375 3.25279 0.898801 0.0879165 +threshold=1.3050000000000004 -5.9999999999999991 1.0000000180025095e-35 9.0104166666666696 1.8541666666666667 0.080538861184771923 39.812500000000007 0.52433570113128136 0.19969875129044842 -1.0875000000000001 0.78248216953695626 118.93750000000001 -13.09523809523809 -16.645833333333325 13.816666666666668 0.51328944240770225 85.062500000000014 23.535714285714288 0.19841107122821819 3.1550000000000007 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=12 2 6 7 -3 8 16 14 11 -10 17 -6 13 -1 -4 -14 -2 -8 -15 -18 +right_child=1 4 3 -5 5 -7 10 -9 9 -11 -12 -13 15 18 -16 -17 19 -19 -20 -21 +leaf_value=0.013447805314569216 -0.026577780088672762 0.034080468035460186 0.017293051569916593 0.048557724592127684 0.007825469574374657 0.040280280315047955 -0.016199932862450934 -0.052623204127446775 -0.047796064224015833 -0.0064826859346349495 -0.043040720837339654 -0.024990244369414837 0.029249467101270982 0.061303641269563863 -0.025610761209309305 -0.014994478156697251 -0.064751343135519002 0.026458734247185672 0.042272167151271647 -0.057522795432648433 +leaf_weight=7.10912762582302 8.7443605959415454 11.499579980969427 8.8282703608274513 6.7955194860696784 70.869819477200508 7.13891777396202 5.5701393187046069 5.2240076065063468 9.2205274105072004 37.017053097486496 5.7464897632598868 8.8745380789041501 7.6564510315656644 6.4316389113664689 5.9041270762681952 5.8124003708362579 5.5334616154432279 5.8780088722705841 10.375598087906836 5.2427052408456802 +leaf_count=31 39 47 37 28 292 29 24 24 39 154 25 37 33 29 25 25 24 25 47 25 +internal_value=0.00154432 -0.00329759 -0.0160366 0.0021131 0.00229294 -0.000453006 -0.029261 -0.0137021 -0.00276121 -0.0147212 -0.0105873 0.0041735 0.0284946 0.0388221 9.90026e-05 0.0101563 -0.0457098 0.005703 0.049555 -0.0612346 +internal_weight=245.473 208.088 63.4671 26.7519 144.62 133.121 36.7152 19.9564 125.982 46.2376 17.1946 79.7444 37.3852 23.9164 14.7324 13.4689 19.5205 11.4481 16.8072 10.7762 +internal_count=1039 874 276 114 598 551 162 86 522 193 74 329 165 107 62 58 88 49 76 49 +is_linear=0 +shrinkage=0.04 + + +Tree=22 +num_leaves=18 +num_cat=0 +split_feature=77 78 25 48 51 25 9 35 9 48 27 30 9 18 43 50 72 +split_gain=18.621 8.70394 7.91958 7.02031 6.21305 7.48529 6.53645 5.17821 7.11278 5.11576 6.50119 6.92842 4.18572 3.88527 3.74781 0.796232 0.173612 +threshold=1.3050000000000004 0.69515163347650677 1.7395833333333337 0.059692514253813854 0.043626967279394004 7.8802083333333348 8.333333333333341 6.6937500000000005 8.7121212121212164 -0.018202373884811395 0.98958333333333315 0.22500000000000142 -13.09523809523809 -16.645833333333325 0.51328944240770225 0.74854124348714812 225.04250000000002 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=12 2 -2 4 6 -6 -4 8 -3 -9 11 -11 13 -1 -14 -15 -17 +right_child=1 7 3 -5 5 -7 -8 9 -10 10 -12 -13 14 15 -16 16 -18 +leaf_value=0.012956313378187341 0.036677286509223014 0.027307971594871701 -0.0022506819015889979 0.032532340700343249 0.001223625287223337 0.048438323769350811 -0.026039625377917281 -0.053747110254001744 -0.024750955618137494 -0.0073465850093818175 0.015119548741830309 -0.055928592960106001 0.028278098346949592 0.036190897477542942 -0.014350124084243559 0.059344625765782907 