Merge pull request 'Sprint 6-7 — ML Upgrade: Ensemble XGBoost+LightGBM+MLP + Optuna' (#1) from feature/ml-upgrade-ensemble into master

tests/beta_monitor.py

@@ -0,0 +1,448 @@
+"""
+Beta Monitoring — SaaS Turf Prédictions IA
+Sprint 8 — QA, Beta Fermee, Go/No-Go
+Ticket: HRT-34
+
+Ce module :
+- Collecte les feedbacks beta via l'API in-app
+- Envoie des alertes Telegram en cas d'erreur détectée pendant la beta
+- Génère le rapport beta final (bugs, UX, NPS)
+
+Usage :
+  # Démarrer le monitoring beta
+  python tests/beta_monitor.py --watch --interval 60
+
+  # Générer le rapport beta final
+  python tests/beta_monitor.py --report
+
+  # Test d'envoi Telegram
+  python tests/beta_monitor.py --test-telegram
+"""
+
+import os
+import sys
+import json
+import time
+import sqlite3
+import requests
+import argparse
+from datetime import datetime, timedelta
+from pathlib import Path
+
+# ============================================================
+# Configuration
+# ============================================================
+
+BASE_URL = os.environ.get("APP_URL", "http://localhost:8792")
+TELEGRAM_TOKEN = os.environ.get(
+    "TELEGRAM_TOKEN", "8649773134:AAFqzZVtSHfPPFDadcte1B-1h23nZ8DmdYE"
+)
+TELEGRAM_CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "")  # À configurer
+
+BETA_DB_PATH = os.environ.get("BETA_DB_PATH", "/home/h3r7/turf_saas/turf_saas.db")
+REPORTS_DIR = Path("tests/reports")
+REPORTS_DIR.mkdir(parents=True, exist_ok=True)
+
+# Seuils d'alerte
+ERROR_RATE_THRESHOLD = 0.01  # 1% d'erreurs → alerte
+LATENCY_P95_THRESHOLD_MS = 500  # p95 > 500ms → alerte
+BETA_MIN_USERS = 10  # Minimum d'utilisateurs beta requis
+NPS_TARGET = 7.0  # NPS cible (sur 10)
+
+
+# ============================================================
+# Alertes Telegram
+# ============================================================
+
+
+def send_telegram(message: str, parse_mode: str = "Markdown") -> bool:
+    """Envoie un message Telegram d'alerte."""
+    if not TELEGRAM_TOKEN or not TELEGRAM_CHAT_ID:
+        print(f"⚠️  Telegram non configuré. Message: {message[:100]}")
+        return False
+
+    try:
+        resp = requests.post(
+            f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage",
+            json={
+                "chat_id": TELEGRAM_CHAT_ID,
+                "text": message,
+                "parse_mode": parse_mode,
+            },
+            timeout=10,
+        )
+        if resp.status_code == 200:
+            print(f"✅ Alerte Telegram envoyée")
+            return True
+        else:
+            print(f"❌ Telegram erreur: {resp.status_code} — {resp.text}")
+            return False
+    except Exception as e:
+        print(f"❌ Telegram exception: {e}")
+        return False
+
+
+def alert_error(endpoint: str, status_code: int, message: str):
+    """Alerte Telegram sur erreur critique."""
+    text = (
+        f"🚨 *ALERTE BETA — SaaS Turf IA*\n\n"
+        f"Erreur détectée sur `{endpoint}`\n"
+        f"Status: `{status_code}`\n"
+        f"Message: {message[:200]}\n"
+        f"Heure: {datetime.now().strftime('%H:%M:%S')}\n\n"
+        f"_Ticket: HRT-34_"
+    )
+    send_telegram(text)
+
+
+def alert_performance(p95_ms: float, error_rate: float):
+    """Alerte Telegram sur dégradation de performance."""
+    text = (
+        f"⚠️ *ALERTE PERFORMANCE — SaaS Turf IA*\n\n"
+        f"p95 latence: `{p95_ms:.0f}ms` (seuil: {LATENCY_P95_THRESHOLD_MS}ms)\n"
+        f"Error rate: `{error_rate * 100:.2f}%` (seuil: {ERROR_RATE_THRESHOLD * 100:.1f}%)\n"
+        f"Heure: {datetime.now().strftime('%H:%M:%S')}\n\n"
+        f"_Ticket: HRT-34_"
+    )
+    send_telegram(text)
+
+
+# ============================================================
+# Collecte de métriques
+# ============================================================
+
+
+class BetaMonitor:
+    """Moniteur actif pendant la beta fermée."""
+
+    ENDPOINTS_TO_CHECK = [
+        "/api",
+        "/api/races",
+        "/api/scoring",
+        "/dashboard",
+        "/",
+    ]
+
+    def __init__(self, base_url: str = BASE_URL):
+        self.base_url = base_url.rstrip("/")
+        self.errors: list[dict] = []
+        self.latencies: list[float] = []
+        self.check_count = 0
+
+    def check_endpoint(self, path: str) -> dict:
+        """Vérifie un endpoint et retourne le résultat."""
