From 8a134239d7f81276cba8e230e3e14790519e0932 Mon Sep 17 00:00:00 2001 From: "mamonov.ep" Date: Sat, 21 Feb 2026 17:22:58 +0300 Subject: [PATCH] =?UTF-8?q?Initial=20commit:=20=D0=B4=D0=BE=D0=B1=D0=B0?= =?UTF-8?q?=D0=B2=D0=BB=D0=B5=D0=BD=D0=B8=D0=B5=20=D0=BF=D1=80=D0=BE=D0=B5?= =?UTF-8?q?=D0=BA=D1=82=D0=B0=20predictV1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 --- .gitignore | 19 + BD.txt | 34 + CLAUDE.md | 0 .../feature_importance_bag_of_heroes.csv | 226 + artifacts/feature_importance_db.csv | 411 ++ artifacts/feature_importance_stacking.csv | 4 + artifacts/feature_importance_with_players.csv | 4509 +++++++++++++++++ artifacts/feature_order_bag_of_heroes.csv | 226 + artifacts/feature_order_db.csv | 411 ++ artifacts/feature_order_with_players.csv | 4509 +++++++++++++++++ artifacts/model_bag_of_heroes.cbm | Bin 0 -> 802720 bytes artifacts/model_from_db_pro_v3.cbm | Bin 0 -> 141224 bytes artifacts/model_stacking.pkl | Bin 0 -> 891 bytes artifacts/model_with_players.cbm | Bin 0 -> 282496 bytes data/dataset_from_db.parquet | Bin 0 -> 42261 bytes data/dataset_with_players.parquet | Bin 0 -> 40813 bytes docker-compose.yml | 18 + educationML/build_dataset_pro.py | 94 + educationML/build_dataset_with_players.py | 139 + educationML/train_model_bag_of_heroes.py | 127 + educationML/train_model_pro.py | 131 + educationML/train_model_pro_long_old.py | 161 + educationML/train_model_pro_old.py | 116 + educationML/train_model_stacking.py | 176 + educationML/train_model_with_players.py | 156 + req.txt | 2 + routes/__init__.py | 0 routes/heroes.py | 24 + routes/match.py | 62 + routes/players.py | 24 + routes/predict.py | 199 + routes/predict_bag_of_heroes.py | 85 + routes/predict_stacking.py | 53 + routes/predict_with_players.py | 123 + routes/teams.py | 24 + run.sh | 236 + serve.py | 31 + start/parse_pro_details_match.py | 106 + start/parse_pro_matches.py | 79 + test_models_accuracy.py | 234 + update_models.sh | 73 + лиги.txt | 9 + 42 files changed, 12831 insertions(+) create mode 100644 .gitignore create mode 100644 BD.txt create mode 100644 CLAUDE.md create mode 100644 artifacts/feature_importance_bag_of_heroes.csv create mode 100644 artifacts/feature_importance_db.csv create mode 100644 artifacts/feature_importance_stacking.csv create mode 100644 artifacts/feature_importance_with_players.csv create mode 100644 artifacts/feature_order_bag_of_heroes.csv create mode 100644 artifacts/feature_order_db.csv create mode 100644 artifacts/feature_order_with_players.csv create mode 100644 artifacts/model_bag_of_heroes.cbm create mode 100644 artifacts/model_from_db_pro_v3.cbm create mode 100644 artifacts/model_stacking.pkl create mode 100644 artifacts/model_with_players.cbm create mode 100644 data/dataset_from_db.parquet create mode 100644 data/dataset_with_players.parquet create mode 100644 docker-compose.yml create mode 100644 educationML/build_dataset_pro.py create mode 100644 educationML/build_dataset_with_players.py create mode 100644 educationML/train_model_bag_of_heroes.py create mode 100644 educationML/train_model_pro.py create mode 100644 educationML/train_model_pro_long_old.py create mode 100644 educationML/train_model_pro_old.py create mode 100644 educationML/train_model_stacking.py create mode 100644 educationML/train_model_with_players.py create mode 100644 req.txt create mode 100644 routes/__init__.py create mode 100644 routes/heroes.py create mode 100644 routes/match.py create mode 100644 routes/players.py create mode 100644 routes/predict.py create mode 100644 routes/predict_bag_of_heroes.py create mode 100644 routes/predict_stacking.py create mode 100644 routes/predict_with_players.py create mode 100644 routes/teams.py create mode 100755 run.sh create mode 100644 serve.py create mode 100644 start/parse_pro_details_match.py create mode 100644 start/parse_pro_matches.py create mode 100644 test_models_accuracy.py create mode 100755 update_models.sh create mode 100644 лиги.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..db5c518 --- /dev/null +++ b/.gitignore @@ -0,0 +1,19 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +.venv/ +venv/ +ENV/ + +# macOS +.DS_Store + +# CatBoost +catboost_info/ + +# IDE +.vscode/ +.idea/ diff --git a/BD.txt b/BD.txt new file mode 100644 index 0000000..9d4457b --- /dev/null +++ b/BD.txt @@ -0,0 +1,34 @@ +CREATE TABLE hero ( + id INTEGER PRIMARY KEY, + name VARCHAR(255) +); + +CREATE TABLE matches ( + id BIGINT PRIMARY KEY, + start_time INTEGER, + leagueid INTEGER, + radiant_team_id INTEGER, + dire_team_id INTEGER, + radiant_win BOOLEAN, + source VARCHAR(255) +); + +CREATE TABLE details_match ( + match_id BIGINT, + hero_id INTEGER, + team INTEGER, + "order" INTEGER, + players_id BIGINT, + pos INTEGER +); + +CREATE TABLE teams ( + id BIGINT PRIMARY KEY, + name VARCHAR(255) +); + +CREATE TABLE pro_players ( + id BIGINT PRIMARY KEY, + name VARCHAR(255), + team_id BIGINT +); \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..e69de29 diff --git a/artifacts/feature_importance_bag_of_heroes.csv b/artifacts/feature_importance_bag_of_heroes.csv new file mode 100644 index 0000000..d6b0775 --- /dev/null +++ b/artifacts/feature_importance_bag_of_heroes.csv @@ -0,0 +1,226 @@ +feature,importance +radiant_hero_96,3.5448903148379154 +is_first_pick_radiant,3.1565223041927504 +radiant_hero_137,2.0235517900167266 +radiant_hero_106,1.987790361290638 +dire_hero_96,1.960286880219167 +dire_hero_95,1.9082867265736683 +dire_hero_64,1.7996110650970016 +radiant_hero_29,1.7315926094694511 +radiant_hero_72,1.6448044545454794 +radiant_hero_114,1.6441341241899101 +dire_hero_13,1.6043480443067073 +dire_hero_29,1.5796733982550784 +dire_hero_100,1.5595810413341753 +radiant_hero_120,1.5349678998581766 +radiant_hero_126,1.4997652826627643 +dire_hero_10,1.4639340774176453 +dire_hero_137,1.4356855498450338 +dire_hero_37,1.3886017253491347 +dire_hero_49,1.374665187198288 +radiant_hero_102,1.3283477995280788 +dire_hero_45,1.3278670924698959 +dire_hero_17,1.2812245277976781 +dire_hero_79,1.2784221281359065 +dire_hero_39,1.2667408091038088 +radiant_hero_49,1.2632189131127254 +radiant_hero_38,1.1962602198205863 +dire_hero_123,1.1872109503677573 +dire_hero_102,1.1228931434997156 +radiant_hero_18,1.1174737519883087 +dire_hero_87,1.077588910539817 +radiant_hero_129,1.0219559481460052 +radiant_hero_46,1.0018953069589642 +dire_hero_38,0.9996211369063055 +radiant_hero_37,0.993539916392573 +dire_hero_120,0.9862796010369532 +dire_hero_66,0.9376575493895805 +dire_hero_3,0.9028065601906793 +dire_hero_2,0.901682474329439 +dire_hero_28,0.8948171866399827 +radiant_hero_89,0.89147406950863 +radiant_hero_53,0.8648249886966961 +radiant_hero_98,0.8585308463944029 +radiant_hero_87,0.8381995066812421 +radiant_hero_66,0.8232327944369873 +radiant_hero_39,0.8207115488272696 +dire_hero_94,0.8003485263994297 +dire_hero_129,0.7788770107522424 +radiant_hero_28,0.7776717128986811 +dire_hero_8,0.7691400847093839 +dire_hero_62,0.7635947687074511 +dire_hero_16,0.7634034659406227 +radiant_hero_8,0.7619724662859222 +dire_hero_77,0.7528422859580692 +dire_hero_59,0.7508683533785772 +radiant_hero_17,0.7476607306553408 +radiant_hero_77,0.7441942841775127 +dire_hero_53,0.7276366474281736 +dire_hero_99,0.7233076956342283 +radiant_hero_13,0.7193942058596804 +radiant_hero_128,0.7001897029702516 +radiant_hero_70,0.6938372976032963 +radiant_hero_64,0.6691934104513008 +radiant_hero_123,0.638529772522028 +dire_hero_98,0.6319093349534167 +radiant_hero_45,0.6062424683655009 +radiant_hero_79,0.5868955332730199 +radiant_hero_52,0.5840206533460277 +dire_hero_72,0.5806209726259308 +dire_hero_89,0.5790685908445176 +dire_hero_18,0.5722360992240108 +radiant_hero_95,0.5635903439598497 +dire_hero_70,0.5583839849822786 +dire_hero_69,0.5520070584047531 +dire_hero_46,0.5396131611619958 +dire_hero_119,0.5358381979706183 +radiant_hero_65,0.5192645247152368 +radiant_hero_62,0.5183279865244088 +radiant_hero_135,0.49936800424506134 +radiant_hero_2,0.4817157203218963 +radiant_hero_69,0.4674047119278839 +dire_hero_105,0.4672531552610654 +dire_hero_128,0.46490506129257847 +dire_hero_52,0.45906683460354497 +dire_hero_131,0.4519549389897154 +radiant_hero_100,0.4237007080091267 +radiant_hero_3,0.4200207882294507 +dire_hero_58,0.41845651287518343 +dire_hero_101,0.39098204345118254 +radiant_hero_58,0.36615994337973956 +radiant_hero_90,0.3654215922377207 +radiant_hero_109,0.36114422894241377 +radiant_hero_85,0.3397063517524785 +dire_hero_47,0.33120483123859173 +dire_hero_65,0.3201442668327861 +radiant_hero_16,0.3054077635508628 +dire_hero_51,0.27585362626776144 +dire_hero_126,0.273936700171308 +radiant_hero_145,0.27080666451804547 +dire_hero_50,0.24193256479700528 +dire_hero_14,0.2307593677866238 +dire_hero_138,0.22995826671565825 +dire_hero_108,0.22299160580230648 +radiant_hero_50,0.22224984591119692 +radiant_hero_55,0.22024426849261636 +radiant_hero_110,0.21815464793464948 +dire_hero_145,0.21662542865055226 +dire_hero_109,0.21105314668783304 +dire_hero_71,0.2037500212802944 +radiant_hero_51,0.20131521608992764 +radiant_hero_99,0.1878406955271859 +dire_hero_114,0.18771311441859298 +radiant_hero_61,0.18042090494382437 +radiant_hero_19,0.17972494076335502 +dire_hero_135,0.17674176940995884 +radiant_hero_138,0.17638797830930697 +radiant_hero_11,0.17173288969835926 +radiant_hero_131,0.17163285185364796 +radiant_hero_71,0.1690626448094322 +dire_hero_63,0.1677136535489791 +dire_hero_136,0.1665628310620548 +radiant_hero_59,0.15482478477426076 +dire_hero_1,0.14761511544285089 +radiant_hero_47,0.14432777647536155 +dire_hero_74,0.14328897339865496 +radiant_hero_60,0.1350544787435433 +dire_hero_54,0.13444883015330528 +radiant_hero_33,0.13418407698461643 +radiant_hero_14,0.13062864684037695 +dire_hero_55,0.11982716113335097 +dire_hero_41,0.11590519381635243 +radiant_hero_101,0.11145841473118147 +radiant_hero_26,0.11042331242123556 +radiant_hero_121,0.10624052682211534 +dire_hero_73,0.10523838204127042 +dire_hero_85,0.10116628070719334 +radiant_hero_75,0.0990778076023039 +radiant_hero_136,0.0971404322283737 +dire_hero_121,0.0882738489185766 +radiant_hero_48,0.08644988028482627 +radiant_hero_105,0.08636504455479098 +dire_hero_78,0.086083302792892 +dire_hero_112,0.08186185440747837 +radiant_hero_21,0.08031441854242871 +dire_hero_11,0.08022938628242476 +radiant_hero_1,0.07993183126994755 +radiant_hero_73,0.07987048197448883 +dire_hero_26,0.0751263061140198 +radiant_hero_40,0.07500706396983621 +radiant_hero_88,0.06448627461148718 +radiant_hero_103,0.0596421467981599 +radiant_hero_35,0.05811051984217868 +radiant_hero_108,0.05783500222406305 +radiant_hero_10,0.048789752327982654 +dire_hero_21,0.04778443130941926 +dire_hero_6,0.04540743981832734 +dire_hero_106,0.04419874118360171 +radiant_hero_9,0.04183971731090627 +dire_hero_7,0.04151823003037786 +radiant_hero_36,0.04071609662932216 +dire_hero_93,0.03917124022761937 +radiant_hero_57,0.03746648579498447 +dire_hero_32,0.035168701377993536 +radiant_hero_54,0.03379848987272846 +radiant_hero_83,0.031345340329813724 +dire_hero_103,0.03072106078174553 +dire_hero_60,0.030582548339197258 +radiant_hero_32,0.029014217937254724 +radiant_hero_80,0.026474334315182813 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+radiant_hero_25,0.005493482170013952 +radiant_hero_22,0.0054192470856789625 +dire_hero_48,0.0051407952840448615 +radiant_hero_7,0.005138603464622757 +dire_hero_90,0.005123300607378032 +radiant_hero_67,0.004597631094333278 +dire_hero_36,0.002401848761390748 +dire_hero_61,0.00032280295629982147 +dire_hero_43,0.0 +radiant_hero_56,0.0 +dire_hero_67,0.0 +dire_hero_56,0.0 +dire_hero_9,0.0 +dire_hero_5,0.0 +dire_hero_57,0.0 +radiant_hero_6,0.0 +dire_hero_80,0.0 +radiant_hero_112,0.0 +dire_hero_22,0.0 +dire_hero_25,0.0 +radiant_hero_43,0.0 +dire_hero_15,0.0 +radiant_hero_91,0.0 +radiant_hero_93,0.0 +radiant_hero_94,0.0 +dire_hero_34,0.0 +radiant_hero_34,0.0 +radiant_hero_119,0.0 +dire_hero_27,0.0 +dire_hero_83,0.0 diff --git a/artifacts/feature_importance_db.csv b/artifacts/feature_importance_db.csv new file mode 100644 index 0000000..9c9c65c --- /dev/null +++ b/artifacts/feature_importance_db.csv @@ -0,0 +1,411 @@ +feature,importance +radiant_h126_p2,5.8458473601590715 +radiant_h96_p4,5.2413063229870165 +dire_h10_p3,4.904683311960459 +dire_h37_p3,4.8751699328103335 +radiant_h89_p1,2.4715406190858067 +radiant_h46_p1,2.455964779498448 +radiant_h29_p4,2.3728880092419993 +dire_h95_p3,2.230103174869408 +radiant_h100_p1,2.055912748373654 +radiant_h137_p2,2.011249388205107 +radiant_h114_p1,1.8834632265713043 +radiant_h106_p2,1.862418726366778 +radiant_h18_p1,1.859907271551847 +radiant_h120_p4,1.768662877638573 +radiant_h37_p1,1.7297278884453906 +radiant_h8_p1,1.4434684244563847 +dire_h94_p3,1.394179590286315 +radiant_h110_p1,1.3797194949204172 +dire_h13_p2,1.364320358951324 +dire_h137_p2,1.3423520562699351 +radiant_h128_p1,1.2200491444875372 +radiant_h38_p2,1.1861633538551983 +dire_h29_p5,1.1845192629048282 +radiant_h95_p1,1.1685338110028223 +radiant_h114_p2,1.108551650488793 +dire_h45_p3,1.0881588369614426 +radiant_h16_p4,1.0870253279315796 +dire_h105_p5,1.026037232399492 +dire_h64_p3,0.9866609384774393 +dire_h50_p3,0.9397591580025132 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a/artifacts/feature_importance_stacking.csv b/artifacts/feature_importance_stacking.csv new file mode 100644 index 0000000..041f77f --- /dev/null +++ b/artifacts/feature_importance_stacking.csv @@ -0,0 +1,4 @@ +feature,importance +pred_with_positions,2.8814398908281142 +pred_bag_of_heroes,6.656601650820854 +pred_with_players,0.3906490762854252 diff --git a/artifacts/feature_importance_with_players.csv b/artifacts/feature_importance_with_players.csv new file mode 100644 index 0000000..c3571d4 --- /dev/null +++ b/artifacts/feature_importance_with_players.csv @@ -0,0 +1,4509 @@ +feature,importance +radiant_p190826739_h29,7.642348424935656 +radiant_p898455820_pos2,7.572347769866246 +dire_p152962063_pos3,6.2108019037315625 +radiant_p183719386_pos4,5.545030656203106 +radiant_p196878136_h64_pos1,5.216189676011742 +dire_p76904792_pos3,4.8169230572489115 +dire_p202217968_pos2,4.674358007420447 +radiant_p210053851_h106,4.513063013774831 +dire_p147767183_pos5,4.440013627372781 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a/artifacts/feature_order_bag_of_heroes.csv b/artifacts/feature_order_bag_of_heroes.csv new file mode 100644 index 0000000..6f6c603 --- /dev/null +++ b/artifacts/feature_order_bag_of_heroes.csv @@ -0,0 +1,226 @@ +feature +is_first_pick_radiant +radiant_hero_1 +dire_hero_1 +radiant_hero_2 +dire_hero_2 +radiant_hero_3 +dire_hero_3 +radiant_hero_5 +dire_hero_5 +radiant_hero_6 +dire_hero_6 +radiant_hero_7 +dire_hero_7 +radiant_hero_8 +dire_hero_8 +radiant_hero_9 +dire_hero_9 +radiant_hero_10 +dire_hero_10 +radiant_hero_11 +dire_hero_11 +radiant_hero_13 +dire_hero_13 +radiant_hero_14 +dire_hero_14 +radiant_hero_15 +dire_hero_15 +radiant_hero_16 +dire_hero_16 +radiant_hero_17 +dire_hero_17 +radiant_hero_18 +dire_hero_18 +radiant_hero_19 +dire_hero_19 +radiant_hero_21 +dire_hero_21 +radiant_hero_22 +dire_hero_22 +radiant_hero_23 +dire_hero_23 +radiant_hero_25 +dire_hero_25 +radiant_hero_26 +dire_hero_26 +radiant_hero_27 +dire_hero_27 +radiant_hero_28 +dire_hero_28 +radiant_hero_29 +dire_hero_29 +radiant_hero_32 +dire_hero_32 +radiant_hero_33 +dire_hero_33 +radiant_hero_34 +dire_hero_34 +radiant_hero_35 +dire_hero_35 +radiant_hero_36 +dire_hero_36 +radiant_hero_37 +dire_hero_37 +radiant_hero_38 +dire_hero_38 +radiant_hero_39 +dire_hero_39 +radiant_hero_40 +dire_hero_40 +radiant_hero_41 +dire_hero_41 +radiant_hero_43 +dire_hero_43 +radiant_hero_45 +dire_hero_45 +radiant_hero_46 +dire_hero_46 +radiant_hero_47 +dire_hero_47 +radiant_hero_48 +dire_hero_48 +radiant_hero_49 +dire_hero_49 +radiant_hero_50 +dire_hero_50 +radiant_hero_51 +dire_hero_51 +radiant_hero_52 +dire_hero_52 +radiant_hero_53 +dire_hero_53 +radiant_hero_54 +dire_hero_54 +radiant_hero_55 +dire_hero_55 +radiant_hero_56 +dire_hero_56 +radiant_hero_57 +dire_hero_57 +radiant_hero_58 +dire_hero_58 +radiant_hero_59 +dire_hero_59 +radiant_hero_60 +dire_hero_60 +radiant_hero_61 +dire_hero_61 +radiant_hero_62 +dire_hero_62 +radiant_hero_63 +dire_hero_63 +radiant_hero_64 +dire_hero_64 +radiant_hero_65 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+dire_hero_101 +radiant_hero_102 +dire_hero_102 +radiant_hero_103 +dire_hero_103 +radiant_hero_104 +dire_hero_104 +radiant_hero_105 +dire_hero_105 +radiant_hero_106 +dire_hero_106 +radiant_hero_108 +dire_hero_108 +radiant_hero_109 +dire_hero_109 +radiant_hero_110 +dire_hero_110 +radiant_hero_112 +dire_hero_112 +radiant_hero_114 +dire_hero_114 +radiant_hero_119 +dire_hero_119 +radiant_hero_120 +dire_hero_120 +radiant_hero_121 +dire_hero_121 +radiant_hero_123 +dire_hero_123 +radiant_hero_126 +dire_hero_126 +radiant_hero_128 +dire_hero_128 +radiant_hero_129 +dire_hero_129 +radiant_hero_131 +dire_hero_131 +radiant_hero_135 +dire_hero_135 +radiant_hero_136 +dire_hero_136 +radiant_hero_137 +dire_hero_137 +radiant_hero_138 +dire_hero_138 +radiant_hero_145 +dire_hero_145 diff --git a/artifacts/feature_order_db.csv b/artifacts/feature_order_db.csv new file mode 100644 index 0000000..9db3a13 --- /dev/null +++ b/artifacts/feature_order_db.csv @@ -0,0 +1,411 @@ +feature +dire_h100_p2 +dire_h100_p3 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zzRCe;&)(WS8*pa@53uHVMi}plH^`sCj-wln6(Kf`ZMhe==Pq*12RSwzuL=Zu!$Uc? z`HT#`Gp)!1wA`l;B(XWvD9V*Yocae0_dzW&JDH<#jQTF9WY_mp7^UMSt)~xMB6Y%M)dx zpZ~S~L_d7O*uSP%Kly=t>|b&*zn;G$!MF5F|Jp9E^90}M=jDU`@A777{jq;_)#Fq3 zxAb%WnqK`>WQVW6@A_l^+L&L@Up|nJ{Y&#MuPMQ|^mG5Js>k~T-{_bA)2_>#q4n4O z+p8b_Kj8Wq);~)zz53DrhxtZ-xPMhKzn*_TkdOU~{txGiJo-Pxb^ketY~O!*(y8@) zheO)nnwQD-Tz)+?t>b(tD#OtrSOETbvB4YqKb$X~AG-r=NU)Ju@U8(H3^} 0 else 0 + row["is_first_pick_radiant"] = 1 if fp_team == 0 else 0 + + return pd.Series(row) + +slots_df = details_df.groupby("match_id").apply(slots_from_picks).reset_index() + +# Объединяем с информацией о матчах +dataset = matches_df.merge(slots_df, on="match_id", how="inner") + +# Добавляем целевую переменную +dataset['y'] = dataset['radiant_win'].astype(int) + +# Выбираем нужные колонки в правильном порядке +final_df = dataset[['match_id', 'is_first_pick_radiant', + 'r_h1', 'r_h2', 'r_h3', 'r_h4', 'r_h5', + 'd_h1', 'd_h2', 'd_h3', 'd_h4', 'd_h5', + 'r_p1', 'r_p2', 'r_p3', 'r_p4', 'r_p5', + 'd_p1', 'd_p2', 'd_p3', 'd_p4', 'd_p5', + 'rp_h1', 'rp_h2', 'rp_h3', 'rp_h4', 'rp_h5', + 'dp_h1', 'dp_h2', 'dp_h3', 'dp_h4', 'dp_h5', + 'y']] + +# Сохраняем +final_df.to_parquet("data/dataset_from_db.parquet", index=False) +print(f"Сохранено {len(final_df)} записей в data/dataset_from_db.parquet") +print(f"Radiant wins: {final_df['y'].sum()}, Dire wins: {len(final_df) - final_df['y'].sum()}") diff --git a/educationML/build_dataset_with_players.py b/educationML/build_dataset_with_players.py new file mode 100644 index 0000000..5923e6e --- /dev/null +++ b/educationML/build_dataset_with_players.py @@ -0,0 +1,139 @@ +import psycopg2 +import pandas as pd +import numpy as np + +print("Подключение к базе данных...") +conn = psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" +) + +print("Загрузка матчей с известными игроками...") + +# Получаем все матчи где есть хотя бы один известный игрок +query = """ +SELECT + m.id as match_id, + m.radiant_win, + m.leagueid +FROM matches m +WHERE EXISTS ( + SELECT 1 + FROM details_match dm + WHERE dm.match_id = m.id + AND dm.players_id IS NOT NULL + AND dm.players_id != 0 +) +ORDER BY m.id +""" + +matches_df = pd.read_sql(query, conn) +print(f"Найдено матчей: {len(matches_df)}") + +# Получаем детали всех этих матчей +query_details = """ +SELECT + dm.match_id, + dm.hero_id, + dm.team, + dm.players_id, + dm.pos, + dm."order" +FROM details_match dm +WHERE dm.match_id IN ( + SELECT DISTINCT m.id + FROM matches m + WHERE EXISTS ( + SELECT 1 + FROM details_match dm2 + WHERE dm2.match_id = m.id + AND dm2.players_id IS NOT NULL + AND dm2.players_id != 0 + ) +) +ORDER BY dm.match_id, dm."order" +""" + +details_df = pd.read_sql(query_details, conn) +conn.close() + +print(f"Загружено {len(details_df)} записей деталей") + +# Преобразуем в wide-format +print("\nПреобразование в wide-format...") + +rows = [] + +for match_id, group in details_df.groupby('match_id'): + match_info = matches_df[matches_df['match_id'] == match_id].iloc[0] + + row = { + 'match_id': match_id, + 'y': int(match_info['radiant_win']), + 'leagueid': int(match_info['leagueid']) + } + + # Radiant (team=0) и Dire (team=1) + radiant_picks = group[group['team'] == 0].sort_values('order') + dire_picks = group[group['team'] == 1].sort_values('order') + + # Заполняем героев и игроков для Radiant (до 5) + for i, (idx, pick) in enumerate(radiant_picks.iterrows(), 1): + if i > 5: + break + row[f'r_h{i}'] = int(pick['hero_id']) + row[f'r_p{i}'] = int(pick['players_id']) if pd.notna(pick['players_id']) and pick['players_id'] != 0 else -1 + row[f'rp_h{i}'] = int(pick['pos']) if pd.notna(pick['pos']) else -1 + + # Заполняем пропуски для Radiant + for i in range(len(radiant_picks) + 1, 6): + row[f'r_h{i}'] = -1 + row[f'r_p{i}'] = -1 + row[f'rp_h{i}'] = -1 + + # Заполняем героев и игроков для Dire (до 5) + for i, (idx, pick) in enumerate(dire_picks.iterrows(), 1): + if i > 5: + break + row[f'd_h{i}'] = int(pick['hero_id']) + row[f'd_p{i}'] = int(pick['players_id']) if pd.notna(pick['players_id']) and pick['players_id'] != 0 else -1 + row[f'dp_h{i}'] = int(pick['pos']) if pd.notna(pick['pos']) else -1 + + # Заполняем пропуски для Dire + for i in range(len(dire_picks) + 1, 6): + row[f'd_h{i}'] = -1 + row[f'd_p{i}'] = -1 + row[f'dp_h{i}'] = -1 + + rows.append(row) + +df = pd.DataFrame(rows) + +print(f"Создано {len(df)} записей в wide-format") +print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)") +print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)") + +# Статистика по игрокам +player_cols = [f'r_p{i}' for i in range(1, 6)] + [f'd_p{i}' for i in range(1, 6)] +all_players = [] +for col in player_cols: + all_players.extend(df[col][df[col] > 0].tolist()) + +unique_players = len(set(all_players)) +print(f"\nУникальных игроков в датасете: {unique_players}") +print(f"Всего записей игроков (не -1): {len(all_players)}") + +# Статистика по турнирам +print(f"\nУникальных турниров (leagueid): {df['leagueid'].nunique()}") + +# Сохранение +output_path = "data/dataset_with_players.parquet" +df.to_parquet(output_path, index=False) +print(f"\n✓ Датасет сохранён: {output_path}") + +# Пример первых записей +print("\nПример данных (первые 3 матча):") +print(df.head(3).to_string()) diff --git a/educationML/train_model_bag_of_heroes.py b/educationML/train_model_bag_of_heroes.py new file mode 100644 index 0000000..942964f --- /dev/null +++ b/educationML/train_model_bag_of_heroes.py @@ -0,0 +1,127 @@ +import os +import pandas as pd +import numpy as np +from catboost import CatBoostClassifier, Pool +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score + +print("Загрузка датасета...") + +df = pd.read_parquet("data/dataset_from_db.parquet") + +print(f"Всего записей: {len(df)}") +print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)") +print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)") + +# --- Bag-of-Heroes