Merge branch 'main' of https://gitea.bilgich.com/fahricansecer/iddaai-be

- Add LeagueTier DB model and Prisma schema
- Add league-tiers service (CRUD, sync, retrain trigger)
- Add league-tiers controller with admin API endpoints
- Add /v1/admin/retrain endpoint in AI engine (extract→train→reload pipeline)
- Retrain V25 Pro with 48 quality leagues (MS accuracy: 26.9%→51.4%)
- Update qualified_leagues.json (443→48 leagues)
- Include V25 model files in repo for Docker deployment

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.gitea/workflows/deploy.yml

@@ -11,13 +11,27 @@ jobs:
       - name: Kodu Cek
         uses: actions/checkout@v4
 
-      - name: Docker Build
+      - name: Docker Build (Backend)
         run: docker build -t iddaai-be:latest .
 
-      - name: Eski Konteyneri Sil
+      - name: Docker Build (AI Engine)
-        run: docker rm -f iddaai-be || true
+        run: docker build -t iddaai-ai-engine:latest ./ai-engine
 
-      - name: Yeni Versiyonu Baslat
+      - name: Eski Konteynerleri Sil
+        run: |
+          docker rm -f iddaai-be || true
+          docker rm -f iddaai-ai-engine || true
+
+      - name: AI Engine'i Baslat
+        run: |
+          docker run -d \
+            --name iddaai-ai-engine \
+            --restart unless-stopped \
+            --network iddaai_iddaai-network \
+            -e DATABASE_URL='${{ secrets.DATABASE_URL }}' \
+            iddaai-ai-engine:latest
+
+      - name: Backend'i Baslat
         run: |
           docker run -d \
             --name iddaai-be \
@@ -25,11 +39,21 @@ jobs:
             --network iddaai_iddaai-network \
             -p 127.0.0.1:1810:3005 \
             -e NODE_ENV=production \
-            -e DATABASE_URL='postgresql://iddaai_user:IddaA1_S4crET!@iddaai-postgres:5432/iddaai_db?schema=public' \
+            -e DATABASE_URL='${{ secrets.DATABASE_URL }}' \
-            -e REDIS_HOST='iddaai-redis' \
+            -e REDIS_HOST='${{ secrets.REDIS_HOST }}' \
-            -e REDIS_PORT='6379' \
+            -e REDIS_PORT='${{ secrets.REDIS_PORT }}' \
-            -e REDIS_PASSWORD='IddaA1_Redis_Pass!' \
+            -e REDIS_PASSWORD='${{ secrets.REDIS_PASSWORD }}' \
             -e AI_ENGINE_URL='http://iddaai-ai-engine:8000' \
-            -e JWT_SECRET='b7V8jM2wP1L5mQxs2RdfFkAsLpI2oG!w' \
+            -e JWT_SECRET='${{ secrets.JWT_SECRET }}' \
             -e JWT_ACCESS_EXPIRATION='1d' \
             iddaai-be:latest /bin/sh -c "npx prisma migrate deploy && node dist/src/main.js"
+
+      - name: Saglik Kontrolu
+        run: |
+          sleep 10
+          echo "=== AI Engine logs ==="
+          docker logs --tail 30 iddaai-ai-engine || true
+          echo "=== Backend logs ==="
+          docker logs --tail 30 iddaai-be || true
+          echo "=== AI Engine health ==="
+          docker exec iddaai-ai-engine python -c "import urllib.request; print(urllib.request.urlopen('http://127.0.0.1:8000/health').read().decode())" || echo "AI engine health check failed"

.gitignore

@@ -21,7 +21,10 @@ venv/
 env/
 
 # Database / Docker Volumes
-data/
+/data/
+ai-engine/data/**/*.csv
+ai-engine/data/v26_shadow/
+ai-engine/data/__pycache__/
 postgres-data/
 redis-data/
 
@@ -42,7 +45,10 @@ uploads/
 public/uploads/
 
 # Large Datasets and ML Models
-ai-engine/models/
+ai-engine/models/*
-models/
+!ai-engine/models/*.py
+!ai-engine/models/v25/
+models/*
+!models/*.py
 colab_export/
 

Dockerfile

@@ -16,7 +16,7 @@ RUN npm ci
 COPY . .
 
 # Generate Prisma client
-RUN npx prisma generate
+RUN DATABASE_URL="postgresql://dummy:dummy@localhost/dummy" npx prisma generate
 
 # Build the application
 RUN npm run build
@@ -38,7 +38,7 @@ RUN apk add --no-cache --virtual .build-deps python3 make g++ cairo-dev pango-de
 
 # Copy Prisma schema and generate client
 COPY prisma ./prisma
-RUN npx prisma generate
+RUN DATABASE_URL="postgresql://dummy:dummy@localhost/dummy" npx prisma generate
 
 # Copy built application
 COPY --from=builder /app/dist ./dist
@@ -47,7 +47,7 @@ COPY --from=builder /app/dist ./dist
 COPY --from=builder /app/src/i18n ./dist/i18n
 
 # Copy league filter config files (critical: without these, feeder stores ALL matches)
-COPY top_leagues.json basketball_top_leagues.json ./
+COPY qualified_leagues.json top_leagues.json basketball_top_leagues.json ./
 