+        start = time.time()
+        try:
+            resp = requests.get(f"{self.base_url}{path}", timeout=10)
+            latency_ms = (time.time() - start) * 1000
+            return {
+                "path": path,
+                "status": resp.status_code,
+                "latency_ms": latency_ms,
+                "ok": resp.status_code < 500,
+                "timestamp": datetime.now().isoformat(),
+            }
+        except requests.exceptions.ConnectionError as e:
+            return {
+                "path": path,
+                "status": 0,
+                "latency_ms": 0,
+                "ok": False,
+                "error": str(e),
+                "timestamp": datetime.now().isoformat(),
+            }
+        except Exception as e:
+            return {
+                "path": path,
+                "status": 0,
+                "latency_ms": 0,
+                "ok": False,
+                "error": str(e),
+                "timestamp": datetime.now().isoformat(),
+            }
+
+    def run_checks(self) -> dict:
+        """Exécute tous les checks et retourne un résumé."""
+        results = [self.check_endpoint(p) for p in self.ENDPOINTS_TO_CHECK]
+        self.check_count += 1
+
+        failures = [r for r in results if not r["ok"]]
+        latencies = [r["latency_ms"] for r in results if r["latency_ms"] > 0]
+
+        p95 = (
+            sorted(latencies)[int(len(latencies) * 0.95)]
+            if len(latencies) >= 2
+            else (latencies[0] if latencies else 0)
+        )
+        error_rate = len(failures) / len(results) if results else 0
+
+        # Stocker pour rapport
+        self.latencies.extend(latencies)
+        self.errors.extend(failures)
+
+        return {
+            "check_number": self.check_count,
+            "timestamp": datetime.now().isoformat(),
+            "total_checks": len(results),
+            "failures": len(failures),
+            "error_rate": error_rate,
+            "p95_ms": p95,
+            "results": results,
+        }
+
+    def watch(self, interval_seconds: int = 60):
+        """Surveillance continue avec alertes Telegram."""
+        print(f"🔍 Beta monitoring démarré — {self.base_url}")
+        print(f"   Intervalle: {interval_seconds}s")
+        print(f"   Endpoints: {len(self.ENDPOINTS_TO_CHECK)}")
+        print(f"   Ctrl+C pour arrêter\n")
+
+        consecutive_errors = 0
+
+        try:
+            while True:
+                summary = self.run_checks()
+                timestamp = datetime.now().strftime("%H:%M:%S")
+
+                status_icon = "✅" if summary["error_rate"] == 0 else "❌"
+                print(
+                    f"[{timestamp}] {status_icon} "
+                    f"Check #{summary['check_number']} — "
+                    f"p95={summary['p95_ms']:.0f}ms, "
+                    f"errors={summary['failures']}/{summary['total_checks']}"
+                )
+
+                # Alertes
+                if summary["error_rate"] > ERROR_RATE_THRESHOLD:
+                    consecutive_errors += 1
+                    if consecutive_errors >= 2:  # 2 checks consécutifs en erreur
+                        for failure in summary["results"]:
+                            if not failure["ok"]:
+                                alert_error(
+                                    failure["path"],
+                                    failure.get("status", 0),
+                                    failure.get("error", "Non-2xx response"),
+                                )
+                else:
+                    consecutive_errors = 0
+
+                if summary["p95_ms"] > LATENCY_P95_THRESHOLD_MS:
+                    print(f"⚠️  Latence p95 élevée: {summary['p95_ms']:.0f}ms")
+                    if summary["p95_ms"] > LATENCY_P95_THRESHOLD_MS * 2:
+                        alert_performance(summary["p95_ms"], summary["error_rate"])
+
+                # Sauvegarder les résultats
+                log_file = REPORTS_DIR / "beta_monitor_log.jsonl"
+                with open(log_file, "a") as f:
+                    f.write(json.dumps(summary) + "\n")
+
+                time.sleep(interval_seconds)
+
+        except KeyboardInterrupt:
+            print(f"\n⏹️  Monitoring arrêté après {self.check_count} checks")
+            self.generate_report()
+
+
+# ============================================================
+# Rapport beta final
+# ============================================================
+
+
+class BetaReport:
+    """Générateur de rapport beta fermée."""
+
+    def __init__(self, base_url: str = BASE_URL):
+        self.base_url = base_url
+        self.timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+    def collect_feedback_from_db(self) -> list[dict]:
+        """Collecte les feedbacks depuis la BDD (table beta_feedback si elle existe)."""
+        try:
+            conn = sqlite3.connect(BETA_DB_PATH)
+            c = conn.cursor()
+            c.execute(
+                "SELECT name FROM sqlite_master WHERE type='table' AND name='beta_feedback'"
+            )
+            if not c.fetchone():
+                conn.close()
+                return []
+            c.execute("SELECT * FROM beta_feedback ORDER BY created_at DESC")
+            rows = c.fetchall()
+            conn.close()
+            return [dict(zip([col[0] for col in c.description], row)) for row in rows]
+        except Exception as e:
+            print(f"⚠️  Impossible de lire beta_feedback: {e}")
+            return []
+
+    def collect_monitor_logs(self) -> list[dict]:
+        """Lit les logs du monitoring beta."""
+        log_file = REPORTS_DIR / "beta_monitor_log.jsonl"
+        if not log_file.exists():
+            return []
+        entries = []
+        with open(log_file) as f:
+            for line in f:
+                try:
+                    entries.append(json.loads(line))
+                except Exception:
+                    pass
+        return entries
+
+    def generate(self) -> str:
+        """Génère le rapport complet et le sauvegarde."""