подход --- +# Создаем бинарные признаки для каждого героя в каждой команде + +# Получаем все уникальные ID героев из данных +hero_cols_r = [f"r_h{i}" for i in range(1, 6)] +hero_cols_d = [f"d_h{i}" for i in range(1, 6)] + +all_hero_ids = set() +for col in hero_cols_r + hero_cols_d: + all_hero_ids.update(df[col].dropna().unique()) + +all_hero_ids = sorted([int(h) for h in all_hero_ids if h >= 0]) +print(f"\nВсего уникальных героев: {len(all_hero_ids)}") + +# Создаем новый датафрейм с bag-of-heroes признаками +X = pd.DataFrame() + +# Добавляем is_first_pick_radiant +X["is_first_pick_radiant"] = df["is_first_pick_radiant"].astype(int) + +# Для каждого героя создаем 2 признака: radiant_hero_{id} и dire_hero_{id} +for hero_id in all_hero_ids: + # Radiant team + X[f"radiant_hero_{hero_id}"] = 0 + for col in hero_cols_r: + X.loc[df[col] == hero_id, f"radiant_hero_{hero_id}"] = 1 + + # Dire team + X[f"dire_hero_{hero_id}"] = 0 + for col in hero_cols_d: + X.loc[df[col] == hero_id, f"dire_hero_{hero_id}"] = 1 + +print(f"Количество признаков: {len(X.columns)}") +print(f" - is_first_pick_radiant: 1") +print(f" - radiant_hero_*: {len(all_hero_ids)}") +print(f" - dire_hero_*: {len(all_hero_ids)}") + +# Целевая переменная +y = df["y"].astype(int).copy() + +# Разбиение +X_train, X_test, y_train, y_test = train_test_split( + X, y, + test_size=0.2, + random_state=42, + stratify=y +) + +print(f"\nTrain: {len(X_train)} записей") +print(f"Test: {len(X_test)} записей") + +# В bag-of-heroes все признаки числовые (0 или 1), категориальных нет +train_pool = Pool(X_train, y_train) +test_pool = Pool(X_test, y_test) + +# Модель +model = CatBoostClassifier( + iterations=2500, + learning_rate=0.03, + depth=7, + l2_leaf_reg=2, + bootstrap_type="Bayesian", + bagging_temperature=1.0, + loss_function="Logloss", + eval_metric="AUC", + random_seed=42, + verbose=100, + od_type="Iter", + od_wait=200 +) + +print("\nНачало обучения...") +model.fit(train_pool, eval_set=test_pool, use_best_model=True) + +# --- Оценка качества --- +best_scores = model.get_best_score() +train_auc_cb = best_scores.get("learn", {}).get("AUC", np.nan) +test_auc_cb = best_scores.get("validation", {}).get("AUC", np.nan) + +y_train_proba = model.predict_proba(train_pool)[:, 1] +y_test_proba = model.predict_proba(test_pool)[:, 1] +train_auc = roc_auc_score(y_train, y_train_proba) +test_auc = roc_auc_score(y_test, y_test_proba) + +print(f"\nCatBoost best AUC (learn/valid): {train_auc_cb:.4f} / {test_auc_cb:.4f}") +print(f"Recomputed AUC (train/test): {train_auc:.4f} / {test_auc:.4f}") + +# --- Сохранение --- +os.makedirs("artifacts", exist_ok=True) +model_path = "artifacts/model_bag_of_heroes.cbm" +model.save_model(model_path) +print(f"\nМодель сохранена: {model_path}") + +# Порядок фичей +feature_cols = list(X.columns) +pd.DataFrame(feature_cols, columns=["feature"]).to_csv( + "artifacts/feature_order_bag_of_heroes.csv", index=False +) +print("Порядок фичей сохранен в artifacts/feature_order_bag_of_heroes.csv") + +# Важность признаков (топ-30) +importance = model.get_feature_importance(train_pool) +importance_df = ( + pd.DataFrame({"feature": X_train.columns, "importance": importance}) + .sort_values("importance", ascending=False) + .reset_index(drop=True) +) + +print("\nВажность признаков (top 30):") +print(importance_df.head(30).to_string(index=False)) + +importance_df.to_csv("artifacts/feature_importance_bag_of_heroes.csv", index=False) diff --git a/educationML/train_model_pro.py b/educationML/train_model_pro.py new file mode 100644 index 0000000..99ee9fd --- /dev/null +++ b/educationML/train_model_pro.py @@ -0,0 +1,131 @@ +import os +import pandas as pd +import numpy as np +from catboost import CatBoostClassifier, Pool +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score + +print("Загрузка датасета...") + +df = pd.read_parquet("data/dataset_from_db.parquet") + +print(f"Всего записей (матчей): {len(df)}") +print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)") +print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)") + +# --- Создаём признаки на уровне матча --- +print("\nСоздание признаков...") + +hero_cols_r = [f"r_h{i}" for i in range(1, 6)] +hero_cols_d = [f"d_h{i}" for i in range(1, 6)] +pos_cols_r = [f"rp_h{i}" for i in range(1, 6)] +pos_cols_d = [f"dp_h{i}" for i in range(1, 6)] + +# Создаём признаки: каждый герой на каждой позиции для каждой команды +# Формат: radiant_{hero_id}_pos_{position}, dire_{hero_id}_pos_{position} + +rows = [] + +for idx, row in df.iterrows(): + features = {} + + # Radiant heroes с позициями + for i in range(5): + hero_id = int(row[hero_cols_r[i]]) + position = int(row[pos_cols_r[i]]) + + if hero_id >= 0 and position >= 0: + features[f"radiant_h{hero_id}_p{position}"] = 1 + + # Dire heroes с позициями + for i in range(5): + hero_id = int(row[hero_cols_d[i]]) + position = int(row[pos_cols_d[i]]) + + if hero_id >= 0 and position >= 0: + features[f"dire_h{hero_id}_p{position}"] = 1 + + features['y'] = int(row['y']) + rows.append(features) + +df_features = pd.DataFrame(rows).fillna(0) + +print(f"Создано признаков: {len(df_features.columns) - 1}") + +# Целевая +y = df_features['y'].astype(int) +X = df_features.drop('y', axis=1) + +# Разбиение +X_train, X_test, y_train, y_test = train_test_split( + X, y, + test_size=0.2, + random_state=42, + stratify=y +) + +print(f"\nTrain: {len(X_train)} матчей") +print(f"Test: {len(X_test)} матчей") + +# Обучение +train_pool = Pool(X_train, y_train) +test_pool = Pool(X_test, y_test) + +model = CatBoostClassifier( + iterations=1000, + learning_rate=0.05, + depth=5, + l2_leaf_reg=3, + min_data_in_leaf=10, + bootstrap_type="Bayesian", + bagging_temperature=0.5, + loss_function="Logloss", + eval_metric="AUC", + random_seed=42, + verbose=50, + od_type="Iter", + od_wait=100, + use_best_model=True +) + +print("\nНачало обучения...") +model.fit(train_pool, eval_set=test_pool) + +# Оценка +best_scores = model.get_best_score() +train_auc_cb = best_scores.get("learn", {}).get("AUC", np.nan) +test_auc_cb = best_scores.get("validation", {}).get("AUC", np.nan) + +y_train_proba = model.predict_proba(train_pool)[:, 1] +y_test_proba = model.predict_proba(test_pool)[:, 1] +train_auc = roc_auc_score(y_train, y_train_proba) +test_auc = roc_auc_score(y_test, y_test_proba) + +print(f"\nCatBoost best AUC (learn/valid): {train_auc_cb:.4f} / {test_auc_cb:.4f}") +print(f"Recomputed AUC (train/test): {train_auc:.4f} / {test_auc:.4f}") + +# Сохранение +os.makedirs("artifacts", exist_ok=True) +model_path = "artifacts/model_from_db_pro_v3.cbm" +model.save_model(model_path) +print(f"\nМодель сохранена: {model_path}") + +# Важность (топ-30) +importance = model.get_feature_importance(train_pool) +importance_df = ( + pd.DataFrame({"feature": X_train.columns, "importance": importance}) + .sort_values("importance", ascending=False) + .reset_index(drop=True) +) + +print("\nВажность признаков (top 30):") +print(importance_df.head(30).to_string(index=False)) + +importance_df.to_csv("artifacts/feature_importance_db.csv", index=False) + +# Сохраняем список всех возможных признаков для инференса +all_features = sorted(X.columns.tolist()) +pd.DataFrame(all_features, columns=["feature"]).to_csv( + "artifacts/feature_order_db.csv", index=False +) +print(f"Порядок фичей сохранен в artifacts/feature_order_db.csv ({len(all_features)} признаков)") diff --git a/educationML/train_model_pro_long_old.py b/educationML/train_model_pro_long_old.py new file mode 100644 index 0000000..8af228e --- /dev/null +++ b/educationML/train_model_pro_long_old.py @@ -0,0 +1,161 @@ +import os +import pandas as pd +import numpy as np +from catboost import CatBoostClassifier, Pool +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score + +print("Загрузка датасета...") + +df = pd.read_parquet("data/dataset_from_db.parquet") + +print(f"Всего записей (матчей): {len(df)}") +print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)") +print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)") + +# --- Преобразование в long-format --- +print("\nПреобразование в long-format...") + +hero_cols_r = [f"r_h{i}" for i in range(1, 6)] +hero_cols_d = [f"d_h{i}" for i in range(1, 6)] +pos_cols_r = [f"rp_h{i}" for i in range(1, 6)] +pos_cols_d = [f"dp_h{i}" for i in range(1, 6)] + +rows = [] + +for idx, row in df.iterrows(): + match_id = idx + is_first_pick_radiant = int(row.get("is_first_pick_radiant", 0)) + radiant_win = int(row["y"]) + + # Radiant team (5 героев) + for i in range(5): + hero_id = int(row[hero_cols_r[i]]) + position = int(row[pos_cols_r[i]]) + + if hero_id >= 0: # Только валидные герои + rows.append({ + "match_id": match_id, + "is_first_pick_radiant": is_first_pick_radiant, + "team": 0, # Radiant + "hero_id": hero_id, + "position": position, + "radiant_win": radiant_win + }) + + # Dire team (5 героев) + for i in range(5): + hero_id = int(row[hero_cols_d[i]]) + position = int(row[pos_cols_d[i]]) + + if hero_id >= 0: # Только валидные герои + rows.append({ + "match_id": match_id, + "is_first_pick_radiant": is_first_pick_radiant, + "team": 1, # Dire + "hero_id": hero_id, + "position": position, + "radiant_win": radiant_win + }) + +df_long = pd.DataFrame(rows) + +print(f"\nLong-format датасет создан:") +print(f"Всего записей (пиков): {len(df_long)}") +print(f"Уникальных матчей: {df_long['match_id'].nunique()}") +print(f"Средних пиков на матч: {len(df_long) / df_long['match_id'].nunique():.1f}") + +# Целевая переменная +y = df_long["radiant_win"].astype(int) + +# Признаки +feature_cols = ["team", "hero_id", "position"] +X = df_long[feature_cols].copy() + +# Убедимся в правильных типах +X["team"] = X["team"].astype(int) +X["hero_id"] = X["hero_id"].astype(int) +X["position"] = X["position"].astype(int) + +# Разбиение (важно: разбиваем по match_id, чтобы пики одного матча были в одном сплите) +unique_matches = df_long["match_id"].unique() +train_matches, test_matches = train_test_split( + unique_matches, + test_size=0.1, + random_state=42 +) + +train_mask = df_long["match_id"].isin(train_matches) +test_mask = df_long["match_id"].isin(test_matches) + +X_train = X[train_mask].reset_index(drop=True) +y_train = y[train_mask].reset_index(drop=True) +X_test = X[test_mask].reset_index(drop=True) +y_test = y[test_mask].reset_index(drop=True) + +print(f"\nTrain: {len(X_train)} пиков ({len(train_matches)} матчей)") +print(f"Test: {len(X_test)} пиков ({len(test_matches)} матчей)") + +# Категориальные признаки +cat_features = ["team", "hero_id", "position"] +train_pool = Pool(X_train, y_train, cat_features=cat_features) +test_pool = Pool(X_test, y_test, cat_features=cat_features) + +# Модель с более агрессивной регуляризацией для малого датасета +model = CatBoostClassifier( + iterations=1000, + learning_rate=0.1, # Увеличили learning rate + depth=4, # Уменьшили глубину + l2_leaf_reg=5, # Увеличили регуляризацию + min_data_in_leaf=20, # Добавили минимум данных в листе + bootstrap_type="Bayesian", + bagging_temperature=0.5, # Уменьшили для меньшего разброса + loss_function="Logloss", + eval_metric="AUC", + random_seed=42, + verbose=50, + od_type="Iter", + od_wait=50, # Уменьшили patience + use_best_model=True +) + +print("\nНачало обучения...") +model.fit(train_pool, eval_set=test_pool, use_best_model=True) + +# --- Оценка качества --- +best_scores = model.get_best_score() +train_auc_cb = best_scores.get("learn", {}).get("AUC", np.nan) +test_auc_cb = best_scores.get("validation", {}).get("AUC", np.nan) + +y_train_proba = model.predict_proba(train_pool)[:, 1] +y_test_proba = model.predict_proba(test_pool)[:, 1] +train_auc = roc_auc_score(y_train, y_train_proba) +test_auc = roc_auc_score(y_test, y_test_proba) + +print(f"\nCatBoost best AUC (learn/valid): {train_auc_cb:.4f} / {test_auc_cb:.4f}") +print(f"Recomputed AUC (train/test): {train_auc:.4f} / {test_auc:.4f}") + +# --- Сохранение --- +os.makedirs("artifacts", exist_ok=True) +model_path = "artifacts/model_from_db_pro_v3.cbm" +model.save_model(model_path) +print(f"\nМодель сохранена: {model_path}") + +# Порядок фичей +pd.DataFrame(feature_cols, columns=["feature"]).to_csv( + "artifacts/feature_order_db.csv", index=False +) +print("Порядок фичей сохранен в artifacts/feature_order_db.csv") + +# Важность признаков +importance = model.get_feature_importance(train_pool) +importance_df = ( + pd.DataFrame({"feature": X_train.columns, "importance": importance}) + .sort_values("importance", ascending=False) + .reset_index(drop=True) +) + +print("\nВажность признаков:") +print(importance_df.to_string(index=False)) + +importance_df.to_csv("artifacts/feature_importance_db.csv", index=False) diff --git a/educationML/train_model_pro_old.py b/educationML/train_model_pro_old.py new file mode 100644 index 0000000..0ca4281 --- /dev/null +++ b/educationML/train_model_pro_old.py @@ -0,0 +1,116 @@ +import os +import pandas as pd +import numpy as np +from catboost import CatBoostClassifier, Pool +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score + +print("Загрузка датасета...") + +df = pd.read_parquet("data/dataset_from_db.parquet") + +print(f"Всего записей: {len(df)}") +print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)") +print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)") + +# --- Фичи под новый формат датасета --- +hero_cols_r = [f"r_h{i}" for i in range(1, 6)] +hero_cols_d = [f"d_h{i}" for i in range(1, 5+1)] +# player_cols_r = [f"r_p{i}" for i in range(1, 6)] +# player_cols_d = [f"d_p{i}" for i in range(1, 6)] +pos_cols_r = [f"rp_h{i}" for i in range(1, 6)] +pos_cols_d = [f"dp_h{i}" for i in range(1, 6)] + +feature_cols = ( + ["is_first_pick_radiant"] + + hero_cols_r + hero_cols_d + # + player_cols_r + player_cols_d # Убрали игроков - мало данных + + pos_cols_r + pos_cols_d +) + +# Целевая +target_col = "y" + +# Отделяем признаки/таргет +X = df[feature_cols].copy() +y = df[target_col].astype(int).copy() + +# На всякий случай убедимся, что бинарный признак int +X["is_first_pick_radiant"] = X["is_first_pick_radiant"].astype(int) + +# Разбиение +X_train, X_test, y_train, y_test = train_test_split( + X, y, + test_size=0.1, + random_state=42, + stratify=y +) + +print(f"\nTrain: {len(X_train)} записей") +print(f"Test: {len(X_test)} записей") + +# Категориальные признаки: герои и позиции (их ID — это категории) +cat_features = hero_cols_r + hero_cols_d + pos_cols_r + pos_cols_d +# CatBoost принимает либо индексы, либо имена колонок. Передаем имена. +train_pool = Pool(X_train, y_train, cat_features=cat_features) +test_pool = Pool(X_test, y_test, cat_features=cat_features) + +# Модель +model = CatBoostClassifier( + iterations=2500, + learning_rate=0.03, + depth=7, + l2_leaf_reg=2, + bootstrap_type="Bayesian", + bagging_temperature=1.0, # <- вместо subsample + loss_function="Logloss", + eval_metric="AUC", + random_seed=42, + verbose=100, + od_type="Iter", + od_wait=200 +) + +print("\nНачало обучения...") +model.fit(train_pool, eval_set=test_pool, use_best_model=True) + +# --- Оценка качества --- +# Лучшие метрики по мнению CatBoost +best_scores = model.get_best_score() +train_auc_cb = best_scores.get("learn", {}).get("AUC", np.nan) +test_auc_cb = best_scores.get("validation", {}).get("AUC", np.nan) + +# Перепроверим AUC напрямую +y_train_proba = model.predict_proba(train_pool)[:, 1] +y_test_proba = model.predict_proba(test_pool)[:, 1] +train_auc = roc_auc_score(y_train, y_train_proba) +test_auc = roc_auc_score(y_test, y_test_proba) + +print(f"\nCatBoost best AUC (learn/valid): {train_auc_cb:.4f} / {test_auc_cb:.4f}") +print(f"Recomputed AUC (train/test): {train_auc:.4f} / {test_auc:.4f}") + +# --- Сохранение --- +os.makedirs("artifacts", exist_ok=True) +model_path = "artifacts/model_from_db_pro_v3.cbm" +model.save_model(model_path) +print(f"\nМодель сохранена: {model_path}") + +# Порядок фичей +pd.DataFrame(feature_cols, columns=["feature"]).to_csv( + "artifacts/feature_order_db.csv", index=False +) +print("Порядок фичей сохранен в artifacts/feature_order_db.csv") + +# Важность признаков +importance = model.get_feature_importance(train_pool) +importance_df = ( + pd.DataFrame({"feature": X_train.columns, "importance": importance}) + .sort_values("importance", ascending=False) + .reset_index(drop=True) +) + +print("\nВажность признаков (top 25):") +print(importance_df.head(25).to_string(index=False)) + +# При желании — сохранить важности целиком +importance_df.to_csv("artifacts/feature_importance_db.csv", index=False) diff --git a/educationML/train_model_stacking.py b/educationML/train_model_stacking.py new file mode 100644 index 0000000..ca4ba38 --- /dev/null +++ b/educationML/train_model_stacking.py @@ -0,0 +1,176 @@ +import os +import sys +import pandas as pd +import numpy as np +from catboost import CatBoostClassifier, Pool +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score +from sklearn.linear_model import LogisticRegression +import pickle + +# Добавляем корневую директорию проекта в путь +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +print("Загрузка датасета...") +df = pd.read_parquet("data/dataset_from_db.parquet") + +print(f"Всего записей: {len(df)}") +print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)") +print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)") + +# Целевая переменная +y = df["y"].astype(int).copy() + +# Разбиение на train/test +_, X_test_indices, _, y_test = train_test_split( + df.index, y, + test_size=0.2, + random_state=42, + stratify=y +) + +print("\n" + "="*60) +print("Загрузка базовых моделей...") +print("="*60) + +# === Модель 1: Heroes + Positions === +from routes.predict import build_long_format_input, modelPro + +# === Модель 2: Bag of Heroes === +from routes.predict_bag_of_heroes import build_bag_of_heroes_features, modelBagOfHeroes + +# === Модель 3: With Players === +from routes.predict_with_players import build_player_features, modelWithPlayers + +print("\n" + "="*60) +print("Генерация предсказаний базовых моделей...") +print("="*60) + +# Подготовим данные для всех моделей +hero_cols_r = [f"r_h{i}" for i in range(1, 6)] +hero_cols_d = [f"d_h{i}" for i in range(1, 6)] +player_cols_r = [f"r_p{i}" for i in range(1, 6)] +player_cols_d = [f"d_p{i}" for i in range(1, 6)] +pos_cols_r = [f"rp_h{i}" for i in range(1, 6)] +pos_cols_d = [f"dp_h{i}" for i in range(1, 6)] + +predictions_list = [] + +for idx in df.index: + row_data = df.loc[idx] + + # Формируем payload для текущей записи + payload = { + "is_first_pick_radiant": int(row_data.get("is_first_pick_radiant", 0)), + } + + # Герои + for col in hero_cols_r + hero_cols_d: + payload[col] = int(row_data.get(col, -1)) + + # Игроки + for col in player_cols_r + player_cols_d: + payload[col] = int(row_data.get(col, -1)) + + # Позиции + for col in pos_cols_r + pos_cols_d: + payload[col] = int(row_data.get(col, -1)) + + # === Предсказание модели 1: Heroes + Positions === + X_with_pos = build_long_format_input(payload) + pred1 = float(modelPro.predict_proba(X_with_pos)[0, 1]) + + # === Предсказание модели 2: Bag of Heroes === + X_bag = build_bag_of_heroes_features(payload) + pred2 = float(modelBagOfHeroes.predict_proba(X_bag)[0, 1]) + + # === Предсказание модели 3: With Players === + X_players = build_player_features(payload) + pred3 = float(modelWithPlayers.predict_proba(X_players)[0, 1]) + + predictions_list.append({ + "pred_with_positions": pred1, + "pred_bag_of_heroes": pred2, + "pred_with_players": pred3 + }) + + if (idx + 1) % 100 == 0: + print(f"Обработано {idx + 1}/{len(df)} записей...") + +# Создаём DataFrame с предсказаниями +X_meta = pd.DataFrame(predictions_list) + +print(f"\nСоздано {len(X_meta)} мета-признаков") +print(f"Колонки: {list(X_meta.columns)}") + +# Разбиение на train/test по тем же индексам +X_meta_train = X_meta.loc[~X_meta.index.isin(X_test_indices)] +X_meta_test = X_meta.loc[X_meta.index.isin(X_test_indices)] +y_meta_train = y.loc[~y.index.isin(X_test_indices)] +y_meta_test = y.loc[y.index.isin(X_test_indices)] + +print(f"\nMeta Train: {len(X_meta_train)} записей") +print(f"Meta Test: {len(X_meta_test)} записей") + +# Обучение мета-модели +print("\n" + "="*60) +print("Обучение мета-модели (Логистическая регрессия)...") +print("="*60) + +# Используем логистическую регрессию вместо CatBoost для избежания переобучения +meta_model = LogisticRegression( + random_state=42, + max_iter=1000, + C=1.0 # Регуляризация +) + +meta_model.fit(X_meta_train, y_meta_train) + +# Оценка качества +y_train_proba = meta_model.predict_proba(X_meta_train)[:, 1] +y_test_proba = meta_model.predict_proba(X_meta_test)[:, 1] +train_auc = roc_auc_score(y_meta_train, y_train_proba) +test_auc = roc_auc_score(y_meta_test, y_test_proba) + +print(f"\nLogistic Regression AUC (train/test): {train_auc:.4f} / {test_auc:.4f}") + +# Сохранение мета-модели +os.makedirs("artifacts", exist_ok=True) +model_path = "artifacts/model_stacking.pkl" +with open(model_path, 'wb') as f: + pickle.dump(meta_model, f) +print(f"\nМета-модель сохранена: {model_path}") + +# Важность признаков (коэффициенты логистической регрессии) +coefficients = meta_model.coef_[0] +intercept = meta_model.intercept_[0] + +importance_df = pd.DataFrame({ + "feature": X_meta_train.columns, + "coefficient": coefficients +}).sort_values("coefficient", ascending=False).reset_index(drop=True) + +print("\nКоэффициенты логистической регрессии:") +print(f"Intercept: {intercept:.4f}") +print(importance_df.to_string(index=False)) + +# Сохраняем в старом формате для совместимости +importance_df_compat = pd.DataFrame({ + "feature": X_meta_train.columns, + "importance": np.abs(coefficients) # Абсолютные значения коэффициентов +}) +importance_df_compat.to_csv("artifacts/feature_importance_stacking.csv", index=False) + +print("\n" + "="*60) +print("Сравнение моделей на тестовой выборке:") +print("="*60) + +# AUC базовых моделей +auc1 = roc_auc_score(y_meta_test, X_meta_test["pred_with_positions"]) +auc2 = roc_auc_score(y_meta_test, X_meta_test["pred_bag_of_heroes"]) +auc3 = roc_auc_score(y_meta_test, X_meta_test["pred_with_players"]) + +print(f"Модель 1 (Heroes + Positions): AUC = {auc1:.4f}") +print(f"Модель 2 (Bag of Heroes): AUC = {auc2:.4f}") +print(f"Модель 3 (With Players): AUC = {auc3:.4f}") +print(f"Мета-модель (Stacking): AUC = {test_auc:.4f}") diff --git a/educationML/train_model_with_players.py b/educationML/train_model_with_players.py new file mode 100644 index 0000000..63cef08 --- /dev/null +++ b/educationML/train_model_with_players.py @@ -0,0 +1,156 @@ +import os +import pandas as pd +import numpy as np +from catboost import CatBoostClassifier, Pool +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score + +print("Загрузка датасета...") + +df = pd.read_parquet("data/dataset_with_players.parquet") + +print(f"Всего записей (матчей): {len(df)}") +print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)") +print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)") + +# --- Создаём признаки на уровне матча --- +print("\nСоздание признаков...") + +hero_cols_r = [f"r_h{i}" for i in range(1, 6)] +hero_cols_d = [f"d_h{i}" for i in range(1, 6)] +player_cols_r = [f"r_p{i}" for i in range(1, 6)] +player_cols_d = [f"d_p{i}" for