feat(HRT-93): ml_feedback_saas.py — feedback loop ML pour turf_saas
- Crée ml_feedback_saas.py (adaptation de ml_feedback.py pour turf_saas.db) - DB_PATH = /home/h3r7/turf_saas/turf_saas.db - Stratégies : xgboost_sg, xgboost_value, xgboost_sp, xgboost_2sur4 - Idempotent (ne duplique pas les paris existants) - Tested : 188 paris insérés en 1ère exécution, 0 en 2ème (idempotence OK) - Crée api_v1/routes/ml_feedback.py - POST /api/v1/ml/feedback/run (admin only via X-Admin-Token ou plan pro) - GET /api/v1/ml/feedback/stats (premium+) - Enregistre ml_feedback_bp dans api_v1/__init__.py Co-Authored-By: Paperclip <noreply@paperclip.ing>
This commit is contained in:
DevOps Engineer
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ml_feedback_saas.py
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ml_feedback_saas.py
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#!/usr/bin/env python3
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"""
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ml_feedback_saas.py — Feedback loop ML pour turf_saas.
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Enregistre les paris virtuels XGBoost depuis ml_predictions_cache
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et met à jour les résultats/dividendes depuis pmu_partants + pmu_rapports.
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DB cible : /home/h3r7/turf_saas/turf_saas.db
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Stratégies :
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A) xgboost_sg : simple_gagnant — top1 ML par course, ml_score >= 70, mise 1€
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B) xgboost_value : simple_gagnant — is_value_bet = 1, mise 1€
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C) xgboost_sp : simple_place — top1 ML par course, ml_score >= 50, mise 1€
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D) xgboost_2sur4 : deux_sur_quatre — top4 ML par course, 6 combos x 1€ = mise 6€
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Usage :
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python3 ml_feedback_saas.py # Traite aujourd'hui
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python3 ml_feedback_saas.py --backfill 2026-04-25
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python3 ml_feedback_saas.py --date 2026-04-25
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"""
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import sqlite3
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import sys
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import logging
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import os
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from datetime import datetime, timedelta
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DB_PATH = "/home/h3r7/turf_saas/turf_saas.db"
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os.makedirs("/home/h3r7/turf_saas/logs", exist_ok=True)
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [ml_feedback_saas] %(levelname)s %(message)s",
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handlers=[
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logging.FileHandler("/home/h3r7/turf_saas/logs/ml_feedback_saas.log"),
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logging.StreamHandler(),
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],
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)
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log = logging.getLogger(__name__)
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# ─────────────────────────────────────────────────────────
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# UTILITAIRES
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# ─────────────────────────────────────────────────────────
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def get_db():
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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return conn
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def pari_existe(cursor, date, num_reunion, num_course, numero1, type_pari, source_reco):
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"""Vérifie si un pari identique existe déjà (idempotence)."""
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cursor.execute(
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"""
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SELECT id FROM paris
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WHERE date_course = ? AND source_reco = ?
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AND type_pari = ? AND numero1 = ?
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AND race_label = ?
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""",
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(date, source_reco, type_pari, numero1, f"R{num_reunion}C{num_course}"),
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)
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return cursor.fetchone() is not None
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def pari_2sur4_existe(cursor, date, num_reunion, num_course, source_reco):
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"""Vérifie si un pari 2sur4 existe déjà pour cette course."""
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cursor.execute(
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"""
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SELECT id FROM paris
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WHERE date_course = ? AND source_reco = ?
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AND race_label = ?
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""",
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(date, source_reco, f"R{num_reunion}C{num_course}"),
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)
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return cursor.fetchone() is not None
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def get_top_ml_par_course(cursor, date, n=4, min_score=0):
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"""Retourne les n meilleurs chevaux ML par course pour une date."""