 # Set environment
 ENV NODE_ENV=production

ai-engine/catboost_info/catboost_training.json

@@ -0,0 +1,874 @@
+{
+"meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Min","name":"Logloss"}],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":2000,"learn_sets":["learn"],"name":"experiment"},
+"iterations":[
+{"learn":[0.692389481],"iteration":0,"passed_time":0.04679785798,"remaining_time":93.54891809,"test":[0.6924099937]},
+{"learn":[0.6916338586],"iteration":1,"passed_time":0.08350330552,"remaining_time":83.41980222,"test":[0.6916660956]},
+{"learn":[0.6910159214],"iteration":2,"passed_time":0.132821758,"remaining_time":88.41501689,"test":[0.691108145]},
+{"learn":[0.6903417151],"iteration":3,"passed_time":0.162826233,"remaining_time":81.25029026,"test":[0.6904585078]},
+{"learn":[0.6896961461],"iteration":4,"passed_time":0.1969265393,"remaining_time":78.57368918,"test":[0.689812816]},
+{"learn":[0.6890979366],"iteration":5,"passed_time":0.2309352918,"remaining_time":76.74749531,"test":[0.689192261]},
+{"learn":[0.6884946167],"iteration":6,"passed_time":0.2693987513,"remaining_time":76.70167304,"test":[0.6886032715]},
+{"learn":[0.6879503686],"iteration":7,"passed_time":0.3199759681,"remaining_time":79.67401607,"test":[0.6880706742]},
+{"learn":[0.6874528094],"iteration":8,"passed_time":0.3645802206,"remaining_time":80.65324659,"test":[0.6876192378]},
+{"learn":[0.6869036785],"iteration":9,"passed_time":0.4116507506,"remaining_time":81.91849936,"test":[0.6870868859]},
+{"learn":[0.6863761921],"iteration":10,"passed_time":0.4562469316,"remaining_time":82.49774064,"test":[0.6865493528]},
+{"learn":[0.6859038678],"iteration":11,"passed_time":0.491541699,"remaining_time":81.43207481,"test":[0.686105086]},
+{"learn":[0.685410175],"iteration":12,"passed_time":0.5221556769,"remaining_time":79.80948692,"test":[0.6856345086]},
+{"learn":[0.6849483392],"iteration":13,"passed_time":0.5553110353,"remaining_time":78.77483686,"test":[0.6852027185]},
+{"learn":[0.6845417792],"iteration":14,"passed_time":0.5952927147,"remaining_time":78.77706925,"test":[0.6848238481]},
+{"learn":[0.6841038875],"iteration":15,"passed_time":0.6300274185,"remaining_time":78.12339989,"test":[0.6844045699]},
+{"learn":[0.6836957422],"iteration":16,"passed_time":0.662600544,"remaining_time":77.29040464,"test":[0.6840077621]},
+{"learn":[0.6832947461],"iteration":17,"passed_time":0.7004221698,"remaining_time":77.12426337,"test":[0.6836197496]},
+{"learn":[0.6829014105],"iteration":18,"passed_time":0.7300844347,"remaining_time":76.12090869,"test":[0.6832475033]},
+{"learn":[0.6825264546],"iteration":19,"passed_time":0.7641559459,"remaining_time":75.65143865,"test":[0.6829012069]},
+{"learn":[0.6822106577],"iteration":20,"passed_time":0.8040792063,"remaining_time":75.77489282,"test":[0.6825880966]},
+{"learn":[0.6818649349],"iteration":21,"passed_time":0.8356039756,"remaining_time":75.12839381,"test":[0.6822424968]},
+{"learn":[0.6815467855],"iteration":22,"passed_time":0.8861440327,"remaining_time":76.16985881,"test":[0.6819180513]},
+{"learn":[0.6812293319],"iteration":23,"passed_time":0.920219319,"remaining_time":75.76472393,"test":[0.6816384467]},
+{"learn":[0.6808837443],"iteration":24,"passed_time":0.960164738,"remaining_time":75.8530143,"test":[0.6813262593]},
+{"learn":[0.6805816494],"iteration":25,"passed_time":0.9895547925,"remaining_time":75.13004463,"test":[0.6810353411]},
+{"learn":[0.6803209634],"iteration":26,"passed_time":1.025550161,"remaining_time":74.94112844,"test":[0.6808138172]},
+{"learn":[0.6800350862],"iteration":27,"passed_time":1.060852064,"remaining_time":74.71429535,"test":[0.6805550049]},
+{"learn":[0.6797703947],"iteration":28,"passed_time":1.10467538,"remaining_time":75.07983357,"test":[0.680347991]},
+{"learn":[0.6794926675],"iteration":29,"passed_time":1.141766834,"remaining_time":74.97602208,"test":[0.680089679]},
+{"learn":[0.6792251865],"iteration":30,"passed_time":1.180421588,"remaining_time":74.9758099,"test":[0.6798451919]},
+{"learn":[0.6789670166],"iteration":31,"passed_time":1.213674604,"remaining_time":74.64098814,"test":[0.6796090443]},
+{"learn":[0.678722402],"iteration":32,"passed_time":1.245848393,"remaining_time":74.26011482,"test":[0.6793890865]},
+{"learn":[0.678476935],"iteration":33,"passed_time":1.287262512,"remaining_time":74.43406171,"test":[0.6791683772]},
+{"learn":[0.6782297335],"iteration":34,"passed_time":1.327473991,"remaining_time":74.52818262,"test":[0.6789766369]},
+{"learn":[0.6780226701],"iteration":35,"passed_time":1.3760549,"remaining_time":75.07143955,"test":[0.6787930242]},
+{"learn":[0.6778291026],"iteration":36,"passed_time":1.427620019,"remaining_time":75.74102965,"test":[0.6786087714]},
+{"learn":[0.6776045324],"iteration":37,"passed_time":1.468182407,"remaining_time":75.80457587,"test":[0.6784161299]},
+{"learn":[0.6773969079],"iteration":38,"passed_time":1.508647379,"remaining_time":75.85788487,"test":[0.6782227897]},
+{"learn":[0.6771819602],"iteration":39,"passed_time":1.549435187,"remaining_time":75.92232419,"test":[0.6780242369]},
+{"learn":[0.6769816736],"iteration":40,"passed_time":1.586036608,"remaining_time":75.78160282,"test":[0.6778499631]},
+{"learn":[0.6767984027],"iteration":41,"passed_time":1.621458864,"remaining_time":75.59086802,"test":[0.6776975784]},
+{"learn":[0.6766201184],"iteration":42,"passed_time":1.663424818,"remaining_time":75.70517136,"test":[0.6775231674]},
+{"learn":[0.6764394377],"iteration":43,"passed_time":1.70110089,"remaining_time":75.62166686,"test":[0.6773582124]},
+{"learn":[0.6762698797],"iteration":44,"passed_time":1.739954496,"remaining_time":75.59135644,"test":[0.6772234666]},
+{"learn":[0.6760974263],"iteration":45,"passed_time":1.776461223,"remaining_time":75.46098325,"test":[0.6770659843]},
+{"learn":[0.6759245179],"iteration":46,"passed_time":1.819761638,"remaining_time":75.61690381,"test":[0.6769049529]},
+{"learn":[0.6757673909],"iteration":47,"passed_time":1.869479807,"remaining_time":76.02551217,"test":[0.6767664194]},
+{"learn":[0.6756172628],"iteration":48,"passed_time":1.916010121,"remaining_time":76.28848462,"test":[0.6766584917]},
+{"learn":[0.675474531],"iteration":49,"passed_time":1.953635244,"remaining_time":76.19177452,"test":[0.6765507257]},
+{"learn":[0.6753286933],"iteration":50,"passed_time":1.993876686,"remaining_time":76.19736591,"test":[0.6764489911]},
+{"learn":[0.6751900513],"iteration":51,"passed_time":2.038943041,"remaining_time":76.38194316,"test":[0.6763947956]},
+{"learn":[0.6750574835],"iteration":52,"passed_time":2.080276765,"remaining_time":76.42073325,"test":[0.6762778712]},
+{"learn":[0.6749329567],"iteration":53,"passed_time":2.158576742,"remaining_time":77.78871001,"test":[0.6761865366]},
+{"learn":[0.6748033265],"iteration":54,"passed_time":2.220619687,"remaining_time":78.52918711,"test":[0.6760679685]},
+{"learn":[0.6746797823],"iteration":55,"passed_time":2.286959228,"remaining_time":79.39015604,"test":[0.6759774874]},
+{"learn":[0.674535525],"iteration":56,"passed_time":2.328472096,"remaining_time":79.3723032,"test":[0.6758500622]},
+{"learn":[0.6744256514],"iteration":57,"passed_time":2.367031568,"remaining_time":79.25474665,"test":[0.6757625065]},
+{"learn":[0.674310819],"iteration":58,"passed_time":2.409161286,"remaining_time":79.25732298,"test":[0.6756876412]},
+{"learn":[0.6741967947],"iteration":59,"passed_time":2.444825903,"remaining_time":79.04937087,"test":[0.6756151069]},
+{"learn":[0.6740879654],"iteration":60,"passed_time":2.48484996,"remaining_time":78.98564055,"test":[0.6755303655]},
+{"learn":[0.6739772476],"iteration":61,"passed_time":2.521603395,"remaining_time":78.8204416,"test":[0.6754565036]},
+{"learn":[0.67388281],"iteration":62,"passed_time":2.554102332,"remaining_time":78.5285114,"test":[0.6753738983]},
+{"learn":[0.6737789726],"iteration":63,"passed_time":2.593937938,"remaining_time":78.46662263,"test":[0.6752897299]},
+{"learn":[0.6736812332],"iteration":64,"passed_time":2.623889155,"remaining_time":78.11116175,"test":[0.6752115539]},
+{"learn":[0.6735930009],"iteration":65,"passed_time":2.660795108,"remaining_time":77.96935967,"test":[0.6751595431]},
+{"learn":[0.6734947116],"iteration":66,"passed_time":2.695822592,"remaining_time":77.77649358,"test":[0.6750764658]},
+{"learn":[0.6733961481],"iteration":67,"passed_time":2.725876686,"remaining_time":77.44696703,"test":[0.6750179194]},
+{"learn":[0.6732990195],"iteration":68,"passed_time":2.761848366,"remaining_time":77.29172746,"test":[0.6749408803]},
+{"learn":[0.6732133575],"iteration":69,"passed_time":2.791847449,"remaining_time":76.97522253,"test":[0.6748795802]},
+{"learn":[0.673111539],"iteration":70,"passed_time":2.824541003,"remaining_time":76.73999429,"test":[0.674790372]},
+{"learn":[0.6730080451],"iteration":71,"passed_time":2.861023716,"remaining_time":76.61185729,"test":[0.6747239773]},
+{"learn":[0.6729157861],"iteration":72,"passed_time":2.897136588,"remaining_time":76.47646857,"test":[0.6746701254]},
+{"learn":[0.6728347949],"iteration":73,"passed_time":2.935718661,"remaining_time":76.40802894,"test":[0.6746120937]},
+{"learn":[0.6727640693],"iteration":74,"passed_time":3.040023476,"remaining_time":78.02726921,"test":[0.6745550085]},
+{"learn":[0.6726808811],"iteration":75,"passed_time":3.097341794,"remaining_time":78.41165279,"test":[0.6744855074]},
+{"learn":[0.6726029645],"iteration":76,"passed_time":3.152948955,"remaining_time":78.74182909,"test":[0.6744264172]},
+{"learn":[0.6725356026],"iteration":77,"passed_time":3.216126808,"remaining_time":79.24866314,"test":[0.674381715]},
+{"learn":[0.6724606887],"iteration":78,"passed_time":3.256861302,"remaining_time":79.19532355,"test":[0.6743331681]},
+{"learn":[0.6723849561],"iteration":79,"passed_time":3.305679851,"remaining_time":79.33631641,"test":[0.67428564]},
+{"learn":[0.6723050519],"iteration":80,"passed_time":3.348083566,"remaining_time":79.32064647,"test":[0.6742202413]},
+{"learn":[0.6722508802],"iteration":81,"passed_time":3.38129387,"remaining_time":79.08928832,"test":[0.6741620971]},
+{"learn":[0.6721773904],"iteration":82,"passed_time":3.41660066,"remaining_time":78.91112609,"test":[0.6741109453]},
+{"learn":[0.6721007598],"iteration":83,"passed_time":3.48099347,"remaining_time":79.39980344,"test":[0.6740556003]},
+{"learn":[0.6720353564],"iteration":84,"passed_time":3.535359896,"remaining_time":79.64957884,"test":[0.6740146772]},
+{"learn":[0.6719790902],"iteration":85,"passed_time":3.581806996,"remaining_time":79.71603012,"test":[0.673983295]},
+{"learn":[0.6719140024],"iteration":86,"passed_time":3.612293661,"remaining_time":79.42893993,"test":[0.6739595301]},
+{"learn":[0.6718573633],"iteration":87,"passed_time":3.644530261,"remaining_time":79.18570293,"test":[0.6739336659]},
+{"learn":[0.671795602],"iteration":88,"passed_time":3.67809653,"remaining_time":78.97575809,"test":[0.673890361]},
+{"learn":[0.6717369134],"iteration":89,"passed_time":3.712417516,"remaining_time":78.78574951,"test":[0.673863586]},
+{"learn":[0.6716711079],"iteration":90,"passed_time":3.743502971,"remaining_time":78.53128759,"test":[0.6738190616]},
+{"learn":[0.6716070843],"iteration":91,"passed_time":3.775351679,"remaining_time":78.2975109,"test":[0.6737799295]},
+{"learn":[0.6715517232],"iteration":92,"passed_time":3.806186247,"remaining_time":78.04728142,"test":[0.6737364374]},
+{"learn":[0.6714957378],"iteration":93,"passed_time":3.83798807,"remaining_time":77.82133257,"test":[0.6737093719]},
+{"learn":[0.6714364567],"iteration":94,"passed_time":3.871278973,"remaining_time":77.62933099,"test":[0.6736630475]},
+{"learn":[0.6713881758],"iteration":95,"passed_time":3.913531039,"remaining_time":77.6183656,"test":[0.67364367]},
+{"learn":[0.6713336502],"iteration":96,"passed_time":3.945433866,"remaining_time":77.40371802,"test":[0.6735998081]},
+{"learn":[0.6712700267],"iteration":97,"passed_time":3.989716281,"remaining_time":77.43306496,"test":[0.6735526984]},
+{"learn":[0.6712154424],"iteration":98,"passed_time":4.020621946,"remaining_time":77.20406384,"test":[0.6735012924]},
+{"learn":[0.6711600413],"iteration":99,"passed_time":4.053732144,"remaining_time":77.02091074,"test":[0.6734818024]},
+{"learn":[0.6711060533],"iteration":100,"passed_time":4.084124711,"remaining_time":76.78963194,"test":[0.6734379341]},
+{"learn":[0.6710494943],"iteration":101,"passed_time":4.116434744,"remaining_time":76.59797199,"test":[0.6734059869]},
+{"learn":[0.6709936897],"iteration":102,"passed_time":4.148330356,"remaining_time":76.40177365,"test":[0.6733740852]},
+{"learn":[0.6709472183],"iteration":103,"passed_time":4.176511193,"remaining_time":76.14101176,"test":[0.6733330971]},
+{"learn":[0.6708914508],"iteration":104,"passed_time":4.2025065,"remaining_time":75.84523636,"test":[0.6733060254]},
+{"learn":[0.6708388195],"iteration":105,"passed_time":4.232975206,"remaining_time":75.63448151,"test":[0.6732755898]},
+{"learn":[0.6707885854],"iteration":106,"passed_time":4.261364958,"remaining_time":75.39031649,"test":[0.6732294722]},
+{"learn":[0.6707454167],"iteration":107,"passed_time":4.290824713,"remaining_time":75.1688922,"test":[0.6732035176]},
+{"learn":[0.6706973013],"iteration":108,"passed_time":4.324192493,"remaining_time":75.01878903,"test":[0.673196437]},
+{"learn":[0.6706577031],"iteration":109,"passed_time":4.351512102,"remaining_time":74.76688976,"test":[0.6731652709]},
+{"learn":[0.67061108],"iteration":110,"passed_time":4.38641502,"remaining_time":74.64808984,"test":[0.673138808]},
+{"learn":[0.6705625485],"iteration":111,"passed_time":4.424063991,"remaining_time":74.57707871,"test":[0.6731062725]},
+{"learn":[0.6705146484],"iteration":112,"passed_time":4.45863849,"remaining_time":74.45531709,"test":[0.6730726625]},
+{"learn":[0.6704704423],"iteration":113,"passed_time":4.497153675,"remaining_time":74.40027922,"test":[0.6730285927]},
+{"learn":[0.6704155922],"iteration":114,"passed_time":4.533368584,"remaining_time":74.30782417,"test":[0.6729872702]},
+{"learn":[0.6703687117],"iteration":115,"passed_time":4.564651269,"remaining_time":74.13623268,"test":[0.6729721425]},
+{"learn":[0.6703324232],"iteration":116,"passed_time":4.596824343,"remaining_time":73.98136956,"test":[0.6729564624]},
+{"learn":[0.6702884624],"iteration":117,"passed_time":4.628377967,"remaining_time":73.81870623,"test":[0.6729312424]},
+{"learn":[0.670253478],"iteration":118,"passed_time":4.668052254,"remaining_time":73.78660748,"test":[0.6729354345]},
+{"learn":[0.6702140804],"iteration":119,"passed_time":4.692108266,"remaining_time":73.50969617,"test":[0.6729085401]},
+{"learn":[0.6701682529],"iteration":120,"passed_time":4.723741667,"remaining_time":73.354633,"test":[0.6728898322]},
+{"learn":[0.6701320588],"iteration":121,"passed_time":4.756626425,"remaining_time":73.22085595,"test":[0.6728773638]},
+{"learn":[0.6700939824],"iteration":122,"passed_time":4.788008428,"remaining_time":73.06578714,"test":[0.6728618874]},
+{"learn":[0.6700655902],"iteration":123,"passed_time":4.815546648,"remaining_time":72.85456058,"test":[0.6728540413]},
+{"learn":[0.6700190743],"iteration":124,"passed_time":4.843186806,"remaining_time":72.64780209,"test":[0.6728441291]},
+{"learn":[0.6699792296],"iteration":125,"passed_time":4.875548614,"remaining_time":72.51411192,"test":[0.672815631]},
+{"learn":[0.6699379404],"iteration":126,"passed_time":4.916953662,"remaining_time":72.51538748,"test":[0.6728082021]},
+{"learn":[0.669895454],"iteration":127,"passed_time":4.952918369,"remaining_time":72.43643115,"test":[0.6727900064]},
+{"learn":[0.6698563938],"iteration":128,"passed_time":4.991585558,"remaining_time":72.39733782,"test":[0.6727649552]},
+{"learn":[0.6698215571],"iteration":129,"passed_time":5.028084166,"remaining_time":72.32705685,"test":[0.6727467657]},
+{"learn":[0.6697857067],"iteration":130,"passed_time":5.059198996,"remaining_time":72.18048033,"test":[0.6727396032]},
+{"learn":[0.6697449303],"iteration":131,"passed_time":5.096035515,"remaining_time":72.1166238,"test":[0.6727245271]},
+{"learn":[0.6697052425],"iteration":132,"passed_time":5.125282589,"remaining_time":71.94663604,"test":[0.6726955143]},
+{"learn":[0.6696695553],"iteration":133,"passed_time":5.156392608,"remaining_time":71.80469109,"test":[0.67269209]},
+{"learn":[0.6696269265],"iteration":134,"passed_time":5.190402292,"remaining_time":71.70444647,"test":[0.672677932]},
+{"learn":[0.6695969271],"iteration":135,"passed_time":5.221466142,"remaining_time":71.56480065,"test":[0.6726540285]},
+{"learn":[0.6695489786],"iteration":136,"passed_time":5.251144663,"remaining_time":71.40790151,"test":[0.6726288583]},
+{"learn":[0.6695173859],"iteration":137,"passed_time":5.274361693,"remaining_time":71.16566285,"test":[0.6725863431]},
+{"learn":[0.6694811164],"iteration":138,"passed_time":5.309398952,"remaining_time":71.08483058,"test":[0.6725837967]},
+{"learn":[0.6694477439],"iteration":139,"passed_time":5.344693175,"remaining_time":71.00806646,"test":[0.6725772977]},
+{"learn":[0.6694082161],"iteration":140,"passed_time":5.377737126,"remaining_time":70.90222211,"test":[0.6725685594]},
+{"learn":[0.6693679185],"iteration":141,"passed_time":5.416087925,"remaining_time":70.8668406,"test":[0.6725553829]},
+{"learn":[0.6693341916],"iteration":142,"passed_time":5.452286939,"remaining_time":70.80347444,"test":[0.6725484347]},
+{"learn":[0.6692933159],"iteration":143,"passed_time":5.490006789,"remaining_time":70.7600875,"test":[0.6725306172]},
+{"learn":[0.6692619696],"iteration":144,"passed_time":5.521869859,"remaining_time":70.64185233,"test":[0.672543149]},
+{"learn":[0.6692229289],"iteration":145,"passed_time":5.553520721,"remaining_time":70.5221056,"test":[0.6725196247]},
+{"learn":[0.6691840164],"iteration":146,"passed_time":5.582178524,"remaining_time":70.3658286,"test":[0.6725226452]},
+{"learn":[0.6691581406],"iteration":147,"passed_time":5.611368671,"remaining_time":70.21793769,"test":[0.6725056913]},
+{"learn":[0.6691177196],"iteration":148,"passed_time":5.636941079,"remaining_time":70.02669757,"test":[0.6724771476]},
+{"learn":[0.6690851126],"iteration":149,"passed_time":5.673704689,"remaining_time":69.97569117,"test":[0.6724439435]},
+{"learn":[0.6690518144],"iteration":150,"passed_time":5.706346207,"remaining_time":69.87439826,"test":[0.672442532]},
+{"learn":[0.6690149711],"iteration":151,"passed_time":5.738210991,"remaining_time":69.76456521,"test":[0.6724303064]},
+{"learn":[0.668993877],"iteration":152,"passed_time":5.765951318,"remaining_time":69.60596133,"test":[0.6724235788]},
+{"learn":[0.6689596579],"iteration":153,"passed_time":5.795573467,"remaining_time":69.47161442,"test":[0.6724294499]},
+{"learn":[0.6689372651],"iteration":154,"passed_time":5.81744896,"remaining_time":69.24640858,"test":[0.6724285935]},
+{"learn":[0.6689003045],"iteration":155,"passed_time":5.853529431,"remaining_time":69.19171968,"test":[0.6724172017]},
+{"learn":[0.6688680182],"iteration":156,"passed_time":5.888380392,"remaining_time":69.12283479,"test":[0.6724130745]},
+{"learn":[0.6688348164],"iteration":157,"passed_time":5.924601775,"remaining_time":69.07035741,"test":[0.6723860878]},
+{"learn":[0.6687947046],"iteration":158,"passed_time":5.964531924,"remaining_time":69.06102687,"test":[0.6723707604]},
+{"learn":[0.6687605251],"iteration":159,"passed_time":5.996805452,"remaining_time":68.9632627,"test":[0.6723566111]},
+{"learn":[0.668726253],"iteration":160,"passed_time":6.022341459,"remaining_time":68.78935368,"test":[0.6723469906]},
+{"learn":[0.6686862718],"iteration":161,"passed_time":6.05082584,"remaining_time":68.65072774,"test":[0.6723287161]},
+{"learn":[0.668663478],"iteration":162,"passed_time":6.079027554,"remaining_time":68.51026759,"test":[0.6723155898]},
+{"learn":[0.6686399521],"iteration":163,"passed_time":6.108511297,"remaining_time":68.38552891,"test":[0.6722970834]},
+{"learn":[0.6686058279],"iteration":164,"passed_time":6.140719309,"remaining_time":68.29224202,"test":[0.6722872244]},
+{"learn":[0.6685761282],"iteration":165,"passed_time":6.169540017,"remaining_time":68.16226742,"test":[0.6722800481]},
+{"learn":[0.6685469327],"iteration":166,"passed_time":6.2020892,"remaining_time":68.07442817,"test":[0.6722550973]},
+{"learn":[0.6685157003],"iteration":167,"passed_time":6.231576547,"remaining_time":67.95385854,"test":[0.6722394313]},
+{"learn":[0.6684805143],"iteration":168,"passed_time":6.263261652,"remaining_time":67.85817802,"test":[0.6722204135]},
+{"learn":[0.6684485765],"iteration":169,"passed_time":6.295102833,"remaining_time":67.7649305,"test":[0.6721982148]},
+{"learn":[0.6684144429],"iteration":170,"passed_time":6.325415964,"remaining_time":67.65605729,"test":[0.6721971176]},
+{"learn":[0.6683849752],"iteration":171,"passed_time":6.35697084,"remaining_time":67.56129474,"test":[0.6721880705]},
+{"learn":[0.6683568537],"iteration":172,"passed_time":6.395913563,"remaining_time":67.5452837,"test":[0.672179176]},
+{"learn":[0.6683266628],"iteration":173,"passed_time":6.437330522,"remaining_time":67.55497433,"test":[0.6721769709]},
+{"learn":[0.6682937842],"iteration":174,"passed_time":6.472195712,"remaining_time":67.49575528,"test":[0.6721693215]},
+{"learn":[0.6682657097],"iteration":175,"passed_time":6.503044842,"remaining_time":67.395192,"test":[0.6721581386]},
+{"learn":[0.6682301443],"iteration":176,"passed_time":6.533528251,"remaining_time":67.29164972,"test":[0.6721638661]},
+{"learn":[0.6681995916],"iteration":177,"passed_time":6.562589882,"remaining_time":67.17437509,"test":[0.6721598475]},
+{"learn":[0.6681658267],"iteration":178,"passed_time":6.590816982,"remaining_time":67.04959623,"test":[0.6721433342]},
+{"learn":[0.6681422687],"iteration":179,"passed_time":6.624646227,"remaining_time":66.98253407,"test":[0.6721335599]},
+{"learn":[0.6681216601],"iteration":180,"passed_time":6.655147334,"remaining_time":66.88239227,"test":[0.6721300594]},
+{"learn":[0.6680899019],"iteration":181,"passed_time":6.687788902,"remaining_time":66.80439684,"test":[0.6721153533]},
+{"learn":[0.6680676394],"iteration":182,"passed_time":6.718057043,"remaining_time":66.7033314,"test":[0.6721076397]},
+{"learn":[0.6680413672],"iteration":183,"passed_time":6.751300957,"remaining_time":66.6324051,"test":[0.6721009911]},
+{"learn":[0.6680088406],"iteration":184,"passed_time":6.784288393,"remaining_time":66.55936991,"test":[0.6720999252]},
+{"learn":[0.6679873982],"iteration":185,"passed_time":6.810905309,"remaining_time":66.42463565,"test":[0.6720953028]},
+{"learn":[0.6679663544],"iteration":186,"passed_time":6.832974292,"remaining_time":66.24696466,"test":[0.6720942505]},
+{"learn":[0.6679417375],"iteration":187,"passed_time":6.867184511,"remaining_time":66.18796986,"test":[0.6720856237]},
+{"learn":[0.6679100197],"iteration":188,"passed_time":6.918652024,"remaining_time":66.29459691,"test":[0.6720876136]},
+{"learn":[0.667881208],"iteration":189,"passed_time":6.96948149,"remaining_time":66.39348156,"test":[0.6720880182]},
+{"learn":[0.6678475427],"iteration":190,"passed_time":7.018176318,"remaining_time":66.47058094,"test":[0.6720743856]},
+{"learn":[0.6678310341],"iteration":191,"passed_time":7.074099623,"remaining_time":66.61443812,"test":[0.6720598415]},
+{"learn":[0.6678060257],"iteration":192,"passed_time":7.117099742,"remaining_time":66.63522919,"test":[0.6720563492]},
+{"learn":[0.6677789336],"iteration":193,"passed_time":7.191058554,"remaining_time":66.94356571,"test":[0.6720389527]},
+{"learn":[0.6677478773],"iteration":194,"passed_time":7.2421897,"remaining_time":67.03667902,"test":[0.6720317324]},
+{"learn":[0.6677212408],"iteration":195,"passed_time":7.282401129,"remaining_time":67.02781447,"test":[0.672000736]},
+{"learn":[0.667704316],"iteration":196,"passed_time":7.317019235,"remaining_time":66.96744,"test":[0.6719895017]},
+{"learn":[0.6676819639],"iteration":197,"passed_time":7.351194179,"remaining_time":66.90329248,"test":[0.6719725302]},
+{"learn":[0.6676554448],"iteration":198,"passed_time":7.389840926,"remaining_time":66.87991712,"test":[0.6719770493]},
+{"learn":[0.6676318346],"iteration":199,"passed_time":7.432994652,"remaining_time":66.89695187,"test":[0.6719667172]},
+{"learn":[0.6676074705],"iteration":200,"passed_time":7.471295231,"remaining_time":66.86995085,"test":[0.6719511616]},
+{"learn":[0.6675849784],"iteration":201,"passed_time":7.506377837,"remaining_time":66.8141948,"test":[0.6719427289]},
+{"learn":[0.6675631744],"iteration":202,"passed_time":7.540821494,"remaining_time":66.75298633,"test":[0.6719299116]},
+{"learn":[0.6675397619],"iteration":203,"passed_time":7.56808212,"remaining_time":66.62880141,"test":[0.6719106583]},
+{"learn":[0.6675169086],"iteration":204,"passed_time":7.605676901,"remaining_time":66.59604896,"test":[0.6718967065]},
+{"learn":[0.6674864762],"iteration":205,"passed_time":7.638300222,"remaining_time":66.51995436,"test":[0.671890967]},
+{"learn":[0.6674670714],"iteration":206,"passed_time":7.665554951,"remaining_time":66.39777791,"test":[0.6718896293]},
+{"learn":[0.6674375599],"iteration":207,"passed_time":7.700277678,"remaining_time":66.34085384,"test":[0.6718883534]},
+{"learn":[0.6674148457],"iteration":208,"passed_time":7.734145802,"remaining_time":66.27681881,"test":[0.6718827289]},
+{"learn":[0.6673974446],"iteration":209,"passed_time":7.766232144,"remaining_time":66.19788351,"test":[0.6718763224]},
+{"learn":[0.6673812139],"iteration":210,"passed_time":7.796801222,"remaining_time":66.1065279,"test":[0.67187262]},
+{"learn":[0.6673515687],"iteration":211,"passed_time":7.831891449,"remaining_time":66.05387693,"test":[0.6718590402]},
+{"learn":[0.6673197956],"iteration":212,"passed_time":7.871259964,"remaining_time":66.0372843,"test":[0.6718455115]},
+{"learn":[0.6672900754],"iteration":213,"passed_time":7.910110502,"remaining_time":66.01615587,"test":[0.6718253747]},
+{"learn":[0.6672550009],"iteration":214,"passed_time":7.951342226,"remaining_time":66.01463197,"test":[0.671794877]},
+{"learn":[0.6672271563],"iteration":215,"passed_time":7.989001461,"remaining_time":65.98323429,"test":[0.6717873786]},
+{"learn":[0.667204521],"iteration":216,"passed_time":8.025973631,"remaining_time":65.94613357,"test":[0.6717765089]},
+{"learn":[0.667181968],"iteration":217,"passed_time":8.058434478,"remaining_time":65.87215707,"test":[0.6717616726]},
+{"learn":[0.6671640023],"iteration":218,"passed_time":8.087145957,"remaining_time":65.76806826,"test":[0.6717499215]},
+{"learn":[0.66714351],"iteration":219,"passed_time":8.112590578,"remaining_time":65.63823286,"test":[0.6717326052]},
+{"learn":[0.6671167156],"iteration":220,"passed_time":8.148644349,"remaining_time":65.59474342,"test":[0.6717161937]},
+{"learn":[0.6670915937],"iteration":221,"passed_time":8.197662625,"remaining_time":65.65515382,"test":[0.6717056951]},
+{"learn":[0.6670595279],"iteration":222,"passed_time":8.239228431,"remaining_time":65.65519696,"test":[0.6717021438]},
+{"learn":[0.667033994],"iteration":223,"passed_time":8.268371203,"remaining_time":65.55637168,"test":[0.6716868488]},
+{"learn":[0.6670008246],"iteration":224,"passed_time":8.298555216,"remaining_time":65.46638004,"test":[0.6716751909]},
+{"learn":[0.6669858319],"iteration":225,"passed_time":8.327401394,"remaining_time":65.36641625,"test":[0.671670116]},
+{"learn":[0.6669553964],"iteration":226,"passed_time":8.357648377,"remaining_time":65.27802014,"test":[0.6716558757]},
+{"learn":[0.6669274683],"iteration":227,"passed_time":8.384989701,"remaining_time":65.16755154,"test":[0.6716559962]},
+{"learn":[0.666896348],"iteration":228,"passed_time":8.418297538,"remaining_time":65.1039517,"test":[0.6716487875]},
+{"learn":[0.6668698686],"iteration":229,"passed_time":8.453919972,"remaining_time":65.05842761,"test":[0.6716427451]},
+{"learn":[0.6668513411],"iteration":230,"passed_time":8.49049033,"remaining_time":65.02024846,"test":[0.6716323255]},
+{"learn":[0.6668309985],"iteration":231,"passed_time":8.523986676,"remaining_time":64.95865708,"test":[0.6716303547]},
+{"learn":[0.6668058585],"iteration":232,"passed_time":8.550998228,"remaining_time":64.84812819,"test":[0.6716309509]},
+{"learn":[0.6667845908],"iteration":233,"passed_time":8.575382398,"remaining_time":64.71848425,"test":[0.6716215401]},
+{"learn":[0.6667582863],"iteration":234,"passed_time":8.607602961,"remaining_time":64.64859245,"test":[0.6716162103]},
+{"learn":[0.6667332943],"iteration":235,"passed_time":8.6353786,"remaining_time":64.54579597,"test":[0.6716135097]},
+{"learn":[0.6667070085],"iteration":236,"passed_time":8.66085309,"remaining_time":64.42651476,"test":[0.6716156696]},
+{"learn":[0.6666907315],"iteration":237,"passed_time":8.691362456,"remaining_time":64.34529684,"test":[0.6716020054]},
+{"learn":[0.6666633028],"iteration":238,"passed_time":8.719983169,"remaining_time":64.25058728,"test":[0.6715921704]},
+{"learn":[0.6666406707],"iteration":239,"passed_time":8.746012652,"remaining_time":64.13742611,"test":[0.6715804466]},
+{"learn":[0.6666134624],"iteration":240,"passed_time":8.773898765,"remaining_time":64.03853912,"test":[0.6715882966]},
+{"learn":[0.6665850522],"iteration":241,"passed_time":8.803292064,"remaining_time":63.9511878,"test":[0.6715753942]},
+{"learn":[0.6665631193],"iteration":242,"passed_time":8.833976809,"remaining_time":63.87365125,"test":[0.6715752261]},
+{"learn":[0.6665412643],"iteration":243,"passed_time":8.862338006,"remaining_time":63.7797768,"test":[0.6715625509]},
+{"learn":[0.6665168385],"iteration":244,"passed_time":8.892424073,"remaining_time":63.69879285,"test":[0.6715628214]},
+{"learn":[0.6664904845],"iteration":245,"passed_time":8.932383667,"remaining_time":63.68862175,"test":[0.6715601629]},
+{"learn":[0.6664678274],"iteration":246,"passed_time":8.962911123,"remaining_time":63.61126801,"test":[0.6715576255]},
+{"learn":[0.6664539777],"iteration":247,"passed_time":8.991624872,"remaining_time":63.52147894,"test":[0.6715550274]},
+{"learn":[0.6664334121],"iteration":248,"passed_time":9.021847081,"remaining_time":63.44278811,"test":[0.6715448645]},
+{"learn":[0.6664121724],"iteration":249,"passed_time":9.05121341,"remaining_time":63.35849387,"test":[0.6715308166]},
+{"learn":[0.666392034],"iteration":250,"passed_time":9.085113431,"remaining_time":63.30622865,"test":[0.671519334]},
+{"learn":[0.666366899],"iteration":251,"passed_time":9.110250512,"remaining_time":63.19332498,"test":[0.6715184071]},
+{"learn":[0.6663414098],"iteration":252,"passed_time":9.137253573,"remaining_time":63.09399997,"test":[0.6715163019]},
+{"learn":[0.6663157816],"iteration":253,"passed_time":9.174559864,"remaining_time":63.06606899,"test":[0.6715096094]},
+{"learn":[0.6662989799],"iteration":254,"passed_time":9.196898204,"remaining_time":62.93563673,"test":[0.6714992963]},
+{"learn":[0.6662696102],"iteration":255,"passed_time":9.238149902,"remaining_time":62.9348962,"test":[0.6714917256]},
+{"learn":[0.6662479711],"iteration":256,"passed_time":9.267818291,"remaining_time":62.85528125,"test":[0.671477406]},
+{"learn":[0.6662231874],"iteration":257,"passed_time":9.297538986,"remaining_time":62.77640665,"test":[0.6714741542]},
+{"learn":[0.6661947927],"iteration":258,"passed_time":9.324772701,"remaining_time":62.68119411,"test":[0.6714576155]},
+{"learn":[0.6661669951],"iteration":259,"passed_time":9.357824574,"remaining_time":62.62544138,"test":[0.6714473645]},
+{"learn":[0.6661426137],"iteration":260,"passed_time":9.388345461,"remaining_time":62.55299907,"test":[0.6714427232]},
+{"learn":[0.6661216749],"iteration":261,"passed_time":9.427290804,"remaining_time":62.53676114,"test":[0.6714364275]},
+{"learn":[0.6660983123],"iteration":262,"passed_time":9.461913185,"remaining_time":62.49179925,"test":[0.6714339587]},
+{"learn":[0.6660803402],"iteration":263,"passed_time":9.496090562,"remaining_time":62.44398945,"test":[0.6714336287]},
+{"learn":[0.6660617842],"iteration":264,"passed_time":9.524189317,"remaining_time":62.35648477,"test":[0.6714283568]},
+{"learn":[0.6660443878],"iteration":265,"passed_time":9.55372419,"remaining_time":62.27878852,"test":[0.6714271895]},
+{"learn":[0.6660176079],"iteration":266,"passed_time":9.590356068,"remaining_time":62.2475171,"test":[0.671413471]},
+{"learn":[0.6659967546],"iteration":267,"passed_time":9.620235131,"remaining_time":62.17256436,"test":[0.6714072396]},
+{"learn":[0.6659751467],"iteration":268,"passed_time":9.645948482,"remaining_time":62.0711406,"test":[0.6714002677]},
+{"learn":[0.6659539329],"iteration":269,"passed_time":9.682675077,"remaining_time":62.04084401,"test":[0.6714001163]},
+{"learn":[0.6659263951],"iteration":270,"passed_time":9.711914203,"remaining_time":61.96272936,"test":[0.6713933952]},
+{"learn":[0.6659038921],"iteration":271,"passed_time":9.739142426,"remaining_time":61.87219894,"test":[0.6713926761]},
+{"learn":[0.6658767418],"iteration":272,"passed_time":9.768751964,"remaining_time":61.79719649,"test":[0.6713836619]},
+{"learn":[0.6658510507],"iteration":273,"passed_time":9.804576737,"remaining_time":61.76167682,"test":[0.6713772112]},
+{"learn":[0.6658210119],"iteration":274,"passed_time":9.848653906,"remaining_time":61.77791996,"test":[0.6713603715]},
+{"learn":[0.6657963011],"iteration":275,"passed_time":9.88663261,"remaining_time":61.75563268,"test":[0.6713560246]},
+{"learn":[0.6657748552],"iteration":276,"passed_time":9.925808942,"remaining_time":61.74068161,"test":[0.6713837913]},
+{"learn":[0.6657490013],"iteration":277,"passed_time":9.965409489,"remaining_time":61.72818396,"test":[0.6713684274]},
+{"learn":[0.665732402],"iteration":278,"passed_time":9.99537326,"remaining_time":61.65604796,"test":[0.6713619356]},
+{"learn":[0.6657118786],"iteration":279,"passed_time":10.02216777,"remaining_time":61.5647449,"test":[0.6713584836]},
+{"learn":[0.665684467],"iteration":280,"passed_time":10.05593393,"remaining_time":61.51654955,"test":[0.6713673572]},
+{"learn":[0.6656584634],"iteration":281,"passed_time":10.08025153,"remaining_time":61.41089406,"test":[0.6713625568]},
+{"learn":[0.6656309991],"iteration":282,"passed_time":10.11102202,"remaining_time":61.34496401,"test":[0.6713542652]},
+{"learn":[0.6656073482],"iteration":283,"passed_time":10.14714598,"remaining_time":61.31162855,"test":[0.6713512017]},
+{"learn":[0.6655890957],"iteration":284,"passed_time":10.17528061,"remaining_time":61.23019734,"test":[0.671342038]},
+{"learn":[0.6655665563],"iteration":285,"passed_time":10.2021403,"remaining_time":61.14149818,"test":[0.6713279798]},
+{"learn":[0.6655452454],"iteration":286,"passed_time":10.23423432,"remaining_time":61.08447174,"test":[0.6713123285]},
+{"learn":[0.6655255286],"iteration":287,"passed_time":10.26481698,"remaining_time":61.0186343,"test":[0.6713035326]},
+{"learn":[0.6655053548],"iteration":288,"passed_time":10.29945844,"remaining_time":60.97707056,"test":[0.6713022203]},
+{"learn":[0.6654893396],"iteration":289,"passed_time":10.32366496,"remaining_time":60.87402441,"test":[0.671296041]},
+{"learn":[0.6654648912],"iteration":290,"passed_time":10.35344703,"remaining_time":60.80426453,"test":[0.6712829551]},
+{"learn":[0.6654442759],"iteration":291,"passed_time":10.3949915,"remaining_time":60.8035804,"test":[0.6712769751]},
+{"learn":[0.6654173127],"iteration":292,"passed_time":10.43148765,"remaining_time":60.77320621,"test":[0.6712702915]},
+{"learn":[0.6653914518],"iteration":293,"passed_time":10.47162738,"remaining_time":60.76393303,"test":[0.6712379343]},
+{"learn":[0.6653648946],"iteration":294,"passed_time":10.50360107,"remaining_time":60.70725362,"test":[0.6712192006]},
+{"learn":[0.665344141],"iteration":295,"passed_time":10.53460819,"remaining_time":60.64517686,"test":[0.6712074061]},
+{"learn":[0.6653140817],"iteration":296,"passed_time":10.57659448,"remaining_time":60.64626395,"test":[0.6711953324]},
+{"learn":[0.665295365],"iteration":297,"passed_time":10.61260262,"remaining_time":60.61291829,"test":[0.6711891001]},
+{"learn":[0.6652787488],"iteration":298,"passed_time":10.63910358,"remaining_time":60.52546889,"test":[0.6711870526]},
+{"learn":[0.6652502991],"iteration":299,"passed_time":10.6681867,"remaining_time":60.45305797,"test":[0.6711812809]},
+{"learn":[0.665231168],"iteration":300,"passed_time":10.70260503,"remaining_time":60.41104967,"test":[0.6711768946]},
+{"learn":[0.6652136682],"iteration":301,"passed_time":10.72952096,"remaining_time":60.32690925,"test":[0.6711845012]},
+{"learn":[0.6651903001],"iteration":302,"passed_time":10.76489952,"remaining_time":60.29054288,"test":[0.6711869636]},
+{"learn":[0.6651697153],"iteration":303,"passed_time":10.80197155,"remaining_time":60.26363073,"test":[0.671186884]},
+{"learn":[0.6651525958],"iteration":304,"passed_time":10.82922271,"remaining_time":60.18207375,"test":[0.6711890401]},
+{"learn":[0.6651322685],"iteration":305,"passed_time":10.8578399,"remaining_time":60.10843394,"test":[0.6711868603]},
+{"learn":[0.6651113828],"iteration":306,"passed_time":10.89228879,"remaining_time":60.06724727,"test":[0.6711900892]},
+{"learn":[0.6650886807],"iteration":307,"passed_time":10.93056436,"remaining_time":60.04712628,"test":[0.6711884242]},
+{"learn":[0.6650622251],"iteration":308,"passed_time":10.97231236,"remaining_time":60.04589061,"test":[0.6711837119]},
+{"learn":[0.6650429987],"iteration":309,"passed_time":11.00296848,"remaining_time":59.98392494,"test":[0.6711766645]},
+{"learn":[0.665015513],"iteration":310,"passed_time":11.03002276,"remaining_time":59.90259947,"test":[0.671172959]},
+{"learn":[0.6650019022],"iteration":311,"passed_time":11.05828865,"remaining_time":59.82817707,"test":[0.6711740433]},
+{"learn":[0.664979951],"iteration":312,"passed_time":11.09287745,"remaining_time":59.78812863,"test":[0.6711715069]},
+{"learn":[0.6649549638],"iteration":313,"passed_time":11.1177757,"remaining_time":59.69608229,"test":[0.6711589843]},
+{"learn":[0.6649340455],"iteration":314,"passed_time":11.14959087,"remaining_time":59.64146228,"test":[0.6711446402]},
+{"learn":[0.6649162445],"iteration":315,"passed_time":11.18718772,"remaining_time":59.61779784,"test":[0.6711415366]},
+{"learn":[0.6649048119],"iteration":316,"passed_time":11.21179073,"remaining_time":59.52505932,"test":[0.6711359351]},
+{"learn":[0.6648796463],"iteration":317,"passed_time":11.24311165,"remaining_time":59.46828238,"test":[0.671143361]},
+{"learn":[0.6648605481],"iteration":318,"passed_time":11.27486028,"remaining_time":59.41391889,"test":[0.6711353638]},
+{"learn":[0.6648429084],"iteration":319,"passed_time":11.30400807,"remaining_time":59.34604237,"test":[0.6711444387]},
+{"learn":[0.6648238121],"iteration":320,"passed_time":11.33488419,"remaining_time":59.28744721,"test":[0.6711487352]},
+{"learn":[0.6647969527],"iteration":321,"passed_time":11.36208838,"remaining_time":59.20988915,"test":[0.67114436]},
+{"learn":[0.6647854723],"iteration":322,"passed_time":11.39429642,"remaining_time":59.15862259,"test":[0.6711444722]},
+{"learn":[0.6647589304],"iteration":323,"passed_time":11.4363998,"remaining_time":59.15866068,"test":[0.6711325635]},
+{"learn":[0.6647429024],"iteration":324,"passed_time":11.47751019,"remaining_time":59.15332173,"test":[0.6711269403]},
+{"learn":[0.6647237508],"iteration":325,"passed_time":11.5136833,"remaining_time":59.12241054,"test":[0.6711154078]},
+{"learn":[0.6647059396],"iteration":326,"passed_time":11.54795566,"remaining_time":59.08174257,"test":[0.6711203043]},
+{"learn":[0.664686288],"iteration":327,"passed_time":11.57245915,"remaining_time":58.99131613,"test":[0.6711241333]},
+{"learn":[0.6646532527],"iteration":328,"passed_time":11.60790333,"remaining_time":58.95685857,"test":[0.6711213497]},
+{"learn":[0.6646306438],"iteration":329,"passed_time":11.63787346,"remaining_time":58.89469298,"test":[0.6711231641]},
+{"learn":[0.6646098516],"iteration":330,"passed_time":11.66805718,"remaining_time":58.83379887,"test":[0.6711049215]},
+{"learn":[0.6645858284],"iteration":331,"passed_time":11.70070223,"remaining_time":58.78545579,"test":[0.6711031963]},
+{"learn":[0.6645707188],"iteration":332,"passed_time":11.724753,"remaining_time":58.69418391,"test":[0.6710996314]},
+{"learn":[0.6645485788],"iteration":333,"passed_time":11.75795297,"remaining_time":58.64895104,"test":[0.6710867309]},
+{"learn":[0.6645305696],"iteration":334,"passed_time":11.78053066,"remaining_time":58.55099567,"test":[0.6710914578]},
+{"learn":[0.6645108881],"iteration":335,"passed_time":11.81570271,"remaining_time":58.51586106,"test":[0.6710929585]},
+{"learn":[0.6644923286],"iteration":336,"passed_time":11.8448851,"remaining_time":58.45116888,"test":[0.6710984779]},
+{"learn":[0.6644805222],"iteration":337,"passed_time":11.86964023,"remaining_time":58.36491734,"test":[0.6710923199]},
+{"learn":[0.6644572776],"iteration":338,"passed_time":11.90591446,"remaining_time":58.33546879,"test":[0.6710893917]},
+{"learn":[0.6644320741],"iteration":339,"passed_time":11.94145444,"remaining_time":58.30239521,"test":[0.6710923306]},
+{"learn":[0.6644115048],"iteration":340,"passed_time":11.98658051,"remaining_time":58.31594449,"test":[0.6710927901]},
+{"learn":[0.6643949013],"iteration":341,"passed_time":12.02038848,"remaining_time":58.27428098,"test":[0.6711092802]},
+{"learn":[0.6643619789],"iteration":342,"passed_time":12.06653941,"remaining_time":58.29229096,"test":[0.6711012995]},
+{"learn":[0.6643389502],"iteration":343,"passed_time":12.12283646,"remaining_time":58.35877087,"test":[0.6711015305]},
+{"learn":[0.6643088915],"iteration":344,"passed_time":12.17733618,"remaining_time":58.41591705,"test":[0.6710975574]},
+{"learn":[0.664286972],"iteration":345,"passed_time":12.22133732,"remaining_time":58.42223099,"test":[0.6710899474]},
+{"learn":[0.664274149],"iteration":346,"passed_time":12.2642467,"remaining_time":58.42305415,"test":[0.671085152]},
+{"learn":[0.6642536926],"iteration":347,"passed_time":12.30091895,"remaining_time":58.39401755,"test":[0.6710814533]},
+{"learn":[0.6642357634],"iteration":348,"passed_time":12.32484094,"remaining_time":58.30462002,"test":[0.6710701892]},
+{"learn":[0.664207914],"iteration":349,"passed_time":12.35469303,"remaining_time":58.24355287,"test":[0.67105503]},
+{"learn":[0.6641853097],"iteration":350,"passed_time":12.40148755,"remaining_time":58.26225919,"test":[0.6710527861]},
+{"learn":[0.6641654917],"iteration":351,"passed_time":12.43803877,"remaining_time":58.23263605,"test":[0.6710508715]},
+{"learn":[0.664143804],"iteration":352,"passed_time":12.47995438,"remaining_time":58.22800245,"test":[0.6710560803]},
+{"learn":[0.6641290647],"iteration":353,"passed_time":12.51241326,"remaining_time":58.17918707,"test":[0.6710465693]},
+{"learn":[0.6641117244],"iteration":354,"passed_time":12.5417829,"remaining_time":58.11614893,"test":[0.6710440741]},
+{"learn":[0.6640880219],"iteration":355,"passed_time":12.5692936,"remaining_time":58.0447154,"test":[0.6710496913]},
+{"learn":[0.6640669415],"iteration":356,"passed_time":12.5976392,"remaining_time":57.97737034,"test":[0.6710404659]},
+{"learn":[0.6640462999],"iteration":357,"passed_time":12.62815847,"remaining_time":57.92021287,"test":[0.6710293986]},
+{"learn":[0.664030296],"iteration":358,"passed_time":12.65342509,"remaining_time":57.8391938,"test":[0.6710353817]},
+{"learn":[0.6640028542],"iteration":359,"passed_time":12.68233453,"remaining_time":57.77507954,"test":[0.6710271815]},
+{"learn":[0.6639813347],"iteration":360,"passed_time":12.72037964,"remaining_time":57.75263774,"test":[0.6710288077]},
+{"learn":[0.6639597941],"iteration":361,"passed_time":12.744473,"remaining_time":57.66698004,"test":[0.6710169894]},
+{"learn":[0.6639429832],"iteration":362,"passed_time":12.77086568,"remaining_time":57.59203063,"test":[0.6710119848]},
+{"learn":[0.6639222708],"iteration":363,"passed_time":12.81194554,"remaining_time":57.58335961,"test":[0.6710114775]},
+{"learn":[0.6639065546],"iteration":364,"passed_time":12.84133287,"remaining_time":57.52213492,"test":[0.6710013614]},
+{"learn":[0.6638823236],"iteration":365,"passed_time":12.87057337,"remaining_time":57.46042866,"test":[0.6709985657]},
+{"learn":[0.6638648195],"iteration":366,"passed_time":12.8971183,"remaining_time":57.38690512,"test":[0.6709948954]},
+{"learn":[0.6638436235],"iteration":367,"passed_time":12.93825161,"remaining_time":57.37833324,"test":[0.6709970591]},
+{"learn":[0.6638208732],"iteration":368,"passed_time":12.97444296,"remaining_time":57.3477411,"test":[0.6709739289]},
+{"learn":[0.6637956357],"iteration":369,"passed_time":13.00974924,"remaining_time":57.31321963,"test":[0.6709754911]},
+{"learn":[0.6637718453],"iteration":370,"passed_time":13.03832239,"remaining_time":57.24912984,"test":[0.6709717066]},
+{"learn":[0.663756918],"iteration":371,"passed_time":13.07843077,"remaining_time":57.23571316,"test":[0.67096845]},
+{"learn":[0.6637353525],"iteration":372,"passed_time":13.11729124,"remaining_time":57.21671005,"test":[0.6709739445]},
+{"learn":[0.6637143112],"iteration":373,"passed_time":13.14745329,"remaining_time":57.15978354,"test":[0.6709728881]},
+{"learn":[0.6636956547],"iteration":374,"passed_time":13.18118022,"remaining_time":57.11844761,"test":[0.6709694284]},
+{"learn":[0.663680995],"iteration":375,"passed_time":13.20539229,"remaining_time":57.03605604,"test":[0.6709604166]},
+{"learn":[0.66366728],"iteration":376,"passed_time":13.23563977,"remaining_time":56.97995583,"test":[0.6709605025]},
+{"learn":[0.6636487567],"iteration":377,"passed_time":13.27428255,"remaining_time":56.96001665,"test":[0.6709603727]},
+{"learn":[0.6636266904],"iteration":378,"passed_time":13.30625754,"remaining_time":56.91146033,"test":[0.670944339]},
+{"learn":[0.6636116064],"iteration":379,"passed_time":13.33327871,"remaining_time":56.84187241,"test":[0.6709447187]},
+{"learn":[0.6635902746],"iteration":380,"passed_time":13.36632239,"remaining_time":56.79809961,"test":[0.6709538679]},
+{"learn":[0.6635654896],"iteration":381,"passed_time":13.39639051,"remaining_time":56.74177969,"test":[0.6709640912]},
+{"learn":[0.6635393029],"iteration":382,"passed_time":13.42189438,"remaining_time":56.66632694,"test":[0.6709534847]},
+{"learn":[0.6635171734],"iteration":383,"passed_time":13.46730432,"remaining_time":56.6749057,"test":[0.6709471555]},
+{"learn":[0.663500789],"iteration":384,"passed_time":13.50832777,"remaining_time":56.66480351,"test":[0.6709506783]},
+{"learn":[0.663477743],"iteration":385,"passed_time":13.54029627,"remaining_time":56.61667921,"test":[0.6709546729]},
+{"learn":[0.6634584806],"iteration":386,"passed_time":13.56996301,"remaining_time":56.5590448,"test":[0.670930774]},
+{"learn":[0.6634337499],"iteration":387,"passed_time":13.59835745,"remaining_time":56.4962686,"test":[0.6709287322]},
+{"learn":[0.6634135584],"iteration":388,"passed_time":13.6279617,"remaining_time":56.43867943,"test":[0.6709198643]},
+{"learn":[0.6633868455],"iteration":389,"passed_time":13.65633448,"remaining_time":56.37615005,"test":[0.6709220389]},
+{"learn":[0.6633755323],"iteration":390,"passed_time":13.68565529,"remaining_time":56.31769658,"test":[0.6709230923]},
+{"learn":[0.663356103],"iteration":391,"passed_time":13.71789303,"remaining_time":56.27135714,"test":[0.670930414]},
+{"learn":[0.6633337631],"iteration":392,"passed_time":13.75060752,"remaining_time":56.2270389,"test":[0.6709354296]},
+{"learn":[0.663319422],"iteration":393,"passed_time":13.77167974,"remaining_time":56.13532403,"test":[0.6709351544]},
+{"learn":[0.6632911566],"iteration":394,"passed_time":13.80416242,"remaining_time":56.09033084,"test":[0.6709414935]},
+{"learn":[0.6632687875],"iteration":395,"passed_time":13.82525369,"remaining_time":55.9992599,"test":[0.6709445943]},
+{"learn":[0.6632431997],"iteration":396,"passed_time":13.85836516,"remaining_time":55.95707646,"test":[0.6709475685]},
+{"learn":[0.6632189331],"iteration":397,"passed_time":13.88898168,"remaining_time":55.90489613,"test":[0.6709533591]},
+{"learn":[0.663201035],"iteration":398,"passed_time":13.91726355,"remaining_time":55.84345598,"test":[0.6709592222]},
+{"learn":[0.6631898553],"iteration":399,"passed_time":13.95316828,"remaining_time":55.81267311,"test":[0.6709508704]},
+{"learn":[0.6631712482],"iteration":400,"passed_time":13.99418497,"remaining_time":55.80224881,"test":[0.6709479912]},
+{"learn":[0.663143025],"iteration":401,"passed_time":14.0253575,"remaining_time":55.75254052,"test":[0.6709417519]},
+{"learn":[0.663121538],"iteration":402,"passed_time":14.04844239,"remaining_time":55.67087467,"test":[0.6709476082]},
+{"learn":[0.6631087792],"iteration":403,"passed_time":14.0761289,"remaining_time":55.60767753,"test":[0.6709480979]},
+{"learn":[0.6630859067],"iteration":404,"passed_time":14.10555105,"remaining_time":55.55149118,"test":[0.6709448724]},
+{"learn":[0.663066483],"iteration":405,"passed_time":14.1427661,"remaining_time":55.52603242,"test":[0.6709421934]},
+{"learn":[0.6630443652],"iteration":406,"passed_time":14.18285552,"remaining_time":55.51176619,"test":[0.6709386261]},
+{"learn":[0.6630250376],"iteration":407,"passed_time":14.21458769,"remaining_time":55.46476372,"test":[0.6709461564]},
+{"learn":[0.6630007822],"iteration":408,"passed_time":14.24035708,"remaining_time":55.39464088,"test":[0.670934384]},
+{"learn":[0.6629768728],"iteration":409,"passed_time":14.26711915,"remaining_time":55.32858403,"test":[0.6709312987]},
+{"learn":[0.6629528093],"iteration":410,"passed_time":14.29943785,"remaining_time":55.28420133,"test":[0.670931806]},
+{"learn":[0.6629260936],"iteration":411,"passed_time":14.32489173,"remaining_time":55.21341763,"test":[0.6709286111]},
+{"learn":[0.6629102182],"iteration":412,"passed_time":14.35119075,"remaining_time":55.14610101,"test":[0.6709224729]},
+{"learn":[0.6628863488],"iteration":413,"passed_time":14.37946054,"remaining_time":55.08653242,"test":[0.6709236504]},
+{"learn":[0.6628648972],"iteration":414,"passed_time":14.41005914,"remaining_time":55.03600899,"test":[0.6709245901]},
+{"learn":[0.6628454339],"iteration":415,"passed_time":14.45103793,"remaining_time":55.02510598,"test":[0.6709463437]},
+{"learn":[0.6628200274],"iteration":416,"passed_time":14.48428995,"remaining_time":54.98472661,"test":[0.6709567049]},
+{"learn":[0.6627942591],"iteration":417,"passed_time":14.5135184,"remaining_time":54.92915339,"test":[0.670945606]},
+{"learn":[0.6627744647],"iteration":418,"passed_time":14.53698524,"remaining_time":54.85196578,"test":[0.6709479298]},
+{"learn":[0.662765485],"iteration":419,"passed_time":14.56542473,"remaining_time":54.79374067,"test":[0.6709464351]},
+{"learn":[0.6627503257],"iteration":420,"passed_time":14.58728594,"remaining_time":54.71098455,"test":[0.6709414048]},
+{"learn":[0.6627323029],"iteration":421,"passed_time":14.61501375,"remaining_time":54.65045425,"test":[0.6709414427]},
+{"learn":[0.6627111509],"iteration":422,"passed_time":14.64231614,"remaining_time":54.58849302,"test":[0.6709296343]},
+{"learn":[0.6626785863],"iteration":423,"passed_time":14.66665432,"remaining_time":54.51567739,"test":[0.670924721]},
+{"learn":[0.6626576561],"iteration":424,"passed_time":14.69050441,"remaining_time":54.44128104,"test":[0.670906284]},
+{"learn":[0.6626363113],"iteration":425,"passed_time":14.71910475,"remaining_time":54.38467341,"test":[0.6708996826]},
+{"learn":[0.6626181065],"iteration":426,"passed_time":14.73941058,"remaining_time":54.2976413,"test":[0.6708987677]},
+{"learn":[0.66259794],"iteration":427,"passed_time":14.77242451,"remaining_time":54.25759657,"test":[0.670909526]},
+{"learn":[0.6625765658],"iteration":428,"passed_time":14.79088688,"remaining_time":54.1642967,"test":[0.6709033226]},
+{"learn":[0.6625526572],"iteration":429,"passed_time":14.82430966,"remaining_time":54.12596783,"test":[0.6708750209]},
+{"learn":[0.66253135],"iteration":430,"passed_time":14.84439175,"remaining_time":54.03909666,"test":[0.6708752079]},
+{"learn":[0.6625035695],"iteration":431,"passed_time":14.8764415,"remaining_time":53.99597284,"test":[0.6708776566]},
+{"learn":[0.662480212],"iteration":432,"passed_time":14.90666075,"remaining_time":53.94627573,"test":[0.6708736133]},
+{"learn":[0.6624611632],"iteration":433,"passed_time":14.93845927,"remaining_time":53.90236684,"test":[0.6708754298]},
+{"learn":[0.6624332625],"iteration":434,"passed_time":14.98024104,"remaining_time":53.89443041,"test":[0.6708751084]},
+{"learn":[0.6624120584],"iteration":435,"passed_time":15.00605075,"remaining_time":53.82904442,"test":[0.6708642042]},
+{"learn":[0.6623941719],"iteration":436,"passed_time":15.03384083,"remaining_time":53.77092268,"test":[0.6708610465]},
+{"learn":[0.6623766304],"iteration":437,"passed_time":15.05972545,"remaining_time":53.70614417,"test":[0.6708574768]},
+{"learn":[0.6623623329],"iteration":438,"passed_time":15.08505889,"remaining_time":53.63958297,"test":[0.6708557953]},
+{"learn":[0.6623442925],"iteration":439,"passed_time":15.11080547,"remaining_time":53.57467393,"test":[0.670871378]},
+{"learn":[0.6623212715],"iteration":440,"passed_time":15.13466304,"remaining_time":53.50326458,"test":[0.6708640187]},
+{"learn":[0.6623025941],"iteration":441,"passed_time":15.16037021,"remaining_time":53.43859001,"test":[0.6708700565]},
+{"learn":[0.6622749791],"iteration":442,"passed_time":15.18471062,"remaining_time":53.36928767,"test":[0.6708667534]},
+{"learn":[0.6622534499],"iteration":443,"passed_time":15.21140556,"remaining_time":53.30843931,"test":[0.6708675383]},
+{"learn":[0.6622305473],"iteration":444,"passed_time":15.23498219,"remaining_time":53.23684787,"test":[0.6708740175]},
+{"learn":[0.6622059333],"iteration":445,"passed_time":15.26647355,"remaining_time":53.19304911,"test":[0.6708774523]},
+{"learn":[0.6621871707],"iteration":446,"passed_time":15.28793136,"remaining_time":53.11444609,"test":[0.6708697231]},
+{"learn":[0.6621638454],"iteration":447,"passed_time":15.31613827,"remaining_time":53.05947899,"test":[0.6708614971]},
+{"learn":[0.6621511296],"iteration":448,"passed_time":15.33689091,"remaining_time":52.9788815,"test":[0.6708607946]},
+{"learn":[0.6621349978],"iteration":449,"passed_time":15.36674634,"remaining_time":52.92990406,"test":[0.6708740865]},
+{"learn":[0.6621120424],"iteration":450,"passed_time":15.393642,"remaining_time":52.87084582,"test":[0.6708729562]},
+{"learn":[0.6620958271],"iteration":451,"passed_time":15.42984657,"remaining_time":52.84381082,"test":[0.6708674017]},
+{"learn":[0.6620793528],"iteration":452,"passed_time":15.46956188,"remaining_time":52.82872456,"test":[0.6708693088]},
+{"learn":[0.6620572713],"iteration":453,"passed_time":15.49032259,"remaining_time":52.74898396,"test":[0.6708712037]},
+{"learn":[0.6620395025],"iteration":454,"passed_time":15.52379393,"remaining_time":52.71266289,"test":[0.6708703905]},
+{"learn":[0.6620188044],"iteration":455,"passed_time":15.55053135,"remaining_time":52.65355352,"test":[0.6708577595]},
+{"learn":[0.6620017347],"iteration":456,"passed_time":15.57735398,"remaining_time":52.59487352,"test":[0.6708493546]},
+{"learn":[0.6619811454],"iteration":457,"passed_time":15.60434803,"remaining_time":52.53690973,"test":[0.6708523777]},
+{"learn":[0.6619695569],"iteration":458,"passed_time":15.63056555,"remaining_time":52.47647387,"test":[0.6708454134]},
+{"learn":[0.661952377],"iteration":459,"passed_time":15.656355,"remaining_time":52.41475368,"test":[0.6708404483]},
+{"learn":[0.6619237442],"iteration":460,"passed_time":15.68232112,"remaining_time":52.35377918,"test":[0.6708274771]},
+{"learn":[0.6619089407],"iteration":461,"passed_time":15.71164945,"remaining_time":52.30414904,"test":[0.6708244992]},
+{"learn":[0.6618886168],"iteration":462,"passed_time":15.7361944,"remaining_time":52.23872743,"test":[0.6708344314]},
+{"learn":[0.6618831383],"iteration":463,"passed_time":15.76527735,"remaining_time":52.18850433,"test":[0.6708279081]},
+{"learn":[0.6618690774],"iteration":464,"passed_time":15.78652262,"remaining_time":52.11249942,"test":[0.6708258106]},
+{"learn":[0.661845878],"iteration":465,"passed_time":15.81756836,"remaining_time":52.06899113,"test":[0.6708049714]},
+{"learn":[0.6618290213],"iteration":466,"passed_time":15.83979966,"remaining_time":51.99660146,"test":[0.670810989]},
+{"learn":[0.6618050064],"iteration":467,"passed_time":15.87342473,"remaining_time":51.9617237,"test":[0.6708212237]},
+{"learn":[0.6617832833],"iteration":468,"passed_time":15.90381555,"remaining_time":51.9162934,"test":[0.6708221741]},
+{"learn":[0.6617652311],"iteration":469,"passed_time":15.93502938,"remaining_time":51.87360627,"test":[0.6708259658]},
+{"learn":[0.6617443144],"iteration":470,"passed_time":15.96919221,"remaining_time":51.84054117,"test":[0.6708159692]},
+{"learn":[0.6617202619],"iteration":471,"passed_time":15.99477329,"remaining_time":51.77968981,"test":[0.6708136212]},
+{"learn":[0.6617005831],"iteration":472,"passed_time":16.02279091,"remaining_time":51.72685354,"test":[0.6708224942]},
+{"learn":[0.6616824419],"iteration":473,"passed_time":16.04763422,"remaining_time":51.66390258,"test":[0.6708363084]},
+{"learn":[0.6616538226],"iteration":474,"passed_time":16.07374645,"remaining_time":51.60518598,"test":[0.670850875]},
+{"learn":[0.6616314155],"iteration":475,"passed_time":16.09993591,"remaining_time":51.54685363,"test":[0.6708527236]},
+{"learn":[0.6616127861],"iteration":476,"passed_time":16.12811357,"remaining_time":51.49500411,"test":[0.6708453401]},
+{"learn":[0.6616029072],"iteration":477,"passed_time":16.15264086,"remaining_time":51.43163051,"test":[0.6708413844]},
+{"learn":[0.6615843751],"iteration":478,"passed_time":16.17696751,"remaining_time":51.36778201,"test":[0.6708364569]},
+{"learn":[0.661563216],"iteration":479,"passed_time":16.20551145,"remaining_time":51.31745293,"test":[0.6708251774]},
+{"learn":[0.6615432257],"iteration":480,"passed_time":16.22860577,"remaining_time":51.2500045,"test":[0.6708154393]},
+{"learn":[0.6615263324],"iteration":481,"passed_time":16.25544093,"remaining_time":51.19452144,"test":[0.6708111613]},
+{"learn":[0.6615033259],"iteration":482,"passed_time":16.27729221,"remaining_time":51.12350369,"test":[0.6708102339]},
+{"learn":[0.661484293],"iteration":483,"passed_time":16.30502335,"remaining_time":51.07110619,"test":[0.6707929623]},
+{"learn":[0.6614678231],"iteration":484,"passed_time":16.32842702,"remaining_time":51.00529266,"test":[0.6707900226]},
+{"learn":[0.6614463024],"iteration":485,"passed_time":16.36272839,"remaining_time":50.97360242,"test":[0.6707832384]},
+{"learn":[0.6614155436],"iteration":486,"passed_time":16.39272506,"remaining_time":50.92852776,"test":[0.6707739118]},
+{"learn":[0.6613958945],"iteration":487,"passed_time":16.42636604,"remaining_time":50.89480625,"test":[0.6707737538]},
+{"learn":[0.661380611],"iteration":488,"passed_time":16.4597142,"remaining_time":50.86018027,"test":[0.6707730234]},
+{"learn":[0.6613677802],"iteration":489,"passed_time":16.48056007,"remaining_time":50.78703206,"test":[0.6707796291]},
+{"learn":[0.6613530086],"iteration":490,"passed_time":16.51091177,"remaining_time":50.74331132,"test":[0.670791408]},
+{"learn":[0.6613248211],"iteration":491,"passed_time":16.53097438,"remaining_time":50.66810846,"test":[0.6707944906]},
+{"learn":[0.6613059359],"iteration":492,"passed_time":16.56161402,"remaining_time":50.62546112,"test":[0.6707835635]},
+{"learn":[0.6612729965],"iteration":493,"passed_time":16.5854633,"remaining_time":50.56216139,"test":[0.6707908928]},
+{"learn":[0.6612624948],"iteration":494,"passed_time":16.61302735,"remaining_time":50.51031547,"test":[0.670796262]},
+{"learn":[0.6612401679],"iteration":495,"passed_time":16.63896978,"remaining_time":50.45365029,"test":[0.6707877825]},
+{"learn":[0.6612191637],"iteration":496,"passed_time":16.663707,"remaining_time":50.39346403,"test":[0.6707854132]},
+{"learn":[0.6611912219],"iteration":497,"passed_time":16.69040179,"remaining_time":50.33932428,"test":[0.6707756206]},
+{"learn":[0.6611773017],"iteration":498,"passed_time":16.71612789,"remaining_time":50.28238068,"test":[0.6707707899]},
+{"learn":[0.6611638216],"iteration":499,"passed_time":16.74072553,"remaining_time":50.2221766,"test":[0.6707704386]},
+{"learn":[0.6611450533],"iteration":500,"passed_time":16.77346538,"remaining_time":50.18647626,"test":[0.6707621465]},
+{"learn":[0.6611179111],"iteration":501,"passed_time":16.80230735,"remaining_time":50.13915621,"test":[0.6707661931]},
+{"learn":[0.6610959069],"iteration":502,"passed_time":16.83637769,"remaining_time":50.10747,"test":[0.6707651988]},
+{"learn":[0.6610728788],"iteration":503,"passed_time":16.87382128,"remaining_time":50.08578697,"test":[0.6707607827]},
+{"learn":[0.6610436668],"iteration":504,"passed_time":16.92151611,"remaining_time":50.09438927,"test":[0.670760242]},
+{"learn":[0.6610188976],"iteration":505,"passed_time":16.9898618,"remaining_time":50.16374216,"test":[0.6707506008]},
+{"learn":[0.6610030555],"iteration":506,"passed_time":17.03818668,"remaining_time":50.17359509,"test":[0.6707452886]},
+{"learn":[0.6609831174],"iteration":507,"passed_time":17.06933058,"remaining_time":50.13275833,"test":[0.6707355189]},
+{"learn":[0.6609586562],"iteration":508,"passed_time":17.1106164,"remaining_time":50.12166807,"test":[0.6707312551]},
+{"learn":[0.660935882],"iteration":509,"passed_time":17.14537899,"remaining_time":50.09140137,"test":[0.6707199485]},
+{"learn":[0.6609202024],"iteration":510,"passed_time":17.19066307,"remaining_time":50.09177556,"test":[0.6707131947]},
+{"learn":[0.6609011137],"iteration":511,"passed_time":17.21958034,"remaining_time":50.04440537,"test":[0.6707154112]},
+{"learn":[0.6608726737],"iteration":512,"passed_time":17.24756917,"remaining_time":49.99441591,"test":[0.6706982346]},
+{"learn":[0.6608608849],"iteration":513,"passed_time":17.27150822,"remaining_time":49.93280391,"test":[0.6706988941]},
+{"learn":[0.6608387256],"iteration":514,"passed_time":17.29800365,"remaining_time":49.87870957,"test":[0.6706989098]},
+{"learn":[0.6608136063],"iteration":515,"passed_time":17.34332283,"remaining_time":49.87885868,"test":[0.670693306]},
+{"learn":[0.6607946343],"iteration":516,"passed_time":17.37393636,"remaining_time":49.83664916,"test":[0.6706944515]},
+{"learn":[0.6607703935],"iteration":517,"passed_time":17.4173655,"remaining_time":49.83114994,"test":[0.6706899688]},
+{"learn":[0.6607509625],"iteration":518,"passed_time":17.46008645,"remaining_time":49.82348368,"test":[0.6706909374]},
+{"learn":[0.6607238109],"iteration":519,"passed_time":17.4906988,"remaining_time":49.78121967,"test":[0.6706855074]},
+{"learn":[0.6606999858],"iteration":520,"passed_time":17.5186435,"remaining_time":49.7314275,"test":[0.6706787779]},
+{"learn":[0.6606813873],"iteration":521,"passed_time":17.54613056,"remaining_time":49.6804233,"test":[0.6706737082]},
+{"learn":[0.6606610372],"iteration":522,"passed_time":17.57100039,"remaining_time":49.62211774,"test":[0.6706761225]},
+{"learn":[0.660638456],"iteration":523,"passed_time":17.60084283,"remaining_time":49.5779466,"test":[0.670685455]},
+{"learn":[0.6606156483],"iteration":524,"passed_time":17.62599925,"remaining_time":49.52066456,"test":[0.6706693855]},
+{"learn":[0.6605968623],"iteration":525,"passed_time":17.65519625,"remaining_time":49.47482751,"test":[0.6706647216]},
+{"learn":[0.6605735776],"iteration":526,"passed_time":17.67910836,"remaining_time":49.41428199,"test":[0.6706569188]},
+{"learn":[0.6605517294],"iteration":527,"passed_time":17.70744827,"remaining_time":49.36621942,"test":[0.6706549134]},
+{"learn":[0.6605309239],"iteration":528,"passed_time":17.72943083,"remaining_time":49.3005534,"test":[0.6706547978]},
+{"learn":[0.6605086434],"iteration":529,"passed_time":17.75830336,"remaining_time":49.25416215,"test":[0.6706564214]},
+{"learn":[0.6604803349],"iteration":530,"passed_time":17.78141858,"remaining_time":49.19190939,"test":[0.6706559196]},
+{"learn":[0.6604566326],"iteration":531,"passed_time":17.80870208,"remaining_time":49.14130574,"test":[0.6706515072]},
+{"learn":[0.6604430839],"iteration":532,"passed_time":17.82904188,"remaining_time":49.07167811,"test":[0.6706474616]},
+{"learn":[0.6604273738],"iteration":533,"passed_time":17.86246645,"remaining_time":49.03815696,"test":[0.6706424204]},
+{"learn":[0.6604048016],"iteration":534,"passed_time":17.90552779,"remaining_time":49.03102469,"test":[0.6706520008]},
+{"learn":[0.6603845173],"iteration":535,"passed_time":18.02843143,"remaining_time":49.24183511,"test":[0.6706448306]},
+{"learn":[0.6603669212],"iteration":536,"passed_time":18.07245966,"remaining_time":49.23651485,"test":[0.6706415789]},
+{"learn":[0.6603488983],"iteration":537,"passed_time":18.10631942,"remaining_time":49.20341819,"test":[0.6706305359]},
+{"learn":[0.6603176881],"iteration":538,"passed_time":18.13531438,"remaining_time":49.1571323,"test":[0.6706152774]},
+{"learn":[0.6602953862],"iteration":539,"passed_time":18.16575265,"remaining_time":49.11481272,"test":[0.670616585]},
+{"learn":[0.6602672025],"iteration":540,"passed_time":18.20025584,"remaining_time":49.08349958,"test":[0.6705963243]},
+{"learn":[0.6602568636],"iteration":541,"passed_time":18.22381751,"remaining_time":49.02274158,"test":[0.6706027368]},
+{"learn":[0.660235705],"iteration":542,"passed_time":18.25438575,"remaining_time":48.98092088,"test":[0.6706003522]},
+{"learn":[0.6602152295],"iteration":543,"passed_time":18.28070524,"remaining_time":48.9277699,"test":[0.6706044301]},
+{"learn":[0.6601897709],"iteration":544,"passed_time":18.30768805,"remaining_time":48.87648827,"test":[0.6706047241]},
+{"learn":[0.6601683731],"iteration":545,"passed_time":18.33807201,"remaining_time":48.83435294,"test":[0.6706038235]},
+{"learn":[0.6601472267],"iteration":546,"passed_time":18.36776304,"remaining_time":48.79041993,"test":[0.6706026913]},
+{"learn":[0.6601262337],"iteration":547,"passed_time":18.41134623,"remaining_time":48.78334803,"test":[0.6705845786]},
+{"learn":[0.6601119991],"iteration":548,"passed_time":18.44405381,"remaining_time":48.74739905,"test":[0.6705873967]},
+{"learn":[0.6600869973],"iteration":549,"passed_time":18.47010718,"remaining_time":48.69391893,"test":[0.6705755426]},
+{"learn":[0.6600667497],"iteration":550,"passed_time":18.5036553,"remaining_time":48.66024779,"test":[0.6705715731]},
+{"learn":[0.6600397508],"iteration":551,"passed_time":18.53164471,"remaining_time":48.61199556,"test":[0.6705757153]},
+{"learn":[0.660016863],"iteration":552,"passed_time":18.5577607,"remaining_time":48.55891452,"test":[0.6705516814]},
+{"learn":[0.6599933158],"iteration":553,"passed_time":18.58492994,"remaining_time":48.50867995,"test":[0.6705530864]},
+{"learn":[0.6599632649],"iteration":554,"passed_time":18.62562092,"remaining_time":48.49373376,"test":[0.6705552479]},
+{"learn":[0.6599446007],"iteration":555,"passed_time":18.65010209,"remaining_time":48.43659608,"test":[0.6705563336]},
+{"learn":[0.6599138126],"iteration":556,"passed_time":18.67796421,"remaining_time":48.38833458,"test":[0.6705718544]},
+{"learn":[0.6598965504],"iteration":557,"passed_time":18.70319381,"remaining_time":48.33334314,"test":[0.6705688384]},
+{"learn":[0.6598785723],"iteration":558,"passed_time":18.72995694,"remaining_time":48.28241136,"test":[0.6705641528]},
+{"learn":[0.659860838],"iteration":559,"passed_time":18.75657945,"remaining_time":48.23120429,"test":[0.6705628467]},
+{"learn":[0.6598408724],"iteration":560,"passed_time":18.78181322,"remaining_time":48.17652269,"test":[0.670558488]},
+{"learn":[0.6598244857],"iteration":561,"passed_time":18.80867415,"remaining_time":48.12610931,"test":[0.6705544404]},
+{"learn":[0.6598082469],"iteration":562,"passed_time":18.83488797,"remaining_time":48.0741279,"test":[0.6705617451]},
+{"learn":[0.6597851673],"iteration":563,"passed_time":18.86939449,"remaining_time":48.04335193,"test":[0.6705631717]},
+{"learn":[0.6597683521],"iteration":564,"passed_time":18.90235988,"remaining_time":48.00864854,"test":[0.6705636201]},
+{"learn":[0.6597479006],"iteration":565,"passed_time":18.93001053,"remaining_time":47.96048604,"test":[0.6705537522]},
+{"learn":[0.6597310938],"iteration":566,"passed_time":18.95858079,"remaining_time":47.91472006,"test":[0.670555083]},
+{"learn":[0.6597096581],"iteration":567,"passed_time":18.9833487,"remaining_time":47.85942842,"test":[0.6705524541]},
+{"learn":[0.6596862311],"iteration":568,"passed_time":19.0162481,"remaining_time":47.82469425,"test":[0.6705503132]},
+{"learn":[0.6596574779],"iteration":569,"passed_time":19.03781666,"remaining_time":47.76154004,"test":[0.6705354602]},
+{"learn":[0.6596385418],"iteration":570,"passed_time":19.0681355,"remaining_time":47.72043018,"test":[0.6705387012]},
+{"learn":[0.6596189903],"iteration":571,"passed_time":19.09073714,"remaining_time":47.66009201,"test":[0.6705411923]},
+{"learn":[0.65959275],"iteration":572,"passed_time":19.11146842,"remaining_time":47.59522765,"test":[0.6705390018]},
+{"learn":[0.6595730662],"iteration":573,"passed_time":19.141368,"remaining_time":47.55329403,"test":[0.6705354939]},
+{"learn":[0.6595566809],"iteration":574,"passed_time":19.16428373,"remaining_time":47.49409447,"test":[0.670531296]},
+{"learn":[0.6595365076],"iteration":575,"passed_time":19.19652276,"remaining_time":47.45807015,"test":[0.6705377163]},
+{"learn":[0.6595163446],"iteration":576,"passed_time":19.21727405,"remaining_time":47.39372785,"test":[0.6705248875]},
+{"learn":[0.6594816637],"iteration":577,"passed_time":19.24969594,"remaining_time":47.35824848,"test":[0.6705252902]},
+{"learn":[0.6594570142],"iteration":578,"passed_time":19.27445137,"remaining_time":47.30396442,"test":[0.6705181562]},
+{"learn":[0.6594353055],"iteration":579,"passed_time":19.29822455,"remaining_time":47.24737734,"test":[0.6705123446]},
+{"learn":[0.6594162362],"iteration":580,"passed_time":19.32403522,"remaining_time":47.19587948,"test":[0.6705128345]},
+{"learn":[0.659395036],"iteration":581,"passed_time":19.35739555,"remaining_time":47.16286408,"test":[0.6705173712]},
+{"learn":[0.6593798831],"iteration":582,"passed_time":19.39112791,"remaining_time":47.13075172,"test":[0.670541941]},
+{"learn":[0.6593556719],"iteration":583,"passed_time":19.42704318,"remaining_time":47.1039266,"test":[0.6705463243]},
+{"learn":[0.6593292627],"iteration":584,"passed_time":19.46022169,"remaining_time":47.07045077,"test":[0.6705513215]},
+{"learn":[0.6592976737],"iteration":585,"passed_time":19.48332075,"remaining_time":47.01265452,"test":[0.6705455889]},
+{"learn":[0.6592754841],"iteration":586,"passed_time":19.5115578,"remaining_time":46.9673444,"test":[0.6705408087]},
+{"learn":[0.6592510441],"iteration":587,"passed_time":19.54275193,"remaining_time":46.92919341,"test":[0.6705510193]},
+{"learn":[0.6592290326],"iteration":588,"passed_time":19.56411389,"remaining_time":46.86751222,"test":[0.6705456751]},
+{"learn":[0.6592097404],"iteration":589,"passed_time":19.59700884,"remaining_time":46.8335296,"test":[0.6705402427]},
+{"learn":[0.6591876204],"iteration":590,"passed_time":19.62169623,"remaining_time":46.77998306,"test":[0.6705443402]},
+{"learn":[0.6591705995],"iteration":591,"passed_time":19.64747626,"remaining_time":46.72913272,"test":[0.67054441]},
+{"learn":[0.6591456195],"iteration":592,"passed_time":19.67090184,"remaining_time":46.67278059,"test":[0.6705441955]},
+{"learn":[0.6591107122],"iteration":593,"passed_time":19.69910949,"remaining_time":46.62785848,"test":[0.6705319356]},
+{"learn":[0.6590819533],"iteration":594,"passed_time":19.72694709,"remaining_time":46.58211876,"test":[0.6705358843]},
+{"learn":[0.6590551327],"iteration":595,"passed_time":19.7530808,"remaining_time":46.53242523,"test":[0.6705334396]},
+{"learn":[0.6590373916],"iteration":596,"passed_time":19.77835609,"remaining_time":46.48079328,"test":[0.6705320462]},
+{"learn":[0.6590177149],"iteration":597,"passed_time":19.80378809,"remaining_time":46.4296169,"test":[0.6705332043]},
+{"learn":[0.6589946095],"iteration":598,"passed_time":19.83052585,"remaining_time":46.38158048,"test":[0.6705328363]},
+{"learn":[0.6589697628],"iteration":599,"passed_time":19.8579153,"remaining_time":46.33513569,"test":[0.6705315638]},
+{"learn":[0.6589442269],"iteration":600,"passed_time":19.89600309,"remaining_time":46.31365777,"test":[0.6705274435]},
+{"learn":[0.6589182437],"iteration":601,"passed_time":19.92518872,"remaining_time":46.27145155,"test":[0.670509808]},
+{"learn":[0.6588837179],"iteration":602,"passed_time":19.95754179,"remaining_time":46.23662666,"test":[0.6705077789]},
+{"learn":[0.6588674101],"iteration":603,"passed_time":19.99116426,"remaining_time":46.20474388,"test":[0.6705212132]},
+{"learn":[0.6588406916],"iteration":604,"passed_time":20.01900069,"remaining_time":46.15951398,"test":[0.6705098442]},
+{"learn":[0.6588149945],"iteration":605,"passed_time":20.04735837,"remaining_time":46.11554053,"test":[0.6705061509]},
+{"learn":[0.6587866031],"iteration":606,"passed_time":20.07232044,"remaining_time":46.06382599,"test":[0.6705003071]},
+{"learn":[0.6587636648],"iteration":607,"passed_time":20.09871086,"remaining_time":46.01546959,"test":[0.6705045031]},
+{"learn":[0.6587502469],"iteration":608,"passed_time":20.12348304,"remaining_time":45.96348917,"test":[0.6705083194]},
+{"learn":[0.6587292784],"iteration":609,"passed_time":20.14920752,"remaining_time":45.91376797,"test":[0.6705329997]},
+{"learn":[0.6587104112],"iteration":610,"passed_time":20.17662353,"remaining_time":45.86797068,"test":[0.6705269987]},
+{"learn":[0.6586953782],"iteration":611,"passed_time":20.20202219,"remaining_time":45.81765818,"test":[0.6705315607]},
+{"learn":[0.6586641191],"iteration":612,"passed_time":20.23050051,"remaining_time":45.77439512,"test":[0.6705142835]},
+{"learn":[0.6586450136],"iteration":613,"passed_time":20.25381994,"remaining_time":45.71953492,"test":[0.6705165015]},
+{"learn":[0.6586136263],"iteration":614,"passed_time":20.28518384,"remaining_time":45.68289369,"test":[0.6705001061]},
+{"learn":[0.6585862768],"iteration":615,"passed_time":20.3078175,"remaining_time":45.62665489,"test":[0.6705013916]},
+{"learn":[0.6585585235],"iteration":616,"passed_time":20.33878033,"remaining_time":45.5891948,"test":[0.6705037253]},
+{"learn":[0.6585371631],"iteration":617,"passed_time":20.36122842,"remaining_time":45.53271469,"test":[0.67049647]},
+{"learn":[0.6585092632],"iteration":618,"passed_time":20.3943397,"remaining_time":45.50013429,"test":[0.6705005632]},
+{"learn":[0.6584914317],"iteration":619,"passed_time":20.42384285,"remaining_time":45.45952119,"test":[0.6704957943]},
+{"learn":[0.6584662432],"iteration":620,"passed_time":20.45411533,"remaining_time":45.42065225,"test":[0.6704955333]},
+{"learn":[0.6584454668],"iteration":621,"passed_time":20.488223,"remaining_time":45.39030754,"test":[0.6704961207]},
+{"learn":[0.6584249408],"iteration":622,"passed_time":20.51043528,"remaining_time":45.33365872,"test":[0.6704921459]},
+{"learn":[0.6583931228],"iteration":623,"passed_time":20.54384208,"remaining_time":45.30180561,"test":[0.6704751713]},
+{"learn":[0.6583660767],"iteration":624,"passed_time":20.56912557,"remaining_time":45.25207624,"test":[0.6704753101]},
+{"learn":[0.658354264],"iteration":625,"passed_time":20.59414123,"remaining_time":45.20183714,"test":[0.6704620888]},
+{"learn":[0.6583253625],"iteration":626,"passed_time":20.61901142,"remaining_time":45.15135993,"test":[0.6704604282]},
+{"learn":[0.6582968632],"iteration":627,"passed_time":20.6468542,"remaining_time":45.10745855,"test":[0.6704663192]},
+{"learn":[0.6582687399],"iteration":628,"passed_time":20.67583093,"remaining_time":45.06607981,"test":[0.6704680085]},
+{"learn":[0.658242535],"iteration":629,"passed_time":20.7010198,"remaining_time":45.01650336,"test":[0.670453228]},
+{"learn":[0.6582199874],"iteration":630,"passed_time":20.72783977,"remaining_time":44.97054302,"test":[0.6704577785]},
+{"learn":[0.6581918101],"iteration":631,"passed_time":20.75222724,"remaining_time":44.91937795,"test":[0.67046675]},
+{"learn":[0.6581735218],"iteration":632,"passed_time":20.78264004,"remaining_time":44.88130954,"test":[0.6704731863]},
+{"learn":[0.6581445869],"iteration":633,"passed_time":20.80459182,"remaining_time":44.82503538,"test":[0.6704811116]},
+{"learn":[0.6581202427],"iteration":634,"passed_time":20.83717209,"remaining_time":44.79171637,"test":[0.6704839644]},
+{"learn":[0.6580977862],"iteration":635,"passed_time":20.86231353,"remaining_time":44.74244599,"test":[0.6704854798]},
+{"learn":[0.6580724179],"iteration":636,"passed_time":20.89269601,"remaining_time":44.70446572,"test":[0.6704835837]},
+{"learn":[0.6580426322],"iteration":637,"passed_time":20.93117347,"remaining_time":44.68379039,"test":[0.6704736198]},
+{"learn":[0.6580111256],"iteration":638,"passed_time":20.96066949,"remaining_time":44.64392985,"test":[0.6704640242]},
+{"learn":[0.6579834747],"iteration":639,"passed_time":20.9941179,"remaining_time":44.61250055,"test":[0.670465663]},
+{"learn":[0.6579541367],"iteration":640,"passed_time":21.0224519,"remaining_time":44.57022174,"test":[0.6704646829]},
+{"learn":[0.6579254503],"iteration":641,"passed_time":21.0522529,"remaining_time":44.53108946,"test":[0.6704600961]},
+{"learn":[0.657898555],"iteration":642,"passed_time":21.08260618,"remaining_time":44.49315178,"test":[0.6704643207]},
+{"learn":[0.6578676875],"iteration":643,"passed_time":21.10716702,"remaining_time":44.44304112,"test":[0.6704600533]},
+{"learn":[0.6578324163],"iteration":644,"passed_time":21.13594828,"remaining_time":44.40187584,"test":[0.6704614691]},
+{"learn":[0.6578062223],"iteration":645,"passed_time":21.1601277,"remaining_time":44.35110357,"test":[0.6704728212]},
+{"learn":[0.6577760631],"iteration":646,"passed_time":21.18552999,"remaining_time":44.30297075,"test":[0.6704758731]},
+{"learn":[0.6577483474],"iteration":647,"passed_time":21.21048648,"remaining_time":44.25397797,"test":[0.6704833026]},
+{"learn":[0.6577249642],"iteration":648,"passed_time":21.23686209,"remaining_time":44.20801337,"test":[0.6704767664]},
+{"learn":[0.6576974966],"iteration":649,"passed_time":21.26287585,"remaining_time":44.16135753,"test":[0.6704702727]},
+{"learn":[0.657675114],"iteration":650,"passed_time":21.28806218,"remaining_time":44.11305051,"test":[0.6704671372]},
+{"learn":[0.6576447891],"iteration":651,"passed_time":21.31506267,"remaining_time":44.06856515,"test":[0.6704699936]},
+{"learn":[0.6576102356],"iteration":652,"passed_time":21.3435081,"remaining_time":44.02711394,"test":[0.6704587989]},
+{"learn":[0.6575793887],"iteration":653,"passed_time":21.37776713,"remaining_time":43.99766753,"test":[0.6704637668]},
+{"learn":[0.6575543309],"iteration":654,"passed_time":21.40301154,"remaining_time":43.94969545,"test":[0.6704653717]},
+{"learn":[0.6575340787],"iteration":655,"passed_time":21.44023109,"remaining_time":43.92632711,"test":[0.6704598273]},
+{"learn":[0.6575061464],"iteration":656,"passed_time":21.4778965,"remaining_time":43.903828,"test":[0.6704522865]},
+{"learn":[0.657476113],"iteration":657,"passed_time":21.50245582,"remaining_time":43.85455275,"test":[0.6704558586]},
+{"learn":[0.6574447014],"iteration":658,"passed_time":21.53379663,"remaining_time":43.81915217,"test":[0.6704466331]},
+{"learn":[0.6574247361],"iteration":659,"passed_time":21.55955041,"remaining_time":43.77242053,"test":[0.6704405886]},
+{"learn":[0.6574034983],"iteration":660,"passed_time":21.58626671,"remaining_time":43.72770215,"test":[0.6704463767]},
+{"learn":[0.6573783832],"iteration":661,"passed_time":21.61183918,"remaining_time":43.68072633,"test":[0.6704475216]},
+{"learn":[0.657357694],"iteration":662,"passed_time":21.6373217,"remaining_time":43.63363366,"test":[0.6704572386]},
+{"learn":[0.6573411592],"iteration":663,"passed_time":21.66283476,"remaining_time":43.58666753,"test":[0.6704658153]},
+{"learn":[0.6573118559],"iteration":664,"passed_time":21.68841321,"remaining_time":43.5398972,"test":[0.6704600945]},
+{"learn":[0.6572819076],"iteration":665,"passed_time":21.71420973,"remaining_time":43.4936273,"test":[0.6704561998]},
+{"learn":[0.6572430097],"iteration":666,"passed_time":21.74213421,"remaining_time":43.45167151,"test":[0.6704535154]},
+{"learn":[0.6572160391],"iteration":667,"passed_time":21.77174463,"remaining_time":43.41311953,"test":[0.6704413781]},
+{"learn":[0.6571931413],"iteration":668,"passed_time":21.81895309,"remaining_time":43.40960622,"test":[0.6704450013]},
+{"learn":[0.6571737099],"iteration":669,"passed_time":21.84627583,"remaining_time":43.36648784,"test":[0.6704422199]},
+{"learn":[0.6571532872],"iteration":670,"passed_time":21.88834724,"remaining_time":43.35262814,"test":[0.67044342]},
+{"learn":[0.6571208939],"iteration":671,"passed_time":21.93403139,"remaining_time":43.34582395,"test":[0.6704415341]},
+{"learn":[0.6570887673],"iteration":672,"passed_time":21.9714274,"remaining_time":43.32256191,"test":[0.6704439539]},
+{"learn":[0.6570633692],"iteration":673,"passed_time":22.01942449,"remaining_time":43.32011406,"test":[0.6704498197]},
+{"learn":[0.6570454361],"iteration":674,"passed_time":22.05319867,"remaining_time":43.2896122,"test":[0.6704452194]},
+{"learn":[0.6570231031],"iteration":675,"passed_time":22.09079747,"remaining_time":43.26659149,"test":[0.6704366524]},
+{"learn":[0.6570052089],"iteration":676,"passed_time":22.14192346,"remaining_time":43.26996269,"test":[0.6704427124]},
+{"learn":[0.6569855794],"iteration":677,"passed_time":22.17624471,"remaining_time":43.24040635,"test":[0.6704395579]},
+{"learn":[0.6569579709],"iteration":678,"passed_time":22.213192,"remaining_time":43.21594497,"test":[0.6704401246]},
+{"learn":[0.6569333354],"iteration":679,"passed_time":22.23966403,"remaining_time":43.17111253,"test":[0.6704415621]},
+{"learn":[0.6569069617],"iteration":680,"passed_time":22.27051241,"remaining_time":43.13481039,"test":[0.6704341343]},
+{"learn":[0.6568931857],"iteration":681,"passed_time":22.29625075,"remaining_time":43.08864881,"test":[0.6704369615]},
+{"learn":[0.6568734532],"iteration":682,"passed_time":22.32160622,"remaining_time":43.04180877,"test":[0.6704357425]},
+{"learn":[0.6568435196],"iteration":683,"passed_time":22.35059872,"remaining_time":43.00202911,"test":[0.6704294622]},
+{"learn":[0.6568108038],"iteration":684,"passed_time":22.37956576,"remaining_time":42.96223208,"test":[0.6704289794]},
+{"learn":[0.6567811374],"iteration":685,"passed_time":22.41993338,"remaining_time":42.94430389,"test":[0.6704272409]},
+{"learn":[0.6567467284],"iteration":686,"passed_time":22.45285267,"remaining_time":42.91207504,"test":[0.6704101162]},
+{"learn":[0.6567172734],"iteration":687,"passed_time":22.4848431,"remaining_time":42.8780729,"test":[0.6704069439]},
+{"learn":[0.6566967606],"iteration":688,"passed_time":22.51193834,"remaining_time":42.83476221,"test":[0.6704100747]},
+{"learn":[0.6566720128],"iteration":689,"passed_time":22.53798671,"remaining_time":42.78951101,"test":[0.6704122261]},
+{"learn":[0.6566441608],"iteration":690,"passed_time":22.57108439,"remaining_time":42.75766928,"test":[0.6704137826]},
+{"learn":[0.6566172287],"iteration":691,"passed_time":22.59836588,"remaining_time":42.7148303,"test":[0.6704207952]},
+{"learn":[0.6565952549],"iteration":692,"passed_time":22.62447507,"remaining_time":42.66982528,"test":[0.6704154834]},
+{"learn":[0.6565702687],"iteration":693,"passed_time":22.65349415,"remaining_time":42.63035067,"test":[0.6704253514]},
+{"learn":[0.6565392213],"iteration":694,"passed_time":22.68028991,"remaining_time":42.58673141,"test":[0.6704155636]},
+{"learn":[0.6565157938],"iteration":695,"passed_time":22.70844406,"remaining_time":42.54570555,"test":[0.6704141298]},
+{"learn":[0.6564902789],"iteration":696,"passed_time":22.73944116,"remaining_time":42.51003133,"test":[0.6704207635]},
+{"learn":[0.6564644734],"iteration":697,"passed_time":22.7613976,"remaining_time":42.45750671,"test":[0.6704268341]},
+{"learn":[0.6564349549],"iteration":698,"passed_time":22.79216825,"remaining_time":42.42147482,"test":[0.6704243126]},
+{"learn":[0.6564046572],"iteration":699,"passed_time":22.8167121,"remaining_time":42.37389389,"test":[0.6704235165]},
+{"learn":[0.6563744107],"iteration":700,"passed_time":22.84507296,"remaining_time":42.33345189,"test":[0.6704257736]},
+{"learn":[0.6563525063],"iteration":701,"passed_time":22.87088832,"remaining_time":42.28833766,"test":[0.6704247758]},
+{"learn":[0.6563189867],"iteration":702,"passed_time":22.90238907,"remaining_time":42.25376759,"test":[0.6704331799]},
+{"learn":[0.6562939062],"iteration":703,"passed_time":22.94246813,"remaining_time":42.23499815,"test":[0.6704252722]},
+{"learn":[0.6562739297],"iteration":704,"passed_time":22.97441688,"remaining_time":42.20123385,"test":[0.6704146644]},
+{"learn":[0.656256438],"iteration":705,"passed_time":23.00262167,"remaining_time":42.16061253,"test":[0.6704164122]},
+{"learn":[0.6562366475],"iteration":706,"passed_time":23.033437,"remaining_time":42.12480062,"test":[0.6704118954]},
+{"learn":[0.6562073096],"iteration":707,"passed_time":23.0545813,"remaining_time":42.07135458,"test":[0.6704043129]},
+{"learn":[0.6561864222],"iteration":708,"passed_time":23.08699831,"remaining_time":42.03852584,"test":[0.6703978198]},
+{"learn":[0.6561578826],"iteration":709,"passed_time":23.11590694,"remaining_time":41.99932387,"test":[0.6703935976]},
+{"learn":[0.6561208567],"iteration":710,"passed_time":23.14362702,"remaining_time":41.9579961,"test":[0.6703839683]},
+{"learn":[0.6560924703],"iteration":711,"passed_time":23.16985155,"remaining_time":41.91400112,"test":[0.6703843723]},
+{"learn":[0.6560656907],"iteration":712,"passed_time":23.19510285,"remaining_time":41.86829925,"test":[0.6703879502]},
+{"learn":[0.6560362588],"iteration":713,"passed_time":23.23034771,"remaining_time":41.84065429,"test":[0.6703895978]},
+{"learn":[0.6560124527],"iteration":714,"passed_time":23.25923754,"remaining_time":41.80156678,"test":[0.6703894359]},
+{"learn":[0.6559875055],"iteration":715,"passed_time":23.28703452,"remaining_time":41.76054794,"test":[0.6703928777]},
+{"learn":[0.6559547281],"iteration":716,"passed_time":23.31161175,"remaining_time":41.71380457,"test":[0.6703933128]},
+{"learn":[0.6559230866],"iteration":717,"passed_time":23.34170355,"remaining_time":41.67696929,"test":[0.6703844355]},
+{"learn":[0.6558924823],"iteration":718,"passed_time":23.37263658,"remaining_time":41.64165155,"test":[0.6703825151]},
+{"learn":[0.6558676469],"iteration":719,"passed_time":23.40571088,"remaining_time":41.61015268,"test":[0.6703983542]},
+{"learn":[0.6558459277],"iteration":720,"passed_time":23.4389719,"remaining_time":41.57898067,"test":[0.670399556]},
+{"learn":[0.6558149638],"iteration":721,"passed_time":23.48304084,"remaining_time":41.56693379,"test":[0.6703931808]},
+{"learn":[0.6557812248],"iteration":722,"passed_time":23.50734531,"remaining_time":41.5198893,"test":[0.6703886918]},
+{"learn":[0.6557546502],"iteration":723,"passed_time":23.54055835,"remaining_time":41.48860836,"test":[0.6703847574]},
+{"learn":[0.6557274948],"iteration":724,"passed_time":23.56652491,"remaining_time":41.44457829,"test":[0.6703885941]},
+{"learn":[0.6557044723],"iteration":725,"passed_time":23.59580183,"remaining_time":41.40640708,"test":[0.6703788615]},
+{"learn":[0.6556751811],"iteration":726,"passed_time":23.62334313,"remaining_time":41.36522119,"test":[0.6703799906]},
+{"learn":[0.6556539158],"iteration":727,"passed_time":23.64879831,"remaining_time":41.32042782,"test":[0.6703774518]},
+{"learn":[0.6556182915],"iteration":728,"passed_time":23.67755213,"remaining_time":41.28143862,"test":[0.6703783496]},
+{"learn":[0.6555977079],"iteration":729,"passed_time":23.70012944,"remaining_time":41.23173204,"test":[0.6703648854]},
+{"learn":[0.6555667903],"iteration":730,"passed_time":23.72866102,"remaining_time":41.19243615,"test":[0.6703716654]},
+{"learn":[0.6555394075],"iteration":731,"passed_time":23.75226732,"remaining_time":41.14463793,"test":[0.6703550938]},
+{"learn":[0.6555122742],"iteration":732,"passed_time":23.7844108,"remaining_time":41.11166233,"test":[0.6703467057]},
+{"learn":[0.6554814941],"iteration":733,"passed_time":23.80747563,"remaining_time":41.06303017,"test":[0.6703484503]},
+{"learn":[0.6554517373],"iteration":734,"passed_time":23.84023587,"remaining_time":41.03115425,"test":[0.6703549183]},
+{"learn":[0.655429552],"iteration":735,"passed_time":23.87042124,"remaining_time":40.99485387,"test":[0.6703501504]},
+{"learn":[0.655396579],"iteration":736,"passed_time":23.9087808,"remaining_time":40.97257823,"test":[0.6703672622]},
+{"learn":[0.6553735864],"iteration":737,"passed_time":23.94161529,"remaining_time":40.94081097,"test":[0.6703560249]},
+{"learn":[0.6553472597],"iteration":738,"passed_time":23.97478791,"remaining_time":40.90961779,"test":[0.6703547155]},
+{"learn":[0.6553252832],"iteration":739,"passed_time":24.00628859,"remaining_time":40.87557247,"test":[0.6703593236]},
+{"learn":[0.6552971659],"iteration":740,"passed_time":24.03623034,"remaining_time":40.83888528,"test":[0.6703606827]},
+{"learn":[0.6552763852],"iteration":741,"passed_time":24.06404686,"remaining_time":40.79861313,"test":[0.6703511404]},
+{"learn":[0.6552488203],"iteration":742,"passed_time":24.09270947,"remaining_time":40.75980593,"test":[0.6703431646]},
+{"learn":[0.65521229],"iteration":743,"passed_time":24.12724624,"remaining_time":40.73094258,"test":[0.6703475116]},
+{"learn":[0.6551949744],"iteration":744,"passed_time":24.15397955,"remaining_time":40.68891857,"test":[0.6703483634]},
+{"learn":[0.6551673797],"iteration":745,"passed_time":24.17955779,"remaining_time":40.64499392,"test":[0.6703475713]},
+{"learn":[0.6551421856],"iteration":746,"passed_time":24.20715317,"remaining_time":40.60450191,"test":[0.670360457]},
+{"learn":[0.6551255516],"iteration":747,"passed_time":24.23336836,"remaining_time":40.5617342,"test":[0.6703664352]},
+{"learn":[0.6551019608],"iteration":748,"passed_time":24.2614437,"remaining_time":40.52211759,"test":[0.6703617612]},
+{"learn":[0.6550758728],"iteration":749,"passed_time":24.29512083,"remaining_time":40.49186805,"test":[0.6703669926]},
+{"learn":[0.655051966],"iteration":750,"passed_time":24.31839238,"remaining_time":40.44430371,"test":[0.6703670837]},
+{"learn":[0.6550351058],"iteration":751,"passed_time":24.34977118,"remaining_time":40.41025856,"test":[0.6703706628]},
+{"learn":[0.6549998756],"iteration":752,"passed_time":24.3762114,"remaining_time":40.36804198,"test":[0.670369618]},
+{"learn":[0.6549721212],"iteration":753,"passed_time":24.40831154,"remaining_time":40.3352204,"test":[0.6703692351]},
+{"learn":[0.6549401744],"iteration":754,"passed_time":24.44267281,"remaining_time":40.30612934,"test":[0.6703624433]},
+{"learn":[0.6549207325],"iteration":755,"passed_time":24.47460721,"remaining_time":40.27303091,"test":[0.6703686285]},
+{"learn":[0.6548900891],"iteration":756,"passed_time":24.50826603,"remaining_time":40.24276708,"test":[0.6703598432]},
+{"learn":[0.6548682731],"iteration":757,"passed_time":24.54826542,"remaining_time":40.22288345,"test":[0.6703618766]},
+{"learn":[0.6548418938],"iteration":758,"passed_time":24.57546587,"remaining_time":40.18201996,"test":[0.6703694148]},
+{"learn":[0.6548234717],"iteration":759,"passed_time":24.60502723,"remaining_time":40.14504442,"test":[0.6703683652]},
+{"learn":[0.6547996833],"iteration":760,"passed_time":24.63261096,"remaining_time":40.10486856,"test":[0.6703604855]},
+{"learn":[0.6547726174],"iteration":761,"passed_time":24.66001655,"remaining_time":40.06443634,"test":[0.6703758987]},
+{"learn":[0.6547509314],"iteration":762,"passed_time":24.68929907,"remaining_time":40.02708119,"test":[0.6703773302]},
+{"learn":[0.6547168175],"iteration":763,"passed_time":24.71425118,"remaining_time":39.98274144,"test":[0.6703641028]},
+{"learn":[0.6546907846],"iteration":764,"passed_time":24.74589169,"remaining_time":39.94924999,"test":[0.6703649602]},
+{"learn":[0.6546671611],"iteration":765,"passed_time":24.76625006,"remaining_time":39.89758822,"test":[0.6703567811]},
+{"learn":[0.6546475893],"iteration":766,"passed_time":24.79734832,"remaining_time":39.86327312,"test":[0.6703544688]},
+{"learn":[0.6546206223],"iteration":767,"passed_time":24.82531049,"remaining_time":39.82393558,"test":[0.6703611821]},
+{"learn":[0.6545874193],"iteration":768,"passed_time":24.85435247,"remaining_time":39.78635616,"test":[0.6703527821]},
+{"learn":[0.6545620629],"iteration":769,"passed_time":24.88095966,"remaining_time":39.74490958,"test":[0.6703523616]},
+{"learn":[0.6545346297],"iteration":770,"passed_time":24.90935211,"remaining_time":39.70634726,"test":[0.6703616298]},
+{"learn":[0.6545172316],"iteration":771,"passed_time":24.94098876,"remaining_time":39.67297175,"test":[0.6703603551]},
+{"learn":[0.6544943049],"iteration":772,"passed_time":24.97035098,"remaining_time":39.6359905,"test":[0.6703675655]},
+{"learn":[0.6544632323],"iteration":773,"passed_time":25.00434422,"remaining_time":39.60636436,"test":[0.6703582411]},
+{"learn":[0.6544384097],"iteration":774,"passed_time":25.03067441,"remaining_time":39.56461439,"test":[0.6703581437]},
+{"learn":[0.6544084745],"iteration":775,"passed_time":25.05692652,"remaining_time":39.522781,"test":[0.6703551885]},
+{"learn":[0.6543765257],"iteration":776,"passed_time":25.08660163,"remaining_time":39.48637554,"test":[0.6703608491]},
+{"learn":[0.6543536123],"iteration":777,"passed_time":25.10764591,"remaining_time":39.43643098,"test":[0.6703674554]},
+{"learn":[0.6543303593],"iteration":778,"passed_time":25.13940138,"remaining_time":39.40334928,"test":[0.6703679619]},
+{"learn":[0.6543005831],"iteration":779,"passed_time":25.15916899,"remaining_time":39.35152074,"test":[0.6703701757]},
+{"learn":[0.6542678123],"iteration":780,"passed_time":25.18841105,"remaining_time":39.31456219,"test":[0.6703603462]},
+{"learn":[0.6542439303],"iteration":781,"passed_time":25.21444083,"remaining_time":39.27262012,"test":[0.670359801]},
+{"learn":[0.6542100401],"iteration":782,"passed_time":25.24017824,"remaining_time":39.23026426,"test":[0.6703523669]},
+{"learn":[0.6541836178],"iteration":783,"passed_time":25.2660091,"remaining_time":39.18809574,"test":[0.6703365674]},
+{"learn":[0.654158129],"iteration":784,"passed_time":25.28891553,"remaining_time":39.1414425,"test":[0.6703486118]},
+{"learn":[0.6541343464],"iteration":785,"passed_time":25.31589904,"remaining_time":39.10114686,"test":[0.6703450011]},
+{"learn":[0.6541092921],"iteration":786,"passed_time":25.34123581,"remaining_time":39.05834694,"test":[0.6703473135]},
+{"learn":[0.6540812254],"iteration":787,"passed_time":25.36728606,"remaining_time":39.01668871,"test":[0.670350998]},
+{"learn":[0.654060259],"iteration":788,"passed_time":25.39177931,"remaining_time":38.97268028,"test":[0.6703417767]},
+{"learn":[0.6540467253],"iteration":789,"passed_time":25.41712461,"remaining_time":38.9300263,"test":[0.6703349821]},
+{"learn":[0.6540306837],"iteration":790,"passed_time":25.44804125,"remaining_time":38.89593157,"test":[0.6703457717]},
+{"learn":[0.6540103667],"iteration":791,"passed_time":25.48249341,"remaining_time":38.86723743,"test":[0.6703506266]},
+{"learn":[0.6539821302],"iteration":792,"passed_time":25.51450657,"remaining_time":38.83481643,"test":[0.6703596395]},
+{"learn":[0.6539577914],"iteration":793,"passed_time":25.54216564,"remaining_time":38.79578307,"test":[0.6703799895]},
+{"learn":[0.653923724],"iteration":794,"passed_time":25.56982738,"remaining_time":38.75678238,"test":[0.6703687687]},
+{"learn":[0.6539086888],"iteration":795,"passed_time":25.59539769,"remaining_time":38.71464675,"test":[0.6703780675]},
+{"learn":[0.6538798424],"iteration":796,"passed_time":25.61874122,"remaining_time":38.66919157,"test":[0.670374835]},
+{"learn":[0.6538566996],"iteration":797,"passed_time":25.64394874,"remaining_time":38.62659947,"test":[0.6703831387]},
+{"learn":[0.6538290752],"iteration":798,"passed_time":25.66776244,"remaining_time":38.58195581,"test":[0.670377656]},
+{"learn":[0.6538051255],"iteration":799,"passed_time":25.69593415,"remaining_time":38.54390122,"test":[0.6703689741]},
+{"learn":[0.6537917354],"iteration":800,"passed_time":25.71651353,"remaining_time":38.49450652,"test":[0.6703709756]},
+{"learn":[0.6537684302],"iteration":801,"passed_time":25.74304126,"remaining_time":38.45406912,"test":[0.6703737517]},
+{"learn":[0.6537402991],"iteration":802,"passed_time":25.77084871,"remaining_time":38.41557398,"test":[0.6703818964]},
+{"learn":[0.6537165427],"iteration":803,"passed_time":25.79028824,"remaining_time":38.36465763,"test":[0.6703812173]},
+{"learn":[0.6536853601],"iteration":804,"passed_time":25.82203653,"remaining_time":38.3320915,"test":[0.6703960068]},
+{"learn":[0.6536681479],"iteration":805,"passed_time":25.84395064,"remaining_time":38.28495914,"test":[0.6703976729]},
+{"learn":[0.6536409101],"iteration":806,"passed_time":25.87390688,"remaining_time":38.24977808,"test":[0.6704024604]},
+{"learn":[0.6536120189],"iteration":807,"passed_time":25.89606204,"remaining_time":38.20310143,"test":[0.6704085008]},
+{"learn":[0.6535912493],"iteration":808,"passed_time":25.92585483,"remaining_time":38.16772942,"test":[0.6704076633]},
+{"learn":[0.6535617421],"iteration":809,"passed_time":25.95539059,"remaining_time":38.13199358,"test":[0.6704111719]},
+{"learn":[0.6535315174],"iteration":810,"passed_time":25.98822968,"remaining_time":38.10111601,"test":[0.6704220803]},
+{"learn":[0.6534972927],"iteration":811,"passed_time":26.02835773,"remaining_time":38.08089777,"test":[0.6704265011]},
+{"learn":[0.6534818476],"iteration":812,"passed_time":26.0558565,"remaining_time":38.04219146,"test":[0.6704251162]},
+{"learn":[0.6534498323],"iteration":813,"passed_time":26.08151817,"remaining_time":38.00083606,"test":[0.6704375472]},
+{"learn":[0.6534305025],"iteration":814,"passed_time":26.10848988,"remaining_time":37.96142393,"test":[0.6704319336]},
+{"learn":[0.6534081059],"iteration":815,"passed_time":26.13143346,"remaining_time":37.91619757,"test":[0.670437614]},
+{"learn":[0.6533765804],"iteration":816,"passed_time":26.15923661,"remaining_time":37.87806231,"test":[0.6704554331]},
+{"learn":[0.6533441549],"iteration":817,"passed_time":26.18805523,"remaining_time":37.84141966,"test":[0.6704603317]},
+{"learn":[0.6533053405],"iteration":818,"passed_time":26.2140726,"remaining_time":37.8007567,"test":[0.6704548042]},
+{"learn":[0.6532838469],"iteration":819,"passed_time":26.24289367,"remaining_time":37.76416405,"test":[0.6704502654]},
+{"learn":[0.6532604302],"iteration":820,"passed_time":26.27260776,"remaining_time":37.72887277,"test":[0.6704512072]},
+{"learn":[0.6532364412],"iteration":821,"passed_time":26.29880394,"remaining_time":37.68855358,"test":[0.6704433481]},
+{"learn":[0.6532100089],"iteration":822,"passed_time":26.32785215,"remaining_time":37.65234749,"test":[0.6704095112]},
+{"learn":[0.6531782515],"iteration":823,"passed_time":26.35925682,"remaining_time":37.61952188,"test":[0.6704086019]},
+{"learn":[0.6531449701],"iteration":824,"passed_time":26.38596096,"remaining_time":37.580005,"test":[0.6703987131]},
+{"learn":[0.653115452],"iteration":825,"passed_time":26.40854839,"remaining_time":37.53466805,"test":[0.6704019708]},
+{"learn":[0.6530787602],"iteration":826,"passed_time":26.44419918,"remaining_time":37.50791492,"test":[0.6704046556]},
+{"learn":[0.653052397],"iteration":827,"passed_time":26.47784276,"remaining_time":37.47829917,"test":[0.6704091961]},
+{"learn":[0.6530313579],"iteration":828,"passed_time":26.51701028,"remaining_time":37.45647652,"test":[0.6704103204]},
+{"learn":[0.6530010363],"iteration":829,"passed_time":26.53963123,"remaining_time":37.41128739,"test":[0.6704074257]},
+{"learn":[0.6529752146],"iteration":830,"passed_time":26.57362226,"remaining_time":37.38214732,"test":[0.6704115335]},
+{"learn":[0.652954801],"iteration":831,"passed_time":26.59767057,"remaining_time":37.33903754,"test":[0.6704041275]},
+{"learn":[0.6529330351],"iteration":832,"passed_time":26.62378941,"remaining_time":37.29887425,"test":[0.6704004556]},
+{"learn":[0.6528993709],"iteration":833,"passed_time":26.65024746,"remaining_time":37.25921887,"test":[0.6704037097]},
+{"learn":[0.6528665883],"iteration":834,"passed_time":26.67774911,"remaining_time":37.22105115,"test":[0.6704035477]},
+{"learn":[0.6528413041],"iteration":835,"passed_time":26.70473813,"remaining_time":37.1821952,"test":[0.6704025281]},
+{"learn":[0.6528217161],"iteration":836,"passed_time":26.72833235,"remaining_time":37.13865056,"test":[0.6704024549]},
+{"learn":[0.6527978782],"iteration":837,"passed_time":26.76384162,"remaining_time":37.11167537,"test":[0.670405721]},
+{"learn":[0.6527789461],"iteration":838,"passed_time":26.79137369,"remaining_time":37.07364106,"test":[0.6703983189]},
+{"learn":[0.6527432001],"iteration":839,"passed_time":26.82295602,"remaining_time":37.04122498,"test":[0.6704035256]},
+{"learn":[0.6527139767],"iteration":840,"passed_time":26.87217031,"remaining_time":37.03310985,"test":[0.6704047613]},
+{"learn":[0.6526857244],"iteration":841,"passed_time":26.92488006,"remaining_time":37.0297044,"test":[0.6704139617]},
+{"learn":[0.652657086],"iteration":842,"passed_time":26.98258041,"remaining_time":37.03303147,"test":[0.6704066193]},
+{"learn":[0.6526355016],"iteration":843,"passed_time":27.05424841,"remaining_time":37.05534497,"test":[0.670402892]},
+{"learn":[0.6526054936],"iteration":844,"passed_time":27.09765154,"remaining_time":37.03880181,"test":[0.6704081961]},
+{"learn":[0.6525793707],"iteration":845,"passed_time":27.12038959,"remaining_time":36.99400661,"test":[0.6704029862]},
+{"learn":[0.6525584692],"iteration":846,"passed_time":27.14691224,"remaining_time":36.95441537,"test":[0.6704014281]},
+{"learn":[0.6525279747],"iteration":847,"passed_time":27.18096334,"remaining_time":36.92508227,"test":[0.6704036115]},
+{"learn":[0.6525038765],"iteration":848,"passed_time":27.20686017,"remaining_time":36.88468322,"test":[0.6704016777]},
+{"learn":[0.6524849104],"iteration":849,"passed_time":27.23465701,"remaining_time":36.8468889,"test":[0.6704085392]},
+{"learn":[0.6524610603],"iteration":850,"passed_time":27.26094834,"remaining_time":36.80708536,"test":[0.6704042952]},
+{"learn":[0.6524357337],"iteration":851,"passed_time":27.28945577,"remaining_time":36.77029957,"test":[0.670394789]},
+{"learn":[0.6524082286],"iteration":852,"passed_time":27.31865398,"remaining_time":36.73446203,"test":[0.6703885644]},
+{"learn":[0.65238051],"iteration":853,"passed_time":27.34791322,"remaining_time":36.69872195,"test":[0.6703946813]},
+{"learn":[0.6523557826],"iteration":854,"passed_time":27.3865535,"remaining_time":36.67555995,"test":[0.6704042137]},
+{"learn":[0.6523391233],"iteration":855,"passed_time":27.41370907,"remaining_time":36.63701306,"test":[0.6704077517]},
+{"learn":[0.652325347],"iteration":856,"passed_time":27.43905921,"remaining_time":36.5960848,"test":[0.6704118698]},
+{"learn":[0.6522924958],"iteration":857,"passed_time":27.47159295,"remaining_time":36.56475425,"test":[0.6704114259]},
+{"learn":[0.6522623584],"iteration":858,"passed_time":27.50124299,"remaining_time":36.52959052,"test":[0.6704157567]},
+{"learn":[0.6522343891],"iteration":859,"passed_time":27.53509105,"remaining_time":36.50000442,"test":[0.6703837005]},
+{"learn":[0.6522094424],"iteration":860,"passed_time":27.57211091,"remaining_time":36.47460432,"test":[0.6703829482]},
+{"learn":[0.6521841478],"iteration":861,"passed_time":27.59555719,"remaining_time":36.43125764,"test":[0.6703818491]},
+{"learn":[0.6521657946],"iteration":862,"passed_time":27.6272049,"remaining_time":36.39876242,"test":[0.6703826129]},
+{"learn":[0.6521304278],"iteration":863,"passed_time":27.65462267,"remaining_time":36.36070759,"test":[0.6703834487]},
+{"learn":[0.6521045712],"iteration":864,"passed_time":27.68321566,"remaining_time":36.3242194,"test":[0.6703868275]},
+{"learn":[0.6520753696],"iteration":865,"passed_time":27.71151671,"remaining_time":36.28736714,"test":[0.6703853357]},
+{"learn":[0.6520519528],"iteration":866,"passed_time":27.73884016,"remaining_time":36.2492571,"test":[0.670450644]},
+{"learn":[0.6520216555],"iteration":867,"passed_time":27.76583897,"remaining_time":36.21074851,"test":[0.6704556991]},
+{"learn":[0.6519926935],"iteration":868,"passed_time":27.79498714,"remaining_time":36.17506382,"test":[0.6704535742]},
+{"learn":[0.6519734186],"iteration":869,"passed_time":27.82082723,"remaining_time":36.13509744,"test":[0.6704495915]}
+]}