i in range(1, 6)] +pos_cols_r = [f"rp_h{i}" for i in range(1, 6)] +pos_cols_d = [f"dp_h{i}" for i in range(1, 6)] + +# Создаём признаки: player_hero_pos для каждой команды +# Формат: radiant_p{player_id}_h{hero_id}_pos{position}, dire_p{player_id}_h{hero_id}_pos{position} + +rows = [] + +for idx, row in df.iterrows(): + features = {} + + # Radiant heroes с игроками и позициями + for i in range(5): + hero_id = int(row[hero_cols_r[i]]) + player_id = int(row[player_cols_r[i]]) + position = int(row[pos_cols_r[i]]) + + # Признак: игрок + герой + позиция + if player_id > 0 and hero_id >= 0 and position >= 0: + features[f"radiant_p{player_id}_h{hero_id}_pos{position}"] = 1 + + # Признак: только игрок + герой (если позиция неизвестна) + if player_id > 0 and hero_id >= 0: + features[f"radiant_p{player_id}_h{hero_id}"] = 1 + + # Признак: только игрок + позиция + if player_id > 0 and position >= 0: + features[f"radiant_p{player_id}_pos{position}"] = 1 + + # Dire heroes с игроками и позициями + for i in range(5): + hero_id = int(row[hero_cols_d[i]]) + player_id = int(row[player_cols_d[i]]) + position = int(row[pos_cols_d[i]]) + + # Признак: игрок + герой + позиция + if player_id > 0 and hero_id >= 0 and position >= 0: + features[f"dire_p{player_id}_h{hero_id}_pos{position}"] = 1 + + # Признак: только игрок + герой (если позиция неизвестна) + if player_id > 0 and hero_id >= 0: + features[f"dire_p{player_id}_h{hero_id}"] = 1 + + # Признак: только игрок + позиция + if player_id > 0 and position >= 0: + features[f"dire_p{player_id}_pos{position}"] = 1 + + features['y'] = int(row['y']) + rows.append(features) + + if (idx + 1) % 100 == 0: + print(f"Обработано {idx + 1}/{len(df)} матчей...") + +df_features = pd.DataFrame(rows).fillna(0) + +print(f"\nСоздано признаков: {len(df_features.columns) - 1}") + +# Целевая +y = df_features['y'].astype(int) +X = df_features.drop('y', axis=1) + +# Разбиение +X_train, X_test, y_train, y_test = train_test_split( + X, y, + test_size=0.2, + random_state=42, + stratify=y +) + +print(f"\nTrain: {len(X_train)} матчей") +print(f"Test: {len(X_test)} матчей") + +# Обучение +train_pool = Pool(X_train, y_train) +test_pool = Pool(X_test, y_test) + +model = CatBoostClassifier( + iterations=1000, + learning_rate=0.05, + depth=5, + l2_leaf_reg=3, + min_data_in_leaf=5, + bootstrap_type="Bayesian", + bagging_temperature=0.5, + loss_function="Logloss", + eval_metric="AUC", + random_seed=42, + verbose=50, + od_type="Iter", + od_wait=100, + use_best_model=True +) + +print("\nНачало обучения...") +model.fit(train_pool, eval_set=test_pool) + +# Оценка +best_scores = model.get_best_score() +train_auc_cb = best_scores.get("learn", {}).get("AUC", np.nan) +test_auc_cb = best_scores.get("validation", {}).get("AUC", np.nan) + +y_train_proba = model.predict_proba(train_pool)[:, 1] +y_test_proba = model.predict_proba(test_pool)[:, 1] +train_auc = roc_auc_score(y_train, y_train_proba) +test_auc = roc_auc_score(y_test, y_test_proba) + +print(f"\nCatBoost best AUC (learn/valid): {train_auc_cb:.4f} / {test_auc_cb:.4f}") +print(f"Recomputed AUC (train/test): {train_auc:.4f} / {test_auc:.4f}") + +# Сохранение +os.makedirs("artifacts", exist_ok=True) +model_path = "artifacts/model_with_players.cbm" +model.save_model(model_path) +print(f"\nМодель сохранена: {model_path}") + +# Важность (топ-30) +importance = model.get_feature_importance(train_pool) +importance_df = ( + pd.DataFrame({"feature": X_train.columns, "importance": importance}) + .sort_values("importance", ascending=False) + .reset_index(drop=True) +) + +print("\nВажность признаков (top 30):") +print(importance_df.head(30).to_string(index=False)) + +importance_df.to_csv("artifacts/feature_importance_with_players.csv", index=False) + +# Сохраняем список всех возможных признаков для инференса +all_features = sorted(X.columns.tolist()) +pd.DataFrame(all_features, columns=["feature"]).to_csv( + "artifacts/feature_order_with_players.csv", index=False +) +print(f"\nПорядок фичей сохранен в artifacts/feature_order_with_players.csv ({len(all_features)} признаков)") diff --git a/req.txt b/req.txt new file mode 100644 index 0000000..7f6bed9 --- /dev/null +++ b/req.txt @@ -0,0 +1,2 @@ +requests +psycopg2-binary \ No newline at end of file diff --git a/routes/__init__.py b/routes/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/routes/heroes.py b/routes/heroes.py new file mode 100644 index 0000000..0f1a21a --- /dev/null +++ b/routes/heroes.py @@ -0,0 +1,24 @@ +from fastapi import APIRouter +import psycopg2 +from psycopg2.extras import RealDictCursor + +router = APIRouter() + +def get_db_connection(): + return psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" + ) + +@router.get("/heroes") +def get_heroes(): + conn = get_db_connection() + cursor = conn.cursor(cursor_factory=RealDictCursor) + cursor.execute("SELECT id, name FROM hero ORDER BY id") + heroes = cursor.fetchall() + cursor.close() + conn.close() + return heroes diff --git a/routes/match.py b/routes/match.py new file mode 100644 index 0000000..f4860c6 --- /dev/null +++ b/routes/match.py @@ -0,0 +1,62 @@ +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel +from typing import List +import psycopg2 +from psycopg2.extras import RealDictCursor + +router = APIRouter() + +def get_db_connection(): + return psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" + ) + +class HeroDetail(BaseModel): + hero_id: int + team: int + order: int + +class MatchData(BaseModel): + id: int + start_time: int + leagueid: int + radiant_team_id: int + dire_team_id: int + radiant_win: bool + heroes: List[HeroDetail] + +@router.post("/match/pro/add") +def add_pro_match(match: MatchData): + conn = get_db_connection() + cursor = conn.cursor() + + try: + # Добавляем матч в pro_matches + cursor.execute(""" + INSERT INTO pro_matches (id, start_time, leagueid, radiant_team_id, dire_team_id, radiant_win) + VALUES (%s, %s, %s, %s, %s, %s) + ON CONFLICT (id) DO NOTHING + """, (match.id, match.start_time, match.leagueid, match.radiant_team_id, match.dire_team_id, match.radiant_win)) + + # Добавляем детали героев в pro_details_match + for hero in match.heroes: + cursor.execute(""" + INSERT INTO pro_details_match (match_id, hero_id, team, "order") + VALUES (%s, %s, %s, %s) + """, (match.id, hero.hero_id, hero.team, hero.order)) + + conn.commit() + cursor.close() + conn.close() + + return {"status": "success", "message": f"Match {match.id} added successfully"} + + except Exception as e: + conn.rollback() + cursor.close() + conn.close() + raise HTTPException(status_code=500, detail=str(e)) diff --git a/routes/players.py b/routes/players.py new file mode 100644 index 0000000..f230ead --- /dev/null +++ b/routes/players.py @@ -0,0 +1,24 @@ +from fastapi import APIRouter +import psycopg2 +from psycopg2.extras import RealDictCursor + +router = APIRouter() + +def get_db_connection(): + return psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" + ) + +@router.get("/pro-players") +def get_pro_players(): + conn = get_db_connection() + cursor = conn.cursor(cursor_factory=RealDictCursor) + cursor.execute("SELECT id, name, team_id FROM pro_players ORDER BY id") + players = cursor.fetchall() + cursor.close() + conn.close() + return players diff --git a/routes/predict.py b/routes/predict.py new file mode 100644 index 0000000..1da6b12 --- /dev/null +++ b/routes/predict.py @@ -0,0 +1,199 @@ +from fastapi import APIRouter, Request +from pydantic import BaseModel, Field +from typing import Optional, Dict, Any +from catboost import CatBoostClassifier +import pandas as pd +import numpy as np +from routes.predict_bag_of_heroes import predict_bag_of_heroes +from routes.predict_with_players import predict_with_players + +router = APIRouter() + +# ========================= +# Загрузка модели +# ========================= +modelPro = CatBoostClassifier() +modelPro.load_model("artifacts/model_from_db_pro_v3.cbm") + +# ========================= +# Загрузка порядка фич +# ========================= +def load_feature_order(path: str) -> list[str]: + fo = pd.read_csv(path) + first_col = fo.columns[0] + return fo[first_col].tolist() + +FEATURE_ORDER_PRO: list[str] = load_feature_order("artifacts/feature_order_db.csv") + +# ========================= +# Дефолты для недостающих фич +# ========================= +DEFAULTS: Dict[str, Any] = { + "is_first_pick_radiant": 0, + # Radiant heroes + "r_h1": -1, "r_h2": -1, "r_h3": -1, "r_h4": -1, "r_h5": -1, + # Dire heroes + "d_h1": -1, "d_h2": -1, "d_h3": -1, "d_h4": -1, "d_h5": -1, + # # Radiant players + "r_p1": -1, "r_p2": -1, "r_p3": -1, "r_p4": -1, "r_p5": -1, + # # Dire players + "d_p1": -1, "d_p2": -1, "d_p3": -1, "d_p4": -1, "d_p5": -1, + # Radiant positions + "rp_h1": -1, "rp_h2": -1, "rp_h3": -1, "rp_h4": -1, "rp_h5": -1, + # Dire positions + "dp_h1": -1, "dp_h2": -1, "dp_h3": -1, "dp_h4": -1, "dp_h5": -1, +} + +# ========================= +# Входная схема +# ========================= +class DraftPayload(BaseModel): + # флаг первого пика (0 — Dire first pick/неизвестно, 1 — Radiant first pick) + is_first_pick_radiant: Optional[int] = Field(default=DEFAULTS["is_first_pick_radiant"]) + + # герои (IDs) + r_h1: Optional[int] = Field(default=DEFAULTS["r_h1"]) + r_h2: Optional[int] = Field(default=DEFAULTS["r_h2"]) + r_h3: Optional[int] = Field(default=DEFAULTS["r_h3"]) + r_h4: Optional[int] = Field(default=DEFAULTS["r_h4"]) + r_h5: Optional[int] = Field(default=DEFAULTS["r_h5"]) + + d_h1: Optional[int] = Field(default=DEFAULTS["d_h1"]) + d_h2: Optional[int] = Field(default=DEFAULTS["d_h2"]) + d_h3: Optional[int] = Field(default=DEFAULTS["d_h3"]) + d_h4: Optional[int] = Field(default=DEFAULTS["d_h4"]) + d_h5: Optional[int] = Field(default=DEFAULTS["d_h5"]) + + # игроки (IDs) + r_p1: Optional[int] = Field(default=DEFAULTS["r_p1"]) + r_p2: Optional[int] = Field(default=DEFAULTS["r_p2"]) + r_p3: Optional[int] = Field(default=DEFAULTS["r_p3"]) + r_p4: Optional[int] = Field(default=DEFAULTS["r_p4"]) + r_p5: Optional[int] = Field(default=DEFAULTS["r_p5"]) + + d_p1: Optional[int] = Field(default=DEFAULTS["d_p1"]) + d_p2: Optional[int] = Field(default=DEFAULTS["d_p2"]) + d_p3: Optional[int] = Field(default=DEFAULTS["d_p3"]) + d_p4: Optional[int] = Field(default=DEFAULTS["d_p4"]) + d_p5: Optional[int] = Field(default=DEFAULTS["d_p5"]) + + # позиции героев (1-5) + rp_h1: Optional[int] = Field(default=DEFAULTS["rp_h1"]) + rp_h2: Optional[int] = Field(default=DEFAULTS["rp_h2"]) + rp_h3: Optional[int] = Field(default=DEFAULTS["rp_h3"]) + rp_h4: Optional[int] = Field(default=DEFAULTS["rp_h4"]) + rp_h5: Optional[int] = Field(default=DEFAULTS["rp_h5"]) + + dp_h1: Optional[int] = Field(default=DEFAULTS["dp_h1"]) + dp_h2: Optional[int] = Field(default=DEFAULTS["dp_h2"]) + dp_h3: Optional[int] = Field(default=DEFAULTS["dp_h3"]) + dp_h4: Optional[int] = Field(default=DEFAULTS["dp_h4"]) + dp_h5: Optional[int] = Field(default=DEFAULTS["dp_h5"]) + +# ========================= +# Хелперы +# ========================= +def build_long_format_input(payload: dict) -> pd.DataFrame: + """ + Конвертирует payload в hero+position combination features для модели. + + Создаёт бинарные признаки вида radiant_h{hero_id}_p{position} и dire_h{hero_id}_p{position} + """ + features = {} + + # Инициализируем все признаки нулями + for feat in FEATURE_ORDER_PRO: + features[feat] = 0 + + # Radiant heroes с позициями + for i in range(1, 6): + hero_id = int(payload.get(f"r_h{i}", -1)) + position = int(payload.get(f"rp_h{i}", -1)) + + if hero_id >= 0 and position >= 0: + feature_name = f"radiant_h{hero_id}_p{position}" + if