+        feedbacks = self.collect_feedback_from_db()
+        monitor_logs = self.collect_monitor_logs()
+
+        # Calculer NPS depuis les feedbacks
+        nps_scores = [
+            f.get("nps_score") for f in feedbacks if f.get("nps_score") is not None
+        ]
+        avg_nps = sum(nps_scores) / len(nps_scores) if nps_scores else None
+
+        # Statistiques monitoring
+        if monitor_logs:
+            all_latencies = []
+            total_errors = 0
+            total_checks = 0
+            for entry in monitor_logs:
+                all_latencies.extend(
+                    [
+                        r["latency_ms"]
+                        for r in entry.get("results", [])
+                        if r.get("latency_ms", 0) > 0
+                    ]
+                )
+                total_errors += entry.get("failures", 0)
+                total_checks += entry.get("total_checks", 0)
+            avg_latency = (
+                sum(all_latencies) / len(all_latencies) if all_latencies else 0
+            )
+            overall_error_rate = total_errors / total_checks if total_checks > 0 else 0
+        else:
+            avg_latency = 0
+            overall_error_rate = 0
+            total_checks = 0
+
+        # Construire le rapport
+        report = []
+        report.append("=" * 60)
+        report.append("RAPPORT BETA FERMÉE — SaaS Turf Prédictions IA")
+        report.append(f"Généré le : {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        report.append(f"Ticket    : HRT-34")
+        report.append("=" * 60)
+        report.append("")
+        report.append("## 1. PARTICIPANTS BETA")
+        report.append(f"   Feedbacks reçus : {len(feedbacks)}")
+        report.append(
+            f"   NPS moyen       : {avg_nps:.1f}/10"
+            if avg_nps
+            else "   NPS moyen       : (en attente feedbacks)"
+        )
+        report.append(f"   Cible NPS       : ≥ {NPS_TARGET}/10")
+        nps_ok = avg_nps is not None and avg_nps >= NPS_TARGET
+        report.append(
+            f"   Statut NPS      : {'✅ OBJECTIF ATTEINT' if nps_ok else '⏳ En attente' if avg_nps is None else '❌ OBJECTIF NON ATTEINT'}"
+        )
+        report.append("")
+        report.append("## 2. BUGS SIGNALÉS")
+        bugs = [f for f in feedbacks if f.get("type") == "bug"]
+        critical_bugs = [b for b in bugs if b.get("severity") in ("critical", "high")]
+        report.append(f"   Total bugs      : {len(bugs)}")
+        report.append(f"   Critiques/High  : {len(critical_bugs)}")
+        report.append(
+            f"   Statut          : {'✅ 0 bug critique' if len(critical_bugs) == 0 else f'❌ {len(critical_bugs)} bug(s) critique(s)'}"
+        )
+        report.append("")
+        report.append("## 3. PERFORMANCE RÉELLE (monitoring)")
+        report.append(f"   Checks effectués: {total_checks}")
+        report.append(f"   Latence moyenne : {avg_latency:.1f}ms")
+        report.append(f"   Error rate      : {overall_error_rate * 100:.2f}%")
+        report.append(f"   Seuil latence   : {LATENCY_P95_THRESHOLD_MS}ms")
+        perf_ok = (
+            avg_latency < LATENCY_P95_THRESHOLD_MS
+            and overall_error_rate < ERROR_RATE_THRESHOLD
+        )
+        report.append(
+            f"   Statut          : {'✅ OBJECTIF ATTEINT' if perf_ok else '⏳ Données insuffisantes' if total_checks == 0 else '❌ OBJECTIF NON ATTEINT'}"
+        )
+        report.append("")
+        report.append("## 4. FEEDBACKS UX")
+        ux_feedbacks = [f for f in feedbacks if f.get("type") == "ux"]
+        report.append(f"   Retours UX      : {len(ux_feedbacks)}")
+        if ux_feedbacks:
+            for fb in ux_feedbacks[:5]:  # Top 5
+                report.append(f"   - {fb.get('comment', '')[:100]}")
+        report.append("")
+        report.append("## 5. VERDICT BETA FERMÉE")
+        users_ok = len(feedbacks) >= 5  # Au moins 5 feedbacks = 5 users satisfaits
+        verdict = all([users_ok, nps_ok, len(critical_bugs) == 0])
+        report.append(
+            f"   Participants suffisants (≥5)  : {'✅' if users_ok else '❌'}"
+        )
+        report.append(f"   NPS ≥ 7/10                   : {'✅' if nps_ok else '❌'}")
+        report.append(
+            f"   0 bug critique               : {'✅' if len(critical_bugs) == 0 else '❌'}"
+        )
+        report.append("")
+        report.append(
+            f"   VERDICT GLOBAL : {'✅ GO — Beta réussie' if verdict else '❌ NO-GO — Conditions non remplies'}"
+        )
+        report.append("=" * 60)
+
+        report_text = "\n".join(report)
+
+        # Sauvegarder
+        report_file = REPORTS_DIR / f"beta_report_{self.timestamp}.txt"
+        with open(report_file, "w") as f:
+            f.write(report_text)
+
+        print(report_text)
+        print(f"\nRapport sauvegardé : {report_file}")
+
+        return report_text
+
+
+# ============================================================
+# CLI
+# ============================================================
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Beta Monitor — SaaS Turf IA")
+    parser.add_argument("--watch", action="store_true", help="Surveillance continue")
+    parser.add_argument(
+        "--interval", type=int, default=60, help="Intervalle en secondes (défaut: 60)"
+    )
+    parser.add_argument(
+        "--report", action="store_true", help="Générer le rapport beta final"
+    )
+    parser.add_argument(
+        "--test-telegram", action="store_true", help="Tester l'envoi Telegram"
+    )
+    parser.add_argument(
+        "--url", default=BASE_URL, help=f"URL de l'app (défaut: {BASE_URL})"
+    )
+    args = parser.parse_args()
+
+    if args.test_telegram:
+        print("Test d'envoi Telegram...")
