138 lines
5.2 KiB
Markdown
Executable File
138 lines
5.2 KiB
Markdown
Executable File
# V17 "Galacticos" Migration & Training Guide
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**Date:** February 6, 2026
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**Status:** Architecture Implemented / Training Ready
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---
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## 1. Executive Summary
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This document details the transition of the Suggest-Bet AI Engine from a monolithic, script-based system to a scalable, service-oriented architecture (SOA) powered by FastAPI and Docker. It also documents the rigorous data analysis that led to the "Strict Filtering" training strategy for the V17 Model.
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**Core Achievement:** The system now treats players as first-class citizens (Embeddings) and evaluates match context (Odds, Form) dynamically, served via a high-performance HTTP API.
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---
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## 2. Architecture Overhaul
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### Before (Legacy)
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* **Execution:** Backend spawned new Python processes (`child_process.spawn`) for EVERY prediction request.
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* **Performance:** ~3-5 seconds latency per request (loading PyTorch models from disk repeatedly).
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* **Maintenance:** Spaghetti code mixing API logic, feature engineering, and training scripts.
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* **Integration:** Brittle stdout parsing (Backend read text output from Python).
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### After (V17 Beast Mode)
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* **Execution:** Persistent Dockerized Service (`ai-engine`).
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* **Performance:** <100ms latency (Models kept in RAM).
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* **Structure:** Clean separation of concerns:
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* `ai-engine/app`: FastAPI Routes (HTTP Layer).
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* `ai-engine/core`: Pure Business Logic (Model, Features).
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* `ai-engine/data`: Database Abstraction.
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* **Integration:** Type-safe HTTP requests via `axios` from NestJS.
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---
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## 3. Data Analysis & Strategy Shift
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We analyzed the database (~167k matches) and discovered critical data quality issues that were poisoning previous models.
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### The "Garbage Data" Problem
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* **Total Matches:** ~167,000
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* **Matches with Lineups:** Only ~73,000 (44%)
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* **Matches with Odds:** ~94,000 (56%)
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* **Intersection (Quality Data):** < 50,000 matches.
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**Conclusion:** Training on the full dataset forces the model to learn from "blind" matches (missing lineups) or "contextless" matches (missing odds), leading to hallucinations.
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### The "Top Leagues" Solution
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We analyzed 20 Top Leagues (Premier League, LaLiga, etc.) and found elite data quality:
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* **Premier League:** 77% Lineup Coverage.
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* **Championship:** 88% Lineup Coverage.
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**Decision:** The V17 training pipeline now **strictly filters** for:
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1. Top 20 Leagues (`top_leagues.json`).
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2. Full Lineup Availability (11+ players per team).
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3. Odds Availability.
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This reduces the dataset size but drastically increases **Signal-to-Noise Ratio**.
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---
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## 4. The V17 Model "Galacticos"
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### Philosophy
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Instead of rating "Team A vs Team B", V17 rates "These 11 Players vs Those 11 Players" in the context of current odds and form.
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### Input Vector (The Brain)
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1. **Player Embeddings:** Each of the ~17,000 elite players has a learnable 32-dimensional vector.
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2. **Context Vector (32-dim):**
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* **Odds (9):** 1X2, Over/Under, BTTS (Normalized).
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* **Form (12):** Goals Scored/Conceded, Win Rate (Home/Away).
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* **H2H (3):** Historical dominance.
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* **Advanced (8):** Rest Days (Fatigue), League Standing Diff.
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### Output Heads (Multi-Task Learning)
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The model is trained to predict **everything at once** to understand the game deeply:
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* **Match Result (1X2):** CrossEntropy Loss.
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* **Goals (Home/Away):** MSE Loss.
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* **BTTS & Over 2.5:** Binary CrossEntropy Loss.
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---
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## 5. How to Train (The Beast Trainer)
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The training logic is consolidated into a single robust script: `ai-engine/core/training/trainer_v17.py`.
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### Prerequisites
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* Docker container running (`ai-engine`).
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* Database populated with historical data.
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### Execution Command
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Run this from your host terminal (project root):
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```bash
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# 1. Install dependencies (if running locally outside docker)
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pip3 install torch numpy pandas tqdm psycopg2-binary scikit-learn python-dotenv
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# 2. Run Training
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export DATABASE_URL="postgresql://suggestbet:SuGGesT2026SecuRe@127.0.0.1:15432/boilerplate_db?schema=public"
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python3 ai-engine/core/training/trainer_v17.py
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```
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### Key Metrics to Watch
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The trainer reports **"High Confidence Accuracy"**.
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* *Bad:* Overall Acc 50%, High Conf Acc 55%.
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* *Good:* Overall Acc 55%, High Conf Acc **>85%**. (This is our goal).
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---
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## 6. Deployment & Usage
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### 1. Docker
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The AI Engine is now part of `docker-compose.yml`.
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```bash
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docker-compose up -d --build
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```
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### 2. Backend Usage
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NestJS services (`SmartCouponService`, `PredictionsProcessor`) now use `axios` to call the AI Engine:
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```typescript
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// Example call
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const response = await axios.post(`http://ai-engine:8000/predict/v17/${matchId}`);
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```
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### 3. API Endpoints
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* `POST /predict/v17/{match_id}`: Single match prediction.
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* `GET /health`: Health check.
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---
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## 7. Future Roadmap (TODOs)
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1. **Curriculum Learning V2:** Sort training data by "Difficulty" (Goal Difference) to teach easy matches first. (Partially implemented).
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2. **Live Dashboard:** A simple frontend page to visualize `v17_comprehensive.pth` training metrics in real-time.
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3. **Auto-Retraining:** A Cron job (BullMQ) to retrain the model every Monday with the weekend's results.
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4. **Feedback Loop:** Integrate `ai_predictions_log` table to feed "Wrong Predictions" back into training with higher weight.
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---
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*Generated by Gemini CLI Agent - Senior ML Engineer*
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