ai-engine/catboost_info/learn_error.tsv

@@ -0,0 +1,871 @@
+iter	Logloss
+0	0.692389481
+1	0.6916338586
+2	0.6910159214
+3	0.6903417151
+4	0.6896961461
+5	0.6890979366
+6	0.6884946167
+7	0.6879503686
+8	0.6874528094
+9	0.6869036785
+10	0.6863761921
+11	0.6859038678
+12	0.685410175
+13	0.6849483392
+14	0.6845417792
+15	0.6841038875
+16	0.6836957422
+17	0.6832947461
+18	0.6829014105
+19	0.6825264546
+20	0.6822106577
+21	0.6818649349
+22	0.6815467855
+23	0.6812293319
+24	0.6808837443
+25	0.6805816494
+26	0.6803209634
+27	0.6800350862
+28	0.6797703947
+29	0.6794926675
+30	0.6792251865
+31	0.6789670166
+32	0.678722402
+33	0.678476935
+34	0.6782297335
+35	0.6780226701
+36	0.6778291026
+37	0.6776045324
+38	0.6773969079
+39	0.6771819602
+40	0.6769816736
+41	0.6767984027
+42	0.6766201184
+43	0.6764394377
+44	0.6762698797
+45	0.6760974263
+46	0.6759245179
+47	0.6757673909
+48	0.6756172628
+49	0.675474531
+50	0.6753286933
+51	0.6751900513
+52	0.6750574835
+53	0.6749329567
+54	0.6748033265
+55	0.6746797823
+56	0.674535525
+57	0.6744256514
+58	0.674310819
+59	0.6741967947
+60	0.6740879654
+61	0.6739772476
+62	0.67388281
+63	0.6737789726
+64	0.6736812332
+65	0.6735930009
+66	0.6734947116
+67	0.6733961481
+68	0.6732990195
+69	0.6732133575
+70	0.673111539
+71	0.6730080451
+72	0.6729157861
+73	0.6728347949
+74	0.6727640693
+75	0.6726808811
+76	0.6726029645
+77	0.6725356026
+78	0.6724606887
+79	0.6723849561
+80	0.6723050519
+81	0.6722508802
+82	0.6721773904
+83	0.6721007598
+84	0.6720353564
+85	0.6719790902
+86	0.6719140024
+87	0.6718573633
+88	0.671795602
+89	0.6717369134
+90	0.6716711079
+91	0.6716070843
+92	0.6715517232
+93	0.6714957378
+94	0.6714364567
+95	0.6713881758
+96	0.6713336502
+97	0.6712700267
+98	0.6712154424
+99	0.6711600413
+100	0.6711060533
+101	0.6710494943
+102	0.6709936897
+103	0.6709472183
+104	0.6708914508
+105	0.6708388195
+106	0.6707885854
+107	0.6707454167
+108	0.6706973013
+109	0.6706577031
+110	0.67061108
+111	0.6705625485
+112	0.6705146484
+113	0.6704704423
+114	0.6704155922
+115	0.6703687117
+116	0.6703324232
+117	0.6702884624
+118	0.670253478
+119	0.6702140804
+120	0.6701682529
+121	0.6701320588
+122	0.6700939824
+123	0.6700655902
+124	0.6700190743
+125	0.6699792296
+126	0.6699379404
+127	0.669895454
+128	0.6698563938
+129	0.6698215571
+130	0.6697857067
+131	0.6697449303
+132	0.6697052425
+133	0.6696695553
+134	0.6696269265
+135	0.6695969271
+136	0.6695489786
+137	0.6695173859
+138	0.6694811164
+139	0.6694477439
+140	0.6694082161
+141	0.6693679185
+142	0.6693341916
+143	0.6692933159
+144	0.6692619696
+145	0.6692229289
+146	0.6691840164
+147	0.6691581406
+148	0.6691177196
+149	0.6690851126
+150	0.6690518144
+151	0.6690149711
+152	0.668993877
+153	0.6689596579
+154	0.6689372651
+155	0.6689003045
+156	0.6688680182
+157	0.6688348164
+158	0.6687947046
+159	0.6687605251
+160	0.668726253
+161	0.6686862718
+162	0.668663478
+163	0.6686399521
+164	0.6686058279
+165	0.6685761282
+166	0.6685469327
+167	0.6685157003
+168	0.6684805143
+169	0.6684485765
+170	0.6684144429
+171	0.6683849752
+172	0.6683568537
+173	0.6683266628
+174	0.6682937842
+175	0.6682657097
+176	0.6682301443
+177	0.6681995916
+178	0.6681658267
+179	0.6681422687
+180	0.6681216601
+181	0.6680899019
+182	0.6680676394
+183	0.6680413672
+184	0.6680088406
+185	0.6679873982
+186	0.6679663544
+187	0.6679417375
+188	0.6679100197
+189	0.667881208
+190	0.6678475427
+191	0.6678310341
+192	0.6678060257
+193	0.6677789336
+194	0.6677478773
+195	0.6677212408
+196	0.667704316
+197	0.6676819639
+198	0.6676554448
+199	0.6676318346
+200	0.6676074705
+201	0.6675849784
+202	0.6675631744
+203	0.6675397619
+204	0.6675169086
+205	0.6674864762
+206	0.6674670714
+207	0.6674375599
+208	0.6674148457
+209	0.6673974446
+210	0.6673812139
+211	0.6673515687
+212	0.6673197956
+213	0.6672900754
+214	0.6672550009
+215	0.6672271563
+216	0.667204521
+217	0.667181968
+218	0.6671640023
+219	0.66714351
+220	0.6671167156
+221	0.6670915937
+222	0.6670595279
+223	0.667033994
+224	0.6670008246
+225	0.6669858319
+226	0.6669553964
+227	0.6669274683
+228	0.666896348
+229	0.6668698686
+230	0.6668513411
+231	0.6668309985
+232	0.6668058585
+233	0.6667845908
+234	0.6667582863
+235	0.6667332943
+236	0.6667070085
+237	0.6666907315
+238	0.6666633028
+239	0.6666406707
+240	0.6666134624
+241	0.6665850522
+242	0.6665631193
+243	0.6665412643
+244	0.6665168385
+245	0.6664904845
+246	0.6664678274
+247	0.6664539777
+248	0.6664334121
+249	0.6664121724
+250	0.666392034
+251	0.666366899
+252	0.6663414098
+253	0.6663157816
+254	0.6662989799
+255	0.6662696102
+256	0.6662479711
+257	0.6662231874
+258	0.6661947927
+259	0.6661669951
+260	0.6661426137
+261	0.6661216749
+262	0.6660983123
+263	0.6660803402
+264	0.6660617842
+265	0.6660443878
+266	0.6660176079
+267	0.6659967546
+268	0.6659751467
+269	0.6659539329
+270	0.6659263951
+271	0.6659038921
+272	0.6658767418
+273	0.6658510507
+274	0.6658210119
+275	0.6657963011
+276	0.6657748552
+277	0.6657490013
+278	0.665732402
+279	0.6657118786
+280	0.665684467
+281	0.6656584634
+282	0.6656309991
+283	0.6656073482
+284	0.6655890957
+285	0.6655665563
+286	0.6655452454
+287	0.6655255286
+288	0.6655053548
+289	0.6654893396
+290	0.6654648912
+291	0.6654442759
+292	0.6654173127
+293	0.6653914518
+294	0.6653648946
+295	0.665344141
+296	0.6653140817
+297	0.665295365
+298	0.6652787488
+299	0.6652502991
+300	0.665231168
+301	0.6652136682
+302	0.6651903001
+303	0.6651697153
+304	0.6651525958
+305	0.6651322685
+306	0.6651113828
+307	0.6650886807
+308	0.6650622251
+309	0.6650429987
+310	0.665015513
+311	0.6650019022
+312	0.664979951
+313	0.6649549638
+314	0.6649340455
+315	0.6649162445
+316	0.6649048119
+317	0.6648796463
+318	0.6648605481
+319	0.6648429084
+320	0.6648238121
+321	0.6647969527
+322	0.6647854723
+323	0.6647589304
+324	0.6647429024
+325	0.6647237508
+326	0.6647059396
+327	0.664686288
+328	0.6646532527
+329	0.6646306438
+330	0.6646098516
+331	0.6645858284
+332	0.6645707188
+333	0.6645485788
+334	0.6645305696
+335	0.6645108881
+336	0.6644923286
+337	0.6644805222
+338	0.6644572776
+339	0.6644320741
+340	0.6644115048
+341	0.6643949013
+342	0.6643619789
+343	0.6643389502
+344	0.6643088915
+345	0.664286972
+346	0.664274149
+347	0.6642536926
+348	0.6642357634
+349	0.664207914
+350	0.6641853097
+351	0.6641654917
+352	0.664143804
+353	0.6641290647
+354	0.6641117244
+355	0.6640880219
+356	0.6640669415
+357	0.6640462999
+358	0.664030296
+359	0.6640028542
+360	0.6639813347
+361	0.6639597941
+362	0.6639429832
+363	0.6639222708
+364	0.6639065546
+365	0.6638823236
+366	0.6638648195
+367	0.6638436235
+368	0.6638208732
+369	0.6637956357
+370	0.6637718453
+371	0.663756918
+372	0.6637353525
+373	0.6637143112
+374	0.6636956547
+375	0.663680995
+376	0.66366728
+377	0.6636487567
+378	0.6636266904
+379	0.6636116064
+380	0.6635902746
+381	0.6635654896
+382	0.6635393029
+383	0.6635171734
+384	0.663500789
+385	0.663477743
+386	0.6634584806
+387	0.6634337499
+388	0.6634135584
+389	0.6633868455
+390	0.6633755323
+391	0.663356103
+392	0.6633337631
+393	0.663319422
+394	0.6632911566
+395	0.6632687875
+396	0.6632431997
+397	0.6632189331
+398	0.663201035
+399	0.6631898553
+400	0.6631712482
+401	0.663143025
+402	0.663121538
+403	0.6631087792
+404	0.6630859067
+405	0.663066483
+406	0.6630443652
+407	0.6630250376
+408	0.6630007822
+409	0.6629768728
+410	0.6629528093
+411	0.6629260936
+412	0.6629102182
+413	0.6628863488
+414	0.6628648972
+415	0.6628454339
+416	0.6628200274
+417	0.6627942591
+418	0.6627744647
+419	0.662765485
+420	0.6627503257
+421	0.6627323029
+422	0.6627111509
+423	0.6626785863
+424	0.6626576561
+425	0.6626363113
+426	0.6626181065
+427	0.66259794
+428	0.6625765658
+429	0.6625526572
+430	0.66253135
+431	0.6625035695
+432	0.662480212
+433	0.6624611632
+434	0.6624332625
+435	0.6624120584
+436	0.6623941719
+437	0.6623766304
+438	0.6623623329
+439	0.6623442925
+440	0.6623212715
+441	0.6623025941
+442	0.6622749791
+443	0.6622534499
+444	0.6622305473
+445	0.6622059333
+446	0.6621871707
+447	0.6621638454
+448	0.6621511296
+449	0.6621349978
+450	0.6621120424
+451	0.6620958271
+452	0.6620793528
+453	0.6620572713
+454	0.6620395025
+455	0.6620188044
+456	0.6620017347
+457	0.6619811454
+458	0.6619695569
+459	0.661952377
+460	0.6619237442
+461	0.6619089407
+462	0.6618886168
+463	0.6618831383
+464	0.6618690774
+465	0.661845878
+466	0.6618290213
+467	0.6618050064
+468	0.6617832833
+469	0.6617652311
+470	0.6617443144
+471	0.6617202619
+472	0.6617005831
+473	0.6616824419
+474	0.6616538226
+475	0.6616314155
+476	0.6616127861
+477	0.6616029072
+478	0.6615843751
+479	0.661563216
+480	0.6615432257
+481	0.6615263324
+482	0.6615033259
+483	0.661484293
+484	0.6614678231
+485	0.6614463024
+486	0.6614155436
+487	0.6613958945
+488	0.661380611
+489	0.6613677802
+490	0.6613530086
+491	0.6613248211
+492	0.6613059359
+493	0.6612729965
+494	0.6612624948
+495	0.6612401679
+496	0.6612191637
+497	0.6611912219
+498	0.6611773017
+499	0.6611638216
+500	0.6611450533
+501	0.6611179111
+502	0.6610959069
+503	0.6610728788
+504	0.6610436668
+505	0.6610188976
+506	0.6610030555
+507	0.6609831174
+508	0.6609586562
+509	0.660935882
+510	0.6609202024
+511	0.6609011137
+512	0.6608726737
+513	0.6608608849
+514	0.6608387256
+515	0.6608136063
+516	0.6607946343
+517	0.6607703935
+518	0.6607509625
+519	0.6607238109
+520	0.6606999858
+521	0.6606813873
+522	0.6606610372
+523	0.660638456
+524	0.6606156483
+525	0.6605968623
+526	0.6605735776
+527	0.6605517294
+528	0.6605309239
+529	0.6605086434
+530	0.6604803349
+531	0.6604566326
+532	0.6604430839
+533	0.6604273738
+534	0.6604048016
+535	0.6603845173
+536	0.6603669212
+537	0.6603488983
+538	0.6603176881
+539	0.6602953862
+540	0.6602672025
+541	0.6602568636
+542	0.660235705
+543	0.6602152295
+544	0.6601897709
+545	0.6601683731
+546	0.6601472267
+547	0.6601262337
+548	0.6601119991
+549	0.6600869973
+550	0.6600667497
+551	0.6600397508
+552	0.660016863
+553	0.6599933158
+554	0.6599632649
+555	0.6599446007
+556	0.6599138126
+557	0.6598965504
+558	0.6598785723
+559	0.659860838
+560	0.6598408724
+561	0.6598244857
+562	0.6598082469
+563	0.6597851673
+564	0.6597683521
+565	0.6597479006
+566	0.6597310938
+567	0.6597096581
+568	0.6596862311
+569	0.6596574779
+570	0.6596385418
+571	0.6596189903
+572	0.65959275
+573	0.6595730662
+574	0.6595566809
+575	0.6595365076
+576	0.6595163446
+577	0.6594816637
+578	0.6594570142
+579	0.6594353055
+580	0.6594162362
+581	0.659395036
+582	0.6593798831
+583	0.6593556719
+584	0.6593292627
+585	0.6592976737
+586	0.6592754841
+587	0.6592510441
+588	0.6592290326
+589	0.6592097404
+590	0.6591876204
+591	0.6591705995
+592	0.6591456195
+593	0.6591107122
+594	0.6590819533
+595	0.6590551327
+596	0.6590373916
+597	0.6590177149
+598	0.6589946095
+599	0.6589697628
+600	0.6589442269
+601	0.6589182437
+602	0.6588837179
+603	0.6588674101
+604	0.6588406916
+605	0.6588149945
+606	0.6587866031
+607	0.6587636648
+608	0.6587502469
+609	0.6587292784
+610	0.6587104112
+611	0.6586953782
+612	0.6586641191
+613	0.6586450136
+614	0.6586136263
+615	0.6585862768
+616	0.6585585235
+617	0.6585371631
+618	0.6585092632
+619	0.6584914317
+620	0.6584662432
+621	0.6584454668
+622	0.6584249408
+623	0.6583931228
+624	0.6583660767
+625	0.658354264
+626	0.6583253625
+627	0.6582968632
+628	0.6582687399
+629	0.658242535
+630	0.6582199874
+631	0.6581918101
+632	0.6581735218
+633	0.6581445869
+634	0.6581202427
+635	0.6580977862
+636	0.6580724179
+637	0.6580426322
+638	0.6580111256
+639	0.6579834747
+640	0.6579541367
+641	0.6579254503
+642	0.657898555
+643	0.6578676875
+644	0.6578324163
+645	0.6578062223
+646	0.6577760631
+647	0.6577483474
+648	0.6577249642
+649	0.6576974966
+650	0.657675114
+651	0.6576447891
+652	0.6576102356
+653	0.6575793887
+654	0.6575543309
+655	0.6575340787
+656	0.6575061464
+657	0.657476113
+658	0.6574447014
+659	0.6574247361
+660	0.6574034983
+661	0.6573783832
+662	0.657357694
+663	0.6573411592
+664	0.6573118559
+665	0.6572819076
+666	0.6572430097
+667	0.6572160391
+668	0.6571931413
+669	0.6571737099
+670	0.6571532872
+671	0.6571208939
+672	0.6570887673
+673	0.6570633692
+674	0.6570454361
+675	0.6570231031
+676	0.6570052089
+677	0.6569855794
+678	0.6569579709
+679	0.6569333354
+680	0.6569069617
+681	0.6568931857
+682	0.6568734532
+683	0.6568435196
+684	0.6568108038
+685	0.6567811374
+686	0.6567467284
+687	0.6567172734
+688	0.6566967606
+689	0.6566720128
+690	0.6566441608
+691	0.6566172287
+692	0.6565952549
+693	0.6565702687
+694	0.6565392213
+695	0.6565157938
+696	0.6564902789
+697	0.6564644734
+698	0.6564349549
+699	0.6564046572
+700	0.6563744107
+701	0.6563525063
+702	0.6563189867
+703	0.6562939062
+704	0.6562739297
+705	0.656256438
+706	0.6562366475
+707	0.6562073096
+708	0.6561864222
+709	0.6561578826
+710	0.6561208567
+711	0.6560924703
+712	0.6560656907
+713	0.6560362588
+714	0.6560124527
+715	0.6559875055
+716	0.6559547281
+717	0.6559230866
+718	0.6558924823
+719	0.6558676469
+720	0.6558459277
+721	0.6558149638
+722	0.6557812248
+723	0.6557546502
+724	0.6557274948
+725	0.6557044723
+726	0.6556751811
+727	0.6556539158
+728	0.6556182915
+729	0.6555977079
+730	0.6555667903
+731	0.6555394075
+732	0.6555122742
+733	0.6554814941
+734	0.6554517373
+735	0.655429552
+736	0.655396579
+737	0.6553735864
+738	0.6553472597
+739	0.6553252832
+740	0.6552971659
+741	0.6552763852
+742	0.6552488203
+743	0.65521229
+744	0.6551949744
+745	0.6551673797
+746	0.6551421856
+747	0.6551255516
+748	0.6551019608
+749	0.6550758728
+750	0.655051966
+751	0.6550351058
+752	0.6549998756
+753	0.6549721212
+754	0.6549401744
+755	0.6549207325
+756	0.6548900891
+757	0.6548682731
+758	0.6548418938
+759	0.6548234717
+760	0.6547996833
+761	0.6547726174
+762	0.6547509314
+763	0.6547168175
+764	0.6546907846
+765	0.6546671611
+766	0.6546475893
+767	0.6546206223
+768	0.6545874193
+769	0.6545620629
+770	0.6545346297
+771	0.6545172316
+772	0.6544943049
+773	0.6544632323
+774	0.6544384097
+775	0.6544084745
+776	0.6543765257
+777	0.6543536123
+778	0.6543303593
+779	0.6543005831
+780	0.6542678123
+781	0.6542439303
+782	0.6542100401
+783	0.6541836178
+784	0.654158129
+785	0.6541343464
+786	0.6541092921
+787	0.6540812254
+788	0.654060259
+789	0.6540467253
+790	0.6540306837
+791	0.6540103667
+792	0.6539821302
+793	0.6539577914
+794	0.653923724
+795	0.6539086888
+796	0.6538798424
+797	0.6538566996
+798	0.6538290752
+799	0.6538051255
+800	0.6537917354
+801	0.6537684302
+802	0.6537402991
+803	0.6537165427
+804	0.6536853601
+805	0.6536681479
+806	0.6536409101
+807	0.6536120189
+808	0.6535912493
+809	0.6535617421
+810	0.6535315174
+811	0.6534972927
+812	0.6534818476
+813	0.6534498323
+814	0.6534305025
+815	0.6534081059
+816	0.6533765804
+817	0.6533441549
+818	0.6533053405
+819	0.6532838469
+820	0.6532604302
+821	0.6532364412
+822	0.6532100089
+823	0.6531782515
+824	0.6531449701
+825	0.653115452
+826	0.6530787602
+827	0.653052397
+828	0.6530313579
+829	0.6530010363
+830	0.6529752146
+831	0.652954801
+832	0.6529330351
+833	0.6528993709
+834	0.6528665883
+835	0.6528413041
+836	0.6528217161
+837	0.6527978782
+838	0.6527789461
+839	0.6527432001
+840	0.6527139767
+841	0.6526857244
+842	0.652657086
+843	0.6526355016
+844	0.6526054936
+845	0.6525793707
+846	0.6525584692
+847	0.6525279747
+848	0.6525038765
+849	0.6524849104
+850	0.6524610603
+851	0.6524357337
+852	0.6524082286
+853	0.65238051
+854	0.6523557826
+855	0.6523391233
+856	0.652325347
+857	0.6522924958
+858	0.6522623584
+859	0.6522343891
+860	0.6522094424
+861	0.6521841478
+862	0.6521657946
+863	0.6521304278
+864	0.6521045712
+865	0.6520753696
+866	0.6520519528
+867	0.6520216555
+868	0.6519926935
+869	0.6519734186

ai-engine/catboost_info/time_left.tsv

@@ -0,0 +1,871 @@
+iter	Passed	Remaining
+0	46	93548
+1	83	83419
+2	132	88415
+3	162	81250
+4	196	78573
+5	230	76747
+6	269	76701
+7	319	79674
+8	364	80653
+9	411	81918
+10	456	82497
+11	491	81432
+12	522	79809
+13	555	78774
+14	595	78777
+15	630	78123
+16	662	77290
+17	700	77124
+18	730	76120
+19	764	75651
+20	804	75774
+21	835	75128
+22	886	76169
+23	920	75764
+24	960	75853
+25	989	75130
+26	1025	74941
+27	1060	74714
+28	1104	75079
+29	1141	74976
+30	1180	74975
+31	1213	74640
+32	1245	74260
+33	1287	74434
+34	1327	74528
+35	1376	75071
+36	1427	75741
+37	1468	75804
+38	1508	75857
+39	1549	75922
+40	1586	75781
+41	1621	75590
+42	1663	75705
+43	1701	75621
+44	1739	75591
+45	1776	75460
+46	1819	75616
+47	1869	76025
+48	1916	76288
+49	1953	76191
+50	1993	76197
+51	2038	76381
+52	2080	76420
+53	2158	77788
+54	2220	78529
+55	2286	79390
+56	2328	79372
+57	2367	79254
+58	2409	79257
+59	2444	79049
+60	2484	78985
+61	2521	78820
+62	2554	78528
+63	2593	78466
+64	2623	78111
+65	2660	77969
+66	2695	77776
+67	2725	77446
+68	2761	77291
+69	2791	76975
+70	2824	76739
+71	2861	76611
+72	2897	76476
+73	2935	76408
+74	3040	78027
+75	3097	78411
+76	3152	78741
+77	3216	79248
+78	3256	79195
+79	3305	79336
+80	3348	79320
+81	3381	79089
+82	3416	78911
+83	3480	79399
+84	3535	79649
+85	3581	79716
+86	3612	79428
+87	3644	79185
+88	3678	78975
+89	3712	78785
+90	3743	78531
+91	3775	78297
+92	3806	78047
+93	3837	77821
+94	3871	77629
+95	3913	77618
+96	3945	77403
+97	3989	77433
+98	4020	77204
+99	4053	77020
+100	4084	76789
+101	4116	76597
+102	4148	76401
+103	4176	76141
+104	4202	75845
+105	4232	75634
+106	4261	75390
+107	4290	75168
+108	4324	75018
+109	4351	74766
+110	4386	74648
+111	4424	74577
+112	4458	74455
+113	4497	74400
+114	4533	74307
+115	4564	74136
+116	4596	73981
+117	4628	73818
+118	4668	73786
+119	4692	73509
+120	4723	73354
+121	4756	73220
+122	4788	73065
+123	4815	72854
+124	4843	72647
+125	4875	72514
+126	4916	72515
+127	4952	72436
+128	4991	72397
+129	5028	72327
+130	5059	72180
+131	5096	72116
+132	5125	71946
+133	5156	71804
+134	5190	71704
+135	5221	71564
+136	5251	71407
+137	5274	71165
+138	5309	71084
+139	5344	71008
+140	5377	70902
+141	5416	70866
+142	5452	70803
+143	5490	70760
+144	5521	70641
+145	5553	70522
+146	5582	70365
+147	5611	70217
+148	5636	70026
+149	5673	69975
+150	5706	69874
+151	5738	69764
+152	5765	69605
+153	5795	69471
+154	5817	69246
+155	5853	69191
+156	5888	69122
+157	5924	69070
+158	5964	69061
+159	5996	68963
+160	6022	68789
+161	6050	68650
+162	6079	68510
+163	6108	68385
+164	6140	68292
+165	6169	68162
+166	6202	68074
+167	6231	67953
+168	6263	67858
+169	6295	67764
+170	6325	67656
+171	6356	67561
+172	6395	67545
+173	6437	67554
+174	6472	67495
+175	6503	67395
+176	6533	67291
+177	6562	67174
+178	6590	67049
+179	6624	66982
+180	6655	66882
+181	6687	66804
+182	6718	66703
+183	6751	66632
+184	6784	66559
+185	6810	66424
+186	6832	66246
+187	6867	66187
+188	6918	66294
+189	6969	66393
+190	7018	66470
+191	7074	66614
+192	7117	66635
+193	7191	66943
+194	7242	67036
+195	7282	67027
+196	7317	66967
+197	7351	66903
+198	7389	66879
+199	7432	66896
+200	7471	66869
+201	7506	66814
+202	7540	66752
+203	7568	66628
+204	7605	66596
+205	7638	66519
+206	7665	66397
+207	7700	66340
+208	7734	66276
+209	7766	66197
+210	7796	66106
+211	7831	66053
+212	7871	66037
+213	7910	66016
+214	7951	66014
+215	7989	65983
+216	8025	65946
+217	8058	65872
+218	8087	65768
+219	8112	65638
+220	8148	65594
+221	8197	65655
+222	8239	65655
+223	8268	65556
+224	8298	65466
+225	8327	65366
+226	8357	65278
+227	8384	65167
+228	8418	65103
+229	8453	65058
+230	8490	65020
+231	8523	64958
+232	8550	64848
+233	8575	64718
+234	8607	64648
+235	8635	64545
+236	8660	64426
+237	8691	64345
+238	8719	64250
+239	8746	64137
+240	8773	64038
+241	8803	63951
+242	8833	63873
+243	8862	63779
+244	8892	63698
+245	8932	63688
+246	8962	63611
+247	8991	63521
+248	9021	63442
+249	9051	63358
+250	9085	63306
+251	9110	63193
+252	9137	63093
+253	9174	63066
+254	9196	62935
+255	9238	62934
+256	9267	62855
+257	9297	62776
+258	9324	62681
+259	9357	62625
+260	9388	62552
+261	9427	62536
+262	9461	62491
+263	9496	62443
+264	9524	62356
+265	9553	62278
+266	9590	62247
+267	9620	62172
+268	9645	62071
+269	9682	62040
+270	9711	61962
+271	9739	61872
+272	9768	61797
+273	9804	61761
+274	9848	61777
+275	9886	61755
+276	9925	61740
+277	9965	61728
+278	9995	61656
+279	10022	61564
+280	10055	61516
+281	10080	61410
+282	10111	61344
+283	10147	61311
+284	10175	61230
+285	10202	61141
+286	10234	61084
+287	10264	61018
+288	10299	60977
+289	10323	60874
+290	10353	60804
+291	10394	60803
+292	10431	60773
+293	10471	60763
+294	10503	60707
+295	10534	60645
+296	10576	60646
+297	10612	60612
+298	10639	60525
+299	10668	60453
+300	10702	60411
+301	10729	60326
+302	10764	60290
+303	10801	60263
+304	10829	60182
+305	10857	60108
+306	10892	60067
+307	10930	60047
+308	10972	60045
+309	11002	59983
+310	11030	59902
+311	11058	59828
+312	11092	59788
+313	11117	59696
+314	11149	59641
+315	11187	59617
+316	11211	59525
+317	11243	59468
+318	11274	59413
+319	11304	59346
+320	11334	59287
+321	11362	59209
+322	11394	59158
+323	11436	59158
+324	11477	59153
+325	11513	59122
+326	11547	59081
+327	11572	58991
+328	11607	58956
+329	11637	58894
+330	11668	58833
+331	11700	58785
+332	11724	58694
+333	11757	58648
+334	11780	58550
+335	11815	58515
+336	11844	58451
+337	11869	58364
+338	11905	58335
+339	11941	58302
+340	11986	58315
+341	12020	58274
+342	12066	58292
+343	12122	58358
+344	12177	58415
+345	12221	58422
+346	12264	58423
+347	12300	58394
+348	12324	58304
+349	12354	58243
+350	12401	58262
+351	12438	58232
+352	12479	58228
+353	12512	58179
+354	12541	58116
+355	12569	58044
+356	12597	57977
+357	12628	57920
+358	12653	57839
+359	12682	57775
+360	12720	57752
+361	12744	57666
+362	12770	57592
+363	12811	57583
+364	12841	57522
+365	12870	57460
+366	12897	57386
+367	12938	57378
+368	12974	57347
+369	13009	57313
+370	13038	57249
+371	13078	57235
+372	13117	57216
+373	13147	57159
+374	13181	57118
+375	13205	57036
+376	13235	56979
+377	13274	56960
+378	13306	56911
+379	13333	56841
+380	13366	56798
+381	13396	56741
+382	13421	56666
+383	13467	56674
+384	13508	56664
+385	13540	56616
+386	13569	56559
+387	13598	56496
+388	13627	56438
+389	13656	56376
+390	13685	56317
+391	13717	56271
+392	13750	56227
+393	13771	56135
+394	13804	56090
+395	13825	55999
+396	13858	55957
+397	13888	55904
+398	13917	55843
+399	13953	55812
+400	13994	55802
+401	14025	55752
+402	14048	55670
+403	14076	55607
+404	14105	55551
+405	14142	55526
+406	14182	55511
+407	14214	55464
+408	14240	55394
+409	14267	55328
+410	14299	55284
+411	14324	55213
+412	14351	55146
+413	14379	55086
+414	14410	55036
+415	14451	55025
+416	14484	54984
+417	14513	54929
+418	14536	54851
+419	14565	54793
+420	14587	54710
+421	14615	54650
+422	14642	54588
+423	14666	54515
+424	14690	54441
+425	14719	54384
+426	14739	54297
+427	14772	54257
+428	14790	54164
+429	14824	54125
+430	14844	54039
+431	14876	53995
+432	14906	53946
+433	14938	53902
+434	14980	53894
+435	15006	53829
+436	15033	53770
+437	15059	53706
+438	15085	53639
+439	15110	53574
+440	15134	53503
+441	15160	53438
+442	15184	53369
+443	15211	53308
+444	15234	53236
+445	15266	53193
+446	15287	53114
+447	15316	53059
+448	15336	52978
+449	15366	52929
+450	15393	52870
+451	15429	52843
+452	15469	52828
+453	15490	52748
+454	15523	52712
+455	15550	52653
+456	15577	52594
+457	15604	52536
+458	15630	52476
+459	15656	52414
+460	15682	52353
+461	15711	52304
+462	15736	52238
+463	15765	52188
+464	15786	52112
+465	15817	52068
+466	15839	51996
+467	15873	51961
+468	15903	51916
+469	15935	51873
+470	15969	51840
+471	15994	51779
+472	16022	51726
+473	16047	51663
+474	16073	51605
+475	16099	51546
+476	16128	51495
+477	16152	51431
+478	16176	51367
+479	16205	51317
+480	16228	51250
+481	16255	51194
+482	16277	51123
+483	16305	51071
+484	16328	51005
+485	16362	50973
+486	16392	50928
+487	16426	50894
+488	16459	50860
+489	16480	50787
+490	16510	50743
+491	16530	50668
+492	16561	50625
+493	16585	50562
+494	16613	50510
+495	16638	50453
+496	16663	50393
+497	16690	50339
+498	16716	50282
+499	16740	50222
+500	16773	50186
+501	16802	50139
+502	16836	50107
+503	16873	50085
+504	16921	50094
+505	16989	50163
+506	17038	50173
+507	17069	50132
+508	17110	50121
+509	17145	50091
+510	17190	50091
+511	17219	50044
+512	17247	49994
+513	17271	49932
+514	17298	49878
+515	17343	49878
+516	17373	49836
+517	17417	49831
+518	17460	49823
+519	17490	49781
+520	17518	49731
+521	17546	49680
+522	17571	49622
+523	17600	49577
+524	17625	49520
+525	17655	49474
+526	17679	49414
+527	17707	49366
+528	17729	49300
+529	17758	49254
+530	17781	49191
+531	17808	49141
+532	17829	49071
+533	17862	49038
+534	17905	49031
+535	18028	49241
+536	18072	49236
+537	18106	49203
+538	18135	49157
+539	18165	49114
+540	18200	49083
+541	18223	49022
+542	18254	48980
+543	18280	48927
+544	18307	48876
+545	18338	48834
+546	18367	48790
+547	18411	48783
+548	18444	48747
+549	18470	48693
+550	18503	48660
+551	18531	48611
+552	18557	48558
+553	18584	48508
+554	18625	48493
+555	18650	48436
+556	18677	48388
+557	18703	48333
+558	18729	48282
+559	18756	48231
+560	18781	48176
+561	18808	48126
+562	18834	48074
+563	18869	48043
+564	18902	48008
+565	18930	47960
+566	18958	47914
+567	18983	47859
+568	19016	47824
+569	19037	47761
+570	19068	47720
+571	19090	47660
+572	19111	47595
+573	19141	47553
+574	19164	47494
+575	19196	47458
+576	19217	47393
+577	19249	47358
+578	19274	47303
+579	19298	47247
+580	19324	47195
+581	19357	47162
+582	19391	47130
+583	19427	47103
+584	19460	47070
+585	19483	47012
+586	19511	46967
+587	19542	46929
+588	19564	46867
+589	19597	46833
+590	19621	46779
+591	19647	46729
+592	19670	46672
+593	19699	46627
+594	19726	46582
+595	19753	46532
+596	19778	46480
+597	19803	46429
+598	19830	46381
+599	19857	46335
+600	19896	46313
+601	19925	46271
+602	19957	46236
+603	19991	46204
+604	20019	46159
+605	20047	46115
+606	20072	46063
+607	20098	46015
+608	20123	45963
+609	20149	45913
+610	20176	45867
+611	20202	45817
+612	20230	45774
+613	20253	45719
+614	20285	45682
+615	20307	45626
+616	20338	45589
+617	20361	45532
+618	20394	45500
+619	20423	45459
+620	20454	45420
+621	20488	45390
+622	20510	45333
+623	20543	45301
+624	20569	45252
+625	20594	45201
+626	20619	45151
+627	20646	45107
+628	20675	45066
+629	20701	45016
+630	20727	44970
+631	20752	44919
+632	20782	44881
+633	20804	44825
+634	20837	44791
+635	20862	44742
+636	20892	44704
+637	20931	44683
+638	20960	44643
+639	20994	44612
+640	21022	44570
+641	21052	44531
+642	21082	44493
+643	21107	44443
+644	21135	44401
+645	21160	44351
+646	21185	44302
+647	21210	44253
+648	21236	44208
+649	21262	44161
+650	21288	44113
+651	21315	44068
+652	21343	44027
+653	21377	43997
+654	21403	43949
+655	21440	43926
+656	21477	43903
+657	21502	43854
+658	21533	43819
+659	21559	43772
+660	21586	43727
+661	21611	43680
+662	21637	43633
+663	21662	43586
+664	21688	43539
+665	21714	43493
+666	21742	43451
+667	21771	43413
+668	21818	43409
+669	21846	43366
+670	21888	43352
+671	21934	43345
+672	21971	43322
+673	22019	43320
+674	22053	43289
+675	22090	43266
+676	22141	43269
+677	22176	43240
+678	22213	43215
+679	22239	43171
+680	22270	43134
+681	22296	43088
+682	22321	43041
+683	22350	43002
+684	22379	42962
+685	22419	42944
+686	22452	42912
+687	22484	42878
+688	22511	42834
+689	22537	42789
+690	22571	42757
+691	22598	42714
+692	22624	42669
+693	22653	42630
+694	22680	42586
+695	22708	42545
+696	22739	42510
+697	22761	42457
+698	22792	42421
+699	22816	42373
+700	22845	42333
+701	22870	42288
+702	22902	42253
+703	22942	42234
+704	22974	42201
+705	23002	42160
+706	23033	42124
+707	23054	42071
+708	23086	42038
+709	23115	41999
+710	23143	41957
+711	23169	41914
+712	23195	41868
+713	23230	41840
+714	23259	41801
+715	23287	41760
+716	23311	41713
+717	23341	41676
+718	23372	41641
+719	23405	41610
+720	23438	41578
+721	23483	41566
+722	23507	41519
+723	23540	41488
+724	23566	41444
+725	23595	41406
+726	23623	41365
+727	23648	41320
+728	23677	41281
+729	23700	41231
+730	23728	41192
+731	23752	41144
+732	23784	41111
+733	23807	41063
+734	23840	41031
+735	23870	40994
+736	23908	40972
+737	23941	40940
+738	23974	40909
+739	24006	40875
+740	24036	40838
+741	24064	40798
+742	24092	40759
+743	24127	40730
+744	24153	40688
+745	24179	40644
+746	24207	40604
+747	24233	40561
+748	24261	40522
+749	24295	40491
+750	24318	40444
+751	24349	40410
+752	24376	40368
+753	24408	40335
+754	24442	40306
+755	24474	40273
+756	24508	40242
+757	24548	40222
+758	24575	40182
+759	24605	40145
+760	24632	40104
+761	24660	40064
+762	24689	40027
+763	24714	39982
+764	24745	39949
+765	24766	39897
+766	24797	39863
+767	24825	39823
+768	24854	39786
+769	24880	39744
+770	24909	39706
+771	24940	39672
+772	24970	39635
+773	25004	39606
+774	25030	39564
+775	25056	39522
+776	25086	39486
+777	25107	39436
+778	25139	39403
+779	25159	39351
+780	25188	39314
+781	25214	39272
+782	25240	39230
+783	25266	39188
+784	25288	39141
+785	25315	39101
+786	25341	39058
+787	25367	39016
+788	25391	38972
+789	25417	38930
+790	25448	38895
+791	25482	38867
+792	25514	38834
+793	25542	38795
+794	25569	38756
+795	25595	38714
+796	25618	38669
+797	25643	38626
+798	25667	38581
+799	25695	38543
+800	25716	38494
+801	25743	38454
+802	25770	38415
+803	25790	38364
+804	25822	38332
+805	25843	38284
+806	25873	38249
+807	25896	38203
+808	25925	38167
+809	25955	38131
+810	25988	38101
+811	26028	38080
+812	26055	38042
+813	26081	38000
+814	26108	37961
+815	26131	37916
+816	26159	37878
+817	26188	37841
+818	26214	37800
+819	26242	37764
+820	26272	37728
+821	26298	37688
+822	26327	37652
+823	26359	37619
+824	26385	37580
+825	26408	37534
+826	26444	37507
+827	26477	37478
+828	26517	37456
+829	26539	37411
+830	26573	37382
+831	26597	37339
+832	26623	37298
+833	26650	37259
+834	26677	37221
+835	26704	37182
+836	26728	37138
+837	26763	37111
+838	26791	37073
+839	26822	37041
+840	26872	37033
+841	26924	37029
+842	26982	37033
+843	27054	37055
+844	27097	37038
+845	27120	36994
+846	27146	36954
+847	27180	36925
+848	27206	36884
+849	27234	36846
+850	27260	36807
+851	27289	36770
+852	27318	36734
+853	27347	36698
+854	27386	36675
+855	27413	36637
+856	27439	36596
+857	27471	36564
+858	27501	36529
+859	27535	36500
+860	27572	36474
+861	27595	36431
+862	27627	36398
+863	27654	36360
+864	27683	36324
+865	27711	36287
+866	27738	36249
+867	27765	36210
+868	27794	36175
+869	27820	36135

ai-engine/config/config_loader.py

@@ -1,17 +1,19 @@
 import os
+import json
 import yaml
 from typing import Dict, Any, Optional
 
+
 class EnsembleConfig:
     _instance: Optional['EnsembleConfig'] = None
     _config: Dict[str, Any] = {}
-    
+
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super(EnsembleConfig, cls).__new__(cls)
             cls._instance._load_config()
         return cls._instance
-    
+
     def _load_config(self):
         """Load configuration from YAML file."""
         config_path = os.path.join(os.path.dirname(__file__), 'ensemble_config.yaml')
@@ -22,12 +24,12 @@ class EnsembleConfig:
         except Exception as e:
             print(f"❌ Failed to load ensemble config: {e}")
             self._config = {}
-    
+
     def get(self, key: str, default: Any = None) -> Any:
         """Get configuration value by key (supports dot notation for nested keys)."""
         keys = key.split('.')
         value = self._config
-        
+
         try:
             for k in keys:
                 value = value[k]
@@ -35,12 +37,79 @@ class EnsembleConfig:
         except (KeyError, TypeError):
             return default
 
+
 # Singleton accessor
 def get_config() -> EnsembleConfig:
     return EnsembleConfig()
 
+
+# ── Market Thresholds Loader ────────────────────────────────────────────
+
+_market_thresholds_cache: Optional[Dict[str, Any]] = None
+
+
+def load_market_thresholds() -> Dict[str, Any]:
+    """
+    Load market thresholds from JSON config file.
+    Returns the full config dict with 'markets' and 'defaults' keys.
+    Caches after first load for performance.
+    """
+    global _market_thresholds_cache
+    if _market_thresholds_cache is not None:
+        return _market_thresholds_cache
+
+    config_path = os.path.join(os.path.dirname(__file__), 'market_thresholds.json')
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+        _market_thresholds_cache = data
+        print(f"✅ Market thresholds loaded: {len(data.get('markets', {}))} markets (v={data.get('_meta', {}).get('version', '?')})")
+        return data
+    except Exception as e:
+        print(f"❌ Failed to load market thresholds: {e} — using built-in defaults")
+        _market_thresholds_cache = {"markets": {}, "defaults": {
+            "calibration": 0.55,
+            "min_conf": 55.0,
+            "min_play_score": 68.0,
+            "min_edge": 0.02,
+            "odds_band_min_sample": 0.0,
+            "odds_band_min_edge": 0.0,
+        }}
+        return _market_thresholds_cache
+
+
+def build_threshold_dict(field: str) -> Dict[str, float]:
+    """
+    Build a flat {market: value} dict for a specific threshold field.
+
+    Usage:
+        calibration_map = build_threshold_dict("calibration")
+        # → {"MS": 0.62, "DC": 0.82, ...}
+    """
+    data = load_market_thresholds()
+    markets = data.get("markets", {})
+    result: Dict[str, float] = {}
+    for market, cfg in markets.items():
+        if field in cfg:
+            result[market] = float(cfg[field])
+    return result
+
+
+def get_threshold_default(field: str) -> float:
+    """Get the default fallback value for a threshold field."""
+    data = load_market_thresholds()
+    defaults = data.get("defaults", {})
+    return float(defaults.get(field, 0.0))
+
+
 if __name__ == "__main__":
     # Test
     cfg = get_config()
     print(f"Weights: {cfg.get('engine_weights')}")
     print(f"Team Weight: {cfg.get('engine_weights.team')}")
+    print()
+    print("--- Market Thresholds ---")
+    for field in ["calibration", "min_conf", "min_play_score", "min_edge"]:
+        d = build_threshold_dict(field)
+        print(f"{field}: {d}")
+    print(f"Default calibration: {get_threshold_default('calibration')}")

ai-engine/config/ensemble_config.yaml

@@ -1,3 +1,14 @@
+model_ensemble:
+  xgb_weight: 0.50
+  lgb_weight: 0.50
+  temperature: 1.5
+  default_ms_odds:
+    home: 2.65
+    draw: 3.20
+    away: 2.65
+  elo_staleness_days: 14
+  odds_staleness_hours: 48
+
 engine_weights:
   team: 0.30
   player: 0.25

ai-engine/config/market_thresholds.json

@@ -0,0 +1,115 @@
+{
+  "_meta": {
+    "version": "v34",
+    "description": "Market-specific thresholds for the betting engine pipeline — V34 odds-aware gate fix",
+    "rule": "max_reachable (100 × calibration) MUST be > min_conf + 8",
+    "updated_at": "2026-05-10",
+    "changelog": "V34: Reduced min_edge to realistic levels for odds-aware V25 model. Model output ≈ market-implied, so large EV edges are mathematically impossible."
+  },
+  "markets": {
+    "MS": {
+      "calibration": 0.62,
+      "min_conf": 20.0,
+      "min_play_score": 28.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "DC": {
+      "calibration": 0.82,
+      "min_conf": 40.0,
+      "min_play_score": 50.0,
+      "min_edge": 0.003,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU15": {
+      "calibration": 0.84,
+      "min_conf": 45.0,
+      "min_play_score": 50.0,
+      "min_edge": 0.003,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU25": {
+      "calibration": 0.68,
+      "min_conf": 30.0,
+      "min_play_score": 40.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU35": {
+      "calibration": 0.60,
+      "min_conf": 20.0,
+      "min_play_score": 30.0,
+      "min_edge": 0.008,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "BTTS": {
+      "calibration": 0.65,
+      "min_conf": 30.0,
+      "min_play_score": 40.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "HT": {
+      "calibration": 0.58,
+      "min_conf": 20.0,
+      "min_play_score": 28.0,
+      "min_edge": 0.01,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "HT_OU05": {
+      "calibration": 0.68,
+      "min_conf": 35.0,
+      "min_play_score": 42.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "HT_OU15": {
+      "calibration": 0.60,
+      "min_conf": 25.0,
+      "min_play_score": 32.0,
+      "min_edge": 0.008,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "OE": {
+      "calibration": 0.62,
+      "min_conf": 35.0,
+      "min_play_score": 32.0,
+      "min_edge": 0.005
+    },
+    "CARDS": {
+      "calibration": 0.58,
+      "min_conf": 30.0,
+      "min_play_score": 35.0,
+      "min_edge": 0.008
+    },
+    "HCAP": {
+      "calibration": 0.56,
+      "min_conf": 25.0,
+      "min_play_score": 30.0,
+      "min_edge": 0.015
+    },
+    "HTFT": {
+      "calibration": 0.45,
+      "min_conf": 10.0,
+      "min_play_score": 18.0,
+      "min_edge": 0.02
+    }
+  },
+  "defaults": {
+    "calibration": 0.55,
+    "min_conf": 55.0,
+    "min_play_score": 60.0,
+    "min_edge": 0.008,
+    "odds_band_min_sample": 0.0,
+    "odds_band_min_edge": 0.0
+  }
+}

ai-engine/core/calculators/base_calculator.py

@@ -40,7 +40,7 @@ class CalculationContext:
     is_surprise: bool = False
 
     # XGBoost Predictions (New)
-    xgboost_preds: dict[str, dict[str, Any]] = field(default_factory=dict)
+    xgboost_preds: dict[str, Any] = field(default_factory=dict)
 
 
 class BaseCalculator:

ai-engine/core/calculators/bet_recommender.py

@@ -28,7 +28,7 @@ class RecommendationResult:
 
 
 class BetRecommender(BaseCalculator):
-    def calculate(self,
+    def calculate(self,  # type: ignore[override]
                   ctx: CalculationContext,
                   ms_res: MatchResultPrediction,
                   ou_res: OverUnderPrediction,

ai-engine/core/calculators/expert_recommender.py

@@ -36,7 +36,7 @@ class ExpertResult:
 
 
 class ExpertRecommender(BaseCalculator):
-    def calculate(self,
+    def calculate(self,  # type: ignore[override]
                   ctx: CalculationContext,
                   ms_res: MatchResultPrediction,
                   ou_res: OverUnderPrediction,

ai-engine/core/calculators/half_time_calculator.py

@@ -31,7 +31,7 @@ class HalfTimeCalculator(BaseCalculator):
             return 1.0 if k == 0 else 0.0
         return (lam ** k) * math.exp(-lam) / math.factorial(k)
         
-    def calculate(self, ctx: CalculationContext) -> HalfTimePrediction:
+    def calculate(self, ctx: CalculationContext) -> HalfTimePrediction:  # type: ignore[override]
         team_pred = ctx.team_pred
         odds_pred = ctx.odds_pred
         

ai-engine/core/calculators/match_result_calculator.py

@@ -22,9 +22,9 @@ class MatchResultCalculator(BaseCalculator):
     def _get_engine_winner(self, home_prob: float, draw_prob: float, away_prob: float) -> str:
         """Determine which outcome an engine favors."""
         probs = {"1": home_prob, "X": draw_prob, "2": away_prob}
-        return max(probs, key=probs.get)
+        return max(probs, key=probs.__getitem__)
     
-    def calculate(self, ctx: CalculationContext) -> MatchResultPrediction:
+    def calculate(self, ctx: CalculationContext) -> MatchResultPrediction:  # type: ignore[override]
         # Weights
         w_team = ctx.weights["team"]
         w_player = ctx.weights["player"]

ai-engine/core/calculators/other_markets_calculator.py

@@ -28,7 +28,7 @@ class OtherMarketsPrediction:
 
 
 class OtherMarketsCalculator(BaseCalculator):
-    def calculate(
+    def calculate(  # type: ignore[override]
         self,
         ctx: CalculationContext,
         ms_result: MatchResultPrediction,

ai-engine/core/calculators/over_under_calculator.py

@@ -55,7 +55,7 @@ class OverUnderCalculator(BaseCalculator):
         
         return over_15, over_25, over_35, btts_yes
     
-    def calculate(self, ctx: CalculationContext) -> OverUnderPrediction:
+    def calculate(self, ctx: CalculationContext) -> OverUnderPrediction:  # type: ignore[override]
         odds_pred = ctx.odds_pred
         referee_mods = ctx.referee_mods
         

ai-engine/core/calculators/risk_assessor.py

@@ -67,12 +67,14 @@ class RiskAssessor(BaseCalculator):
 
         if sport_key == "basketball":
             if is_top_league:
-                return float(
+                top_val = self.config.get("risk.surprise_threshold_basketball_top")
-                    self.config.get("risk.surprise_threshold_basketball_top", self.config.get("risk.surprise_threshold_basketball", 0.30)),
+                if top_val is not None:
-                )
+                    return float(top_val)
-            return float(
+                base_val = self.config.get("risk.surprise_threshold_basketball")
-                self.config.get("risk.surprise_threshold_basketball_non_top", 0.34),
+                return float(base_val) if base_val is not None else 0.30
-            )
+            
+            non_top_val = self.config.get("risk.surprise_threshold_basketball_non_top")
+            return float(non_top_val) if non_top_val is not None else 0.34
 
         if top_label not in ("1/2", "2/1"):
             return base_threshold
@@ -81,27 +83,30 @@ class RiskAssessor(BaseCalculator):
         favorite_side, gap = self._favorite_profile_from_odds(ctx.odds_data)
 
         if is_top_league:
-            favorite_winner_threshold = float(
+            top_fav = self.config.get("risk.surprise_threshold_favorite_reversal_top")
-                self.config.get(
+            if top_fav is not None:
-                    "risk.surprise_threshold_favorite_reversal_top",
+                favorite_winner_threshold = float(top_fav)
-                    self.config.get("risk.surprise_threshold_favorite_reversal", 0.26),
+            else:
-                ),
+                base_fav = self.config.get("risk.surprise_threshold_favorite_reversal")
-            )
+                favorite_winner_threshold = float(base_fav) if base_fav is not None else 0.26
-            underdog_winner_threshold = float(
+                
-                self.config.get(
+            top_ud = self.config.get("risk.surprise_threshold_underdog_reversal_top")
-                    "risk.surprise_threshold_underdog_reversal_top",
+            if top_ud is not None:
-                    self.config.get("risk.surprise_threshold_underdog_reversal", 0.20),
+                underdog_winner_threshold = float(top_ud)
-                ),
+            else:
-            )
+                base_ud = self.config.get("risk.surprise_threshold_underdog_reversal")
+                underdog_winner_threshold = float(base_ud) if base_ud is not None else 0.20
         else:
-            favorite_winner_threshold = float(
+            nt_fav = self.config.get("risk.surprise_threshold_favorite_reversal_non_top")
-                self.config.get("risk.surprise_threshold_favorite_reversal_non_top", 0.30),
+            favorite_winner_threshold = float(nt_fav) if nt_fav is not None else 0.30
-            )
+            nt_ud = self.config.get("risk.surprise_threshold_underdog_reversal_non_top")
-            underdog_winner_threshold = float(
+            underdog_winner_threshold = float(nt_ud) if nt_ud is not None else 0.24
-                self.config.get("risk.surprise_threshold_underdog_reversal_non_top", 0.24),
+            
-            )
+        gm = self.config.get("risk.htft_reversal_gap_medium")
-        gap_medium = float(self.config.get("risk.htft_reversal_gap_medium", 0.50))
+        gap_medium = float(gm) if gm is not None else 0.50
-        gap_strong = float(self.config.get("risk.htft_reversal_gap_strong", 1.00))
+        
+        gs = self.config.get("risk.htft_reversal_gap_strong")
+        gap_strong = float(gs) if gs is not None else 1.00
 
         if favorite_side in ("H", "A"):
             threshold = (
@@ -117,7 +122,7 @@ class RiskAssessor(BaseCalculator):
 
         return base_threshold
     
-    def calculate(self, ctx: CalculationContext, ms_result=None) -> RiskAnalysis:
+    def calculate(self, ctx: CalculationContext, ms_result: Any = None) -> RiskAnalysis:  # type: ignore[override]
         """
         Wrapper for assess_risk to match BaseCalculator interface but with extra arg.
         """
@@ -173,9 +178,15 @@ class RiskAssessor(BaseCalculator):
 
                 threshold = self._dynamic_reversal_threshold(ctx, top_label)
                 if getattr(ctx, "is_top_league", False):
-                    min_gap = float(self.config.get("risk.surprise_min_top_gap_top", self.config.get("risk.surprise_min_top_gap", 0.02)))
+                    top_gap_val = self.config.get("risk.surprise_min_top_gap_top")
+                    if top_gap_val is not None:
+                        min_gap = float(top_gap_val)
+                    else:
+                        base_gap_val = self.config.get("risk.surprise_min_top_gap")
+                        min_gap = float(base_gap_val) if base_gap_val is not None else 0.02
                 else:
-                    min_gap = float(self.config.get("risk.surprise_min_top_gap_non_top", 0.03))
+                    non_top_gap_val = self.config.get("risk.surprise_min_top_gap_non_top")
+                    min_gap = float(non_top_gap_val) if non_top_gap_val is not None else 0.03
 
                 # Trigger surprise only when reversal class is:
                 # - top HT/FT outcome

ai-engine/core/calculators/score_calculator.py

@@ -3,7 +3,7 @@ import pickle
 import pandas as pd
 import xgboost as xgb
 from dataclasses import dataclass
-from typing import List, Dict, Tuple
+from typing import List, Dict, Tuple, Optional
 import math
 from .base_calculator import BaseCalculator, CalculationContext
 from .confidence import calc_confidence_3way, calc_confidence_dc
@@ -16,7 +16,7 @@ class ScorePrediction:
     ft_scores_top5: List[Dict]
     
     # Reconciled MS/DC predictions (can be updated here)
-    reconciled_ms: MatchResultPrediction = None
+    reconciled_ms: Optional[MatchResultPrediction] = None
 
 class ScoreCalculator(BaseCalculator):
 
@@ -57,7 +57,8 @@ class ScoreCalculator(BaseCalculator):
             return 1.0 if k == 0 else 0.0
         return (lam ** k) * math.exp(-lam) / math.factorial(k)
         
-    def calculate(self, ctx: CalculationContext, ms_result: MatchResultPrediction) -> ScorePrediction:
+    def calculate(self, ctx: CalculationContext, ms_result: MatchResultPrediction) -> ScorePrediction:  # type: ignore[override]
+        predicted_ht = None
         # Default Lambdas (fallback)
         lambda_home = max(0.5, ctx.home_xg)
         lambda_away = max(0.5, ctx.away_xg)
@@ -199,7 +200,7 @@ class ScoreCalculator(BaseCalculator):
             predicted_ft = top_overall_score
             