0.049204905261110286 +leaf_weight=7.0841730237007132 9.6115598380565626 8.2046131193637866 86.82922887802124 9.458725377917288 19.787809953093532 7.3747483491897574 23.477235645055771 8.3150889277458173 8.6016630232334119 10.108060508966451 7.039140820503234 8.7735196799039823 7.6037576496601087 5.6597618609666887 5.8300840854644775 5.8845966756343824 4.9951938986778259 +leaf_count=31 39 35 359 39 81 30 97 39 39 45 31 40 33 25 25 27 24 +internal_value=0.00148631 -0.00317645 0.00150065 -0.0008005 -0.00309401 0.0140426 -0.00731384 -0.0175205 0.000663559 -0.026447 -0.0176894 -0.0299207 0.0276052 0.0377427 0.00977809 0.0483592 0.0546892 +internal_weight=244.639 207.581 156.539 146.928 137.469 27.1626 110.306 51.0421 16.8063 34.2358 25.9207 18.8816 37.0576 23.6237 13.4338 16.5396 10.8798 +internal_count=1039 874 645 606 567 111 456 229 74 155 116 85 165 107 58 76 51 +is_linear=0 +shrinkage=0.04 + + +end of trees + +feature_importances: +Column_77=25 +Column_46=15 +Column_76=15 +Column_97=14 +Column_43=13 +Column_50=13 +Column_49=12 +Column_27=11 +Column_78=11 +Column_9=10 +Column_26=10 +Column_51=10 +Column_55=10 +Column_61=10 +Column_17=9 +Column_18=8 +Column_25=8 +Column_36=8 +Column_42=8 +Column_52=8 +Column_74=8 +Column_75=8 +Column_81=8 +Column_33=7 +Column_40=7 +Column_48=7 +Column_73=7 +Column_86=7 +Column_10=6 +Column_31=6 +Column_32=6 +Column_44=6 +Column_57=6 +Column_88=6 +Column_13=5 +Column_29=5 +Column_35=5 +Column_60=5 +Column_64=5 +Column_85=5 +Column_90=5 +Column_15=4 +Column_23=4 +Column_30=4 +Column_37=4 +Column_38=4 +Column_41=4 +Column_45=4 +Column_47=4 +Column_54=4 +Column_58=4 +Column_59=4 +Column_62=4 +Column_65=4 +Column_83=4 +Column_95=4 +Column_98=4 +Column_8=3 +Column_12=3 +Column_63=3 +Column_72=3 +Column_82=3 +Column_7=2 +Column_11=2 +Column_14=2 +Column_16=2 +Column_56=2 +Column_68=2 +Column_21=1 +Column_22=1 +Column_28=1 +Column_34=1 +Column_39=1 +Column_53=1 +Column_69=1 +Column_79=1 +Column_89=1 +Column_96=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1200] +[learning_rate: 0.04] +[num_leaves: 31] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 6] +[min_data_in_leaf: 24] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.84] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 5] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.82] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 30056] +[extra_trees: 0] +[extra_seed: 12879] +[early_stopping_round: 0] +[early_stopping_min_delta: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0] +[lambda_l2: 0] +[linear_lambda: 0] +[min_gain_to_split: 0] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 17869] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[monotone_constraints_method: basic] +[monotone_penalty: 0] +[feature_contri: ] +[forcedsplits_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[path_smooth: 0] +[interaction_constraints: ] +[verbosity: -1] +[saved_feature_importance_type: 0] +[use_quantized_grad: 0] +[num_grad_quant_bins: 4] +[quant_train_renew_leaf: 0] +[stochastic_rounding: 1] +[linear_tree: 0] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[data_random_seed: 175] +[is_enable_sparse: 1] +[enable_bundle: 1] +[use_missing: 1] +[zero_as_missing: 0] +[feature_pre_filter: 