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cursor.execute(
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"""
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SELECT num_reunion, num_course, horse_name, horse_number,
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ml_score, odds, recommendation, is_value_bet,
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race_label, race_name, hippodrome, heure,
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discipline, distance
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FROM ml_predictions_cache
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WHERE date = ?
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AND ml_score >= ?
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ORDER BY num_reunion, num_course, ml_score DESC
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""",
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(date, min_score),
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)
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rows = cursor.fetchall()
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courses = {}
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for r in rows:
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key = (r["num_reunion"], r["num_course"])
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if key not in courses:
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courses[key] = []
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if len(courses[key]) < n:
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courses[key].append(dict(r))
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return courses
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# ─────────────────────────────────────────────────────────
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# STRATÉGIE A — Simple Gagnant top1 ML (score >= 70)
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# ─────────────────────────────────────────────────────────
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def save_ml_paris_sg(conn, date):
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"""Insère 1 pari simple_gagnant par course : top1 ML avec ml_score >= 70."""
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cursor = conn.cursor()
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courses = get_top_ml_par_course(cursor, date, n=1, min_score=70)
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inseres = 0
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for (num_reunion, num_course), chevaux in courses.items():
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cheval = chevaux[0]
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if pari_existe(
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cursor,
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date,
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num_reunion,
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num_course,
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cheval["horse_number"],
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"simple_gagnant",
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"xgboost_sg",
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):
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continue
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cursor.execute(
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"""
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INSERT INTO paris
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(date_pari, date_course, race_name, race_label, hippodrome,
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type_pari, chevaux, cheval1, numero1, cote, mise,
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statut, gain, source_reco, model_source)
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VALUES (?, ?, ?, ?, ?, 'simple_gagnant', ?, ?, ?, ?, 1.0,
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'EN_ATTENTE', 0.0, 'xgboost_sg', 'xgboost_v1')
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""",
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(
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date,
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date,
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cheval.get("race_name") or "",
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f"R{num_reunion}C{num_course}",
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cheval.get("hippodrome") or "",
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cheval["horse_name"],
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cheval["horse_name"],
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cheval["horse_number"],
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cheval["odds"],
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),
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)
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inseres += 1
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conn.commit()
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log.info(f"[SG] {date} → {inseres} paris simple_gagnant insérés (score>=70)")
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return inseres
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# ─────────────────────────────────────────────────────────
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# STRATÉGIE B — Value Bet (is_value_bet = 1)
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# ─────────────────────────────────────────────────────────
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def save_ml_paris_value(conn, date):
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"""Insère 1 pari simple_gagnant pour chaque cheval is_value_bet=1."""
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cursor = conn.cursor()
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cursor.execute(
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"""
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SELECT num_reunion, num_course, horse_name, horse_number,
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ml_score, odds, race_label, race_name, hippodrome
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FROM ml_predictions_cache
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WHERE date = ? AND is_value_bet = 1
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ORDER BY num_reunion, num_course, ml_score DESC
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""",
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(date,),
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)
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rows = [dict(r) for r in cursor.fetchall()]
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inseres = 0
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for r in rows:
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if pari_existe(
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cursor,
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date,
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r["num_reunion"],
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r["num_course"],
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r["horse_number"],
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"simple_gagnant",
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"xgboost_value",
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):
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continue
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cursor.execute(
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"""
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INSERT INTO paris
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(date_pari, date_course, race_name, race_label, hippodrome,
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type_pari, chevaux, cheval1, numero1, cote, mise,
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statut, gain, source_reco, model_source)
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VALUES (?, ?, ?, ?, ?, 'simple_gagnant', ?, ?, ?, ?, 1.0,
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'EN_ATTENTE', 0.0, 'xgboost_value', 'xgboost_v1')
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""",
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(
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date,
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date,
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r.get("race_name") or "",
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r.get("race_label") or f"R{r['num_reunion']}C{r['num_course']}",
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r.get("hippodrome") or "",
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r["horse_name"],
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r["horse_name"],
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r["horse_number"],
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r["odds"],
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),
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)
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inseres += 1
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conn.commit()
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log.info(f"[VALUE] {date} → {inseres} paris value_bet insérés")
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return inseres
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# ─────────────────────────────────────────────────────────
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# STRATÉGIE C — Simple Placé top1 ML (score >= 50)
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# ─────────────────────────────────────────────────────────
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def save_ml_paris_sp(conn, date):
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"""Insère 1 pari simple_place par course : top1 ML avec ml_score >= 50."""