+        ok = send_telegram(
+            "✅ *Test alerte Beta* — SaaS Turf IA\n_Ceci est un test du système d'alertes QA_\nTicket: HRT-34"
+        )
+        sys.exit(0 if ok else 1)
+
+    if args.report:
+        reporter = BetaReport(args.url)
+        reporter.generate()
+        sys.exit(0)
+
+    if args.watch:
+        monitor = BetaMonitor(args.url)
+        monitor.watch(interval_seconds=args.interval)
+        sys.exit(0)
+
+    parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

tests/conftest.py

@@ -0,0 +1,124 @@
+"""
+conftest.py — Configuration pytest globale
+SaaS Turf Prédictions IA — Sprint 8 QA
+Ticket: HRT-34
+"""
+
+import os
+import asyncio
+import pytest
+from pathlib import Path
+from datetime import datetime
+
+# ============================================================
+# Répertoires de sortie
+# ============================================================
+
+REPORTS_DIR = Path("tests/reports")
+SCREENSHOTS_DIR = Path("tests/screenshots")
+
+for d in [REPORTS_DIR, SCREENSHOTS_DIR]:
+    d.mkdir(parents=True, exist_ok=True)
+
+
+# ============================================================
+# Variables d'environnement
+# ============================================================
+
+BASE_URL = os.environ.get("APP_URL", "http://localhost:8792")
+
+
+# ============================================================
+# Fixtures globales
+# ============================================================
+
+
+@pytest.fixture(scope="session")
+def base_url():
+    return BASE_URL
+
+
+@pytest.fixture(scope="session")
+def event_loop():
+    """Event loop partagé pour les tests async de la session."""
+    policy = asyncio.get_event_loop_policy()
+    loop = policy.new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest.fixture(scope="session")
+def reports_dir():
+    return REPORTS_DIR
+
+
+@pytest.fixture(scope="session")
+def screenshots_dir():
+    return SCREENSHOTS_DIR
+
+
+# ============================================================
+# Hook : screenshot automatique sur échec
+# ============================================================
+
+
+@pytest.hookimpl(tryfirst=True, hookwrapper=True)
+def pytest_runtest_makereport(item, call):
+    """Capture screenshot automatiquement sur tout test E2E en échec."""
+    outcome = yield
+    report = outcome.get_result()
+
+    if report.when == "call" and report.failed:
+        # Récupérer la page Playwright si disponible dans les fixtures
+        page = None
+        for fixture_name in ("page", "context_page"):
+            if fixture_name in item.funcargs:
+                val = item.funcargs[fixture_name]
+                if isinstance(val, tuple):
+                    page = val[0]  # (page, browser_name)
+                else:
+                    page = val
+                break
+
+        if page is not None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            test_name = item.name.replace("/", "_").replace(":", "_")
+            screenshot_path = SCREENSHOTS_DIR / f"FAIL_{test_name}_{timestamp}.png"
+            try:
+                # Playwright page.screenshot est synchrone dans les fixtures sync
+                # Pour les fixtures async, on force la capture
+                import asyncio as _asyncio
+
+                if _asyncio.iscoroutinefunction(page.screenshot):
+                    loop = _asyncio.get_event_loop()
+                    loop.run_until_complete(page.screenshot(path=str(screenshot_path)))
+                else:
+                    page.screenshot(path=str(screenshot_path))
+                report.sections.append(
+                    ("Screenshot", f"Sauvegardé : {screenshot_path}")
+                )
+            except Exception as e:
+                report.sections.append(
+                    ("Screenshot Error", f"Impossible de capturer : {e}")
+                )
+
+
+# ============================================================
+# Marqueurs personnalisés
+# ============================================================
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "e2e: Tests End-to-End Playwright")
+    config.addinivalue_line("markers", "load: Tests de charge Locust")
+    config.addinivalue_line("markers", "security: Tests de sécurité")
+    config.addinivalue_line(
+        "markers", "smoke: Tests rapides de smoke (sans infra complète)"
+    )
+    config.addinivalue_line("markers", "beta: Tests spécifiques beta fermée")
+    config.addinivalue_line(
+        "markers", "requires_billing: Nécessite HRT-31 (Billing Stripe)"
+    )
+    config.addinivalue_line(
+        "markers", "requires_infra: Nécessite HRT-33 (infra staging)"
+    )

tests/test_ml_ensemble.py

@@ -0,0 +1,333 @@
+"""
+Tests ML Ensemble — HRT-32 Sprint 6-7
+Tests de régression, benchmark et latence pour le nouveau modèle ensemble.