1] +[pre_partition: 0] +[two_round: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[forcedbins_filename: ] +[precise_float_parser: 0] +[parser_config_file: ] +[objective_seed: 16083] +[num_class: 1] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[lambdarank_truncation_level: 30] +[lambdarank_norm: 1] +[label_gain: ] +[lambdarank_position_bias_regularization: 0] +[eval_at: ] +[multi_error_top_k: 1] +[auc_mu_weights: ] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] +[num_gpu: 1] + +end of parameters + +pandas_categorical:null diff --git a/ai-engine/models/basketball_v25/lgb_basketball_v25_spread.txt b/ai-engine/models/basketball_v25/lgb_basketball_v25_spread.txt new file mode 100644 index 0000000..ec0cbd2 --- /dev/null +++ b/ai-engine/models/basketball_v25/lgb_basketball_v25_spread.txt @@ -0,0 +1,790 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=98 +objective=binary sigmoid:1 +feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 +feature_infos=none none none none none none none [0:100] [0:100] [-100:100] [54:128.875] [61:138] [-41.333333333333329:24] [61:130.125] [59:133] [-50:34.916666666666671] [-28.666666666666657:32.5] [-24.5:27] [-35.5:31.833333333333329] [0:1] [0:1] [-1:1] [0:12] [0:12] [-12:10] [0.91666666666666663:186.1875] [0.85416666666666663:185.08333333333337] [-179.02083333333334:184.16666666666663] [20:54] [22:56] [-21:15.833333333333332] [11:34.5] [9:36] [-15:13.333333333333332] [2:13] [3:13] [-8:5.5] [0.875:9] [0:12] [-9:4.875] [7:22] [8:23] [-10:10] [0.41025641025641019:0.67647058823529416] [0.39730639730639727:0.76744186046511631] [-0.3174418604651163:0.22647058823529409] [0.20419254658385089:0.4825000000000001] [0.1875:0.54166666666666663] [-0.22171945701357459:0.17923280423280419] [0.47826086956521741:0.87898457080200498] [0.42857142857142849:0.88636363636363635] [-0.29647435897435898:0.38962801311591622] [11:39.666666666666664] [15.5:40] [10:31.875] [11.5:34] [15:56] [17:57] [12:32.125] [10:38] [8:20] [7:22] [0.40000000000000002:0.69047619047619047] [0.3902439024390244:0.71739130434782605] [0.23753976670201479:0.51851851851851849] [0.1707317073170731:0.54545454545454541] [0:8] [0:1] [120:295] [-38:36] [0:1] [0:1] [140.39130434782609:233.09999999999999] 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124.817 99.2441 25.573 21.6318 47.1584 15.6549 12.2265 +internal_count=1026 983 894 275 243 619 104 515 408 107 89 193 64 50 +is_linear=0 +shrinkage=0.04 + + +Tree=26 +num_leaves=13 +num_cat=0 +split_feature=48 87 75 13 13 26 98 83 54 28 63 26 +split_gain=6.07316 5.62173 8.54781 6.21306 5.95377 6.62941 7.29602 6.07248 5.64674 6.51449 5.46195 3.27744 +threshold=0.064872666259492118 1.5850000000000002 0.69045454545454554 104.77500000000002 120.56250000000001 15.072916666666668 -11.562499999999998 0.49675474226372424 20.535714285714288 40.187500000000007 0.51085383914331295 6.072916666666667 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 3 4 -1 5 6 -3 -7 -4 11 -2 -10 +right_child=10 2 8 -5 -6 7 -8 -9 9 -11 -12 -13 +leaf_value=-0.039674658535771484 -0.052512934017087537 -0.014023176104202862 -0.005798976431383154 0.0036351517375994916 -0.026305412473366974 0.0062814492481233046 0.010148203473004854 -0.041049983242601396 0.028561765576098901 