         # If we didn't calculate HT via ML (exception case), do it now
-        if 'predicted_ht' not in locals():
+        if predicted_ht is None:
             ft_to_ht = self.config.get("half_time.ft_to_ht_ratio", 0.42)
             ht_h = round(lambda_home * ft_to_ht)
             ht_a = round(lambda_away * ft_to_ht)

ai-engine/core/engines/__init__.py

@@ -1,16 +1,10 @@
 # ai-engine/core/engines/__init__.py
 """
-V20 Ensemble Prediction Engines
+Prediction Engines
 """
 
-from .team_predictor import TeamPredictorEngine, get_team_predictor
 from .player_predictor import PlayerPredictorEngine, get_player_predictor
-from .odds_predictor import OddsPredictorEngine, get_odds_predictor
-from .referee_predictor import RefereePredictorEngine, get_referee_predictor
 
 __all__ = [
-    "TeamPredictorEngine", "get_team_predictor",
     "PlayerPredictorEngine", "get_player_predictor",
-    "OddsPredictorEngine", "get_odds_predictor",
-    "RefereePredictorEngine", "get_referee_predictor"
 ]

ai-engine/core/engines/odds_predictor.py

@@ -1,237 +0,0 @@
-"""
-Odds Predictor Engine - V20 Ensemble Component
-Uses market odds and Poisson mathematics for predictions.
-
-Weight: 30% in ensemble
-"""
-
-import os
-import sys
-from typing import Dict, Optional
-from dataclasses import dataclass
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-from features.poisson_engine import get_poisson_engine
-from features.value_calculator import get_value_calculator
-
-
-@dataclass
-class OddsPrediction:
-    """Odds engine prediction output."""
-    # Market-implied probabilities
-    market_home_prob: float = 0.33
-    market_draw_prob: float = 0.33
-    market_away_prob: float = 0.33
-    
-    # Poisson xG
-    poisson_home_xg: float = 1.3
-    poisson_away_xg: float = 1.1
-    
-    # Over/Under probabilities
-    over_15_prob: float = 0.75
-    over_25_prob: float = 0.55
-    over_35_prob: float = 0.30
-    
-    # BTTS
-    btts_yes_prob: float = 0.50
-    
-    # Most likely scores
-    most_likely_score: str = "1-1"
-    second_likely_score: str = "1-0"
-    third_likely_score: str = "2-1"
-    
-    # Value bet opportunities
-    value_bets: list = None
-    
-    confidence: float = 0.0
-    
-    def __post_init__(self):
-        if self.value_bets is None:
-            self.value_bets = []
-    
-    def to_dict(self) -> dict:
-        return {
-            "market_home_prob": round(self.market_home_prob * 100, 1),
-            "market_draw_prob": round(self.market_draw_prob * 100, 1),
-            "market_away_prob": round(self.market_away_prob * 100, 1),
-            "poisson_home_xg": round(self.poisson_home_xg, 2),
-            "poisson_away_xg": round(self.poisson_away_xg, 2),
-            "over_15_prob": round(self.over_15_prob * 100, 1),
-            "over_25_prob": round(self.over_25_prob * 100, 1),
-            "over_35_prob": round(self.over_35_prob * 100, 1),
-            "btts_yes_prob": round(self.btts_yes_prob * 100, 1),
-            "most_likely_score": self.most_likely_score,
-            "second_likely_score": self.second_likely_score,
-            "third_likely_score": self.third_likely_score,
-            "value_bets": self.value_bets,
-            "confidence": round(self.confidence, 1)
-        }
-
-
-class OddsPredictorEngine:
-    """
-    Odds-based prediction engine.
-    
-    Uses:
-    - Market odds to extract implied probabilities
-    - Poisson distribution for mathematical xG
-    - Value calculator for EV+ opportunities
-    """
-    
-    def __init__(self):
-        self.poisson_engine = get_poisson_engine()
-        try:
-            self.value_calc = get_value_calculator()
-        except Exception:
-            self.value_calc = None
-        self.default_ms_h = 2.65
-        self.default_ms_d = 3.20
-        self.default_ms_a = 2.65
-        print("✅ OddsPredictorEngine initialized")
-    
-    def _odds_to_prob(self, odds: float) -> float:
-        """Convert decimal odds to probability."""
-        try:
-            odds = float(odds)
-        except (TypeError, ValueError):
-            return 0.0
-        if odds <= 1.0:
-            return 0.0
-        return 1.0 / odds
-    
-    def predict(self,
-                odds_data: Dict[str, float],
-                home_goals_avg: float = 1.5,
-                home_conceded_avg: float = 1.2,
-                away_goals_avg: float = 1.2,
-                away_conceded_avg: float = 1.4) -> OddsPrediction:
-        """
-        Generate odds-based prediction.
-        
-        Args:
-            odds_data: Dict with keys like 'ms_h', 'ms_d', 'ms_a', 'ou25_o', 'btts_y'
-            home_goals_avg: Home team's average goals scored
-            home_conceded_avg: Home team's average goals conceded
-            away_goals_avg: Away team's average goals scored
-            away_conceded_avg: Away team's average goals conceded
-            
-        Returns:
-            OddsPrediction with market and Poisson analysis
-        """
-        
-        # 1. Extract market probabilities from odds
-        ms_h = odds_data.get("ms_h", self.default_ms_h)
-        ms_d = odds_data.get("ms_d", self.default_ms_d)
-        ms_a = odds_data.get("ms_a", self.default_ms_a)
-        
-        # Remove vig to get fair probabilities
-        raw_probs = [
-            self._odds_to_prob(ms_h),
-            self._odds_to_prob(ms_d),
-            self._odds_to_prob(ms_a)
-        ]
-        total = sum(raw_probs) or 1
-        
-        market_home = raw_probs[0] / total
-        market_draw = raw_probs[1] / total
-        market_away = raw_probs[2] / total
-        
-        # 2. Poisson prediction
-        poisson_pred = self.poisson_engine.predict(
-            home_goals_avg, home_conceded_avg,
-            away_goals_avg, away_conceded_avg
-        )
-        
-        # 3. Get most likely scores
-        likely_scores = poisson_pred.most_likely_scores[:3] if poisson_pred.most_likely_scores else []
-        score_1 = likely_scores[0]["score"] if len(likely_scores) > 0 else "1-1"
-        score_2 = likely_scores[1]["score"] if len(likely_scores) > 1 else "1-0"
-        score_3 = likely_scores[2]["score"] if len(likely_scores) > 2 else "2-1"
-        
-        # 4. Value bet detection
-        value_bets = []
-        
-        # Check if our Poisson model disagrees with market significantly
-        if abs(poisson_pred.home_win_prob - market_home) > 0.10:
-            if poisson_pred.home_win_prob > market_home:
-                value_bets.append({
-                    "market": "MS 1",
-                    "edge": round((poisson_pred.home_win_prob - market_home) * 100, 1),
-                    "confidence": "medium"
-                })
-            else:
-                value_bets.append({
-                    "market": "MS 2",
-                    "edge": round((poisson_pred.away_win_prob - market_away) * 100, 1),
-                    "confidence": "medium"
-                })
-        
-        # O/U value check
-        ou25_o = odds_data.get("ou25_o", 1.9)
-        market_over25 = self._odds_to_prob(ou25_o)
-        if abs(poisson_pred.over_25_prob - market_over25) > 0.08:
-            pick = "2.5 Üst" if poisson_pred.over_25_prob > market_over25 else "2.5 Alt"
-            edge = abs(poisson_pred.over_25_prob - market_over25) * 100
-            value_bets.append({
-                "market": pick,
-                "edge": round(edge, 1),
-                "confidence": "high" if edge > 10 else "medium"
-            })
-        
-        # Calculate confidence
-        # Higher when market and Poisson agree
-        agreement = 1.0 - abs(poisson_pred.home_win_prob - market_home)
-        confidence = 50.0 + (agreement * 40) + (len(value_bets) * 5)
-        
-        return OddsPrediction(
-            market_home_prob=market_home,
-            market_draw_prob=market_draw,
-            market_away_prob=market_away,
-            poisson_home_xg=poisson_pred.home_xg,
-            poisson_away_xg=poisson_pred.away_xg,
-            over_15_prob=poisson_pred.over_15_prob,
-            over_25_prob=poisson_pred.over_25_prob,
-            over_35_prob=poisson_pred.over_35_prob,
-            btts_yes_prob=poisson_pred.btts_yes_prob,
-            most_likely_score=score_1,
-            second_likely_score=score_2,
-            third_likely_score=score_3,
-            value_bets=value_bets,
-            confidence=min(99.9, confidence)
-        )
-
-
-# Singleton
-_engine: Optional[OddsPredictorEngine] = None
-
-
-def get_odds_predictor() -> OddsPredictorEngine:
-    global _engine
-    if _engine is None:
-        _engine = OddsPredictorEngine()
-    return _engine
-
-
-if __name__ == "__main__":
-    engine = get_odds_predictor()
-    
-    print("\n🧪 Odds Predictor Engine Test")
-    print("=" * 50)
-    
-    pred = engine.predict(
-        odds_data={
-            "ms_h": 1.85,
-            "ms_d": 3.40,
-            "ms_a": 4.20,
-            "ou25_o": 1.90
-        },
-        home_goals_avg=1.8,
-        home_conceded_avg=1.0,
-        away_goals_avg=1.2,
-        away_conceded_avg=1.5
-    )
-    
-    print(f"\n📊 Prediction:")
-    for k, v in pred.to_dict().items():
-        print(f"   {k}: {v}")

ai-engine/core/engines/player_predictor.py

@@ -18,33 +18,35 @@ from features.sidelined_analyzer import get_sidelined_analyzer
 
 @dataclass
 class PlayerPrediction:
-    """Player engine prediction output."""
+    """Player engine prediction output.
-    home_squad_quality: float = 50.0  # 0-100
+
-    away_squad_quality: float = 50.0
+    IMPORTANT: squad_quality uses the SAME composite formula as
-    squad_diff: float = 0.0  # -100 to +100
+    extract_training_data.py so that inference values match the
+    distribution the model was trained on (~3-36 range).
+    """
+    home_squad_quality: float = 12.0
+    away_squad_quality: float = 12.0
+    squad_diff: float = 0.0
     home_key_players: int = 0
     away_key_players: int = 0
-    home_missing_impact: float = 0.0  # 0-1, how much weaker due to missing players
+    home_missing_impact: float = 0.0
     away_missing_impact: float = 0.0
-    home_goals_form: int = 0  # Goals in last 5 matches
+    home_goals_form: int = 0
     away_goals_form: int = 0
+    home_lineup_goals_per90: float = 0.0
+    away_lineup_goals_per90: float = 0.0
+    home_lineup_assists_per90: float = 0.0
+    away_lineup_assists_per90: float = 0.0
+    home_squad_continuity: float = 0.5
+    away_squad_continuity: float = 0.5
+    home_top_scorer_form: int = 0
+    away_top_scorer_form: int = 0
+    home_avg_player_exp: float = 0.0
+    away_avg_player_exp: float = 0.0
+    home_goals_diversity: float = 0.0
+    away_goals_diversity: float = 0.0
     lineup_available: bool = False
     confidence: float = 0.0
-    
-    def to_dict(self) -> dict:
-        return {
-            "home_squad_quality": round(self.home_squad_quality, 1),
-            "away_squad_quality": round(self.away_squad_quality, 1),
-            "squad_diff": round(self.squad_diff, 1),
-            "home_key_players": self.home_key_players,
-            "away_key_players": self.away_key_players,
-            "home_missing_impact": round(self.home_missing_impact, 2),
-            "away_missing_impact": round(self.away_missing_impact, 2),
-            "home_goals_form": self.home_goals_form,
-            "away_goals_form": self.away_goals_form,
-            "lineup_available": self.lineup_available,
-            "confidence": round(self.confidence, 1)
-        }
 
 
 class PlayerPredictorEngine:
@@ -67,9 +69,9 @@ class PlayerPredictorEngine:
                 match_id: str,
                 home_team_id: str,
                 away_team_id: str,
-                home_lineup: List[str] = None,
+                home_lineup: Optional[List[str]] = None,
-                away_lineup: List[str] = None,
+                away_lineup: Optional[List[str]] = None,
-                sidelined_data: Dict = None) -> PlayerPrediction:
+                sidelined_data: Optional[Dict] = None) -> PlayerPrediction:
         """
         Generate player-based prediction.
         
@@ -85,8 +87,9 @@ class PlayerPredictorEngine:
         """
         
         # Get squad features
+        home_analysis = None
+        away_analysis = None
         if home_lineup and away_lineup:
-            # Use provided lineups (for live matches)
             home_analysis = self.squad_engine.analyze_squad_from_list(
                 home_lineup, home_team_id
             )
@@ -94,19 +97,19 @@ class PlayerPredictorEngine:
                 away_lineup, away_team_id
             )
             lineup_available = True
-            # Build features dict from analysis objects
             features = {
                 "home_starting_11": home_analysis.starting_count or 11,
                 "home_goals_last_5": home_analysis.total_goals_last_5,
                 "home_assists_last_5": home_analysis.total_assists_last_5,
                 "home_key_players": home_analysis.key_players_count,
+                "home_forwards": home_analysis.forward_count or 2,
                 "away_starting_11": away_analysis.starting_count or 11,
                 "away_goals_last_5": away_analysis.total_goals_last_5,
                 "away_assists_last_5": away_analysis.total_assists_last_5,
                 "away_key_players": away_analysis.key_players_count,
+                "away_forwards": away_analysis.forward_count or 2,
             }
         elif match_id:
-            # Try to get from database
             try:
                 features = self.squad_engine.get_features(
                     match_id, home_team_id, away_team_id
@@ -125,40 +128,42 @@ class PlayerPredictorEngine:
                 home_team_id, away_team_id
             )
             lineup_available = False
-        
+
-        # Extract features
+        home_goals = int(features.get("home_goals_last_5", 0))
-        home_goals = features.get("home_goals_last_5", 0)
+        away_goals = int(features.get("away_goals_last_5", 0))
-        away_goals = features.get("away_goals_last_5", 0)
+        home_key = int(features.get("home_key_players", 0))
-        home_key = features.get("home_key_players", 0)
+        away_key = int(features.get("away_key_players", 0))
-        away_key = features.get("away_key_players", 0)
+        home_starting = features.get("home_starting_11", 11)
-        
+        away_starting = features.get("away_starting_11", 11)
-        # Calculate squad quality (0-100)
+        home_fwd = features.get("home_forwards", 2)
-        # Based on: goals scored, key players, assists
+        away_fwd = features.get("away_forwards", 2)
-        home_quality = min(100, 50 + (home_goals * 3) + (home_key * 5) + 
+
-                          features.get("home_assists_last_5", 0) * 2)
+        # Squad quality — matches V25 extract_training_data.py:579
-        away_quality = min(100, 50 + (away_goals * 3) + (away_key * 5) + 
+        home_quality = home_starting * 0.3 + home_key * 3.0 + home_fwd * 1.5
-                          features.get("away_assists_last_5", 0) * 2)
+        away_quality = away_starting * 0.3 + away_key * 3.0 + away_fwd * 1.5
-        
-        # Squad difference
         squad_diff = home_quality - away_quality
-        
+
         # Missing player impact
-        # Priority: sidelined data (position-weighted) > lineup count (basic)
         if sidelined_data:
             home_impact, away_impact = self.sidelined_analyzer.analyze_match(sidelined_data)
-            home_missing = home_impact.impact_score
+            home_missing = min(1.0, max(0.0, home_impact.impact_score))
-            away_missing = away_impact.impact_score
+            away_missing = min(1.0, max(0.0, away_impact.impact_score))
             sidelined_available = True
         else:
-            # Fallback: basic lineup count method
             expected_xi = 11
             actual_home_xi = features.get("home_starting_11", 11)
             actual_away_xi = features.get("away_starting_11", 11)
             home_missing = (expected_xi - actual_home_xi) / expected_xi if actual_home_xi < expected_xi else 0
             away_missing = (expected_xi - actual_away_xi) / expected_xi if actual_away_xi < expected_xi else 0
             sidelined_available = False
-        
+
-        # Confidence: more data sources = higher confidence
+        # Player-level features (matches extract_training_data.py:594-650)
+        player_feats = self._compute_player_level_features(
+            home_lineup or [], away_lineup or [],
+            home_team_id, away_team_id,
+            home_analysis, away_analysis,
+        )
+
         confidence = 70.0 if lineup_available else 35.0
         if home_goals + away_goals > 10:
             confidence += 15
@@ -166,7 +171,7 @@ class PlayerPredictorEngine:
             confidence += self.sidelined_analyzer.config.get("sidelined.confidence_boost", 10)
         if not lineup_available:
             confidence -= 5.0
-        
+
         return PlayerPrediction(
             home_squad_quality=home_quality,
             away_squad_quality=away_quality,
@@ -177,17 +182,147 @@ class PlayerPredictorEngine:
             away_missing_impact=away_missing,
             home_goals_form=home_goals,
             away_goals_form=away_goals,
+            home_lineup_goals_per90=player_feats['home_lineup_goals_per90'],
+            away_lineup_goals_per90=player_feats['away_lineup_goals_per90'],
+            home_lineup_assists_per90=player_feats['home_lineup_assists_per90'],
+            away_lineup_assists_per90=player_feats['away_lineup_assists_per90'],
+            home_squad_continuity=player_feats['home_squad_continuity'],
+            away_squad_continuity=player_feats['away_squad_continuity'],
+            home_top_scorer_form=player_feats['home_top_scorer_form'],
+            away_top_scorer_form=player_feats['away_top_scorer_form'],
+            home_avg_player_exp=player_feats['home_avg_player_exp'],
+            away_avg_player_exp=player_feats['away_avg_player_exp'],
+            home_goals_diversity=player_feats['home_goals_diversity'],
+            away_goals_diversity=player_feats['away_goals_diversity'],
             lineup_available=lineup_available,
             confidence=max(5.0, confidence)
         )
+
+    def _compute_player_level_features(
+        self,
+        home_lineup: List[str],
+        away_lineup: List[str],
+        home_team_id: str,
+        away_team_id: str,
+        home_analysis,
+        away_analysis,
+    ) -> Dict[str, float]:
+        defaults = {
+            'home_lineup_goals_per90': 0.0, 'away_lineup_goals_per90': 0.0,
+            'home_lineup_assists_per90': 0.0, 'away_lineup_assists_per90': 0.0,
+            'home_squad_continuity': 0.5, 'away_squad_continuity': 0.5,
+            'home_top_scorer_form': 0, 'away_top_scorer_form': 0,
+            'home_avg_player_exp': 0.0, 'away_avg_player_exp': 0.0,
+            'home_goals_diversity': 0.0, 'away_goals_diversity': 0.0,
+        }
+        conn = self.squad_engine.get_conn()
+        if conn is None:
+            return defaults
+
+        try:
+            from psycopg2.extras import RealDictCursor
+            result = {}
+            for prefix, lineup, team_id in [
+                ('home', home_lineup, home_team_id),
+                ('away', away_lineup, away_team_id),
+            ]:
+                if not lineup:
+                    for k in ('lineup_goals_per90', 'lineup_assists_per90',
+                              'squad_continuity', 'top_scorer_form',
+                              'avg_player_exp', 'goals_diversity'):
+                        result[f'{prefix}_{k}'] = defaults[f'{prefix}_{k}']
+                    continue
+
+                g90, a90, total_exp = 0.0, 0.0, 0
+                best_scorer_total, best_scorer_id = 0, None
+                scorers_in_lineup = 0
+
+                with conn.cursor(cursor_factory=RealDictCursor) as cur:
+                    for pid in lineup:
+                        cur.execute("""
+                            SELECT
+                                COUNT(*) as starts,
+                                COALESCE(SUM(CASE WHEN e.event_type = 'goal'
+                                    AND (e.event_subtype IS NULL OR e.event_subtype NOT ILIKE '%%penaltı kaçırma%%')
+                                    THEN 1 ELSE 0 END), 0) as goals,
+                                COALESCE((SELECT COUNT(*) FROM match_player_events
+                                    WHERE assist_player_id = %s), 0) as assists
+                            FROM match_player_participation mpp
+                            LEFT JOIN match_player_events e
+                                ON e.match_id = mpp.match_id AND e.player_id = mpp.player_id
+                            WHERE mpp.player_id = %s AND mpp.is_starting = true
+                        """, (pid, pid))
+                        row = cur.fetchone()
+                        if not row or not row['starts']:
+                            continue
+                        starts = row['starts']
+                        goals = row['goals'] or 0
+                        assists = row['assists'] or 0
+                        g90 += goals / starts
+                        a90 += assists / starts
+                        total_exp += starts
+                        if goals > 0:
+                            scorers_in_lineup += 1
+                        if goals > best_scorer_total:
+                            best_scorer_total = goals
+                            best_scorer_id = pid
+
+                    n_st = len(lineup) or 1
+
+                    # Top scorer recent form (goals in last 5 starts)
+                    top_scorer_form = 0
+                    if best_scorer_id:
+                        cur.execute("""
+                            SELECT COUNT(*) as goals
+                            FROM match_player_events mpe
+                            WHERE mpe.player_id = %s AND mpe.event_type = 'goal'
+                              AND mpe.match_id IN (
+                                  SELECT match_id FROM match_player_participation
+                                  WHERE player_id = %s AND is_starting = true
+                                  ORDER BY match_id DESC LIMIT 5
+                              )
+                        """, (best_scorer_id, best_scorer_id))
+                        tsf_row = cur.fetchone()
+                        if tsf_row:
+                            top_scorer_form = tsf_row['goals'] or 0
+
+                    # Squad continuity (overlap with previous match lineup)
+                    squad_continuity = 0.5
+                    cur.execute("""
+                        SELECT mpp.player_id
+                        FROM match_player_participation mpp
+                        JOIN matches m ON mpp.match_id = m.id
+                        WHERE mpp.team_id = %s AND mpp.is_starting = true
+                          AND m.status = 'FT'
+                        ORDER BY m.mst_utc DESC
+                        LIMIT 11
+                    """, (team_id,))
+                    prev_starters = {r['player_id'] for r in cur.fetchall()}
+                    if prev_starters:
+                        overlap = len(set(lineup) & prev_starters)
+                        squad_continuity = overlap / n_st
+
+                result[f'{prefix}_lineup_goals_per90'] = round(g90, 3)
+                result[f'{prefix}_lineup_assists_per90'] = round(a90, 3)
+                result[f'{prefix}_squad_continuity'] = round(squad_continuity, 3)
+                result[f'{prefix}_top_scorer_form'] = top_scorer_form
+                result[f'{prefix}_avg_player_exp'] = round(total_exp / n_st, 1)
+                result[f'{prefix}_goals_diversity'] = round(scorers_in_lineup / n_st, 3)
+
+            return result
+        except Exception as e:
+            print(f"[PlayerPredictor] Player-level features failed: {e}")
+            return defaults
     
     def get_1x2_modifier(self, prediction: PlayerPrediction) -> Dict[str, float]:
         """
         Calculate 1X2 probability modifiers based on squad analysis.
         
         Returns modifiers to apply to base probabilities.
+        squad_diff is in training scale (~-33 to +33), normalize to -1..+1.
         """
-        diff = prediction.squad_diff / 100  # -1 to +1
+        diff = prediction.squad_diff / 33.0  # training-scale normalisation
+        diff = max(-1.0, min(1.0, diff))      # clamp
         
         return {
             "home_modifier": 1.0 + (diff * 0.3),  # Up to +/-30%
@@ -214,7 +349,7 @@ if __name__ == "__main__":
     print("=" * 50)
     
     pred = engine.predict(
-        match_id=None,
+        match_id="test_match",
         home_team_id="test_home",
         away_team_id="test_away"
     )

ai-engine/core/engines/referee_predictor.py

@@ -1,188 +0,0 @@
-"""
-Referee Predictor Engine - V20 Ensemble Component
-Analyzes referee patterns for cards, goals, and home bias.
-
-Weight: 15% in ensemble
-"""
-
-import os
-import sys
-from typing import Dict, Optional
-from dataclasses import dataclass
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-from features.referee_engine import get_referee_engine
-
-
-@dataclass
-class RefereePrediction:
-    """Referee engine prediction output."""
-    referee_name: str = ""
-    matches_officiated: int = 0
-    
-    # Card tendencies
-    avg_yellow_cards: float = 4.0
-    avg_red_cards: float = 0.2
-    is_card_heavy: bool = False  # Above average cards
-    
-    # Goal tendencies
-    avg_goals_per_match: float = 2.5
-    over_25_rate: float = 0.50
-    is_high_scoring: bool = False  # Above average goals
-    
-    # Home bias
-    home_win_rate: float = 0.45
-    home_bias: float = 0.0  # -1 to +1, positive = favors home
-    
-    # Penalty tendency
-    penalty_rate: float = 0.15
-    
-    confidence: float = 0.0
-    
-    def to_dict(self) -> dict:
-        return {
-            "referee_name": self.referee_name,
-            "matches_officiated": self.matches_officiated,
-            "avg_yellow_cards": round(self.avg_yellow_cards, 1),
-            "avg_red_cards": round(self.avg_red_cards, 2),
-            "is_card_heavy": self.is_card_heavy,
-            "avg_goals_per_match": round(self.avg_goals_per_match, 2),
-            "over_25_rate": round(self.over_25_rate * 100, 1),
-            "is_high_scoring": self.is_high_scoring,
-            "home_win_rate": round(self.home_win_rate * 100, 1),
-            "home_bias": round(self.home_bias, 2),
-            "penalty_rate": round(self.penalty_rate * 100, 1),
-            "confidence": round(self.confidence, 1)
-        }
-
-
-class RefereePredictorEngine:
-    """
-    Referee-based prediction engine.
-    
-    Analyzes:
-    - Card tendency (sarı/kırmızı kart ortalaması)
-    - Goal tendency (maç başına gol, 2.5 üst oranı)
-    - Home bias (ev sahibi lehine karar oranı)
-    - Penalty tendency (penaltı verme oranı)
-    """
-    
-    # League average benchmarks
-    LEAGUE_AVG_GOALS = 2.65
-    LEAGUE_AVG_YELLOW = 4.0
-    LEAGUE_HOME_WIN_RATE = 0.45
-    
-    def __init__(self):
-        self.referee_engine = get_referee_engine()
-        print("✅ RefereePredictorEngine initialized")
-    
-    def predict(self,
-                match_id: str = None,
-                referee_name: str = None,
-                league_id: str = None) -> RefereePrediction:
-        """
-        Generate referee-based prediction.
-        
-        Args:
-            match_id: Match ID to find referee
-            referee_name: Or provide referee name directly
-            league_id: League ID to scope stats (prevents name collisions)
-            
-        Returns:
-            RefereePrediction with referee analysis
-        """
-        
-        # Get referee features
-        if match_id:
-            features = self.referee_engine.get_features(match_id, league_id=league_id)
-            # Live flows may already have referee_name while match_officials table is sparse.
-            # Prefer the richer profile if direct-name lookup has more history.
-            if referee_name:
-                name_features = self.referee_engine.get_features_by_name(referee_name, league_id=league_id)
-                if (name_features.get("referee_matches", 0) or 0) > (features.get("referee_matches", 0) or 0):
-                    features = name_features
-        elif referee_name:
-            features = self.referee_engine.get_features_by_name(referee_name, league_id=league_id)
-        else:
-            # Return default
-            return RefereePrediction(confidence=10.0)
-        
-        ref_name = features.get("referee_name", "Unknown")
-        matches = features.get("referee_matches", 0)
-        
-        if matches < 5:
-            # Not enough data
-            return RefereePrediction(
-                referee_name=ref_name,
-                matches_officiated=matches,
-                confidence=20.0
-            )
-        
-        # Extract features
-        avg_yellow = features.get("referee_avg_yellow", 4.0)
-        avg_red = features.get("referee_avg_red", 0.2)
-        avg_goals = features.get("referee_avg_goals", 2.5)
-        over25_rate = features.get("referee_over25_rate", 0.5)
-        home_win_rate = features.get("referee_home_win_rate", 0.45) if "referee_home_win_rate" in features else 0.45
-        home_bias = features.get("referee_home_bias", 0.0)
-        penalty_rate = features.get("referee_penalty_rate", 0.15)
-        
-        # Determine tendencies
-        is_card_heavy = (avg_yellow + avg_red * 4) > (self.LEAGUE_AVG_YELLOW + 1)
-        is_high_scoring = avg_goals > self.LEAGUE_AVG_GOALS
-        
-        # Confidence based on matches officiated
-        confidence = min(90.0, 30.0 + matches * 2)
-        
-        return RefereePrediction(
-            referee_name=ref_name,
-            matches_officiated=matches,
-            avg_yellow_cards=avg_yellow,
-            avg_red_cards=avg_red,
-            is_card_heavy=is_card_heavy,
-            avg_goals_per_match=avg_goals,
-            over_25_rate=over25_rate,
-            is_high_scoring=is_high_scoring,
-            home_win_rate=home_win_rate,
-            home_bias=home_bias,
-            penalty_rate=penalty_rate,
-            confidence=confidence
-        )
-    
-    def get_modifiers(self, prediction: RefereePrediction) -> Dict[str, float]:
-        """
-        Get modifiers to apply to other predictions based on referee profile.
-        """
-        return {
-            # Home team gets slight boost if referee has home bias
-            "home_modifier": 1.0 + (prediction.home_bias * 0.05),
-            # O/U modifier
-            "over_25_modifier": 1.0 + (prediction.avg_goals_per_match - self.LEAGUE_AVG_GOALS) * 0.1,
-            # Card modifier for card markets
-            "cards_modifier": 1.0 + (prediction.avg_yellow_cards - self.LEAGUE_AVG_YELLOW) * 0.05
-        }
-
-
-# Singleton
-_engine: Optional[RefereePredictorEngine] = None
-
-
-def get_referee_predictor() -> RefereePredictorEngine:
-    global _engine
-    if _engine is None:
-        _engine = RefereePredictorEngine()
-    return _engine
-
-
-if __name__ == "__main__":
-    engine = get_referee_predictor()
-    
-    print("\n🧪 Referee Predictor Engine Test")
-    print("=" * 50)
-    
-    pred = engine.predict(referee_name="Cüneyt Çakır")
-    
-    print(f"\n📊 Prediction:")
-    for k, v in pred.to_dict().items():
-        print(f"   {k}: {v}")

ai-engine/core/engines/team_predictor.py

@@ -1,286 +0,0 @@
-"""
-Team Predictor Engine - V20 Ensemble Component
-Combines ELO ratings, form stats, H2H records and team statistics.
-
-Weight: 30% in ensemble
-"""
-
-import os
-import sys
-from typing import Dict, Optional, Tuple, Any
-from dataclasses import dataclass, field
-
-# Add parent to path
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-from features.elo_system import get_elo_system
-from features.h2h_engine import get_h2h_engine
-from features.momentum_engine import get_momentum_engine, MomentumData
-from features.team_stats_engine import get_team_stats_engine
-
-
-@dataclass
-class TeamPrediction:
-    """Team engine prediction output."""
-    home_win_prob: float = 0.33
-    draw_prob: float = 0.33
-    away_win_prob: float = 0.33
-    home_xg: float = 1.3
-    away_xg: float = 1.1
-    form_advantage: float = 0.0  # -1 to +1, positive = home advantage
-    h2h_advantage: float = 0.0   # -1 to +1
-    elo_diff: float = 0.0
-    confidence: float = 0.0
-    
-    def to_dict(self) -> dict:
-        return {
-            "home_win_prob": round(self.home_win_prob * 100, 1),
-            "draw_prob": round(self.draw_prob * 100, 1),
-            "away_win_prob": round(self.away_win_prob * 100, 1),
-            "home_xg": round(self.home_xg, 2),
-            "away_xg": round(self.away_xg, 2),
-            "form_advantage": round(self.form_advantage, 2),
-            "h2h_advantage": round(self.h2h_advantage, 2),
-            "elo_diff": round(self.elo_diff, 0),
-            "confidence": round(self.confidence, 1)
-        }
-    
-    raw_features: Dict[str, Any] = field(default_factory=dict)
-
-
-class TeamPredictorEngine:
-    """
-    Team-based prediction engine.
-    
-    Uses:
-    - ELO Rating System (venue-adjusted, league-weighted)
-    - H2H Engine (head-to-head history)
-    - Momentum Engine (recent form)
-    - Team Stats Engine (possession, shots, corners)
-    """
-    
-    def __init__(self):
-        self.elo_system = get_elo_system()
-        self.h2h_engine = get_h2h_engine()
-        self.momentum_engine = get_momentum_engine()
-        self.team_stats_engine = get_team_stats_engine()
-        
-        print("✅ TeamPredictorEngine initialized")
-    
-    def predict(self, 
-                home_team_id: str, 
-                away_team_id: str,
-                match_date_ms: int,
-                home_team_name: str = "",
-                away_team_name: str = "") -> TeamPrediction:
-        """
-        Generate team-based prediction.
-        
-        Args:
-            home_team_id: Home team ID
-            away_team_id: Away team ID  
-            match_date_ms: Match date in milliseconds
-            home_team_name: Home team name (for ELO)
-            away_team_name: Away team name (for ELO)
-            
-        Returns:
-            TeamPrediction with 1X2 probabilities and xG
-        """
-        
-        # 1. Get ELO predictions
-        elo_pred = self.elo_system.predict_match(home_team_id, away_team_id)
-        elo_features = self.elo_system.get_match_features(home_team_id, away_team_id)
-        
-        # 2. Get H2H features
-        try:
-            h2h_features = self.h2h_engine.get_features(
-                home_team_id, away_team_id, match_date_ms
-            )
-        except Exception:
-            h2h_features = {
-                "h2h_home_win_rate": 0.5,
-                "h2h_away_win_rate": 0.5,
-                "h2h_avg_goals": 2.5,
-                "h2h_btts_rate": 0.5
-            }
-        
-        # 3. Get Momentum/Form features
-        try:
-            # key: form_score should be 0-1 derived from momentum_score (-1 to 1)
-            home_mom_data = self.momentum_engine.calculate_momentum(home_team_id, match_date_ms)
-            away_mom_data = self.momentum_engine.calculate_momentum(away_team_id, match_date_ms)
-            
-            home_form_score = (home_mom_data.momentum_score + 1) / 2
-            away_form_score = (away_mom_data.momentum_score + 1) / 2
-        except Exception as e:
-            print(f"⚠️ MomentumEngine error: {e}")
-            home_mom_data = MomentumData()
-            away_mom_data = MomentumData()
-            home_form_score = 0.5
-            away_form_score = 0.5
-        
-        # 4. Get Team Stats
-        home_stats = self.team_stats_engine.get_features(home_team_id, match_date_ms)
-        away_stats = self.team_stats_engine.get_features(away_team_id, match_date_ms)
-        
-        # 5. Combine predictions
-        # ELO-based 1X2 (60% weight)
-        elo_home = elo_pred.get("home_win_prob", 0.33)
-        elo_draw = elo_pred.get("draw_prob", 0.33)
-        elo_away = elo_pred.get("away_win_prob", 0.33)
-        
-        # Adjust based on H2H (20% weight)
-        h2h_home_rate = h2h_features.get("h2h_home_win_rate", 0.5)
-        h2h_away_rate = h2h_features.get("h2h_away_win_rate", 0.5)
-        
-        # Adjust based on form (20% weight)
-        home_form = home_form_score
-        away_form = away_form_score
-        form_diff = (home_form - away_form)  # -1 to +1
-        
-        # Weighted combination
-        final_home = elo_home * 0.6 + h2h_home_rate * 0.2 + (0.5 + form_diff * 0.3) * 0.2
-        final_away = elo_away * 0.6 + h2h_away_rate * 0.2 + (0.5 - form_diff * 0.3) * 0.2
-        final_draw = 1.0 - final_home - final_away
-        
-        # Normalize
-        total = final_home + final_draw + final_away
-        if total > 0:
-            final_home /= total
-            final_draw /= total
-            final_away /= total
-        
-        # Calculate xG based on stats and form (conservative base)
-        home_conversion = home_stats.get("shot_conversion_rate", 0.1)
-        away_conversion = away_stats.get("shot_conversion_rate", 0.1)
-        
-        base_home_xg = 1.35 + (home_conversion * 3.0)
-        base_away_xg = 1.10 + (away_conversion * 2.5)
-        
-        # Defense weakness factor: opponent's defensive quality affects xG
-        # Higher shots on target against = weaker defense
-        away_def_weakness = away_stats.get("shot_accuracy", 0.35)  # opponent's shot accuracy as proxy
-        home_def_weakness = home_stats.get("shot_accuracy", 0.35)
-        
-        # Adjust xG: stronger opponent defense → lower xG
-        home_xg = base_home_xg * (1 + form_diff * 0.15) * (0.8 + away_def_weakness * 0.6)
-        away_xg = base_away_xg * (1 - form_diff * 0.15) * (0.8 + home_def_weakness * 0.6)
-        
-        # Apply xG Underperformance Penalty directly to calculated xG
-        # If a team chronically underperforms its xG, we subtract that historical difference here
-        if hasattr(home_mom_data, 'xg_underperformance') and home_mom_data.xg_underperformance > 0.2:
-            home_xg -= min(0.5, home_mom_data.xg_underperformance * 0.5)
-            
-        if hasattr(away_mom_data, 'xg_underperformance') and away_mom_data.xg_underperformance > 0.2:
-            away_xg -= min(0.5, away_mom_data.xg_underperformance * 0.5)
-        
-        # H2H adjustment (more conservative)
-        h2h_avg_goals = h2h_features.get("h2h_avg_goals", 2.5)
-        if h2h_avg_goals > 3.0:
-            home_xg *= 1.05
-            away_xg *= 1.05
-        elif h2h_avg_goals < 2.0:
-            home_xg *= 0.95
-            away_xg *= 0.95
-        
-        # Clamp xG to reasonable range
-        home_xg = max(0.5, min(3.5, home_xg))
-        away_xg = max(0.3, min(3.0, away_xg))
-        
-        # Calculate confidence
-        # Higher when ELO, H2H, and Form all agree
-        elo_winner = "H" if elo_home > max(elo_draw, elo_away) else ("A" if elo_away > elo_draw else "D")
-        h2h_winner = "H" if h2h_home_rate > h2h_away_rate else "A"
-        form_winner = "H" if form_diff > 0.1 else ("A" if form_diff < -0.1 else "D")
-        
-        agreement = sum([
-            elo_winner == h2h_winner,
-            elo_winner == form_winner,
-            h2h_winner == form_winner
-        ])
-        
-        max_prob = max(final_home, final_draw, final_away)
-        confidence = max_prob * 100 * (0.7 + agreement * 0.1)
-        
-        # Collect Raw Features for XGBoost
-        # Note: home_mom_data is an object now
-        def get_rate(val): return val if val is not None else 0.5
-        
-        raw_features = {
-            **elo_features,  # 8 features
-            
-            # Form Features (need key mapping to match extract_training_data.py)
-            "home_goals_avg": 1.5 + home_mom_data.goals_trend, # Proxy
-            "home_conceded_avg": 1.5 - home_mom_data.conceded_trend, # Proxy
-            "away_goals_avg": 1.5 + away_mom_data.goals_trend,
-            "away_conceded_avg": 1.5 - away_mom_data.conceded_trend,
-            
-            "home_clean_sheet_rate": 0.2, # Not in new MomentumData
-            "away_clean_sheet_rate": 0.2,
-            "home_scoring_rate": 0.8,
-            "away_scoring_rate": 0.8,
-            
-            "home_winning_streak": home_mom_data.winning_streak,
-            "away_winning_streak": away_mom_data.winning_streak,
-            "home_unbeaten_streak": home_mom_data.unbeaten_streak,
-            "away_unbeaten_streak": away_mom_data.unbeaten_streak,
-            
-            # H2H Features
-            **h2h_features,
-            
-            # Team Stats
-            "home_avg_possession": home_stats.get("avg_possession", 0.5),
-            "away_avg_possession": away_stats.get("avg_possession", 0.5),
-            "home_avg_shots_on_target": home_stats.get("avg_shots_on_target", 3.5),
-            "away_avg_shots_on_target": away_stats.get("avg_shots_on_target", 3.5),
-            "home_shot_conversion": home_stats.get("shot_conversion_rate", 0.1),
-            "away_shot_conversion": away_stats.get("shot_conversion_rate", 0.1),
-            "home_avg_corners": home_stats.get("avg_corners", 4.5),
-            "away_avg_corners": away_stats.get("avg_corners", 4.5),
-            
-            # Derived
-            "home_xga": 1.5 - home_mom_data.conceded_trend, # reusing as proxy
-            "away_xga": 1.5 - away_mom_data.conceded_trend
-        }
-        
-        return TeamPrediction(
-            home_win_prob=final_home,
-            draw_prob=final_draw,
-            away_win_prob=final_away,
-            home_xg=home_xg,
-            away_xg=away_xg,
-            form_advantage=form_diff,
-            h2h_advantage=h2h_home_rate - h2h_away_rate,
-            elo_diff=elo_features.get("elo_diff", 0),
-            confidence=confidence,
-            raw_features=raw_features
-        )
-
-
-# Singleton
-_engine: Optional[TeamPredictorEngine] = None
-
-
-def get_team_predictor() -> TeamPredictorEngine:
-    global _engine
-    if _engine is None:
-        _engine = TeamPredictorEngine()
-    return _engine
-
-
-if __name__ == "__main__":
-    engine = get_team_predictor()
-    
-    print("\n🧪 Team Predictor Engine Test")
-    print("=" * 50)
-    
-    # Test with sample IDs
-    pred = engine.predict(
-        home_team_id="test_home",
-        away_team_id="test_away",
-        match_date_ms=1707393600000
-    )
-    
-    print(f"\n📊 Prediction:")
-    for k, v in pred.to_dict().items():
-        print(f"   {k}: {v}")

ai-engine/data/__init__.py

@@ -0,0 +1 @@
+# data package

ai-engine/data/database.py

@@ -0,0 +1,97 @@
+"""
+Async Database Module — V2 Betting Engine
+==========================================
+Provides async SQLAlchemy sessions via asyncpg for the V2 router.
+
+Usage:
+    async with get_session() as session:
+        result = await session.execute(text("SELECT ..."))
+"""
+
+from __future__ import annotations
+
+import os
+from contextlib import asynccontextmanager
+from typing import AsyncGenerator
+
+from dotenv import load_dotenv
+from sqlalchemy.ext.asyncio import (
+    AsyncEngine,
+    AsyncSession,
+    async_sessionmaker,
+    create_async_engine,
+)
+
+load_dotenv()
+
+_engine: AsyncEngine | None = None
+_session_maker: async_sessionmaker[AsyncSession] | None = None
+
+
+def _get_async_dsn() -> str:
+    """
+    Convert DATABASE_URL to asyncpg-compatible format.
+
+    Handles:
+      1. Prisma's ``?schema=public`` suffix  → stripped
+      2. ``postgresql://`` driver prefix     → ``postgresql+asyncpg://``
+    """
+    dsn = os.getenv(
+        "DATABASE_URL",
+        "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db",
+    )
+
+    # Strip Prisma's ?schema= parameter
+    if "?" in dsn:
+        base, query = dsn.split("?", 1)
+        kept_parts = [
+            part for part in query.split("&") if part and not part.startswith("schema=")
+        ]
+        dsn = base if not kept_parts else f"{base}?{'&'.join(kept_parts)}"
+
+    # Convert driver prefix for asyncpg
+    if dsn.startswith("postgresql://"):
+        dsn = dsn.replace("postgresql://", "postgresql+asyncpg://", 1)
+    elif dsn.startswith("postgres://"):
+        dsn = dsn.replace("postgres://", "postgresql+asyncpg://", 1)
+
+    return dsn
+
+
+def _ensure_engine() -> AsyncEngine:
+    global _engine, _session_maker
+    if _engine is None:
+        _engine = create_async_engine(
+            _get_async_dsn(),
+            pool_size=5,
+            max_overflow=5,
+            pool_timeout=10,
+            pool_pre_ping=True,
+            echo=False,
+        )
+        _session_maker = async_sessionmaker(
+            bind=_engine,
+            class_=AsyncSession,
+            expire_on_commit=False,
+        )
+        print("✅ Async database engine created (asyncpg)")
+    return _engine
+
+
+@asynccontextmanager
+async def get_session() -> AsyncGenerator[AsyncSession, None]:
+    """Provide an async session context manager."""
+    _ensure_engine()
+    assert _session_maker is not None
+    async with _session_maker() as session:
+        yield session
+
+
+async def dispose_engine() -> None:
+    """Shut down the async engine cleanly."""
+    global _engine, _session_maker
+    if _engine is not None:
+        await _engine.dispose()
+        _engine = None
+        _session_maker = None
+        print("ℹ️  Async database engine disposed")

ai-engine/data/db.py

@@ -0,0 +1,92 @@
+"""
+Synchronous psycopg2 database helper for the AI Engine.
+Uses a thread-safe connection pool for legacy V20+ endpoints.
+"""
+
+from __future__ import annotations
+
+import os
+from contextlib import contextmanager
+from typing import Generator
+
+import psycopg2
+from psycopg2 import pool
+from psycopg2.extensions import connection as PgConnection
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Safe default with no credentials — will fail fast if not configured.
+_DEFAULT_DSN = "postgresql://postgres:postgres@localhost:15432/boilerplate_db"
+
+
+def get_clean_dsn() -> str:
+    """
+    Return a psycopg2-compatible DSN from DATABASE_URL.
+
+    Handles DSN cleanup issues that break raw usage:
+      1. Prisma appends '?schema=public' which psycopg2 cannot parse.
+    """
+    dsn: str = os.getenv("DATABASE_URL", _DEFAULT_DSN)
+    connect_timeout: str = os.getenv("PGCONNECT_TIMEOUT", "5").strip() or "5"
+
+    # Strip Prisma's ?schema= query parameter while preserving any other query args.
+    if "?" in dsn:
+        base, query = dsn.split("?", 1)
+        kept_parts: list[str] = [
+            part for part in query.split("&") if part and not part.startswith("schema=")
+        ]
+        dsn = base if not kept_parts else f"{base}?{'&'.join(kept_parts)}"
+
+    # Force bounded DB connect attempts so API calls do not hang indefinitely.
+    if "connect_timeout=" not in dsn:
+        separator = "&" if "?" in dsn else "?"
+        dsn = f"{dsn}{separator}connect_timeout={connect_timeout}"
+    return dsn
+
+
+class Database:
+    _pool: pool.ThreadedConnectionPool | None = None
+
+    @classmethod
+    def initialize(cls) -> None:
+        if cls._pool is None:
+            dsn: str = get_clean_dsn()
+            try:
+                cls._pool = pool.ThreadedConnectionPool(
+                    minconn=1,
+                    maxconn=10,
+                    dsn=dsn,
+                )
+                print("✅ Database connection pool created")
+            except Exception as e:
+                print(f"❌ Failed to create DB pool: {e}")
+                raise
+
+    @classmethod
+    def get_conn(cls) -> PgConnection:
+        if cls._pool is None:
+            cls.initialize()
+        assert cls._pool is not None  # guaranteed by initialize()
+        return cls._pool.getconn()
+
+    @classmethod
+    def return_conn(cls, conn: PgConnection) -> None:
+        if cls._pool:
+            cls._pool.putconn(conn)
+
+    @classmethod
+    @contextmanager
+    def connection(cls) -> Generator[PgConnection, None, None]:
+        """Context manager for safe connection handling."""
+        conn: PgConnection = cls.get_conn()
+        try:
+            yield conn
+        finally:
+            cls.return_conn(conn)
+
+    @classmethod
+    def close_all(cls) -> None:
+        if cls._pool:
+            cls._pool.closeall()
+            print("ℹ️  Database connection pool closed")

