diff --git a/.gitignore b/.gitignore index 6085122..1e72b00 100644 --- a/.gitignore +++ b/.gitignore @@ -44,6 +44,7 @@ public/uploads/ # Large Datasets and ML Models ai-engine/models/* !ai-engine/models/*.py +!ai-engine/models/v25/ models/* !models/*.py colab_export/ diff --git a/ai-engine/main.py b/ai-engine/main.py index 323117a..6caa455 100755 --- a/ai-engine/main.py +++ b/ai-engine/main.py @@ -7,11 +7,14 @@ import time from contextlib import asynccontextmanager from typing import Any +from datetime import datetime + import uvicorn from dotenv import load_dotenv from fastapi import FastAPI, HTTPException, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse +import subprocess from pydantic import BaseModel try: @@ -38,6 +41,23 @@ class CouponRequest(BaseModel): min_confidence: float | None = None +class RetrainRequest(BaseModel): + reason: str | None = "manual" + markets: str | None = None # comma-separated, e.g. "MS,OU25,BTTS" + trials: int | None = 50 + + +# ─── Retrain state tracking ────────────────────────────────── +_retrain_state: dict[str, Any] = { + "running": False, + "last_started": None, + "last_completed": None, + "last_status": None, + "last_error": None, + "pid": None, +} + + @asynccontextmanager async def lifespan(_: FastAPI): try: @@ -115,6 +135,8 @@ def read_root() -> dict[str, Any]: "GET /v20plus/reversal-watchlist", "POST /v20plus/coupon", "GET /v20plus/daily-banker", + "POST /v1/admin/retrain", + "GET /v1/admin/retrain/status", ], } @@ -298,6 +320,111 @@ async def get_reversal_watchlist_v20plus( ) +# ─── ADMIN: Retrain Pipeline ───────────────────────────────── + +def _run_retrain_pipeline(markets: str | None, trials: int): + """Background function: extract data → train model → reload.""" + global _retrain_state + ai_dir = os.path.dirname(os.path.abspath(__file__)) + scripts_dir = os.path.join(ai_dir, "scripts") + python = os.path.join(ai_dir, "venv", "bin", "python3") + if not os.path.exists(python): + python = sys.executable # fallback + + try: + # Step 1: Extract training data + print("🔄 [RETRAIN] Step 1/3: Extracting training data...", flush=True) + result = subprocess.run( + [python, os.path.join(scripts_dir, "extract_training_data.py")], + capture_output=True, text=True, timeout=600, cwd=ai_dir, + ) + if result.returncode != 0: + raise RuntimeError(f"Extract failed:\n{result.stderr[-500:]}") + print(f"✅ [RETRAIN] Extract done", flush=True) + + # Step 2: Train V25 Pro + print("🔄 [RETRAIN] Step 2/3: Training V25 Pro model...", flush=True) + train_cmd = [python, os.path.join(scripts_dir, "train_v25_pro.py")] + if markets: + train_cmd += ["--markets", markets] + train_cmd += ["--trials", str(trials)] + + result = subprocess.run( + train_cmd, capture_output=True, text=True, timeout=3600, cwd=ai_dir, + ) + if result.returncode != 0: + raise RuntimeError(f"Training failed:\n{result.stderr[-500:]}") + print(f"✅ [RETRAIN] Training done", flush=True) + + # Step 3: Reload models in memory + print("🔄 [RETRAIN] Step 3/3: Reloading models...", flush=True) + try: + orchestrator = get_single_match_orchestrator() + v25 = orchestrator._get_v25_predictor() + v25._loaded = False + v25.load_models() + print("✅ [RETRAIN] Models reloaded in memory", flush=True) + except Exception as reload_err: + print(f"⚠️ [RETRAIN] Hot reload failed (restart needed): {reload_err}", flush=True) + + _retrain_state.update({ + "running": False, + "last_completed": datetime.now().isoformat(), + "last_status": "success", + "last_error": None, + }) + print("🎉 [RETRAIN] Pipeline complete!", flush=True) + + except Exception as err: + _retrain_state.update({ + "running": False, + "last_completed": datetime.now().isoformat(), + "last_status": "failed", + "last_error": str(err), + }) + print(f"❌ [RETRAIN] Pipeline failed: {err}", flush=True) + + +@app.post("/v1/admin/retrain") +async def admin_retrain(request: RetrainRequest) -> dict[str, Any]: + """Trigger full retrain pipeline: extract → train → reload.""" + if _retrain_state["running"]: + return { + "status": "already_running", + "message": f"Retrain in progress since {_retrain_state['last_started']}", + } + + _retrain_state.update({ + "running": True, + "last_started": datetime.now().isoformat(), + "last_status": "running", + "last_error": None, + }) + + # Run in background thread + import threading + thread = threading.Thread( + target=_run_retrain_pipeline, + args=(request.markets, request.trials or 50), + daemon=True, + ) + thread.start() + + return { + "status": "triggered", + "message": "Retrain pipeline started in background", + "reason": request.reason, + "markets": request.markets or "all", + "trials": request.trials or 50, + } + + +@app.get("/v1/admin/retrain/status") +async def admin_retrain_status() -> dict[str, Any]: + """Check retrain pipeline status.""" + return {**_retrain_state} + + if __name__ == "__main__": port = int(os.getenv("PORT", "8000")) uvicorn.run("main:app", host="0.0.0.0", port=port, reload=True) diff --git a/ai-engine/models/v25/feature_cols.json b/ai-engine/models/v25/feature_cols.json new file mode 100644 index 0000000..8503c3e --- /dev/null +++ b/ai-engine/models/v25/feature_cols.json @@ -0,0 +1,154 @@ +[ + "home_overall_elo", + "away_overall_elo", + "elo_diff", + "home_home_elo", + "away_away_elo", + "home_form_elo", + "away_form_elo", + "form_elo_diff", + "home_goals_avg", + "home_conceded_avg", + "away_goals_avg", + "away_conceded_avg", + "home_clean_sheet_rate", + "away_clean_sheet_rate", + "home_scoring_rate", + "away_scoring_rate", + "home_winning_streak", + "away_winning_streak", + "home_unbeaten_streak", + "away_unbeaten_streak", + "h2h_total_matches", + "h2h_home_win_rate", + "h2h_draw_rate", + "h2h_avg_goals", + "h2h_btts_rate", + "h2h_over25_rate", + "home_avg_possession", + "away_avg_possession", + "home_avg_shots_on_target", + "away_avg_shots_on_target", + "home_shot_conversion", + "away_shot_conversion", + "home_avg_corners", + "away_avg_corners", + "odds_ms_h", + "odds_ms_d", + "odds_ms_a", + "implied_home", + "implied_draw", + "implied_away", + "odds_ht_ms_h", + "odds_ht_ms_d", + "odds_ht_ms_a", + "odds_ou05_o", + "odds_ou05_u", + "odds_ou15_o", + "odds_ou15_u", + "odds_ou25_o", + "odds_ou25_u", + "odds_ou35_o", + "odds_ou35_u", + "odds_ht_ou05_o", + "odds_ht_ou05_u", + "odds_ht_ou15_o", + "odds_ht_ou15_u", + "odds_btts_y", + "odds_btts_n", + "odds_ms_h_present", + "odds_ms_d_present", + "odds_ms_a_present", + "odds_ht_ms_h_present", + "odds_ht_ms_d_present", + "odds_ht_ms_a_present", + "odds_ou05_o_present", + "odds_ou05_u_present", + "odds_ou15_o_present", + "odds_ou15_u_present", + "odds_ou25_o_present", + "odds_ou25_u_present", + "odds_ou35_o_present", + "odds_ou35_u_present", + "odds_ht_ou05_o_present", + "odds_ht_ou05_u_present", + "odds_ht_ou15_o_present", + "odds_ht_ou15_u_present", + "odds_btts_y_present", + "odds_btts_n_present", + "home_xga", + "away_xga", + "league_avg_goals", + "league_zero_goal_rate", + "upset_atmosphere", + "upset_motivation", + "upset_fatigue", + "upset_potential", + "referee_home_bias", + "referee_avg_goals", + "referee_cards_total", + "referee_avg_yellow", + "referee_experience", + "home_momentum_score", + "away_momentum_score", + "momentum_diff", + "home_squad_quality", + "away_squad_quality", + "squad_diff", + "home_key_players", + "away_key_players", + "home_missing_impact", + "away_missing_impact", + "home_goals_form", + "away_goals_form", + "home_lineup_goals_per90", + "away_lineup_goals_per90", + "home_lineup_assists_per90", + "away_lineup_assists_per90", + "home_squad_continuity", + "away_squad_continuity", + "home_top_scorer_form", + "away_top_scorer_form", + "home_avg_player_exp", + "away_avg_player_exp", + "home_goals_diversity", + "away_goals_diversity", + "h2h_home_goals_avg", + "h2h_away_goals_avg", + "h2h_recent_trend", + "h2h_venue_advantage", + "home_rolling5_goals", + "home_rolling5_conceded", + "home_rolling10_goals", + "home_rolling10_conceded", + "home_rolling20_goals", + "home_rolling20_conceded", + "away_rolling5_goals", + "away_rolling5_conceded", + "away_rolling10_goals", + "away_rolling10_conceded", + "home_rolling5_cs", + "away_rolling5_cs", + "home_venue_goals", + "home_venue_conceded", + "away_venue_goals", + "away_venue_conceded", + "home_goal_trend", + "away_goal_trend", + "home_days_rest", + "away_days_rest", + "match_month", + "is_season_start", + "is_season_end", + "attack_vs_defense_home", + "attack_vs_defense_away", + "xg_diff", + "form_momentum_interaction", + "elo_form_consistency", + "upset_x_elo_gap", + "league_home_win_rate", + "league_draw_rate", + "league_btts_rate", + "league_ou25_rate", + "league_reliability_score" +] \ No newline at end of file diff --git a/ai-engine/models/v25/iso_lgb_v25_btts.pkl b/ai-engine/models/v25/iso_lgb_v25_btts.pkl new file mode 100644 index 0000000..54fa702 Binary files /dev/null and b/ai-engine/models/v25/iso_lgb_v25_btts.pkl differ diff --git 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and b/ai-engine/models/v25/iso_xgb_v25_ou25.pkl differ diff --git a/ai-engine/models/v25/iso_xgb_v25_ou35.pkl b/ai-engine/models/v25/iso_xgb_v25_ou35.pkl new file mode 100644 index 0000000..55c8e4d Binary files /dev/null and b/ai-engine/models/v25/iso_xgb_v25_ou35.pkl differ diff --git a/ai-engine/models/v25/lgb_v25_btts.txt b/ai-engine/models/v25/lgb_v25_btts.txt new file mode 100644 index 0000000..5e0086c --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_btts.txt @@ -0,0 +1,3306 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 +feature_infos=[1150.3663761896189:1903.4781806887747] [1158.5088961211511:1916.84579108047] [-496.81477567713546:573.10259120534784] [1159.8767670517543:1884.5959848901657] [1151.7894548779084:1919.4116678360419] [1426.1496448360797:1585.9817930954068] [1427.9817118206745:1588.9895054335384] [-113.02538114266532:114.69704651598067] [0:6.7333333333333334] [0:7.5333333333333332] [0:7.7333333333333334] [0:6.583333333333333] [0:1] [0:1] [0:1] [0:1] [0:5] [0:5] [0:5] [0:5] [0:8] [0:1] [0:1] [0:12] [0:1] [0:1] [0.20999999999999999:0.81000000000000005] [0.22500000000000001:0.76000000000000001] [0:13] [0:13] [0:30] [0:24] [0:4.5] [0:4.5] [0:22.550000000000001] [0:17.5] [0:35.5] [0.065285302506677897:0.80962131918207236] [0.090561906180844906:0.4288719106684401] [0.056651501780225801:0.79902050363656307] [0:26] [0:5.0899999999999999] [0:26.5] [0:1.0900000000000001] [0:13.050000000000001] [0:1.76] [0:13.25] [0:3.7400000000000002] [0:12.6] [0:8.5600000000000005] [0:3.5299999999999998] [0:1.72] [0:7.3300000000000001] [0:5.0199999999999996] [0:2.5800000000000001] [0:3.77] [0:4.9100000000000001] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:7.5333333333333332] [0:6.583333333333333] [2.0046118370484245:4.1840324763193504] [0.018042399639151999:0.1552651806302843] [0:0.45000000000000001] none none [0:0.1575] [-0.92000000000000004:1] [0:7] none none [0:1] [-1:0.61250000000000004] [-1:0.59166666666666667] [-1.3574999999999999:1.3799999999999999] [0:40.799999999999997] [0:36.299999999999997] [-29.999999999999996:31.499999999999996] [0:10] [0:10] [0:1] [0:1] [0:5.7999999999999998] [0:5] [0:5] [0:6.25] [0:4] [0:5] [0:1] [0:1] [0:10] [0:11] [0:42.100000000000001] 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+feature_importances: +Column_86=188 +Column_56=111 +Column_55=98 +Column_85=96 +Column_4=94 +Column_26=81 +Column_3=78 +Column_31=78 +Column_145=78 +Column_149=72 +Column_94=66 +Column_131=66 +Column_136=66 +Column_7=63 +Column_29=63 +Column_28=62 +Column_99=61 +Column_27=60 +Column_142=60 +Column_98=59 +Column_137=59 +Column_141=58 +Column_102=57 +Column_126=56 +Column_30=53 +Column_36=53 +Column_105=53 +Column_23=52 +Column_34=52 +Column_38=52 +Column_123=52 +Column_130=52 +Column_134=52 +Column_9=51 +Column_6=49 +Column_110=49 +Column_132=49 +Column_103=48 +Column_8=47 +Column_40=47 +Column_92=47 +Column_46=46 +Column_93=46 +Column_111=46 +Column_144=46 +Column_0=45 +Column_10=45 +Column_11=45 +Column_35=45 +Column_48=45 +Column_91=45 +Column_120=45 +Column_122=45 +Column_104=44 +Column_133=44 +Column_2=43 +Column_135=42 +Column_1=41 +Column_90=41 +Column_146=41 +Column_54=40 +Column_121=39 +Column_143=39 +Column_39=36 +Column_42=36 +Column_89=35 +Column_5=34 +Column_49=34 +Column_79=34 +Column_95=34 +Column_97=33 +Column_148=32 +Column_37=31 +Column_147=31 +Column_41=30 +Column_125=30 +Column_106=29 +Column_109=29 +Column_127=29 +Column_138=27 +Column_44=26 +Column_115=26 +Column_50=25 +Column_53=25 +Column_112=25 +Column_114=25 +Column_47=24 +Column_52=23 +Column_12=22 +Column_84=21 +Column_100=20 +Column_96=19 +Column_124=19 +Column_22=18 +Column_45=18 +Column_150=18 +Column_80=17 +Column_119=17 +Column_51=16 +Column_24=14 +Column_77=14 +Column_19=13 +Column_78=12 +Column_16=11 +Column_17=11 +Column_18=11 +Column_20=11 +Column_21=11 +Column_108=11 +Column_14=10 +Column_107=10 +Column_113=10 +Column_101=9 +Column_118=8 +Column_25=7 +Column_151=7 +Column_13=5 +Column_15=5 +Column_128=3 +Column_81=2 +Column_116=2 +Column_117=2 +Column_139=2 +Column_140=2 +Column_74=1 +Column_129=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0193364] +[num_leaves: 31] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 8] +[min_data_in_leaf: 15] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.632973] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 5] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.700458] +[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: 0.00260895] +[lambda_l2: 0.00363081] +[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/v25/lgb_v25_cards_ou45.txt b/ai-engine/models/v25/lgb_v25_cards_ou45.txt new file mode 100644 index 0000000..cd8b4db --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_cards_ou45.txt @@ -0,0 +1,6686 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 +feature_infos=[1150.3663761896189:1903.4781806887747] [1158.5088961211511:1916.84579108047] [-496.81477567713546:573.10259120534784] [1159.8767670517543:1884.5959848901657] [1151.7894548779084:1919.4116678360419] [1426.1496448360797:1585.9817930954068] [1427.9817118206745:1588.9895054335384] [-113.02538114266532:114.69704651598067] [0:6.7333333333333334] [0:7.5333333333333332] [0:7.7333333333333334] [0:6.583333333333333] [0:1] [0:1] [0:1] [0:1] [0:5] [0:5] [0:5] [0:5] [0:8] [0:1] [0:1] [0:12] [0:1] [0:1] [0.20999999999999999:0.81000000000000005] [0.22500000000000001:0.76000000000000001] [0:13] [0:13] [0:30] [0:24] [0:4.5] [0:4.5] [0:22.550000000000001] [0:17.5] [0:35.5] [0.065285302506677897:0.80962131918207236] [0.090561906180844906:0.4288719106684401] [0.056651501780225801:0.79902050363656307] [0:26] [0:5.0899999999999999] [0:26.5] none [0:13.050000000000001] [0:1.76] [0:13.25] [0:3.7400000000000002] [0:12.6] [0:8.5600000000000005] [0:3.5299999999999998] [0:1.72] [0:7.3300000000000001] [0:5.0199999999999996] [0:2.5800000000000001] [0:3.77] [0:4.9100000000000001] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:7.5333333333333332] [0:6.583333333333333] [2.0046118370484245:4.1840324763193504] [0.018042399639151999:0.1552651806302843] [0:0.45000000000000001] none none [0:0.1575] [-0.92000000000000004:1] [0:7] none none [0:1] [-1:0.61250000000000004] [-1:0.59166666666666667] [-1.3574999999999999:1.3799999999999999] [0:40.799999999999997] [0:36.299999999999997] [-29.999999999999996:31.499999999999996] [0:10] [0:10] [0:1] [0:1] [0:5.7999999999999998] [0:5] [0:5] [0:6.25] [0:4] [0:5] [0:1] [0:1] [0:10] [0:11] [0:42.100000000000001] [0:41.799999999999997] [0:1] [0:1] [0:11] [0:9] [-1:1] [0:1] [0:6.2000000000000002] [0:7] [0:5.2000000000000002] [0:6.5] [0:5] [0:6.5] [0:6] [0:7] [0:5.3330000000000002] [0:7] [0:1] [0:1] [0:8] [0:8] [0:11] [0:8.5] [-2.2999999999999998:1.8] [-2.2000000000000002:2] [1:30] [1:30] [1:12] [0:1] [0:1] [-5.1669999999999998:5.4000000000000004] [-6.4669999999999996:5.7329999999999997] [-4.5830000000000002:5.7329999999999997] [-0.045499999999999999:0.088099999999999998] [0.063600000000000004:1] [0:0.088800000000000004] [0.35623409669211198:0.51556156968876865] [0.1051136363636363:0.32744043043812449] [0.40430438124519602:0.64185836716283262] [0.32129131437355879:0.77537212449255755] [0.70399999999999996:1] +tree_sizes=788 794 809 912 791 796 807 910 1015 915 919 924 898 909 922 1018 912 919 913 915 920 915 1023 701 915 914 697 912 913 920 917 913 918 921 917 704 920 922 919 924 918 914 1022 698 912 920 920 919 700 913 921 690 912 915 917 920 913 912 701 920 922 923 916 920 702 927 702 918 915 912 910 911 917 1014 923 698 698 917 1022 918 701 911 919 917 1023 700 918 910 927 921 901 895 1024 1016 1032 1026 918 1024 914 1019 1024 1026 1019 1027 1026 1018 1016 913 1026 1018 1018 1024 1015 1013 915 1029 1016 1018 1021 1020 1015 934 908 1016 912 1014 898 1022 1017 914 1028 1032 1025 1008 906 1012 1018 1014 1007 1020 701 1015 1027 1007 1013 1025 1020 1011 1013 1012 1012 1025 1029 1008 906 903 801 1026 1009 1018 915 1006 1013 1019 1018 1015 1027 1020 929 1017 909 1022 912 1006 1027 1021 905 1018 1020 1030 988 1007 812 816 1002 909 1016 1008 916 1016 1014 1028 1021 919 1014 1024 908 1032 1012 789 1019 1024 905 913 903 1033 1002 1028 1008 808 918 917 798 1020 1026 908 1012 1021 1020 1023 916 1029 1027 1014 1022 1022 908 1015 914 904 1019 1024 904 700 1014 1015 1018 916 909 1018 1007 695 692 693 1023 1025 693 1017 895 1005 1032 1013 914 1026 1020 1019 1011 1017 814 916 1022 805 1011 1022 1018 1013 1030 916 698 1032 1017 910 1006 1022 1021 1030 798 801 1033 1023 913 1028 1010 1022 1013 910 1024 1019 1007 1036 1019 1034 1022 919 913 908 1016 702 1013 907 1014 810 912 904 1023 1018 911 1014 919 1002 907 707 703 1024 1023 1007 903 912 918 807 903 1020 1018 914 1021 803 1004 698 1010 1017 911 1020 1024 1016 1015 1017 1026 700 + +Tree=0 +num_leaves=6 +num_cat=0 +split_feature=100 53 35 14 138 +split_gain=1111.31 154.37 46.8507 45.1075 30.6796 +threshold=1.0000000180025095e-35 2.4350000000000005 5.5250000000000012 1.0000000180025095e-35 7.5000000000000009 +decision_type=2 2 2 2 2 +left_child=3 2 -2 -1 -3 +right_child=1 4 -4 -5 -6 +leaf_value=-0.56890106979215649 -0.58151362048288946 -0.57262198384104734 -0.60321338740306618 -0.64280524214731727 -0.56162854723799793 +leaf_weight=12.414002269506453 2471.0761184245348 777.25447542965412 154.48536157608032 433.57052370905876 709.66712974011898 +leaf_count=54 10749 3381 672 1886 3087 +internal_value=-0.583432 -0.577217 -0.58279 -0.640748 -0.567375 +internal_weight=4558.47 4112.48 2625.56 445.985 1486.92 +internal_count=19829 17889 11421 1940 6468 +is_linear=0 +shrinkage=1 + + +Tree=1 +num_leaves=6 +num_cat=0 +split_feature=101 80 147 47 98 +split_gain=1074.25 235.545 148.19 32.0531 18.9281 +threshold=1.0000000180025095e-35 0.045785126783846462 0.4519630965593785 1.175 0.48684210526315791 +decision_type=2 2 2 2 2 +left_child=4 3 -3 -2 -1 +right_child=1 2 -4 -5 -6 +leaf_value=-0.058334633550069605 -0.034192791794547289 0.0046906718619574433 0.022145858087145177 -0.012198851178154731 -0.0042292261262535111 +leaf_weight=427.81068673729897 123.81990058720112 2569.8557803034782 982.47593878209591 444.2217410504818 9.6622041910886747 +leaf_count=1894 539 11158 4265 1931 42 +internal_value=-0.000183794 0.00586339 0.00951829 -0.016993 -0.0571396 +internal_weight=4557.85 4120.37 3552.33 568.042 437.473 +internal_count=19829 17893 15423 2470 1936 +is_linear=0 +shrinkage=0.0382273 + + +Tree=2 +num_leaves=6 +num_cat=0 +split_feature=101 80 147 34 98 +split_gain=1015.51 220.21 137.014 23.9685 18.7106 +threshold=1.0000000180025095e-35 0.045785126783846462 0.4519630965593785 1.0000000180025095e-35 0.48684210526315791 +decision_type=2 2 2 2 2 +left_child=4 3 -3 -2 -1 +right_child=1 2 -4 -5 -6 +leaf_value=-0.057196037560745976 -0.035808401241098235 0.0046921912774161544 0.021437232063054007 -0.013195325050206482 -0.0027208289972841257 +leaf_weight=419.14815951883793 79.829620882868767 2575.2811836898327 988.01961982250214 482.47344189882278 9.4209049642086011 +leaf_count=1891 351 11167 4263 2106 41 +internal_value=8.75377e-06 0.00582682 0.0093352 -0.0164057 -0.0559986 +internal_weight=4554.17 4125.6 3563.3 562.303 428.569 +internal_count=19819 17887 15430 2457 1932 +is_linear=0 +shrinkage=0.0382273 + + +Tree=3 +num_leaves=7 +num_cat=0 +split_feature=100 80 147 98 84 55 +split_gain=930.901 203.71 131.86 38.7706 26.7808 0.476574 +threshold=1.0000000180025095e-35 0.045785126783846462 0.40573489010989011 0.31666666666666671 0.047500000000000007 1.7950000000000002 +decision_type=2 2 2 2 2 2 +left_child=3 4 -3 5 -2 -1 +right_child=1 2 -4 -5 -6 -7 +leaf_value=-0.056144301348653426 -0.018851323813430673 -0.012286704886275708 0.011431463144863683 0.020346230377800454 0.0079623127737035101 -0.046623426348493667 +leaf_weight=403.0488645285368 498.45010693371296 383.76123978197575 3187.9443215876818 9.9654673188924772 61.105284988880157 7.8490427136421195 +leaf_count=1855 2189 1662 13766 43 268 36 +internal_value=5.63914e-06 0.0055232 0.00888307 -0.0541556 -0.0159232 -0.0559625 +internal_weight=4552.12 4131.26 3571.71 420.863 559.555 410.898 +internal_count=19819 17885 15428 1934 2457 1891 +is_linear=0 +shrinkage=0.0382273 + + +Tree=4 +num_leaves=6 +num_cat=0 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3690.4 +internal_count=19880 1560 845 715 18320 486 17834 +is_linear=0 +shrinkage=0.0382273 + + +Tree=336 +num_leaves=8 +num_cat=0 +split_feature=112 107 102 134 93 80 34 +split_gain=4.62027 7.88783 16.7872 7.06946 6.58586 6.38803 6.03081 +threshold=0.52250000000000008 0.59050000000000014 2.0515000000000003 -0.81099999999999983 15.900000000000002 0.057580777954459206 1.9550000000000003 +decision_type=2 2 2 2 2 2 2 +left_child=3 2 -2 5 -5 -1 -3 +right_child=1 6 -4 4 -6 -7 -8 +leaf_value=0.0015168890871476012 -0.010669911888365217 -0.0028317852635999334 0.031150676595115428 -0.0003976627268608293 0.003519056286493751 -0.02619213781519027 0.003004421322848329 +leaf_weight=22.29735913942568 231.14609422162175 662.64948134496808 14.932050935924051 1692.1188173880801 996.99291909858584 26.737082932144403 424.48114617913961 +leaf_count=184 1027 2969 67 9256 4360 119 1870 +internal_value=-0.000106408 -0.00195192 -0.00813218 0.000792175 0.00105447 -0.0135922 -0.000552979 +internal_weight=4071.35 1333.21 246.078 2738.15 2689.11 49.0344 1087.13 +internal_count=19852 5933 1094 13919 13616 303 4839 +is_linear=0 +shrinkage=0.0382273 + + +Tree=337 +num_leaves=5 +num_cat=0 +split_feature=96 135 4 135 +split_gain=4.10573 3.86443 8.19798 5.16006 +threshold=9.5000000000000018 0.83450000000000013 1470.0218464596569 0.51950000000000018 +decision_type=2 2 2 2 +left_child=1 3 -3 -1 +right_child=-2 2 -4 -5 +leaf_value=0.00013218707610153976 -0.027758840899893628 0.035894846464125267 0.0022764626536127906 -0.0058919795107644172 +leaf_weight=3778.772225719993 7.8285715207457534 13.358703764271921 51.319166925619356 219.87102640606463 +leaf_count=18362 38 80 277 1095 +internal_value=-0.000102415 -4.91273e-05 0.00922029 -0.000199061 +internal_weight=4071.15 4063.32 64.6779 3998.64 +internal_count=19852 19814 357 19457 +is_linear=0 +shrinkage=0.0382273 + + +end of trees + +feature_importances: +Column_148=130 +Column_147=104 +Column_79=89 +Column_80=85 +Column_101=84 +Column_35=52 +Column_150=52 +Column_98=47 +Column_26=39 +Column_100=39 +Column_137=35 +Column_145=35 +Column_27=33 +Column_138=33 +Column_133=32 +Column_149=32 +Column_30=30 +Column_29=29 +Column_102=28 +Column_110=28 +Column_142=28 +Column_47=26 +Column_105=25 +Column_134=25 +Column_3=23 +Column_31=23 +Column_136=23 +Column_92=22 +Column_120=22 +Column_7=21 +Column_53=21 +Column_8=20 +Column_41=20 +Column_103=20 +Column_111=20 +Column_135=20 +Column_40=19 +Column_130=19 +Column_28=18 +Column_0=17 +Column_5=17 +Column_84=17 +Column_114=17 +Column_121=17 +Column_1=16 +Column_51=16 +Column_86=16 +Column_123=16 +Column_146=16 +Column_6=15 +Column_55=15 +Column_132=15 +Column_4=13 +Column_90=13 +Column_96=13 +Column_99=13 +Column_143=13 +Column_34=12 +Column_36=12 +Column_38=12 +Column_46=12 +Column_49=12 +Column_56=12 +Column_85=12 +Column_93=12 +Column_95=12 +Column_141=12 +Column_21=10 +Column_112=10 +Column_122=10 +Column_9=9 +Column_10=9 +Column_125=9 +Column_127=9 +Column_48=8 +Column_54=8 +Column_89=8 +Column_106=8 +Column_2=7 +Column_11=7 +Column_39=7 +Column_42=7 +Column_91=7 +Column_94=7 +Column_107=7 +Column_113=7 +Column_126=7 +Column_144=7 +Column_13=6 +Column_23=6 +Column_77=6 +Column_15=5 +Column_16=5 +Column_19=5 +Column_37=5 +Column_44=5 +Column_52=5 +Column_97=5 +Column_104=5 +Column_116=5 +Column_12=4 +Column_14=4 +Column_119=4 +Column_129=4 +Column_131=4 +Column_17=3 +Column_78=3 +Column_115=3 +Column_20=2 +Column_22=2 +Column_45=2 +Column_50=2 +Column_81=2 +Column_108=2 +Column_109=2 +Column_118=2 +Column_128=2 +Column_18=1 +Column_57=1 +Column_61=1 +Column_67=1 +Column_124=1 +Column_139=1 +Column_151=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0382273] +[num_leaves: 8] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 3] +[min_data_in_leaf: 35] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.963728] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 2] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.517194] +[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: 3.11665e-06] +[lambda_l2: 0.000479305] +[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/v25/lgb_v25_handicap_ms.txt b/ai-engine/models/v25/lgb_v25_handicap_ms.txt new file mode 100644 index 0000000..bff4724 --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_handicap_ms.txt @@ -0,0 +1,13440 @@ +tree +version=v4 +num_class=3 +num_tree_per_iteration=3 +label_index=0 +max_feature_idx=151 +objective=multiclass num_class:3 +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 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-0.000646838 -0.016348 -0.000315728 +internal_weight=3577.86 3562.54 513.615 3048.92 15.3163 6.87251 +internal_count=15094 15014 2047 12967 80 39 +is_linear=0 +shrinkage=0.0326735 + + +Tree=688 +num_leaves=6 +num_cat=0 +split_feature=55 143 143 4 17 +split_gain=3.83068 3.45066 4.19927 3.17589 3.03975 +threshold=2.5450000000000004 2.4145000000000003 3.1500000000000004 1470.0218464596569 1.0000000180025095e-35 +decision_type=2 2 2 2 2 +left_child=1 4 -3 -2 -1 +right_child=3 2 -4 -5 -6 +leaf_value=0.00083473198230290125 -0.0027271524260766289 -0.020581000899974007 0.015567593767882122 0.044570370622176142 -0.0010694923722384246 +leaf_weight=2201.8901387639344 2.4027663096785536 15.929688397794964 3.7221366539597502 2.998071450740099 1506.6331942584366 +leaf_count=8662 9 106 28 11 6278 +internal_value=2.98632e-05 -1.26633e-05 -0.0134109 0.0257793 6.1057e-05 +internal_weight=3733.58 3728.18 19.6518 5.40084 3708.52 +internal_count=15094 15074 134 20 14940 +is_linear=0 +shrinkage=0.0326735 + + +Tree=689 +num_leaves=8 +num_cat=0 +split_feature=130 56 137 135 36 41 147 +split_gain=6.60263 7.08106 6.05946 9.90423 6.00697 4.86043 4.05771 +threshold=1.6125000000000003 2.3250000000000006 6.1500000000000012 -0.53799999999999992 4.5650000000000004 2.2250000000000005 0.39487318826102363 +decision_type=2 2 2 2 2 2 2 +left_child=1 4 3 -2 -1 -3 -4 +right_child=2 5 6 -5 -6 -7 -8 +leaf_value=-6.2255427305135956e-05 0.012499075867261334 0.001451424115346431 0.013265532335962891 -0.0020727295325411788 -0.0053421393935911493 -0.014396666133479306 0.0024110700210588937 +leaf_weight=2249.3522648103535 53.098220858722925 29.512641444802284 37.021806381642818 660.33288056775928 255.48774338513613 67.116355083882809 1439.800467999652 +leaf_count=7080 169 86 109 2069 850 205 4526 +internal_value=0.000174258 -0.0009385 0.0014958 -0.000976104 -0.000602242 -0.00958264 0.00268983 +internal_weight=4791.72 2601.47 2190.25 713.431 2504.84 96.629 1476.82 +internal_count=15094 8221 6873 2238 7930 291 4635 +is_linear=0 +shrinkage=0.0326735 + + +Tree=690 +num_leaves=8 +num_cat=0 +split_feature=114 38 113 7 36 111 31 +split_gain=5.14392 8.401 8.04129 5.20272 4.92299 4.93925 3.90533 +threshold=0.45000000000000007 0.29286528700929276 0.68150000000000011 38.531334035160633 14.175000000000002 11.250000000000002 0.13596116504854369 +decision_type=2 2 2 2 2 2 2 +left_child=1 2 -1 -3 6 -6 -2 +right_child=4 3 -4 -5 5 -7 -8 +leaf_value=0.0047133415000742331 -0.0023429583213419964 -0.005777739444842965 0.023817634419512856 0.033944211392219439 0.012902633231912685 -0.011309795984919546 3.238417419473903e-05 +leaf_weight=219.73506440967321 1164.3157215793617 97.993006499484181 25.691967558115721 2.9033224731683722 49.09123669564724 10.35843466222286 2020.0805989066139 +leaf_count=998 4723 510 148 10 174 33 8543 +internal_value=-0.000257294 0.00352337 0.0067803 -0.00439052 -0.000661657 0.00859475 -0.000836466 +internal_weight=3590.17 346.323 245.427 100.896 3243.85 59.4497 3184.4 +internal_count=15139 1666 1146 520 13473 207 13266 +is_linear=0 +shrinkage=0.0326735 + + +Tree=691 +num_leaves=8 +num_cat=0 +split_feature=110 48 44 4 22 143 56 +split_gain=4.8929 6.0413 6.99131 6.00493 4.42588 6.48078 4.8287 +threshold=28.550000000000004 1.635 7.4650000000000007 1428.3239778243289 1.0000000180025095e-35 0.66100000000000014 1.2850000000000004 +decision_type=2 2 2 2 2 2 2 +left_child=4 2 -2 -3 5 -1 -6 +right_child=1 3 -4 -5 6 -7 -8 +leaf_value=-0.0031161853769771188 0.0082694691219209401 0.029301243316708192 0.044729732821269561 -0.0055806519292195857 0.004420705219023973 0.0024500322425632572 2.8802498501142282e-05 +leaf_weight=954.04168075509369 16.493897616863254 5.601911023259162 8.129031725227831 29.980012565851212 304.73005631752312 290.85322504490614 2135.1439720094204 +leaf_count=3770 55 19 27 106 1270 1306 8586 +internal_value=-7.84856e-05 0.00910248 0.0212814 0.000406917 -0.00023033 -0.00181501 0.000578534 +internal_weight=3744.97 60.2049 24.6229 35.5819 3684.77 1244.89 2439.87 +internal_count=15139 207 82 125 14932 5076 9856 +is_linear=0 +shrinkage=0.0326735 + + +Tree=692 +num_leaves=7 +num_cat=0 +split_feature=85 85 114 92 42 56 +split_gain=4.98727 4.74407 4.42269 3.01927 3.54154 2.55603 +threshold=0.54060606060606065 -0.85333333333333328 0.45000000000000007 0.40041666666666681 4.2550000000000008 2.1850000000000005 +decision_type=2 2 2 2 2 2 +left_child=1 -1 -3 5 -5 -2 +right_child=3 2 -4 4 -6 -7 +leaf_value=0.022384553818511795 -0.020592166537843338 -0.0025671087517330289 0.00063614698612212274 0.035197067755681606 -0.017765898699232209 0.0094058470337872058 +leaf_weight=9.6401844713836891 22.916942974552519 515.2273776344955 4251.692470561713 2.1665031239390364 1.8581913411617277 3.0324314832687369 +leaf_count=111 140 1634 13218 10 14 12 +internal_value=0.000253346 0.000337706 0.000289574 -0.0128671 0.0118026 -0.0169194 +internal_weight=4806.53 4776.56 4766.92 29.9741 4.02469 25.9494 +internal_count=15139 14963 14852 176 24 152 +is_linear=0 +shrinkage=0.0326735 + + +end of trees + +feature_importances: +Column_85=511 +Column_95=245 +Column_34=215 +Column_36=179 +Column_40=144 +Column_35=86 +Column_86=73 +Column_31=68 +Column_2=67 +Column_27=67 +Column_39=65 +Column_123=65 +Column_56=64 +Column_145=60 +Column_26=58 +Column_37=58 +Column_42=57 +Column_10=55 +Column_143=53 +Column_28=50 +Column_136=50 +Column_4=49 +Column_53=49 +Column_5=48 +Column_90=47 +Column_110=44 +Column_121=44 +Column_144=44 +Column_1=43 +Column_3=43 +Column_0=42 +Column_6=41 +Column_91=41 +Column_41=40 +Column_120=39 +Column_122=39 +Column_134=39 +Column_147=39 +Column_149=38 +Column_9=37 +Column_49=37 +Column_141=37 +Column_29=36 +Column_11=35 +Column_102=35 +Column_104=35 +Column_131=35 +Column_133=35 +Column_38=34 +Column_146=34 +Column_7=33 +Column_113=33 +Column_99=32 +Column_142=32 +Column_44=31 +Column_47=30 +Column_130=30 +Column_105=29 +Column_98=28 +Column_103=28 +Column_54=27 +Column_111=27 +Column_132=27 +Column_55=26 +Column_92=26 +Column_30=25 +Column_96=25 +Column_93=24 +Column_148=24 +Column_8=23 +Column_46=23 +Column_124=23 +Column_135=23 +Column_50=22 +Column_48=21 +Column_127=21 +Column_23=18 +Column_94=18 +Column_97=18 +Column_115=18 +Column_22=17 +Column_137=17 +Column_126=15 +Column_138=15 +Column_51=14 +Column_52=14 +Column_77=14 +Column_79=14 +Column_89=14 +Column_100=14 +Column_114=14 +Column_16=13 +Column_17=13 +Column_101=13 +Column_109=13 +Column_112=13 +Column_106=11 +Column_108=11 +Column_118=11 +Column_78=10 +Column_119=10 +Column_12=9 +Column_125=9 +Column_150=9 +Column_107=8 +Column_128=8 +Column_45=7 +Column_13=6 +Column_80=6 +Column_84=6 +Column_14=5 +Column_15=5 +Column_24=5 +Column_117=5 +Column_19=4 +Column_21=4 +Column_116=4 +Column_129=4 +Column_25=3 +Column_43=3 +Column_151=3 +Column_20=2 +Column_57=2 +Column_81=2 +Column_140=2 +Column_18=1 +Column_33=1 +Column_61=1 +Column_63=1 +Column_69=1 +Column_70=1 +Column_71=1 +Column_139=1 + +parameters: +[boosting: gbdt] +[objective: multiclass] +[metric: multi_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0326735] +[num_leaves: 8] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 3] +[min_data_in_leaf: 10] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.733612] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 1] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.500947] +[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: 1.38802e-06] +[lambda_l2: 0.756809] +[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: 3] +[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/v25/lgb_v25_ht_ou05.txt b/ai-engine/models/v25/lgb_v25_ht_ou05.txt new file mode 100644 index 0000000..16ece75 --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_ht_ou05.txt @@ -0,0 +1,891 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 +feature_infos=[1150.3663761896189:1903.4781806887747] [1158.5088961211511:1916.84579108047] [-496.81477567713546:573.10259120534784] [1159.8767670517543:1884.5959848901657] [1151.7894548779084:1919.4116678360419] [1426.1496448360797:1585.9817930954068] [1427.9817118206745:1588.9895054335384] [-113.02538114266532:114.69704651598067] [0:5.9333333333333336] [0:6.2666666666666666] [0:6.0666666666666664] [0:5.2000000000000002] [0:1] [0:1] [0:1] [0:1] [0:5] [0:5] [0:5] [0:5] [0:8] [0:1] [0:1] [0:11] [0:1] [0:1] [0.20999999999999999:0.81000000000000005] [0.22500000000000001:0.76000000000000001] [0:13] [0:13] [0:6.333333333333333] [0:6.666666666666667] [0:4.5] [0:4.5] [0:22.550000000000001] [0:17.5] [0:35.5] [0.065285302506677897:0.80962131918207236] [0.1044438556117265:0.4288719106684401] [0.056651501780225801:0.79902050363656307] [0:26] [0:5.0899999999999999] [0:26.5] [0:1.0900000000000001] [0:13.050000000000001] [0:1.76] [0:13.25] [0:3.7400000000000002] [0:7.6500000000000004] [0:8.5600000000000005] [0:3.5299999999999998] [0:1.72] [0:7.3300000000000001] [0:5.0199999999999996] [0:2.5800000000000001] [0:3.77] [0:4.1299999999999999] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:6.2666666666666666] [0:5.2000000000000002] [2.0046118370484245:4.1840324763193504] [0.018042399639151999:0.1552651806302843] [0:0.45000000000000001] none none [0:0.1575] [-0.92000000000000004:1] [0:7] none none [0:1] [-1:0.61250000000000004] [-1:0.59166666666666667] [-1.2908333333333333:1.3799999999999999] [0:40.799999999999997] [0:36.299999999999997] [-29.999999999999996:31.499999999999996] [0:10] [0:10] [0:1] [0:1] [0:5.7999999999999998] [0:5] [0:5] [0:6.25] [0:4] [0:5] [0:1] [0:1] [0:10] [0:11] [0:42.100000000000001] [0:41.799999999999997] [0:1] [0:1] [0:8] [0:8] [-1:1] [0:1] [0:5.7999999999999998] [0:6.5] [0:5.2000000000000002] [0:6.5] [0:4.4119999999999999] [0:6.5] [0:5.3330000000000002] [0:4.7999999999999998] [0:5.3330000000000002] [0:4.5] [0:1] [0:1] [0:7] [0:8] [0:6] [0:7] [-1.8:1.8] [-2.2000000000000002:2] [1:30] [1.2:30] [1:12] [0:1] [0:1] [-4.4669999999999996:4.5999999999999996] [-5.2000000000000002:5] [-4.133:5.3330000000000002] [-0.045499999999999999:0.088099999999999998] 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+Column_27=1 +Column_28=1 +Column_31=1 +Column_34=1 +Column_37=1 +Column_39=1 +Column_46=1 +Column_47=1 +Column_78=1 +Column_80=1 +Column_84=1 +Column_95=1 +Column_99=1 +Column_107=1 +Column_112=1 +Column_119=1 +Column_121=1 +Column_125=1 +Column_126=1 +Column_145=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.104222] +[num_leaves: 8] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 3] +[min_data_in_leaf: 17] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.709098] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 3] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.58888] +[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: 7.81041e-07] +[lambda_l2: 0.00942891] +[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/v25/lgb_v25_ht_ou15.txt b/ai-engine/models/v25/lgb_v25_ht_ou15.txt new file mode 100644 index 0000000..db96939 --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_ht_ou15.txt @@ -0,0 +1,6822 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 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[0:3.5299999999999998] [0:1.72] [0:7.3300000000000001] [0:5.0199999999999996] [0:2.5800000000000001] [0:3.77] [0:4.1299999999999999] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:6.2666666666666666] [0:5.2000000000000002] [2.0046118370484245:4.1840324763193504] [0.018042399639151999:0.1552651806302843] [0:0.45000000000000001] none none [0:0.1575] [-0.92000000000000004:1] [0:7] none none [0:1] [-1:0.61250000000000004] [-1:0.59166666666666667] [-1.2908333333333333:1.3799999999999999] [0:40.799999999999997] [0:36.299999999999997] [-29.999999999999996:31.499999999999996] [0:10] [0:10] [0:1] [0:1] [0:5.7999999999999998] [0:5] [0:5] [0:6.25] [0:4] [0:5] [0:1] [0:1] [0:10] [0:11] [0:42.100000000000001] [0:41.799999999999997] [0:1] [0:1] [0:8] [0:8] [-1:1] [0:1] [0:5.7999999999999998] [0:6.5] [0:5.2000000000000002] [0:6.5] [0:4.4119999999999999] [0:6.5] [0:5.3330000000000002] [0:4.7999999999999998] [0:5.3330000000000002] [0:4.5] [0:1] [0:1] [0:7] [0:8] [0:6] [0:7] [-1.8:1.8] [-2.2000000000000002:2] [1:30] [1.2:30] [1:12] [0:1] [0:1] [-4.4669999999999996:4.5999999999999996] [-5.2000000000000002:5] [-4.133:5.3330000000000002] [-0.045499999999999999:0.088099999999999998] [0.063600000000000004:1] [0:0.088800000000000004] [0.35623409669211198:0.51556156968876865] [0.1051136363636363:0.32744043043812449] [0.40430438124519602:0.64185836716283262] [0.32129131437355879:0.77537212449255755] [0.70399999999999996:1] +tree_sizes=3386 3479 3485 3424 3478 3506 3514 3444 3488 3464 3444 3473 3473 3489 3473 3498 3508 3443 3421 3441 3455 3518 3479 3460 3496 3445 3497 3443 3466 3462 3483 3454 3483 3459 3447 3438 3437 3486 3529 3451 3511 3479 3491 3436 3451 3469 3467 3443 3494 3513 3454 3471 3486 3472 3457 3469 3465 3477 3465 3423 3490 3460 3435 3502 3510 3499 3499 3491 3534 3523 3483 3497 3525 3504 3420 3472 3450 3523 3487 3501 3474 3477 3457 3476 3474 3517 3505 3468 3462 3473 3466 3452 3511 3437 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3421 3428 3509 3504 3442 3476 3444 3469 3474 3518 3479 3524 3505 3459 3470 3449 3453 3468 3497 3444 3435 1609 3495 3469 3469 3447 3515 3469 3491 3443 3494 3537 3511 3485 3496 3476 3405 3465 3464 3431 3494 3458 3490 3423 3488 3450 3420 3446 3508 3447 3455 + +Tree=0 +num_leaves=31 +num_cat=0 +split_feature=45 54 49 48 147 99 150 136 85 134 2 55 31 27 99 40 147 35 102 28 97 97 150 8 142 1 31 104 85 22 +split_gain=194.237 76.2311 27.3356 19.9253 18.003 13.6954 15.6158 13.6206 11.5197 11.2907 10.8269 12.3285 11.4424 10.115 10.9979 10.0133 9.99943 10.9836 9.85539 9.45724 9.42775 9.36667 8.91588 8.48467 8.46457 10.4356 8.37333 10.9664 8.34377 8.12369 +threshold=1.135 1.4850000000000001 3.7950000000000004 1.9950000000000003 0.42111881931569756 0.75595238095238104 0.62017637310606066 8.8500000000000032 -0.14902097902097897 0.10550000000000002 -127.16874079307875 1.635 0.21353768953856223 0.49211111111111117 0.61721611721611735 1.9750000000000003 0.47199397614381194 5.1350000000000007 0.25050000000000006 7.4722222222222232 4.5000000000000009 4.5000000000000009 0.47465257807797773 1.0333333333333337 0.73100000000000009 1477.0007987809656 0.18768939393939393 0.38450000000000006 -0.052704402515723246 0.61250000000000016 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 3 7 4 22 9 -7 -2 -9 18 20 12 -12 14 -13 -8 -5 -18 28 -3 -10 -15 -1 -24 25 -25 27 -11 -6 -4 +right_child=2 19 29 16 5 6 15 8 10 26 11 13 -14 21 -16 -17 17 -19 -20 -21 -22 -23 23 24 -26 -27 -28 -29 -30 -31 +leaf_value=-0.62543942436525124 -0.62404927858892212 -0.61413954022520778 -0.62721318293831174 -0.61711894248666954 -0.62074587056703301 -0.62191199267812569 -0.60717203578830814 -0.62671311071478919 -0.62049401499028223 -0.62334667351025552 -0.6195730668152003 -0.62074587056703301 -0.606667479719877 -0.61805792914691871 -0.62831388145958367 -0.62074587056703301 -0.62303678662245776 -0.61096879578076102 -0.61834127674029393 -0.60524453218363006 -0.60926876938583774 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10.5345 10.0878 9.28949 8.74629 8.63329 8.48114 11.3942 8.43238 8.28554 +threshold=1.2150000000000001 1.4850000000000001 2.6228448275862073 3.3279569892473124 2.0650000000000004 22.050000000000004 1.0000000180025095e-35 3.0450000000000004 1.3945000000000001 2.1835000000000004 0.072307692307692267 2.6350000000000002 0.00015000000000000004 1.5350000000000004 0.5124309890665032 0.0049500000000000004 7.9500000000000011 0.77500000000000013 1600.7799905922766 0.45000000000000007 -0.51791666666666658 0.45000000000000007 3.5327702702702708 2.0550000000000002 1.3945000000000001 2.4152298850574714 14.65 1524.3277801427464 -0.068999999999999992 0.17305000000000001 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 3 4 5 6 7 16 8 24 21 -5 -12 25 -9 15 -15 26 -18 -7 -8 -21 -10 -4 -19 -1 -6 -2 -28 -24 -26 +right_child=2 -3 22 10 12 18 19 13 9 -11 11 -13 -14 14 -16 -17 17 23 -20 20 -22 -23 28 -25 29 -27 27 -29 -30 -31 +leaf_value=-0.0064129056366581212 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+[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/v25/lgb_v25_ht_result.txt b/ai-engine/models/v25/lgb_v25_ht_result.txt new file mode 100644 index 0000000..8209932 --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_ht_result.txt @@ -0,0 +1,13779 @@ +tree +version=v4 +num_class=3 +num_tree_per_iteration=3 +label_index=0 +max_feature_idx=151 +objective=multiclass num_class:3 +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 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 +feature_infos=[1150.3663761896189:1903.4781806887747] 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[0:7.3300000000000001] [0:5.0199999999999996] [0:2.5800000000000001] [0:3.77] [0:4.1299999999999999] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:6.2666666666666666] [0:5.2000000000000002] [2.0046118370484245:4.1840324763193504] [0.018042399639151999:0.1552651806302843] [0:0.45000000000000001] none none [0:0.1575] [-0.92000000000000004:1] [0:7] none none [0:1] [-1:0.61250000000000004] [-1:0.59166666666666667] [-1.2908333333333333:1.3799999999999999] [0:40.799999999999997] [0:36.299999999999997] [-29.999999999999996:31.499999999999996] [0:10] [0:10] [0:1] [0:1] [0:5.7999999999999998] [0:5] [0:5] [0:6.25] [0:4] [0:5] [0:1] [0:1] [0:10] [0:11] [0:42.100000000000001] [0:41.799999999999997] [0:1] [0:1] [0:8] [0:8] [-1:1] [0:1] [0:5.7999999999999998] [0:6.5] [0:5.2000000000000002] [0:6.5] [0:4.4119999999999999] [0:6.5] [0:5.3330000000000002] [0:4.7999999999999998] [0:5.3330000000000002] [0:4.5] [0:1] [0:1] [0:7] [0:8] [0:6] [0:7] [-1.8:1.8] [-2.2000000000000002:2] [1:30] [1.2:30] [1:12] [0:1] [0:1] [-4.4669999999999996:4.5999999999999996] [-5.2000000000000002:5] [-4.133:5.3330000000000002] [-0.045499999999999999:0.088099999999999998] [0.063600000000000004:1] [0:0.088800000000000004] [0.35623409669211198:0.51556156968876865] [0.1051136363636363:0.32744043043812449] [0.40430438124519602:0.64185836716283262] [0.32129131437355879:0.77537212449255755] [0.70399999999999996:1] +tree_sizes=972 976 968 1007 1009 1020 1018 1007 1018 1001 1020 1014 1002 1014 1011 984 1014 1022 1014 1020 1018 996 1017 1008 1014 1018 1013 1019 1019 1017 1004 1022 1018 1006 1018 1012 1006 1016 1012 975 1018 1009 1006 1010 1019 997 993 1017 1004 1002 1012 1016 1010 1017 1013 1017 1009 1003 1011 1012 1001 1016 1021 1005 1022 1010 1016 1005 1011 1015 1010 1009 1011 992 1013 1002 1018 1020 1000 1025 1017 1014 914 999 1001 1022 1024 997 1001 1015 1029 1016 1011 1021 1017 1012 1025 1010 1016 1021 1019 1021 1002 1027 1008 1025 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920 910 894 1019 912 1006 801 1014 912 804 907 1008 1021 1019 1014 888 913 1013 888 909 1011 803 1011 1015 705 911 1019 1006 1010 998 903 1018 1008 1013 1016 1023 999 1016 917 1002 1019 1006 1026 1012 1015 1024 906 1010 799 1024 1016 1020 1019 917 1018 911 903 993 904 1022 802 918 1025 1012 1024 1007 1012 907 905 1014 904 897 1019 1007 917 1013 1006 910 1027 1012 1000 1015 1022 1029 1019 1012 1020 1014 1015 1022 1017 1018 1023 1025 1013 1006 702 705 1013 699 703 1016 700 699 902 906 919 908 1011 1018 1011 1017 910 1024 1023 1025 1020 1023 914 1010 1029 1023 907 1018 1026 1011 914 909 1023 1018 914 1016 1021 1018 1021 706 910 1014 907 910 1018 1024 1024 1002 1018 1014 1014 1015 1021 1024 1011 1024 1027 908 1017 1021 908 1024 1031 908 899 1022 905 1022 1028 907 910 1012 588 900 1021 696 1010 1021 588 916 907 1018 920 916 1009 1023 899 1019 1021 910 913 916 911 1018 1020 907 1010 1020 + +Tree=0 +num_leaves=8 +num_cat=0 +split_feature=36 34 34 34 95 35 7 +split_gain=275.385 84.1013 51.9669 32.2495 22.2076 10.9167 8.78481 +threshold=4.0650000000000004 2.0050000000000003 1.2250000000000003 3.2950000000000004 -1.0000000180025095e-35 7.455000000000001 -18.44627404077232 +decision_type=2 2 2 2 2 2 2 +left_child=1 4 5 -3 -1 -2 -4 +right_child=2 3 6 -5 -6 -7 -8 +leaf_value=-1.0675794439612276 -1.0532201526036671 -1.0685273820548493 -1.0496944040670773 -1.0737389038446994 -1.0632953940067922 -1.0443167536003837 -1.0604613086982253 +leaf_weight=488.60509949922562 306.30999302864075 1398.0476009249687 22.363340198993683 446.92796549201012 1175.091875910759 45.065518885850906 645.82615786790848 +leaf_count=1442 904 4126 66 1319 3468 133 1906 +internal_value=-1.06506 -1.06731 -1.05734 -1.06979 -1.06455 -1.05208 -1.0601 +internal_weight=4528.24 3508.67 1019.57 1844.98 1663.7 351.376 668.189 +internal_count=13364 10355 3009 5445 4910 1037 1972 +is_linear=0 +shrinkage=1 + + +Tree=1 +num_leaves=8 +num_cat=0 +split_feature=49 35 35 4 86 126 108 +split_gain=78.6374 23.5953 15.4952 9.44244 8.96189 8.95593 5.16644 +threshold=2.9450000000000007 4.4150000000000009 2.7450000000000006 1704.4687685416313 3.0956959706959712 1.8660000000000003 1.0000000180025095e-35 +decision_type=2 2 2 2 2 2 2 +left_child=1 4 5 -4 -1 -2 -3 +right_child=2 6 3 -5 -6 -7 -8 +leaf_value=-0.90127731675696665 -0.89558996242962496 -0.9084996895097639 -0.89886817347230463 -0.88770694861278765 -0.90417147383004814 -0.8865749922379077 -0.90456251575894542 +leaf_weight=2532.2756417691708 414.99073597788811 142.9131130874157 1057.9188421964645 22.070126324892044 346.97133025527 34.009702861309052 284.01719942688942 +leaf_count=6999 1147 395 2924 61 959 94 785 +internal_value=-0.900711 -0.902175 -0.897544 -0.89864 -0.901626 -0.894907 -0.905881 +internal_weight=4835.17 3306.18 1528.99 1079.99 2879.25 449 426.93 +internal_count=13364 9138 4226 2985 7958 1241 1180 +is_linear=0 +shrinkage=1 + + +Tree=2 +num_leaves=8 +num_cat=0 +split_feature=34 34 95 35 39 2 27 +split_gain=245.509 86.6275 50.2122 30.268 27.7209 20.8533 15.4692 +threshold=2.0250000000000004 4.5250000000000012 -1.5000000000000002 3.9950000000000006 0.42976513239726083 -206.31690219533411 0.61866071428571445 +decision_type=2 2 2 2 2 2 2 +left_child=2 4 5 -4 -2 -1 -3 +right_child=1 6 3 -5 -6 -7 -8 +leaf_value=-1.3663745258992592 -1.3878921673142646 -1.3705974786172266 -1.3938361562887283 -1.3992177372668018 -1.3825606152536147 -1.3891551714079229 -1.3837626546729944 +leaf_weight=11.782702088356016 933.91941076517105 129.32918244600296 1452.0777621269226 375.08268314599991 396.1232225894928 419.12754571437836 31.701079428195953 +leaf_count=42 3329 461 5176 1337 1412 1494 113 +internal_value=-1.39021 -1.38489 -1.39372 -1.39494 -1.3863 -1.38853 -1.37319 +internal_weight=3749.14 1491.07 2258.07 1827.16 1330.04 430.91 161.03 +internal_count=13364 5315 8049 6513 4741 1536 574 +is_linear=0 +shrinkage=1 + + +Tree=3 +num_leaves=8 +num_cat=0 +split_feature=36 34 34 34 96 99 52 +split_gain=266.145 81.3002 49.6164 31.5494 13.4184 10.6176 8.57219 +threshold=4.0650000000000004 2.0050000000000003 1.2250000000000003 4.745000000000001 4.5000000000000009 0.73508771929824568 3.8950000000000005 +decision_type=2 2 2 2 2 2 2 +left_child=1 4 5 -3 -1 -2 -4 +right_child=2 3 6 -5 -6 -7 -8 +leaf_value=-0.00069559553459769188 0.010234696490579514 -0.0040322016388853717 0.0045661970952341601 -0.01169532638408783 0.0024844828259294833 0.016212382885025682 0.020430809812638166 +leaf_weight=1082.4841198027134 212.28176686167717 1672.0471458733082 659.66987147927284 168.69350317120552 581.836445748806 141.11150676012039 9.8599047660827619 +leaf_count=3194 623 4945 1943 500 1716 414 29 +internal_value=-7.69131e-05 -0.0022888 0.00750213 -0.0047345 0.000416126 0.0126217 0.00479987 +internal_weight=4527.98 3505.06 1022.92 1840.74 1664.32 353.393 669.53 +internal_count=13364 10355 3009 5445 4910 1037 1972 +is_linear=0 +shrinkage=0.0168881 + + +Tree=4 +num_leaves=8 +num_cat=0 +split_feature=49 35 35 4 86 126 53 +split_gain=76.5403 22.6651 15.0694 8.9646 8.76914 7.60561 6.47498 +threshold=2.9450000000000007 4.4150000000000009 2.7650000000000001 1704.4687685416313 3.0956959706959712 1.9500000000000002 1.5750000000000004 +decision_type=2 2 2 2 2 2 2 +left_child=1 4 5 -4 -1 -2 -3 +right_child=2 6 3 -5 -6 -7 -8 +leaf_value=-0.0003930069600231963 0.0051839413837609901 -0.0095756286665282294 0.001899591550178949 0.013037892732564322 -0.0032565772205568949 0.014092876847093247 -0.0039817001245813473 +leaf_weight=2532.2348488271236 469.80109861493111 70.775065153837204 1010.255486369133 21.038822740316391 346.77775746583939 29.022426664829254 355.47570630908012 +leaf_count=6999 1297 196 2791 58 959 80 984 +internal_value=0.00016962 -0.00127605 0.00329248 0.00212684 -0.000737933 0.00570233 -0.00491058 +internal_weight=4835.38 3305.26 1530.12 1031.29 2879.01 498.824 426.251 +internal_count=13364 9138 4226 2849 7958 1377 1180 +is_linear=0 +shrinkage=0.0168881 + + +Tree=5 +num_leaves=8 +num_cat=0 +split_feature=42 34 42 95 42 17 95 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9.43411 5.60277 5.1977 4.79863 4.27471 +threshold=-0.0017499999999999996 3.1500000000000008 27.850000000000005 -0.00074999999999999991 4.5000000000000009 1.9950000000000003 -2.4334999999999996 +decision_type=2 2 2 2 2 2 2 +left_child=4 2 -2 -3 6 -6 -1 +right_child=1 3 -4 -5 5 -7 -8 +leaf_value=0.024876230683378589 0.0023351234908217788 0.003708327319401369 0.02209660394622795 -2.2734230803227081e-06 0.0055416734962823481 -0.01089311140325069 -0.002728854998614538 +leaf_weight=1.6077006459236134 196.60187643021345 120.93700414896011 7.1405251473188391 2876.8189431726933 40.378643028438091 5.7930708974599829 313.54743275791407 +leaf_count=5 777 448 26 10747 142 20 1202 +internal_value=0.000113356 0.000330731 0.00302785 0.000147414 -0.0018126 0.00347906 -0.00258794 +internal_weight=3562.83 3201.5 203.742 2997.76 361.327 46.1717 315.155 +internal_count=13367 11998 803 11195 1369 162 1207 +is_linear=0 +shrinkage=0.0168881 + + +Tree=708 +num_leaves=7 +num_cat=0 +split_feature=95 31 48 85 79 130 +split_gain=6.15061 7.26923 5.72771 5.80008 5.33787 3.5667 +threshold=-8.2499999999999964 1.4173913043478261 3.3350000000000004 -0.35223443223443218 2.5967863419533024 2.2360000000000002 +decision_type=2 2 2 2 2 2 +left_child=1 4 3 -2 -1 -4 +right_child=2 -3 5 -5 -6 -7 +leaf_value=0.00046350607985591011 -0.0040735542447122635 0.021789995410993166 0.016201618842264436 8.289025104210155e-05 -0.0038761763484376609 0.0031873621918397962 +leaf_weight=119.53393113613129 98.2313202470541 3.5558714717626563 9.4691885262727755 3795.3525827676058 249.65275703370571 16.439448237419128 +leaf_count=485 346 12 30 11448 994 52 +internal_value=-0.00016645 -0.0022395 3.06902e-05 -2.19667e-05 -0.00247102 0.00794491 +internal_weight=4292.24 372.743 3919.49 3893.58 369.187 25.9086 +internal_count=13367 1491 11876 11794 1479 82 +is_linear=0 +shrinkage=0.0168881 + + +Tree=709 +num_leaves=8 +num_cat=0 +split_feature=130 37 7 56 104 104 135 +split_gain=4.94132 7.1931 4.89904 4.85345 6.93182 4.73708 3.26664 +threshold=0.41450000000000004 0.20137679838008113 12.248910181587123 2.3950000000000005 1.1845000000000001 1.2005000000000001 0.027000000000000007 +decision_type=2 2 2 2 2 2 2 +left_child=1 6 -3 5 -5 -2 -1 +right_child=3 2 -4 4 -6 -7 -8 +leaf_value=0.018507585044332718 6.8185343864898698e-05 -0.0077968863878921163 0.010705496831119467 -0.0016769944587721442 -0.013942044248838364 0.0021795744729230055 -0.0016586161876984775 +leaf_weight=5.1260463297367078 4265.8914237320423 56.85904061794281 4.3958612382411948 98.160828351974487 15.17514708638191 326.24718108773232 4.13825723528862 +leaf_count=15 11896 159 12 288 45 940 12 +internal_value=6.64749e-05 -0.00437082 -0.00646866 0.00013298 -0.00331947 0.000218194 0.00949712 +internal_weight=4775.99 70.5192 61.2549 4705.47 113.336 4592.14 9.2643 +internal_count=13367 198 171 13169 333 12836 27 +is_linear=0 +shrinkage=0.0168881 + + +Tree=710 +num_leaves=8 +num_cat=0 +split_feature=96 26 102 92 55 133 145 +split_gain=5.64109 11.4004 7.12092 9.26085 8.72753 7.09116 4.81856 +threshold=2.5000000000000004 0.55414285714285716 1.0105000000000002 0.81416666666666682 1.8450000000000004 1.6125000000000003 0.94975000000000009 +decision_type=2 2 2 2 2 2 2 +left_child=1 5 3 -2 -4 -1 -3 +right_child=2 6 4 -5 -6 -7 -8 +leaf_value=0.00059338451073421585 -0.0015579579289755464 -0.0043200486450900881 6.3245910237407348e-05 0.021246175426076062 0.0055822125047362611 0.0033170534634245166 0.0090506749074731521 +leaf_weight=852.40333483368158 1025.1217227876186 135.35283450037241 1047.3770984560251 5.1040273308753958 88.638933099806309 400.84991509467363 8.1488171815872175 +leaf_count=2906 3839 563 4133 20 450 1425 31 +internal_value=0.000111405 0.000948263 -0.000428176 -0.00144495 0.000493892 0.00146456 -0.0035606 +internal_weight=3563 1396.75 2166.24 1030.23 1136.02 1253.25 143.502 +internal_count=13367 4925 8442 3859 4583 4331 594 +is_linear=0 +shrinkage=0.0168881 + + +end of trees + +feature_importances: +Column_95=327 +Column_85=281 +Column_34=233 +Column_36=214 +Column_35=145 +Column_42=134 +Column_86=127 +Column_27=97 +Column_40=88 +Column_49=79 +Column_6=71 +Column_99=70 +Column_29=69 +Column_145=69 +Column_141=68 +Column_39=63 +Column_96=63 +Column_28=60 +Column_26=58 +Column_2=57 +Column_144=57 +Column_30=55 +Column_53=53 +Column_41=52 +Column_37=51 +Column_130=51 +Column_137=50 +Column_31=49 +Column_47=49 +Column_7=48 +Column_11=48 +Column_4=45 +Column_91=43 +Column_143=43 +Column_1=42 +Column_3=42 +Column_123=40 +Column_133=39 +Column_0=38 +Column_148=38 +Column_9=37 +Column_93=37 +Column_120=36 +Column_134=36 +Column_136=36 +Column_132=35 +Column_142=35 +Column_48=34 +Column_97=34 +Column_38=33 +Column_147=33 +Column_44=31 +Column_102=31 +Column_135=29 +Column_111=28 +Column_10=27 +Column_80=27 +Column_92=27 +Column_127=27 +Column_56=26 +Column_55=25 +Column_100=25 +Column_110=25 +Column_5=24 +Column_8=24 +Column_50=24 +Column_94=24 +Column_78=23 +Column_89=23 +Column_54=22 +Column_121=22 +Column_126=22 +Column_51=21 +Column_103=21 +Column_149=21 +Column_77=20 +Column_90=20 +Column_104=20 +Column_105=20 +Column_106=20 +Column_115=20 +Column_131=20 +Column_98=19 +Column_109=19 +Column_138=19 +Column_12=18 +Column_52=18 +Column_122=18 +Column_17=17 +Column_124=17 +Column_23=16 +Column_79=16 +Column_125=15 +Column_146=14 +Column_45=13 +Column_46=12 +Column_101=12 +Column_150=10 +Column_84=9 +Column_119=9 +Column_112=8 +Column_113=8 +Column_114=8 +Column_128=8 +Column_14=7 +Column_118=7 +Column_151=6 +Column_13=5 +Column_22=5 +Column_107=5 +Column_15=4 +Column_116=4 +Column_117=4 +Column_16=3 +Column_18=3 +Column_19=3 +Column_21=3 +Column_24=3 +Column_64=3 +Column_129=3 +Column_20=2 +Column_69=2 +Column_81=2 +Column_108=2 +Column_140=2 +Column_62=1 +Column_65=1 +Column_70=1 +Column_71=1 +Column_73=1 + +parameters: +[boosting: gbdt] +[objective: multiclass] +[metric: multi_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0168881] +[num_leaves: 8] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 3] +[min_data_in_leaf: 12] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.70695] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 3] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.543465] +[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: 0.00145086] +[lambda_l2: 0.000235889] +[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: 3] +[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/v25/lgb_v25_htft.txt b/ai-engine/models/v25/lgb_v25_htft.txt new file mode 100644 index 0000000..4e36b95 --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_htft.txt @@ -0,0 +1,43358 @@ +tree +version=v4 +num_class=9 +num_tree_per_iteration=9 +label_index=0 +max_feature_idx=151 +objective=multiclass num_class:9 +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 Column_99 Column_100 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+shrinkage=0.0130895 + + +Tree=2267 +num_leaves=8 +num_cat=0 +split_feature=95 42 123 137 34 7 31 +split_gain=10.3979 12.5363 9.90909 7.93112 7.34196 14.4617 9.72394 +threshold=-2.2499999999999982 1.5850000000000002 1.5665000000000002 7.7500000000000009 1.425 -25.78318322104337 0.12352614787071711 +decision_type=2 2 2 2 2 2 2 +left_child=1 2 -1 -3 5 -2 -6 +right_child=4 3 -4 -5 6 -7 -8 +leaf_value=0.001208048045939925 0.0070710489833403249 -0.00069687974918569717 0.0086896176083588426 0.0019958687257931283 0.0019901707672341992 -0.0033052653187668323 -0.00092774213766257606 +leaf_weight=66.077649034559727 15.880545008927582 412.26368103176355 52.781533420085907 328.27730911970139 258.30592950060964 267.46529527381063 714.2157204002142 +leaf_count=308 105 2122 217 1677 1898 3084 4751 +internal_value=6.79495e-05 0.00116992 0.00494225 0.00050736 -0.000689386 -0.00250507 -0.000140512 +internal_weight=2115.27 859.4 118.859 740.541 1255.87 283.346 972.522 +internal_count=14162 4324 525 3799 9838 3189 6649 +is_linear=0 +shrinkage=0.0130895 + + +end of trees + +feature_importances: +Column_85=1429 +Column_95=692 +Column_36=557 +Column_34=521 +Column_35=453 +Column_40=422 +Column_42=384 +Column_4=283 +Column_148=268 +Column_145=256 +Column_27=245 +Column_99=242 +Column_3=231 +Column_26=206 +Column_30=203 +Column_29=192 +Column_31=188 +Column_86=187 +Column_28=168 +Column_136=167 +Column_39=158 +Column_56=157 +Column_149=157 +Column_130=153 +Column_96=148 +Column_1=144 +Column_110=144 +Column_2=143 +Column_7=141 +Column_104=140 +Column_103=137 +Column_6=136 +Column_137=136 +Column_147=136 +Column_44=134 +Column_49=134 +Column_142=132 +Column_105=131 +Column_98=129 +Column_38=126 +Column_5=124 +Column_141=124 +Column_132=121 +Column_93=120 +Column_80=118 +Column_91=118 +Column_133=117 +Column_134=117 +Column_11=116 +Column_92=115 +Column_131=115 +Column_135=113 +Column_111=111 +Column_8=109 +Column_0=108 +Column_41=106 +Column_123=106 +Column_120=104 +Column_144=104 +Column_97=102 +Column_37=101 +Column_121=99 +Column_102=98 +Column_127=97 +Column_94=96 +Column_90=93 +Column_9=90 +Column_53=89 +Column_122=89 +Column_114=81 +Column_143=81 +Column_10=77 +Column_22=73 +Column_115=71 +Column_47=70 +Column_126=70 +Column_106=67 +Column_79=65 +Column_55=61 +Column_51=60 +Column_46=59 +Column_52=58 +Column_118=58 +Column_89=57 +Column_100=57 +Column_101=50 +Column_113=50 +Column_23=49 +Column_150=49 +Column_109=47 +Column_107=46 +Column_12=42 +Column_138=40 +Column_146=39 +Column_13=37 +Column_16=37 +Column_54=37 +Column_124=34 +Column_20=33 +Column_48=33 +Column_125=33 +Column_19=31 +Column_119=31 +Column_50=30 +Column_116=30 +Column_45=29 +Column_78=27 +Column_84=27 +Column_14=24 +Column_117=24 +Column_112=22 +Column_18=21 +Column_77=20 +Column_15=19 +Column_17=19 +Column_108=18 +Column_21=17 +Column_25=17 +Column_24=16 +Column_128=16 +Column_140=14 +Column_139=13 +Column_151=6 +Column_129=5 +Column_81=3 +Column_58=1 + +parameters: +[boosting: gbdt] +[objective: multiclass] +[metric: multi_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0130895] +[num_leaves: 8] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 3] +[min_data_in_leaf: 44] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.744087] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 4] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.790605] +[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: 2.01297e-05] +[lambda_l2: 9.87919] +[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: 9] +[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/v25/lgb_v25_ms.txt b/ai-engine/models/v25/lgb_v25_ms.txt new file mode 100644 index 0000000..f41f6c9 --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_ms.txt @@ -0,0 +1,10014 @@ +tree +version=v4 +num_class=3 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Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 +feature_infos=[1150.3663761896189:1903.4781806887747] [1158.5088961211511:1916.84579108047] [-496.81477567713546:573.10259120534784] [1159.8767670517543:1884.5959848901657] [1151.7894548779084:1919.4116678360419] [1426.1496448360797:1585.9817930954068] [1427.9817118206745:1588.9895054335384] 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+Column_142=23 +Column_1=22 +Column_92=21 +Column_93=21 +Column_130=21 +Column_141=21 +Column_105=20 +Column_122=20 +Column_28=19 +Column_110=19 +Column_9=18 +Column_91=18 +Column_94=18 +Column_131=18 +Column_135=18 +Column_6=16 +Column_80=16 +Column_102=16 +Column_121=16 +Column_147=16 +Column_5=15 +Column_47=15 +Column_48=15 +Column_90=15 +Column_144=15 +Column_10=14 +Column_126=14 +Column_133=14 +Column_137=13 +Column_11=12 +Column_30=12 +Column_46=12 +Column_89=12 +Column_104=12 +Column_111=12 +Column_146=12 +Column_8=11 +Column_103=11 +Column_127=11 +Column_15=10 +Column_23=10 +Column_52=10 +Column_79=10 +Column_114=10 +Column_55=9 +Column_86=9 +Column_98=9 +Column_132=9 +Column_138=9 +Column_149=9 +Column_45=8 +Column_77=8 +Column_99=8 +Column_101=8 +Column_106=8 +Column_119=8 +Column_124=8 +Column_14=7 +Column_100=7 +Column_109=7 +Column_118=7 +Column_150=7 +Column_50=6 +Column_78=6 +Column_84=6 +Column_112=6 +Column_12=5 +Column_20=5 +Column_44=5 +Column_107=5 +Column_115=5 +Column_125=5 +Column_13=4 +Column_24=4 +Column_25=4 +Column_108=4 +Column_57=3 +Column_113=3 +Column_18=2 +Column_117=2 +Column_140=2 +Column_151=2 +Column_16=1 +Column_17=1 +Column_19=1 +Column_21=1 +Column_22=1 +Column_58=1 +Column_67=1 +Column_75=1 +Column_81=1 +Column_128=1 +Column_129=1 + +parameters: +[boosting: gbdt] +[objective: multiclass] +[metric: multi_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0209926] +[num_leaves: 8] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 3] +[min_data_in_leaf: 16] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.645261] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 5] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.595124] +[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: 0.227704] +[lambda_l2: 1.15312e-05] +[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: 3] +[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/v25/lgb_v25_odd_even.txt b/ai-engine/models/v25/lgb_v25_odd_even.txt new file mode 100644 index 0000000..23bc6db --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_odd_even.txt @@ -0,0 +1,1556 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 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+[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/v25/lgb_v25_ou15.txt b/ai-engine/models/v25/lgb_v25_ou15.txt new file mode 100644 index 0000000..822fb85 --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_ou15.txt @@ -0,0 +1,6944 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 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-0.0192202 -0.00256776 -0.00011286 0.00453495 -0.000282613 -0.000284028 0.0013605 0.00144816 0.00112048 0.00615881 0.000718026 0.0074621 0.00317632 0.0153706 0.0191022 +internal_weight=2726.77 2007.81 127.602 91.4787 21.8887 69.5901 36.1232 1880.21 153.014 6.34111 146.673 1727.19 60.8597 1666.33 31.4561 718.962 716.008 679.011 50.2268 628.784 36.9971 23.9941 13.003 4.66712 +internal_count=15890 11320 788 564 127 437 224 10532 995 47 948 9537 346 9191 193 4570 4553 4228 255 3973 325 219 106 31 +is_linear=0 +shrinkage=0.0149824 + + +end of trees + +feature_importances: +Column_86=402 +Column_26=170 +Column_27=151 +Column_30=137 +Column_3=134 +Column_41=132 +Column_28=131 +Column_136=131 +Column_31=130 +Column_141=128 +Column_90=125 +Column_145=121 +Column_144=117 +Column_6=116 +Column_92=114 +Column_35=112 +Column_4=111 +Column_49=111 +Column_89=108 +Column_111=107 +Column_93=105 +Column_102=102 +Column_98=101 +Column_85=100 +Column_126=98 +Column_143=97 +Column_1=96 +Column_91=96 +Column_29=94 +Column_131=94 +Column_130=93 +Column_137=93 +Column_11=92 +Column_96=92 +Column_53=91 +Column_9=88 +Column_79=88 +Column_133=88 +Column_47=87 +Column_135=87 +Column_149=86 +Column_110=85 +Column_80=84 +Column_38=83 +Column_56=80 +Column_142=80 +Column_8=79 +Column_55=79 +Column_10=78 +Column_5=76 +Column_120=76 +Column_123=76 +Column_7=74 +Column_52=74 +Column_104=74 +Column_121=74 +Column_122=74 +Column_34=73 +Column_51=71 +Column_134=71 +Column_0=70 +Column_23=70 +Column_36=70 +Column_37=70 +Column_147=69 +Column_94=68 +Column_99=67 +Column_132=66 +Column_39=65 +Column_103=65 +Column_54=64 +Column_114=64 +Column_48=60 +Column_46=59 +Column_40=58 +Column_45=58 +Column_50=57 +Column_119=55 +Column_148=55 +Column_127=54 +Column_2=53 +Column_105=51 +Column_44=49 +Column_95=48 +Column_138=47 +Column_106=46 +Column_42=44 +Column_146=44 +Column_100=42 +Column_101=41 +Column_150=37 +Column_84=36 +Column_125=36 +Column_78=35 +Column_115=35 +Column_24=30 +Column_107=30 +Column_118=30 +Column_19=29 +Column_77=28 +Column_14=27 +Column_124=26 +Column_15=25 +Column_16=25 +Column_109=25 +Column_117=25 +Column_12=24 +Column_18=24 +Column_22=24 +Column_112=23 +Column_108=22 +Column_97=21 +Column_20=18 +Column_128=17 +Column_13=16 +Column_113=16 +Column_116=16 +Column_43=15 +Column_17=13 +Column_21=13 +Column_25=13 +Column_140=11 +Column_151=9 +Column_81=8 +Column_129=8 +Column_63=6 +Column_139=5 +Column_65=3 +Column_32=2 +Column_33=1 +Column_64=1 +Column_68=1 +Column_72=1 +Column_73=1 +Column_74=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0149824] +[num_leaves: 31] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 5] +[min_data_in_leaf: 14] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.774602] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 7] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.535942] +[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: 8.50081e-07] +[lambda_l2: 2.53648e-05] +[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/v25/lgb_v25_ou25.txt b/ai-engine/models/v25/lgb_v25_ou25.txt new file mode 100644 index 0000000..aee7efc --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_ou25.txt @@ -0,0 +1,1387 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 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+Column_81=1 +Column_84=1 +Column_104=1 +Column_113=1 +Column_120=1 +Column_125=1 +Column_131=1 +Column_135=1 +Column_136=1 +Column_138=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0910033] +[num_leaves: 8] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 3] +[min_data_in_leaf: 8] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.891603] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 6] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.853429] +[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: 7.37439e-06] +[lambda_l2: 1.10363e-07] +[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/v25/lgb_v25_ou35.txt b/ai-engine/models/v25/lgb_v25_ou35.txt new file mode 100644 index 0000000..61faafb --- /dev/null +++ b/ai-engine/models/v25/lgb_v25_ou35.txt @@ -0,0 +1,2866 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=151 +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 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 +feature_infos=[1150.3663761896189:1903.4781806887747] [1158.5088961211511:1916.84579108047] [-496.81477567713546:573.10259120534784] [1159.8767670517543:1884.5959848901657] [1151.7894548779084:1919.4116678360419] [1426.1496448360797:1585.9817930954068] [1427.9817118206745:1588.9895054335384] [-113.02538114266532:114.69704651598067] [0:6.7333333333333334] [0:7.5333333333333332] [0:7.7333333333333334] [0:6.583333333333333] [0:1] [0:1] [0:1] [0:1] [0:5] [0:5] [0:5] [0:5] [0:8] [0:1] [0:1] [0:12] [0:1] [0:1] [0.20999999999999999:0.81000000000000005] [0.22500000000000001:0.76000000000000001] [0:13] [0:13] [0:30] [0:24] [0:4.5] [0:4.5] [0:22.550000000000001] [0:17.5] [0:35.5] [0.065285302506677897:0.80962131918207236] [0.090561906180844906:0.4288719106684401] [0.056651501780225801:0.79902050363656307] [0:26] [0:5.0899999999999999] [0:26.5] none [0:13.050000000000001] [0:1.76] [0:13.25] [0:3.7400000000000002] [0:12.6] [0:8.5600000000000005] [0:3.5299999999999998] [0:1.72] [0:7.3300000000000001] [0:5.0199999999999996] [0:2.5800000000000001] [0:3.77] [0:4.9100000000000001] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:7.5333333333333332] [0:6.583333333333333] [2.0046118370484245:4.1840324763193504] [0.018042399639151999:0.1552651806302843] [0:0.45000000000000001] none none [0:0.1575] [-0.92000000000000004:1] [0:7] none none [0:1] [-1:0.61250000000000004] [-1:0.59166666666666667] [-1.3574999999999999:1.3799999999999999] [0:40.799999999999997] [0:36.299999999999997] [-29.999999999999996:31.499999999999996] [0:10] [0:10] [0:1] [0:1] [0:5.7999999999999998] [0:5] [0:5] [0:6.25] [0:4] [0:5] [0:1] [0:1] [0:10] [0:11] [0:42.100000000000001] [0:41.799999999999997] [0:1] [0:1] [0:11] [0:9] [-1:1] [0:1] [0:6.2000000000000002] [0:7] [0:5.2000000000000002] [0:6.5] [0:5] [0:6.5] [0:6] [0:7] [0:5.3330000000000002] [0:7] [0:1] [0:1] [0:8] [0:8] [0:11] [0:8.5] [-2.2999999999999998:1.8] [-2.2000000000000002:2] [1:30] [1:30] [1:12] [0:1] [0:1] [-5.1669999999999998:5.4000000000000004] [-6.4669999999999996:5.7329999999999997] [-4.5830000000000002:5.7329999999999997] [-0.045499999999999999:0.088099999999999998] [0.063600000000000004:1] [0:0.088800000000000004] [0.35623409669211198:0.51556156968876865] [0.1051136363636363:0.32744043043812449] [0.40430438124519602:0.64185836716283262] [0.32129131437355879:0.77537212449255755] [0.70399999999999996:1] +tree_sizes=2962 3216 3255 3098 3229 3462 2897 3356 2916 3242 3034 2910 3336 3134 3257 3318 3032 3328 3233 2920 3452 3462 3466 3254 2919 3054 3029 3351 3150 3144 3035 3144 3047 3045 2920 3246 3154 3353 3061 3128 3189 3267 3171 2622 3045 3061 2957 2507 2826 3040 3260 2718 3040 2077 2298 2411 3056 2603 2301 3360 3249 2936 3144 3062 2823 2399 2182 2505 3049 2501 2833 2521 2939 3076 2950 2944 3061 3267 2960 3249 2592 2736 2747 3142 2700 2954 2500 2838 2719 2848 2815 2187 2523 2626 2301 3052 3053 3388 2525 2859 2733 2421 2951 2734 1975 2283 2611 2077 2306 2194 2504 3043 2281 2951 2614 2415 1768 2732 3046 2281 3059 2407 3071 2606 1544 2198 3286 3163 2088 2493 2074 2190 1638 1754 1651 3078 1979 + +Tree=0 +num_leaves=27 +num_cat=0 +split_feature=54 45 150 54 38 35 150 148 120 48 27 81 99 41 55 79 50 114 26 46 46 7 53 145 28 121 +split_gain=424.808 180.707 95.7328 68.1568 31.8626 25.5157 19.1515 17.7496 15.7681 15.5018 13.5013 11.6217 12.5737 11.3145 10.8755 10.2953 9.34621 9.56768 8.15417 7.75648 6.69457 6.64778 6.36922 6.1314 5.35715 4.45995 +threshold=1.3950000000000002 1.135 0.62017637310606066 1.5950000000000004 0.30113296764963554 7.5150000000000006 0.46962878840809003 0.26796584848230992 2.5280000000000005 2.0650000000000004 0.62050000000000016 0.22500000000000003 0.36931818181818182 3.4850000000000008 1.6850000000000003 2.8098448471773501 1.8250000000000004 2.4500000000000006 0.43683333333333335 7.205000000000001 2.8450000000000002 2.1416577046417165 2.2550000000000003 0.52015000000000011 4.8819444444444455 1.6835000000000002 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 2 6 7 10 8 22 11 -4 -8 14 12 -2 16 -3 23 17 -5 25 -18 -12 -20 -1 -9 -17 -6 +right_child=3 4 5 13 18 -7 9 15 -10 -11 20 -13 -14 -15 -16 24 19 -19 21 -21 -22 -23 -24 -25 -26 -27 +leaf_value=-0.74168991532519768 -0.72595558796999948 -0.73561191879415433 -0.71969868672153181 -0.7128078688323185 -0.7547274047438095 -0.68645657215591205 -0.73032581841141109 -0.73173539073032556 -0.70315048749743858 -0.71815396452613434 -0.73325777151773464 -0.73373713280703956 -0.71575654796609078 -0.68408021870861802 -0.740673009624088 -0.70979727361993794 -0.69843674417510171 -0.69579264475049951 -0.7465419983248659 -0.71621086859978011 -0.71081056141788901 -0.73898492416349393 -0.73260082533780901 -0.75129969636346683 -0.73019295716017185 -0.74035087800752097 +leaf_weight=77.034438729286194 57.062547206878662 333.37695848941803 142.21742534637451 87.130120158195496 51.136821150779724 11.851452112197874 945.70198428630829 18.874534845352176 27.872859597206116 44.333209753036499 10.754095435142519 17.996649503707886 343.03369724750519 9.8762100934982282 354.66567802429199 10.534624099731447 68.255585312843323 15.801936149597166 137.16958463191986 11.631980776786802 10.754095435142515 71.767126679420471 52.453649163246155 9.8762100934982282 10.315152764320372 10.534624099731444 +leaf_count=351 260 1519 648 397 233 54 4309 86 127 202 49 82 1563 45 1616 48 311 72 625 53 49 327 239 45 47 48 +internal_value=-0.729333 -0.733446 -0.728533 -0.715124 -0.73997 -0.714998 -0.730732 -0.719272 -0.716987 -0.729781 -0.73773 -0.717923 -0.717211 -0.705055 -0.738221 -0.730651 -0.706188 -0.710196 -0.745844 -0.701025 -0.722034 -0.743946 -0.738008 -0.738456 -0.719888 -0.752272 +internal_weight=2942.01 2281.62 1301.47 660.389 980.159 181.942 1119.52 467.693 170.09 990.035 709.551 418.093 400.096 192.696 688.043 49.6005 182.82 102.932 270.608 79.8876 21.5082 208.937 129.488 28.7507 20.8498 61.6714 +internal_count=13405 10396 5930 3009 4466 829 5101 2131 775 4511 3233 1905 1823 878 3135 226 833 469 1233 364 98 952 590 131 95 281 +is_linear=0 +shrinkage=1 + + +Tree=1 +num_leaves=29 +num_cat=0 +split_feature=47 54 86 150 47 35 86 53 89 47 46 56 86 96 36 96 93 29 26 86 145 98 119 4 92 34 120 124 +split_gain=440.563 136.247 95.7124 67.1278 58.2511 44.4262 29.2145 21.9333 21.9209 20.126 14.8957 12.0126 11.9254 15.5148 11.4219 10.7589 10.3237 9.8267 9.40391 9.3898 8.16894 7.98735 7.87988 7.74473 7.25661 7.13625 6.55258 4.66647 +threshold=1.4650000000000001 1.4950000000000003 3.0750469043151978 0.47994140021624965 2.0050000000000003 8.3900000000000023 3.3279569892473124 1.6150000000000004 0.17000000000000001 1.6950000000000001 3.0950000000000002 3.0450000000000004 2.9128787878787885 4.5000000000000009 4.0850000000000009 3.5000000000000004 19.050000000000004 3.683333333333334 0.46316666666666667 2.9205263157894739 0.52505000000000013 0.58114035087719318 1.1000000000000003 1554.5945392565027 0.027083333333333303 1.905 1.1550000000000002 2.5500000000000003 +decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 +left_child=1 3 4 16 9 6 -5 11 22 19 15 24 13 -9 -11 -10 20 -12 25 -2 -1 -14 -4 -18 -3 -6 -20 -13 +right_child=2 7 8 5 18 -7 -8 12 10 14 17 27 21 -15 -16 -17 23 -19 26 -21 -22 -23 -24 -25 -26 -27 -28 -29 +leaf_value=-0.0078344325971515544 -0.0039374879117112611 0.027829983148718065 0.016854212065858326 0.0052990197405180092 -0.010115304041419486 0.042942902985927484 0.018286415236901877 0.009031340142167112 -0.011729136443923269 -0.0073498004788129512 0.027623714611151588 0.012027375359343118 0.039746520424980211 0.020952197314502433 -0.015332844530670346 0.011466398847477374 -0.0078093859065286337 0.0079050480423784805 -0.015962774584830447 0.006337151825499091 -0.020773059540905952 0.020245478474479391 0.039445300352486029 0.01232731301479186 0.041903829968767181 -0.022254607214906772 -0.0090934139421602958 0.027620314143079798 +leaf_weight=47.731353551149368 575.47346901893616 64.256763190031052 12.503900289535524 757.75079092383385 24.677432283759117 13.762317091226576 77.246921703219414 105.81705455482006 22.330225020647049 485.08535474538803 14.708136379718779 23.902182891964912 12.124556124210356 75.872730880975723 85.294927284121513 12.69371461868286 19.261691108345985 33.590544447302818 121.66737654805183 38.400411948561668 33.691724762320518 28.452211514115334 12.49541652202606 12.912292093038557 19.279730141162872 95.298493966460228 104.46207454800606 11.509792760014532 +leaf_count=218 2623 290 57 3445 113 62 351 480 102 2216 67 108 55 344 390 58 88 153 557 175 154 129 57 59 87 437 478 52 +internal_value=0.00029217 0.0090204 -0.00665114 0.00514759 -0.00794468 0.00709142 0.00650051 0.0199432 0.0116239 -0.0058228 0.00666654 0.0269156 0.0162118 0.0140095 -0.0085436 -0.00332218 -0.00937598 0.01391 -0.015205 -0.00329475 -0.0131883 0.0260727 0.0281462 0.000271796 0.0310782 -0.0197578 -0.0127895 0.0170957 +internal_weight=2942.25 1303.57 1638.68 962.357 1530.36 848.76 834.998 341.215 108.322 1184.25 83.3226 118.948 222.267 181.69 570.38 35.0239 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+Column_84=5 +Column_12=3 +Column_108=3 +Column_129=3 +Column_140=3 +Column_21=2 +Column_67=1 +Column_68=1 +Column_81=1 +Column_139=1 + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 1500] +[learning_rate: 0.0201179] +[num_leaves: 31] +[num_threads: 4] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 5] +[min_data_in_leaf: 45] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.656252] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 5] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.620895] +[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: 0.00036062] +[lambda_l2: 3.05269e-05] +[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] 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b/ai-engine/models/v25/xgb_v25_btts.json new file mode 100644 index 0000000..d41d3e5 --- /dev/null +++ b/ai-engine/models/v25/xgb_v25_btts.json @@ -0,0 +1 @@ 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2026-05-20 + +Bu dokuman, 20 Mayis 2026 tarihinde yapilan **League Tier Sistemi** calismasini ozetler. Amac: model egitiminde kullanilan ligleri kalite bazli filtreleyerek tahmin dogrulugunu artirmak. + +## 1. Problem Analizi + +- Model 443 lig ile egitiliyordu (DB'de toplam 1142 lig). +- Dusuk kaliteli ligler (zayif bahis pazari, eksik veri) modelin sinyalini bozuyordu. +- Mevcut performans: **MS %26.9** (rastgeleden kotu), **OU2.5 %57.7**, **BTTS %53.8** +- Model hic "X" (draw) tahmini yapmiyordu — agir home bias vardi. +- 19 gercek bahisten 5 kazanc, 14 kayip = **%26.3 win rate, -9.68 unit** + +## 2. Cozum: League Tier Sistemi + +Bookmaker margin (overround) analizine dayanarak 48 kaliteli lig secildi: + +| Tier | Isim | Lig Sayisi | Kriter | +|------|------|-----------|--------| +| 1 | Elmas | 10 | En iyi veri kalitesi, en dusuk margin (Premier League, La Liga, Serie A vb.) | +| 2 | Altin | 20 | Iyi veri kalitesi (Eredivisie, Ligue 2, Championship vb.) | +| 3 | Gumus | 18 | Kabul edilebilir veri (Ekstraklasa, MLS, Liga MX vb.) | + +## 3. Veritabani Degisiklikleri + +### Yeni tablo: `league_tiers` +```sql +CREATE TABLE league_tiers ( + id SERIAL PRIMARY KEY, + league_id TEXT NOT NULL UNIQUE REFERENCES leagues(id) ON DELETE CASCADE, + tier INT NOT NULL DEFAULT 1, + is_active BOOLEAN NOT NULL DEFAULT TRUE, + added_by TEXT, + notes TEXT, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW() +); +CREATE INDEX idx_league_tiers_tier_active ON league_tiers(tier, is_active); +``` + +### Prisma schema guncellendi +- `LeagueTier` modeli eklendi (`prisma/schema.prisma`) +- `League` modeline `leagueTier LeagueTier?` iliskisi eklendi +- Not: Migration shadow DB hatasi nedeniyle tablo dogrudan SQL ile olusturuldu + +## 4. Backend Degisiklikleri + +### Yeni dosyalar + +**`src/modules/leagues/league-tiers.service.ts`** +- `findAll()` — tum tier'lari listele +- `findActive()` — aktif tier'lari listele +- `findByTier(tier)` — belirli tier'daki ligler +- `addLeague(leagueId, tier, notes, addedBy)` — lig ekle/guncelle (upsert), otomatik JSON sync + retrain tetikle +- `removeLeague(leagueId)` — ligi pasif yap (soft delete), JSON sync +- `updateTier(leagueId, tier)` — tier seviyesi degistir +- `deleteLeague(leagueId)` — kalici sil +- `syncQualifiedLeagues()` — DB'den `qualified_leagues.json` dosyasini guncelle +- `triggerModelRetrain()` — AI engine'e POST `/v1/admin/retrain` +- `getStats()` — tier istatistikleri + toplam qualified match sayisi + +**`src/modules/leagues/league-tiers.controller.ts`** +- `GET /admin/league-tiers` — listele (`?active=true` filtreli) +- `GET /admin/league-tiers/stats` — istatistikler +- `GET /admin/league-tiers/tier/:tier` — tier bazli filtre +- `POST /admin/league-tiers` — lig ekle (body: `{ leagueId, tier?, notes?, addedBy? }`) +- `PUT /admin/league-tiers/:leagueId/tier` — tier guncelle +- `PUT /admin/league-tiers/:leagueId/deactivate` — pasif yap +- `DELETE /admin/league-tiers/:leagueId` — kalici sil +- `POST /admin/league-tiers/sync` — JSON sync tetikle +- `POST /admin/league-tiers/retrain` — model retrain tetikle +- Tum endpointler `@Roles("superadmin")` ile korumali + +### Guncellenen dosyalar + +**`src/modules/leagues/leagues.module.ts`** +- `HttpModule` import eklendi (retrain HTTP cagirisi icin) +- `LeagueTiersController` ve `LeagueTiersService` register edildi + +## 5. qualified_leagues.json Sync + +- Onceki durum: **443 lig** +- Yeni durum: **48 lig** (DB'deki aktif tier'lardan) +- `data-fetcher.task.ts` zaten bu dosyayi okuyor — yeni mac