feature_name in features: + features[feature_name] = 1 + + # Dire heroes с позициями + for i in range(1, 6): + hero_id = int(payload.get(f"d_h{i}", -1)) + position = int(payload.get(f"dp_h{i}", -1)) + + if hero_id >= 0 and position >= 0: + feature_name = f"dire_h{hero_id}_p{position}" + if feature_name in features: + features[feature_name] = 1 + + # Создаём DataFrame с одной строкой в правильном порядке + df = pd.DataFrame([features], columns=FEATURE_ORDER_PRO) + + return df + +def proba_percent(p: float) -> float: + """Перевод вероятности в проценты (0..100) с отсечкой.""" + return round(float(np.clip(p * 100.0, 0.0, 100.0))) + +# ========================= +# Роут +# ========================= +@router.post("/draft/predict") +async def predict(request: Request): + body = await request.json() + + # Конвертируем все значения героев, игроков и позиций в int + for key in body: + if key.startswith(("r_h", "d_h", "is_first_pick_radiant")): + if body[key] is not None and body[key] != "": + try: + body[key] = int(body[key]) + except (ValueError, TypeError): + body[key] = -1 + else: + body[key] = -1 + elif key.startswith(("rp_h", "dp_h")): + if body[key] == 0: + body[key] = -1 + else: + body[key] = -1 + + # Hero+position combination предсказание + X_pro = build_long_format_input(body) + + # Получаем предсказание для матча (одна строка) + radiant_pro = float(modelPro.predict_proba(X_pro)[0, 1]) + + rp = proba_percent(radiant_pro) + rd = 100.0 - rp + + # Предсказание bag-of-heroes модели + bag_prediction = predict_bag_of_heroes(body) + + # Предсказание модели с игроками + players_prediction = predict_with_players(body) + + # Предсказание стекинг модели (ленивый импорт для избежания циклической зависимости) + try: + from routes.predict_stacking import predict_stacking + stacking_prediction = predict_stacking(body) + except Exception: + stacking_prediction = {"radiant_win": 50, "dire_win": 50} + + return { + "pro-with-pos": { + "radiant_win": rp, + "dire_win": rd + }, + "pro": { + "radiant_win": bag_prediction["radiant_win"], + "dire_win": bag_prediction["dire_win"] + }, + "with-players": { + "radiant_win": players_prediction["radiant_win"], + "dire_win": players_prediction["dire_win"] + }, + "stacking": { + "radiant_win": stacking_prediction["radiant_win"], + "dire_win": stacking_prediction["dire_win"] + } + } diff --git a/routes/predict_bag_of_heroes.py b/routes/predict_bag_of_heroes.py new file mode 100644 index 0000000..99fd302 --- /dev/null +++ b/routes/predict_bag_of_heroes.py @@ -0,0 +1,85 @@ +from catboost import CatBoostClassifier +import pandas as pd +import numpy as np +from typing import Dict, Any + +# Загрузка модели +modelBagOfHeroes = CatBoostClassifier() +modelBagOfHeroes.load_model("artifacts/model_bag_of_heroes.cbm") + +# Загрузка порядка фич +def load_feature_order(path: str) -> list[str]: + fo = pd.read_csv(path) + first_col = fo.columns[0] + return fo[first_col].tolist() + +FEATURE_ORDER_BAG: list[str] = load_feature_order("artifacts/feature_order_bag_of_heroes.csv") + +def build_bag_of_heroes_features(payload: Dict[str, Any]) -> pd.DataFrame: + """ + Конвертирует payload в bag-of-heroes формат. + + payload содержит: + - is_first_pick_radiant + - r_h1, r_h2, r_h3, r_h4, r_h5 + - d_h1, d_h2, d_h3, d_h4, d_h5 + + Возвращает DataFrame с колонками: + - is_first_pick_radiant + - radiant_hero_{1-145} + - dire_hero_{1-145} + """ + # Получаем героев из payload + radiant_heroes = [] + dire_heroes = [] + + for i in range(1, 6): + r_hero = payload.get(f"r_h{i}", -1) + d_hero = payload.get(f"d_h{i}", -1) + + if r_hero and r_hero != -1: + radiant_heroes.append(int(r_hero)) + if d_hero and d_hero != -1: + dire_heroes.append(int(d_hero)) + + # Создаем словарь признаков + features = {feat: 0 for feat in FEATURE_ORDER_BAG} + + # Устанавливаем is_first_pick_radiant + features["is_first_pick_radiant"] = int(payload.get("is_first_pick_radiant", 0)) + + # Устанавливаем бинарные признаки для героев Radiant + for hero_id in radiant_heroes: + feat_name = f"radiant_hero_{hero_id}" + if feat_name in features: + features[feat_name] = 1 + + # Устанавливаем бинарные признаки для героев Dire + for hero_id in dire_heroes: + feat_name = f"dire_hero_{hero_id}" + if feat_name in features: + features[feat_name] = 1 + + # Создаем DataFrame с правильным порядком колонок + return pd.DataFrame([features], columns=FEATURE_ORDER_BAG) + +def predict_bag_of_heroes(payload: Dict[str, Any]) -> Dict[str, float]: + """ + Делает предсказание с использованием bag-of-heroes модели. + + Возвращает: + { + "radiant_win": вероятность победы Radiant (0-100), + "dire_win": вероятность победы Dire (0-100) + } + """ + X = build_bag_of_heroes_features(payload) + proba = modelBagOfHeroes.predict_proba(X)[0, 1] + + radiant_win = round(float(np.clip(proba * 100.0, 0.0, 100.0))) + dire_win = 100.0 - radiant_win + + return { + "radiant_win": radiant_win, + "dire_win": dire_win + } diff --git a/routes/predict_stacking.py b/routes/predict_stacking.py new file mode 100644 index 0000000..44a9d09 --- /dev/null +++ b/routes/predict_stacking.py @@ -0,0 +1,53 @@ +import pandas as pd +import numpy as np +import pickle +from typing import Dict, Any +from routes.predict import build_long_format_input, modelPro +from routes.predict_bag_of_heroes import build_bag_of_heroes_features, modelBagOfHeroes +from routes.predict_with_players import build_player_features, modelWithPlayers + +# Загрузка мета-модели (Logistic Regression) +with open("artifacts/model_stacking.pkl", 'rb') as f: + modelStacking = pickle.load(f) + +def predict_stacking(payload: Dict[str, Any]) -> Dict[str, float]: + """ + Делает предсказание с использованием стекинг-модели. + + Сначала получает предсказания от всех базовых моделей, + затем использует их как признаки для мета-модели. + + Возвращает: + { + "radiant_win": вероятность победы Radiant (0-100), + "dire_win": вероятность победы Dire (0-100) + } + """ + # === Предсказание модели 1: Heroes + Positions === + X_with_pos = build_long_format_input(payload) + pred1 = float(modelPro.predict_proba(X_with_pos)[0, 1]) + + # === Предсказание модели 2: Bag of Heroes === + X_bag = build_bag_of_heroes_features(payload) + pred2 = float(modelBagOfHeroes.predict_proba(X_bag)[0, 1]) + + # === Предсказание модели 3: With Players === + X_players = build_player_features(payload) + pred3 = float(modelWithPlayers.predict_proba(X_players)[0, 1]) + + # === Мета-модель === + X_meta = pd.DataFrame([{ + "pred_with_positions": pred1, + "pred_bag_of_heroes": pred2, + "pred_with_players": pred3 + }]) + + proba = modelStacking.predict_proba(X_meta)[0, 1] + + radiant_win = round(float(np.clip(proba * 100.0, 0.0, 100.0))) + dire_win = 100.0 - radiant_win + + return { + "radiant_win": radiant_win, + "dire_win": dire_win + } diff --git a/routes/predict_with_players.py b/routes/predict_with_players.py new file mode 100644 index 0000000..361f5f6 --- /dev/null +++ b/routes/predict_with_players.py @@ -0,0 +1,123 @@ +from catboost import CatBoostClassifier +import pandas as pd +import numpy as np +from typing import Dict, Any + +# Загрузка модели с игроками +modelWithPlayers = CatBoostClassifier() +modelWithPlayers.load_model("artifacts/model_with_players.cbm") + +# Загрузка порядка фич +def load_feature_order(path: str) -> list: + fo = pd.read_csv(path) + first_col = fo.columns[0] + return fo[first_col].tolist() + +FEATURE_ORDER_WITH_PLAYERS = load_feature_order("artifacts/feature_order_with_players.csv") + +def build_player_features(payload: Dict[str, Any]) -> pd.DataFrame: + """ + Создаёт бинарные признаки для модели с игроками. + + Признаки: + - radiant_p{player_id}_h{hero_id}_pos{position} + - radiant_p{player_id}_h{hero_id} + - radiant_p{player_id}_pos{position} + (аналогично для dire) + """ + features = {} + + # Инициализируем все признаки нулями + for feat in FEATURE_ORDER_WITH_PLAYERS: + features[feat] = 0 + + # Radiant: игроки + герои + позиции + for i in range(1, 6): + hero_id = int(payload.get(f"r_h{i}", -1)) + player_id = int(payload.get(f"r_p{i}", -1)) + position = int(payload.get(f"rp_h{i}", -1)) + + # Признак: игрок + герой + позиция + if player_id > 0 and hero_id >= 0 and position >= 0: + feature_name = f"radiant_p{player_id}_h{hero_id}_pos{position}" + if feature_name in features: + features[feature_name] = 1 + + # Признак: только игрок + герой + if player_id > 0 and hero_id >= 0: + feature_name = f"radiant_p{player_id}_h{hero_id}" + if feature_name in features: + features[feature_name] = 1 + + # Признак: только игрок + позиция + if player_id > 0 and position >= 0: + feature_name = f"radiant_p{player_id}_pos{position}" + if feature_name in features: + features[feature_name] = 1 + + # Dire: игроки + герои + позиции + for i in range(1, 6): + hero_id = int(payload.get(f"d_h{i}", -1)) + player_id = int(payload.get(f"d_p{i}", -1)) + position = int(payload.get(f"dp_h{i}", -1)) + + # Признак: игрок + герой + позиция + if player_id > 0 and hero_id >= 0 and position >= 0: + feature_name = f"dire_p{player_id}_h{hero_id}_pos{position}" + if feature_name in features: + features[feature_name] = 1 + + # Признак: только игрок + герой + if player_id > 0 and hero_id >= 0: + feature_name = f"dire_p{player_id}_h{hero_id}" + if feature_name in features: + features[feature_name] = 1 + + # Признак: только игрок + позиция + if player_id > 0 and position >= 0: + feature_name = f"dire_p{player_id}_pos{position}" + if feature_name in features: + features[feature_name] = 1 + + # Создаём DataFrame с одной строкой в правильном порядке + df = pd.DataFrame([features], columns=FEATURE_ORDER_WITH_PLAYERS) + + return df + +def predict_with_players(payload: Dict[str, Any]) -> Dict[str, float]: + """ + Делает предсказание с использованием модели с игроками. + + Возвращает: + { + "radiant_win": вероятность победы Radiant (0-100), + "dire_win": вероятность победы Dire (0-100) + } + """ + # Проверяем, есть ли хотя бы один игрок в payload + has_players = False + for i in range(1, 6): + if payload.get(f"r_p{i}", -1) > 0 or payload.get(f"d_p{i}", -1) > 0: + has_players = True + break + + # Если нет игроков, возвращаем 50/50 + if not has_players: + return { + "radiant_win": 50, + "dire_win": 50 + } + + # Создаём признаки + X = build_player_features(payload) + + # Предсказание + proba = modelWithPlayers.predict_proba(X)[0, 1] + + radiant_win = round(float(np.clip(proba * 100.0, 0.0, 100.0))) + dire_win = 100 - radiant_win + + return { + "radiant_win": radiant_win, + "dire_win": dire_win + } diff --git a/routes/teams.py b/routes/teams.py new file mode 100644 index 0000000..62fec6c --- /dev/null +++ b/routes/teams.py @@ -0,0 +1,24 @@ +from fastapi import APIRouter +import psycopg2 +from psycopg2.extras import RealDictCursor + +router = APIRouter() + +def get_db_connection(): + return psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" + ) + +@router.get("/teams") +def get_teams(): + conn = get_db_connection() + cursor = conn.cursor(cursor_factory=RealDictCursor) + cursor.execute("SELECT id, name FROM teams ORDER BY id") + teams = cursor.fetchall() + cursor.close() + conn.close() + return teams diff --git a/run.sh b/run.sh new file mode 100755 index 0000000..94eca66 --- /dev/null +++ b/run.sh @@ -0,0 +1,236 @@ +#!