ai-engine/data/league_reliability.json

@@ -0,0 +1,726 @@
+{
+  "version": "v1",
+  "description": "Per-league odds reliability scores computed from Brier Score analysis",
+  "min_matches_threshold": 50,
+  "total_leagues": 265,
+  "default_reliability": 0.35,
+  "lookup": {
+    "bx57cmq1edfq53ckfk791supi": 0.9476,
+    "55hcphd1ccc6eai1ms77460on": 0.9445,
+    "d9eaigzyfnfiraqc3ius757tl": 0.9402,
+    "1gxlzw2ezkyeykhcaa5x8ozkk": 0.9259,
+    "5jd0k2txwnq69frs79eulba8j": 0.9233,
+    "6694fff47wqxl10lrd9tb91f8": 0.9193,
+    "4jg7he1n3rb5dniq6hf49xorq": 0.9061,
+    "59tpnfrwnvhnhzmnvfyug68hj": 0.8988,
+    "ac42gi3penartj88fe9l6plpk": 0.8937,
+    "3j81qr7yc4gdnakfwnxf95ovh": 0.8771,
+    "9z5643nd06afqu01ea2wt8y4g": 0.8734,
+    "482ofyysbdbeoxauk19yg7tdt": 0.8722,
+    "ahl3vljaignq9ebaos4uqkrvo": 0.8696,
+    "8x3sbh85gc8qir50utw39jl04": 0.865,
+    "agpweohvn9tugnyl6ry4rhivp": 0.8428,
+    "4c1nfi2j1m731hcay25fcgndq": 0.8425,
+    "1j4ehtrbry9depwt6oghaq3lu": 0.8299,
+    "40yjcbx2sq6oq736iqqqczwt1": 0.8237,
+    "145hkd59i6foieuwr4mwi6wlq": 0.823,
+    "34pl8szyvrbwcmfkuocjm3r6t": 0.8227,
+    "cse5oqqt2pzfcy8uz6yz3tkbj": 0.8212,
+    "zs18qaehvhg3w1208874zvfa": 0.8176,
+    "57nu0wygurzkp6fuy5hhrtaa2": 0.8099,
+    "1eruend45vd20g9hbrpiggs5u": 0.8083,
+    "595nsvo7ykvoe690b1e4u5n56": 0.7987,
+    "6vq8j5p3av14nr3iuyi4okhjt": 0.793,
+    "486rhdgz7yc0sygziht7hje65": 0.7901,
+    "9hh6n2f84k31zmlcxyvmc1w2y": 0.789,
+    "3n5046abeu3x482ds3jwda238": 0.7863,
+    "8yi6ejjd1zudcqtbn07haahg6": 0.7752,
+    "byhmntnl1b4lxw0zz21im3zkd": 0.7719,
+    "2bmwykmdlcc2u1c40ytoc39vy": 0.7668,
+    "82jkgccg7phfjpd0mltdl3pat": 0.7643,
+    "2nttcoriwf5co73vmz1vr8frm": 0.7641,
+    "dr2xk7muj8aqcjdz2b3li1c0k": 0.759,
+    "4yngyfinzd6bb1k7anqtqs0wt": 0.7586,
+    "eog6knrkfei68si736fpquyzc": 0.756,
+    "eg6s9f1jj7jr6stmbosn0g6c8": 0.7538,
+    "ae1wva3zrzcp2zd15gpvsntg6": 0.7517,
+    "cesdwwnxbc5fmajgroc0hqzy2": 0.7466,
+    "8k1xcsyvxapl4jlsluh3eomre": 0.7463,
+    "bdtat25m14jy85y484z3e6lf": 0.7437,
+    "iu1vi94p4p28oozl1h9bvplr": 0.7411,
+    "1r097lpxe0xn03ihb7wi98kao": 0.7391,
+    "2kwbbcootiqqgmrzs6o5inle5": 0.7386,
+    "9fuwphq8kvugrlc3ckm7k8wes": 0.7358,
+    "civf31q1inxohs4a03y8reetf": 0.735,
+    "ili150pwfuf39f7yfdch9lhw": 0.7286,
+    "abs7n2ae3oydilk0tgmpnsj89": 0.7277,
+    "9nbpdi9q3ywcm4q0j5u0ekwcq": 0.7254,
+    "6by3h89i2eykc341oz7lv1ddd": 0.7252,
+    "4qehj8hfxmy6o2ohp4fxinnzo": 0.7244,
+    "9u4pm8x0lfmfq3r0pypmrls71": 0.7244,
+    "c7b8o53flg36wbuevfzy3lb10": 0.7144,
+    "89ovpy1rarewwzqvi30bfdr8b": 0.7068,
+    "4d5d3sf6805n5u6jdoa0hdlog": 0.7052,
+    "eqz64pn0qsp2y7aq4m9id3fn6": 0.7031,
+    "8q60vlvn3krynkob6igrncdjq": 0.703,
+    "6ihotpaocgiovlxw18e9r9prx": 0.7019,
+    "c0r21rtokgnbtc0o2rldjmkxu": 0.7013,
+    "1mpjd0vbxbtu9zw89yj09xk3z": 0.6996,
+    "4zwgbb66rif2spcoeeol2motx": 0.6995,
+    "bu1l7ckihyr0errxw61p0m05": 0.6995,
+    "cv3tuitw3ho3v0opjjxpn83b9": 0.6974,
+    "8r98daokeuzsamu5fmjtblqx5": 0.6922,
+    "dvstmwnvw0mt5p38twn9yttyb": 0.688,
+    "8y29fg2s85ppcb8uugm5ee8s4": 0.6866,
+    "19q13y6ruzo0o84ipblcuouzs": 0.6858,
+    "f4jc2cc5nq7flaoptpi5ua4k4": 0.6852,
+    "4oogyu6o156iphvdvphwpck10": 0.684,
+    "3e40pestup9xzagsu2o6c0i8u": 0.6824,
+    "4rls982p5uzil6x30mhyhv9f3": 0.6812,
+    "e21cf135btr8t3upw0vl6n6x0": 0.6771,
+    "65q4uwm6ol1rkf5dp89m8omny": 0.6754,
+    "46b141eaqq9q7o4gz5gtdpikk": 0.6752,
+    "75i269i1ak43magshljadydrh": 0.6741,
+    "3ab1uwtoyjopdj1y1fynyy9jg": 0.6737,
+    "4mbfidy8zum5u0aqjqo0vuqs2": 0.673,
+    "7wssxdqi4xihseeam8grqa2b8": 0.666,
+    "61fzfjogstjuukzcehighq7mu": 0.6641,
+    "6g8hw3acenrw828la7gwx4mvs": 0.663,
+    "e1kxdivp5g4cpldgpwvnzl1vv": 0.6626,
+    "9ikchyu9fb8bvx0s673jofj6s": 0.6622,
+    "a9vrdkelbgif0gtu3wxsr75xo": 0.6618,
+    "6sxm2iln2w45ux498pty9miw8": 0.6615,
+    "ea0h6cf3bhl698hkxhpulh2zz": 0.661,
+    "apdwh753fupxheygs8seahh7x": 0.6604,
+    "er5745q30wnr8jv9nr863omzg": 0.659,
+    "2z7257m7hj58zuxcjrsg4erzc": 0.6551,
+    "2o9svokc5s7diish3ycrzk7jm": 0.655,
+    "8usjlmziv3p2re0r2wwzezki9": 0.6549,
+    "c0yqkbilbbg70ij2473xymmqv": 0.6506,
+    "du6jsenbjql5e8f3yk880ox4g": 0.6494,
+    "cbdbziaqczfuyuwqsylqi26zd": 0.6478,
+    "725gd73msyt08xm76v7gkxj7u": 0.6445,
+    "enzlj1as2raqm4ids1zyb07y1": 0.6442,
+    "scf9p4y91yjvqvg5jndxzhxj": 0.6414,
+    "5z8v4mj6cjs9ex6hdrpourjzh": 0.6389,
+    "4zwjlzdszduqmxzusysvzymms": 0.6387,
+    "7nmz249q89qg5ezcvzlheljji": 0.6381,
+    "2mdmx668tyhy4u4z9zszwjv5v": 0.6345,
+    "4a7o9rf7ytl8g3ejwpblc6p5n": 0.6306,
+    "2ty8ihceabty8yddmu31iuuej": 0.6283,
+    "dy8zaksw5e9nwrs1p5ss4o1nu": 0.628,
+    "1b70m6qtxrp75b4vtk8hxh8c3": 0.6261,
+    "ajxs0e0g6ryg5ol8qvw3evrcz": 0.6249,
+    "a4fgj2rfbpf4ejo1qi624fefo": 0.6184,
+    "akmkihra9ruad09ljapsm84b3": 0.6182,
+    "907l7wtxdvugdo9i2249wcmr0": 0.6171,
+    "6lwpjhktjhl9g7x2w7njmzva6": 0.6164,
+    "ax1yf4nlzqpcji4j8epdgx3zl": 0.6163,
+    "6ybvtzejh91761lqe7y1csrqo": 0.6158,
+    "3btdfgw79qiz3jmyfudovtbu2": 0.6122,
+    "5cwsxtx37les6m10xj71htkgf": 0.6101,
+    "9p3nnxhdjahfn8qswpzy8oyc3": 0.61,
+    "2xg0qvif1rh7du6wmk2eleku3": 0.6091,
+    "1wwro3z1eb3fl601dju6inlc6": 0.6084,
+    "gfskxsdituog2kqp9yiu7bzi": 0.6076,
+    "zilopfej2h0n3vpan5tcynpo": 0.6051,
+    "2hsidwomhjsaaytdy9u5niyi4": 0.6012,
+    "1klyfth8tl6lu6ra7k8zmy2n2": 0.5996,
+    "cegl2ivkc25blcatxp4jmk1ec": 0.5993,
+    "7qf0jaayyxy3ruamsexv5p1kl": 0.5988,
+    "erpufio3qaujd9gkszcqvb0bf": 0.5972,
+    "cfesxhzb83yl8b779uv3revz1": 0.597,
+    "3ww12jab49q8q8mk9avdwjqgk": 0.5961,
+    "8t2o4huu2e48ij23dxnl9w5qx": 0.5928,
+    "5vq1bl8h8dxdr34w0jaanokto": 0.5919,
+    "ac112osli9fvox1epcg4ld3t6": 0.59,
+    "3frp1zxrqulrlrnk503n6l4l": 0.5808,
+    "c76z5d6j7dpi1e79tm8fpm39z": 0.5807,
+    "6ifaeunfdelecgticvxanikzu": 0.5796,
+    "81txfenlgw75nq3u2nfdkj92o": 0.5789,
+    "yv73ms6v1995b5wny16jcfi3": 0.5787,
+    "b3ufcd24wfnnd5j98ped6irfu": 0.5752,
+    "29actv1ohj8r10kd9hu0jnb0n": 0.5737,
+    "bfqezwfhot1l3p1cpk4oonh25": 0.5705,
+    "5taraea6mqjjldg9zxswo825y": 0.5696,
+    "7qdv1xae7ikfe8dft3oj29yqc": 0.5692,
+    "dm5ka0os1e3dxcp3vh05kmp33": 0.5678,
+    "ay4u6j7lfkcg7x21mx5q121j": 0.5676,
+    "7af85xa75vozt2l4hzi6ryts7": 0.5663,
+    "5k620c7y6dlbmcm88dt3eb7t": 0.5644,
+    "ejunkmfhjz9weugd2bqrkgobb": 0.564,
+    "3428tckxcirwwh3o3jgc1m8ji": 0.5597,
+    "d6zovb8puwgcmsg91iya6rbtm": 0.5593,
+    "2wolc27r8z03itcvwp43e38c5": 0.5592,
+    "alpfd99yd3lfv7bhjo0biuq7b": 0.5582,
+    "beqqnubkv05mamuwvimeum015": 0.5577,
+    "4w7x0s5gfs5abasphlha5de8k": 0.5558,
+    "9ynnnx1qmkizq1o3qr3v0nsuk": 0.554,
+    "722fdbecxzcq9788l6jqclzlw": 0.5539,
+    "287tckirbfj9nb8ar2k9r60vn": 0.5529,
+    "esrunz7rjb0td98mx9e5cedoy": 0.5516,
+    "32n2r9bl6x90psj0wa7bfs6vq": 0.5487,
+    "50ap4sua1xyut3mpu7ehesp63": 0.5483,
+    "5c96g1zm7vo5ons9c42uy2w3r": 0.5469,
+    "3p81ltz6845appgkbgkzxueii": 0.5454,
+    "3n9mk5b2mxmq831wfmv6pu86i": 0.5437,
+    "5zr0b05eyx25km7z1k03ca9jx": 0.5424,
+    "1owhvvge4wlx7e0e431b4vhqx": 0.5423,
+    "3iwftmprsznl6yribr11a8l9m": 0.5393,
+    "7r1f93t6ddrsa5n8v1nq6qlzm": 0.5393,
+    "1gwajyt0pk2jm5fx5mu36v114": 0.5389,
+    "581t4mywybx21wcpmpykhyzr3": 0.5388,
+    "6wubmo7di3kdpflluf6s8c7vs": 0.5375,
+    "bq89wbdvedtov6auzuh6rsv7s": 0.5363,
+    "byu00jvt1j6csyv4y1lkt2fm2": 0.5359,
+    "af79lqrc0ntom74zq13ccjslo": 0.5357,
+    "3ri6juw2w6ma0jezszdlv1uqm": 0.5356,
+    "3l29w00m506ex93t5bbh9cg2a": 0.5355,
+    "1zp1du9n4rj36p1ss9zbxtqfb": 0.5353,
+    "9chuiarcjofld1dkj9kysehmb": 0.5346,
+    "5aw6uyw4pz2bpj24t5z8aacim": 0.5333,
+    "by5nibd18nkt40t0j8a0j5yzx": 0.5332,
+    "4yzidekywejmxxp77gqmdgopg": 0.5323,
+    "7ntvbsyq31jnzoqoa8850b9b8": 0.5305,
+    "a7247po5qs29o3zsfmt222ydu": 0.5299,
+    "117yqo02rs8dykkxpm274w3bd": 0.5298,
+    "193wqkyb0v5jnsblhvd2ocmyo": 0.5296,
+    "8jh0jejuxfhrpawnoztz2jlv4": 0.5295,
+    "5y0z0l2epprzbscvzsgldw8vu": 0.5288,
+    "47s2kt0e8m444ftqvsrqa3bvq": 0.5268,
+    "2hj3286pqov1g1g59k2t2qcgm": 0.5245,
+    "7swf4kpu3v38i2it4h94c5s9k": 0.5227,
+    "78wml3z5wrfxe5iky50tiotgu": 0.5196,
+    "f39uq10c8xhg5e6rwwcf6lhgc": 0.5186,
+    "bbajzna018c79opa1kl5kmkqo": 0.5172,
+    "4davonpqws4a4ejl1awu98zdg": 0.5168,
+    "1fedahp0rws09tj451onten8r": 0.5163,
+    "aho73e5udydy96iun3tkzdzsi": 0.5149,
+    "3aa4mumjl6zyetg6o9hwd5hhx": 0.5125,
+    "7cwemnr3vi40znjq451zxkus6": 0.5115,
+    "ajm86skyzse4ym8g6fpgzncxa": 0.5112,
+    "bgen5kjer2ytfp7lo9949t72g": 0.5102,
+    "8ey0ww2zsosdmwr8ehsorh6t7": 0.51,
+    "8najqkluatpaxvqws78b9s17c": 0.5082,
+    "8v97rcbthsxmzqk4ufxws9mug": 0.506,
+    "degxm4y6gmvp011ccyrev6z5p": 0.5049,
+    "3oa9e03e7w9nr8kqwqc3tlqz9": 0.5049,
+    "5dycj9wdhxh3n33qubw18ohlk": 0.5036,
+    "3is4bkgf3loxv9qfg3hm8zfqb": 0.5033,
+    "f47f3717z2vtpxfxrpdd4jl1x": 0.498,
+    "8ivsfwex4dfx1tvgsiq8askcx": 0.4972,
+    "8vbck9a4mxjms783lf72779uu": 0.4946,
+    "aql5z4osw5wmun0emnakfpwji": 0.4946,
+    "e6vzdkz6l236s9p288mharefy": 0.4925,
+    "4nidzmunvpvxk1ir9b6m8mpay": 0.4874,
+    "ein4fkggto3pdh5msp8huafiq": 0.4856,
+    "1q4ab2bpg5e8jl1g2udnakrju": 0.4852,
+    "8ztsv3pzrsyq5w1r3a0nfk1y5": 0.4842,
+    "1qd0wvt30rlswa4g6nu4na660": 0.4826,
+    "jznihqxle06xych9ygwiwnsa": 0.4796,
+    "2y8bntiif3a9y6gtmauv30gt": 0.4782,
+    "477yyajzheg2z8u7uick0e13e": 0.4706,
+    "bockl24qpr7ryjl8b6obukga": 0.4671,
+    "7mxwwunvot2pi69pj1yr1kh8i": 0.466,
+    "3w1hkk9k9gr8fwssyn4icvdfo": 0.4657,
+    "1txej2dzohnydl21zc9pgx6hy": 0.464,
+    "b8rae0ib0frjmwlca429bq19q": 0.4624,
+    "b5udgm9vakjqz8dcmy5b2g0xt": 0.4582,
+    "eitf7hulqfv1clb7toewkil24": 0.458,
+    "7hl0svs2hg225i2zud0g3xzp2": 0.4559,
+    "2aso72utuctat2ecs6nahjss6": 0.4521,
+    "3ymqchdzk8tt6lfphf26xfvh0": 0.4519,
+    "2yyjcbbryf1r10apyzl7c7jvp": 0.4507,
+    "bly7ema5au6j40i0grhl0pnub": 0.4476,
+    "b1rveez5u792gess9w3e7v5le": 0.4444,
+    "8sdpk4aerruf515yh76ezo7vi": 0.4434,
+    "32vph7vcjqgo1ksj1548di90n": 0.44,
+    "65ggsqdi6drpa4m8y3gkll25k": 0.4394,
+    "xaouuwuk8qyhv1libkeexwjh": 0.4347,
+    "6qitd9h242qkvjenaytfdnsf2": 0.4312,
+    "duuc1qczfnawwncru1ly6o66": 0.4213,
+    "b60nisd3qn427jm0hrg9kvmab": 0.4203,
+    "xwnjb1az11zffwty3m6vn8y6": 0.4197,
+    "dkarmrybx9vx10rg7cywumth0": 0.4158,
+    "75434tz9rc14xkkvudex742ui": 0.4137,
+    "c1d9p6b2e9zr5tqlzx3ktjplg": 0.4129,
+    "b73zounsynk9d3u1p9nvpu7i2": 0.4049,
+    "913mb508il6jzwtlj28fl892h": 0.4044,
+    "e0lck99w8meo9qoalfrxgo33o": 0.401,
+    "8dn0w8zh7nbn2i904603eigwf": 0.3984,
+    "ddyrh5latwfhesgfh4w401n92": 0.3973,
+    "avs3xposm3t9x1x2vzsoxzcbu": 0.3957,
+    "eu2g5j36zzxiazpd729osx0wm": 0.3924,
+    "67uya58idol2eq18ljecsru5o": 0.3912,
+    "23e698ls3x6vi9x8wl0mz7bsa": 0.3838,
+    "6321dlqv4ziuwqte4xpohijtw": 0.382,
+    "8o5tv5viv4hy1qg9jp94k7ayb": 0.381,
+    "53tknno09wqihmwxrqcuwq9sa": 0.3782,
+    "82wo38rqeizxlfjjhfjy4rx7u": 0.3781,
+    "dvtl8sf1262pd2aqgu641qa7u": 0.3767,
+    "663a54fmymndjeev47qm7d3nf": 0.3522,
+    "macko16888165594668885588": 0.3309,
+    "macko16698982162572521585": 0.3262,
+    "6lkj3o21cr4g7bql6tb3fk222": 0.3261,
+    "cu0rmpyff5692eo06ltddjo8a": 0.3161,
+    "1cnx2c8g3hhp8ssxnwwli0mjb": 0.3121,
+    "4vt0ldrcl6thpxpcs8zmpdq1g": 0.2926,
+    "etta63x1t7tnkn4jheisjwk4p": 0.2907,
+    "1n9l0ex47bu0762qg574hzjtd": 0.2626,
+    "6jgwiu2gq3dllmrwt45pfdn2z": 0.2416,
+    "392slbmf1kdqlr6sd1ckt71rs": 0.24,
+    "8z3180hhw2pj1i65uftlk54uz": 0.2096
+  },
+  "details": [
+    {
+      "league_id": "bx57cmq1edfq53ckfk791supi",
+      "league_name": "CAF Konfederasyon Kupası",
+      "match_count": 98,
+      "brier_score": 0.3046,
+      "heavy_fav_win_pct": 84.1,
+      "fav_win_pct": 63.3,
+      "odds_reliability": 0.9476
+    },
+    {
+      "league_id": "55hcphd1ccc6eai1ms77460on",
+      "league_name": "Şampiyonlar Ligi Kadınlar",
+      "match_count": 89,
+      "brier_score": 0.3258,
+      "heavy_fav_win_pct": 83.3,
+      "fav_win_pct": 74.2,
+      "odds_reliability": 0.9445
+    },
+    {
+      "league_id": "d9eaigzyfnfiraqc3ius757tl",
+      "league_name": "Kupa",
+      "match_count": 78,
+      "brier_score": 0.3141,
+      "heavy_fav_win_pct": 81.2,
+      "fav_win_pct": 73.1,
+      "odds_reliability": 0.9402
+    },
+    {
+      "league_id": "1gxlzw2ezkyeykhcaa5x8ozkk",
+      "league_name": "Concacaf Orta Amerika Kupası",
+      "match_count": 88,
+      "brier_score": 0.3338,
+      "heavy_fav_win_pct": 79.4,
+      "fav_win_pct": 61.4,
+      "odds_reliability": 0.9259
+    },
+    {
+      "league_id": "5jd0k2txwnq69frs79eulba8j",
+      "league_name": "Kupa",
+      "match_count": 69,
+      "brier_score": 0.3223,
+      "heavy_fav_win_pct": 78.4,
+      "fav_win_pct": 66.7,
+      "odds_reliability": 0.9233
+    },
+    {
+      "league_id": "6694fff47wqxl10lrd9tb91f8",
+      "league_name": "Kupa",
+      "match_count": 55,
+      "brier_score": 0.3099,
+      "heavy_fav_win_pct": 78.8,
+      "fav_win_pct": 67.3,
+      "odds_reliability": 0.9193
+    },
+    {
+      "league_id": "4jg7he1n3rb5dniq6hf49xorq",
+      "league_name": "Premier Lig",
+      "match_count": 79,
+      "brier_score": 0.3333,
+      "heavy_fav_win_pct": 77.1,
+      "fav_win_pct": 64.6,
+      "odds_reliability": 0.9061
+    },
+    {
+      "league_id": "59tpnfrwnvhnhzmnvfyug68hj",
+      "league_name": "Libertadores Kupası",
+      "match_count": 180,
+      "brier_score": 0.3408,
+      "heavy_fav_win_pct": 76.2,
+      "fav_win_pct": 61.7,
+      "odds_reliability": 0.8988
+    },
+    {
+      "league_id": "ac42gi3penartj88fe9l6plpk",
+      "league_name": "Premier Lig",
+      "match_count": 185,
+      "brier_score": 0.3148,
+      "heavy_fav_win_pct": 70.7,
+      "fav_win_pct": 68.1,
+      "odds_reliability": 0.8937
+    },
+    {
+      "league_id": "3j81qr7yc4gdnakfwnxf95ovh",
+      "league_name": "Premier Lig",
+      "match_count": 106,
+      "brier_score": 0.333,
+      "heavy_fav_win_pct": 72.2,
+      "fav_win_pct": 60.4,
+      "odds_reliability": 0.8771
+    },
+    {
+      "league_id": "9z5643nd06afqu01ea2wt8y4g",
+      "league_name": "Kuu Bara Ligi",
+      "match_count": 110,
+      "brier_score": 0.3294,
+      "heavy_fav_win_pct": 70.3,
+      "fav_win_pct": 53.6,
+      "odds_reliability": 0.8734
+    },
+    {
+      "league_id": "482ofyysbdbeoxauk19yg7tdt",
+      "league_name": "Trendyol Süper Lig",
+      "match_count": 342,
+      "brier_score": 0.3627,
+      "heavy_fav_win_pct": 80.7,
+      "fav_win_pct": 59.6,
+      "odds_reliability": 0.8722
+    },
+    {
+      "league_id": "ahl3vljaignq9ebaos4uqkrvo",
+      "league_name": "Kupa",
+      "match_count": 105,
+      "brier_score": 0.331,
+      "heavy_fav_win_pct": 70.4,
+      "fav_win_pct": 63.8,
+      "odds_reliability": 0.8696
+    },
+    {
+      "league_id": "8x3sbh85gc8qir50utw39jl04",
+      "league_name": "UEFA Kadınlar Euro 2025 Elemeleri",
+      "match_count": 88,
+      "brier_score": 0.3421,
+      "heavy_fav_win_pct": 75.5,
+      "fav_win_pct": 61.4,
+      "odds_reliability": 0.865
+    },
+    {
+      "league_id": "agpweohvn9tugnyl6ry4rhivp",
+      "league_name": "Eredivisie Kadınlar",
+      "match_count": 51,
+      "brier_score": 0.3356,
+      "heavy_fav_win_pct": 72.0,
+      "fav_win_pct": 56.9,
+      "odds_reliability": 0.8428
+    },
+    {
+      "league_id": "4c1nfi2j1m731hcay25fcgndq",
+      "league_name": "Avrupa Ligi",
+      "match_count": 242,
+      "brier_score": 0.3625,
+      "heavy_fav_win_pct": 77.6,
+      "fav_win_pct": 61.6,
+      "odds_reliability": 0.8425
+    },
+    {
+      "league_id": "1j4ehtrbry9depwt6oghaq3lu",
+      "league_name": "Süper Lig",
+      "match_count": 84,
+      "brier_score": 0.3201,
+      "heavy_fav_win_pct": 65.9,
+      "fav_win_pct": 60.7,
+      "odds_reliability": 0.8299
+    },
+    {
+      "league_id": "40yjcbx2sq6oq736iqqqczwt1",
+      "league_name": "DK Elemeler",
+      "match_count": 88,
+      "brier_score": 0.3383,
+      "heavy_fav_win_pct": 68.6,
+      "fav_win_pct": 55.7,
+      "odds_reliability": 0.8237
+    },
+    {
+      "league_id": "145hkd59i6foieuwr4mwi6wlq",
+      "league_name": "Pro Lig",
+      "match_count": 143,
+      "brier_score": 0.3546,
+      "heavy_fav_win_pct": 73.8,
+      "fav_win_pct": 60.1,
+      "odds_reliability": 0.823
+    },
+    {
+      "league_id": "34pl8szyvrbwcmfkuocjm3r6t",
+      "league_name": "LaLiga",
+      "match_count": 364,
+      "brier_score": 0.3773,
+      "heavy_fav_win_pct": 80.2,
+      "fav_win_pct": 56.6,
+      "odds_reliability": 0.8227
+    },
+    {
+      "league_id": "cse5oqqt2pzfcy8uz6yz3tkbj",
+      "league_name": "CAF Şampiyonlar Ligi",
+      "match_count": 91,
+      "brier_score": 0.3513,
+      "heavy_fav_win_pct": 73.9,
+      "fav_win_pct": 57.1,
+      "odds_reliability": 0.8212
+    },
+    {
+      "league_id": "zs18qaehvhg3w1208874zvfa",
+      "league_name": "1. Lig",
+      "match_count": 225,
+      "brier_score": 0.3744,
+      "heavy_fav_win_pct": 82.1,
+      "fav_win_pct": 59.6,
+      "odds_reliability": 0.8176
+    },
+    {
+      "league_id": "57nu0wygurzkp6fuy5hhrtaa2",
+      "league_name": "1. Lig",
+      "match_count": 286,
+      "brier_score": 0.3626,
+      "heavy_fav_win_pct": 72.9,
+      "fav_win_pct": 59.1,
+      "odds_reliability": 0.8099
+    },
+    {
+      "league_id": "1eruend45vd20g9hbrpiggs5u",
+      "league_name": "Botola Pro",
+      "match_count": 265,
+      "brier_score": 0.3625,
+      "heavy_fav_win_pct": 72.9,
+      "fav_win_pct": 50.2,
+      "odds_reliability": 0.8083
+    },
+    {
+      "league_id": "595nsvo7ykvoe690b1e4u5n56",
+      "league_name": "UEFA Uluslar Ligi",
+      "match_count": 67,
+      "brier_score": 0.3687,
+      "heavy_fav_win_pct": 83.3,
+      "fav_win_pct": 50.7,
+      "odds_reliability": 0.7987
+    },
+    {
+      "league_id": "6vq8j5p3av14nr3iuyi4okhjt",
+      "league_name": "Süper Lig Kadınlar",
+      "match_count": 70,
+      "brier_score": 0.356,
+      "heavy_fav_win_pct": 73.5,
+      "fav_win_pct": 58.6,
+      "odds_reliability": 0.793
+    },
+    {
+      "league_id": "486rhdgz7yc0sygziht7hje65",
+      "league_name": "Kupa",
+      "match_count": 62,
+      "brier_score": 0.3704,
+      "heavy_fav_win_pct": 81.1,
+      "fav_win_pct": 66.1,
+      "odds_reliability": 0.7901
+    },
+    {
+      "league_id": "9hh6n2f84k31zmlcxyvmc1w2y",
+      "league_name": "2. Lig",
+      "match_count": 204,
+      "brier_score": 0.357,
+      "heavy_fav_win_pct": 69.2,
+      "fav_win_pct": 62.3,
+      "odds_reliability": 0.789
+    },
+    {
+      "league_id": "3n5046abeu3x482ds3jwda238",
+      "league_name": "WE Lig Kadınlar",
+      "match_count": 102,
+      "brier_score": 0.3761,
+      "heavy_fav_win_pct": 85.4,
+      "fav_win_pct": 58.8,
+      "odds_reliability": 0.7863
+    },
+    {
+      "league_id": "8yi6ejjd1zudcqtbn07haahg6",
+      "league_name": "Premier Lig",
+      "match_count": 302,
+      "brier_score": 0.3712,
+      "heavy_fav_win_pct": 72.1,
+      "fav_win_pct": 56.3,
+      "odds_reliability": 0.7752
+    },
+    {
+      "league_id": "byhmntnl1b4lxw0zz21im3zkd",
+      "league_name": "Kupa",
+      "match_count": 96,
+      "brier_score": 0.3528,
+      "heavy_fav_win_pct": 68.2,
+      "fav_win_pct": 58.3,
+      "odds_reliability": 0.7719
+    },
+    {
+      "league_id": "2bmwykmdlcc2u1c40ytoc39vy",
+      "league_name": "Açık Kupası",
+      "match_count": 93,
+      "brier_score": 0.3807,
+      "heavy_fav_win_pct": 84.6,
+      "fav_win_pct": 66.7,
+      "odds_reliability": 0.7668
+    },
+    {
+      "league_id": "82jkgccg7phfjpd0mltdl3pat",
+      "league_name": "Süper Lig",
+      "match_count": 289,
+      "brier_score": 0.3782,
+      "heavy_fav_win_pct": 74.0,
+      "fav_win_pct": 57.4,
+      "odds_reliability": 0.7643
+    },
+    {
+      "league_id": "2nttcoriwf5co73vmz1vr8frm",
+      "league_name": "Nesine 2. Lig",
+      "match_count": 525,
+      "brier_score": 0.3782,
+      "heavy_fav_win_pct": 71.8,
+      "fav_win_pct": 55.2,
+      "odds_reliability": 0.7641
+    },
+    {
+      "league_id": "dr2xk7muj8aqcjdz2b3li1c0k",
+      "league_name": "Meistaradeildin",
+      "match_count": 129,
+      "brier_score": 0.3714,
+      "heavy_fav_win_pct": 73.6,
+      "fav_win_pct": 61.2,
+      "odds_reliability": 0.759
+    },
+    {
+      "league_id": "4yngyfinzd6bb1k7anqtqs0wt",
+      "league_name": "Premier Lig",
+      "match_count": 195,
+      "brier_score": 0.3772,
+      "heavy_fav_win_pct": 74.4,
+      "fav_win_pct": 57.4,
+      "odds_reliability": 0.7586
+    },
+    {
+      "league_id": "eog6knrkfei68si736fpquyzc",
+      "league_name": "Lig Kupası",
+      "match_count": 120,
+      "brier_score": 0.3632,
+      "heavy_fav_win_pct": 69.9,
+      "fav_win_pct": 66.7,
+      "odds_reliability": 0.756
+    },
+    {
+      "league_id": "eg6s9f1jj7jr6stmbosn0g6c8",
+      "league_name": "Süper Lig",
+      "match_count": 108,
+      "brier_score": 0.3657,
+      "heavy_fav_win_pct": 71.2,
+      "fav_win_pct": 55.6,
+      "odds_reliability": 0.7538
+    },
+    {
+      "league_id": "ae1wva3zrzcp2zd15gpvsntg6",
+      "league_name": "Ulusal Lig",
+      "match_count": 278,
+      "brier_score": 0.3783,
+      "heavy_fav_win_pct": 72.7,
+      "fav_win_pct": 55.0,
+      "odds_reliability": 0.7517
+    },
+    {
+      "league_id": "cesdwwnxbc5fmajgroc0hqzy2",
+      "league_name": "Hazırlık Maçları Ülkeler",
+      "match_count": 235,
+      "brier_score": 0.3669,
+      "heavy_fav_win_pct": 67.6,
+      "fav_win_pct": 56.2,
+      "odds_reliability": 0.7466
+    },
+    {
+      "league_id": "8k1xcsyvxapl4jlsluh3eomre",
+      "league_name": "Premier Lig",
+      "match_count": 328,
+      "brier_score": 0.385,
+      "heavy_fav_win_pct": 74.2,
+      "fav_win_pct": 45.7,
+      "odds_reliability": 0.7463
+    },
+    {
+      "league_id": "bdtat25m14jy85y484z3e6lf",
+      "league_name": "Kupa",
+      "match_count": 90,
+      "brier_score": 0.3772,
+      "heavy_fav_win_pct": 75.7,
+      "fav_win_pct": 55.6,
+      "odds_reliability": 0.7437
+    },
+    {
+      "league_id": "iu1vi94p4p28oozl1h9bvplr",
+      "league_name": "1. Lig",
+      "match_count": 158,
+      "brier_score": 0.3729,
+      "heavy_fav_win_pct": 71.2,
+      "fav_win_pct": 50.0,
+      "odds_reliability": 0.7411
+    },
+    {
+      "league_id": "1r097lpxe0xn03ihb7wi98kao",
+      "league_name": "Serie A",
+      "match_count": 359,
+      "brier_score": 0.3732,
+      "heavy_fav_win_pct": 67.8,
+      "fav_win_pct": 56.5,
+      "odds_reliability": 0.7391
+    },
+    {
+      "league_id": "2kwbbcootiqqgmrzs6o5inle5",
+      "league_name": "Premier Lig",
+      "match_count": 369,
+      "brier_score": 0.3791,
+      "heavy_fav_win_pct": 70.2,
+      "fav_win_pct": 54.2,
+      "odds_reliability": 0.7386
+    },
+    {
+      "league_id": "9fuwphq8kvugrlc3ckm7k8wes",
+      "league_name": "Ligler Kupası",
+      "match_count": 143,
+      "brier_score": 0.3934,
+      "heavy_fav_win_pct": 81.6,
+      "fav_win_pct": 50.3,
+      "odds_reliability": 0.7358
+    },
+    {
+      "league_id": "civf31q1inxohs4a03y8reetf",
+      "league_name": "Premier Lig",
+      "match_count": 320,
+      "brier_score": 0.3721,
+      "heavy_fav_win_pct": 67.2,
+      "fav_win_pct": 57.8,
+      "odds_reliability": 0.735
+    },
+    {
+      "league_id": "ili150pwfuf39f7yfdch9lhw",
+      "league_name": "UEFA U21 Şampiyonası Elemeler",
+      "match_count": 112,
+      "brier_score": 0.3715,
+      "heavy_fav_win_pct": 70.4,
+      "fav_win_pct": 67.9,
+      "odds_reliability": 0.7286
+    },
+    {
+      "league_id": "abs7n2ae3oydilk0tgmpnsj89",
+      "league_name": "Azadegan Ligi",
+      "match_count": 217,
+      "brier_score": 0.3801,
+      "heavy_fav_win_pct": 71.4,
+      "fav_win_pct": 45.2,
+      "odds_reliability": 0.7277
+    },
+    {
+      "league_id": "9nbpdi9q3ywcm4q0j5u0ekwcq",
+      "league_name": "Serie D",
+      "match_count": 232,
+      "brier_score": 0.3718,
+      "heavy_fav_win_pct": 67.2,
+      "fav_win_pct": 54.7,
+      "odds_reliability": 0.7254
+    }
+  ]
+}

ai-engine/features/rolling_features.py

@@ -0,0 +1,243 @@
+"""
+V27 Rolling Window Feature Calculator
+======================================
+Computes rolling averages over 5/10/20 match windows,
+with home/away splits and trend detection.
+"""
+from __future__ import annotations
+from typing import Dict, List, Tuple
+import math
+
+
+def calc_rolling_features(
+    team_matches: List[Tuple],  # [(mst, is_home, team_goals, opp_goals, opp_id), ...]
+    before_date: int,
+    team_is_home: bool,
+) -> Dict[str, float]:
+    """Calculate rolling window features for a team before a given date."""
+    valid = [m for m in team_matches if m[0] < before_date]
+
+    defaults = {
+        "rolling5_goals_avg": 1.3, "rolling5_conceded_avg": 1.2,
+        "rolling10_goals_avg": 1.3, "rolling10_conceded_avg": 1.2,
+        "rolling20_goals_avg": 1.3, "rolling20_conceded_avg": 1.2,
+        "rolling5_clean_sheets": 0.25,
+        "venue_goals_avg": 1.3, "venue_conceded_avg": 1.2,
+        "goal_trend": 0.0,
+    }
+
+    if len(valid) < 3:
+        return defaults
+
+    result = {}
+
+    for window in [5, 10, 20]:
+        recent = valid[-window:] if len(valid) >= window else valid
+        n = len(recent)
+        g_sum = sum(m[2] for m in recent)
+        c_sum = sum(m[3] for m in recent)
+        result[f"rolling{window}_goals_avg"] = g_sum / n
+        result[f"rolling{window}_conceded_avg"] = c_sum / n
+
+    # Clean sheet rate (last 5)
+    r5 = valid[-5:] if len(valid) >= 5 else valid
+    result["rolling5_clean_sheets"] = sum(1 for m in r5 if m[3] == 0) / len(r5)
+
+    # Venue-specific (home-only or away-only)
+    venue_matches = [m for m in valid if m[1] == team_is_home]
+    if venue_matches:
+        vm = venue_matches[-10:] if len(venue_matches) >= 10 else venue_matches
+        result["venue_goals_avg"] = sum(m[2] for m in vm) / len(vm)
+        result["venue_conceded_avg"] = sum(m[3] for m in vm) / len(vm)
+    else:
+        result["venue_goals_avg"] = defaults["venue_goals_avg"]
+        result["venue_conceded_avg"] = defaults["venue_conceded_avg"]
+
+    # Goal trend: compare last 3 vs previous 3
+    if len(valid) >= 6:
+        last3 = sum(m[2] for m in valid[-3:]) / 3
+        prev3 = sum(m[2] for m in valid[-6:-3]) / 3
+        result["goal_trend"] = last3 - prev3
+    else:
+        result["goal_trend"] = 0.0
+
+    return result
+
+
+def calc_league_quality(
+    all_matches: List[Tuple],  # all FT matches in this league
+) -> Dict[str, float]:
+    """Calculate league-level quality features."""
+    defaults = {
+        "league_home_win_rate": 0.45,
+        "league_draw_rate": 0.25,
+        "league_btts_rate": 0.50,
+        "league_ou25_rate": 0.50,
+        "league_reliability_score": 0.50,
+    }
+
+    if len(all_matches) < 20:
+        return defaults
+
+    n = len(all_matches)
+    home_wins = sum(1 for m in all_matches if m[2] > m[3])
+    draws = sum(1 for m in all_matches if m[2] == m[3])
+    btts = sum(1 for m in all_matches if m[2] > 0 and m[3] > 0)
+    ou25 = sum(1 for m in all_matches if (m[2] + m[3]) > 2.5)
+
+    hw_rate = home_wins / n
+    dr_rate = draws / n
+    btts_rate = btts / n
+    ou25_rate = ou25 / n
+
+    # Reliability: leagues closer to averages are more predictable
+    predictability = 1.0 - abs(hw_rate - 0.45) - abs(dr_rate - 0.27) * 0.5
+    reliability = max(0.2, min(0.95, predictability))
+
+    return {
+        "league_home_win_rate": round(hw_rate, 4),
+        "league_draw_rate": round(dr_rate, 4),
+        "league_btts_rate": round(btts_rate, 4),
+        "league_ou25_rate": round(ou25_rate, 4),
+        "league_reliability_score": round(reliability, 4),
+    }
+
+
+def calc_time_features(
+    team_matches: List[Tuple],
+    match_mst: int,
+) -> Dict[str, float]:
+    """Calculate time-based features."""
+    from datetime import datetime
+
+    # Days since last match
+    valid = [m for m in team_matches if m[0] < match_mst]
+    if valid:
+        last_mst = valid[-1][0]
+        days_rest = (match_mst - last_mst) / 86_400_000  # ms to days
+        days_rest = min(days_rest, 60.0)  # cap at 60 days
+    else:
+        days_rest = 14.0
+
+    # Month and season flags
+    try:
+        dt = datetime.utcfromtimestamp(match_mst / 1000)
+        month = dt.month
+        is_season_start = 1.0 if month in (7, 8) else 0.0
+        is_season_end = 1.0 if month in (5, 6) else 0.0
+    except Exception:
+        month = 6
+        is_season_start = 0.0
+        is_season_end = 0.0
+
+    return {
+        "days_rest": round(days_rest, 2),
+        "match_month": month,
+        "is_season_start": is_season_start,
+        "is_season_end": is_season_end,
+    }
+
+
+def calc_advanced_h2h(
+    team_matches: List[Tuple],
+    home_id: int,
+    away_id: int,
+    before_date: int,
+) -> Dict[str, float]:
+    """Calculate advanced H2H features."""
+    defaults = {
+        "h2h_home_goals_avg": 1.3,
+        "h2h_away_goals_avg": 1.1,
+        "h2h_recent_trend": 0.0,
+        "h2h_venue_advantage": 0.0,
+    }
+
+    h2h = [m for m in team_matches if m[4] == away_id and m[0] < before_date]
+    if not h2h:
+        return defaults
+
+    recent = h2h[-10:]
+    home_goals_total = 0
+    away_goals_total = 0
+    venue_home_wins = 0
+    venue_total = 0
+
+    for mst, is_home, team_goals, opp_goals, _ in recent:
+        if is_home:
+            home_goals_total += team_goals
+            away_goals_total += opp_goals
+            venue_total += 1
+            if team_goals > opp_goals:
+                venue_home_wins += 1
+        else:
+            home_goals_total += opp_goals
+            away_goals_total += team_goals
+
+    n = len(recent)
+    result = {
+        "h2h_home_goals_avg": home_goals_total / n,
+        "h2h_away_goals_avg": away_goals_total / n,
+        "h2h_venue_advantage": venue_home_wins / venue_total if venue_total > 0 else 0.5,
+    }
+
+    # Recent trend: last 3 vs overall
+    if len(h2h) >= 4:
+        last3_pts = sum(
+            1.0 if m[2] > m[3] else (0.5 if m[2] == m[3] else 0.0)
+            for m in h2h[-3:]
+        ) / 3
+        overall_pts = sum(
+            1.0 if m[2] > m[3] else (0.5 if m[2] == m[3] else 0.0)
+            for m in h2h
+        ) / len(h2h)
+        result["h2h_recent_trend"] = round(last3_pts - overall_pts, 4)
+    else:
+        result["h2h_recent_trend"] = 0.0
+
+    return result
+
+
+def calc_strength_diff(
+    home_form: Dict[str, float],
+    away_form: Dict[str, float],
+    home_elo: Dict[str, float],
+    away_elo: Dict[str, float],
+    home_momentum: float,
+    away_momentum: float,
+    upset_potential: float,
+) -> Dict[str, float]:
+    """Calculate strength differential features."""
+    # Attack vs Defense mismatches
+    h_attack = home_form.get("goals_avg", 1.3)
+    a_defense = away_form.get("conceded_avg", 1.2)
+    a_attack = away_form.get("goals_avg", 1.3)
+    h_defense = home_form.get("conceded_avg", 1.2)
+
+    atk_def_home = h_attack - a_defense  # positive = home attack > away defense
+    atk_def_away = a_attack - h_defense
+
+    # XG diff approximation
+    xg_diff = (h_attack + a_defense) / 2 - (a_attack + h_defense) / 2
+
+    # Form × Momentum interaction
+    form_mom = (home_momentum - away_momentum) * (
+        home_form.get("scoring_rate", 0.75) - away_form.get("scoring_rate", 0.75)
+    )
+
+    # ELO-Form consistency
+    elo_diff = home_elo.get("overall", 1500) - away_elo.get("overall", 1500)
+    form_diff = h_attack - a_attack
+    elo_form_consistency = 1.0 if (elo_diff > 0 and form_diff > 0) or (elo_diff < 0 and form_diff < 0) else 0.0
+
+    # Upset × ELO gap
+    elo_gap = abs(elo_diff)
+    upset_x_elo = upset_potential * (elo_gap / 400.0)
+
+    return {
+        "attack_vs_defense_home": round(atk_def_home, 4),
+        "attack_vs_defense_away": round(atk_def_away, 4),
+        "xg_diff": round(xg_diff, 4),
+        "form_momentum_interaction": round(form_mom, 4),
+        "elo_form_consistency": elo_form_consistency,
+        "upset_x_elo_gap": round(upset_x_elo, 4),
+    }

ai-engine/features/upset_engine_v2.py

@@ -15,13 +15,9 @@ Orijinal Faktörler:
 - Tarihsel upset pattern
 """
 
-import os
-import sys
 from typing import Dict, Any, Optional, Tuple, List
 from dataclasses import dataclass, field
 
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
 try:
     import psycopg2
     from psycopg2.extras import RealDictCursor

ai-engine/main.py

@@ -7,16 +7,24 @@ import time
 from contextlib import asynccontextmanager
 from typing import Any
 
+from datetime import datetime
+
 import uvicorn
 from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
+import subprocess
 from pydantic import BaseModel
 
-from models.basketball_v25 import get_basketball_v25_predictor
+try:
+    from models.basketball_v25 import get_basketball_v25_predictor
+    HAS_BASKETBALL = True
+except ImportError:
+    HAS_BASKETBALL = False
 from services.single_match_orchestrator import get_single_match_orchestrator
-from data.database import dispose_engine
+from services.v26_shadow_engine import get_v26_shadow_engine
+from models.league_model import get_league_model_loader
 
 load_dotenv()
 
@@ -33,12 +41,30 @@ class CouponRequest(BaseModel):
     min_confidence: float | None = None
 
 
+class RetrainRequest(BaseModel):
+    reason: str | None = "manual"
+    markets: str | None = None  # comma-separated, e.g. "MS,OU25,BTTS"
+    trials: int | None = 50
+
+
+# ─── Retrain state tracking ──────────────────────────────────
+_retrain_state: dict[str, Any] = {
+    "running": False,
+    "last_started": None,
+    "last_completed": None,
+    "last_status": None,
+    "last_error": None,
+    "pid": None,
+}
+
+
 @asynccontextmanager
 async def lifespan(_: FastAPI):
     try:
-        print("🚀 Initializing V25 orchestrator...", flush=True)
+        print("🚀 Initializing V28 orchestrator...", flush=True)
         get_single_match_orchestrator()
-        print("✅ V25 orchestrator ready", flush=True)
+        get_v26_shadow_engine()
+        print("✅ V28 orchestrator ready", flush=True)
     except Exception as error:
         print(f"❌ Failed to initialize orchestrator: {error}", flush=True)
         import traceback
@@ -47,14 +73,11 @@ async def lifespan(_: FastAPI):
 
     yield
 
-    # Cleanup async DB connections on shutdown
-    await dispose_engine()
-
 
 app = FastAPI(
     title="Suggest-Bet AI Engine",
-    version="25.0.0",
+    version="28.0.0",
-    description="V25 Single Match Prediction Package API",
+    description="V28 Single Match Prediction Package API",
     lifespan=lifespan,
 )
 
@@ -102,8 +125,9 @@ async def global_exception_handler(_: Request, exc: Exception):
 @app.get("/")
 def read_root() -> dict[str, Any]:
     return {
-        "status": "Suggest-Bet AI Engine v25",
+        "status": "Suggest-Bet AI Engine v28",
-        "engine": "V25 Single Match Orchestrator",
+        "engine": "V28 Single Match Orchestrator",
+        "mode": os.getenv("AI_ENGINE_MODE", "v28"),
         "routes": [
             "POST /v20plus/analyze/{match_id}",
             "GET /v20plus/analyze-htms/{match_id}",
@@ -111,6 +135,8 @@ def read_root() -> dict[str, Any]:
             "GET /v20plus/reversal-watchlist",
             "POST /v20plus/coupon",
             "GET /v20plus/daily-banker",
+            "POST /v1/admin/retrain",
+            "GET /v1/admin/retrain/status",
         ],
     }
 
@@ -118,33 +144,70 @@ def read_root() -> dict[str, Any]:
 @app.get("/health")
 def health_check() -> dict[str, Any]:
     try:
-        get_single_match_orchestrator()
+        orchestrator = get_single_match_orchestrator()
-        basketball_predictor = get_basketball_v25_predictor()
+        shadow_engine = get_v26_shadow_engine()
-        basketball_readiness = basketball_predictor.readiness_summary()
+
-        ready = bool(basketball_readiness["fully_loaded"])
+        # Per-market V25 model status
+        v25_readiness: dict[str, Any] = {"fully_loaded": False}
+        try:
+            v25_predictor = orchestrator._get_v25_predictor()
+            v25_readiness = v25_predictor.readiness_summary()
+        except Exception as v25_err:
+            v25_readiness = {"fully_loaded": False, "error": str(v25_err)}
+
+        if HAS_BASKETBALL:
+            basketball_predictor = get_basketball_v25_predictor()
+            basketball_readiness = basketball_predictor.readiness_summary()
+            ready = bool(basketball_readiness.get("fully_loaded", True))
+        else:
+            basketball_readiness = {"fully_loaded": False, "error": "Basketball module not found"}
+            ready = True
+
+        league_readiness = get_league_model_loader().readiness_summary()
+        overall_ready = ready and v25_readiness.get("fully_loaded", False)
         return {
-            "status": "healthy" if ready else "degraded",
+            "status": "healthy" if overall_ready else "degraded",
-            "engine": "v25.main",
+            "engine": "v28.main",
-            "ready": ready,
+            "mode": os.getenv("AI_ENGINE_MODE", "v28"),
+            "ready": overall_ready,
+            "v25_football": v25_readiness,
+            "league_specific": league_readiness,
             "basketball_v25": basketball_readiness,
+            "v26_shadow": shadow_engine.readiness_summary(),
+            "prediction_service_ready": True,
+            "model_loaded": overall_ready,
+            "orchestrator_mode": getattr(orchestrator, "engine_mode", "v28"),
         }
     except Exception as error:
         return {"status": "unhealthy", "ready": False, "error": str(error)}
 
 
+_REQUIRED_RESPONSE_FIELDS = ("match_info", "market_board", "main_pick", "bet_summary", "data_quality")
+
+
 @app.post("/v20plus/analyze/{match_id}")
 async def analyze_match_v20plus(match_id: str) -> dict[str, Any]:
+    started_at = time.time()
     orchestrator = get_single_match_orchestrator()
-    result = orchestrator.analyze_match(match_id)
+    result = await asyncio.to_thread(orchestrator.analyze_match, match_id)
+    elapsed_ms = int((time.time() - started_at) * 1000)
+
     if not result:
         raise HTTPException(status_code=404, detail=f"Match not found: {match_id}")
+
+    # Response validation: log missing required fields (non-fatal)
+    missing_fields = [f for f in _REQUIRED_RESPONSE_FIELDS if f not in result]
+    if missing_fields:
+        print(f"⚠️ [API] analyze/{match_id} response missing fields: {missing_fields} ({elapsed_ms}ms)")
+
+    result["timing_ms"] = elapsed_ms
     return result
 
 
 @app.get("/v20plus/analyze-htms/{match_id}")
 async def analyze_match_htms_v20plus(match_id: str) -> dict[str, Any]:
     orchestrator = get_single_match_orchestrator()
-    result = orchestrator.analyze_match_htms(match_id)
+    result = await asyncio.to_thread(orchestrator.analyze_match_htms, match_id)
     if not result:
         raise HTTPException(status_code=404, detail=f"Match not found: {match_id}")
     return result
@@ -196,7 +259,7 @@ async def analyze_match_htft_v20plus(match_id: str, timeout_sec: int = 30) -> di
             key=lambda item: float(item[1]),
         )
     return {
-        "engine": "v25.main",
+        "engine": "v28.main",
         "match_info": result.get("match_info", {}),
         "timing_ms": int((time.time() - started_at) * 1000),
         "ht_ft_probs": htft_probs,
@@ -215,11 +278,12 @@ async def analyze_match_htft_v20plus(match_id: str, timeout_sec: int = 30) -> di
 @app.post("/v20plus/coupon")
 async def generate_coupon_v20plus(request: CouponRequest) -> dict[str, Any]:
     orchestrator = get_single_match_orchestrator()
-    return orchestrator.build_coupon(
+    return await asyncio.to_thread(
-        match_ids=request.match_ids,
+        orchestrator.build_coupon,
-        strategy=request.strategy or "BALANCED",
+        request.match_ids,
-        max_matches=request.max_matches,
+        request.strategy or "BALANCED",
-        min_confidence=request.min_confidence,
+        request.max_matches,
+        request.min_confidence,
     )
 
 
@@ -229,7 +293,7 @@ async def get_daily_banker_v20plus(count: int = 3) -> dict[str, Any]:
         raise HTTPException(status_code=400, detail="count must be >= 1")
 
     orchestrator = get_single_match_orchestrator()
-    bankers = orchestrator.get_daily_bankers(count=count)
+    bankers = await asyncio.to_thread(orchestrator.get_daily_bankers, count)
     return {"count": len(bankers), "bankers": bankers}
 
 @app.get("/v20plus/reversal-watchlist")
@@ -247,14 +311,120 @@ async def get_reversal_watchlist_v20plus(
         raise HTTPException(status_code=400, detail="min_score must be between 0 and 100")
 
     orchestrator = get_single_match_orchestrator()
-    return orchestrator.get_reversal_watchlist(
+    return await asyncio.to_thread(
-        count=count,
+        orchestrator.get_reversal_watchlist,
-        horizon_hours=horizon_hours,
+        count,
-        min_score=min_score,
+        horizon_hours,
-        top_leagues_only=top_leagues_only,
+        min_score,
+        top_leagues_only,
     )
 
 
+# ─── ADMIN: Retrain Pipeline ─────────────────────────────────
+
+def _run_retrain_pipeline(markets: str | None, trials: int):
+    """Background function: extract data → train model → reload."""
+    global _retrain_state
+    ai_dir = os.path.dirname(os.path.abspath(__file__))
+    scripts_dir = os.path.join(ai_dir, "scripts")
+    python = os.path.join(ai_dir, "venv", "bin", "python3")
+    if not os.path.exists(python):
+        python = sys.executable  # fallback
+
+    try:
+        # Step 1: Extract training data
+        print("🔄 [RETRAIN] Step 1/3: Extracting training data...", flush=True)
+        result = subprocess.run(
+            [python, os.path.join(scripts_dir, "extract_training_data.py")],
+            capture_output=True, text=True, timeout=600, cwd=ai_dir,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"Extract failed:\n{result.stderr[-500:]}")
+        print(f"✅ [RETRAIN] Extract done", flush=True)
+
+        # Step 2: Train V25 Pro
+        print("🔄 [RETRAIN] Step 2/3: Training V25 Pro model...", flush=True)
+        train_cmd = [python, os.path.join(scripts_dir, "train_v25_pro.py")]
+        if markets:
+            train_cmd += ["--markets", markets]
+        train_cmd += ["--trials", str(trials)]
+
+        result = subprocess.run(
+            train_cmd, capture_output=True, text=True, timeout=3600, cwd=ai_dir,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"Training failed:\n{result.stderr[-500:]}")
+        print(f"✅ [RETRAIN] Training done", flush=True)
+
+        # Step 3: Reload models in memory
+        print("🔄 [RETRAIN] Step 3/3: Reloading models...", flush=True)
+        try:
+            orchestrator = get_single_match_orchestrator()
+            v25 = orchestrator._get_v25_predictor()
+            v25._loaded = False
+            v25.load_models()
+            print("✅ [RETRAIN] Models reloaded in memory", flush=True)
+        except Exception as reload_err:
+            print(f"⚠️ [RETRAIN] Hot reload failed (restart needed): {reload_err}", flush=True)
+
+        _retrain_state.update({
+            "running": False,
+            "last_completed": datetime.now().isoformat(),
+            "last_status": "success",
+            "last_error": None,
+        })
+        print("🎉 [RETRAIN] Pipeline complete!", flush=True)
+
+    except Exception as err:
+        _retrain_state.update({
+            "running": False,
+            "last_completed": datetime.now().isoformat(),
+            "last_status": "failed",
+            "last_error": str(err),
+        })
+        print(f"❌ [RETRAIN] Pipeline failed: {err}", flush=True)
+
+
+@app.post("/v1/admin/retrain")
+async def admin_retrain(request: RetrainRequest) -> dict[str, Any]:
+    """Trigger full retrain pipeline: extract → train → reload."""
+    if _retrain_state["running"]:
+        return {
+            "status": "already_running",
+            "message": f"Retrain in progress since {_retrain_state['last_started']}",
+        }
+
+    _retrain_state.update({
+        "running": True,
+        "last_started": datetime.now().isoformat(),
+        "last_status": "running",
+        "last_error": None,
+    })
+
+    # Run in background thread
+    import threading
+    thread = threading.Thread(
+        target=_run_retrain_pipeline,
+        args=(request.markets, request.trials or 50),
+        daemon=True,
+    )
+    thread.start()
+
+    return {
+        "status": "triggered",
+        "message": "Retrain pipeline started in background",
+        "reason": request.reason,
+        "markets": request.markets or "all",
+        "trials": request.trials or 50,
+    }
+
+
+@app.get("/v1/admin/retrain/status")
+async def admin_retrain_status() -> dict[str, Any]:
+    """Check retrain pipeline status."""
+    return {**_retrain_state}
+
+
 if __name__ == "__main__":
     port = int(os.getenv("PORT", "8000"))
     uvicorn.run("main:app", host="0.0.0.0", port=port, reload=True)

ai-engine/models/calibration.py

@@ -0,0 +1,456 @@
+"""
+Calibration Module for XGBoost Models
+=====================================
+Calibrates raw probabilities from XGBoost models using Isotonic Regression.
+Ensures that a predicted probability of 70% actually corresponds to a 70% win rate.
+
+Usage:
+    from ai_engine.models.calibration import Calibrator
+    calibrator = Calibrator()
+    calibrated_prob = calibrator.calibrate("ms", raw_prob)
+    
+    # Training new calibration models:
+    calibrator.train_calibration(valid_df, market="ms")
+"""
+
+import os
+import pickle
+import json
+import numpy as np
+import pandas as pd
+from datetime import datetime
+from typing import Dict, List, Optional, Tuple, Any
+from sklearn.isotonic import IsotonicRegression
+from sklearn.calibration import calibration_curve
+from sklearn.metrics import brier_score_loss
+
+AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+CALIBRATION_DIR = os.path.join(AI_ENGINE_DIR, "models", "calibration")
+
+os.makedirs(CALIBRATION_DIR, exist_ok=True)
+
+# Supported markets for calibration
+SUPPORTED_MARKETS = [
+    "ms",        # Match Result (1X2) - multi-class, calibrated per class
+    "ms_home",   # Standard Home win probability
+    "ms_home_heavy_fav", # Context: home odds <= 1.40
+    "ms_home_fav",       # Context: 1.40 < home odds <= 1.80
+    "ms_home_balanced",  # Context: 1.80 < home odds <= 2.50
+    "ms_home_underdog",  # Context: home odds > 2.50
+    "ms_draw",   # Draw probability  
+    "ms_away",   # Away win probability
+    "ou15",      # Over/Under 1.5
+    "ou25",      # Over/Under 2.5
+    "ou35",      # Over/Under 3.5
+    "btts",      # Both Teams to Score
+    "ht_ft",     # Half-Time/Full-Time
+    "dc",        # Double Chance
+    "ht",        # Half-Time Result
+    "ht_home",   # Half-Time Home win
+    "ht_draw",   # Half-Time Draw
+    "ht_away",   # Half-Time Away win
+]
+
+
+class CalibrationMetrics:
+    """Stores calibration quality metrics for a market."""
+    
+    def __init__(self):
+        self.brier_score: float = 0.0
+        self.calibration_error: float = 0.0
+        self.sample_count: int = 0
+        self.last_trained: str = ""
+        self.mean_predicted: float = 0.0
+        self.mean_actual: float = 0.0
+        
+    def to_dict(self) -> Dict:
+        return {
+            "brier_score": round(self.brier_score, 4),
+            "calibration_error": round(self.calibration_error, 4),
+            "sample_count": self.sample_count,
+            "last_trained": self.last_trained,
+            "mean_predicted": round(self.mean_predicted, 4),
+            "mean_actual": round(self.mean_actual, 4),
+        }
+
+
+class Calibrator:
+    """
+    Probability calibration using Isotonic Regression.
+    
+    Isotonic Regression is a non-parametric method that fits a piecewise
+    constant function that is monotonically increasing. It's ideal for
+    calibrating probabilities because:
+    
+    1. It preserves ranking (if P(A) > P(B) before, P(A) > P(B) after)
+    2. It doesn't assume a specific distribution shape
+    3. It can correct systematic over/under-confidence
+    
+    Example:
+        # Before calibration: model predicts 70% but actual win rate is 60%
+        # After calibration: model predicts 70% → calibrated to 60%
+    """
+    
+    def __init__(self):
+        self.calibrators: Dict[str, IsotonicRegression] = {}
+        self.metrics: Dict[str, CalibrationMetrics] = {}
+        # Less aggressive shrinkage — only meaningful overconfident bands are pulled.
+        # Default raised from ~0.85-0.90 to 0.95+ since the orchestrator and config
+        # already apply market-level multipliers; double-shrinkage was the root cause
+        # of 24-35pt avg calibrated-vs-raw drops in production traces.
+        self.heuristic_fallback: Dict[str, float] = {
+            "ms": 0.96,
+            "ms_home": 0.96,
+            "ms_home_heavy_fav": 0.98,
+            "ms_home_fav": 0.96,
+            "ms_home_balanced": 0.94,
+            "ms_home_underdog": 0.92,
+            "ms_draw": 0.94,
+            "ms_away": 0.96,
+            "ou15": 0.96,
+            "ou25": 0.96,
+            "ou35": 0.94,
+            "btts": 0.96,
+            "ht_ft": 0.92,
+            "dc": 0.97,
+            "ht": 0.92,
+            "ht_home": 0.92,
+            "ht_draw": 0.92,
+            "ht_away": 0.92,
+        }
+        self._load_calibrators()
+        
+    def _load_calibrators(self):
+        """Load trained calibrators for each market from disk."""
+        for market in SUPPORTED_MARKETS:
+            model_path = os.path.join(CALIBRATION_DIR, f"{market}_calibrator.pkl")
+            metrics_path = os.path.join(CALIBRATION_DIR, f"{market}_metrics.json")
+            
+            if os.path.exists(model_path):
+                try:
+                    with open(model_path, "rb") as f:
+                        self.calibrators[market] = pickle.load(f)
+                    print(f"[Calibrator] Loaded calibration model for {market}")
+                except Exception as e:
+                    print(f"[Calibrator] Warning: Failed to load {market}: {e}")
+                    
+            if os.path.exists(metrics_path):
+                try:
+                    with open(metrics_path, "r") as f:
+                        data = json.load(f)
+                        metrics = CalibrationMetrics()
+                        metrics.brier_score = data.get("brier_score", 0.0)
+                        metrics.calibration_error = data.get("calibration_error", 0.0)
+                        metrics.sample_count = data.get("sample_count", 0)
+                        metrics.last_trained = data.get("last_trained", "")
+                        metrics.mean_predicted = data.get("mean_predicted", 0.0)
+                        metrics.mean_actual = data.get("mean_actual", 0.0)
+                        self.metrics[market] = metrics
+                except Exception as e:
+                    print(f"[Calibrator] Warning: Failed to load metrics for {market}: {e}")
+
+    # Below this sample count, blend isotonic with raw_prob to dampen overfit jumps.
+    # Above this count, trust isotonic fully.
+    TRUSTED_SAMPLE_FLOOR = 30
+    TRUSTED_SAMPLE_CEILING = 200
+    # Hard cap on how far calibration can move probability in either direction.
+    MAX_DELTA = 0.20
+
+    def calibrate(self, market_type: str, raw_prob: float, odds_val: Optional[float] = None) -> float:
+        """
+        Calibrate a raw probability using Isotonic Regression with safeguards.
+
+        Args:
+            market_type (str): 'ms_home', 'ou25', 'btts', 'ht_ft', etc.
+            raw_prob (float): The raw probability from XGBoost (0.0 - 1.0)
+            odds_val (float, optional): The pre-match odds, used for context-aware bucket mapping
+
+        Returns:
+            float: Calibrated probability (0.0 - 1.0)
+
+        Safeguards:
+          * Low-sample trained models are blended with raw_prob to dampen overfit.
+          * MAX_DELTA caps the per-call adjustment (prevents 40pp swings).
+        """
+        # Normalize market type
+        market_key = market_type.lower().replace("-", "_")
+
+        # Route to bucket if ms_home and odds provided
+        if market_key == "ms_home" and odds_val is not None and odds_val > 1.0:
+            if odds_val <= 1.40:
+                bucket_key = "ms_home_heavy_fav"
+            elif odds_val <= 1.80:
+                bucket_key = "ms_home_fav"
+            elif odds_val <= 2.50:
+                bucket_key = "ms_home_balanced"
+            else:
+                bucket_key = "ms_home_underdog"
+
+            if bucket_key in self.calibrators:
+                market_key = bucket_key
+
+        # If we have a trained Isotonic Regression model, use it (with safeguards)
+        if market_key in self.calibrators:
+            try:
+                iso_pred = float(self.calibrators[market_key].predict([raw_prob])[0])
+
+                # Sample-count weighted blend with raw probability.
+                # Sparse models barely move probability; mature models dominate.
+                metrics = self.metrics.get(market_key)
+                n_samples = metrics.sample_count if metrics else 0
+                if n_samples >= self.TRUSTED_SAMPLE_CEILING:
+                    iso_weight = 1.0
+                elif n_samples <= self.TRUSTED_SAMPLE_FLOOR:
+                    # Very sparse: at least 30% trust to surface the signal
+                    iso_weight = max(0.30, n_samples / self.TRUSTED_SAMPLE_CEILING)
+                else:
+                    # Linearly ramp 30% → 100% between floor and ceiling
+                    span = self.TRUSTED_SAMPLE_CEILING - self.TRUSTED_SAMPLE_FLOOR
+                    iso_weight = 0.30 + 0.70 * (n_samples - self.TRUSTED_SAMPLE_FLOOR) / span
+                blended = iso_weight * iso_pred + (1.0 - iso_weight) * raw_prob
+
+                # Cap delta to avoid huge swings on noisy calibrators
+                delta = blended - raw_prob
+                if delta > self.MAX_DELTA:
+                    blended = raw_prob + self.MAX_DELTA
+                elif delta < -self.MAX_DELTA:
+                    blended = raw_prob - self.MAX_DELTA
+
+                return float(np.clip(blended, 0.01, 0.99))
+            except Exception as e:
+                print(f"[Calibrator] Warning: Isotonic failed for {market_key}: {e}")
+                # Fall through to heuristic
+
+        # Fallback to heuristic calibration
+        return self._heuristic_calibrate(market_key, raw_prob)
+    
+    def _heuristic_calibrate(self, market_type: str, raw_prob: float) -> float:
+        """
+        Heuristic calibration fallback when no trained model exists.
+        
+        This applies a conservative shrinkage towards the mean:
+        - Binary markets (OU, BTTS): shrink towards 0.5
+        - Multi-class (MS): shrink towards 0.33
+        - HT/FT: stronger shrinkage due to higher variance
+        """
+        # Get shrinkage factor for this market
+        shrinkage = self.heuristic_fallback.get(market_type, 0.90)
+        
+        if market_type in ["ms", "ms_home", "ms_home_heavy_fav", "ms_home_fav", "ms_home_balanced", "ms_home_underdog", "ms_draw", "ms_away"]:
+            # Pull towards 0.33 (uniform for 3-class)
+            return (raw_prob * shrinkage) + (0.33 * (1.0 - shrinkage))
+            
+        elif market_type in ["ou15", "ou25", "ou35", "btts"]:
+            # Pull towards 0.5 (uniform for binary)
+            return (raw_prob * shrinkage) + (0.5 * (1.0 - shrinkage))
+            
+        elif market_type in ["ht_ft", "ht"]:
+            # Stronger shrinkage for high-variance markets
+            return raw_prob * shrinkage
+            
+        elif market_type == "dc":
+            # Double chance is more reliable
+            return (raw_prob * shrinkage) + (0.66 * (1.0 - shrinkage))
+            
+        return raw_prob
+
+    def train_calibration(
+        self,
+        df: pd.DataFrame,
+        market: str,
+        prob_col: str,
+        actual_col: str,
+        min_samples: int = 100,
+        save: bool = True,
+    ) -> CalibrationMetrics:
+        """
+        Train an Isotonic Regression calibration model for a specific market.
+        
+        Args:
+            df: DataFrame with predictions and actual outcomes
+            market: Market identifier (e.g., 'ms_home', 'ou25', 'btts')
+            prob_col: Column name for raw probabilities
+            actual_col: Column name for actual outcomes (0 or 1)
+            min_samples: Minimum samples required to train
+            save: Whether to save the model to disk
+            
+        Returns:
+            CalibrationMetrics with quality metrics
+        """
+        # Filter valid data
+        valid_df = df[[prob_col, actual_col]].dropna()
+        n_samples = len(valid_df)
+        
+        if n_samples < min_samples:
+            print(f"[Calibrator] Warning: Only {n_samples} samples for {market}, "
+                  f"need at least {min_samples}")
+            metrics = CalibrationMetrics()
+            metrics.sample_count = n_samples
+            return metrics
+        
+        # Extract arrays
+        raw_probs = valid_df[prob_col].values
+        actuals = valid_df[actual_col].values
+        
+        # Train Isotonic Regression
+        iso = IsotonicRegression(out_of_bounds="clip", increasing=True)
+        iso.fit(raw_probs, actuals)
+        
+        # Calculate calibrated probabilities
+        calibrated_probs = iso.predict(raw_probs)
+        
+        # Calculate metrics
+        metrics = CalibrationMetrics()
+        metrics.sample_count = n_samples
+        metrics.last_trained = datetime.utcnow().isoformat()
+        metrics.brier_score = brier_score_loss(actuals, calibrated_probs)
+        metrics.mean_predicted = np.mean(raw_probs)
+        metrics.mean_actual = np.mean(actuals)
+        
+        # Calculate Expected Calibration Error (ECE)
+        metrics.calibration_error = self._calculate_ece(
+            calibrated_probs, actuals, n_bins=10
+        )
+        
+        # Store in memory
+        self.calibrators[market] = iso
+        self.metrics[market] = metrics
+        
+        # Save to disk
+        if save:
+            self._save_calibration(market, iso, metrics)
+        
+        print(f"[Calibrator] Trained {market}: "
+              f"Brier={metrics.brier_score:.4f}, "
+              f"ECE={metrics.calibration_error:.4f}, "
+              f"n={n_samples}")
+        
+        return metrics
+    
+    def train_all_markets(
+        self,
+        df: pd.DataFrame,
+        market_config: Dict[str, Tuple[str, str]],
+        min_samples: int = 100,
+    ) -> Dict[str, CalibrationMetrics]:
+        """
+        Train calibration models for multiple markets at once.
+        
+        Args:
+            df: DataFrame with all predictions and outcomes
+            market_config: Dict mapping market -> (prob_col, actual_col)
+                          e.g., {'ou25': ('ou25_over_prob', 'ou25_over_actual')}
+            min_samples: Minimum samples per market
+            
+        Returns:
+            Dict of market -> CalibrationMetrics
+        """
+        results = {}
+        
+        for market, (prob_col, actual_col) in market_config.items():
+            print(f"\n[Calibrator] Training {market}...")
+            try:
+                metrics = self.train_calibration(
+                    df=df,
+                    market=market,
+                    prob_col=prob_col,
+                    actual_col=actual_col,
+                    min_samples=min_samples,
+                    save=True,
+                )
+                results[market] = metrics
+            except Exception as e:
+                print(f"[Calibrator] Failed to train {market}: {e}")
+                
+        return results
+    
+    def _calculate_ece(
+        self, 
+        probs: np.ndarray, 
+        actuals: np.ndarray, 
+        n_bins: int = 10
+    ) -> float:
+        """
+        Calculate Expected Calibration Error (ECE).
+        
+        ECE = sum(|bin_accuracy - bin_confidence| * bin_weight)
+        
+        Lower is better. Perfect calibration = 0.
+        """
+        bin_boundaries = np.linspace(0, 1, n_bins + 1)
+        ece = 0.0
+        
+        for i in range(n_bins):
+            in_bin = (probs >= bin_boundaries[i]) & (probs < bin_boundaries[i + 1])
+            prop_in_bin = np.mean(in_bin)
+            
+            if prop_in_bin > 0:
+                accuracy_in_bin = np.mean(actuals[in_bin])
+                avg_confidence_in_bin = np.mean(probs[in_bin])
+                ece += np.abs(accuracy_in_bin - avg_confidence_in_bin) * prop_in_bin
+                
+        return ece
+    
+    def _save_calibration(
+        self, 
+        market: str, 
+        calibrator: IsotonicRegression,
+        metrics: CalibrationMetrics
+    ):
+        """Save calibration model and metrics to disk."""
+        # Save model
+        model_path = os.path.join(CALIBRATION_DIR, f"{market}_calibrator.pkl")
+        with open(model_path, "wb") as f:
+            pickle.dump(calibrator, f)
+            
+        # Save metrics
+        metrics_path = os.path.join(CALIBRATION_DIR, f"{market}_metrics.json")
+        with open(metrics_path, "w") as f:
+            json.dump(metrics.to_dict(), f, indent=2)
+            
+        print(f"[Calibrator] Saved {market} to {CALIBRATION_DIR}")
+    
+    def get_calibration_report(self) -> Dict[str, Any]:
+        """Generate a summary report of all calibration models."""
+        report = {
+            "trained_markets": list(self.calibrators.keys()),
+            "metrics": {},
+            "heuristic_only": [],
+        }
+        
+        for market in SUPPORTED_MARKETS:
+            if market in self.metrics:
+                report["metrics"][market] = self.metrics[market].to_dict()
+            elif market not in self.calibrators:
+                report["heuristic_only"].append(market)
+                
+        return report
+    
+    def get_calibrated_probabilities(
+        self, 
+        market: str, 
+        raw_probs: np.ndarray
+    ) -> np.ndarray:
+        """
+        Batch calibration for array of probabilities.
+        
+        Args:
+            market: Market type
+            raw_probs: Array of raw probabilities
+            
+        Returns:
+            Array of calibrated probabilities
+        """
+        return np.array([self.calibrate(market, p) for p in raw_probs])
+
+
+# Singleton instance
+_calibrator_instance: Optional[Calibrator] = None
+
+
+def get_calibrator() -> Calibrator:
+    """Get or create the global Calibrator instance."""
+    global _calibrator_instance
+    if _calibrator_instance is None:
+        _calibrator_instance = Calibrator()
+    return _calibrator_instance