+
+Usage:
+    pytest tests/test_ml_ensemble.py -v
+    pytest tests/test_ml_ensemble.py -v -m regression
+    pytest tests/test_ml_ensemble.py -v -m latency
+"""
+
+import json
+import os
+import pickle
+import sqlite3
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+import requests
+
+BASE_URL = os.environ.get("APP_URL", "http://localhost:8790")
+DB_PATH = os.environ.get("DB_PATH", "/home/h3r7/turf_saas/turf.db")
+MODELS_DIR = Path("/home/h3r7/turf_saas/models")
+ENSEMBLE_PATH = MODELS_DIR / "ensemble_top3.pkl"
+BENCHMARK_PATH = MODELS_DIR / "benchmark_report.json"
+
+
+# ─── Fixtures ────────────────────────────────────────────────────────────────
+
+
+@pytest.fixture(scope="session")
+def ensemble_model():
+    """Load ensemble model (skip tests if not yet trained)."""
+    if not ENSEMBLE_PATH.exists():
+        pytest.skip(
+            f"Ensemble model not found at {ENSEMBLE_PATH}. Run train_ensemble.py first."
+        )
+    with open(ENSEMBLE_PATH, "rb") as f:
+        return pickle.load(f)
+
+
+@pytest.fixture(scope="session")
+def benchmark_report():
+    """Load benchmark report (skip if not generated)."""
+    if not BENCHMARK_PATH.exists():
+        pytest.skip(f"Benchmark report not found at {BENCHMARK_PATH}.")
+    with open(BENCHMARK_PATH) as f:
+        return json.load(f)
+
+
+@pytest.fixture(scope="session")
+def holdout_data():
+    """Load holdout slice (last 20% temporal) for regression tests."""
+    conn = sqlite3.connect(DB_PATH)
+    df = pd.read_sql_query(
+        """
+        SELECT p.*, c.distance, c.discipline, c.specialite,
+               c.nb_declares_partants, c.montant_prix, c.penetrometre_intitule
+        FROM pmu_partants p
+        LEFT JOIN pmu_courses c ON p.date_programme=c.date_programme
+            AND p.num_reunion=c.num_reunion AND p.num_course=c.num_course
+        WHERE p.ordre_arrivee > 0
+        ORDER BY p.date_programme, p.num_reunion, p.num_course, p.num_pmu
+        """,
+        conn,
+    )
+    conn.close()
+    n = len(df)
+    cutoff = int(n * 0.80)
+    return df.iloc[cutoff:].copy()
+
+
+@pytest.fixture(scope="session")
+def predict_v2():
+    """Import predict_v2 module."""
+    import importlib.util
+
+    spec = importlib.util.spec_from_file_location(
+        "predict_v2", "/home/h3r7/turf_saas/predict_v2.py"
+    )
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+# ─── Model Existence Tests ────────────────────────────────────────────────────
+
+
+class TestModelFiles:
+    """Verify all expected model files exist."""
+
+    def test_ensemble_model_exists(self):
+        assert ENSEMBLE_PATH.exists(), f"Ensemble model missing: {ENSEMBLE_PATH}"
+
+    def test_benchmark_report_exists(self):
+        assert BENCHMARK_PATH.exists(), f"Benchmark report missing: {BENCHMARK_PATH}"
+
+    def test_models_dir_contains_expected_files(self):
+        expected = ["ensemble_top3.pkl", "benchmark_report.json", "benchmark_report.md"]
+        for fname in expected:
+            assert (MODELS_DIR / fname).exists(), f"Missing: {MODELS_DIR / fname}"
+
+
+# ─── Benchmark Tests ──────────────────────────────────────────────────────────
+
+
+class TestBenchmark:
+    """Validate benchmark metrics from the training report."""
+
+    @pytest.mark.regression
+    def test_ensemble_beats_baseline_or_meets_threshold(self, benchmark_report):
+        """Ensemble Precision@3 must be >= baseline XGBoost."""
+        baseline = benchmark_report["baseline"]["precision_at3"]
+        ensemble = benchmark_report["ensemble"]["precision_at3"]
+        assert ensemble >= baseline, (
+            f"Ensemble Precision@3 {ensemble:.4f} < baseline {baseline:.4f}"
+        )
+
+    @pytest.mark.regression
+    def test_ensemble_auc_above_random(self, benchmark_report):
+        """Ensemble AUC must be > 0.60 (significantly above random 0.50)."""
+        auc = benchmark_report["ensemble"]["auc"]
+        assert auc > 0.60, f"Ensemble AUC {auc:.4f} <= 0.60"
+
+    @pytest.mark.regression
+    def test_optuna_ran_minimum_trials(self, benchmark_report):
+        """Optuna must have run at least 100 trials per model."""
+        n_trials = benchmark_report["optuna"]["n_trials"]
+        assert n_trials >= 100, f"Only {n_trials} Optuna trials (minimum 100 required)"
+
+    @pytest.mark.regression
+    def test_no_precision_regression(self, benchmark_report):
+        """Ensemble Precision@3 must not be below naive random baseline (~30%)."""
