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75c7c56 53ed85e 75c7c56 53ed85e 75c7c56 53ed85e 75c7c56 53ed85e 75c7c56 53ed85e 75c7c56 53ed85e 75c7c56 53ed85e fcec78d 75c7c56 53ed85e 75c7c56 53ed85e 75c7c56 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from dotenv import load_dotenv
load_dotenv()
import pandas as pd
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine
from app.db.models import Employee
from ml_model.loader import load_pipeline
from app.schemas.prediction import PredictionInput
EXAMPLES_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "examples")
INPUT_PATH = os.path.join(EXAMPLES_DIR, "input_sample.csv")
OUTPUT_PATH = os.path.join(EXAMPLES_DIR, "output_sample.csv")
N_SAMPLES = 10
FEATURE_COLUMNS = list(PredictionInput.model_fields.keys())
def generate():
db_url = (
f"postgresql+psycopg2://{os.getenv('DB_USER')}:{os.getenv('DB_PASSWORD')}"
f"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}"
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=create_engine(db_url))
session = SessionLocal()
try:
rows = session.query(Employee).limit(N_SAMPLES).all()
inputs = pd.DataFrame(
[{col: getattr(e, col) for col in FEATURE_COLUMNS} for e in rows]
)
finally:
session.close()
pipeline, threshold = load_pipeline()
probas = pipeline.predict_proba(inputs.copy())[:, 1]
predictions = (probas >= threshold).astype(int)
os.makedirs(EXAMPLES_DIR, exist_ok=True)
inputs.to_csv(INPUT_PATH, index=False)
print(f"Fichier généré : {INPUT_PATH} ({N_SAMPLES} lignes)")
outputs = pd.DataFrame({
"prediction": predictions,
"label": ["Quitte" if p == 1 else "Reste" for p in predictions],
"probabilite": probas.round(4),
})
outputs.to_csv(OUTPUT_PATH, index=False)
print(f"Fichier généré : {OUTPUT_PATH} ({N_SAMPLES} lignes)")
if __name__ == "__main__":
generate()
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