ai-engine/models/league_model.py

@@ -0,0 +1,191 @@
+"""
+League-Specific Model Loader
+=============================
+Loads per-league XGBoost models + isotonic calibrators trained by
+scripts/train_league_models.py and provides a unified prediction interface.
+
+Falls back to general V25 for any market/league without a dedicated model.
+"""
+
+import os
+import json
+import pickle
+from functools import lru_cache
+from typing import Dict, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+
+AI_ENGINE_DIR    = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+LEAGUE_MODEL_DIR = os.path.join(AI_ENGINE_DIR, "models", "league_specific")
+
+# Market file name → (num_class, label_list)
+MARKET_META: Dict[str, Tuple[int, list]] = {
+    "ms":       (3, ["1", "X", "2"]),
+    "ou15":     (2, ["Over", "Under"]),
+    "ou25":     (2, ["Over", "Under"]),
+    "ou35":     (2, ["Over", "Under"]),
+    "btts":     (2, ["Yes", "No"]),
+    "ht":       (3, ["1", "X", "2"]),
+    "ht_ou05":  (2, ["Over", "Under"]),
+    "ht_ou15":  (2, ["Over", "Under"]),
+    "htft":     (9, ["1/1","1/X","1/2","X/1","X/X","X/2","2/1","2/X","2/2"]),
+    "oe":       (2, ["Odd", "Even"]),
+    "cards":    (2, ["Over", "Under"]),
+    "handicap": (3, ["1", "X", "2"]),
+}
+
+# Signal key map (file key → uppercase signal key used in _get_v25_signal)
+FILE_TO_SIGNAL = {
+    "ms": "MS", "ou15": "OU15", "ou25": "OU25", "ou35": "OU35",
+    "btts": "BTTS", "ht": "HT", "ht_ou05": "HT_OU05", "ht_ou15": "HT_OU15",
+    "htft": "HTFT", "oe": "OE", "cards": "CARDS", "handicap": "HCAP",
+}
+
+
+class LeagueModel:
+    """Holds XGBoost models + isotonic calibrators for one league."""
+
+    def __init__(self, league_id: str):
+        self.league_id = league_id
+        self.league_dir = os.path.join(LEAGUE_MODEL_DIR, league_id)
+        self.models: Dict[str, xgb.Booster] = {}       # market_key → booster
+        self.calibrators: Dict[str, object] = {}        # cal_key → isotonic
+        self.feature_cols: Optional[list] = None
+        self._loaded = False
+
+    def load(self) -> bool:
+        if not os.path.isdir(self.league_dir):
+            return False
+        try:
+            fc_path = os.path.join(self.league_dir, "feature_cols.json")
+            if os.path.exists(fc_path):
+                with open(fc_path) as f:
+                    self.feature_cols = json.load(f)
+
+            for mkey in MARKET_META:
+                xgb_path = os.path.join(self.league_dir, f"xgb_{mkey}.json")
+                if os.path.exists(xgb_path) and os.path.getsize(xgb_path) > 100:
+                    b = xgb.Booster()
+                    b.load_model(xgb_path)
+                    self.models[mkey] = b
+
+            for fname in os.listdir(self.league_dir):
+                if fname.startswith("cal_") and fname.endswith(".pkl"):
+                    cal_key = fname[4:-4]  # strip cal_ and .pkl
+                    with open(os.path.join(self.league_dir, fname), "rb") as f:
+                        self.calibrators[cal_key] = pickle.load(f)
+
+            self._loaded = bool(self.models or self.calibrators)
+            return self._loaded
+        except Exception as e:
+            print(f"[LeagueModel] Load failed for {self.league_id}: {e}")
+            return False
+
+    def has_market(self, mkey: str) -> bool:
+        return mkey in self.models
+
+    def predict_market(
+        self,
+        mkey: str,
+        feature_row: Dict[str, float],
+    ) -> Optional[Dict[str, float]]:
+        """
+        Predict one market using league-specific XGBoost + isotonic calibration.
+        Returns {label: prob} dict or None if no model available.
+        """
+        if mkey not in self.models:
+            return None
+
+        num_class, labels = MARKET_META[mkey]
+        fc = self.feature_cols
+        if fc is None:
+            # Fallback to whatever the booster expects (it knows its feature names)
+            fc = list(self.models[mkey].feature_names or [])
+
+        try:
+            X = pd.DataFrame([{col: feature_row.get(col, 0.0) for col in fc}])
+            dmat = xgb.DMatrix(X)
+            raw = self.models[mkey].predict(dmat)
+
+            if num_class > 2:
+                probs_arr = raw.reshape(-1, num_class)[0]
+                probs = {labels[i]: float(probs_arr[i]) for i in range(num_class)}
+                # Apply isotonic calibration per class
+                cal_total = 0.0
+                for i, label in enumerate(labels):
+                    cal_key = f"{mkey}_{i}"
+                    if cal_key in self.calibrators:
+                        p_cal = float(self.calibrators[cal_key].predict([probs_arr[i]])[0])
+                        probs[label] = max(0.01, min(0.99, p_cal))
+                    cal_total += probs[label]
+                if cal_total > 0:
+                    probs = {k: v / cal_total for k, v in probs.items()}
+            else:
+                p = float(raw[0])
+                cal_key = mkey
+                if cal_key in self.calibrators:
+                    p = float(self.calibrators[cal_key].predict([p])[0])
+                    p = max(0.01, min(0.99, p))
+                probs = {labels[0]: p, labels[1]: 1.0 - p}
+
+            return probs
+        except Exception as e:
+            print(f"[LeagueModel] predict_market({mkey}) failed for {self.league_id}: {e}")
+            return None
+
+
+class LeagueModelLoader:
+    """
+    In-memory cache for league-specific models.
+    Thread-safe for single-process async servers (FastAPI/uvicorn).
+    """
+
+    def __init__(self, max_cached: int = 80):
+        self._cache: Dict[str, Optional[LeagueModel]] = {}
+        self._max_cached = max_cached
+
+    def get(self, league_id: str) -> Optional[LeagueModel]:
+        """Return loaded LeagueModel for this league, or None if unavailable."""
+        if league_id in self._cache:
+            return self._cache[league_id]
+
+        # Evict oldest entry if cache is full
+        if len(self._cache) >= self._max_cached:
+            oldest = next(iter(self._cache))
+            del self._cache[oldest]
+
+        model = LeagueModel(league_id)
+        loaded = model.load()
+        self._cache[league_id] = model if loaded else None
+        if loaded:
+            n_models = len(model.models)
+            n_cals   = len(model.calibrators)
+            print(f"[LeagueModel] Loaded {league_id}: {n_models} XGB models, {n_cals} calibrators")
+        return self._cache[league_id]
+
+    def available_leagues(self) -> list:
+        if not os.path.isdir(LEAGUE_MODEL_DIR):
+            return []
+        return [d for d in os.listdir(LEAGUE_MODEL_DIR)
+                if os.path.isdir(os.path.join(LEAGUE_MODEL_DIR, d))]
+
+    def readiness_summary(self) -> dict:
+        leagues = self.available_leagues()
+        return {
+            "league_specific_dir": LEAGUE_MODEL_DIR,
+            "available_leagues": len(leagues),
+            "cached": len([v for v in self._cache.values() if v is not None]),
+        }
+
+
+# ── Singleton ──────────────────────────────────────────────────────
+_loader: Optional[LeagueModelLoader] = None
+
+
+def get_league_model_loader() -> LeagueModelLoader:
+    global _loader
+    if _loader is None:
+        _loader = LeagueModelLoader()
+    return _loader

ai-engine/models/v25/feature_cols.json

@@ -0,0 +1,154 @@
+[
+  "home_overall_elo",
+  "away_overall_elo",
+  "elo_diff",
+  "home_home_elo",
+  "away_away_elo",
+  "home_form_elo",
+  "away_form_elo",
+  "form_elo_diff",
+  "home_goals_avg",
+  "home_conceded_avg",
+  "away_goals_avg",
+  "away_conceded_avg",
+  "home_clean_sheet_rate",
+  "away_clean_sheet_rate",
+  "home_scoring_rate",
+  "away_scoring_rate",
+  "home_winning_streak",
+  "away_winning_streak",
+  "home_unbeaten_streak",
+  "away_unbeaten_streak",
+  "h2h_total_matches",
+  "h2h_home_win_rate",
+  "h2h_draw_rate",
+  "h2h_avg_goals",
+  "h2h_btts_rate",
+  "h2h_over25_rate",
+  "home_avg_possession",
+  "away_avg_possession",
+  "home_avg_shots_on_target",
+  "away_avg_shots_on_target",
+  "home_shot_conversion",
+  "away_shot_conversion",
+  "home_avg_corners",
+  "away_avg_corners",
+  "odds_ms_h",
+  "odds_ms_d",
+  "odds_ms_a",
+  "implied_home",
+  "implied_draw",
+  "implied_away",
+  "odds_ht_ms_h",
+  "odds_ht_ms_d",
+  "odds_ht_ms_a",
+  "odds_ou05_o",
+  "odds_ou05_u",
+  "odds_ou15_o",
+  "odds_ou15_u",
+  "odds_ou25_o",
+  "odds_ou25_u",
+  "odds_ou35_o",
+  "odds_ou35_u",
+  "odds_ht_ou05_o",
+  "odds_ht_ou05_u",
+  "odds_ht_ou15_o",
+  "odds_ht_ou15_u",
+  "odds_btts_y",
+  "odds_btts_n",
+  "odds_ms_h_present",
+  "odds_ms_d_present",
+  "odds_ms_a_present",
+  "odds_ht_ms_h_present",
+  "odds_ht_ms_d_present",
+  "odds_ht_ms_a_present",
+  "odds_ou05_o_present",
+  "odds_ou05_u_present",
+  "odds_ou15_o_present",
+  "odds_ou15_u_present",
+  "odds_ou25_o_present",
+  "odds_ou25_u_present",
+  "odds_ou35_o_present",
+  "odds_ou35_u_present",
+  "odds_ht_ou05_o_present",
+  "odds_ht_ou05_u_present",
+  "odds_ht_ou15_o_present",
+  "odds_ht_ou15_u_present",
+  "odds_btts_y_present",
+  "odds_btts_n_present",
+  "home_xga",
+  "away_xga",
+  "league_avg_goals",
+  "league_zero_goal_rate",
+  "upset_atmosphere",
+  "upset_motivation",
+  "upset_fatigue",
+  "upset_potential",
+  "referee_home_bias",
+  "referee_avg_goals",
+  "referee_cards_total",
+  "referee_avg_yellow",
+  "referee_experience",
+  "home_momentum_score",
+  "away_momentum_score",
+  "momentum_diff",
+  "home_squad_quality",
+  "away_squad_quality",
+  "squad_diff",
+  "home_key_players",
+  "away_key_players",
+  "home_missing_impact",
+  "away_missing_impact",
+  "home_goals_form",
+  "away_goals_form",
+  "home_lineup_goals_per90",
+  "away_lineup_goals_per90",
+  "home_lineup_assists_per90",
+  "away_lineup_assists_per90",
+  "home_squad_continuity",
+  "away_squad_continuity",
+  "home_top_scorer_form",
+  "away_top_scorer_form",
+  "home_avg_player_exp",
+  "away_avg_player_exp",
+  "home_goals_diversity",
+  "away_goals_diversity",
+  "h2h_home_goals_avg",
+  "h2h_away_goals_avg",
+  "h2h_recent_trend",
+  "h2h_venue_advantage",
+  "home_rolling5_goals",
+  "home_rolling5_conceded",
+  "home_rolling10_goals",
+  "home_rolling10_conceded",
+  "home_rolling20_goals",
+  "home_rolling20_conceded",
+  "away_rolling5_goals",
+  "away_rolling5_conceded",
+  "away_rolling10_goals",
+  "away_rolling10_conceded",
+  "home_rolling5_cs",
+  "away_rolling5_cs",
+  "home_venue_goals",
+  "home_venue_conceded",
+  "away_venue_goals",
+  "away_venue_conceded",
+  "home_goal_trend",
+  "away_goal_trend",
+  "home_days_rest",
+  "away_days_rest",
+  "match_month",
+  "is_season_start",
+  "is_season_end",
+  "attack_vs_defense_home",
+  "attack_vs_defense_away",
+  "xg_diff",
+  "form_momentum_interaction",
+  "elo_form_consistency",
+  "upset_x_elo_gap",
+  "league_home_win_rate",
+  "league_draw_rate",
+  "league_btts_rate",
+  "league_ou25_rate",
+  "league_reliability_score"
+]

ai-engine/models/v25/lgb_v25_ht_ou05.txt

@@ -0,0 +1,891 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=151
+objective=binary sigmoid:1
+feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151
+feature_infos=[1150.3663761896189:1903.4781806887747] [1158.5088961211511:1916.84579108047] [-496.81477567713546:573.10259120534784] [1159.8767670517543:1884.5959848901657] [1151.7894548779084:1919.4116678360419] [1426.1496448360797:1585.9817930954068] [1427.9817118206745:1588.9895054335384] [-113.02538114266532:114.69704651598067] [0:5.9333333333333336] [0:6.2666666666666666] [0:6.0666666666666664] [0:5.2000000000000002] [0:1] [0:1] [0:1] [0:1] [0:5] [0:5] [0:5] [0:5] [0:8] [0:1] [0:1] [0:11] [0:1] [0:1] [0.20999999999999999:0.81000000000000005] [0.22500000000000001:0.76000000000000001] [0:13] [0:13] [0:6.333333333333333] [0:6.666666666666667] [0:4.5] [0:4.5] [0:22.550000000000001] [0:17.5] [0:35.5] [0.065285302506677897:0.80962131918207236] [0.1044438556117265:0.4288719106684401] [0.056651501780225801:0.79902050363656307] [0:26] [0:5.0899999999999999] [0:26.5] [0:1.0900000000000001] [0:13.050000000000001] [0:1.76] [0:13.25] [0:3.7400000000000002] [0:7.6500000000000004] [0:8.5600000000000005] [0:3.5299999999999998] [0:1.72] [0:7.3300000000000001] [0:5.0199999999999996] [0:2.5800000000000001] [0:3.77] [0:4.1299999999999999] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:6.2666666666666666] [0:5.2000000000000002] [2.0046118370484245:4.1840324763193504] [0.018042399639151999:0.1552651806302843] [0:0.45000000000000001] none none [0:0.1575] [-0.92000000000000004:1] [0:7] none none [0:1] [-1:0.61250000000000004] [-1:0.59166666666666667] [-1.2908333333333333:1.3799999999999999] [0:40.799999999999997] [0:36.299999999999997] [-29.999999999999996:31.499999999999996] [0:10] [0:10] [0:1] [0:1] [0:5.7999999999999998] [0:5] [0:5] [0:6.25] [0:4] [0:5] [0:1] [0:1] [0:10] [0:11] [0:42.100000000000001] [0:41.799999999999997] [0:1] [0:1] [0:8] [0:8] [-1:1] [0:1] [0:5.7999999999999998] [0:6.5] [0:5.2000000000000002] [0:6.5] [0:4.4119999999999999] [0:6.5] [0:5.3330000000000002] [0:4.7999999999999998] [0:5.3330000000000002] [0:4.5] [0:1] [0:1] [0:7] [0:8] [0:6] [0:7] [-1.8:1.8] [-2.2000000000000002:2] [1:30] [1.2:30] [1:12] [0:1] [0:1] [-4.4669999999999996:4.5999999999999996] [-5.2000000000000002:5] [-4.133:5.3330000000000002] [-0.045499999999999999:0.088099999999999998] [0.063600000000000004:1] [0:0.088800000000000004] [0.35623409669211198:0.51556156968876865] [0.1051136363636363:0.32744043043812449] [0.40430438124519602:0.64185836716283262] [0.32129131437355879:0.77537212449255755] [0.70399999999999996:1]
+tree_sizes=946 1005 993 986 1007 997 994 1005 992 1009 984 1007 997 684 1002 999 897 992 991 990 1001 905 1003 995 896 1010 908 1005 1007 1014 1012 1005 1005 691 688
+
+Tree=0
+num_leaves=8
+num_cat=0
+split_feature=45 54 46 96 4 3 16
+split_gain=174.584 35.5641 24.9346 16.8552 14.3832 11.0732 10.8094
+threshold=1.155 1.405 2.5850000000000004 4.5000000000000009 1704.4687685416313 1441.3975280143418 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2
+left_child=1 3 5 -1 -4 -2 -3
+right_child=2 6 4 -5 -6 -7 -8
+leaf_value=0.87683759709368503 0.84782001969484888 0.90404985782878589 0.85793883408869098 0.90097077069702014 0.73127673195863474 0.81008235044809496 0.93515098554525133
+leaf_weight=939.87957760691643 113.51603642106056 283.89385342597961 418.36988925933838 472.32632339000702 9.9611878395080549 330.17187193036079 212.09029108285904
+leaf_count=4529 547 1368 2016 2276 48 1591 1022
+internal_value=0.875686 0.893342 0.837052 0.884909 0.85499 0.819737 0.91735
+internal_weight=2780.21 1908.19 872.019 1412.21 428.331 443.688 495.984
+internal_count=13397 9195 4202 6805 2064 2138 2390
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=8
+num_cat=0
+split_feature=47 48 49 86 141 133 150
+split_gain=140.072 28.5634 26.9673 23.4898 19.0464 14.6224 10.7313
+threshold=1.6850000000000003 2.1650000000000005 3.3850000000000002 3.3279569892473124 1.2620000000000002 0.31650000000000006 0.48538789856891795
+decision_type=2 2 2 2 2 2 2
+left_child=1 3 5 -1 -4 -2 -3
+right_child=2 6 4 -5 -6 -7 -8
+leaf_value=0.0068305934170468773 -0.15031955758812821 -0.031384052219233315 -0.052289652303345355 0.062911487393225371 0.031971580220150592 -0.011449057668411599 0.04709896679065162
+leaf_weight=1284.6507234424353 8.3659095764160138 19.989385187625885 501.71516834199429 86.587033972144127 30.929726287722588 495.32643516361713 353.17835873365402
+leaf_count=6221 40 98 2370 419 146 2373 1730
+internal_value=-0.000699774 0.0173296 -0.0310475 0.0103722 -0.0473963 -0.0137584 0.0428942
+internal_weight=2780.74 1744.41 1036.34 1371.24 532.645 503.692 373.168
+internal_count=13397 8468 4929 6640 2516 2413 1828
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=2
+num_leaves=8
+num_cat=0
+split_feature=45 50 53 39 96 28 150
+split_gain=110.988 23.3948 18.1465 13.3271 12.0676 11.8858 11.7377
+threshold=1.155 1.4150000000000003 3.3250000000000006 0.36187197152743827 4.5000000000000009 3.9500000000000006 0.48538789856891795
+decision_type=2 2 2 2 2 2 2
+left_child=1 4 5 -4 -1 -2 -3
+right_child=2 6 3 -5 -6 -7 -8
+leaf_value=0.0012888166907150367 -0.037269165059036741 -0.040251887739062138 -0.05134333704385069 -0.13896201271441588 0.020859848952961575 -0.011322757210899898 0.041785901483228478
+leaf_weight=991.85861267149448 395.00907251238823 20.026126876473427 106.85419258475304 22.895305529236794 522.50144763290882 372.75306884944439 348.38899676501751
+leaf_count=4802 1851 97 494 106 2555 1751 1741
+internal_value=-0.000605268 0.0137719 -0.0307651 -0.0668133 0.00804152 -0.0246723 0.0373256
+internal_weight=2780.29 1882.78 897.512 129.749 1514.36 767.762 368.415
+internal_count=13397 9195 4202 600 7357 3602 1838
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=3
+num_leaves=8
+num_cat=0
+split_feature=45 48 86 11 53 144 104
+split_gain=91.2855 30.6398 20.2224 17.4942 14.4128 10.3549 10.2483
+threshold=1.155 2.2850000000000006 3.3279569892473124 0.30952380952380959 3.4950000000000006 0.0075500000000000003 1.0005000000000002
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 -3 6 -6 -2
+right_child=4 3 -4 -5 5 -7 -8
+leaf_value=0.0040069597334026 -0.027086096902533729 -0.1715719381102207 0.053726932967164444 0.048773966775503941 -0.061900987427835959 -0.23396750965062757 0.016338062312498316
+leaf_weight=1510.6300098896027 782.38381478190422 3.9652889966964713 94.400425717234612 257.22131448984146 73.248439565300941 3.9991354644298545 63.84617380797863
+leaf_count=7369 3628 20 471 1306 331 18 297
+internal_value=-0.000943352 0.0123188 0.00693156 0.0454226 -0.0277441 -0.0708364 -0.0238097
+internal_weight=2789.69 1866.22 1605.03 261.187 923.478 77.2476 846.23
+internal_count=13440 9166 7840 1326 4274 349 3925
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=4
+num_leaves=8
+num_cat=0
+split_feature=49 35 6 53 44 126 38
+split_gain=70.6071 28.8927 21.165 13.8246 12.9797 16.2351 8.96833
+threshold=2.8350000000000004 3.3550000000000004 1541.1282734213053 1.9650000000000001 6.5150000000000006 1.8450000000000002 0.27603289214361998
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 -3 5 -2 -6
+right_child=4 3 -4 -5 6 -7 -8
+leaf_value=-0.0027517198054468608 -0.029319791370752923 0.043695199840511914 0.083541694807877556 0.015347179328011443 -0.028790250204289367 -0.099217570104222469 -0.0022072689999685109
+leaf_weight=937.29022094607353 417.85833202302456 320.03798474371433 31.915322333574295 449.0833810120821 218.10735833644867 39.50090055167675 374.63410261273384
+leaf_count=4582 1898 1642 157 2211 1027 181 1742
+internal_value=-0.000829778 0.0120602 9.06525e-05 0.0271434 -0.0221677 -0.0353586 -0.0119891
+internal_weight=2788.43 1738.33 969.206 769.121 1050.1 457.359 592.741
+internal_count=13440 8592 4739 3853 4848 2079 2769
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=5
+num_leaves=8
+num_cat=0
+split_feature=51 50 35 143 44 147 11
+split_gain=63.712 19.0005 13.7227 11.8206 10.6018 10.4763 6.84163
+threshold=1.2550000000000001 1.4650000000000001 5.035000000000001 -1.7974999999999997 7.2550000000000008 0.47199397614381194 2.6904761904761911
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 6 -5 -3 -2
+right_child=3 5 -4 4 -6 -7 -8
+leaf_value=0.0047544573525090221 -0.065940592898403594 0.046851958603355899 0.063639968219378742 -0.028402060075216558 -0.0037183287940315865 -0.010933729592520421 -0.2419531318087022
+leaf_weight=1547.0932241082191 6.1367039680480939 207.7856714874506 44.20663620531559 674.41967558860779 262.60369572043419 40.760551080107689 3.9397892653942108
+leaf_count=7584 28 1099 222 3053 1221 215 18
+internal_value=-0.000729499 0.0105766 0.00639068 -0.0226927 -0.0214845 0.0373753 -0.134921
+internal_weight=2786.95 1839.85 1591.3 947.1 937.023 248.546 10.0765
+internal_count=13440 9120 7806 4320 4274 1314 46
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=6
+num_leaves=8
+num_cat=0
+split_feature=41 130 54 48 99 34 105
+split_gain=42.6482 15.4832 11.8244 11.5616 10.5481 8.08864 5.28375
+threshold=1.9950000000000003 2.1835000000000004 1.6950000000000001 2.0250000000000004 0.13392857142857142 2.1550000000000007 1.0765000000000002
+decision_type=2 2 2 2 2 2 2
+left_child=1 3 4 -1 -2 -3 -4
+right_child=2 5 6 -5 -6 -7 -8
+leaf_value=-0.012930103603477631 -0.025837088447404372 0.009985288944886676 0.093598551064641447 0.0827918792790409 0.017739499999769957 0.067684395437451708 -0.011462427181900188
+leaf_weight=1452.7631230950356 63.837418377399445 116.17519751191139 30.260349631309509 13.827319353818892 1098.0612086355686 34.140560820698738 6.2758191227912894
+leaf_count=6763 321 560 163 70 5525 162 34
+internal_value=0.00228156 -0.00876197 0.0171813 -0.0120271 0.0153451 0.0230937 0.0755539
+internal_weight=2815.34 1616.91 1198.43 1466.59 1161.9 150.316 36.5362
+internal_count=13598 7555 6043 6833 5846 722 197
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=7
+num_leaves=8
+num_cat=0
+split_feature=41 135 130 21 53 102 16
+split_gain=34.421 15.4189 13.9177 13.4109 11.0563 13.2179 9.32957
+threshold=1.9950000000000003 -0.78349999999999997 2.1340000000000003 0.4642857142857143 2.0250000000000004 2.1120000000000005 1.5000000000000002
+decision_type=2 2 2 2 2 2 2
+left_child=1 3 -3 -1 6 -6 -2
+right_child=4 2 -4 -5 5 -7 -8
+leaf_value=-0.039029586512136658 0.021256889774663217 -0.0093694924607743823 0.024219953555788796 -0.18450744298912586 0.010656362766355395 -0.067587490182026186 0.059808579820180507
+leaf_weight=29.468837857246399 324.64188092947006 1435.6618839651346 147.77664601802826 8.9744612574577314 753.81818389892578 24.195777088403702 86.313260063529015
+leaf_count=138 1721 6659 716 42 3733 122 467
+internal_value=0.00205658 -0.00781819 -0.00623458 -0.0730278 0.0155268 0.00822224 0.0293551
+internal_weight=2810.85 1621.88 1583.44 38.4433 1188.97 778.014 410.955
+internal_count=13598 7555 7375 180 6043 3855 2188
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=8
+num_leaves=8
+num_cat=0
+split_feature=86 41 30 92 147 8 85
+split_gain=36.407 25.9777 15.256 11.9724 10.8984 10.2187 7.72256
+threshold=3.2679487179487183 1.8750000000000002 0.46841755319148942 -0.76249999999999984 0.40846280364372473 1.1083333333333336 -0.021724137931034445
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 -3 5 -2 -6
+right_child=4 3 -4 -5 6 -7 -8
+leaf_value=-0.012159457825842523 0.064307005786210347 -0.085506751396820332 -0.10592968794102031 0.0081406001340509019 0.027805156010338752 -0.1311253894779951 0.074917050140623179
+leaf_weight=1030.2416722476482 5.0686567574739438 14.960604444146155 19.18836584687233 1572.8768468499184 63.189212292432785 6.7883874922990799 94.08995346724987
+leaf_count=4724 25 75 92 7835 328 34 485
+internal_value=0.00185481 -0.00115146 -0.0138749 0.00725775 0.0487278 -0.0475971 0.055992
+internal_weight=2806.4 2637.27 1049.43 1587.84 169.136 11.857 157.279
+internal_count=13598 12726 4816 7910 872 59 813
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=9
+num_leaves=8
+num_cat=0
+split_feature=49 150 52 133 30 91 30
+split_gain=26.3296 13.6848 11.0511 10.5586 10.2017 14.0051 13.9274
+threshold=2.7850000000000006 0.60942003008724055 2.9650000000000003 2.7320000000000007 0.10822072072072071 -0.0024999999999999497 0.12681311751103638
+decision_type=2 2 2 2 2 2 2
+left_child=1 3 -3 -1 5 -2 -6
+right_child=4 2 -4 -5 6 -7 -8
+leaf_value=0.0056197323214787877 0.0092359089138059399 0.013793895571876906 0.055269467591862222 -0.056030521488072138 0.020769131467870516 -0.044189842169712931 -0.012941111969465513
+leaf_weight=1335.4049420952797 73.188210651278496 194.0794630497694 108.95535556972027 30.86374768614769 165.2346598058939 196.06340071558952 684.92604845762253
+leaf_count=6712 329 987 589 157 747 879 3130
+internal_value=0.000378046 0.00867093 0.0287076 0.00422669 -0.0119885 -0.0296678 -0.00638913
+internal_weight=2788.72 1669.3 303.035 1366.27 1119.41 269.252 850.161
+internal_count=13530 8445 1576 6869 5085 1208 3877
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=10
+num_leaves=8
+num_cat=0
+split_feature=86 41 0 6 110 102 53
+split_gain=38.5149 20.3558 13.2957 10.1636 9.93058 14.9217 6.02783
+threshold=3.0790020790020796 1.925 1413.5914823844723 1484.8073996434571 2.9500000000000006 0.65050000000000019 2.2150000000000003
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 -3 6 -6 -2
+right_child=4 3 -4 -5 5 -7 -8
+leaf_value=0.0140954684441122 0.085154679849317738 0.026410531501952182 -0.017501548280296479 0.0014621478522681489 -0.046636790759956186 0.037447698249607933 0.031794502065436499
+leaf_weight=167.52074213325977 60.752241030335426 211.7988056242466 1059.5275938510895 1090.8869259208441 27.780392900109291 130.96362222731113 37.02421498298645
+leaf_count=765 323 1076 4848 5512 140 685 181
+internal_value=0.000347101 -0.00355495 -0.0131877 0.00551857 0.0388267 0.0227309 0.0649542
+internal_weight=2786.25 2529.73 1227.05 1302.69 256.52 158.744 97.7765
+internal_count=13530 12201 5613 6588 1329 825 504
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=11
+num_leaves=8
+num_cat=0
+split_feature=86 122 0 132 136 148 145
+split_gain=31.5628 19.7563 17.5348 15.2483 8.699 11.6526 8.53686
+threshold=3.0790020790020796 1.0250000000000001 1444.5595683182339 0.68350000000000011 11.950000000000001 0.25070180469638537 0.63955000000000006
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 -3 6 -6 -2
+right_child=4 3 -4 -5 5 -7 -8
+leaf_value=-0.0029563172992086786 0.032749371502154893 -0.027078470578097272 -0.04142343191974563 0.004355282116605525 0.042836068356502124 -0.043229223026257771 0.082015555649337976
+leaf_weight=268.76511310040951 128.29142379760742 184.50364246964455 247.01660700142384 1831.7398000508547 37.845243006944656 31.138597756624222 54.413731098175049
+leaf_count=1259 680 882 1128 8932 205 158 286
+internal_value=0.000321355 -0.0031776 -0.0213793 0.00147871 0.0355201 0.00398648 0.0474254
+internal_weight=2783.71 2532.03 515.782 2016.24 251.689 68.9838 182.705
+internal_count=13530 12201 2387 9814 1329 363 966
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=12
+num_leaves=8
+num_cat=0
+split_feature=86 41 94 4 100 132 111
+split_gain=16.7583 14.9692 11.4051 9.36529 9.15568 6.8883 6.86331
+threshold=3.2679487179487183 2.5250000000000004 19.050000000000004 1454.7040164610194 2.1000000000000005 2.5855000000000006 1.6500000000000001
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 4 -2 -3 -5
+right_child=3 5 -4 6 -6 -7 -8
+leaf_value=-0.0077031612935488866 -0.037300665068035477 0.045270021664037818 0.0069515620157151858 0.08783804546768309 0.1145092464596736 -0.063294467679354607 0.032433508236858831
+leaf_weight=1642.7547204941511 28.476281866431236 95.693837076425552 889.01127083599567 33.729012683033943 5.0763863474130622 6.7911695241928092 86.767844557762146
+leaf_count=7824 156 540 4317 188 29 38 478
+internal_value=0.000977474 -0.000976496 -0.00255725 0.0343884 -0.0143067 0.0380708 0.0479478
+internal_weight=2788.3 2634.25 2531.77 154.05 33.5527 102.485 120.497
+internal_count=13570 12719 12141 851 185 578 666
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=13
+num_leaves=5
+num_cat=0
+split_feature=49 91 105 52
+split_gain=12.5399 11.9283 13.2239 11.4266
+threshold=6.7950000000000008 0.46333333333333332 0.66550000000000009 3.1450000000000005
+decision_type=2 2 2 2
+left_child=1 3 -3 -1
+right_child=-2 2 -4 -5
+leaf_value=-0.00096173862485333158 -0.18852641170062942 -0.26944729151735436 0.0083924991906715318 0.019698628791158867
+leaf_weight=2444.542382568121 3.7820855826139441 3.7145474255084983 3.7179097086191177 330.02280321717262
+leaf_count=11674 16 18 18 1844
+internal_value=0.000884212 0.00114235 -0.13063 0.00149578
+internal_weight=2785.78 2782 7.43246 2774.57
+internal_count=13570 13554 36 13518
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=14
+num_leaves=8
+num_cat=0
+split_feature=35 136 1 3 120 119 137
+split_gain=12.9995 12.1396 9.67922 10.2069 8.87434 8.76233 7.82142
+threshold=3.4050000000000007 3.1500000000000008 1408.1043210310934 1436.2136975970254 1.4145000000000001 0.55000000000000016 12.850000000000003
+decision_type=2 2 2 2 2 2 2
+left_child=1 5 3 -2 -3 -1 -4
+right_child=2 4 6 -5 -6 -7 -8
+leaf_value=-0.065982224130305259 -0.028897601514020019 -0.010800039930687538 0.014598348000302748 0.051779760894765055 0.0040416852382515198 0.035952394418601007 -0.011457004864636652
+leaf_weight=9.93890532851219 20.277691304683685 1238.979572609067 441.32176646590233 106.29307742416859 676.56466819345951 115.69292894005775 174.66667002439499
+leaf_count=46 107 5755 2341 568 3282 548 923
+internal_value=0.000796137 -0.00349957 0.0126042 0.0388534 -0.00555799 0.0278839 0.00721016
+internal_weight=2783.74 2041.18 742.559 126.571 1915.54 125.632 615.988
+internal_count=13570 9631 3939 675 9037 594 3264
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=15
+num_leaves=8
+num_cat=0
+split_feature=94 53 50 125 104 49 120
+split_gain=15.421 19.7004 11.5702 10.8982 8.46618 7.68903 0.284565
+threshold=17.550000000000004 3.5150000000000001 2.0050000000000003 0.45000000000000007 0.45750000000000007 1.8950000000000002 2.4365000000000001
+decision_type=2 2 2 2 2 2 2
+left_child=2 5 3 -1 -3 -2 -4
+right_child=1 4 6 -5 -6 -7 -8
+leaf_value=-0.040237205117941127 0.029193543717025244 -0.027898057080941927 0.13173751892789104 -0.0049749588986612815 -0.15821400423700094 0.0070086816806797644 0.089170490566897243
+leaf_weight=101.41680394113064 210.2422444075346 11.58236499130726 5.4282936453819257 1551.7522683441639 10.168696627020834 879.85487067699432 2.6328659802675247
+leaf_count=475 1114 49 33 7519 43 4276 17
+internal_value=-0.000172415 0.00932761 -0.00653072 -0.00713841 -0.0888634 0.0112876 0.117955
+internal_weight=2773.08 1111.85 1661.23 1653.17 21.7511 1090.1 8.06116
+internal_count=13526 5482 8044 7994 92 5390 50
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=16
+num_leaves=7
+num_cat=0
+split_feature=54 49 149 143 133 120
+split_gain=13.5064 12.5454 12.7609 9.52104 4.64479 6.69198
+threshold=1.6950000000000001 4.1950000000000012 0.53851874003189804 -2.5639999999999996 1.2110000000000001 3.0500000000000003
+decision_type=2 2 2 2 2 2
+left_child=1 3 -3 -1 -2 -6
+right_child=4 2 -4 -5 5 -7
+leaf_value=-0.11165723984306615 -0.022805170561939463 -0.034049153155287885 0.071318390164509041 0.0013999129723307169 0.11023664759120146 -0.064625521974505357
+leaf_weight=8.1074528247117978 4.7248373478651073 177.6567175835371 13.41981780529022 2544.4961924999952 20.898206591606144 2.6760432869195929
+leaf_count=41 29 758 58 12491 132 17
+internal_value=-0.000148839 -0.000887801 -0.0266458 0.00104042 0.0714722 0.0903692
+internal_weight=2771.98 2743.68 191.077 2552.6 28.2991 23.5742
+internal_count=13526 13348 816 12532 178 149
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=17
+num_leaves=8
+num_cat=0
+split_feature=86 89 86 49 89 92 95
+split_gain=12.3529 16.6857 29.867 13.2745 12.903 19.095 7.28162
+threshold=2.9298029556650254 1.0000000180025095e-35 1.8603896103896107 3.3850000000000002 0.27000000000000007 -0.62124999999999986 8.25
+decision_type=2 2 2 2 2 2 2
+left_child=1 3 -3 -1 6 -6 -2
+right_child=4 2 -4 -5 5 -7 -8
+leaf_value=0.0076740310795042049 0.058197093639699833 -0.094247277907232085 -0.010401082561217358 -0.014939641022089629 -0.10188514738956803 0.012348640807380216 -0.023647947189664047
+leaf_weight=1310.5064302459359 93.891582764685154 49.551189616322517 670.03426378965378 359.25059229135513 16.932199478149414 257.1773796081543 13.500689059495924
+leaf_count=6543 499 228 3182 1562 87 1350 75
+internal_value=-0.000128236 -0.00290891 -0.016176 0.00280861 0.0172867 0.00528897 0.0479069
+internal_weight=2770.84 2389.34 719.585 1669.76 381.502 274.11 107.392
+internal_count=13526 11515 3410 8105 2011 1437 574
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=18
+num_leaves=8
+num_cat=0
+split_feature=149 56 148 55 148 53 92
+split_gain=15.7381 11.9383 11.3441 10.9087 9.49514 8.82734 5.7139
+threshold=0.53433908520280893 1.7550000000000001 0.26796584848230992 1.905 0.24185130950380487 2.1250000000000004 -0.0099999999999999482
+decision_type=2 2 2 2 2 2 2
+left_child=1 4 -3 5 -1 -2 -5
+right_child=3 2 -4 6 -6 -7 -8
+leaf_value=-0.054435660661113758 0.0153280247340685 0.02819107947162831 -0.017585503842290271 -0.01840079708748131 -0.010724207322160621 -0.0015876644117591623 0.10180483948952145
+leaf_weight=56.825720146298409 617.56049958616495 165.87729875743389 91.08600726723671 5.4966678321361568 1074.6435647159815 732.60994145274162 19.602076262235641
+leaf_count=283 3334 829 448 27 4917 3565 97
+internal_value=-0.000486987 -0.00831442 0.0119646 0.00741537 -0.01292 0.00614953 0.0754826
+internal_weight=2763.7 1388.43 256.963 1375.27 1131.47 1350.17 25.0987
+internal_count=13500 6477 1277 7023 5200 6899 124
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=19
+num_leaves=8
+num_cat=0
+split_feature=149 55 135 1 27 115 54
+split_gain=12.6285 9.13776 8.91602 12.9607 10.4883 7.46851 5.43256
+threshold=0.53433908520280893 1.905 0.30550000000000005 1494.2870431281676 0.50845238095238099 2.3665000000000007 1.3650000000000004
+decision_type=2 2 2 2 2 2 2
+left_child=2 5 3 -1 -4 -2 -3
+right_child=1 6 4 -5 -6 -7 -8
+leaf_value=0.00092966247827827358 0.0028048803388890822 0.099877401856329082 -0.0069739024068449437 -0.021042388628781803 0.03967412519492558 0.027656530085268364 -0.011168774035797255
+leaf_weight=536.86709239333868 1198.4237125739455 17.76616628468037 128.78820982575417 638.33134125173092 88.204329490661621 147.51364935934544 6.5457842200994483
+leaf_count=2501 6137 84 598 2961 417 762 40
+internal_value=-0.000430022 0.0066729 -0.007421 -0.0110049 0.0119887 0.00552878 0.0699864
+internal_weight=2762.44 1370.25 1392.19 1175.2 216.993 1345.94 24.312
+internal_count=13500 7023 6477 5462 1015 6899 124
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=20
+num_leaves=8
+num_cat=0
+split_feature=86 29 98 85 111 54 1
+split_gain=13.4852 11.6743 11.2269 10.3561 7.51975 9.02896 6.85752
+threshold=3.3717607973421928 3.8452380952380958 0.53589743589743599 -0.45393665158371038 1.7500000000000002 1.3250000000000004 1430.9302281483838
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 -3 6 -6 -2
+right_child=4 3 -4 -5 5 -7 -8
+leaf_value=0.010161271773601144 -0.030831897558424644 -0.065476683381506764 -0.016436452248208454 0.004566632375329463 -0.017458853996767588 0.051773327327659352 0.10280712840651235
+leaf_weight=220.18627671897411 5.0186129659414318 23.250052616000175 793.86604422330856 1612.9528871029615 39.005949392914772 43.020874515175819 24.570655010640621
+leaf_count=1065 29 113 3791 7879 209 264 150
+internal_value=-0.000380007 -0.0018746 -0.0106611 0.00357096 0.0351053 0.0188533 0.0801381
+internal_weight=2761.87 2650.26 1014.05 1636.2 111.616 82.0268 29.5893
+internal_count=13500 12848 4856 7992 652 473 179
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=21
+num_leaves=7
+num_cat=0
+split_feature=96 132 136 141 123 120
+split_gain=13.2003 16.5388 14.2713 13.2653 11.1928 9.3524
+threshold=4.5000000000000009 0.64600000000000013 7.8500000000000005 -2.3269999999999995 0.73000000000000009 3.1835000000000004
+decision_type=2 2 2 2 2 2
+left_child=1 2 -1 -2 -3 -5
+right_child=3 4 -4 5 -6 -7
+leaf_value=-0.060960557629992744 -0.18516760103419116 0.033438636082733196 -0.0037890386549494811 0.011554321509567651 -0.0045660462097220139 -0.095189759824081052
+leaf_weight=104.0578725785017 3.772067964076995 88.859501846134663 87.140555322170258 867.02598301321268 1593.5797092542052 8.9989516139030439
+leaf_count=484 19 416 405 4401 7693 51
+internal_value=-0.000914735 -0.00585967 -0.0349056 0.00961608 -0.00255862 0.0104568
+internal_weight=2753.43 1873.64 191.198 879.797 1682.44 876.025
+internal_count=13469 8998 889 4471 8109 4452
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=22
+num_leaves=8
+num_cat=0
+split_feature=86 96 132 53 17 141 14
+split_gain=11.7147 10.9619 13.1395 10.1877 3.25091 5.30827 0.81688
+threshold=3.9466666666666668 4.5000000000000009 0.64600000000000013 2.0250000000000004 1.0000000180025095e-35 0.45950000000000008 0.63333333333333341
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 -3 5 -2 -6
+right_child=4 3 -4 -5 6 -7 -8
+leaf_value=-0.03177439277102298 0.091437039897303479 0.024857814401883283 -0.0029740838488449607 0.00058337476930965298 0.07050981561257999 -0.054418786094624118 0.13381725959363758
+leaf_weight=191.83759662508965 7.081265255808832 274.07831323891878 1669.0744777023792 596.54252921044827 3.1656967699527723 4.3793987929821014 7.6569998264312744
+leaf_count=880 41 1511 8020 2927 18 26 46
+internal_value=-0.000813155 -0.00142725 -0.00594319 0.00822535 0.0744319 0.0357031 0.115377
+internal_weight=2753.82 2731.53 1860.91 870.621 22.2834 11.4607 10.8227
+internal_count=13469 13338 8900 4438 131 67 64
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=23
+num_leaves=8
+num_cat=0
+split_feature=54 86 91 40 130 51 10
+split_gain=12.222 10.6549 10.0211 7.99429 8.88031 6.65012 3.52018
+threshold=1.5750000000000004 3.9115384615384619 0.46333333333333332 1.4550000000000003 3.4720000000000004 1.0650000000000002 1.2583333333333335
+decision_type=2 2 2 2 2 2 2
+left_child=1 2 -1 4 -2 -5 -3
+right_child=3 6 -4 5 -6 -7 -8
+leaf_value=-0.0022469666970611943 0.016170707495608474 0.01400989076087111 -0.12852821652405796 0.092762096676041614 -0.16668567356812367 0.014555154480083921 0.10191215472125625
+leaf_weight=2637.7453966140747 32.849811799824238 7.6601853668689754 6.834140643477439 32.402883179485798 3.1530902385711661 18.586706772446632 13.987728200852869
+leaf_count=12765 209 44 31 200 20 112 88
+internal_value=-0.000723185 -0.00197755 -0.00257376 0.0377177 0.000116917 0.0642621 0.0708273
+internal_weight=2753.22 2666.23 2644.58 86.9925 36.0029 50.9896 21.6479
+internal_count=13469 12928 12796 541 229 312 132
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=24
+num_leaves=7
+num_cat=0
+split_feature=49 137 127 10 121 40
+split_gain=12.6198 16.4964 12.4085 9.96103 9.53044 3.42942
+threshold=3.3850000000000002 2.8500000000000001 1.4365000000000003 4.3000000000000007 2.7750000000000004 2.7850000000000006
+decision_type=2 2 2 2 2 2
+left_child=3 5 -3 4 -1 -2
+right_child=1 2 -4 -5 -6 -7
+leaf_value=0.0033265147337535523 -0.2293764266460791 -0.0035570344953408817 -0.040820787979045939 -0.15680120567767367 0.077284263807364459 -0.095337183505289247
+leaf_weight=2143.9148783534765 3.9466704875230771 459.63925896584988 123.04180367290974 4.1842633336782447 19.085369817912579 4.4060309380292892
+leaf_count=10807 17 1977 532 22 100 19
+internal_value=-1.32262e-05 -0.0135124 -0.0114264 0.00366831 0.00397942 -0.158858
+internal_weight=2758.22 591.034 582.681 2167.18 2163 8.3527
+internal_count=13474 2545 2509 10929 10907 36
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=25
+num_leaves=8
+num_cat=0
+split_feature=49 6 94 56 127 104 7
+split_gain=10.0943 10.6231 11.9381 10.4719 9.83816 10.0485 9.11168
+threshold=3.3850000000000002 1485.1782059621762 17.550000000000004 2.1050000000000004 1.4365000000000003 0.42950000000000005 3.0523414722088096
+decision_type=2 2 2 2 2 2 2
+left_child=1 3 -3 -1 6 -6 -2
+right_child=4 2 -4 -5 5 -7 -8
+leaf_value=0.014478237478344286 -0.015463014973786505 -0.0073632514981839545 0.0096110031081186563 0.078530598053871026 -0.0086524748202200501 -0.067456935030922358 0.015332839885013407
+leaf_weight=308.73068219423294 309.28241093456745 1017.4353533536196 807.05070595443249 30.452116876840591 63.547168910503387 62.713204026222229 157.49458535015583
+leaf_count=1561 1327 5053 4143 172 271 269 678
+internal_value=-7.78723e-06 0.00329399 0.000145221 0.0202309 -0.012054 -0.0378634 -0.00507213
+internal_weight=2756.71 2163.67 1824.49 339.183 593.037 126.26 466.777
+internal_count=13474 10929 9196 1733 2545 540 2005
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=26
+num_leaves=7
+num_cat=0
+split_feature=86 78 86 86 106 45
+split_gain=11.0807 19.2113 13.2975 16.5366 9.93615 9.16974
+threshold=2.6909814323607431 3.2071428571428577 1.0000000180025095e-35 1.8603896103896107 0.59050000000000014 1.1950000000000001
+decision_type=2 2 2 2 2 2
+left_child=2 4 5 -4 -2 -1
+right_child=1 -3 3 -5 -6 -7
+leaf_value=0.0057285473288771786 0.034425187503696976 -0.17261249206353166 -0.072289780038289356 -0.011539802081962551 0.0051253977786566941 -0.012137044156937154
+leaf_weight=1235.6497117057443 188.36661138385534 5.9902653545141211 54.218099623918533 475.07240612059832 377.99835128337145 417.49042452871799
+leaf_count=6255 973 33 239 2206 1968 1800
+internal_value=-1.60817e-06 0.0129056 -0.00338664 -0.0177639 0.0148706 0.00121666
+internal_weight=2754.79 572.355 2182.43 529.291 566.365 1653.14
+internal_count=13474 2974 10500 2445 2941 8055
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=27
+num_leaves=8
+num_cat=0
+split_feature=148 141 115 107 104 4 134
+split_gain=10.1298 10.8135 12.2053 9.7232 15.8813 8.97442 7.29505
+threshold=0.17399267399267399 -0.79299999999999993 1.2250000000000003 0.40950000000000003 0.41250000000000003 1460.7202799166059 -0.30549999999999994
+decision_type=2 2 2 2 2 2 2
+left_child=3 5 -3 6 -5 -2 -1
+right_child=1 2 -4 4 -6 -7 -8
+leaf_value=-0.10259253098907423 -0.03269054108208802 -0.0021082423175915925 0.015308582355162925 -0.13734624687852556 0.036154293999346325 -0.0035202193944307817 0.042506627852751083
+leaf_weight=4.9914454817771938 183.7718341127038 1603.1649219617248 600.83727415651083 20.186357498168949 7.9962391257286063 304.1783049851656 15.20464117079973
+leaf_count=25 894 7830 2977 102 43 1483 78
+internal_value=-0.0013178 -0.00046824 0.00263984 -0.0485807 -0.0881234 -0.0145068 0.00661435
+internal_weight=2740.33 2691.95 2204 48.3787 28.1826 487.95 20.1961
+internal_count=13432 13184 10807 248 145 2377 103
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=28
+num_leaves=8
+num_cat=0
+split_feature=86 134 41 106 29 91 3
+split_gain=10.2371 10.3112 10.5364 10.1993 11.3955 9.81442 8.99213
+threshold=2.864250614250615 -0.43849999999999995 2.1050000000000004 0.95450000000000013 1.3875000000000002 -0.0054166666666666486 1670.9226550788155
+decision_type=2 2 2 2 2 2 2
+left_child=3 2 -2 5 -5 -1 -3
+right_child=1 6 -4 4 -6 -7 -8
+leaf_value=-0.015667034131642239 -0.063328583368563382 0.021380817595676553 0.036600112461665557 -0.21945686012767968 -0.026560041934415114 0.0014656039323343243 -0.12083840209161807
+leaf_weight=461.02843705564737 34.460375130176544 373.77398503571749 17.162777505815029 3.4011466801166526 135.53743974119425 1711.2739861980081 4.8830340132117263
+leaf_count=2173 176 1994 103 17 658 8282 29
+internal_value=-0.0011736 0.0135866 -0.0301054 -0.00392153 -0.0312966 -0.00217051 0.0195438
+internal_weight=2741.52 430.28 51.6232 2311.24 138.939 2172.3 378.657
+internal_count=13432 2302 279 11130 675 10455 2023
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=29
+num_leaves=8
+num_cat=0
+split_feature=12 84 115 141 136 11 110
+split_gain=9.65816 11.3219 10.1518 11.5361 10.0734 9.44977 6.86501
+threshold=1.0000000180025095e-35 0.017970779220779203 1.2915000000000003 -0.60349999999999981 3.6500000000000008 2.1083333333333338 27.750000000000004
+decision_type=2 2 2 2 2 2 2
+left_child=1 6 4 -4 -2 -3 -1
+right_child=2 5 3 -5 -6 -7 -8
+leaf_value=0.01695486197013276 0.018272032851518873 -0.047853351303007219 -0.020306195413698006 0.015936397489881831 -0.011004436565708025 0.099082325881264394 -0.086337197330871959
+leaf_weight=520.42061326652765 139.83185759186745 50.192012257874012 122.51054417341948 430.93686553835869 1465.7739738970995 5.2428159564733496 7.0750899761915198
+leaf_count=2585 694 257 602 2140 7090 29 35
+internal_value=-0.00104481 0.0108592 -0.00425885 0.00791357 -0.00845466 -0.0339422 0.0155679
+internal_weight=2741.98 582.931 2159.05 553.447 1605.61 55.4348 527.496
+internal_count=13432 2906 10526 2742 7784 286 2620
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=30
+num_leaves=8
+num_cat=0
+split_feature=120 37 38 38 105 85 100
+split_gain=12.0364 15.2453 11.9342 13.3835 10.0958 10.0578 10.0546
+threshold=1.4145000000000001 0.42084520042422408 0.23906113829447367 0.14593794095256588 1.1225000000000003 -0.37141065830721004 1.3650000000000002
+decision_type=2 2 2 2 2 2 2
+left_child=2 6 3 -1 -3 -4 -2
+right_child=1 4 5 -5 -6 -7 -8
+leaf_value=-0.12038760226397151 -0.0090962232080863334 0.0034107878159782353 -0.060847190395801561 0.022337300771774038 -0.044228760615057698 -0.0060312321358531873 0.030222541925148897
+leaf_weight=7.3577561527490607 84.787891268730164 592.17002998292446 37.370823763310909 228.1118380650878 52.602927520871162 1330.5593820437789 423.39983003586531
+leaf_count=43 418 3057 171 1205 280 6147 2165
+internal_value=0.00204154 0.0101633 -0.00379863 0.0178729 -0.00047641 -0.00752917 0.0236624
+internal_weight=2756.36 1152.96 1603.4 235.47 644.773 1367.93 508.188
+internal_count=13486 5920 7566 1248 3337 6318 2583
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=31
+num_leaves=8
+num_cat=0
+split_feature=86 86 86 1 127 31 122
+split_gain=10.4188 10.4465 11.9432 10.3703 15.906 10.0392 7.58651
+threshold=2.6939799331103687 1.0000000180025095e-35 1.8603896103896107 1467.9553804385575 2.1340000000000003 0.22980739360049704 2.3515000000000006
+decision_type=2 2 2 2 2 2 2
+left_child=1 6 -3 5 -5 -2 -1
+right_child=3 2 -4 4 -6 -7 -8
+leaf_value=0.0014256124570572787 0.0018941270616234518 -0.058645654322910731 -0.0086447877444392647 0.02733676763309234 -0.12590863729503893 -0.082423971345859881 0.042831761918242235
+leaf_weight=1608.7813730537891 166.28092505782843 58.273459360003471 473.13907171785831 372.2649027556181 7.4961845725774756 16.887378051877022 49.535993173718452
+leaf_count=7823 854 251 2181 1963 43 94 277
+internal_value=0.00183871 -0.00141235 -0.0141287 0.0144848 0.0243088 -0.0058835 0.0026627
+internal_weight=2752.66 2189.73 531.413 562.929 379.761 183.168 1658.32
+internal_count=13486 10532 2432 2954 2006 948 8100
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=32
+num_leaves=8
+num_cat=0
+split_feature=93 144 112 94 104 93 120
+split_gain=10.1721 16.1941 11.2364 13.1765 9.78364 8.96284 8.48134
+threshold=20.550000000000004 0.013450000000000002 0.22750000000000004 5.5500000000000007 1.2865000000000002 25.050000000000004 2.0500000000000003
+decision_type=2 2 2 2 2 2 2
+left_child=2 4 6 -4 -2 -3 -1
+right_child=1 5 3 -5 -6 -7 -8
+leaf_value=0.0034609471658556285 0.020050548549636428 -0.009082334773132807 -0.11004659616173273 -0.007594432301873808 -0.021486441758403186 -0.13291664093499478 0.038221762386595907
+leaf_weight=820.33589915931225 594.67776323109865 13.120999380946161 13.791079476475714 1142.1561977639794 68.70460732281208 12.293409973382948 84.046657353639603
+leaf_count=3903 3054 71 65 5495 382 70 446
+internal_value=0.0016558 0.0126202 -0.00200979 -0.00881763 0.0157484 -0.0690103 0.00669173
+internal_weight=2749.13 688.797 2060.33 1155.95 663.382 25.4144 904.383
+internal_count=13486 3577 9909 5560 3436 141 4349
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=33
+num_leaves=5
+num_cat=0
+split_feature=91 141 123 7
+split_gain=9.64033 9.54985 10.2025 8.96435
+threshold=0.46333333333333332 -0.77399999999999991 2.2565000000000004 33.104396552584838
+decision_type=2 2 2 2
+left_child=1 3 -3 -1
+right_child=-2 2 -4 -5
+leaf_value=-0.015732035707484552 -0.12133869515037762 0.0038526132779362976 0.057630556320633561 0.031446003016811871
+leaf_weight=450.80555958300829 6.9056807309389105 2188.2216669544578 38.992827542126179 48.440718069672585
+leaf_count=2162 31 10769 213 250
+internal_value=0.00156231 0.00187402 0.00479437 -0.011154
+internal_weight=2733.37 2726.46 2227.21 499.246
+internal_count=13425 13394 10982 2412
+is_linear=0
+shrinkage=0.104222
+
+
+Tree=34
+num_leaves=5
+num_cat=0
+split_feature=45 98 80 123
+split_gain=8.19305 8.37131 12.6079 10.3403
+threshold=1.6150000000000004 0.58114035087719318 0.043836402184014855 2.1715000000000004
+decision_type=2 2 2 2
+left_child=1 3 -3 -1
+right_child=-2 2 -4 -5
+leaf_value=0.008356015193296689 -0.14816646007318571 -0.033602776052007143 0.00019618273212945178 0.070940775625300892
+leaf_weight=749.49989224970341 3.9628616422414771 128.32899699360132 1819.463518589735 29.807201541960239
+leaf_count=3713 16 704 8830 162
+internal_value=0.00140384 0.0016217 -0.00203079 0.0107506
+internal_weight=2731.06 2727.1 1947.79 779.307
+internal_count=13425 13409 9534 3875
+is_linear=0
+shrinkage=0.104222
+
+
+end of trees
+
+feature_importances:
+Column_86=18
+Column_49=9
+Column_53=8
+Column_41=6
+Column_54=6
+Column_120=6
+Column_141=6
+Column_45=5
+Column_91=5
+Column_104=5
+Column_1=4
+Column_94=4
+Column_96=4
+Column_132=4
+Column_136=4
+Column_148=4
+Column_3=3
+Column_4=3
+Column_6=3
+Column_11=3
+Column_30=3
+Column_35=3
+Column_38=3
+Column_48=3
+Column_50=3
+Column_85=3
+Column_92=3
+Column_105=3
+Column_115=3
+Column_123=3
+Column_127=3
+Column_130=3
+Column_133=3
+Column_149=3
+Column_150=3
+Column_0=2
+Column_7=2
+Column_10=2
+Column_16=2
+Column_29=2
+Column_40=2
+Column_44=2
+Column_51=2
+Column_52=2
+Column_55=2
+Column_56=2
+Column_89=2
+Column_93=2
+Column_98=2
+Column_100=2
+Column_102=2
+Column_106=2
+Column_110=2
+Column_111=2
+Column_122=2
+Column_134=2
+Column_135=2
+Column_137=2
+Column_143=2
+Column_144=2
+Column_147=2
+Column_8=1
+Column_12=1
+Column_14=1
+Column_17=1
+Column_21=1
+Column_27=1
+Column_28=1
+Column_31=1
+Column_34=1
+Column_37=1
+Column_39=1
+Column_46=1
+Column_47=1
+Column_78=1
+Column_80=1
+Column_84=1
+Column_95=1
+Column_99=1
+Column_107=1
+Column_112=1
+Column_119=1
+Column_121=1
+Column_125=1
+Column_126=1
+Column_145=1
+
+parameters:
+[boosting: gbdt]
+[objective: binary]
+[metric: binary_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1500]
+[learning_rate: 0.104222]
+[num_leaves: 8]
+[num_threads: 4]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 3]
+[min_data_in_leaf: 17]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.709098]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 3]
+[bagging_seed: 400]
+[bagging_by_query: 0]
+[feature_fraction: 0.58888]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 30056]
+[extra_trees: 0]
+[extra_seed: 12879]
+[early_stopping_round: 0]
+[early_stopping_min_delta: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 7.81041e-07]
+[lambda_l2: 0.00942891]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 17869]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: -1]
+[saved_feature_importance_type: 0]
+[use_quantized_grad: 0]
+[num_grad_quant_bins: 4]
+[quant_train_renew_leaf: 0]
+[stochastic_rounding: 1]
+[linear_tree: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 175]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[parser_config_file: ]
+[objective_seed: 16083]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[lambdarank_position_bias_regularization: 0]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:null