+        ensemble_p3 = benchmark_report["ensemble"]["precision_at3"]
+        assert ensemble_p3 >= 0.30, (
+            f"Precision@3 {ensemble_p3:.4f} is below random baseline (~0.30)"
+        )
+
+    def test_benchmark_has_all_required_models(self, benchmark_report):
+        """Benchmark must include results for all 3 models."""
+        required = {"xgboost", "lightgbm", "mlp"}
+        found = set(benchmark_report.get("individual_models", {}).keys())
+        missing = required - found
+        assert not missing, f"Missing model benchmarks: {missing}"
+
+
+# ─── Regression Tests ─────────────────────────────────────────────────────────
+
+
+class TestPrecisionRegression:
+    """Holdout regression: ensure precision doesn't degrade."""
+
+    @pytest.mark.regression
+    def test_precision_at3_on_holdout(self, ensemble_model, holdout_data):
+        """Precision@3 on holdout must be above naive baseline."""
+        from predict_v2 import build_feature_df, FEATURE_COLS
+
+        df = holdout_data.copy()
+        df["top3"] = (df["ordre_arrivee"] <= 3).astype(int)
+
+        partants = df.to_dict("records")
+        feature_df = build_feature_df(partants)
+        available = [c for c in FEATURE_COLS if c in feature_df.columns]
+        X = feature_df[available].fillna(0)
+
+        proba = ensemble_model.predict_proba(X)[:, 1]
+
+        # Per-race Precision@3
+        tmp = df[["date_programme", "num_reunion", "num_course"]].copy()
+        tmp["proba"] = proba
+        tmp["actual"] = df["top3"].values
+
+        precisions = []
+        for _, group in tmp.groupby(["date_programme", "num_reunion", "num_course"]):
+            if len(group) >= 3:
+                top3_pred = group.nlargest(3, "proba")
+                precisions.append(top3_pred["actual"].sum() / 3.0)
+
+        p_at3 = float(np.mean(precisions)) if precisions else 0.0
+        print(f"\n  Holdout Precision@3: {p_at3:.4f} over {len(precisions)} races")
+
+        # Must beat random baseline (30%)
+        assert p_at3 >= 0.30, f"Holdout Precision@3 {p_at3:.4f} < 0.30"
+
+    @pytest.mark.regression
+    def test_no_all_zero_predictions(self, ensemble_model, holdout_data):
+        """Ensemble must not predict 0 probability for all horses."""
+        from predict_v2 import build_feature_df, FEATURE_COLS
+
+        partants = holdout_data.head(50).to_dict("records")
+        feature_df = build_feature_df(partants)
+        available = [c for c in FEATURE_COLS if c in feature_df.columns]
+        X = feature_df[available].fillna(0)
+
+        proba = ensemble_model.predict_proba(X)[:, 1]
+        assert proba.max() > 0.01, "All predictions are near 0 — model appears broken"
+        assert proba.std() > 0.01, (
+            "All predictions have identical probability — no discrimination"
+        )
+
+
+# ─── Latency Tests ────────────────────────────────────────────────────────────
+
+
+class TestPredictionLatency:
+    """Prediction latency must be < 200ms per race."""
+
+    @pytest.mark.latency
+    def test_single_race_latency(self, ensemble_model, holdout_data):
+        """Prediction for a single race (<=20 horses) must be < 200ms."""
+        from predict_v2 import build_feature_df, FEATURE_COLS
+
+        # Take one race
+        first_race = (
+            holdout_data.groupby(["date_programme", "num_reunion", "num_course"])
+            .first()
+            .reset_index()
+            .iloc[0]
+        )
+        mask = (
+            (holdout_data["date_programme"] == first_race["date_programme"])
+            & (holdout_data["num_reunion"] == first_race["num_reunion"])
+            & (holdout_data["num_course"] == first_race["num_course"])
+        )
+        race_df = holdout_data[mask]
+        partants = race_df.to_dict("records")
+
+        # Warm-up
+        feature_df = build_feature_df(partants)
+        available = [c for c in FEATURE_COLS if c in feature_df.columns]
+        X = feature_df[available].fillna(0)
+        ensemble_model.predict_proba(X)
+
+        # Timed run
+        t0 = time.perf_counter()
+        for _ in range(10):
+            ensemble_model.predict_proba(X)
+        elapsed_ms = (time.perf_counter() - t0) / 10 * 1000
+
+        print(f"\n  Single-race latency: {elapsed_ms:.2f} ms ({len(partants)} horses)")
+        assert elapsed_ms < 200, (
+            f"Prediction latency {elapsed_ms:.1f} ms exceeds 200 ms limit"
+        )
+
+    @pytest.mark.latency
+    def test_full_day_latency(self, ensemble_model, holdout_data):
+        """Prediction for a full day (all races) must complete < 5 seconds."""
+        from predict_v2 import build_feature_df, FEATURE_COLS
+
+        # Take one day
+        day = holdout_data["date_programme"].iloc[0]
+        day_df = holdout_data[holdout_data["date_programme"] == day]
+        partants = day_df.to_dict("records")
+
+        feature_df = build_feature_df(partants)
+        available = [c for c in FEATURE_COLS if c in feature_df.columns]
+        X = feature_df[available].fillna(0)
+
+        t0 = time.perf_counter()
+        proba = ensemble_model.predict_proba(X)
+        elapsed_ms = (time.perf_counter() - t0) * 1000
+
+        print(
+            f"\n  Full day latency: {elapsed_ms:.2f} ms ({len(partants)} horses, {day})"
+        )
+        assert elapsed_ms < 5000, (
+            f"Full-day prediction {elapsed_ms:.0f} ms exceeds 5s limit"
+        )
+
+
+# ─── API Endpoint Tests ───────────────────────────────────────────────────────
+
+
+class TestV1PredictionsAPI:
+    """Tests for the new /api/v1/predictions endpoint."""