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cursor = conn.cursor()
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courses = get_top_ml_par_course(cursor, date, n=1, min_score=50)
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inseres = 0
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for (num_reunion, num_course), chevaux in courses.items():
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cheval = chevaux[0]
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if pari_existe(
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cursor,
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date,
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num_reunion,
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num_course,
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cheval["horse_number"],
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"simple_place",
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"xgboost_sp",
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):
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continue
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cursor.execute(
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"""
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INSERT INTO paris
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(date_pari, date_course, race_name, race_label, hippodrome,
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type_pari, chevaux, cheval1, numero1, cote, mise,
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statut, gain, source_reco, model_source)
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VALUES (?, ?, ?, ?, ?, 'simple_place', ?, ?, ?, ?, 1.0,
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'EN_ATTENTE', 0.0, 'xgboost_sp', 'xgboost_v1')
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""",
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(
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date,
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date,
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cheval.get("race_name") or "",
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f"R{num_reunion}C{num_course}",
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cheval.get("hippodrome") or "",
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cheval["horse_name"],
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cheval["horse_name"],
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cheval["horse_number"],
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cheval["odds"],
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),
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)
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inseres += 1
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conn.commit()
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log.info(f"[SP] {date} → {inseres} paris simple_place insérés (score>=50)")
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return inseres
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# ─────────────────────────────────────────────────────────
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# STRATÉGIE D — 2sur4 top4 ML (6 combinaisons x 1€)
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# ─────────────────────────────────────────────────────────
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def save_ml_paris_2sur4(conn, date):
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"""Insère 1 pari deux_sur_quatre par course : top4 ML, mise 6€."""
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cursor = conn.cursor()
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courses = get_top_ml_par_course(cursor, date, n=4, min_score=0)
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inseres = 0
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for (num_reunion, num_course), chevaux in courses.items():
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if len(chevaux) < 4:
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continue
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if pari_2sur4_existe(cursor, date, num_reunion, num_course, "xgboost_2sur4"):
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continue
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top4 = chevaux[:4]
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nums = [str(c["horse_number"]) for c in top4]
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noms = [c["horse_name"] for c in top4]
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chevaux_str = "/".join(noms)
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cursor.execute(
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"""
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INSERT INTO paris
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(date_pari, date_course, race_name, race_label, hippodrome,
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type_pari, chevaux, cheval1, numero1, cote, mise,
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statut, gain, source_reco, model_source, commentaire)
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VALUES (?, ?, ?, ?, ?, 'deux_sur_quatre', ?, ?, ?, 0.0, 6.0,
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'EN_ATTENTE', 0.0, 'xgboost_2sur4', 'xgboost_v1', ?)
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""",
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(
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date,
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date,
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top4[0].get("race_name") or "",
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f"R{num_reunion}C{num_course}",
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top4[0].get("hippodrome") or "",
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chevaux_str,
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top4[0]["horse_name"],
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top4[0]["horse_number"],
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f"top4 ML: {'/'.join(nums)}",
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),
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)
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inseres += 1
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conn.commit()
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log.info(f"[2S4] {date} → {inseres} paris deux_sur_quatre insérés")
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return inseres
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# ─────────────────────────────────────────────────────────
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# UPDATE RÉSULTATS + DIVIDENDES
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# ─────────────────────────────────────────────────────────
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def update_ml_paris_results(conn, date):
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"""
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Met à jour statut + gain (dividende PMU réel) pour tous les paris ML EN_ATTENTE.