ai-engine/models/v25_ensemble.py

@@ -0,0 +1,735 @@
+"""
+V25 Ensemble Predictor - NO TARGET LEAKAGE
+===========================================
+Multi-model ensemble for match prediction using XGBoost and LightGBM.
+
+Features:
+- 73 engineered features (NO target leakage)
+- Market-specific models (MS, OU25, BTTS)
+- Weighted ensemble predictions
+- Value bet detection
+"""
+
+import os
+import json
+import numpy as np
+import pandas as pd
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass, field
+
+import xgboost as xgb
+import lightgbm as lgb
+
+import sys
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+try:
+    from config.config_loader import get_config as _get_cfg
+except ImportError:
+    _get_cfg = None  # type: ignore[assignment]
+
+# CatBoost is optional
+try:
+    from catboost import CatBoostClassifier
+    CATBOOST_AVAILABLE = True
+except ImportError:
+    CatBoostClassifier = None
+    CATBOOST_AVAILABLE = False
+
+# Paths
+MODELS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'v25')
+
+
+@dataclass
+class MarketPrediction:
+    """Prediction for a single betting market."""
+    market_type: str
+    pick: str
+    probability: float
+    confidence: float
+    odds: float = 0.0
+    is_value_bet: bool = False
+    edge: float = 0.0
+
+    def to_dict(self) -> dict:
+        return {
+            'market_type': self.market_type,
+            'pick': self.pick,
+            'probability': round(self.probability * 100, 1),
+            'confidence': round(self.confidence, 1),
+            'odds': self.odds,
+            'is_value_bet': self.is_value_bet,
+            'edge': round(self.edge * 100, 1),
+        }
+
+
+@dataclass
+class ValueBet:
+    """Detected value bet opportunity."""
+    market_type: str
+    pick: str
+    probability: float
+    odds: float
+    edge: float
+    confidence: float
+
+    def to_dict(self) -> dict:
+        return {
+            'market_type': self.market_type,
+            'pick': self.pick,
+            'probability': round(self.probability * 100, 1),
+            'odds': self.odds,
+            'edge': round(self.edge * 100, 1),
+            'confidence': round(self.confidence, 1),
+        }
+
+
+@dataclass
+class MatchPrediction:
+    """Complete match prediction with all markets."""
+    match_id: str
+    home_team: str
+    away_team: str
+    
+    # MS predictions
+    home_prob: float = 0.0
+    draw_prob: float = 0.0
+    away_prob: float = 0.0
+    ms_pick: str = ''
+    ms_confidence: float = 0.0
+    
+    # OU25 predictions
+    over_prob: float = 0.0
+    under_prob: float = 0.0
+    ou25_pick: str = ''
+    ou25_confidence: float = 0.0
+    
+    # BTTS predictions
+    btts_yes_prob: float = 0.0
+    btts_no_prob: float = 0.0
+    btts_pick: str = ''
+    btts_confidence: float = 0.0
+    
+    # Value bets
+    value_bets: List[ValueBet] = field(default_factory=list)
+    
+    def to_dict(self) -> dict:
+        return {
+            'match_id': self.match_id,
+            'home_team': self.home_team,
+            'away_team': self.away_team,
+            'ms': {
+                'home_prob': round(self.home_prob * 100, 1),
+                'draw_prob': round(self.draw_prob * 100, 1),
+                'away_prob': round(self.away_prob * 100, 1),
+                'pick': self.ms_pick,
+                'confidence': round(self.ms_confidence, 1),
+            },
+            'ou25': {
+                'over_prob': round(self.over_prob * 100, 1),
+                'under_prob': round(self.under_prob * 100, 1),
+                'pick': self.ou25_pick,
+                'confidence': round(self.ou25_confidence, 1),
+            },
+            'btts': {
+                'yes_prob': round(self.btts_yes_prob * 100, 1),
+                'no_prob': round(self.btts_no_prob * 100, 1),
+                'pick': self.btts_pick,
+                'confidence': round(self.btts_confidence, 1),
+            },
+            'value_bets': [vb.to_dict() for vb in self.value_bets],
+        }
+
+
+class V25Predictor:
+    """
+    V25 Ensemble Predictor - NO TARGET LEAKAGE
+    
+    Uses market-specific XGBoost and LightGBM models.
+    Each market (MS, OU25, BTTS) has its own trained models.
+    """
+    
+    # Feature columns — loaded dynamically from feature_cols.json to stay
+    # in sync with the trained models.  The hardcoded list below is only a
+    # fallback in case the JSON file is missing.
+    _FALLBACK_FEATURE_COLS = [
+        # ELO Features (8)
+        'home_overall_elo', 'away_overall_elo', 'elo_diff',
+        'home_home_elo', 'away_away_elo',
+        'home_form_elo', 'away_form_elo', 'form_elo_diff',
+        
+        # Form Features (12)
+        'home_goals_avg', 'home_conceded_avg',
+        'away_goals_avg', 'away_conceded_avg',
+        'home_clean_sheet_rate', 'away_clean_sheet_rate',
+        'home_scoring_rate', 'away_scoring_rate',
+        'home_winning_streak', 'away_winning_streak',
+        'home_unbeaten_streak', 'away_unbeaten_streak',
+        
+        # H2H Features (6)
+        'h2h_total_matches', 'h2h_home_win_rate', 'h2h_draw_rate',
+        'h2h_avg_goals', 'h2h_btts_rate', 'h2h_over25_rate',
+        
+        # Team Stats Features (8)
+        'home_avg_possession', 'away_avg_possession',
+        'home_avg_shots_on_target', 'away_avg_shots_on_target',
+        'home_shot_conversion', 'away_shot_conversion',
+        'home_avg_corners', 'away_avg_corners',
+        
+        # Odds Features (24)
+        'odds_ms_h', 'odds_ms_d', 'odds_ms_a',
+        'implied_home', 'implied_draw', 'implied_away',
+        'odds_ht_ms_h', 'odds_ht_ms_d', 'odds_ht_ms_a',
+        'odds_ou05_o', 'odds_ou05_u',
+        'odds_ou15_o', 'odds_ou15_u',
+        'odds_ou25_o', 'odds_ou25_u',
+        'odds_ou35_o', 'odds_ou35_u',
+        'odds_ht_ou05_o', 'odds_ht_ou05_u',
+        'odds_ht_ou15_o', 'odds_ht_ou15_u',
+        'odds_btts_y', 'odds_btts_n',
+        
+        # Odds Presence Flags (20)
+        'odds_ms_h_present', 'odds_ms_d_present', 'odds_ms_a_present',
+        'odds_ht_ms_h_present', 'odds_ht_ms_d_present', 'odds_ht_ms_a_present',
+        'odds_ou05_o_present', 'odds_ou05_u_present',
+        'odds_ou15_o_present', 'odds_ou15_u_present',
+        'odds_ou25_o_present', 'odds_ou25_u_present',
+        'odds_ou35_o_present', 'odds_ou35_u_present',
+        'odds_ht_ou05_o_present', 'odds_ht_ou05_u_present',
+        'odds_ht_ou15_o_present', 'odds_ht_ou15_u_present',
+        'odds_btts_y_present', 'odds_btts_n_present',
+        
+        # League Features (4)
+        'home_xga', 'away_xga',
+        'league_avg_goals', 'league_zero_goal_rate',
+        
+        # Upset Engine (4)
+        'upset_atmosphere', 'upset_motivation', 'upset_fatigue', 'upset_potential',
+        
+        # Referee Engine (5)
+        'referee_home_bias', 'referee_avg_goals', 'referee_cards_total',
+        'referee_avg_yellow', 'referee_experience',
+        
+        # Momentum Engine (3)
+        'home_momentum_score', 'away_momentum_score', 'momentum_diff',
+
+        # Squad Features (9)
+        'home_squad_quality', 'away_squad_quality', 'squad_diff',
+        'home_key_players', 'away_key_players',
+        'home_missing_impact', 'away_missing_impact',
+        'home_goals_form', 'away_goals_form',
+    ]
+
+    @staticmethod
+    def _load_feature_cols() -> list:
+        """Load feature columns from feature_cols.json, falling back to hardcoded list."""
+        feature_json = os.path.join(MODELS_DIR, 'feature_cols.json')
+        try:
+            if os.path.exists(feature_json):
+                with open(feature_json, 'r', encoding='utf-8') as f:
+                    cols = json.load(f)
+                if isinstance(cols, list) and len(cols) > 0:
+                    print(f"[V25] Loaded {len(cols)} feature columns from feature_cols.json")
+                    return cols
+        except Exception as e:
+            print(f"[V25] Warning: could not load feature_cols.json: {e}")
+        print(f"[V25] Using fallback feature columns ({len(V25Predictor._FALLBACK_FEATURE_COLS)} features)")
+        return V25Predictor._FALLBACK_FEATURE_COLS
+
+    # Model weights for ensemble (overridden from config in __init__)
+    DEFAULT_WEIGHTS = {
+        'xgb': 0.50,
+        'lgb': 0.50,
+    }
+
+    def __init__(self, models_dir: Optional[str] = None):
+        """
+        Initialize V25 Predictor.
+        
+        Args:
+            models_dir: Directory containing model files. Defaults to v25/ directory.
+        """
+        self.models_dir = models_dir or MODELS_DIR
+        self.models = {}  # market -> {'xgb': model, 'lgb': model}
+        self._loaded = False
+        self.FEATURE_COLS = self._load_feature_cols()
+        # Load weights from config (falls back to class default 0.50/0.50)
+        if _get_cfg is not None:
+            try:
+                cfg = _get_cfg()
+                self.DEFAULT_WEIGHTS = {
+                    'xgb': float(cfg.get('model_ensemble.xgb_weight', 0.50)),
+                    'lgb': float(cfg.get('model_ensemble.lgb_weight', 0.50)),
+                }
+            except Exception:
+                pass  # keep class-level defaults
+    
+    # All trained market models available in V25
+    ALL_MARKETS = [
+        'ms', 'ou25', 'btts',           # Core markets
+        'ou15', 'ou35',                  # Additional OU lines
+        'ht_result', 'ht_ou05', 'ht_ou15',  # HT markets
+        'htft',                          # HT/FT combo
+        'cards_ou45',                    # Cards market
+        'handicap_ms',                   # Handicap
+        'odd_even',                      # Odd/Even goals
+    ]
+    
+    # Multi-class markets (output > 2 classes)
+    MULTICLASS_MARKETS = {'ms', 'ht_result', 'htft', 'handicap_ms'}
+    
+    def load_models(self) -> bool:
+        """Load all market-specific models from disk."""
+        try:
+            loaded_count = 0
+            
+            for market in self.ALL_MARKETS:
+                self.models[market] = {}
+                
+                # Load XGBoost (read content in Python to avoid non-ASCII path issues)
+                xgb_path = os.path.join(self.models_dir, f'xgb_v25_{market}.json')
+                if os.path.exists(xgb_path) and os.path.getsize(xgb_path) > 0:
+                    with open(xgb_path, 'r', encoding='utf-8') as f:
+                        xgb_content = f.read()
+                    booster = xgb.Booster()
+                    booster.load_model(bytearray(xgb_content, 'utf-8'))
+                    # Corruption detection: verify model can run a dummy prediction
+                    try:
+                        _dummy = pd.DataFrame([{col: 0.0 for col in self.FEATURE_COLS}])
+                        booster.predict(xgb.DMatrix(_dummy))
+                        self.models[market]['xgb'] = booster
+                        loaded_count += 1
+                    except Exception as _ce:
+                        print(f"[V25] ⚠️ XGB model for {market} failed integrity check: {_ce} — skipping")
+
+                # Load LightGBM (read content in Python to avoid non-ASCII path issues)
+                lgb_path = os.path.join(self.models_dir, f'lgb_v25_{market}.txt')
+                if os.path.exists(lgb_path) and os.path.getsize(lgb_path) > 0:
+                    with open(lgb_path, 'r', encoding='utf-8') as f:
+                        model_str = f.read()
+                    lgb_model = lgb.Booster(model_str=model_str)
+                    # Corruption detection: verify model can run a dummy prediction
+                    try:
+                        _dummy = pd.DataFrame([{col: 0.0 for col in self.FEATURE_COLS}])
+                        lgb_model.predict(_dummy)
+                        self.models[market]['lgb'] = lgb_model
+                        loaded_count += 1
+                    except Exception as _ce:
+                        print(f"[V25] ⚠️ LGB model for {market} failed integrity check: {_ce} — skipping")
+
+                # Remove empty entries
+                if not self.models[market]:
+                    del self.models[market]
+
+            print(f"[V25] Loaded {loaded_count} model files across {len(self.models)} markets: {list(self.models.keys())}")
+            self._loaded = loaded_count > 0
+            return self._loaded
+            
+        except Exception as e:
+            print(f"[ERROR] Error loading models: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+    
+    def _ensure_loaded(self):
+        """Ensure models are loaded before prediction."""
+        if not self._loaded:
+            if not self.load_models():
+                raise RuntimeError("Failed to load V25 models")
+
+    def readiness_summary(self) -> Dict[str, Any]:
+        """Return per-market model status for health check endpoint."""
+        if not self._loaded:
+            self.load_models()
+        market_status = {}
+        for market in self.ALL_MARKETS:
+            m = self.models.get(market, {})
+            market_status[market] = {
+                "xgb": "xgb" in m,
+                "lgb": "lgb" in m,
+                "ready": bool(m),
+            }
+        loaded_markets = [k for k, v in market_status.items() if v["ready"]]
+        return {
+            "fully_loaded": len(loaded_markets) == len(self.ALL_MARKETS),
+            "loaded_markets": loaded_markets,
+            "missing_markets": [m for m in self.ALL_MARKETS if m not in loaded_markets],
+            "weights": self.DEFAULT_WEIGHTS,
+        }
+
+    def _prepare_features(self, features: Dict[str, float]) -> pd.DataFrame:
+        """Prepare feature vector for prediction."""
+        X = pd.DataFrame([{col: features.get(col, 0.0) for col in self.FEATURE_COLS}])
+        return X
+    
+    def predict_ms(self, features: Dict[str, float]) -> tuple:
+        """
+        Predict match result (1X2).
+        
+        Returns:
+            (home_prob, draw_prob, away_prob)
+        """
+        self._ensure_loaded()
+        
+        X = self._prepare_features(features)
+        probs = []
+        
+        # XGBoost
+        if 'xgb' in self.models.get('ms', {}):
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models['ms']['xgb'].predict(dmat)
+            if len(xgb_proba.shape) == 1:
+                xgb_proba = np.array([xgb_proba])
+            probs.append(xgb_proba[0] * self.DEFAULT_WEIGHTS['xgb'])
+        
+        # LightGBM
+        if 'lgb' in self.models.get('ms', {}):
+            lgb_proba = self.models['ms']['lgb'].predict(X)
+            if len(lgb_proba.shape) == 2:
+                probs.append(lgb_proba[0] * self.DEFAULT_WEIGHTS['lgb'])
+        
+        if not probs:
+            return 0.33, 0.33, 0.33
+        
+        ensemble_proba = np.sum(probs, axis=0)
+        ensemble_proba = ensemble_proba / ensemble_proba.sum()
+        
+        return float(ensemble_proba[0]), float(ensemble_proba[1]), float(ensemble_proba[2])
+    
+    def predict_ou25(self, features: Dict[str, float]) -> tuple:
+        """
+        Predict Over/Under 2.5 goals.
+        
+        Returns:
+            (over_prob, under_prob)
+        """
+        self._ensure_loaded()
+        
+        X = self._prepare_features(features)
+        probs = []
+        
+        # XGBoost
+        if 'xgb' in self.models.get('ou25', {}):
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models['ou25']['xgb'].predict(dmat)
+            if isinstance(xgb_proba, np.ndarray) and len(xgb_proba.shape) == 1:
+                probs.append(xgb_proba[0])
+        
+        # LightGBM
+        if 'lgb' in self.models.get('ou25', {}):
+            lgb_proba = self.models['ou25']['lgb'].predict(X)
+            if isinstance(lgb_proba, np.ndarray):
+                probs.append(lgb_proba[0])
+        
+        if not probs:
+            return 0.5, 0.5
+        
+        # Average probability
+        avg_prob = np.mean(probs)
+        
+        return float(avg_prob), float(1 - avg_prob)
+    
+    def predict_btts(self, features: Dict[str, float]) -> tuple:
+        """
+        Predict Both Teams To Score.
+        
+        Returns:
+            (yes_prob, no_prob)
+        """
+        self._ensure_loaded()
+        
+        X = self._prepare_features(features)
+        probs = []
+        
+        # XGBoost
+        if 'xgb' in self.models.get('btts', {}):
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models['btts']['xgb'].predict(dmat)
+            if isinstance(xgb_proba, np.ndarray) and len(xgb_proba.shape) == 1:
+                probs.append(xgb_proba[0])
+        
+        # LightGBM
+        if 'lgb' in self.models.get('btts', {}):
+            lgb_proba = self.models['btts']['lgb'].predict(X)
+            if isinstance(lgb_proba, np.ndarray):
+                probs.append(lgb_proba[0])
+        
+        if not probs:
+            return 0.5, 0.5
+        
+        # Average probability
+        avg_prob = np.mean(probs)
+        
+        return float(avg_prob), float(1 - avg_prob)
+    
+    def predict_market(self, market: str, features: Dict[str, float]) -> Optional[np.ndarray]:
+        """
+        Generic prediction for any loaded market.
+        
+        Args:
+            market: Market key (e.g. 'ht_result', 'htft', 'cards_ou45')
+            features: Feature dictionary.
+            
+        Returns:
+            numpy array of probabilities.
+            For binary markets: [positive_prob]
+            For multi-class markets: [class0_prob, class1_prob, ...]
+        """
+        self._ensure_loaded()
+        
+        if market not in self.models:
+            return None
+        
+        X = self._prepare_features(features)
+        probs = []
+        weights = []
+        is_multiclass = market in self.MULTICLASS_MARKETS
+        
+        # XGBoost
+        if 'xgb' in self.models[market]:
+            dmat = xgb.DMatrix(X)
+            xgb_proba = self.models[market]['xgb'].predict(dmat)
+            if isinstance(xgb_proba, np.ndarray):
+                if is_multiclass and len(xgb_proba.shape) == 2:
+                    probs.append(xgb_proba[0])
+                elif is_multiclass and len(xgb_proba.shape) == 1:
+                    probs.append(xgb_proba)
+                else:
+                    probs.append(np.array([xgb_proba[0]]))
+            weights.append(self.DEFAULT_WEIGHTS['xgb'])
+        
+        # LightGBM
+        if 'lgb' in self.models[market]:
+            lgb_proba = self.models[market]['lgb'].predict(X)
+            if isinstance(lgb_proba, np.ndarray):
+                if is_multiclass and len(lgb_proba.shape) == 2:
+                    probs.append(lgb_proba[0])
+                elif is_multiclass and len(lgb_proba.shape) == 1:
+                    probs.append(lgb_proba)
+                else:
+                    probs.append(np.array([lgb_proba[0]]))
+            weights.append(self.DEFAULT_WEIGHTS['lgb'])
+        
+        if not probs:
+            return None
+        
+        # Weighted average
+        if len(probs) == 1:
+            return probs[0]
+        
+        total_w = sum(weights[:len(probs)])
+        result = np.zeros_like(probs[0])
+        for p, w in zip(probs, weights):
+            result += p * (w / total_w)
+        
+        # Normalize multi-class
+        if is_multiclass and result.sum() > 0:
+            result = result / result.sum()
+        
+        return result
+    
+    def has_market(self, market: str) -> bool:
+        """Check if a specific market model is loaded."""
+        return market in self.models
+    
+    def predict_match(
+        self,
+        match_id: str,
+        home_team: str,
+        away_team: str,
+        features: Dict[str, float],
+        odds: Optional[Dict[str, float]] = None,
+    ) -> MatchPrediction:
+        """
+        Predict all markets for a match.
+        
+        Args:
+            match_id: Match identifier.
+            home_team: Home team name.
+            away_team: Away team name.
+            features: Feature dictionary.
+            odds: Optional odds dictionary for value bet detection.
+            
+        Returns:
+            MatchPrediction object.
+        """
+        # Get predictions for each market
+        home_prob, draw_prob, away_prob = self.predict_ms(features)
+        over_prob, under_prob = self.predict_ou25(features)
+        btts_yes_prob, btts_no_prob = self.predict_btts(features)
+        
+        # Determine picks
+        ms_probs = {'1': home_prob, 'X': draw_prob, '2': away_prob}
+        ms_pick = max(ms_probs, key=ms_probs.__getitem__)
+        ms_confidence = ms_probs[ms_pick] * 100
+        
+        ou25_probs = {'Over': over_prob, 'Under': under_prob}
+        ou25_pick = max(ou25_probs, key=ou25_probs.__getitem__)
+        ou25_confidence = ou25_probs[ou25_pick] * 100
+        
+        btts_probs = {'Yes': btts_yes_prob, 'No': btts_no_prob}
+        btts_pick = max(btts_probs, key=btts_probs.__getitem__)
+        btts_confidence = btts_probs[btts_pick] * 100
+        
+        # Create prediction
+        prediction = MatchPrediction(
+            match_id=match_id,
+            home_team=home_team,
+            away_team=away_team,
+            home_prob=home_prob,
+            draw_prob=draw_prob,
+            away_prob=away_prob,
+            ms_pick=ms_pick,
+            ms_confidence=ms_confidence,
+            over_prob=over_prob,
+            under_prob=under_prob,
+            ou25_pick=ou25_pick,
+            ou25_confidence=ou25_confidence,
+            btts_yes_prob=btts_yes_prob,
+            btts_no_prob=btts_no_prob,
+            btts_pick=btts_pick,
+            btts_confidence=btts_confidence,
+        )
+        
+        # Detect value bets
+        if odds:
+            prediction.value_bets = self._detect_value_bets(
+                prediction, odds, home_prob, draw_prob, away_prob,
+                over_prob, under_prob, btts_yes_prob, btts_no_prob
+            )
+        
+        return prediction
+    
+    def _detect_value_bets(
+        self,
+        prediction: MatchPrediction,
+        odds: Dict[str, float],
+        home_prob: float,
+        draw_prob: float,
+        away_prob: float,
+        over_prob: float,
+        under_prob: float,
+        btts_yes_prob: float,
+        btts_no_prob: float,
+    ) -> List[ValueBet]:
+        """Detect value bets based on model vs market odds."""
+        value_bets = []
+        # Market-specific minimum edge thresholds
+        # MS: higher variance → require more edge
+        # OU/BTTS: binary markets → tighter edge acceptable
+        EDGE_THRESHOLDS = {
+            'MS': 0.06,
+            'OU25': 0.04,
+            'BTTS': 0.04,
+        }
+        ms_edge = EDGE_THRESHOLDS['MS']
+        ou_edge = EDGE_THRESHOLDS['OU25']
+        btts_edge = EDGE_THRESHOLDS['BTTS']
+
+        # MS value bets
+        if 'ms_h' in odds and odds['ms_h'] > 0:
+            implied = 1 / odds['ms_h']
+            edge = home_prob - implied
+            if edge > ms_edge:
+                value_bets.append(ValueBet(
+                    market_type='MS',
+                    pick='1',
+                    probability=home_prob,
+                    odds=odds['ms_h'],
+                    edge=edge,
+                    confidence=home_prob * 100,
+                ))
+        
+        if 'ms_d' in odds and odds['ms_d'] > 0:
+            implied = 1 / odds['ms_d']
+            edge = draw_prob - implied
+            if edge > ms_edge:
+                value_bets.append(ValueBet(
+                    market_type='MS',
+                    pick='X',
+                    probability=draw_prob,
+                    odds=odds['ms_d'],
+                    edge=edge,
+                    confidence=draw_prob * 100,
+                ))
+        
+        if 'ms_a' in odds and odds['ms_a'] > 0:
+            implied = 1 / odds['ms_a']
+            edge = away_prob - implied
+            if edge > ms_edge:
+                value_bets.append(ValueBet(
+                    market_type='MS',
+                    pick='2',
+                    probability=away_prob,
+                    odds=odds['ms_a'],
+                    edge=edge,
+                    confidence=away_prob * 100,
+                ))
+        
+        # OU25 value bets
+        if 'ou25_o' in odds and odds['ou25_o'] > 0:
+            implied = 1 / odds['ou25_o']
+            edge = over_prob - implied
+            if edge > ou_edge:
+                value_bets.append(ValueBet(
+                    market_type='OU25',
+                    pick='Over',
+                    probability=over_prob,
+                    odds=odds['ou25_o'],
+                    edge=edge,
+                    confidence=over_prob * 100,
+                ))
+        
+        if 'ou25_u' in odds and odds['ou25_u'] > 0:
+            implied = 1 / odds['ou25_u']
+            edge = under_prob - implied
+            if edge > ou_edge:
+                value_bets.append(ValueBet(
+                    market_type='OU25',
+                    pick='Under',
+                    probability=under_prob,
+                    odds=odds['ou25_u'],
+                    edge=edge,
+                    confidence=under_prob * 100,
+                ))
+        
+        # BTTS value bets
+        if 'btts_y' in odds and odds['btts_y'] > 0:
+            implied = 1 / odds['btts_y']
+            edge = btts_yes_prob - implied
+            if edge > btts_edge:
+                value_bets.append(ValueBet(
+                    market_type='BTTS',
+                    pick='Yes',
+                    probability=btts_yes_prob,
+                    odds=odds['btts_y'],
+                    edge=edge,
+                    confidence=btts_yes_prob * 100,
+                ))
+        
+        if 'btts_n' in odds and odds['btts_n'] > 0:
+            implied = 1 / odds['btts_n']
+            edge = btts_no_prob - implied
+            if edge > btts_edge:
+                value_bets.append(ValueBet(
+                    market_type='BTTS',
+                    pick='No',
+                    probability=btts_no_prob,
+                    odds=odds['btts_n'],
+                    edge=edge,
+                    confidence=btts_no_prob * 100,
+                ))
+        
+        return value_bets
+
+
+# Singleton instance
+_v25_predictor: Optional[V25Predictor] = None
+
+
+def get_v25_predictor() -> V25Predictor:
+    """Get or create V25 predictor instance."""
+    global _v25_predictor
+    if _v25_predictor is None:
+        _v25_predictor = V25Predictor()
+        _v25_predictor.load_models()
+    return _v25_predictor

ai-engine/models/v27_predictor.py

@@ -0,0 +1,343 @@
+"""
+V27 Pro Predictor — Odds-Free Fundamentals + Value Edge Detection
+
+This module loads V27 ensemble models (XGBoost, LightGBM, CatBoost)
+and produces market-independent probability estimates.
+
+The key insight: V27 is trained WITHOUT odds features, so it produces
+"true" probabilities unbiased by market pricing. The divergence between
+V25 (odds-aware) and V27 (odds-free) predictions signals market mispricing.
+"""
+
+import json
+import logging
+import os
+import pickle
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+V27_DIR = Path(__file__).parent / "v27"
+
+
+class V27Predictor:
+    """
+    Loads V27 ensemble models and provides predictions using the
+    82-feature odds-free vector.
+    """
+
+    MARKETS = ['ms', 'ou25', 'btts']
+
+    def __init__(self):
+        self.models: Dict[str, Dict[str, object]] = {}
+        self.feature_cols: List[str] = []
+        self._loaded = False
+
+    def load_models(self) -> bool:
+        """Load all V27 ensemble models and feature column spec."""
+        if self._loaded:
+            return True
+
+        # Feature columns
+        cols_path = V27_DIR / "v27_feature_cols.json"
+        if not cols_path.exists():
+            logger.error("[V27] Feature columns file not found: %s", cols_path)
+            return False
+
+        try:
+            with open(cols_path, "r", encoding="utf-8") as f:
+                self.feature_cols = json.load(f)
+            logger.info("[V27] Loaded %d feature columns", len(self.feature_cols))
+        except Exception as e:
+            logger.error("[V27] Failed to load feature columns: %s", e)
+            return False
+
+        # Load models per market
+        model_types = {"xgb": "xgb", "lgb": "lgb"}
+
+        for market in self.MARKETS:
+            self.models[market] = {}
+            for short, label in model_types.items():
+                # Try market-specific file first: v27_ms_xgb.pkl
+                path = V27_DIR / f"v27_{market}_{short}.pkl"
+                if not path.exists():
+                    # Fallback to generic: v27_xgboost.pkl (for MS only)
+                    generic_names = {"xgb": "v27_xgboost.pkl", "lgb": "v27_lightgbm.pkl", "cb": "v27_catboost.pkl"}
+                    path = V27_DIR / generic_names.get(short, "")
+                    if not path.exists():
+                        logger.warning("[V27] Model file not found for %s/%s", market, short)
+                        continue
+
+                try:
+                    with open(path, "rb") as f:
+                        model = pickle.load(f)
+                    self.models[market][label] = model
+                    logger.info("[V27] ✓ Loaded %s/%s from %s", market, label, path.name)
+                except Exception as e:
+                    logger.error("[V27] ✗ Failed to load %s/%s: %s", market, label, e)
+
+        loaded_count = sum(len(v) for v in self.models.values())
+        if loaded_count == 0:
+            logger.error("[V27] No models loaded!")
+            return False
+
+        self._loaded = True
+        logger.info("[V27] Total models loaded: %d across %d markets", loaded_count, len(self.models))
+        return True
+
+    def _build_feature_array(self, features: Dict[str, float]) -> np.ndarray:
+        """
+        Build ordered feature array from the full feature dict.
+        V27 uses only its 82 features (odds-free subset).
+        """
+        row = []
+        for col in self.feature_cols:
+            row.append(float(features.get(col, 0.0)))
+        return np.array([row])
+
+    def _predict_with_model(self, model, X: np.ndarray, label: str, expected_classes: int) -> Optional[np.ndarray]:
+        """
+        Predict probabilities from a model, handling both sklearn wrappers
+        (predict_proba) and raw Booster objects (predict).
+
+        For raw XGBoost Boosters, DMatrix is created WITH feature_names
+        to match the training schema.
+        """
+        import xgboost as xgb
+        import lightgbm as lgbm
+        import pandas as pd
+
+        # 1. Try sklearn-style predict_proba first
+        if hasattr(model, 'predict_proba'):
+            try:
+                proba = model.predict_proba(X)[0]
+                if len(proba) == expected_classes:
+                    return proba
+                logger.warning("[V27] %s predict_proba returned %d classes, expected %d", label, len(proba), expected_classes)
+            except Exception:
+                pass  # Fall through to raw predict
+
+        # 2. Raw xgboost.Booster — MUST pass feature_names
+        if isinstance(model, xgb.Booster):
+            try:
+                feature_names = self.feature_cols if self.feature_cols else None
+                dmat = xgb.DMatrix(X, feature_names=feature_names)
+                raw = model.predict(dmat)
+                if isinstance(raw, np.ndarray):
+                    if raw.ndim == 2 and raw.shape[1] == expected_classes:
+                        return raw[0]
+                    elif raw.ndim == 1 and expected_classes == 2:
+                        p = float(raw[0])
+                        return np.array([1.0 - p, p])
+                    elif raw.ndim == 1 and len(raw) == expected_classes:
+                        return raw
+            except Exception as e:
+                logger.warning("[V27] %s xgb.Booster predict failed: %s", label, e)
+                return None
+
+        # 3. Raw lightgbm.Booster — pass as DataFrame with column names
+        if isinstance(model, lgbm.Booster):
+            try:
+                if self.feature_cols:
+                    X_named = pd.DataFrame(X, columns=self.feature_cols)
+                    raw = model.predict(X_named)
+                else:
+                    raw = model.predict(X)
+                if isinstance(raw, np.ndarray):
+                    if raw.ndim == 2 and raw.shape[1] == expected_classes:
+                        return raw[0]
+                    elif raw.ndim == 1 and expected_classes == 2:
+                        p = float(raw[0])
+                        return np.array([1.0 - p, p])
+                    elif raw.ndim == 1 and len(raw) == expected_classes:
+                        return raw
+            except Exception as e:
+                logger.warning("[V27] %s lgb.Booster predict failed: %s", label, e)
+                return None
+
+        # 4. Generic fallback (CatBoost, etc.)
+        try:
+            if hasattr(model, 'predict'):
+                raw = model.predict(X)
+                if isinstance(raw, np.ndarray):
+                    if raw.ndim == 2 and raw.shape[1] == expected_classes:
+                        return raw[0]
+                    elif raw.ndim == 1 and expected_classes == 2:
+                        p = float(raw[0])
+                        return np.array([1.0 - p, p])
+                    elif raw.ndim == 1 and len(raw) == expected_classes:
+                        return raw
+        except Exception as e:
+            logger.warning("[V27] %s generic predict failed: %s", label, e)
+
+        return None
+
+    def predict_ms(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Match Score probabilities (Home/Draw/Away).
+        Returns dict with keys: home, draw, away.
+        """
+        if not self._loaded or "ms" not in self.models or not self.models["ms"]:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models["ms"].items():
+            proba = self._predict_with_model(model, X, f"MS/{label}", expected_classes=3)
+            if proba is not None and len(proba) == 3:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        # Ensemble average
+        avg = np.mean(probs_list, axis=0)
+        return {
+            "home": float(avg[0]),
+            "draw": float(avg[1]),
+            "away": float(avg[2]),
+        }
+
+    def predict_ou25(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Over/Under 2.5 probabilities.
+        Returns dict with keys: under, over.
+        """
+        if not self._loaded or "ou25" not in self.models or not self.models["ou25"]:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models["ou25"].items():
+            proba = self._predict_with_model(model, X, f"OU25/{label}", expected_classes=2)
+            if proba is not None and len(proba) == 2:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        avg = np.mean(probs_list, axis=0)
+        return {
+            "under": float(avg[0]),
+            "over": float(avg[1]),
+        }
+
+    def predict_btts(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Both Teams To Score probabilities.
+        Returns dict with keys: no, yes.
+        """
+        if not self._loaded or 'btts' not in self.models or not self.models['btts']:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models['btts'].items():
+            proba = self._predict_with_model(model, X, f'BTTS/{label}', expected_classes=2)
+            if proba is not None and len(proba) == 2:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        avg = np.mean(probs_list, axis=0)
+        return {
+            'no': float(avg[0]),
+            'yes': float(avg[1]),
+        }
+
+    def predict_dc(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Double Chance probabilities.
+
+        DC is algebraically derived from MS predictions:
+          1X = home + draw
+          X2 = draw + away
+          12 = home + away
+
+        This gives an odds-free DC estimate for divergence detection.
+        """
+        ms_probs = self.predict_ms(features)
+        if not ms_probs:
+            return None
+
+        home = ms_probs['home']
+        draw = ms_probs['draw']
+        away = ms_probs['away']
+
+        return {
+            '1x': round(home + draw, 4),
+            'x2': round(draw + away, 4),
+            '12': round(home + away, 4),
+        }
+
+    def predict_all(self, features: Dict[str, float]) -> Dict[str, Optional[Dict[str, float]]]:
+        """Run predictions for all supported markets."""
+        return {
+            'ms': self.predict_ms(features),
+            'ou25': self.predict_ou25(features),
+            'btts': self.predict_btts(features),
+            'dc': self.predict_dc(features),
+        }
+
+
+def compute_divergence(
+    v25_probs: Dict[str, float],
+    v27_probs: Dict[str, float],
+) -> Dict[str, float]:
+    """
+    Compute the divergence signal between V25 (odds-aware) and V27 (odds-free).
+
+    Positive divergence = V27 thinks it's MORE likely than the market → VALUE BET
+    Negative divergence = V27 thinks it's LESS likely than the market → PASS
+
+    Returns per-outcome divergence values.
+    """
+    divergence = {}
+    for key in v27_probs:
+        v25_val = v25_probs.get(key, 0.33)
+        v27_val = v27_probs.get(key, 0.33)
+        divergence[key] = round(v27_val - v25_val, 4)
+    return divergence
+
+
+def compute_value_edge(
+    v25_probs: Dict[str, float],
+    v27_probs: Dict[str, float],
+    odds: Dict[str, float],
+) -> Dict[str, Dict]:
+    """
+    Detect value bets by combining V25/V27 divergence with odds.
+
+    A value bet exists when:
+    1. V27 (odds-free) probability > implied odds probability  (model says it's underpriced)
+    2. V27 and V25 divergence is positive  (V27 sees more signal than the market)
+
+    Returns per-outcome: { probability, implied_prob, edge, is_value }
+    """
+    results = {}
+    for key in v27_probs:
+        v27_p = v27_probs[key]
+        v25_p = v25_probs.get(key, 0.33)
+        odds_val = odds.get(key, 0.0)
+
+        implied_p = (1.0 / odds_val) if odds_val > 1.01 else 0.0
+        divergence = v27_p - v25_p
+        edge = v27_p - implied_p if implied_p > 0 else 0.0
+
+        results[key] = {
+            "v27_prob": round(v27_p, 4),
+            "v25_prob": round(v25_p, 4),
+            "implied_prob": round(implied_p, 4),
+            "divergence": round(divergence, 4),
+            "edge": round(edge, 4),
+            "is_value": edge > 0.05 and divergence > 0.02,  # 5% edge + 2% divergence
+        }
+
+    return results

ai-engine/reports/backtest_consistency.json

@@ -0,0 +1,160 @@
+{
+  "total_test": 23039,
+  "thresholds": {
+    "0.0": {
+      "n_matches": 22227,
+      "pct": 96.5,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.5363,
+          "avg_roi": -0.0046,
+          "total_roi": -103.02
+        },
+        "ou15": {
+          "hit_rate": 0.7463,
+          "avg_roi": 0.0144,
+          "total_roi": 319.02
+        },
+        "ou25": {
+          "hit_rate": 0.6111,
+          "avg_roi": -0.006,
+          "total_roi": -134.41
+        },
+        "ou35": {
+          "hit_rate": 0.7302,
+          "avg_roi": -0.014,
+          "total_roi": -310.51
+        },
+        "btts": {
+          "hit_rate": 0.5848,
+          "avg_roi": 0.0031,
+          "total_roi": 69.5
+        }
+      }
+    },
+    "0.1": {
+      "n_matches": 23033,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.546,
+          "avg_roi": -0.0045,
+          "total_roi": -104.38
+        },
+        "ou15": {
+          "hit_rate": 0.7533,
+          "avg_roi": 0.0145,
+          "total_roi": 335.02
+        },
+        "ou25": {
+          "hit_rate": 0.6193,
+          "avg_roi": -0.0042,
+          "total_roi": -96.97
+        },
+        "ou35": {
+          "hit_rate": 0.7277,
+          "avg_roi": -0.0147,
+          "total_roi": -338.57
+        },
+        "btts": {
+          "hit_rate": 0.5886,
+          "avg_roi": 0.0025,
+          "total_roi": 57.21
+        }
+      }
+    },
+    "0.2": {
+      "n_matches": 23034,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.5459,
+          "avg_roi": -0.0046,
+          "total_roi": -105.38
+        },
+        "ou15": {
+          "hit_rate": 0.7533,
+          "avg_roi": 0.0146,
+          "total_roi": 335.26
+        },
+        "ou25": {
+          "hit_rate": 0.6193,
+          "avg_roi": -0.0043,
+          "total_roi": -97.97
+        },
+        "ou35": {
+          "hit_rate": 0.7276,
+          "avg_roi": -0.0147,
+          "total_roi": -339.57
+        },
+        "btts": {
+          "hit_rate": 0.5887,
+          "avg_roi": 0.0025,
+          "total_roi": 57.62
+        }
+      }
+    },
+    "0.3": {
+      "n_matches": 23039,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.546,
+          "avg_roi": -0.0045,
+          "total_roi": -103.45
+        },
+        "ou15": {
+          "hit_rate": 0.7534,
+          "avg_roi": 0.0146,
+          "total_roi": 335.6
+        },
+        "ou25": {
+          "hit_rate": 0.6194,
+          "avg_roi": -0.0042,
+          "total_roi": -97.44
+        },
+        "ou35": {
+          "hit_rate": 0.7277,
+          "avg_roi": -0.0147,
+          "total_roi": -339.26
+        },
+        "btts": {
+          "hit_rate": 0.5887,
+          "avg_roi": 0.0025,
+          "total_roi": 58.61
+        }
+      }
+    },
+    "0.5": {
+      "n_matches": 23039,
+      "pct": 100.0,
+      "markets": {
+        "ms": {
+          "hit_rate": 0.546,
+          "avg_roi": -0.0045,
+          "total_roi": -103.45
+        },
+        "ou15": {
+          "hit_rate": 0.7534,
+          "avg_roi": 0.0146,
+          "total_roi": 335.6
+        },
+        "ou25": {
+          "hit_rate": 0.6194,
+          "avg_roi": -0.0042,
+          "total_roi": -97.44
+        },
+        "ou35": {
+          "hit_rate": 0.7277,
+          "avg_roi": -0.0147,
+          "total_roi": -339.26
+        },
+        "btts": {
+          "hit_rate": 0.5887,
+          "avg_roi": 0.0025,
+          "total_roi": 58.61
+        }
+      }
+    }
+  }
+}

ai-engine/reports/backtest_real_odds.json

@@ -0,0 +1,5 @@
+[
+  {
+    "market": "MS-Ev",
+    "min_edge": 0.02,
+    "n": 

ai-engine/reports/backtest_results.json

@@ -0,0 +1,267 @@
+{
+  "generated_at": "2026-05-15T21:40:57.995899",
+  "matches_processed": 3000,
+  "matches_skipped": 0,
+  "markets": {
+    "MS": {
+      "overall_accuracy": 54.97,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "<50%": {
+          "accuracy": 38.87,
+          "count": 759,
+          "mean_confidence": 45.58
+        },
+        "50-65%": {
+          "accuracy": 52.62,
+          "count": 1300,
+          "mean_confidence": 57.19
+        },
+        "65-75%": {
+          "accuracy": 66.99,
+          "count": 624,
+          "mean_confidence": 69.49
+        },
+        "75%+": {
+          "accuracy": 79.5,
+          "count": 317,
+          "mean_confidence": 80.69
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 46.77,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 58.73,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 56.25,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 55.03,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "1": {
+          "accuracy": 58.38,
+          "count": 1946,
+          "mean_confidence": 60.84
+        },
+        "2": {
+          "accuracy": 48.72,
+          "count": 1053,
+          "mean_confidence": 56.44
+        },
+        "X": {
+          "accuracy": 0.0,
+          "count": 1,
+          "mean_confidence": 56.07
+        }
+      }
+    },
+    "OU15": {
+      "overall_accuracy": 74.4,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "50-65%": {
+          "accuracy": 70.97,
+          "count": 62,
+          "mean_confidence": 59.63
+        },
+        "65-75%": {
+          "accuracy": 68.0,
+          "count": 275,
+          "mean_confidence": 71.1
+        },
+        "75%+": {
+          "accuracy": 75.14,
+          "count": 2663,
+          "mean_confidence": 89.44
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 67.74,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 76.19,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 70.31,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 74.6,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "Over": {
+          "accuracy": 74.4,
+          "count": 3000,
+          "mean_confidence": 87.14
+        }
+      }
+    },
+    "OU25": {
+      "overall_accuracy": 51.77,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "50-65%": {
+          "accuracy": 49.33,
+          "count": 1267,
+          "mean_confidence": 57.13
+        },
+        "65-75%": {
+          "accuracy": 54.53,
+          "count": 453,
+          "mean_confidence": 69.42
+        },
+        "75%+": {
+          "accuracy": 53.2,
+          "count": 1280,
+          "mean_confidence": 90.2
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 41.94,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 50.79,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 43.75,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 52.19,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "Over": {
+          "accuracy": 51.03,
+          "count": 2432,
+          "mean_confidence": 76.11
+        },
+        "Under": {
+          "accuracy": 54.93,
+          "count": 568,
+          "mean_confidence": 60.17
+        }
+      }
+    },
+    "BTTS": {
+      "overall_accuracy": 51.83,
+      "total_matches": 3000,
+      "by_confidence_band": {
+        "50-65%": {
+          "accuracy": 48.74,
+          "count": 2214,
+          "mean_confidence": 58.66
+        },
+        "65-75%": {
+          "accuracy": 60.42,
+          "count": 758,
+          "mean_confidence": 68.19
+        },
+        "75%+": {
+          "accuracy": 64.29,
+          "count": 28,
+          "mean_confidence": 77.44
+        }
+      },
+      "by_league": {
+        "Bundesliga": {
+          "accuracy": 54.84,
+          "count": 62
+        },
+        "Ligue 1": {
+          "accuracy": 50.79,
+          "count": 63
+        },
+        "Serie A": {
+          "accuracy": 57.81,
+          "count": 64
+        },
+        "Other": {
+          "accuracy": 51.65,
+          "count": 2811
+        }
+      },
+      "by_pick_direction": {
+        "No": {
+          "accuracy": 50.26,
+          "count": 2099,
+          "mean_confidence": 61.56
+        },
+        "Yes": {
+          "accuracy": 55.49,
+          "count": 901,
+          "mean_confidence": 60.51
+        }
+      }
+    }
+  },
+  "calibration": {
+    "ms_home": {
+      "brier_score": 0.2054,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.026574",
+      "mean_predicted": 0.4942,
+      "mean_actual": 0.46
+    },
+    "ms_draw": {
+      "brier_score": 0.1846,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.030886",
+      "mean_predicted": 0.149,
+      "mean_actual": 0.2493
+    },
+    "ms_away": {
+      "brier_score": 0.1726,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.033980",
+      "mean_predicted": 0.3567,
+      "mean_actual": 0.2907
+    },
+    "ou15": {
+      "brier_score": 0.1884,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.037204",
+      "mean_predicted": 0.8714,
+      "mean_actual": 0.744
+    },
+    "ou25": {
+      "brier_score": 0.247,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.041152",
+      "mean_predicted": 0.6924,
+      "mean_actual": 0.499
+    },
+    "btts": {
+      "brier_score": 0.2453,
+      "calibration_error": 0.0,
+      "sample_count": 3000,
+      "last_trained": "2026-05-15T21:40:58.044344",
+      "mean_predicted": 0.4506,
+      "mean_actual": 0.5147
+    }
+  },
+  "runtime_seconds": 94.1
+}

ai-engine/reports/training_v25/v25_market_metrics.json

@@ -1,8 +1,8 @@
 {
-  "trained_at": "2026-04-14 17:20:03",
+  "trained_at": "2026-05-06 15:53:36",
   "market_results": {
     "MS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -107,19 +107,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.8938,
+      "xgb_accuracy": 0.5437,
-      "xgb_logloss": 0.2263,
+      "xgb_logloss": 0.9429,
-      "lgb_accuracy": 0.8938,
+      "lgb_accuracy": 0.5436,
-      "lgb_logloss": 0.2214,
+      "lgb_logloss": 0.9423,
-      "ensemble_accuracy": 0.8945,
+      "ensemble_accuracy": 0.5442,
-      "ensemble_logloss": 0.2226,
+      "ensemble_logloss": 0.9418,
       "class_count": 3
     },
     "OU15": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -224,19 +224,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9088,
+      "xgb_accuracy": 0.753,
-      "xgb_logloss": 0.1758,
+      "xgb_logloss": 0.5256,
-      "lgb_accuracy": 0.9067,
+      "lgb_accuracy": 0.7523,
-      "lgb_logloss": 0.1783,
+      "lgb_logloss": 0.5262,
-      "ensemble_accuracy": 0.9108,
+      "ensemble_accuracy": 0.7533,
-      "ensemble_logloss": 0.1753,
+      "ensemble_logloss": 0.5254,
       "class_count": 2
     },
     "OU25": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -341,19 +341,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9204,
+      "xgb_accuracy": 0.6253,
-      "xgb_logloss": 0.1535,
+      "xgb_logloss": 0.635,
-      "lgb_accuracy": 0.9224,
+      "lgb_accuracy": 0.6246,
-      "lgb_logloss": 0.1523,
+      "lgb_logloss": 0.6347,
-      "ensemble_accuracy": 0.9217,
+      "ensemble_accuracy": 0.6262,
-      "ensemble_logloss": 0.1518,
+      "ensemble_logloss": 0.6343,
       "class_count": 2
     },
     "OU35": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -458,19 +458,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9578,
+      "xgb_accuracy": 0.7283,
-      "xgb_logloss": 0.1171,
+      "xgb_logloss": 0.5463,
-      "lgb_accuracy": 0.9564,
+      "lgb_accuracy": 0.7304,
-      "lgb_logloss": 0.1144,
+      "lgb_logloss": 0.546,
-      "ensemble_accuracy": 0.9571,
+      "ensemble_accuracy": 0.7297,
-      "ensemble_logloss": 0.1149,
+      "ensemble_logloss": 0.5456,
       "class_count": 2
     },
     "BTTS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -575,19 +575,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9238,
+      "xgb_accuracy": 0.5894,
-      "xgb_logloss": 0.1439,
+      "xgb_logloss": 0.6636,
-      "lgb_accuracy": 0.9265,
+      "lgb_accuracy": 0.5928,
-      "lgb_logloss": 0.143,
+      "lgb_logloss": 0.6633,
-      "ensemble_accuracy": 0.9265,
+      "ensemble_accuracy": 0.5897,
-      "ensemble_logloss": 0.1424,
+      "ensemble_logloss": 0.6628,
       "class_count": 2
     },
     "HT_RESULT": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -692,19 +692,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.5627,
+      "xgb_accuracy": 0.4695,
-      "xgb_logloss": 0.8712,
+      "xgb_logloss": 1.0174,
-      "lgb_accuracy": 0.5715,
+      "lgb_accuracy": 0.4677,
-      "lgb_logloss": 0.8649,
+      "lgb_logloss": 1.0166,
-      "ensemble_accuracy": 0.5811,
+      "ensemble_accuracy": 0.4688,
-      "ensemble_logloss": 0.8649,
+      "ensemble_logloss": 1.0164,
       "class_count": 3
     },
     "HT_OU05": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -809,19 +809,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.7221,
+      "xgb_accuracy": 0.7011,
-      "xgb_logloss": 0.5122,
+      "xgb_logloss": 0.5939,
-      "lgb_accuracy": 0.7268,
+      "lgb_accuracy": 0.7002,
-      "lgb_logloss": 0.5092,
+      "lgb_logloss": 0.5936,
-      "ensemble_accuracy": 0.7275,
+      "ensemble_accuracy": 0.7009,
-      "ensemble_logloss": 0.5084,
+      "ensemble_logloss": 0.5932,
       "class_count": 2
     },
     "HT_OU15": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -926,19 +926,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.752,
+      "xgb_accuracy": 0.6723,
-      "xgb_logloss": 0.5252,
+      "xgb_logloss": 0.6126,
-      "lgb_accuracy": 0.7595,
+      "lgb_accuracy": 0.6736,
-      "lgb_logloss": 0.5213,
+      "lgb_logloss": 0.6118,
-      "ensemble_accuracy": 0.7595,
+      "ensemble_accuracy": 0.6734,
-      "ensemble_logloss": 0.5192,
+      "ensemble_logloss": 0.6117,
       "class_count": 2
     },
     "HTFT": {
-      "samples": 9786,
+      "samples": 103208,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -1043,19 +1043,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6850,
+      "train_samples": 72245,
-      "val_samples": 1468,
+      "val_samples": 15481,
-      "test_samples": 1468,
+      "test_samples": 15482,
-      "xgb_accuracy": 0.5136,
+      "xgb_accuracy": 0.3337,
-      "xgb_logloss": 1.1384,
+      "xgb_logloss": 1.8208,
-      "lgb_accuracy": 0.5184,
+      "lgb_accuracy": 0.3332,
-      "lgb_logloss": 1.1469,
+      "lgb_logloss": 1.8203,
-      "ensemble_accuracy": 0.5143,
+      "ensemble_accuracy": 0.3358,
-      "ensemble_logloss": 1.1339,
+      "ensemble_logloss": 1.8186,
       "class_count": 9
     },
     "ODD_EVEN": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -1160,19 +1160,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.8863,
+      "xgb_accuracy": 0.5296,
-      "xgb_logloss": 0.3565,
+      "xgb_logloss": 0.6841,
-      "lgb_accuracy": 0.8802,
+      "lgb_accuracy": 0.5359,
-      "lgb_logloss": 0.3338,
+      "lgb_logloss": 0.6822,
-      "ensemble_accuracy": 0.8863,
+      "ensemble_accuracy": 0.531,
-      "ensemble_logloss": 0.3423,
+      "ensemble_logloss": 0.6826,
       "class_count": 2
     },
     "CARDS_OU45": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -1277,19 +1277,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.6283,
+      "xgb_accuracy": 0.6009,
-      "xgb_logloss": 0.6174,
+      "xgb_logloss": 0.6489,
-      "lgb_accuracy": 0.6413,
+      "lgb_accuracy": 0.5988,
-      "lgb_logloss": 0.615,
+      "lgb_logloss": 0.6487,
-      "ensemble_accuracy": 0.6372,
+      "ensemble_accuracy": 0.6024,
-      "ensemble_logloss": 0.6142,
+      "ensemble_logloss": 0.6479,
       "class_count": 2
     },
     "HANDICAP_MS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -1394,15 +1394,15 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.936,
+      "xgb_accuracy": 0.6058,
-      "xgb_logloss": 0.1903,
+      "xgb_logloss": 0.8691,
-      "lgb_accuracy": 0.9346,
+      "lgb_accuracy": 0.608,
-      "lgb_logloss": 0.1843,
+      "lgb_logloss": 0.8677,
-      "ensemble_accuracy": 0.936,
+      "ensemble_accuracy": 0.6068,
-      "ensemble_logloss": 0.1861,
+      "ensemble_logloss": 0.8677,
       "class_count": 3
     }
   }

ai-engine/reports/training_v25/v25_pro_metrics.json

@@ -0,0 +1,692 @@
+{
+  "trained_at": "2026-05-20 21:06:17",
+  "trainer": "v25_pro",
+  "optuna_trials": 50,
+  "total_features": 152,
+  "markets": {
+    "MS": {
+      "market": "MS",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.01813533005537975,
+        "subsample": 0.6006077004659048,
+        "colsample_bytree": 0.5355789322633149,
+        "min_child_weight": 6,
+        "gamma": 1.3630627811027698e-08,
+        "reg_lambda": 0.000883795032251414,
+        "reg_alpha": 0.8575321662422171
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.020992610123700125,
+        "feature_fraction": 0.5951238173352622,
+        "bagging_fraction": 0.6452606057786048,
+        "bagging_freq": 5,
+        "min_child_samples": 16,
+        "lambda_l1": 0.2277044617439506,
+        "lambda_l2": 1.1531219311866832e-05
+      },
+      "xgb_best_iteration": 329,
+      "lgb_best_iteration": 171,
+      "xgb_optuna_best_logloss": 0.9665,
+      "lgb_optuna_best_logloss": 0.9662,
+      "test_xgb_raw": {
+        "accuracy": 0.5152,
+        "logloss": 0.9923
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5172,
+        "logloss": 0.9983
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5145,
+        "logloss": 0.9916
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5131,
+        "logloss": 0.995
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5131,
+        "logloss": 0.9917
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5139,
+        "logloss": 0.9949
+      }
+    },
+    "OU15": {
+      "market": "OU15",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 4,
+        "eta": 0.010007100652565513,
+        "subsample": 0.6423987996355262,
+        "colsample_bytree": 0.5399591700794417,
+        "min_child_weight": 9,
+        "gamma": 5.180190831403823e-06,
+        "reg_lambda": 0.04589987363138381,
+        "reg_alpha": 0.3607359437854735
+      },
+      "lgb_best_params": {
+        "max_depth": 5,
+        "learning_rate": 0.014982384401894706,
+        "feature_fraction": 0.5359420477684117,
+        "bagging_fraction": 0.7746019204010285,
+        "bagging_freq": 7,
+        "min_child_samples": 14,
+        "lambda_l1": 8.500808557234126e-07,
+        "lambda_l2": 2.536478147708652e-05
+      },
+      "xgb_best_iteration": 374,
+      "lgb_best_iteration": 351,
+      "xgb_optuna_best_logloss": 0.4945,
+      "lgb_optuna_best_logloss": 0.4939,
+      "test_xgb_raw": {
+        "accuracy": 0.7732,
+        "logloss": 0.5214
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.7641,
+        "logloss": 0.5324
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.7729,
+        "logloss": 0.5224
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.7707,
+        "logloss": 0.5427
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.7732,
+        "logloss": 0.5216
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.7705,
+        "logloss": 0.5298
+      }
+    },
+    "OU25": {
+      "market": "OU25",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 4,
+        "eta": 0.01865626593315191,
+        "subsample": 0.7105443580121279,
+        "colsample_bytree": 0.6135695058254393,
+        "min_child_weight": 4,
+        "gamma": 3.0546982020131495e-05,
+        "reg_lambda": 9.868745464853797,
+        "reg_alpha": 0.011307758649267466
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.09100328259258599,
+        "feature_fraction": 0.8534286719238086,
+        "bagging_fraction": 0.8916028672163949,
+        "bagging_freq": 6,
+        "min_child_samples": 8,
+        "lambda_l1": 7.374385355858303e-06,
+        "lambda_l2": 1.1036250149900698e-07
+      },
+      "xgb_best_iteration": 273,
+      "lgb_best_iteration": 61,
+      "xgb_optuna_best_logloss": 0.6481,
+      "lgb_optuna_best_logloss": 0.648,
+      "test_xgb_raw": {
+        "accuracy": 0.5937,
+        "logloss": 0.6629
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5937,
+        "logloss": 0.6805
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5954,
+        "logloss": 0.6633
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5935,
+        "logloss": 0.6976
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5946,
+        "logloss": 0.6628
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5937,
+        "logloss": 0.6709
+      }
+    },
+    "OU35": {
+      "market": "OU35",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.0143268502217739,
+        "subsample": 0.8172822498674736,
+        "colsample_bytree": 0.506201148705069,
+        "min_child_weight": 1,
+        "gamma": 1.8443203495219952e-05,
+        "reg_lambda": 0.0011170943053842998,
+        "reg_alpha": 0.00020927636698484225
+      },
+      "lgb_best_params": {
+        "max_depth": 5,
+        "learning_rate": 0.020117886227998167,
+        "feature_fraction": 0.6208946683116914,
+        "bagging_fraction": 0.6562518216073271,
+        "bagging_freq": 5,
+        "min_child_samples": 45,
+        "lambda_l1": 0.00036062002131658313,
+        "lambda_l2": 3.05269304967777e-05
+      },
+      "xgb_best_iteration": 529,
+      "lgb_best_iteration": 137,
+      "xgb_optuna_best_logloss": 0.6145,
+      "lgb_optuna_best_logloss": 0.6143,
+      "test_xgb_raw": {
+        "accuracy": 0.6898,
+        "logloss": 0.5998
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6871,
+        "logloss": 0.6091
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.6904,
+        "logloss": 0.6001
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6904,
+        "logloss": 0.6008
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.6905,
+        "logloss": 0.5998
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6907,
+        "logloss": 0.6007
+      }
+    },
+    "BTTS": {
+      "market": "BTTS",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.021664948542761242,
+        "subsample": 0.7099186087768442,
+        "colsample_bytree": 0.7590713567483665,
+        "min_child_weight": 6,
+        "gamma": 5.5491508895403115e-06,
+        "reg_lambda": 4.796666289138789,
+        "reg_alpha": 0.00025842183659199874
+      },
+      "lgb_best_params": {
+        "max_depth": 8,
+        "learning_rate": 0.01933635083345398,
+        "feature_fraction": 0.7004575834832659,
+        "bagging_fraction": 0.6329726219722871,
+        "bagging_freq": 5,
+        "min_child_samples": 15,
+        "lambda_l1": 0.002608953323098078,
+        "lambda_l2": 0.0036308084751911743
+      },
+      "xgb_best_iteration": 255,
+      "lgb_best_iteration": 160,
+      "xgb_optuna_best_logloss": 0.6715,
+      "lgb_optuna_best_logloss": 0.6717,
+      "test_xgb_raw": {
+        "accuracy": 0.5743,
+        "logloss": 0.6781
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5743,
+        "logloss": 0.6872
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5746,
+        "logloss": 0.677
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5733,
+        "logloss": 0.7059
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5766,
+        "logloss": 0.6772
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5768,
+        "logloss": 0.6789
+      }
+    },
+    "HT_RESULT": {
+      "market": "HT_RESULT",
+      "samples": 31774,
+      "train": 19064,
+      "val": 4766,
+      "cal": 3177,
+      "test": 4767,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.010169971619421797,
+        "subsample": 0.6203740676748326,
+        "colsample_bytree": 0.5851318310393765,
+        "min_child_weight": 7,
+        "gamma": 2.3628372099543945e-05,
+        "reg_lambda": 2.2038789911767867e-05,
+        "reg_alpha": 1.4397584578583542e-08
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.016888148464727217,
+        "feature_fraction": 0.5434647939749242,
+        "bagging_fraction": 0.7069497418977599,
+        "bagging_freq": 3,
+        "min_child_samples": 12,
+        "lambda_l1": 0.0014508638064857065,
+        "lambda_l2": 0.00023588865029986223
+      },
+      "xgb_best_iteration": 553,
+      "lgb_best_iteration": 237,
+      "xgb_optuna_best_logloss": 1.035,
+      "lgb_optuna_best_logloss": 1.0349,
+      "test_xgb_raw": {
+        "accuracy": 0.4504,
+        "logloss": 1.0411
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.4495,
+        "logloss": 1.0541
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.4491,
+        "logloss": 1.0417
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.4458,
+        "logloss": 1.0579
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.4498,
+        "logloss": 1.0413
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.447,
+        "logloss": 1.0572
+      }
+    },
+    "HT_OU05": {
+      "market": "HT_OU05",
+      "samples": 31774,
+      "train": 19064,
+      "val": 4766,
+      "cal": 3177,
+      "test": 4767,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.017924063045686854,
+        "subsample": 0.8434007408500664,
+        "colsample_bytree": 0.6701120513607601,
+        "min_child_weight": 4,
+        "gamma": 9.836792363275528e-07,
+        "reg_lambda": 1.7187465006176214e-05,
+        "reg_alpha": 3.014327116647346e-07
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.10422189537713934,
+        "feature_fraction": 0.5888797409273676,
+        "bagging_fraction": 0.7090975058803146,
+        "bagging_freq": 3,
+        "min_child_samples": 17,
+        "lambda_l1": 7.810412584545967e-07,
+        "lambda_l2": 0.009428914603962961
+      },
+      "xgb_best_iteration": 202,
+      "lgb_best_iteration": 35,
+      "xgb_optuna_best_logloss": 0.5704,
+      "lgb_optuna_best_logloss": 0.5698,
+      "test_xgb_raw": {
+        "accuracy": 0.7134,
+        "logloss": 0.5902
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.713,
+        "logloss": 0.6081
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.7143,
+        "logloss": 0.5915
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.7145,
+        "logloss": 0.593
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.7134,
+        "logloss": 0.5906
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.7141,
+        "logloss": 0.5921
+      }
+    },
+    "HT_OU15": {
+      "market": "HT_OU15",
+      "samples": 31774,
+      "train": 19064,
+      "val": 4766,
+      "cal": 3177,
+      "test": 4767,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.017001754132211097,
+        "subsample": 0.6180909155642152,
+        "colsample_bytree": 0.6626651653816322,
+        "min_child_weight": 4,
+        "gamma": 1.481809088646707e-06,
+        "reg_lambda": 0.287499823474079,
+        "reg_alpha": 7.145401117237584e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 8,
+        "learning_rate": 0.01064422725257703,
+        "feature_fraction": 0.6662850991902617,
+        "bagging_fraction": 0.7721868286695308,
+        "bagging_freq": 3,
+        "min_child_samples": 30,
+        "lambda_l1": 0.9857429592173135,
+        "lambda_l2": 3.960422300486119e-06
+      },
+      "xgb_best_iteration": 429,
+      "lgb_best_iteration": 345,
+      "xgb_optuna_best_logloss": 0.6435,
+      "lgb_optuna_best_logloss": 0.6435,
+      "test_xgb_raw": {
+        "accuracy": 0.6381,
+        "logloss": 0.6445
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6381,
+        "logloss": 0.6525
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.6419,
+        "logloss": 0.6439
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6402,
+        "logloss": 0.7399
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.6404,
+        "logloss": 0.6439
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6394,
+        "logloss": 0.6495
+      }
+    },
+    "HTFT": {
+      "market": "HTFT",
+      "samples": 31774,
+      "train": 19064,
+      "val": 4766,
+      "cal": 3177,
+      "test": 4767,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.013345313595371834,
+        "subsample": 0.9479953293996353,
+        "colsample_bytree": 0.6615608030428078,
+        "min_child_weight": 9,
+        "gamma": 7.455111012483033e-07,
+        "reg_lambda": 8.89911456381185,
+        "reg_alpha": 5.090564000963078e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.013089530126848842,
+        "feature_fraction": 0.7906053881599783,
+        "bagging_fraction": 0.7440871339717291,
+        "bagging_freq": 4,
+        "min_child_samples": 44,
+        "lambda_l1": 2.0129746481016167e-05,
+        "lambda_l2": 9.879192474391198
+      },
+      "xgb_best_iteration": 366,
+      "lgb_best_iteration": 252,
+      "xgb_optuna_best_logloss": 1.8581,
+      "lgb_optuna_best_logloss": 1.858,
+      "test_xgb_raw": {
+        "accuracy": 0.3168,
+        "logloss": 1.8817
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.3168,
+        "logloss": 1.9133
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.317,
+        "logloss": 1.882
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.3178,
+        "logloss": 1.9643
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.3172,
+        "logloss": 1.8815
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.3184,
+        "logloss": 1.9473
+      }
+    },
+    "ODD_EVEN": {
+      "market": "ODD_EVEN",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.0229001065742241,
+        "subsample": 0.6062940462474563,
+        "colsample_bytree": 0.6480436065939664,
+        "min_child_weight": 3,
+        "gamma": 0.0030457541200722615,
+        "reg_lambda": 7.476931580627848e-05,
+        "reg_alpha": 0.0009042146623162754
+      },
+      "lgb_best_params": {
+        "max_depth": 7,
+        "learning_rate": 0.04326506132261678,
+        "feature_fraction": 0.5682671758256829,
+        "bagging_fraction": 0.7270328719038639,
+        "bagging_freq": 3,
+        "min_child_samples": 16,
+        "lambda_l1": 8.217437741607322e-08,
+        "lambda_l2": 0.3516451122877441
+      },
+      "xgb_best_iteration": 83,
+      "lgb_best_iteration": 68,
+      "xgb_optuna_best_logloss": 0.6925,
+      "lgb_optuna_best_logloss": 0.6922,
+      "test_xgb_raw": {
+        "accuracy": 0.5017,
+        "logloss": 0.6938
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5053,
+        "logloss": 0.6944
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5069,
+        "logloss": 0.6962
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5048,
+        "logloss": 0.722
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5003,
+        "logloss": 0.6944
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5055,
+        "logloss": 0.6942
+      }
+    },
+    "CARDS_OU45": {
+      "market": "CARDS_OU45",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.05954573029858921,
+        "subsample": 0.9384590363195691,
+        "colsample_bytree": 0.7061263810064461,
+        "min_child_weight": 3,
+        "gamma": 0.12648985601647333,
+        "reg_lambda": 0.0005539202839193393,
+        "reg_alpha": 5.4993381294041956e-08
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.03822726574649208,
+        "feature_fraction": 0.5171942605576092,
+        "bagging_fraction": 0.9637281608315128,
+        "bagging_freq": 2,
+        "min_child_samples": 35,
+        "lambda_l1": 3.1166541263980415e-06,
+        "lambda_l2": 0.0004793052550782129
+      },
+      "xgb_best_iteration": 185,
+      "lgb_best_iteration": 338,
+      "xgb_optuna_best_logloss": 0.5583,
+      "lgb_optuna_best_logloss": 0.5582,
+      "test_xgb_raw": {
+        "accuracy": 0.5917,
+        "logloss": 0.6455
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6014,
+        "logloss": 0.6466
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5925,
+        "logloss": 0.6454
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6034,
+        "logloss": 0.6677
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5917,
+        "logloss": 0.6451
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6038,
+        "logloss": 0.6463
+      }
+    },
+    "HANDICAP_MS": {
+      "market": "HANDICAP_MS",
+      "samples": 34335,
+      "train": 20601,
+      "val": 5150,
+      "cal": 3433,
+      "test": 5151,
+      "features_used": 152,
+      "xgb_best_params": {
+        "max_depth": 3,
+        "eta": 0.016879778725504085,
+        "subsample": 0.6294239018550948,
+        "colsample_bytree": 0.6694819528702206,
+        "min_child_weight": 5,
+        "gamma": 2.9804488909930113e-05,
+        "reg_lambda": 0.2590856513676408,
+        "reg_alpha": 7.468057455332845e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 3,
+        "learning_rate": 0.03267347269820124,
+        "feature_fraction": 0.5009468855100953,
+        "bagging_fraction": 0.7336123380897622,
+        "bagging_freq": 1,
+        "min_child_samples": 10,
+        "lambda_l1": 1.3880195780862006e-06,
+        "lambda_l2": 0.7568094447483327
+      },
+      "xgb_best_iteration": 406,
+      "lgb_best_iteration": 231,
+      "xgb_optuna_best_logloss": 0.907,
+      "lgb_optuna_best_logloss": 0.9064,
+      "test_xgb_raw": {
+        "accuracy": 0.5838,
+        "logloss": 0.9235
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5865,
+        "logloss": 0.9347
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5797,
+        "logloss": 0.9257
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5832,
+        "logloss": 0.9467
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5818,
+        "logloss": 0.9244
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5836,
+        "logloss": 0.9387
+      }
+    }
+  }
+}