0.0087152875473318417 -0.0029143682076594587 0.066287713324515871 +leaf_weight=9.8422116041183489 6.0319006294012061 23.719328477978706 7.8761165887117377 11.48290069401264 11.350633054971693 7.9110089093446758 126.75102043151855 9.5997582674026471 6.5257693231105831 10.448119834065436 8.6423251926898939 8.462289631366728 +leaf_count=41 25 97 32 47 47 33 522 41 27 43 36 35 +internal_value=0.00166 0.00322559 0.00518911 -0.0163537 0.00173262 0.00362718 0.00633796 -0.0196666 0.0237966 0.0329606 -0.0233021 0.0498619 +internal_weight=248.643 233.969 212.644 21.3251 179.332 167.981 150.47 17.5108 33.3123 25.4362 14.6742 14.9881 +internal_count=1026 965 877 88 740 693 619 74 137 105 61 62 +is_linear=0 +shrinkage=0.04 + + +Tree=27 +num_leaves=15 +num_cat=0 +split_feature=33 40 43 64 56 29 74 7 16 12 36 53 32 33 +split_gain=6.67537 6.00723 6.58775 6.4086 6.22146 8.22654 7.17113 5.57557 10.8982 10.7588 6.64454 7.95563 6.24334 4.86842 +threshold=5.4583333333333348 10.937500000000002 0.53985767334155887 0.34495732674280544 30.562500000000004 32.791666666666679 0.62250000000000016 59.166666666666679 3.7916666666666647 9.1339285714285712 -0.34166666666666673 22.387500000000003 23.791666666666668 -0.29166666666666602 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 3 7 -1 -4 -6 -7 8 9 -3 12 -12 -9 -5 +right_child=-2 2 4 13 5 6 -8 10 -10 -11 11 -13 -14 -15 +leaf_value=-0.04539631182232725 -0.030331123415790143 0.0036500818553591068 -0.017115468469559102 -0.026046588004955247 0.065457960355147352 0.02547423439415672 -0.018488966847576102 0.017724882848208653 -0.041424367213409224 -0.047501812723565191 -0.0045522301063454888 0.04416916038732871 -0.038718688099103261 0.024752126334750945 +leaf_weight=8.1224037855863589 10.053963825106619 115.14034233987331 6.9894797801971427 5.7684260606765747 6.3827460259199134 27.928439050912857 7.5389710813760749 6.7571853250265104 10.671643391251562 6.9777127951383582 8.1633969545364398 15.628093481063841 5.8501180559396744 6.3321559280157071 +leaf_count=34 43 476 29 24 26 114 31 28 44 29 34 64 24 26 +internal_value=0.00159555 0.00294283 0.00487723 -0.0179123 0.0178183 0.0236527 0.0161294 0.00114151 -0.00266021 0.00072731 0.0150109 0.0274518 -0.00846641 0.000536049 +internal_weight=248.305 238.251 218.028 20.223 48.8396 41.8502 35.4674 169.188 132.79 122.118 36.3988 23.7915 12.6073 12.1006 +internal_count=1026 983 899 84 200 171 145 699 549 505 150 98 52 50 +is_linear=0 +shrinkage=0.04 + + +Tree=28 +num_leaves=13 +num_cat=0 +split_feature=33 27 14 48 24 53 59 57 34 47 81 50 +split_gain=6.19142 5.48502 8.27407 6.44704 5.11208 5.11786 5.55155 5.07182 5.97421 8.76085 8.56983 3.46147 +threshold=5.4583333333333348 -2.3020833333333326 85.312500000000014 0.064872666259492118 -1.0000000180025095e-35 21.291666666666668 23.387500000000003 45.687500000000007 6.8875000000000011 0.37218684558476361 1.7150000000000001 0.74075516458343793 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 2 11 7 -4 -6 -7 8 10 -10 -3 -1 +right_child=-2 3 4 -5 5 6 -8 -9 9 -11 -12 -13 +leaf_value=0.036467676081935275 -0.029325034647802492 0.012704755728250297 -0.051764929617036118 -0.023669451716433799 -0.044508533316403635 0.026446805663908529 -0.027271352696527536 -0.022833580306035422 0.022463638898768467 -0.0033374130186977686 -0.014458359978742813 -0.0028572785771652266 +leaf_weight=6.0484680682420722 9.9843682050704938 40.375474900007248 7.6424536705017116 11.367303296923636 5.5812820345163372 6.6618274450302106 5.7221347242593765 8.4949828088283521 73.357022643089294 29.534479916095734 34.431760281324387 8.7800204604864103 +leaf_count=25 43 166 32 47 24 28 24 35 302 121 143 36 +internal_value=0.0015332 0.00282775 -0.0105946 0.005575 -0.0243635 -0.0127068 0.00162579 0.0073604 0.00880384 0.0150576 0.000202303 0.0131832 +internal_weight=247.982 237.997 40.4362 197.561 25.6077 17.9652 12.384 186.194 177.699 102.892 74.8072 14.8285 +internal_count=1026 983 169 814 108 76 52 767 732 423 309 61 +is_linear=0 +shrinkage=0.04 + + +Tree=29 +num_leaves=17 +num_cat=0 +split_feature=87 85 10 83 41 73 86 39 8 44 13 76 61 36 97 45 +split_gain=5.37924 8.77104 7.1461 6.5763 6.24618 5.74552 7.24104 5.90731 7.82727 5.90939 5.68942 4.92086 12.7968 7.49136 4.69159 2.29423 +threshold=1.5850000000000002 1.0000000180025095e-35 117.22500000000001 0.54234668644906048 10.812500000000002 0.53158244680851074 1.7450000000000003 -0.61249999999999993 47.727272727272734 0.48331411181158185 104.77500000000002 2.4650000000000003 12.866071428571431 0.28869047619047633 1.4062500000000002 -0.013905145374354397 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=10 4 3 11 -2 6 -6 8 -7 -9 -1 13 -13 -3 -12 -5 +right_child=1 2 -4 15 5 7 -8 9 -10 -11 14 12 -14 -15 -16 -17 +leaf_value=-0.039294680659493833 -0.031671221811972641 0.037455766277146953 -0.018600965920339822 0.063774142191859146 0.050889907105456336 -0.036046187526128669 -0.0011468294092016626 -0.016947588402893 -0.00031638211057623142 0.0084049263294318916 0.026762860869410491 0.035225255717854904 -0.041754796373233906 -0.00065805839704901963 -0.023845024484234915 0.029272484285479398 +leaf_weight=10.1972071826458 10.532053381204603 19.837578102946281 8.7409028112888318 6.0329279154539108 7.6620817482471493 16.978984668850899 9.6890967190265638 18.620566442608833 23.233949556946754 70.049215823411942 5.8855016827583295 6.3545227348804456 7.5724885612726212 14.127186641097067 5.8383642733097076 6.3081436455249769 +leaf_count=43 44 82 36 25 32 71 40 78 96 290 24 26 31 58 24 26 +internal_value=0.0014732 0.00331024 0.0151971 0.0201018 -0.00191971 0.000223048 0.021832 -0.00268611 -0.0154025 0.00308092 -0.0174444 0.0133924 -0.00663086 0.0216028 0.00156066 0.0461386 +internal_weight=247.661 225.74 68.9738 60.2328 156.766 146.234 17.3512 128.883 40.2129 88.6698 21.9211 47.8918 13.927 33.9648 11.7239 12.3411 +internal_count=1026 935 284 248 651 607 72 535 167 368 91 197 57 140 48 51 +is_linear=0 +shrinkage=0.04 + + +end of trees + +feature_importances: +Column_33=16 +Column_55=14 +Column_75=13 +Column_95=13 +Column_17=12 +Column_27=12 +Column_40=12 +Column_60=12 +Column_88=12 +Column_87=11 +Column_11=10 +Column_97=10 +Column_14=9 +Column_36=9 +Column_53=9 +Column_64=9 +Column_86=9 +Column_31=8 +Column_54=8 +Column_73=8 +Column_15=7 +Column_26=7 +Column_29=7 +Column_39=7 +Column_62=7 +Column_63=7 +Column_9=6 +Column_10=6 +Column_12=6 +Column_18=6 +Column_28=6 +Column_51=6 +Column_61=6 +Column_77=6 +Column_13=5 +Column_24=5 +Column_35=5 +Column_38=5 +Column_46=5 +Column_48=5 +Column_58=5 +Column_65=5 +Column_74=5 +Column_80=5 +Column_8=4 +Column_30=4 +Column_32=4 +Column_41=4 +Column_43=4 +Column_45=4 +Column_49=4 +Column_72=4 +Column_82=4 +Column_83=4 +Column_16=3 +Column_25=3 +Column_34=3 +Column_47=3 +Column_50=3 +Column_52=3 +Column_56=3 +Column_59=3 +Column_79=3 +Column_81=3 +Column_85=3 +Column_89=3 +Column_90=3 +Column_98=3 +Column_37=2 +Column_42=2 +Column_57=2 +Column_69=2 +Column_76=2 +Column_96=2 +Column_7=1 +Column_21=1 +Column_44=1 +Column_66=1 +Column_68=1 +Column_71=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1200] +[learning_rate: 0.04] +[num_leaves: 31] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 6] +[min_data_in_leaf: 24] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.84] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 5] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.82] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 30056] +[extra_trees: 0] +[extra_seed: 12879] +[early_stopping_round: 0] +[early_stopping_min_delta: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0] +[lambda_l2: 0] +[linear_lambda: 0] +[min_gain_to_split: 0] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 17869] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[monotone_constraints_method: basic] +[monotone_penalty: 0] +[feature_contri: ] +[forcedsplits_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[path_smooth: 0] +[interaction_constraints: ] +[verbosity: -1] +[saved_feature_importance_type: 0] +[use_quantized_grad: 0] +[num_grad_quant_bins: 4] +[quant_train_renew_leaf: 0] +[stochastic_rounding: 1] +[linear_tree: 0] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[data_random_seed: 175] +[is_enable_sparse: 1] +[enable_bundle: 1] +[use_missing: 1] +[zero_as_missing: 0] +[feature_pre_filter: 1] +[pre_partition: 0] +[two_round: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[forcedbins_filename: ] +[precise_float_parser: 0] +[parser_config_file: ] +[objective_seed: 16083] +[num_class: 1] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[lambdarank_truncation_level: 30] +[lambdarank_norm: 1] +[label_gain: ] +[lambdarank_position_bias_regularization: 0] +[eval_at: ] +[multi_error_top_k: 1] +[auc_mu_weights: ] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] +[num_gpu: 1] + +end of parameters + +pandas_categorical:null diff --git a/ai-engine/models/basketball_v25/lgb_basketball_v25_total.txt b/ai-engine/models/basketball_v25/lgb_basketball_v25_total.txt new file mode 100644 index 0000000..3b4e89d --- /dev/null +++ b/ai-engine/models/basketball_v25/lgb_basketball_v25_total.txt @@ -0,0 +1,202 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=98 +objective=binary sigmoid:1 +feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 +feature_infos=none none none none none none none [0:100] [0:100] [-100:100] [54:128.875] [61:138] [-41.333333333333329:24] [61:130.125] [59:133] [-50:34.916666666666671] [-28.666666666666657:32.5] [-24.5:27] [-35.5:31.833333333333329] [0:1] [0:1] [-1:1] [0:12] [0:12] [-12:10] [0.91666666666666663:186.1875] [0.85416666666666663:185.08333333333337] [-179.02083333333334:184.16666666666663] [20:54] [22:56] [-21:15.833333333333332] [11:34.5] [9:36] [-15:13.333333333333332] [2:13] [3:13] [-8:5.5] [0.875:9] [0:12] [-9:4.875] [7:22] [8:23] [-10:10] [0.41025641025641019:0.67647058823529416] [0.39730639730639727:0.76744186046511631] [-0.3174418604651163:0.22647058823529409] [0.20419254658385089:0.4825000000000001] [0.1875:0.54166666666666663] [-0.22171945701357459:0.17923280423280419] [0.47826086956521741:0.87898457080200498] [0.42857142857142849:0.88636363636363635] [-0.29647435897435898:0.38962801311591622] [11:39.666666666666664] [15.5:40] [10:31.875] [11.5:34] [15:56] [17:57] [12:32.125] [10:38] [8:20] [7:22] [0.40000000000000002:0.69047619047619047] [0.3902439024390244:0.71739130434782605] [0.23753976670201479:0.51851851851851849] [0.1707317073170731:0.54545454545454541] [0:8] [0:1] [120:295] [-38:36] [0:1] [0:1] [140.39130434782609:233.09999999999999] [0:1] [0:1] [0:1] [1.05:4.1100000000000003] [1.05:4.1299999999999999] [0.2034883720930232:0.79729729729729726] [0.20270270270270269:0.79651162790697683] [145.5:257.5] [1.3:7.7599999999999998] [1.4099999999999999:7.5599999999999996] [0.1789976133651551:0.75977653631284914] [0.24022346368715081:0.82100238663484482] [-15.5:12.5] [1.3:2.7200000000000002] [1.1799999999999999:2.7799999999999998] [0.36298076923076922:0.64953271028037374] [0.35046728971962621:0.63701923076923073] [0.1918833727344364:0.21555133935749429] none none none none [132:250.6875] [126.25:256.625] [-63.5:88.6875] [-35.5:27.5] +tree_sizes=1714 1917 + +Tree=0 +num_leaves=15 +num_cat=0 +split_feature=72 82 49 25 36 25 17 77 34 69 25 40 80 40 +split_gain=11.2305 10.9895 14.5557 11.7291 11.5678 6.991 6.42947 6.52716 10.0318 9.34909 5.86638 5.89396 5.34606 8.20903 +threshold=232.51500000000001 1.8250000000000004 0.74446554478882077 2.8645833333333335 -0.46874999999999994 1.0104166666666667 12.562500000000002 2.6750000000000003 5.6339285714285721 -5.4166666666666652 3.1145833333333335 12.803571428571431 162.00000000000003 13.937500000000002 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 5 10 4 -4 -1 7 8 -7 -9 -3 -12 -5 -14 +right_child=-2 2 3 12 -6 6 -8 9 -10 -11 11 -13 13 -15 +leaf_value=-0.014742735215146609 -0.095823069621212592 -0.030752227995962191 -0.086785452728816673 0.017792685597478597 -0.021483574280753166 -0.0010203128315903962 -0.085759203191584921 -0.020079232808751797 -0.05043362279072628 -0.074223259488395713 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+[feature_pre_filter: 1] +[pre_partition: 0] +[two_round: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[forcedbins_filename: ] +[precise_float_parser: 0] +[parser_config_file: ] +[objective_seed: 16083] +[num_class: 1] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[lambdarank_truncation_level: 30] +[lambdarank_norm: 1] +[label_gain: ] +[lambdarank_position_bias_regularization: 0] +[eval_at: ] +[multi_error_top_k: 1] +[auc_mu_weights: ] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] +[num_gpu: 1] + +end of parameters + +pandas_categorical:null diff --git a/ai-engine/models/basketball_v25/xgb_basketball_v25_ml.json b/ai-engine/models/basketball_v25/xgb_basketball_v25_ml.json new file mode 100644 index 0000000..4be096d --- /dev/null +++ b/ai-engine/models/basketball_v25/xgb_basketball_v25_ml.json @@ -0,0 +1 @@ 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