verisi sadece 48 lig icin cekilecek +- `extract_training_data.py` zaten bu dosyayi okuyor — egitim verisi sadece 48 ligden gelecek + +## 6. Mevcut Veri Akisi + +``` +Admin Panel (lig ekle/cikar) + | + v +LeagueTiersService.addLeague() / removeLeague() + | + +---> DB guncelle (league_tiers tablosu) + +---> syncQualifiedLeagues() ---> qualified_leagues.json guncelle + +---> triggerModelRetrain() ---> AI Engine POST /v1/admin/retrain + +Data Fetcher (cron): + qualified_leagues.json ---> sadece bu liglerden live_matches cek + +Training Pipeline: + qualified_leagues.json ---> sadece bu liglerden training_data extract et +``` + +## 7. Yapilacaklar + +- [ ] AI engine'de `/v1/admin/retrain` endpoint olustur (Python tarafi) +- [ ] Modeli yeni 48 lig dataseti ile yeniden egit +- [ ] Admin panel frontend'inde league tier yonetim UI'i +- [ ] Draw blindness sorununu arastir (model hic X tahmini yapmiyor) +- [ ] Prisma migration gecmisini duzelt (manual SQL → migration align) diff --git a/prisma/schema.prisma b/prisma/schema.prisma index d0cfe62..97ec65b 100755 --- a/prisma/schema.prisma +++ b/prisma/schema.prisma @@ -35,6 +35,7 @@ model League { country Country? @relation(fields: [countryId], references: [id]) liveMatches LiveMatch[] matches Match[] + leagueTier LeagueTier? @@unique([name, countryId, sport]) @@index([sport]) @@ -807,6 +808,31 @@ model Translation { @@map("translations") } +// ───────────────────────────────────────────────────────────── +// League Tier System — Controls which leagues are used for +// model training, live data fetching, and predictions. +// Managed from admin panel. When a league is added/removed, +// qualified_leagues.json is auto-regenerated and model +// retraining is triggered. +// ───────────────────────────────────────────────────────────── + +model LeagueTier { + id Int @id @default(autoincrement()) + leagueId String @map("league_id") + tier Int @default(1) // 1=Elmas, 2=Altın, 3=Gümüş + isActive Boolean @default(true) @map("is_active") + addedBy String? @map("added_by") // admin user id + notes String? // e.g. "Margin 0.138, 828 maç" + createdAt DateTime @default(now()) @map("created_at") + updatedAt DateTime @updatedAt @map("updated_at") + + league League @relation(fields: [leagueId], references: [id], onDelete: Cascade) + + @@unique([leagueId]) + @@index([tier, isActive]) + @@map("league_tiers") +} + // ───────────────────────────────────────────────────────────── // Enums // ───────────────────────────────────────────────────────────── diff --git a/qualified_leagues.json b/qualified_leagues.json index d070f05..6eaa76f 100644 --- 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LeagueTiersController { + constructor(private readonly leagueTiersService: LeagueTiersService) {} + + // ─── READ ────────────────────────────────────────────────── + + @Get() + @ApiOperation({ summary: "Get all league tiers" }) + @ApiResponse({ status: 200, description: "All league tiers" }) + @ApiQuery({ + name: "active", + required: false, + type: Boolean, + description: "Filter active only", + }) + async findAll(@Query("active") active?: string) { + if (active === "true") { + return this.leagueTiersService.findActive(); + } + return this.leagueTiersService.findAll(); + } + + @Get("stats") + @ApiOperation({ summary: "Get league tier statistics" }) + @ApiResponse({ status: 200, description: "Tier statistics" }) + async getStats() { + return this.leagueTiersService.getStats(); + } + + @Get("tier/:tier") + @ApiOperation({ summary: "Get leagues by tier level" }) + @ApiParam({ name: "tier", description: "Tier level (1=Diamond, 2=Gold, 3=Silver)" }) + async findByTier(@Param("tier") tier: string) { + return this.leagueTiersService.findByTier(parseInt(tier, 10)); + } + + // ─── WRITE ───────────────────────────────────────────────── + + @Post() + @ApiOperation({ summary: "Add a league to tiers (upsert)" }) + @ApiResponse({ status: 201, description: "League added/updated" }) + async addLeague( + @Body() + body: { + leagueId: string; + tier?: number; + notes?: string; + addedBy?: string; + }, + ) { + return this.leagueTiersService.addLeague( + body.leagueId, + body.tier, + body.notes, + body.addedBy, + ); + } + + @Put(":leagueId/tier") + @ApiOperation({ summary: "Update league tier level" }) + @ApiParam({ name: "leagueId", description: "League ID" }) + async updateTier( + @Param("leagueId") leagueId: string, + @Body() body: { tier: number }, + ) { + return this.leagueTiersService.updateTier(leagueId, body.tier); + } + + @Put(":leagueId/deactivate") + @ApiOperation({ summary: "Deactivate a league (soft remove)" }) + @ApiParam({ name: "leagueId", description: "League ID" }) + async removeLeague(@Param("leagueId") leagueId: string) { + return this.leagueTiersService.removeLeague(leagueId); + } + + @Delete(":leagueId") + @ApiOperation({ summary: "Permanently delete a league tier" }) + @ApiParam({ name: "leagueId", description: "League ID" }) + async deleteLeague(@Param("leagueId") leagueId: string) { + return this.leagueTiersService.deleteLeague(leagueId); + } + + // ─── SYNC & RETRAIN ─────────────────────────────────────── + + @Post("sync") + @ApiOperation({ summary: "Sync qualified_leagues.json from DB" }) + @ApiResponse({ status: 200, description: "Sync result with count and path" }) + async syncQualifiedLeagues() { + return this.leagueTiersService.syncQualifiedLeagues(); + } + + @Post("retrain") + @ApiOperation({ summary: "Trigger AI model retrain" }) + @ApiResponse({ status: 200, description: "Retrain trigger result" }) + async triggerRetrain() { + return this.leagueTiersService.triggerModelRetrain(); + } +} diff --git a/src/modules/leagues/league-tiers.service.ts b/src/modules/leagues/league-tiers.service.ts new file mode 100644 index 0000000..3de82be --- /dev/null +++ b/src/modules/leagues/league-tiers.service.ts @@ -0,0 +1,216 @@ +import { Injectable, Logger } from "@nestjs/common"; +import { PrismaService } from "../../database/prisma.service"; +import { HttpService } from "@nestjs/axios"; +import { firstValueFrom } from "rxjs"; +import * as fs from "fs"; +import * as path from "path"; + +@Injectable() +export class LeagueTiersService { + private readonly logger = new Logger(LeagueTiersService.name); + private readonly qualifiedLeaguesPath = path.join( + process.cwd(), + "qualified_leagues.json", + ); + + constructor( + private readonly prisma: PrismaService, + private readonly httpService: HttpService, + ) {} + + // ─── READ ────────────────────────────────────────────────── + + async findAll() { + return this.prisma.leagueTier.findMany({ + include: { + league: { + include: { country: true }, + }, + }, + orderBy: [{ tier: "asc" }, { createdAt: "asc" }], + }); + } + + async findActive() { + return this.prisma.leagueTier.findMany({ + where: { isActive: true }, + include: { + league: { + include: { country: true }, + }, + }, + orderBy: [{ tier: "asc" }, { createdAt: "asc" }], + }); + } + + async findByTier(tier: number) { + return this.prisma.leagueTier.findMany({ + where: { tier, isActive: true }, + include: { + league: { + include: { country: true }, + }, + }, + orderBy: { createdAt: "asc" }, + }); + } + + // ─── WRITE ───────────────────────────────────────────────── + + async addLeague( + leagueId: string, + tier: number = 2, + notes?: string, + addedBy?: string, + ) { + const result = await this.prisma.leagueTier.upsert({ + where: { leagueId }, + create: { + leagueId, + tier, + isActive: true, + notes, + addedBy, + }, + update: { + tier, + isActive: true, + notes: notes || undefined, + addedBy: addedBy || undefined, + }, + include: { + league: { include: { country: true } }, + }, + }); + + // Auto-sync and retrain + await this.syncQualifiedLeagues(); + this.triggerModelRetrain().catch((err) => + this.logger.error(`Retrain trigger failed: ${err.message}`), + ); + + return result; + } + + async removeLeague(leagueId: string) { + const result = await this.prisma.leagueTier.update({ + where: { leagueId }, + data: { isActive: false }, + }); + + await this.syncQualifiedLeagues(); + + return result; + } + + async updateTier(leagueId: string, tier: number) { + return this.prisma.leagueTier.update({ + where: { leagueId }, + data: { tier }, + }); + } + + async deleteLeague(leagueId: string) { + const result = await this.prisma.leagueTier.delete({ + where: { leagueId }, + }); + + await this.syncQualifiedLeagues(); + + return result; + } + + // ─── SYNC qualified_leagues.json ─────────────────────────── + + async syncQualifiedLeagues(): Promise<{ count: number; path: string }> { + const activeTiers = await this.prisma.leagueTier.findMany({ + where: { isActive: true }, + select: { leagueId: true }, + }); + + const leagueIds = activeTiers.map((t) => t.leagueId); + + fs.writeFileSync( + this.qualifiedLeaguesPath, + JSON.stringify(leagueIds, null, 2), + "utf-8", + ); + + this.logger.log( + `Synced ${leagueIds.length} leagues to qualified_leagues.json`, + ); + + return { count: leagueIds.length, path: this.qualifiedLeaguesPath }; + } + + // ─── TRIGGER MODEL RETRAIN ───────────────────────────────── + + async triggerModelRetrain(): Promise<{ status: string; message: string }> { + const aiEngineUrl = process.env.AI_ENGINE_URL || "http://localhost:8000"; + + try { + const response = await firstValueFrom( + this.httpService.post( + `${aiEngineUrl}/v1/admin/retrain`, + { reason: "league_tier_change" }, + { timeout: 10000 }, + ), + ); + + this.logger.log( + `Model retrain triggered: ${JSON.stringify(response.data)}`, + ); + return { + status: "triggered", + message: "Model retraining started in background", + }; + } catch (error: unknown) { + const message = + error instanceof Error ? error.message : String(error); + this.logger.warn( + `Could not trigger retrain (AI engine may not support it yet): ${message}`, + ); + return { + status: "skipped", + message: `AI engine retrain endpoint not available: ${message}`, + }; + } + } + + // ─── STATS ───────────────────────────────────────────────── + + async getStats() { + const tiers = await this.prisma.leagueTier.groupBy({ + by: ["tier"], + where: { isActive: true }, + _count: true, + }); + + const totalActive = tiers.reduce((sum, t) => sum + t._count, 0); + + // Count matches in qualified leagues + const activeLeagueIds = ( + await this.prisma.leagueTier.findMany({ + where: { isActive: true }, + select: { leagueId: true }, + }) + ).map((t) => t.leagueId); + + const matchCount = await this.prisma.match.count({ + where: { + leagueId: { in: activeLeagueIds }, + sport: "football", + status: "FT", + }, + }); + + return { + tiers: tiers.map((t) => ({ + tier: t.tier, + count: t._count, + })), + totalActiveLeagues: totalActive, + totalQualifiedMatches: matchCount, + }; + } +} diff --git a/src/modules/leagues/leagues.module.ts b/src/modules/leagues/leagues.module.ts index 9451747..cec2994 100755 --- a/src/modules/leagues/leagues.module.ts +++ b/src/modules/leagues/leagues.module.ts @@ -1,12 +1,15 @@ import { Module } from "@nestjs/common"; +import { HttpModule } from "@nestjs/axios"; import { LeaguesController } from "./leagues.controller"; +import { LeagueTiersController } from "./league-tiers.controller"; import { LeaguesService } from "./leagues.service"; +import { LeagueTiersService } from "./league-tiers.service"; import { DatabaseModule } from "../../database/database.module"; @Module({ - imports: [DatabaseModule], - controllers: [LeaguesController], - providers: [LeaguesService], - exports: [LeaguesService], + imports: [DatabaseModule, HttpModule], + controllers: [LeaguesController, LeagueTiersController], + providers: [LeaguesService, LeagueTiersService], + exports: [LeaguesService, LeagueTiersService], }) export class LeaguesModule {}