/usr/bin/env bash +set -euo pipefail + +# =========================== +# Конфиг (меняйте по желанию) +# =========================== +PY=python3 +VENV=".venv" + +# Порт REST-сервиса: +PORT="${PORT:-8000}" + +# Принудить IPv4 (полезно при проблемном IPv6 у провайдера): +FORCE_IPV4="${FORCE_IPV4:-1}" + +# API-ключи (необязательно, но улучшает стабильность/квоты): +# export OPENDOTA_API_KEY=... +# export STRATZ_TOKEN=... +OPENDOTA_API_KEY="${OPENDOTA_API_KEY:-}" +STRATZ_TOKEN="${STRATZ_TOKEN:-}" + +# Использовать STRATZ вместо OpenDota +# только для шага 1 (список матчей): USE_STRATZ_LIST=1 +# только для шага 2 (детали/драфт): USE_STRATZ_DETAILS=1 +USE_STRATZ_LIST="${USE_STRATZ_LIST:-0}" +USE_STRATZ_DETAILS="${USE_STRATZ_DETAILS:-0}" + +# Задержка после каждых 100 запросов (смягчить 429): +SLEEP_PER_100="${SLEEP_PER_100:-1.0}" + +# =========================== +# Окружение и зависимости +# =========================== +if [ ! -d "$VENV" ]; then + $PY -m venv "$VENV" +fi +# shellcheck disable=SC1091 +source "$VENV/bin/activate" + +pip install -U pip +pip install pandas pyarrow requests httpx "urllib3>=2.2" certifi catboost scikit-learn fastapi uvicorn + +mkdir -p data artifacts + +# =========================== +# Хелперы +# =========================== +export FORCE_IPV4 +export OPENDOTA_API_KEY +export STRATZ_TOKEN +export PAGES +export SLEEP_PER_100 + +# =========================== +# [1/7] Список pub-матчей +# =========================== +# [1b] Паблики (high-rank) +$PY educationML/fetch_public_matches.py +# [2b] Детали пабликов (герои из players) +$PY educationML/fetch_public_details.py + +# =========================== +# [2/7] Список pro-матчей +# =========================== +echo "[1/6] Fetch pro matches via OpenDota (pages=$PAGES)" +$PY educationML/fetch_pro_matches_opendota.py + +# ========================================= +# [2/6] Детали матча + драфт (устойчивый) +# ========================================= +if [ "$USE_STRATZ_DETAILS" = "1" ]; then + echo "[2/6] Fetch match details + draft via STRATZ" + $PY - <<'PYCODE' + +PYCODE + +else + echo "[2/6] Fetch match details + draft via OpenDota (robust)" + $PY - <<'PYCODE' +import os, time, socket, sys, pandas as pd, requests, httpx +from urllib3.util.retry import Retry +from requests.adapters import HTTPAdapter + +# IPv4-only (если FORCE_IPV4=1) +if os.getenv("FORCE_IPV4","1") == "1": + _orig = socket.getaddrinfo + def _v4(host, port, family=0, type=0, proto=0, flags=0): + return _orig(host, port, socket.AF_INET, type, proto, flags) + socket.getaddrinfo = _v4 + +API_KEY = os.getenv("OPENDOTA_API_KEY") +SLEEP_PER_100 = float(os.getenv("SLEEP_PER_100","1.0")) +BASE = "https://api.opendota.com/api/matches/{mid}" +headers = {"User-Agent":"korobkaGames/1.0","Accept":"application/json","Connection":"close"} + +# список match_id +pro = pd.read_parquet("data/pro_matches.parquet") +match_ids = pro['match_id'].drop_duplicates().tolist() + +# requests с Retry +sess = requests.Session() +retries = Retry(total=6, connect=6, read=6, backoff_factor=0.7, + status_forcelist=[429,500,502,503,504], + allowed_methods=frozenset(["GET"])) +sess.mount("https://", HTTPAdapter(max_retries=retries)) + +def fetch_one(mid: int): + url = BASE.format(mid=mid) + if API_KEY: + url += f"?api_key={API_KEY}" + try: + r = sess.get(url, headers=headers, timeout=(5,40)) + r.raise_for_status() + return r.json() + except requests.exceptions.SSLError: + # fallback: httpx http2 off + with httpx.Client(http2=False, timeout=40, headers=headers) as client: + resp = client.get(url) + resp.raise_for_status() + return resp.json() + +match_rows, draft_rows, failed = [], [], [] +for i, mid in enumerate(match_ids, 1): + try: + m = fetch_one(int(mid)) + match_rows.append({ + "match_id": int(mid), + "date": pd.to_datetime(m.get("start_time",0), unit="s"), + "patch": str(m.get("patch")), + "radiant_win": bool(m.get("radiant_win")), + "duration_sec": m.get("duration"), + "league_id": (m.get("league") or {}).get("id"), + "series_type": m.get("series_type"), + }) + for pb in (m.get("picks_bans") or []): + draft_rows.append({ + "match_id": int(mid), + "is_pick": pb.get("is_pick", False), + "team": pb.get("team"), + "hero_id": pb.get("hero_id"), + "order": pb.get("order") + }) + except Exception: + failed.append(int(mid)) + if i % 100 == 0: + time.sleep(SLEEP_PER_100) + +pd.DataFrame(match_rows).to_parquet("data/matches.parquet", index=False) +pd.DataFrame(draft_rows).to_parquet("data/draft.parquet", index=False) +pd.Series(failed, name="failed_match_id").to_csv("data/matches_failed.csv", index=False) +print(f"Saved via OpenDota: matches={len(match_rows)} draft_rows={len(draft_rows)} failed={len(failed)}") +if not match_rows: + raise SystemExit("OpenDota details: ничего не скачано") +PYCODE +fi + +# =========================== +# [3/6] Простой Elo baseline +# =========================== +echo "[3/6] Build Elo" +$PY - <<'PYCODE' +import pandas as pd +matches = pd.read_parquet("data/matches.parquet").sort_values("date") +pro = pd.read_parquet("data/pro_matches.parquet")[['match_id','radiant_name','dire_name']] +df = matches.merge(pro, on='match_id', how='left') + +K = 24 +elo = {} +def get_elo(t): return elo.get(t, 1500) +def expect(a,b): return 1.0/(1.0+10**((b-a)/400)) + +rows=[] +for _, r in df.iterrows(): + A, B = r['radiant_name'], r['dire_name'] + ra, rb = get_elo(A), get_elo(B) + ea, eb = expect(ra,rb), expect(rb,ra) + y = 1.0 if r['radiant_win'] else 0.0 + rows.append({ + 'match_id': r['match_id'], + 'date': r['date'], + 'elo_radiant': ra, 'elo_dire': rb, + 'elo_diff_90': ra - rb # упрощённо без окон + }) + elo[A] = ra + K*(y-ea) + elo[B] = rb + K*((1-y)-eb) + +pd.DataFrame(rows).to_parquet("data/elo.parquet", index=False) +print("Saved data/elo.parquet") +PYCODE + +# [4] вместо старого build_dataset_draft.py +$PY educationML/build_dataset_mixed.py + +# =========================== +# [5/6] Обучение модели +# =========================== +echo "[5/6] Train CatBoost" +$PY - <<'PYCODE' +import pandas as pd +from catboost import CatBoostClassifier +from sklearn.model_selection import TimeSeriesSplit +from sklearn.metrics import log_loss, brier_score_loss + +df = pd.read_parquet("data/dataset_mixed.parquet").sort_values("date") +cat_cols = ['patch','source','r_h1','r_h2','r_h3','r_h4','r_h5','d_h1','d_h2','d_h3','d_h4','d_h5'] +X = df.drop(columns=['y','date','match_id']) if 'match_id' in df.columns else df.drop(columns=['y','date']) +y = df['y'] +cat_idx = [X.columns.get_loc(c) for c in cat_cols] + +tscv = TimeSeriesSplit(n_splits=5) +ll, br = [], [] +for tr, te in tscv.split(X): + model = CatBoostClassifier( + depth=8, iterations=1200, learning_rate=0.03, + loss_function='Logloss', eval_metric='Logloss', verbose=False + ) + model.fit(X.iloc[tr], y.iloc[tr], cat_features=cat_idx) + p = model.predict_proba(X.iloc[te])[:,1] + ll.append(log_loss(y.iloc[te], p)) + br.append(brier_score_loss(y.iloc[te], p)) +print("CV LogLoss=", sum(ll)/len(ll), " Brier=", sum(br)/len(br)) + +final = CatBoostClassifier(depth=8, iterations=1500, learning_rate=0.03, + loss_function='Logloss', verbose=False) +final.fit(X, y, cat_features=cat_idx) +final.save_model("artifacts/model_draft.cbm") +pd.Series(X.columns).to_csv("artifacts/feature_order.csv", index=False) +print("Saved artifacts/model_draft.cbm and artifacts/feature_order.csv") +PYCODE + +# =========================== +# [6/6] REST-сервис предсказаний +# =========================== +echo "[6/6] Start API → http://127.0.0.1:$PORT" + +exec uvicorn serve:app --host 0.0.0.0 --port "$PORT" diff --git a/serve.py b/serve.py new file mode 100644 index 0000000..4247a2e --- /dev/null +++ b/serve.py @@ -0,0 +1,31 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from routes.predict import router as predict_router + +from routes.heroes import router as heroes_router +from routes.match import router as match_router +from routes.teams import router as teams_router +from routes.players import router as players_router + +app = FastAPI() + +# CORS настройки +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +app.include_router(predict_router) + +app.include_router(heroes_router) +app.include_router(match_router) +app.include_router(teams_router) +app.include_router(players_router) + +@app.get("/features") +def features(): + # Чтобы легко проверить, что ждёт модель + return {"feature_order": feature_order, "defaults": DEFAULTS} diff --git a/start/parse_pro_details_match.py b/start/parse_pro_details_match.py new file mode 100644 index 0000000..1150621 --- /dev/null +++ b/start/parse_pro_details_match.py @@ -0,0 +1,106 @@ +import requests +import psycopg2 +import time +from psycopg2.extras import RealDictCursor, execute_values + +# Подключение к базе данных +conn = psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" +) +cursor = conn.cursor(cursor_factory=RealDictCursor) + +# Функция для определения позиции по lane и lane_role +def get_position(lane, lane_role): + """ + lane 2, lane_role 2 -> pos 2 (Mid Core) + lane 1, lane_role 1 -> pos 1 (Safe Lane Carry) + lane 1, lane_role 3 -> pos 5 (Safe Lane Support) + lane 3, lane_role 1 -> pos 3 (Offlane Core) + lane 3, lane_role 3 -> pos 4 (Offlane Support) + """ + if lane == 2 and lane_role == 2: + return 2 + elif lane == 1 and lane_role == 1: + return 1 + elif lane == 1 and lane_role == 3: + return 5 + elif lane == 3 and lane_role == 1: + return 3 + elif lane == 3 and lane_role == 3: + return 4 + else: + return -1 # Неизвестная комбинация + +# Получение ID матчей из БД +cursor.execute(""" +select m.id +from matches m +left join details_match dm on dm.match_id = m.id +where m."source" = 'pro' and dm.match_id is null +""") +matches = cursor.fetchall() +match_ids = [match['id'] for match in matches] + +print(f"Получено {len(match_ids)} ID матчей из БД") + +# Запрос деталей для каждого матча +for idx, match_id in enumerate(match_ids, 1): + url = f"https://api.opendota.com/api/matches/{match_id}" + + try: + response = requests.get(url, timeout=30) + + if response.status_code == 200: + match_data = response.json() + picks_bans = match_data.get('picks_bans', []) + players = match_data.get('players', []) + + print(f"[{idx}/{len(match_ids)}] Match {match_id}: {len(picks_bans)} picks/bans, {len(players)} players") + + # Создаем словари из players + hero_to_account = {player.get('hero_id'): player.get('account_id') for player in players if player.get('hero_id')} + hero_to_pos = {player.get('hero_id'): get_position(player.get('lane'), player.get('lane_role')) for player in players if player.get('hero_id')} + + # Сохранение picks_bans в БД (только пики) с позициями + data = [ + ( + match_id, + pick_ban.get('hero_id'), + pick_ban.get('team'), + pick_ban.get('order'), + hero_to_account.get(pick_ban.get('hero_id')), # players_id + hero_to_pos.get(pick_ban.get('hero_id'), -1), # pos + 'pro' # source + ) + for pick_ban in picks_bans + if pick_ban.get('is_pick') + ] + + print(f"Вставка {len(data)} записей для матча {match_id}") + + execute_values( + cursor, + """INSERT INTO details_match (match_id, hero_id, team, "order", players_id, pos, source) + VALUES %s""", + data + ) + conn.commit() + time.sleep(1) # пауза между запросами + + else: + print(f"[{idx}/{len(match_ids)}] Ошибка для матча {match_id}: {response.status_code}") + if response.status_code == 429: + print("Превышен лимит запросов, ждем 10 секунд...") + time.sleep(10) + + except Exception as e: + print(f"[{idx}/{len(match_ids)}] Исключение для матча {match_id}: {e}") + +cursor.close() +conn.close() + +print("Завершено сохранение деталей матчей") diff --git a/start/parse_pro_matches.py b/start/parse_pro_matches.py new file mode 100644 index 0000000..29c3034 --- /dev/null +++ b/start/parse_pro_matches.py @@ -0,0 +1,79 @@ +import requests +import psycopg2 +from psycopg2.extras import execute_values + +PAGE = 10 +url = "https://api.opendota.com/api/proMatches" +all_matches = [] +less_than_match_id = None + +# Разрешенные лиги +ALLOWED_LEAGUES = [17420] # Замените на нужные ID лиг + +for page in range(PAGE): + params = {} + if less_than_match_id: + params['less_than_match_id'] = less_than_match_id + + response = requests.get(url, params=params) + + if response.status_code == 200: + matches = response.json() + all_matches.extend(matches) + print(f"Страница {page + 1}: получено {len(matches)} матчей") + + if matches: + less_than_match_id = matches[-1]['match_id'] + else: + print(f"Ошибка на странице {page + 1}: {response.status_code}") + break + +print(f"\nВсего получено {len(all_matches)} матчей") + +# Подключение к базе данных +conn = psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" +) +cursor = conn.cursor() + +# Подготовка данных для вставки (фильтр по leagueid) +data = [ + ( + match['match_id'], + match.get('start_time'), + match.get('leagueid'), + match.get('radiant_team_id'), + match.get('dire_team_id'), + match.get('radiant_win'), + 'pro' # ← добавили source + ) + for match in all_matches + if match.get('leagueid') in ALLOWED_LEAGUES +] + +print(f"Отфильтровано {len(data)} матчей из {len(all_matches)} по разрешенным лигам") + +# Вставка данных в таблицу +execute_values( + cursor, + """INSERT INTO matches (id, start_time, leagueid, radiant_team_id, dire_team_id, radiant_win, source) + VALUES %s + ON CONFLICT (id) DO UPDATE SET + start_time = EXCLUDED.start_time, + leagueid = EXCLUDED.leagueid, + radiant_team_id = EXCLUDED.radiant_team_id, + dire_team_id = EXCLUDED.dire_team_id, + radiant_win = EXCLUDED.radiant_win, + source = EXCLUDED.source""", + data +) + +conn.commit() +cursor.close() +conn.close() + +print(f"Успешно сохранено {len(data)} матчей в БД") diff --git a/test_models_accuracy.py b/test_models_accuracy.py new file mode 100644 index 0000000..ea0a38e --- /dev/null +++ b/test_models_accuracy.py @@ -0,0 +1,234 @@ +import sys +sys.path.insert(0, '.') + +import psycopg2 +import pandas as pd +import numpy as np +from routes.predict import build_long_format_input, modelPro +from routes.predict_bag_of_heroes import build_bag_of_heroes_features, modelBagOfHeroes +from routes.predict_with_players import build_player_features, modelWithPlayers +from routes.predict_stacking import predict_stacking + +# Подключение к БД +conn = psycopg2.connect( + host="localhost", + port=5432, + database="korobka_db", + user="postgres", + password="postgres" +) + +# Получаем случайные матчи с известными игроками +query = """ +SELECT + m.id as match_id, + m.radiant_win, + m.leagueid +FROM matches m +WHERE EXISTS ( + SELECT 1 + FROM details_match dm + WHERE dm.match_id = m.id + AND dm.players_id IS NOT NULL + AND dm.players_id != 0 +) +ORDER BY RANDOM() +LIMIT 100 +""" + +matches_df = pd.read_sql(query, conn) +print(f"Загружено {len(matches_df)} случайных матчей") + +# Получаем детали этих матчей +match_ids = matches_df['match_id'].tolist() +placeholders = ','.join(['%s'] * len(match_ids)) + +query_details = f""" +SELECT + dm.match_id, + dm.hero_id, + dm.team, + dm.players_id, + dm.pos, + dm."order" +FROM details_match dm +WHERE dm.match_id IN ({placeholders}) +ORDER BY dm.match_id, dm."order" +""" + +cursor = conn.cursor() +cursor.execute(query_details, match_ids) +details_rows = cursor.fetchall() +conn.close() + +# Преобразуем детали в словарь по match_id +details_by_match = {} +for row in details_rows: + match_id = row[0] + if match_id not in details_by_match: + details_by_match[match_id] = [] + details_by_match[match_id].append({ + 'hero_id': row[1], + 'team': row[2], + 'players_id': row[3], + 'pos': row[4], + 'order': row[5] + }) + +print(f"Загружено деталей для {len(details_by_match)} матчей\n") + +# Счётчики правильных предсказаний +correct = { + 'model1': 0, + 'model2': 0, + 'model3': 0, + 'stacking': 0 +} + +# Списки для хранения всех предсказаний (для расчёта AUC) +predictions = { + 'model1': [], + 'model2': [], + 'model3': [], + 'stacking': [] +} +actuals = [] + +print("Тестирование моделей...") +print("="*80) + +# Проверяем каждый матч +for idx, match_row in matches_df.iterrows(): + match_id = match_row['match_id'] + radiant_win = match_row['radiant_win'] + + if match_id not in details_by_match: + continue + + details = details_by_match[match_id] + + # Формируем payload + payload = {} + + # Разделяем на Radiant и Dire + radiant_picks = sorted([d for d in details if d['team'] == 0], key=lambda x: x['order']) + dire_picks = sorted([d for d in details if d['team'] == 1], key=lambda x: x['order']) + + # Заполняем Radiant + for i in range(5): + if i < len(radiant_picks): + payload[f'r_h{i+1}'] = radiant_picks[i]['hero_id'] + payload[f'r_p{i+1}'] = radiant_picks[i]['players_id'] if radiant_picks[i]['players_id'] else -1 + payload[f'rp_h{i+1}'] = radiant_picks[i]['pos'] if radiant_picks[i]['pos'] else -1 + else: + payload[f'r_h{i+1}'] = -1 + payload[f'r_p{i+1}'] = -1 + payload[f'rp_h{i+1}'] = -1 + + # Заполняем Dire + for i in range(5): + if i < len(dire_picks): + payload[f'd_h{i+1}'] = dire_picks[i]['hero_id'] + payload[f'd_p{i+1}'] = dire_picks[i]['players_id'] if dire_picks[i]['players_id'] else -1 + payload[f'dp_h{i+1}'] = dire_picks[i]['pos'] if dire_picks[i]['pos'] else -1 + else: + payload[f'd_h{i+1}'] = -1 + payload[f'd_p{i+1}'] = -1 + payload[f'dp_h{i+1}'] = -1 + + # Предсказания + try: + # Модель 1: Heroes + Positions + X1 = build_long_format_input(payload) + pred1_proba = float(modelPro.predict_proba(X1)[0, 1]) + pred1 = pred1_proba >= 0.5 + + # Модель 2: Bag of Heroes + X2 = build_bag_of_heroes_features(payload) + pred2_proba = float(modelBagOfHeroes.predict_proba(X2)[0, 1]) + pred2 = pred2_proba >= 0.5 + + # Модель 3: With Players + X3 = build_player_features(payload) + pred3_proba = float(modelWithPlayers.predict_proba(X3)[0, 1]) + pred3 = pred3_proba >= 0.5 + + # Стекинг + stack_result = predict_stacking(payload) + pred_stack_proba = stack_result['radiant_win'] / 100.0 + pred_stack = pred_stack_proba >= 0.5 + + # Сохраняем предсказания + predictions['model1'].append(pred1_proba) + predictions['model2'].append(pred2_proba) + predictions['model3'].append(pred3_proba) + predictions['stacking'].append(pred_stack_proba) + actuals.append(int(radiant_win)) + + # Подсчитываем правильные предсказания + if pred1 == radiant_win: + correct['model1'] += 1 + if pred2 == radiant_win: + correct['model2'] += 1 + if pred3 == radiant_win: + correct['model3'] += 1 + if pred_stack == radiant_win: + correct['stacking'] += 1 + + # Показываем первые 10 матчей + if idx < 10: + actual_str = "Radiant" if radiant_win else "Dire" + print(f"\nМатч #{idx+1} (ID: {match_id}):") + print(f" Реальный результат: {actual_str} win") + print(f" Модель 1: {pred1_proba*100:.1f}% Radiant ({'✓' if pred1 == radiant_win else '✗'})") + print(f" Модель 2: {pred2_proba*100:.1f}% Radiant ({'✓' if pred2 == radiant_win else '✗'})") + print(f" Модель 3: {pred3_proba*100:.1f}% Radiant ({'✓' if pred3 == radiant_win else '✗'})") + print(f" Стекинг: {pred_stack_proba*100:.1f}% Radiant ({'✓' if pred_stack == radiant_win else '✗'})") + + except Exception as e: + print(f"Ошибка при обработке матча {match_id}: {e}") + continue + +# Итоговая статистика +total = len(actuals) + +print("\n" + "="*80) +print("ИТОГОВАЯ СТАТИСТИКА") +print("="*80) +print(f"Всего проверено матчей: {total}\n") + +print("Точность (Accuracy):") +print(f" Модель 1 (Heroes + Positions): {correct['model1']}/{total} = {correct['model1']/total*100:.2f}%") +print(f" Модель 2 (Bag of Heroes): {correct['model2']}/{total} = {correct['model2']/total*100:.2f}%") +print(f" Модель 3 (With Players): {correct['model3']}/{total} = {correct['model3']/total*100:.2f}%") +print(f" Мета-модель (Stacking): {correct['stacking']}/{total} = {correct['stacking']/total*100:.2f}%") + +# Расчёт AUC +from sklearn.metrics import roc_auc_score + +print("\nAUC (Area Under ROC Curve):") +try: + auc1 = roc_auc_score(actuals, predictions['model1']) + print(f" Модель 1 (Heroes + Positions): {auc1:.4f}") +except: + print(f" Модель 1 (Heroes + Positions): N/A") + +try: + auc2 = roc_auc_score(actuals, predictions['model2']) + print(f" Модель 2 (Bag of Heroes): {auc2:.4f}") +except: + print(f" Модель 2 (Bag of Heroes): N/A") + +try: + auc3 = roc_auc_score(actuals, predictions['model3']) + print(f" Модель 3 (With Players): {auc3:.4f}") +except: + print(f" Модель 3 (With Players): N/A") + +try: + auc_stack = roc_auc_score(actuals, predictions['stacking']) + print(f" Мета-модель (Stacking): {auc_stack:.4f}") +except: + print(f" Мета-модель (Stacking): N/A") + +print("\n" + "="*80) diff --git a/update_models.sh b/update_models.sh new file mode 100755 index 0000000..664b08b --- /dev/null +++ b/update_models.sh @@ -0,0 +1,73 @@ +#!/bin/bash + +# Скрипт для обновления базы данных и переобучения моделей + +set -e # Остановка при ошибке + +echo "======================================" +echo "Начало обновления моделей" +echo "======================================" +echo "" + +# Активация виртуального окружения +source .venv/bin/activate + +# 1. Парсинг про матчей +echo "======================================" +echo "1. Парсинг про матчей..." +echo "======================================" +python start/parse_pro_matches.py +echo "" + +# 2. Парсинг деталей про матчей +echo "======================================" +echo "2. Парсинг деталей про матчей..." +echo "======================================" +python start/parse_pro_details_match.py +echo "" + +# 3. Построение датасета +echo "======================================" +echo "3. Построение датасета из БД..." +echo "======================================" +python educationML/build_dataset_pro.py +echo "" + +# 4. Обучение модели Long-Format (Heroes + Positions) +echo "======================================" +echo "4. Обучение модели Long-Format..." +echo "======================================" +python educationML/train_model_pro.py +echo "" + +# 5. Обучение модели Bag of Heroes +echo "======================================" +echo "5. Обучение модели Bag of Heroes..." +echo "======================================" +python educationML/train_model_bag_of_heroes.py +echo "" + +# 6. Построение датасета с игроками +echo "======================================" +echo "6. Построение датасета с игроками..." +echo "======================================" +python educationML/build_dataset_with_players.py +echo "" + +# 7. Обучение модели With Players +echo "======================================" +echo "7. Обучение модели With Players..." +echo "======================================" +python educationML/train_model_with_players.py +echo "" + +# 8. Обучение мета-модели (Stacking) +echo "======================================" +echo "8. Обучение мета-модели (Stacking)..." +echo "======================================" +python educationML/train_model_stacking.py +echo "" + +echo "======================================" +echo "✅ Все модели успешно обновлены!" +echo "======================================" diff --git a/лиги.txt b/лиги.txt new file mode 100644 index 0000000..f0e1127 --- /dev/null +++ b/лиги.txt @@ -0,0 +1,9 @@ +18038 - FISSURE PLAYGROUND 2 - Closed Qualifiers - EEU +//18667 - FISSURE PLAYGROUND 2 - Closed Qualifiers - China +//18668 - FISSURE PLAYGROUND 2 - Closed Qualifiers - Americas +//18700 - FISSURE PLAYGROUND 2 - Closed Qualifiers - Americas (SA) +//18702 - RES Unchained - A Blast Dota Slam IV Qualifier EU +//18704 - RES Unchained - A Blast Dota Slam IV Qualifier SEA +//18706 - BLAST Slam IV: China Closed Qualifier +18863 - FISSURE PLAYGROUND 2 +18920 - PGL Wallachia 2025 Season 6" \ No newline at end of file