+
+    def _api_available(self):
+        try:
+            requests.get(f"{BASE_URL}/api/v1/model/status", timeout=3)
+            return True
+        except Exception:
+            return False
+
+    @pytest.mark.api
+    def test_model_status_endpoint(self):
+        """GET /api/v1/model/status returns valid JSON."""
+        if not self._api_available():
+            pytest.skip("API server not running")
+        resp = requests.get(f"{BASE_URL}/api/v1/model/status", timeout=10)
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "ensemble_available" in data
+
+    @pytest.mark.api
+    def test_v1_predictions_no_500(self):
+        """GET /api/v1/predictions must not return 5xx."""
+        if not self._api_available():
+            pytest.skip("API server not running")
+        resp = requests.get(f"{BASE_URL}/api/v1/predictions", timeout=30)
+        assert resp.status_code < 500, (
+            f"Server error: {resp.status_code}\n{resp.text[:200]}"
+        )
+
+    @pytest.mark.api
+    def test_v1_predictions_returns_json(self):
+        """GET /api/v1/predictions returns valid JSON with expected keys."""
+        if not self._api_available():
+            pytest.skip("API server not running")
+        resp = requests.get(f"{BASE_URL}/api/v1/predictions", timeout=30)
+        if resp.status_code == 503:
+            pytest.skip("Ensemble model not yet deployed")
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "model_version" in data, "Missing model_version in response"
+        assert "races" in data or "predictions" in data, (
+            "Missing races/predictions in response"
+        )
+
+    @pytest.mark.api
+    def test_v1_predictions_latency(self):
+        """GET /api/v1/predictions must respond in < 3 seconds."""
+        if not self._api_available():
+            pytest.skip("API server not running")
+        resp = requests.get(f"{BASE_URL}/api/v1/predictions", timeout=30)
+        if resp.status_code == 503:
+            pytest.skip("Ensemble model not yet deployed")
+        # Check API-reported latency
+        if resp.status_code == 200:
+            data = resp.json()
+            latency = data.get("latency_ms", 0)
+            assert latency < 3000, f"API latency {latency:.0f} ms > 3000 ms"

tests/test_smoke.py

@@ -0,0 +1,205 @@
+"""
+Tests de smoke — SaaS Turf Prédictions IA
+Sprint 8 — QA, Beta Fermee, Go/No-Go
+Ticket: HRT-34
+
+Vérifications rapides sur l'état de l'application :
+- Routes de base accessibles
+- API répond en JSON valide
+- Base de données accessible
+- Pas d'erreurs 5xx sur les routes principales
+
+Ces tests peuvent tourner SANS infra complète (pas besoin de HRT-31/33).
+Exécuter sur l'app actuelle en staging ou localhost.
+"""
+
+import pytest
+import requests
+import os
+import json
+
+BASE_URL = os.environ.get("APP_URL", "http://localhost:8792")
+
+# Routes qui doivent retourner 200 (publiques)
+PUBLIC_ROUTES_200 = [
+    "/",
+    "/dashboard",
+]
+
+# Routes API qui doivent retourner 200 ou 401 (jamais 500)
+API_ROUTES_NO_500 = [
+    "/api",
+    "/api/races",
+    "/api/scoring",
+    "/api/weather",
+    "/api/odds_history",
+]
+
+
+class TestSmoke:
+    """Tests de smoke : l'app répond correctement aux requêtes de base."""
+
+    @pytest.mark.smoke
+    @pytest.mark.parametrize("route", PUBLIC_ROUTES_200)
+    def test_route_publique_accessible(self, route):
+        """Les routes publiques doivent retourner 200."""
+        try:
+            resp = requests.get(f"{BASE_URL}{route}", timeout=10)
+            assert resp.status_code in (200, 304), (
+                f"Route publique inaccessible: {route} → {resp.status_code}"
+            )
+            assert len(resp.content) > 0, f"Réponse vide sur {route}"
+        except requests.exceptions.ConnectionError:
+            pytest.skip(
+                f"App non accessible sur {BASE_URL} — vérifier que le serveur est démarré"
+            )
+
+    @pytest.mark.smoke
+    @pytest.mark.parametrize("route", API_ROUTES_NO_500)
+    def test_api_pas_derreur_serveur(self, route):
+        """Les routes API ne doivent jamais retourner 5xx."""
+        try:
+            resp = requests.get(f"{BASE_URL}{route}", timeout=10)
+            assert resp.status_code < 500, (
+                f"Erreur serveur sur {route}: {resp.status_code}\n{resp.text[:200]}"
+            )
+        except requests.exceptions.ConnectionError:
+            pytest.skip(f"App non accessible sur {BASE_URL}")
+
+    @pytest.mark.smoke
+    def test_api_today_retourne_json(self):
+        """L'endpoint principal /api doit retourner du JSON valide."""
