CatBoost模型部署与在线预测教程

pip install catboost

from catboost import CatBoostClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# 生成模拟数据
X, y = make_classification(n_samples=1000, n_features=20, n_informative=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 初始化CatBoost分类器
model = CatBoostClassifier(iterations=100, learning_rate=0.1, depth=5)

# 训练模型
model.fit(X_train, y_train, eval_set=(X_test, y_test), verbose=False)

model.save_model('catboost_model.cbm')

pip install flask

from flask import Flask, request, jsonify
from catboost import CatBoostClassifier

app = Flask(__name__)

# 加载模型
model = CatBoostClassifier()
model.load_model('catboost_model.cbm')

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json(force=True)
    prediction = model.predict(data['features'])
    return jsonify({'prediction': int(prediction[0])})

if __name__ == '__main__':
    app.run(debug=True)

python app.py

curl -X POST -H "Content-Type: application/json" -d '{"features":[1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0]}' http://127.0.0.1:5000/predict