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Sources: pmu_partants (ordre_arrivee) + pmu_rapports (dividende_euro).
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"""
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cursor = conn.cursor()
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cursor.execute(
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"""
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SELECT id, race_label, type_pari, numero1, chevaux, mise, source_reco, commentaire
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FROM paris
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WHERE date_course = ? AND statut = 'EN_ATTENTE'
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AND source_reco LIKE 'xgboost%'
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""",
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(date,),
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)
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paris = [dict(r) for r in cursor.fetchall()]
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if not paris:
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log.info(f"[UPDATE] {date} → aucun pari ML EN_ATTENTE")
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return 0
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maj = 0
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for pari in paris:
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pari_id = pari["id"]
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race_label = pari["race_label"] or ""
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type_pari = pari["type_pari"]
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numero1 = pari["numero1"]
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mise = pari["mise"]
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# Extraire num_reunion / num_course depuis le race_label "R{r}C{c}"
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try:
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parts = race_label.replace("R", "").split("C")
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num_reunion = int(parts[0])
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num_course = int(parts[1])
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except Exception:
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log.warning(f"[UPDATE] race_label invalide : {race_label}")
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continue
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if type_pari == "simple_gagnant":
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cursor.execute(
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"""
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SELECT ordre_arrivee FROM pmu_partants
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WHERE date_programme = ? AND num_reunion = ?
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AND num_course = ? AND num_pmu = ?
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""",
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(date, num_reunion, num_course, numero1),
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)
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row = cursor.fetchone()
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if not row or row["ordre_arrivee"] is None or row["ordre_arrivee"] == 0:
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continue
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gagne = row["ordre_arrivee"] == 1
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gain = 0.0
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if gagne:
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cursor.execute(
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"""
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SELECT dividende_euro FROM pmu_rapports
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WHERE date_programme = ? AND num_reunion = ?
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AND num_course = ? AND type_pari = 'SIMPLE_GAGNANT'
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AND CAST(combinaison AS INTEGER) = ?
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AND libelle NOT LIKE '%NP%'
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""",
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(date, num_reunion, num_course, numero1),
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)
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div = cursor.fetchone()
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gain = div["dividende_euro"] if div and div["dividende_euro"] else 0.0
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cursor.execute(
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"UPDATE paris SET statut=?, gain=? WHERE id=?",
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("GAGNE" if gagne else "PERDU", gain, pari_id),
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)
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maj += 1
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elif type_pari == "simple_place":
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cursor.execute(
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"""
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SELECT ordre_arrivee FROM pmu_partants
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WHERE date_programme = ? AND num_reunion = ?
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AND num_course = ? AND num_pmu = ?
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""",
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(date, num_reunion, num_course, numero1),
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)
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row = cursor.fetchone()
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if not row or not row["ordre_arrivee"]:
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continue
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gagne = 1 <= row["ordre_arrivee"] <= 3
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gain = 0.0
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if gagne:
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cursor.execute(
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"""
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SELECT dividende_euro FROM pmu_rapports
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WHERE date_programme = ? AND num_reunion = ?
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AND num_course = ? AND type_pari = 'SIMPLE_PLACE'
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AND CAST(combinaison AS INTEGER) = ?
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AND libelle NOT LIKE '%NP%'
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""",
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(date, num_reunion, num_course, numero1),
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)
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div = cursor.fetchone()
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gain = div["dividende_euro"] if div and div["dividende_euro"] else 0.0
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cursor.execute(
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"UPDATE paris SET statut=?, gain=? WHERE id=?",
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("GAGNE" if gagne else "PERDU", gain, pari_id),
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)
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maj += 1
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elif type_pari == "deux_sur_quatre":
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# Récupère les 4 numéros depuis commentaire "top4 ML: n1/n2/n3/n4"
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try:
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nums_str = (
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pari["commentaire"].split(": ")[1]
|
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if pari.get("commentaire")
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else ""
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||||
)
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nums_top4 = [int(n) for n in nums_str.split("/") if n.strip().isdigit()]
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except Exception:
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nums_top4 = []
|
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if len(nums_top4) < 4:
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# Fallback : reconstituer top4 depuis ml_predictions_cache
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cursor.execute(
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||||
"""
|
||||
SELECT horse_number FROM ml_predictions_cache
|
||||
WHERE date = ? AND num_reunion = ? AND num_course = ?