ai-engine/reports/training_v26_shadow.json

@@ -0,0 +1,19 @@
+{
+  "version": "v26.shadow.0",
+  "calibration_version": "v26.shadow.calib.0",
+  "train_rows": 6853,
+  "validation_rows": 1469,
+  "label_priors": {
+    "MS": 0.4404,
+    "OU25": 0.5214,
+    "BTTS": 0.5398,
+    "HT": 0.4275,
+    "HTFT": 0.26,
+    "CARDS": 0.6052
+  },
+  "artifact_path": "/Users/piton/Documents/GitHub/iddaai/iddaai-be/ai-engine/models/v26_shadow/market_profiles.json",
+  "notes": [
+    "v26.shadow runtime currently uses artifact-based calibration and ROI gating",
+    "market profile JSON remains the source of truth for runtime thresholds"
+  ]
+}

ai-engine/requirements.txt

@@ -17,3 +17,4 @@ pyyaml>=6.0
 # V2 async database
 asyncpg>=0.29.0
 pydantic>=2.5.0
+pytest>=8.0.0

ai-engine/schemas/match_data.py

@@ -0,0 +1,40 @@
+"""
+MatchData dataclass — core data transfer object used throughout the engine.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class MatchData:
+    match_id: str
+    home_team_id: str
+    away_team_id: str
+    home_team_name: str
+    away_team_name: str
+    match_date_ms: int
+    sport: str
+    league_id: Optional[str]
+    league_name: str
+    referee_name: Optional[str]
+    odds_data: Dict[str, float]
+    home_lineup: Optional[List[str]]
+    away_lineup: Optional[List[str]]
+    sidelined_data: Optional[Dict[str, Any]]
+    home_goals_avg: float
+    home_conceded_avg: float
+    away_goals_avg: float
+    away_conceded_avg: float
+    home_position: int
+    away_position: int
+    lineup_source: str
+    status: str = ""
+    state: Optional[str] = None
+    substate: Optional[str] = None
+    current_score_home: Optional[int] = None
+    current_score_away: Optional[int] = None
+    lineup_confidence: float = 0.0
+    source_table: str = "matches"

ai-engine/schemas/prediction.py

@@ -0,0 +1,292 @@
+"""
+Shared prediction dataclasses used across the AI engine.
+
+These were originally defined in models/v20_ensemble.py and are extracted here
+so they can be used without importing the full V20 ensemble.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+from core.calculators.score_calculator import ScorePrediction
+
+
+@dataclass
+class MarketPrediction:
+    """Prediction for a single betting market."""
+    market_type: str
+    pick: str
+    probability: float
+    confidence: float
+    odds: float = 0.0
+    is_recommended: bool = False
+    is_value_bet: bool = False
+    edge: float = 0.0  # Expected edge over market
+
+    def to_dict(self) -> dict:
+        return {
+            "market_type": self.market_type,
+            "pick": self.pick,
+            "probability": round(self.probability * 100, 1),
+            "confidence": round(self.confidence, 1),
+            "odds": self.odds,
+            "is_recommended": self.is_recommended,
+            "is_value_bet": self.is_value_bet,
+            "edge": round(self.edge, 1)
+        }
+
+
+@dataclass
+class FullMatchPrediction:
+    """Complete prediction for a match with ALL markets."""
+    match_id: str
+    home_team: str
+    away_team: str
+    match_date: str = ""
+
+    # === MAÇ SONUCU (1X2) ===
+    ms_home_prob: float = 0.33
+    ms_draw_prob: float = 0.33
+    ms_away_prob: float = 0.33
+    ms_pick: str = ""
+    ms_confidence: float = 0.0
+
+    # === ÇİFTE ŞANS ===
+    dc_1x_prob: float = 0.66
+    dc_x2_prob: float = 0.66
+    dc_12_prob: float = 0.66
+    dc_pick: str = ""
+    dc_confidence: float = 0.0
+
+    # === ALT/ÜST GOLLER ===
+    # 1.5
+    over_15_prob: float = 0.70
+    under_15_prob: float = 0.30
+    ou15_pick: str = ""
+    ou15_confidence: float = 0.0
+
+    # 2.5
+    over_25_prob: float = 0.50
+    under_25_prob: float = 0.50
+    ou25_pick: str = ""
+    ou25_confidence: float = 0.0
+
+    # 3.5
+    over_35_prob: float = 0.30
+    under_35_prob: float = 0.70
+    ou35_pick: str = ""
+    ou35_confidence: float = 0.0
+
+    # === KARŞILIKLI GOL (BTTS) ===
+    btts_yes_prob: float = 0.50
+    btts_no_prob: float = 0.50
+    btts_pick: str = ""
+    btts_confidence: float = 0.0
+
+    # === İLK YARI SONUCU ===
+    ht_home_prob: float = 0.30
+    ht_draw_prob: float = 0.40
+    ht_away_prob: float = 0.30
+    ht_pick: str = ""
+    ht_confidence: float = 0.0
+
+    # === SKOR TAHMİNLERİ ===
+    score: Optional[ScorePrediction] = None
+    predicted_ft_score: str = "1-1"
+    predicted_ht_score: str = "0-0"
+    ft_scores_top5: List[Dict] = field(default_factory=list)
+
+    # === xG (Expected Goals) ===
+    home_xg: float = 1.3
+    away_xg: float = 1.1
+    total_xg: float = 2.4
+
+    # === RISK DEĞERLENDİRMESİ ===
+    risk_level: str = "MEDIUM"  # LOW, MEDIUM, HIGH, EXTREME
+    risk_score: float = 0.0
+    is_surprise_risk: bool = False
+    surprise_type: str = ""
+    risk_warnings: List[str] = field(default_factory=list)
+    ht_ft_probs: Dict[str, float] = field(default_factory=dict)
+
+    # === GLM-5 SÜRPRİZ SKORU ===
+    upset_score: int = 0  # 0-100 arası sürpriz skoru
+    upset_level: str = "LOW"  # LOW, MEDIUM, HIGH, EXTREME
+    upset_reasons: List[str] = field(default_factory=list)
+
+    # === SÜRPRİZ PROFİLİ ===
+    surprise_score: float = 0.0        # 0-100 overall surprise risk score
+    surprise_comment: str = ""         # Human-readable surprise commentary
+    surprise_reasons: List[str] = field(default_factory=list)  # Flagged risk reasons
+    surprise_breakdown: List[Dict[str, Any]] = field(default_factory=list)  # Per-factor {code, points, label}
+
+    # === ENGINE KATKILARI ===
+    team_confidence: float = 0.0
+    player_confidence: float = 0.0
+    odds_confidence: float = 0.0
+    referee_confidence: float = 0.0
+
+    # === KORNER & KART & DİĞER ===
+    total_corners_pred: float = 9.5
+    corner_pick: str = "9.5 Üst"
+
+    total_cards_pred: float = 4.5
+    card_pick: str = "4.5 Alt"
+    cards_over_prob: float = 0.50
+    cards_under_prob: float = 0.50
+    cards_confidence: float = 0.0
+
+    handicap_pick: str = ""
+    handicap_home_prob: float = 0.33
+    handicap_draw_prob: float = 0.34
+    handicap_away_prob: float = 0.33
+    handicap_confidence: float = 0.0
+
+    ht_over_05_prob: float = 0.65
+    ht_under_05_prob: float = 0.35
+    ht_over_15_prob: float = 0.30
+    ht_under_15_prob: float = 0.70
+    ht_ou_pick: str = "İY 0.5 Üst"
+    ht_ou15_pick: str = "İY 1.5 Alt"
+
+    odd_even_pick: str = "Çift"
+    odd_prob: float = 0.50   # Tek olasılığı
+    even_prob: float = 0.50  # Çift olasılığı
+
+    # === TAVSİYELER (RECOMMENDATIONS) ===
+    best_bet: Optional[MarketPrediction] = None
+    recommended_bets: List[MarketPrediction] = field(default_factory=list)
+    alternative_bet: Optional[MarketPrediction] = None
+    expert_recommendation: Dict[str, Any] = field(default_factory=dict)
+
+    # === DETAILED ANALYSIS ===
+    analysis_details: Dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        return {
+            "match_info": {
+                "match_id": self.match_id,
+                "home_team": self.home_team,
+                "away_team": self.away_team,
+                "match_date": self.match_date
+            },
+            "predictions": {
+                "match_result": {
+                    "1": round(self.ms_home_prob * 100, 1),
+                    "X": round(self.ms_draw_prob * 100, 1),
+                    "2": round(self.ms_away_prob * 100, 1),
+                    "pick": self.ms_pick,
+                    "confidence": round(self.ms_confidence, 1)
+                },
+                "double_chance": {
+                    "1X": round(self.dc_1x_prob * 100, 1),
+                    "X2": round(self.dc_x2_prob * 100, 1),
+                    "12": round(self.dc_12_prob * 100, 1),
+                    "pick": self.dc_pick,
+                    "confidence": round(self.dc_confidence, 1)
+                },
+                "over_under": {
+                    "1.5": {
+                        "over": round(self.over_15_prob * 100, 1),
+                        "under": round(self.under_15_prob * 100, 1),
+                        "pick": self.ou15_pick,
+                        "confidence": round(self.ou15_confidence, 1)
+                    },
+                    "2.5": {
+                        "over": round(self.over_25_prob * 100, 1),
+                        "under": round(self.under_25_prob * 100, 1),
+                        "pick": self.ou25_pick,
+                        "confidence": round(self.ou25_confidence, 1)
+                    },
+                    "3.5": {
+                        "over": round(self.over_35_prob * 100, 1),
+                        "under": round(self.under_35_prob * 100, 1),
+                        "pick": self.ou35_pick,
+                        "confidence": round(self.ou35_confidence, 1)
+                    }
+                },
+                "btts": {
+                    "yes": round(self.btts_yes_prob * 100, 1),
+                    "no": round(self.btts_no_prob * 100, 1),
+                    "pick": self.btts_pick,
+                    "confidence": round(self.btts_confidence, 1)
+                },
+                "first_half": {
+                    "1": round(self.ht_home_prob * 100, 1),
+                    "X": round(self.ht_draw_prob * 100, 1),
+                    "2": round(self.ht_away_prob * 100, 1),
+                    "pick": self.ht_pick,
+                    "confidence": round(self.ht_confidence, 1),
+                    "over_under_05": {
+                        "over": round(self.ht_over_05_prob * 100, 1),
+                        "under": round(self.ht_under_05_prob * 100, 1),
+                        "pick": self.ht_ou_pick
+                    },
+                    "over_under_15": {
+                        "over": round(self.ht_over_15_prob * 100, 1),
+                        "under": round(self.ht_under_15_prob * 100, 1),
+                        "pick": self.ht_ou15_pick
+                    }
+                },
+                "scores": {
+                    "predicted_ft": self.predicted_ft_score,
+                    "predicted_ht": self.predicted_ht_score,
+                    "top_5_ft_scores": self.ft_scores_top5
+                },
+                "others": {
+                    "handicap": {
+                        "pick": self.handicap_pick,
+                        "confidence": round(self.handicap_confidence, 1),
+                        "home": round(self.handicap_home_prob * 100, 1),
+                        "draw": round(self.handicap_draw_prob * 100, 1),
+                        "away": round(self.handicap_away_prob * 100, 1)
+                    },
+                    "corners": {
+                        "total": round(self.total_corners_pred, 1),
+                        "pick": self.corner_pick
+                    },
+                    "cards": {
+                        "total": round(self.total_cards_pred, 1),
+                        "pick": self.card_pick,
+                        "confidence": round(self.cards_confidence, 1),
+                        "over": round(self.cards_over_prob * 100, 1),
+                        "under": round(self.cards_under_prob * 100, 1)
+                    },
+                    "odd_even": {
+                        "pick": self.odd_even_pick,
+                        "tek": round(self.odd_prob * 100, 1),
+                        "cift": round(self.even_prob * 100, 1)
+                    }
+                },
+                "xg": {
+                    "home": round(self.home_xg, 2),
+                    "away": round(self.away_xg, 2),
+                    "total": round(self.total_xg, 2)
+                }
+            },
+            "risk": {
+                "level": self.risk_level,
+                "score": round(self.risk_score, 1),
+                "is_surprise_risk": self.is_surprise_risk,
+                "surprise_type": self.surprise_type,
+                "ht_ft_probs": {k: round(v * 100, 1) for k, v in self.ht_ft_probs.items()} if self.ht_ft_probs else {},
+                "warnings": self.risk_warnings
+            },
+            "upset_analysis": {
+                "score": self.upset_score,
+                "level": self.upset_level,
+                "reasons": self.upset_reasons
+            },
+            "engine_breakdown": {
+                "team_engine": round(self.team_confidence, 1),
+                "player_engine": round(self.player_confidence, 1),
+                "odds_engine": round(self.odds_confidence, 1),
+                "referee_engine": round(self.referee_confidence, 1)
+            },
+            "recommendations": {
+                "best_bet": self.best_bet.to_dict() if self.best_bet else None,
+                "all_recommended": [b.to_dict() for b in self.recommended_bets] if self.recommended_bets else [],
+                "alternative_bet": self.alternative_bet.to_dict() if self.alternative_bet else None
+            },
+            "analysis_details": self.analysis_details
+        }

ai-engine/scripts/backfill_calibration.py

@@ -0,0 +1,510 @@
+"""
+Calibration Backfill Script
+============================
+Runs V25 model against historical matches (using pre-computed ai_features + odds)
+to generate calibration training data, then trains isotonic calibration models.
+
+Usage:
+    python ai-engine/scripts/backfill_calibration.py
+    python ai-engine/scripts/backfill_calibration.py --limit 5000
+    python ai-engine/scripts/backfill_calibration.py --min-samples 50
+"""
+
+import argparse
+import json
+import os
+import sys
+import time
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+import psycopg2
+from psycopg2.extras import RealDictCursor
+from dotenv import load_dotenv
+
+AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, AI_ENGINE_DIR)
+
+from models.v25_ensemble import V25Predictor
+from models.calibration import get_calibrator
+
+load_dotenv()
+
+
+def _normalize_pick(pick) -> str:
+    return str(pick or "").strip().casefold()
+
+
+def resolve_actual(market, pick, score_home, score_away, ht_home, ht_away):
+    if score_home is None or score_away is None:
+        return None
+    market = (market or "").upper()
+    p = _normalize_pick(pick)
+    total = score_home + score_away
+    ht_total = (ht_home or 0) + (ht_away or 0) if ht_home is not None else None
+
+    if market == "MS":
+        if p == "1": return int(score_home > score_away)
+        if p in {"x", "0"}: return int(score_home == score_away)
+        if p == "2": return int(score_away > score_home)
+        return None
+    if market in {"OU15", "OU25", "OU35"}:
+        line = {"OU15": 1.5, "OU25": 2.5, "OU35": 3.5}[market]
+        if "over" in p or "üst" in p or "ust" in p: return int(total > line)
+        if "under" in p or "alt" in p: return int(total < line)
+        return None
+    if market == "BTTS":
+        both = score_home > 0 and score_away > 0
+        if "yes" in p or "var" in p: return int(both)
+        if "no" in p or "yok" in p: return int(not both)
+        return None
+    if market == "HT":
+        if ht_home is None or ht_away is None: return None
+        if p == "1": return int(ht_home > ht_away)
+        if p in {"x", "0"}: return int(ht_home == ht_away)
+        if p == "2": return int(ht_away > ht_home)
+        return None
+    if market == "HTFT":
+        if ht_home is None or ht_away is None or "/" not in p: return None
+        ht_p, ft_p = p.split("/")
+        ht_actual = "1" if ht_home > ht_away else "2" if ht_away > ht_home else "x"
+        ft_actual = "1" if score_home > score_away else "2" if score_away > score_home else "x"
+        return int(ht_p.strip() == ht_actual and ft_p.strip() == ft_actual)
+    if market == "DC":
+        norm = p.replace("-", "").upper()
+        if norm == "1X": return int(score_home >= score_away)
+        if norm == "X2": return int(score_away >= score_home)
+        if norm == "12": return int(score_home != score_away)
+        return None
+    return None
+
+
+def calibrator_key(market, pick):
+    m = (market or "").upper()
+    p = _normalize_pick(pick)
+    if m == "MS":
+        if p == "1": return "ms_home"
+        if p in {"x", "0"}: return "ms_draw"
+        if p == "2": return "ms_away"
+        return None
+    if m == "DC": return "dc"
+    if m == "OU15" and ("over" in p or "üst" in p): return "ou15"
+    if m == "OU25" and ("over" in p or "üst" in p): return "ou25"
+    if m == "OU35" and ("over" in p or "üst" in p): return "ou35"
+    if m == "BTTS" and ("yes" in p or "var" in p): return "btts"
+    if m == "HT":
+        if p == "1": return "ht_home"
+        if p in {"x", "0"}: return "ht_draw"
+        if p == "2": return "ht_away"
+        return None
+    if m == "HTFT": return "ht_ft"
+    return None
+
+
+def get_conn():
+    db_url = os.getenv("DATABASE_URL", "")
+    if "?schema=" in db_url:
+        db_url = db_url.split("?schema=")[0]
+    if not db_url:
+        raise ValueError("DATABASE_URL not set")
+    return psycopg2.connect(db_url, cursor_factory=RealDictCursor)
+
+
+ODD_CAT_MAP = {
+    "maç sonucu": {"1": "ms_h", "0": "ms_d", "x": "ms_d", "2": "ms_a"},
+    "1. yarı sonucu": {"1": "ht_ms_h", "0": "ht_ms_d", "x": "ht_ms_d", "2": "ht_ms_a"},
+}
+
+ODD_CAT_KEYWORD_MAP = {
+    "karşılıklı gol": {"var": "btts_y", "yok": "btts_n"},
+    "0,5 alt/üst": {"alt": "ou05_u", "üst": "ou05_o"},
+    "1,5 alt/üst": {"alt": "ou15_u", "üst": "ou15_o"},
+    "2,5 alt/üst": {"alt": "ou25_u", "üst": "ou25_o"},
+    "3,5 alt/üst": {"alt": "ou35_u", "üst": "ou35_o"},
+    "ilk yarı 0,5 alt/üst": {"alt": "ht_ou05_u", "üst": "ht_ou05_o"},
+    "ilk yarı 1,5 alt/üst": {"alt": "ht_ou15_u", "üst": "ht_ou15_o"},
+}
+
+
+def load_matches(cur, limit: int) -> List[Dict]:
+    cur.execute("""
+        SELECT m.id, m.score_home, m.score_away,
+               m.ht_score_home, m.ht_score_away
+        FROM matches m
+        JOIN football_ai_features f ON f.match_id = m.id
+        WHERE m.status = 'FT'
+          AND m.sport = 'football'
+          AND m.score_home IS NOT NULL
+          AND m.score_away IS NOT NULL
+        ORDER BY m.mst_utc DESC
+        LIMIT %s
+    """, (limit,))
+    return cur.fetchall()
+
+
+def load_ai_features_batch(cur, match_ids: List[str]) -> Dict[str, Dict]:
+    if not match_ids:
+        return {}
+    ph = ",".join(["%s"] * len(match_ids))
+    cur.execute(f"""
+        SELECT match_id,
+               home_elo AS home_overall_elo,
+               away_elo AS away_overall_elo,
+               elo_diff,
+               home_home_elo, away_away_elo,
+               home_form_elo, away_form_elo,
+               (home_form_elo - away_form_elo) AS form_elo_diff,
+               home_goals_avg_5 AS home_goals_avg,
+               home_conceded_avg_5 AS home_conceded_avg,
+               away_goals_avg_5 AS away_goals_avg,
+               away_conceded_avg_5 AS away_conceded_avg,
+               home_clean_sheet_rate, away_clean_sheet_rate,
+               home_scoring_rate, away_scoring_rate,
+               home_win_streak AS home_winning_streak,
+               away_win_streak AS away_winning_streak,
+               0 AS home_unbeaten_streak,
+               0 AS away_unbeaten_streak,
+               h2h_total AS h2h_total_matches,
+               h2h_home_win_rate,
+               (1.0 - h2h_home_win_rate - 0.33) AS h2h_draw_rate,
+               h2h_avg_goals,
+               h2h_btts_rate, h2h_over25_rate,
+               home_avg_possession, away_avg_possession,
+               home_avg_shots_on_target, away_avg_shots_on_target,
+               home_shot_conversion, away_shot_conversion,
+               0.0 AS home_avg_corners, 0.0 AS away_avg_corners,
+               implied_home, implied_draw, implied_away,
+               league_avg_goals,
+               0.0 AS league_zero_goal_rate,
+               0.0 AS home_xga, 0.0 AS away_xga,
+               0.0 AS upset_atmosphere, 0.0 AS upset_motivation,
+               0.0 AS upset_fatigue, 0.0 AS upset_potential,
+               referee_home_bias, referee_avg_goals,
+               referee_avg_cards AS referee_cards_total,
+               0.0 AS referee_avg_yellow,
+               0.0 AS referee_experience,
+               0.0 AS home_momentum_score, 0.0 AS away_momentum_score,
+               0.0 AS momentum_diff,
+               0.0 AS home_squad_quality, 0.0 AS away_squad_quality,
+               0.0 AS squad_diff,
+               0 AS home_key_players, 0 AS away_key_players,
+               missing_players_impact AS home_missing_impact,
+               0.0 AS away_missing_impact,
+               home_goals_avg_5 AS home_goals_form,
+               away_goals_avg_5 AS away_goals_form
+        FROM football_ai_features
+        WHERE match_id IN ({ph})
+    """, match_ids)
+    return {str(row["match_id"]): dict(row) for row in cur.fetchall()}
+
+
+def load_odds_batch(cur, match_ids: List[str]) -> Dict[str, Dict[str, float]]:
+    if not match_ids:
+        return {}
+    ph = ",".join(["%s"] * len(match_ids))
+    cur.execute(f"""
+        SELECT oc.match_id, oc.name AS cat_name,
+               os.name AS sel_name, os.odd_value
+        FROM odd_selections os
+        JOIN odd_categories oc ON os.odd_category_db_id = oc.db_id
+        WHERE oc.match_id IN ({ph})
+    """, match_ids)
+
+    odds: Dict[str, Dict[str, float]] = {}
+    for row in cur.fetchall():
+        mid = str(row["match_id"])
+        cat = (row["cat_name"] or "").lower().strip()
+        sel = (row["sel_name"] or "").strip()
+        val = float(row["odd_value"]) if row["odd_value"] else 0
+        if val <= 0:
+            continue
+        if mid not in odds:
+            odds[mid] = {}
+
+        if cat in ODD_CAT_MAP:
+            key = ODD_CAT_MAP[cat].get(sel.lower())
+            if key:
+                odds[mid][key] = val
+        else:
+            for cat_pattern, kw_map in ODD_CAT_KEYWORD_MAP.items():
+                if cat == cat_pattern:
+                    for keyword, key in kw_map.items():
+                        if keyword in sel.lower():
+                            odds[mid][key] = val
+                    break
+    return odds
+
+
+MARKETS_TO_PREDICT = [
+    ("MS", "1", lambda p: p[0]),
+    ("MS", "X", lambda p: p[1]),
+    ("MS", "2", lambda p: p[2]),
+    ("OU25", "Over 2.5", lambda p: p[0]),
+    ("BTTS", "Yes", lambda p: p[0]),
+    ("OU15", "Over 1.5", lambda p: p[0]),
+    ("OU35", "Over 3.5", lambda p: p[0]),
+    ("HT", "1", lambda p: p[0]),
+    ("HT", "X", lambda p: p[1]),
+    ("HT", "2", lambda p: p[2]),
+]
+
+
+def run_backfill(args):
+    print("=" * 70)
+    print("CALIBRATION BACKFILL")
+    print("=" * 70)
+
+    conn = get_conn()
+    cur = conn.cursor(cursor_factory=RealDictCursor)
+
+    t0 = time.time()
+    print(f"Loading matches (limit={args.limit})...")
+    matches = load_matches(cur, args.limit)
+    print(f"  Found {len(matches)} finished matches with ai_features")
+
+    match_ids = [str(m["id"]) for m in matches]
+    match_map = {str(m["id"]): m for m in matches}
+
+    print("Loading ai_features...")
+    features_map = load_ai_features_batch(cur, match_ids)
+    print(f"  Loaded features for {len(features_map)} matches")
+
+    print("Loading odds...")
+    odds_map = load_odds_batch(cur, match_ids)
+    print(f"  Loaded odds for {len(odds_map)} matches")
+
+    print(f"Data loading: {time.time() - t0:.1f}s")
+
+    print("\nLoading V25 model...")
+    predictor = V25Predictor()
+    predictor.load_models()
+
+    feature_cols = predictor.FEATURE_COLS
+
+    samples: List[Dict[str, Any]] = []
+    skipped = 0
+    processed = 0
+
+    print(f"\nRunning predictions on {len(match_ids)} matches...")
+    t1 = time.time()
+
+    for i, mid in enumerate(match_ids):
+        if mid not in features_map:
+            skipped += 1
+            continue
+
+        feat_row = features_map[mid]
+        odds_row = odds_map.get(mid, {})
+        match_row = match_map[mid]
+
+        feat_dict = {}
+        for col in feature_cols:
+            if col in feat_row and feat_row[col] is not None:
+                feat_dict[col] = float(feat_row[col])
+            elif col.startswith("odds_") and not col.endswith("_present"):
+                odds_key = col.replace("odds_", "")
+                feat_dict[col] = float(odds_row.get(odds_key, 0))
+            elif col.endswith("_present"):
+                base = col.replace("_present", "")
+                odds_key = base.replace("odds_", "")
+                feat_dict[col] = 1.0 if odds_row.get(odds_key, 0) > 0 else 0.0
+            else:
+                feat_dict[col] = 0.0
+
+        if odds_row.get("ms_h", 0) > 0:
+            feat_dict["odds_ms_h"] = odds_row["ms_h"]
+        if odds_row.get("ms_d", 0) > 0:
+            feat_dict["odds_ms_d"] = odds_row["ms_d"]
+        if odds_row.get("ms_a", 0) > 0:
+            feat_dict["odds_ms_a"] = odds_row["ms_a"]
+
+        ms_h = feat_dict.get("odds_ms_h", 0)
+        ms_d = feat_dict.get("odds_ms_d", 0)
+        ms_a = feat_dict.get("odds_ms_a", 0)
+        if ms_h > 0 and ms_d > 0 and ms_a > 0:
+            raw_sum = 1/ms_h + 1/ms_d + 1/ms_a
+            feat_dict["implied_home"] = (1/ms_h) / raw_sum
+            feat_dict["implied_draw"] = (1/ms_d) / raw_sum
+            feat_dict["implied_away"] = (1/ms_a) / raw_sum
+
+        sh = match_row["score_home"]
+        sa = match_row["score_away"]
+        ht_h = match_row.get("ht_score_home")
+        ht_a = match_row.get("ht_score_away")
+
+        try:
+            X = pd.DataFrame([{c: feat_dict.get(c, 0.0) for c in feature_cols}])
+
+            for market_name, model_key, market_list in [
+                ("ms", "ms", ["MS"]),
+                ("ou25", "ou25", ["OU25"]),
+                ("btts", "btts", ["BTTS"]),
+                ("ou15", "ou15", ["OU15"]),
+                ("ou35", "ou35", ["OU35"]),
+                ("ht_result", "ht_result", ["HT"]),
+            ]:
+                if model_key not in predictor.models:
+                    continue
+
+                probs = predictor.predict_market(model_key, feat_dict)
+                if probs is None:
+                    continue
+
+                if model_key == "ms":
+                    for pick, prob in [("1", probs[0]), ("X", probs[1]), ("2", probs[2])]:
+                        actual = resolve_actual("MS", pick, sh, sa, ht_h, ht_a)
+                        key = calibrator_key("MS", pick)
+                        if actual is not None and key:
+                            samples.append({
+                                "match_id": mid,
+                                "market": "MS",
+                                "pick": pick,
+                                "key": key,
+                                "raw_prob": float(prob),
+                                "actual": int(actual),
+                            })
+
+                elif model_key == "ht_result":
+                    if ht_h is None or ht_a is None:
+                        continue
+                    for pick, prob in [("1", probs[0]), ("X", probs[1]), ("2", probs[2])]:
+                        actual = resolve_actual("HT", pick, sh, sa, ht_h, ht_a)
+                        key = calibrator_key("HT", pick)
+                        if actual is not None and key:
+                            samples.append({
+                                "match_id": mid,
+                                "market": "HT",
+                                "pick": pick,
+                                "key": key,
+                                "raw_prob": float(prob),
+                                "actual": int(actual),
+                            })
+
+                elif model_key in ("ou25", "ou15", "ou35"):
+                    market_upper = model_key.upper()
+                    over_prob = float(probs[0]) if len(probs) > 0 else 0.5
+                    pick = f"Over"
+                    actual = resolve_actual(market_upper, "Over", sh, sa, ht_h, ht_a)
+                    key = calibrator_key(market_upper, "Over")
+                    if actual is not None and key:
+                        samples.append({
+                            "match_id": mid,
+                            "market": market_upper,
+                            "pick": pick,
+                            "key": key,
+                            "raw_prob": over_prob,
+                            "actual": int(actual),
+                        })
+
+                elif model_key == "btts":
+                    yes_prob = float(probs[0]) if len(probs) > 0 else 0.5
+                    actual = resolve_actual("BTTS", "Yes", sh, sa, ht_h, ht_a)
+                    key = calibrator_key("BTTS", "Yes")
+                    if actual is not None and key:
+                        samples.append({
+                            "match_id": mid,
+                            "market": "BTTS",
+                            "pick": "Yes",
+                            "key": key,
+                            "raw_prob": yes_prob,
+                            "actual": int(actual),
+                        })
+
+            processed += 1
+
+        except Exception as e:
+            skipped += 1
+            if skipped <= 5:
+                print(f"  Error on {mid}: {e}")
+
+        if (i + 1) % 5000 == 0:
+            elapsed = time.time() - t1
+            rate = (i + 1) / elapsed
+            print(f"  Processed {i+1}/{len(match_ids)} ({rate:.0f} matches/s)")
+
+    elapsed = time.time() - t1
+    print(f"\nPrediction complete: {processed} matches, {skipped} skipped, {elapsed:.1f}s")
+
+    if not samples:
+        print("No calibration samples generated!")
+        cur.close()
+        conn.close()
+        return
+
+    df = pd.DataFrame(samples)
+    print(f"\nTotal calibration samples: {len(df)}")
+    print(f"Unique matches: {df['match_id'].nunique()}")
+    print(f"\nPer-key counts:")
+    for key, count in df["key"].value_counts().items():
+        print(f"  {key:<14} {count}")
+
+    print(f"\nTraining isotonic calibration models (min_samples={args.min_samples})...")
+    calibrator = get_calibrator()
+    results: Dict[str, Any] = {}
+    keys = sorted(df["key"].unique())
+
+    for key in keys:
+        sub = df[df["key"] == key].copy()
+        sub = sub.drop_duplicates(subset=["match_id", "key"], keep="first")
+        sub = sub.dropna(subset=["raw_prob", "actual"])
+        sub = sub[(sub["raw_prob"] > 0.0) & (sub["raw_prob"] < 1.0)]
+
+        n = len(sub)
+        if n < args.min_samples:
+            results[key] = {"status": "skipped", "samples": n}
+            continue
+
+        metrics = calibrator.train_calibration(
+            df=sub,
+            market=key,
+            prob_col="raw_prob",
+            actual_col="actual",
+            min_samples=args.min_samples,
+            save=True,
+        )
+        results[key] = {
+            "status": "trained",
+            "samples": metrics.sample_count,
+            "brier": round(metrics.brier_score, 4),
+            "ece": round(metrics.calibration_error, 4),
+            "mean_predicted": round(metrics.mean_predicted, 4),
+            "mean_actual": round(metrics.mean_actual, 4),
+        }
+
+    print("\n" + "=" * 70)
+    print("CALIBRATION RESULTS")
+    print("=" * 70)
+    print(f"{'market':<14} {'status':<10} {'n':<8} {'brier':<9} {'ece':<8} {'pred_avg':<9} {'actual_avg'}")
+    print("-" * 70)
+    for key, info in sorted(results.items()):
+        if info["status"] == "trained":
+            print(
+                f"{key:<14} {'OK':<10} {info['samples']:<8} "
+                f"{info['brier']:<9.4f} {info['ece']:<8.4f} "
+                f"{info['mean_predicted']:<9.4f} {info['mean_actual']}"
+            )
+        else:
+            print(f"{key:<14} {'SKIP':<10} {info['samples']:<8}")
+    print("=" * 70)
+
+    total_time = time.time() - t0
+    print(f"\nTotal time: {total_time:.1f}s")
+    print(f"Calibration models saved to: {os.path.join(AI_ENGINE_DIR, 'models', 'calibration')}/")
+
+    cur.close()
+    conn.close()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Backfill calibration from historical matches")
+    parser.add_argument("--limit", type=int, default=50000,
+                        help="Max matches to process (default: 50000)")
+    parser.add_argument("--min-samples", type=int, default=100,
+                        help="Min samples per market for calibration (default: 100)")
+    args = parser.parse_args()
+    run_backfill(args)
+
+
+if __name__ == "__main__":
+    main()

ai-engine/scripts/backtest_consistency.py

@@ -0,0 +1,352 @@
+"""
+Tutarsızlık Bazlı Backtest
+============================
+Modeller arası tutarsızlığı ölçer, tutarlı maçlarda bahis açılsaydı
+ROI ne olurdu hesaplar.
+
+Mantık:
+- Her maç için market'ler arası çelişkileri tespit et
+- Tutarsız maçları filtrele
+- Tutarlı maçlarda hit rate ve ROI hesapla
+
+Usage:
+  python scripts/backtest_consistency.py
+"""
+
+import os, sys, json
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+from sklearn.metrics import accuracy_score
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+                         'data', 'training_data.csv')
+MODELS_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+                          'models', 'v25')
+
+SKIP_COLS = {
+    'match_id','home_team_id','away_team_id','league_id','mst_utc',
+    'score_home','score_away','total_goals','ht_score_home','ht_score_away','ht_total_goals',
+    'label_ms','label_ou05','label_ou15','label_ou25','label_ou35','label_btts',
+    'label_ht_result','label_ht_ou05','label_ht_ou15','label_ht_ft',
+    'label_odd_even','label_yellow_cards','label_cards_ou45','label_handicap_ms',
+}
+
+
+def load_model(market: str):
+    path = os.path.join(MODELS_DIR, f'xgb_v25_{market}.json')
+    if not os.path.exists(path):
+        return None
+    b = xgb.Booster()
+    b.load_model(path)
+    return b
+
+
+def predict_proba(model, X: np.ndarray, feature_cols: list, n_class: int):
+    dmat = xgb.DMatrix(pd.DataFrame(X, columns=feature_cols))
+    raw = model.predict(dmat)
+    if n_class > 2:
+        return raw.reshape(-1, n_class)
+    return np.column_stack([1 - raw, raw])
+
+
+def consistency_score(probs: dict) -> tuple[float, list]:
+    """
+    Market'ler arası tutarsızlığı hesapla.
+    0 = tamamen tutarlı, 1 = tamamen çelişkili.
+
+    Kontrol edilen çelişkiler:
+    1. OU15 üst yüksek ama OU25 üst de yüksek → ok
+       OU15 üst yüksek ama OU25 alt yüksek → ÇELISKI (1 gol bekleniyor ama 2.5+ da bekleniyor?)
+
+    2. HT_OU05 üst yüksek ama HT sonucu draw yüksek → ÇELISKI
+
+    3. OU35 üst yüksek ama BTTS düşük → şüpheli
+
+    4. MS home yüksek ama HT away yüksek → çelişkili
+    """
+    conflicts = []
+    total_weight = 0
+    total_conflict = 0
+
+    # OU tutarlılığı: P(OU25>0.5) <= P(OU15>0.5) matematiksel zorunluluk
+    ou15_over = probs.get('ou15_over', 0.5)
+    ou25_over = probs.get('ou25_over', 0.5)
+    ou35_over = probs.get('ou35_over', 0.5)
+
+    # OU hiyerarşisi: ou35 <= ou25 <= ou15 olmalı
+    if ou25_over > ou15_over + 0.05:
+        gap = ou25_over - ou15_over
+        conflicts.append(f'OU25>{ou25_over:.0%} > OU15>{ou15_over:.0%} (imkansız)')
+        total_conflict += gap * 2
+    total_weight += 1
+
+    if ou35_over > ou25_over + 0.05:
+        gap = ou35_over - ou25_over
+        conflicts.append(f'OU35>{ou35_over:.0%} > OU25>{ou25_over:.0%} (imkansız)')
+        total_conflict += gap * 2
+    total_weight += 1
+
+    # HT_OU05 ve HT sonuç tutarlılığı
+    ht_ou05_over = probs.get('ht_ou05_over', 0.5)
+    ht_draw_prob = probs.get('ht_draw', 0.34)
+
+    # İlk yarıda gol bekleniyor ama beraberlik de bekleniyor (0-0 draw?)
+    # HT_OU05 >%70 ama HT draw >%50 → çelişkili (0-0 berabere çok?)
+    if ht_ou05_over > 0.70 and ht_draw_prob > 0.50:
+        conflict = min(ht_ou05_over - 0.5, ht_draw_prob - 0.4)
+        conflicts.append(f'HT_OU05>{ht_ou05_over:.0%} ama HT_Draw>{ht_draw_prob:.0%}')
+        total_conflict += conflict
+    total_weight += 1
+
+    # HT_OU05 ve HT_OU15 tutarlılığı
+    ht_ou15_over = probs.get('ht_ou15_over', 0.3)
+    if ht_ou15_over > ht_ou05_over + 0.05:
+        gap = ht_ou15_over - ht_ou05_over
+        conflicts.append(f'HT_OU15>{ht_ou15_over:.0%} > HT_OU05>{ht_ou05_over:.0%} (imkansız)')
+        total_conflict += gap * 2
+    total_weight += 1
+
+    # MS ve OU tutarlılığı
+    ms_home = probs.get('ms_home', 0.33)
+    ms_away = probs.get('ms_away', 0.33)
+    btts_yes = probs.get('btts_yes', 0.5)
+
+    # Tek takım galibiyeti kuvvetli ama BTTS yüksek → şüpheli
+    dominant = max(ms_home, ms_away)
+    if dominant > 0.65 and btts_yes > 0.65:
+        conflict = (dominant - 0.5) * (btts_yes - 0.5)
+        conflicts.append(f'MS dominant>{dominant:.0%} ama BTTS_Yes>{btts_yes:.0%}')
+        total_conflict += conflict * 0.5
+    total_weight += 1
+
+    # OU25 ve BTTS tutarlılığı
+    # BTTS yüksekse en az 2 gol → OU25 üst de yüksek olmalı
+    if btts_yes > 0.65 and ou25_over < 0.45:
+        conflict = btts_yes - ou25_over
+        conflicts.append(f'BTTS_Yes>{btts_yes:.0%} ama OU25>{ou25_over:.0%} düşük')
+        total_conflict += conflict
+    total_weight += 1
+
+    # OU35 üst yüksek ama BTTS düşük → şüpheli (3+ gol ama tek takım mı?)
+    if ou35_over > 0.45 and btts_yes < 0.40:
+        conflict = (ou35_over - 0.35) * (0.5 - btts_yes)
+        conflicts.append(f'OU35>{ou35_over:.0%} ama BTTS_Yes<{btts_yes:.0%}')
+        total_conflict += conflict
+    total_weight += 1
+
+    score = min(1.0, total_conflict / max(total_weight * 0.3, 0.1))
+    return score, conflicts
+
+
+def main():
+    print('Loading data...')
+    df = pd.read_csv(DATA_PATH, low_memory=False)
+
+    # Son %20 = test seti (kronolojik)
+    df = df.sort_values('mst_utc')
+    n_test = int(len(df) * 0.20)
+    df_test = df.tail(n_test).copy()
+    print(f'Test seti: {len(df_test):,} maç')
+
+    feature_cols = [c for c in df.columns if c not in SKIP_COLS]
+
+    # Modelleri yükle
+    print('Modeller yükleniyor...')
+    models = {
+        'ms':       (load_model('ms'),        3),
+        'ou15':     (load_model('ou15'),       2),
+        'ou25':     (load_model('ou25'),       2),
+        'ou35':     (load_model('ou35'),       2),
+        'btts':     (load_model('btts'),       2),
+        'ht_result':(load_model('ht_result'),  3),
+        'ht_ou05':  (load_model('ht_ou05'),    2),
+        'ht_ou15':  (load_model('ht_ou15'),    2),
+    }
+    models = {k: v for k, v in models.items() if v[0] is not None}
+    print(f'Yüklenen model: {list(models.keys())}')
+
+    X = df_test[feature_cols].fillna(0).values
+
+    # Tüm tahminleri al
+    print('Tahminler yapılıyor...')
+    preds = {}
+    for mkey, (model, n_class) in models.items():
+        p = predict_proba(model, X, feature_cols, n_class)
+        preds[mkey] = p
+
+    # Her maç için tutarsızlık skoru ve tahmin kararı
+    results = []
+    for i in range(len(df_test)):
+        row = df_test.iloc[i]
+
+        # Olasılıkları topla
+        probs = {}
+        if 'ms' in preds:
+            probs['ms_home']    = preds['ms'][i][0]
+            probs['ms_draw']    = preds['ms'][i][1]
+            probs['ms_away']    = preds['ms'][i][2]
+        if 'ou15' in preds:
+            probs['ou15_over']  = preds['ou15'][i][1]
+        if 'ou25' in preds:
+            probs['ou25_over']  = preds['ou25'][i][1]
+        if 'ou35' in preds:
+            probs['ou35_over']  = preds['ou35'][i][1]
+        if 'btts' in preds:
+            probs['btts_yes']   = preds['btts'][i][1]
+        if 'ht_result' in preds:
+            probs['ht_home']    = preds['ht_result'][i][0]
+            probs['ht_draw']    = preds['ht_result'][i][1]
+            probs['ht_away']    = preds['ht_result'][i][2]
+        if 'ht_ou05' in preds:
+            probs['ht_ou05_over'] = preds['ht_ou05'][i][1]
+        if 'ht_ou15' in preds:
+            probs['ht_ou15_over'] = preds['ht_ou15'][i][1]
+
+        c_score, conflicts = consistency_score(probs)
+
+        # Gerçek sonuçlar
+        actual = {
+            'ms':    int(row.get('label_ms', -1)),
+            'ou15':  int(row.get('label_ou15', -1)),
+            'ou25':  int(row.get('label_ou25', -1)),
+            'ou35':  int(row.get('label_ou35', -1)),
+            'btts':  int(row.get('label_btts', -1)),
+        }
+
+        # Her market için tahmin ve doğruluk
+        market_results = {}
+        for mkt, label_key in [('ms','ms'),('ou15','ou15'),('ou25','ou25'),
+                                ('ou35','ou35'),('btts','btts')]:
+            if mkt not in preds or actual[label_key] < 0:
+                continue
+            pred_class = int(np.argmax(preds[mkt][i]))
+            correct = int(pred_class == actual[label_key])
+
+            # Odds (implied prob → odds = 1/prob)
+            pred_prob = float(preds[mkt][i][pred_class])
+            implied_odds = 1 / pred_prob if pred_prob > 0.01 else 10.0
+            # ROI hesabı: 1 birim bahis, kazanırsa (odds-1) kazanç, kaybederse -1
+            roi = (implied_odds - 1) * correct - (1 - correct)
+
+            market_results[mkt] = {
+                'pred': pred_class,
+                'actual': actual[label_key],
+                'correct': correct,
+                'prob': pred_prob,
+                'roi': roi,
+            }
+
+        results.append({
+            'idx': i,
+            'consistency_score': c_score,
+            'conflicts': conflicts,
+            'probs': probs,
+            'market_results': market_results,
+        })
+
+    df_results = pd.DataFrame([{
+        'consistency_score': r['consistency_score'],
+        'n_conflicts': len(r['conflicts']),
+        **{f'{m}_correct': r['market_results'].get(m, {}).get('correct', None)
+           for m in ['ms','ou15','ou25','ou35','btts']},
+        **{f'{m}_roi': r['market_results'].get(m, {}).get('roi', None)
+           for m in ['ms','ou15','ou25','ou35','btts']},
+    } for r in results])
+
+    # ── Analiz ──────────────────────────────────────────────────────────
+    print(f'\n{"="*70}')
+    print('TUTARSIZLIK ANALİZİ')
+    print(f'{"="*70}')
+
+    thresholds = [0.0, 0.1, 0.2, 0.3, 0.5]
+    markets = ['ms', 'ou15', 'ou25', 'ou35', 'btts']
+
+    for t in thresholds:
+        mask = df_results['consistency_score'] <= t
+        n = mask.sum()
+        if n < 50:
+            continue
+
+        print(f'\n[Tutarsızlık <= {t:.1f}] → {n:,} maç ({n/len(df_results)*100:.0f}%)')
+        print(f'  {"Market":<8} {"HitRate":>8} {"ROI/bahis":>10} {"Toplam ROI":>12}')
+        print(f'  {"-"*42}')
+        for m in markets:
+            col_c = f'{m}_correct'
+            col_r = f'{m}_roi'
+            if col_c not in df_results.columns:
+                continue
+            sub = df_results[mask][col_c].dropna()
+            roi_sub = df_results[mask][col_r].dropna()
+            if len(sub) < 20:
+                continue
+            hit = sub.mean()
+            avg_roi = roi_sub.mean()
+            total_roi = roi_sub.sum()
+            print(f'  {m:<8} {hit:>7.1%}  {avg_roi:>+9.3f}  {total_roi:>+11.1f}')
+
+    # Çelişki türlerine göre breakdown
+    print(f'\n{"="*70}')
+    print('EN SIK ÇELIŞKILER')
+    print(f'{"="*70}')
+    all_conflicts = [c for r in results for c in r['conflicts']]
+    from collections import Counter
+    for conflict, cnt in Counter(all_conflicts).most_common(10):
+        print(f'  {cnt:>5}x  {conflict}')
+
+    # Tutarsızlık dağılımı
+    print(f'\n{"="*70}')
+    print('TUTARSIZLIK DAĞILIMI')
+    print(f'{"="*70}')
+    for label, lo, hi in [
+        ('Tamamen tutarlı', 0.0, 0.05),
+        ('Çok tutarlı',     0.05, 0.15),
+        ('Orta',            0.15, 0.30),
+        ('Tutarsız',        0.30, 0.50),
+        ('Çok tutarsız',    0.50, 1.01),
+    ]:
+        mask = (df_results['consistency_score'] >= lo) & (df_results['consistency_score'] < hi)
+        n = mask.sum()
+        ou25_hit = df_results[mask]['ou25_correct'].mean()
+        ms_hit = df_results[mask]['ms_correct'].mean()
+        print(f'  {label:<20} {n:>6,} maç ({n/len(df_results)*100:>4.0f}%) | '
+              f'MS={ms_hit:.0%} OU25={ou25_hit:.0%}')
+
+    # Raporu kaydet
+    report = {
+        'total_test': len(df_results),
+        'thresholds': {},
+    }
+    for t in thresholds:
+        mask = df_results['consistency_score'] <= t
+        n = mask.sum()
+        report['thresholds'][str(t)] = {
+            'n_matches': int(n),
+            'pct': round(n/len(df_results)*100, 1),
+            'markets': {},
+        }
+        for m in markets:
+            col_c = f'{m}_correct'
+            col_r = f'{m}_roi'
+            if col_c not in df_results.columns:
+                continue
+            sub_c = df_results[mask][col_c].dropna()
+            sub_r = df_results[mask][col_r].dropna()
+            if len(sub_c) > 0:
+                report['thresholds'][str(t)]['markets'][m] = {
+                    'hit_rate': round(float(sub_c.mean()), 4),
+                    'avg_roi': round(float(sub_r.mean()), 4),
+                    'total_roi': round(float(sub_r.sum()), 2),
+                }
+
+    out_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+                            'reports', 'backtest_consistency.json')
+    with open(out_path, 'w') as f:
+        json.dump(report, f, indent=2)
+    print(f'\nRapor: {out_path}')
+
+
+if __name__ == '__main__':
+    main()

ai-engine/scripts/backtest_day.py

@@ -0,0 +1,146 @@
+import os
+import sys
+import psycopg2
+from psycopg2.extras import RealDictCursor
+
+# Path ayarları
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from services.single_match_orchestrator import SingleMatchOrchestrator
+from services.feature_enrichment import FeatureEnrichmentService
+
+DSN = "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
+
+def run_backtest(target_date="2026-05-03"):
+    conn = psycopg2.connect(DSN)
+    cur = conn.cursor(cursor_factory=RealDictCursor)
+    
+    # 1. Hedef tarihteki bitmiş maçları ve takım isimlerini getir
+    cur.execute("""
+        SELECT m.id, m.score_home, m.score_away, m.mst_utc,
+               t1.name as home_name, t2.name as away_name
+        FROM matches m
+        LEFT JOIN teams t1 ON m.home_team_id = t1.id
+        LEFT JOIN teams t2 ON m.away_team_id = t2.id
+        WHERE m.status IN ('FT', 'AET', 'PEN') 
+          AND to_timestamp(m.mst_utc / 1000.0)::date = %s::date
+          AND m.score_home IS NOT NULL
+        ORDER BY m.mst_utc ASC
+    """, (target_date,))
+    matches = cur.fetchall()
+    
+    if not matches:
+        print(f"❌ {target_date} tarihinde bitmiş maç bulunamadı.")
+        return
+
+    print(f"🚀 {target_date} için Orkestratör Backtesti Başlatılıyor... ({len(matches)} maç bulundu)")
+    print("-" * 60)
+    
+    orchestrator = SingleMatchOrchestrator()
+    
+    bets_placed = 0
+    won = 0
+    lost = 0
+    total_odds_won = 0.0
+    
+    for match in matches:
+        # 3. Üst Akıl (Orkestratör) analizi yapar
+        try:
+            package = orchestrator.analyze_match(match['id'])
+        except Exception as e:
+            print(f"Hata ({match['id']}): {e}")
+            continue
+            
+        if not package:
+            continue
+            
+        package_data = package
+        
+        # 4. Üst akıl bu maça bahis yapmaya karar verdi mi?
+        bet_advice = package_data.get("bet_advice", {})
+        if bet_advice.get("playable") == True:
+            bets_placed += 1
+            main_pick = package_data.get("main_pick", {})
+            market = main_pick.get("market")
+            pick = main_pick.get("pick")
+            odds = float(main_pick.get("odds", 0.0) or 0.0)
+            
+            # Skora göre kazanıp kazanmadığını kontrol et
+            is_won = False
+            h = match['score_home']
+            a = match['score_away']
+            
+            if market == "MS":
+                if pick == "1" and h > a: is_won = True
+                elif pick in ("X", "0") and h == a: is_won = True
+                elif pick == "2" and a > h: is_won = True
+            elif market == "OU25":
+                if pick == "Üst" and (h+a) > 2.5: is_won = True
+                elif pick == "Alt" and (h+a) < 2.5: is_won = True
+            elif market == "OU15":
+                if pick == "Üst" and (h+a) > 1.5: is_won = True
+                elif pick == "Alt" and (h+a) < 1.5: is_won = True
+            elif market == "BTTS":
+                if pick == "KG Var" and h > 0 and a > 0: is_won = True
+                elif pick == "KG Yok" and (h == 0 or a == 0): is_won = True
+            elif market == "DC":
+                if pick == "1X" and h >= a: is_won = True
+                elif pick == "12" and h != a: is_won = True
+                elif pick == "X2" and h <= a: is_won = True
+            
+            if is_won:
+                won += 1
+                total_odds_won += odds
+                res = "✅ KAZANDI"
+            else:
+                lost += 1
+                res = "❌ KAYBETTİ"
+                
+            print(f"[{res}] {match['home_name']} {h}-{a} {match['away_name']} | Tahmin: {market} {pick} (Oran: {odds})")
+        else:
+            main_pick = package_data.get("main_pick", {})
+            reasons = main_pick.get("reasons", ["Bilinmeyen Neden"]) if main_pick else ["No main pick"]
+            reason = " | ".join(reasons) if isinstance(reasons, list) else str(reasons)
+            
+            market_board = package_data.get("market_board", {})
+            main_pick_market = main_pick.get('market', 'N/A') if main_pick else 'N/A'
+            main_pick_pick = main_pick.get('pick', 'N/A') if main_pick else 'N/A'
+            print(f"[PAS] {match['home_name']} {match['score_home']}-{match['score_away']} {match['away_name']} | Reddedilen: {main_pick_market} {main_pick_pick} -> Neden: {reason}")
+            if "market_passed_all_gates" in reason:
+                print(f"      DEBUG: bet_advice = {bet_advice}")
+            
+            v25_ms = market_board.get("MS", {}).get("probs", {})
+            v27_ms = {} # V27 is merged into V25 probabilities in market_board, or we don't have separate V27 access here
+            
+            # Skora göre ms kontrolü
+            h = match['score_home']
+            a = match['score_away']
+            actual_ms = "1" if h > a else ("X" if h == a else "2")
+            
+            v25_top = max(v25_ms, key=v25_ms.get) if v25_ms else "N/A"
+            v27_top = "N/A"
+            
+            rejected_market = main_pick.get("market", "N/A") if main_pick else "N/A"
+            rejected_pick = main_pick.get("pick", "N/A") if main_pick else "N/A"
+            
+            print(f"[PAS] {match['home_name']} {h}-{a} {match['away_name']} | Reddedilen: {rejected_market} {rejected_pick} -> Neden: {reason}")
+            print(f"      [V25 MS Raw: {v25_top}] [Gerçek MS: {actual_ms}]")
+            
+    # Sonuç Raporu
+    print("\n" + "=" * 60)
+    print(f"📊 BACKTEST SONUÇLARI ({target_date})")
+    print("=" * 60)
+    print(f"Toplam Maç Sayısı   : {len(matches)}")
+    print(f"Oynanan Bahis Sayısı: {bets_placed} (Oynama Oranı: %{bets_placed/len(matches)*100:.1f})")
+    print(f"Riskli Bulunup Pas Geçilen: {len(matches) - bets_placed}")
+    
+    if bets_placed > 0:
+        win_rate = won / bets_placed * 100
+        roi = ((total_odds_won - bets_placed) / bets_placed) * 100
+        print(f"Kazanılan           : {won}")
+        print(f"Kaybedilen          : {lost}")
+        print(f"İsabet Oranı        : %{win_rate:.1f}")
+        print(f"Net Kar (ROI)       : %{roi:.1f} {'📈' if roi > 0 else '📉'}")
+
+if __name__ == "__main__":
+    run_backtest("2026-05-03")