我得到以下錯誤當我試圖加載使用mlflow水模型預測
錯誤:
錯誤的工作與關鍵03017 f00000132d4ffffffff _990da74b0db027b33cc49d1d90934149美元失敗的一個例外:. lang。IllegalArgumentException:測試/驗證數據集沒有列與訓練集
源代碼:
# # pip安裝請求! pip安裝彙總# ! pip安裝“彩色光> = 0.3.8 " # ! pip安裝未來# ! pip安裝- f
http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html水# ! pip安裝mlflow # ! wget
https://github.com/mlflow/mlflow-example/blob/master/wine-quality.csv
隨機進口mlflow進口mlflow進口水進口。水從h2o.estimators.random_forest進口H2ORandomForestEstimator h2o.init()酒= h2o.import_file (path =“winequality.csv”) r =葡萄酒('質量'].runif()火車=葡萄酒(r & lt;0.7]測試=酒(0.3 & lt; = r) mlflow.set_tracking_uri mlflow.set_experiment (“https://mlflow.xxxxxxx.cloud/”) (“H2ORandomForestEstimator”) def train_random_forest (ntrees):與mlflow.start_run():射頻= H2ORandomForestEstimator (ntrees = ntrees) train_cols = [n n的葡萄酒。col_names如果n ! =“質量”)射頻。火車(train_cols,“質量”,training_frame =火車,validation_frame =測試)mlflow。log_param mlflow (“ntrees”, ntrees)。log_metric (rmse rf.rmse ()) mlflow。log_metric (r2, rf.r2 ()) mlflow。log_metric(“美”,rf.mae ()) mlflow.h2o。“模型”log_model (rf) h2o.save_model (rf)預測= rf.predict(測試)打印(predict.head())的ntrees (10、20、50、100): train_random_forest (ntrees) < / pre > < pre >進口mlflow logged_model =“s3: / / mlflow-sagemaker / 1/66f7c015fe8d4fb080940f3d31003f49 /工件/模型”# PyFuncModel負載模型。loaded_model = mlflow.pyfunc.load_model (logged_model) #熊貓DataFrame預測。熊貓作為pd loaded_model.predict導入(pd.DataFrame(測試))< / pre >