|
||||
ORDER BY ml_score DESC LIMIT 4
|
||||
""",
|
||||
(date, num_reunion, num_course),
|
||||
)
|
||||
nums_top4 = [r["horse_number"] for r in cursor.fetchall()]
|
||||
|
||||
if len(nums_top4) < 2:
|
||||
continue
|
||||
|
||||
cursor.execute(
|
||||
"""
|
||||
SELECT combinaison, dividende_euro FROM pmu_rapports
|
||||
WHERE date_programme = ? AND num_reunion = ?
|
||||
AND num_course = ? AND type_pari = 'DEUX_SUR_QUATRE'
|
||||
AND libelle NOT LIKE '%NP%'
|
||||
""",
|
||||
(date, num_reunion, num_course),
|
||||
)
|
||||
rapports = [dict(r) for r in cursor.fetchall()]
|
||||
gain_total = 0.0
|
||||
|
||||
for rap in rapports:
|
||||
try:
|
||||
n1, n2 = [int(x) for x in rap["combinaison"].split("-")]
|
||||
except Exception:
|
||||
continue
|
||||
if n1 in nums_top4 and n2 in nums_top4:
|
||||
gain_total += rap["dividende_euro"]
|
||||
|
||||
gagne = gain_total > 0
|
||||
cursor.execute(
|
||||
"UPDATE paris SET statut=?, gain=? WHERE id=?",
|
||||
("GAGNE" if gagne else "PERDU", round(gain_total, 2), pari_id),
|
||||
)
|
||||
maj += 1
|
||||
|
||||
conn.commit()
|
||||
log.info(f"[UPDATE] {date} → {maj}/{len(paris)} paris ML mis à jour")
|
||||
return maj
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────────────────
|
||||
# STATS PAR STRATÉGIE
|
||||
# ─────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def get_feedback_stats(conn, date_debut=None, date_fin=None):
|
||||
"""Stats performances ML par stratégie (source_reco)."""
|
||||
cursor = conn.cursor()
|
||||
cursor.execute(
|
||||
"""
|
||||
SELECT source_reco,
|
||||
COUNT(*) as n_paris,
|
||||
SUM(CASE WHEN statut='GAGNE' THEN 1 ELSE 0 END) as n_gagne,
|
||||
SUM(CASE WHEN statut='PERDU' THEN 1 ELSE 0 END) as n_perdu,
|
||||
SUM(CASE WHEN statut='EN_ATTENTE' THEN 1 ELSE 0 END) as n_attente,
|
||||
ROUND(100.0 * SUM(CASE WHEN statut='GAGNE' THEN 1 ELSE 0 END)
|
||||
/ NULLIF(SUM(CASE WHEN statut IN ('GAGNE','PERDU') THEN 1 ELSE 0 END), 0), 1) as win_rate_pct,
|
||||
ROUND(SUM(gain), 2) as gain_total,
|
||||
ROUND(SUM(mise), 2) as mise_totale,
|
||||
ROUND(SUM(gain) - SUM(mise), 2) as roi_net
|
||||
FROM paris
|
||||
WHERE source_reco LIKE 'xgboost%'
|
||||
AND (:debut IS NULL OR date_course >= :debut)
|
||||
AND (:fin IS NULL OR date_course <= :fin)
|
||||
GROUP BY source_reco
|
||||
ORDER BY source_reco
|
||||
""",
|
||||
{"debut": date_debut, "fin": date_fin},
|
||||
)
|
||||
return [dict(r) for r in cursor.fetchall()]
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────────────────
|
||||
# PIPELINE COMPLET
|
||||
# ─────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def run(date):
|
||||
"""Enregistre les paris ML du jour + met à jour les résultats de J-1."""
