134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
from fastapi import FastAPI
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from pydantic import BaseModel
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import asyncio
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import pickle
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from typing import List, Dict
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import numpy as np
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app = FastAPI()
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# Charger le modèle
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with open('pipeline_xgb.pkl', 'rb') as file:
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model = pickle.load(file)
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# Définir les modèles de requête et de réponse
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class TennisMatch(BaseModel):
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match_id: int
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player_id1: int
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player_id2: int
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p1_niveau_rank: float
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p1_niveau_win_total: float
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p1_niveau_win_surface_total: float
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p1_h2h_win_total: float
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p1_h2h_win_1an: float
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p1_h2h_win_surface_total: float
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p1_h2h_win_sets: float
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p1_progression_win_1an: float
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p1_progression_win_surface_1an: float
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p1_progression_win_last_50matchs: float
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p1_progression_win_last_10matchs: float
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p1_forme_matchs_30j: float
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p1_forme_matchs_length_tournament: float
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p1_tendance_loose_favori_30j: float
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p1_tendance_win_outsider_30j: float
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p1_confiance_first_service_last_match: float
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p1_confiance_points_first_service_last_match: float
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p1_confiance_break_saved_last_match: float
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p2_niveau_rank: float
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p2_niveau_win_total: float
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p2_niveau_win_surface_total: float
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p2_h2h_win_total: float
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p2_h2h_win_1an: float
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p2_h2h_win_surface_total: float
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p2_h2h_win_sets: float
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p2_progression_win_1an: float
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p2_progression_win_surface_1an: float
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p2_progression_win_last_50matchs: float
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p2_progression_win_last_10matchs: float
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p2_forme_matchs_30j: float
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p2_forme_matchs_length_tournament: float
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p2_tendance_loose_favori_30j: float
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p2_tendance_win_outsider_30j: float
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p2_confiance_first_service_last_match: float
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p2_confiance_points_first_service_last_match: float
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p2_confiance_break_saved_last_match: float
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idc_p1_motivation_enjeu: float
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idc_p1_motivation_revanche: float
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idc_p1_motivation_surface: float
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idc_p1_tactique_profil: float
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idc_p1_tactique_service1: float
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idc_p1_tactique_service2: float
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idc_p1_tactique_retourservice1: float
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idc_p1_tactique_retourservice2: float
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idc_p1_tactique_breaksauve: float
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idc_p1_tactique_breakgagne: float
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idc_p1_tactique_tiebreak: float
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idc_p1_historique_match: float
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idc_p1_historique_book: float
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idc_p1_historique_estimated: float
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idc_p2_motivation_enjeu: float
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idc_p2_motivation_revanche: float
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idc_p2_motivation_surface: float
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idc_p2_tactique_profil: float
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idc_p2_tactique_service1: float
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idc_p2_tactique_service2: float
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idc_p2_tactique_retourservice1: float
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idc_p2_tactique_retourservice2: float
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idc_p2_tactique_breaksauve: float
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idc_p2_tactique_breakgagne: float
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idc_p2_tactique_tiebreak: float
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idc_p2_historique_match: float
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idc_p2_historique_book: float
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idc_p2_historique_estimated: float
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tournament_round: float
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tournament_type: float
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tournament_surface: float
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class MatchPrediction(BaseModel):
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match_id: int
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win_probability_j1: float
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win_probability_j2: float
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class BatchPredictionRequest(BaseModel):
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matches: List[TennisMatch]
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class BatchPredictionResponse(BaseModel):
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predictions: List[MatchPrediction]
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# Définir la fonction de prédiction
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async def predict_match(features: Dict[str, float]):
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match_id = int(features.pop('match_id')) # Extraire l'identifiant du match
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prediction = await asyncio.to_thread(model.predict_proba, [features])
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return MatchPrediction(
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match_id=match_id,
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win_probability_j1=prediction[0][0],
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win_probability_j2=prediction[0][1]
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)
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# Définir le point de terminaison pour la prédiction en lot
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@app.post("/predict_batch", response_model=BatchPredictionResponse)
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async def predict_batch(request: BatchPredictionRequest):
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prediction_tasks = [
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predict_match(match.dict())
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for match in request.matches
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]
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predictions = await asyncio.gather(*prediction_tasks)
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return BatchPredictionResponse(predictions=predictions)
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# Définir le point de terminaison pour une seule prédiction
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@app.post("/predict", response_model=MatchPrediction)
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async def predict_match(features: Dict[str, float]):
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match_id = int(features['match_id']) # Extraire l'identifiant du match
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# Assurez-vous que les caractéristiques sont dans le bon format pour le modèle
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feature_list = [features[key] for key in sorted(features.keys())]
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feature_array = np.array(feature_list).reshape(1, -1) # Transformer en tableau 2D avec une seule ligne
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prediction = await asyncio.to_thread(model.predict_proba, feature_array)
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return MatchPrediction(
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match_id=match_id,
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win_probability_j1=prediction[0][0],
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win_probability_j2=prediction[0][1]
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)
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