估值因素 :
包括公里数、使用年数、出场时间、维修次数…
农业机械 履带式拖拉机大轮拖中拖小麦机玉米机免耕播种机打捆机旋耕机深松机液压翻转犁 特专车辆 洗扫车压缩式垃圾车绿化喷洒车多功能吸尘扫路车车厢可卸式垃圾车自卸式垃圾车压缩式垃圾车车厢可卸式垃圾车压缩式对接垃圾车洒水车清洗车抑尘车摆臂式垃圾车吸尘车import h2oimport numpy as npfrom h2o.estimators import H2OGradientBoostingEstimatorh2o.init()from flask import Flask, request, jsonifyapp = Flask(__name__)from h2o.estimators.glm import H2OGeneralizedLinearEstimatorfrom h2o.estimators.deeplearning import H2ODeepLearningEstimator# dataset_url = 'E:/PyCharm_workspace/demo/h2o/regression/cars.csv'# trained_model = 'E:/tmp/mymodel/usedCar_GBM_model'# http://127.0.0.1:5000/model/usedCar/?dataset_url=E:/PyCharm_workspace/demo/h2o/regression/cars.csv&trained_model=E:/tmp/mymodel/usedCar_GBM_model@app.route('/model/usedCar/')def classification_example(): dataset_url = request.args.get('dataset_url') trained_model = request.args.get('trained_model') cars = h2o.import_file(dataset_url) r = cars[0].runif() train = cars[r > .2] valid = cars[r <= .2] response_col = "economy" distribution = "gaussian" # 根据车的“名称”、“生产年份”、“重量”、”加速度“、”马力“ ===》”车当前的价值（economy）“ predictors = ["name","year","weight","acceleration","power"] ########################### 训练过程 ############################################################## # # 可以选择的算法有：梯度提升机（GBM）、深度学习 # # gbm = H2OGradientBoostingEstimator(nfolds=3, distribution=distribution, fold_assignment="Random") # gbm = H2ODeepLearningEstimator(adaptive_rate=True, epochs=800) # # train_model =gbm.train(x=predictors, y=response_col, training_frame=train, validation_frame=valid) # gbm.plot(timestep="AUTO", metric="AUTO",save_plot_path='/temp') # # # # 保存模型 # model_path = h2o.save_model(model=train_model, path="/tmp/mymodel", force=True) # # 打印出保存模型的路径： # print("模型保存在：", model_path) ########################### 应用过程 ############################################################## # load the model,加载模型，要注意模型的位置是/而不是 saved_model = h2o.load_model(trained_model) # saved_model = h2o.load_model("E:/tmp/mymodel/GBM_model_python_1645102695969_1") train_model= saved_model test_file = 'cars_test.csv' test_prostate = h2o.import_file(test_file) # predict using the model and the testing dataset predict = train_model.predict(test_prostate) ## Creating list array from h2o frame column residual_value = np.array(h2o.as_list(predict['predict'])).tolist() # View a summary of the prediction # head()返回对象的前n行 print(predict.head(100)) print(residual_value) t = { 'code': 200, 'residual_value': residual_value } return jsonify(t)if __name__ == "__main__": app.run()