+        try:
+            resp = requests.get(f"{BASE_URL}/api", timeout=10)
+            if resp.status_code == 200:
+                data = resp.json()
+                assert data is not None, "Réponse JSON nulle"
+                assert isinstance(data, (list, dict)), (
+                    f"Type de réponse inattendu: {type(data)}"
+                )
+        except requests.exceptions.ConnectionError:
+            pytest.skip(f"App non accessible sur {BASE_URL}")
+        except json.JSONDecodeError as e:
+            pytest.fail(f"/api ne retourne pas du JSON valide: {e}")
+
+    @pytest.mark.smoke
+    def test_contenu_html_portail_valide(self):
+        """Le portail doit contenir un titre et du contenu significatif."""
+        try:
+            resp = requests.get(f"{BASE_URL}/", timeout=10)
+            if resp.status_code == 200:
+                content = resp.text
+                assert "<html" in content.lower() or "<!doctype" in content.lower(), (
+                    "La page d'accueil ne retourne pas du HTML"
+                )
+                assert len(content) > 500, (
+                    f"Page d'accueil trop courte ({len(content)} chars)"
+                )
+        except requests.exceptions.ConnectionError:
+            pytest.skip(f"App non accessible sur {BASE_URL}")
+
+    @pytest.mark.smoke
+    def test_headers_securite_presents(self):
+        """Les headers de sécurité de base doivent être présents."""
+        try:
+            resp = requests.get(f"{BASE_URL}/", timeout=10)
+            if resp.status_code != 200:
+                return
+
+            # En production (derrière Nginx), ces headers doivent être présents
+            # En dev direct Flask, ils peuvent être absents — on note seulement
+            security_headers = {
+                "X-Content-Type-Options": "nosniff",
+                "X-Frame-Options": None,  # SAMEORIGIN ou DENY
+                "X-XSS-Protection": None,
+            }
+
+            missing = []
+            for header, expected_value in security_headers.items():
+                if header not in resp.headers:
+                    missing.append(header)
+
+            if missing:
+                # Warning seulement — bloquant uniquement en prod derrière Nginx
+                pytest.warns(UserWarning, match=r".*") if False else None
+                print(f"⚠️  Headers sécurité manquants (requis en prod): {missing}")
+
+        except requests.exceptions.ConnectionError:
+            pytest.skip(f"App non accessible sur {BASE_URL}")
+
+    @pytest.mark.smoke
+    def test_api_races_format_reponse(self):
+        """L'endpoint /api/races doit retourner une liste structurée."""
+        try:
+            resp = requests.get(f"{BASE_URL}/api/races", timeout=10)
+            if resp.status_code == 200:
+                data = resp.json()
+                assert isinstance(data, (list, dict)), (
+                    f"Format inattendu pour /api/races: {type(data)}"
+                )
+                if isinstance(data, list) and len(data) > 0:
+                    first = data[0]
+                    # Vérifier la présence de champs clés
+                    expected_fields = ["date", "course", "hippodrome"]
+                    present = [
+                        f
+                        for f in expected_fields
+                        if f in first
+                        or any(k in first for k in [f, f.upper(), f.replace("_", "")])
+                    ]
+                    assert len(present) > 0, (
+                        f"Champs attendus absents de /api/races. Champs présents: {list(first.keys())}"
+                    )
+        except requests.exceptions.ConnectionError:
+            pytest.skip(f"App non accessible sur {BASE_URL}")
+        except json.JSONDecodeError:
+            pytest.fail("/api/races ne retourne pas du JSON valide")
+
+
+class TestSmokeDatabase:
+    """Tests smoke sur la base de données."""
+
+    @pytest.mark.smoke
+    def test_base_donnees_accessible(self):
+        """La base de données SQLite doit être accessible et contenir des données."""
+        import sqlite3
+
+        db_path = "/home/h3r7/turf_saas/turf_saas.db"
+
+        if not __import__("os").path.exists(db_path):
+            pytest.skip(f"Base de données non trouvée: {db_path}")
+
+        conn = sqlite3.connect(db_path)
+        c = conn.cursor()
+
+        # Vérifier que les tables essentielles existent
+        c.execute("SELECT name FROM sqlite_master WHERE type='table'")
+        tables = {row[0] for row in c.fetchall()}
+        conn.close()
+
+        expected_tables = ["predictions", "results"]
+        for table in expected_tables:
+            assert table in tables, (
+                f"Table manquante dans la BDD: {table}. Tables présentes: {tables}"
+            )
+
+    @pytest.mark.smoke
+    def test_donnees_predictions_disponibles(self):
+        """Des prédictions doivent être présentes dans la BDD."""
+        import sqlite3
+
+        db_path = "/home/h3r7/turf_saas/turf_saas.db"
+
+        if not __import__("os").path.exists(db_path):
+            pytest.skip(f"Base de données non trouvée: {db_path}")
+
+        conn = sqlite3.connect(db_path)
+        c = conn.cursor()
+        c.execute("SELECT COUNT(*) FROM predictions")
+        count = c.fetchone()[0]
+        conn.close()
+
+        # Au moins quelques données pour que le SaaS soit utile
+        assert count >= 0, "Table predictions accessible"
+        if count == 0:
+            print("⚠️  Aucune prédiction en base — le scraper doit être lancé")