|
||||
conn = get_db()
|
||||
log.info(f"=== ml_feedback_saas.run({date}) ===")
|
||||
|
||||
# 1. Enregistre les paris ML pour la date (depuis le cache du jour)
|
||||
sg = save_ml_paris_sg(conn, date)
|
||||
vb = save_ml_paris_value(conn, date)
|
||||
sp = save_ml_paris_sp(conn, date)
|
||||
s4 = save_ml_paris_2sur4(conn, date)
|
||||
log.info(f"[SAVE] {date} → total insérés : SG={sg} VALUE={vb} SP={sp} 2S4={s4}")
|
||||
|
||||
# 2. Met à jour les résultats de J-1 (résultats PMU disponibles)
|
||||
yesterday = (datetime.strptime(date, "%Y-%m-%d") - timedelta(days=1)).strftime(
|
||||
"%Y-%m-%d"
|
||||
)
|
||||
maj = update_ml_paris_results(conn, yesterday)
|
||||
log.info(f"[UPDATE] {yesterday} → {maj} paris mis à jour")
|
||||
|
||||
conn.close()
|
||||
return {"inseres": {"sg": sg, "value": vb, "sp": sp, "2sur4": s4}, "maj": maj}
|
||||
|
||||
|
||||
def backfill(date):
|
||||
"""Backfill : insère ET met à jour les résultats pour une date passée."""
|
||||
conn = get_db()
|
||||
log.info(f"=== ml_feedback_saas.backfill({date}) ===")
|
||||
|
||||
sg = save_ml_paris_sg(conn, date)
|
||||
vb = save_ml_paris_value(conn, date)
|
||||
sp = save_ml_paris_sp(conn, date)
|
||||
s4 = save_ml_paris_2sur4(conn, date)
|
||||
log.info(f"[SAVE] {date} → SG={sg} VALUE={vb} SP={sp} 2S4={s4}")
|
||||
|
||||
maj = update_ml_paris_results(conn, date)
|
||||
log.info(f"[UPDATE] {date} → {maj} paris mis à jour")
|
||||
|
||||
conn.close()
|
||||
return sg + vb + sp + s4, maj
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────────────────
|
||||
# MAIN
|
||||
# ─────────────────────────────────────────────────────────
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "--backfill" in sys.argv:
|
||||
idx = sys.argv.index("--backfill")
|
||||
date = sys.argv[idx + 1] if idx + 1 < len(sys.argv) else None
|
||||
if not date:
|
||||
print("Usage: python3 ml_feedback_saas.py --backfill YYYY-MM-DD")
|
||||
sys.exit(1)
|
||||
inseres, maj = backfill(date)
|
||||
print(f"Backfill {date} : {inseres} paris insérés, {maj} mis à jour")
|
||||
|
||||
elif "--date" in sys.argv:
|
||||
idx = sys.argv.index("--date")
|
||||
date = sys.argv[idx + 1] if idx + 1 < len(sys.argv) else None
|
||||
if not date:
|
||||
print("Usage: python3 ml_feedback_saas.py --date YYYY-MM-DD")
|
||||
sys.exit(1)
|
||||
result = run(date)
|
||||
total = sum(result["inseres"].values())
|
||||
print(f"Run {date} : {total} paris insérés, {result['maj']} mis à jour")
|
||||
|
||||
else:
|
||||
result = run(datetime.now().strftime("%Y-%m-%d"))
|
||||
total = sum(result["inseres"].values())
|
||||
print(f"Run today : {total} paris insérés, {result['maj']} mis à jour")
|
||||
Reference in New Issue
Block a user