Neptune.Inger导入NeptuceLogger运行=Neptune.init_run

untune_cbk =neptuneCallk模型

ytark_lighting.loggers导入NeptuneLopune_logger=NeptuneLogger

neptune.sklearns运行gbc.fit/xtrain,y_train运行

from neptune.integrations.lightgbm import NeptuneCallback, create_booster_summary    run = neptune.init_run()  neptune_callback = NeptuneCallback(run=run)    params = {      "boosting_type": "gbdt",      "objective": "multiclass",      "num_class": 10,      "metric": ["multi_logloss", "multi_error"],      "num_leaves": 21,      "learning_rate": 0.05,      "max_depth": 12,  }    # Train the model  gbm = lgb.train(      params,      lgb_train,      num_boost_round=200,      valid_sets=[lgb_train, lgb_eval],      valid_names=["training", "validation"],      callbacks=[neptune_callback],  )    run["lgbm_summary"] = create_booster_summary(      booster=gbm,      log_trees=True,      list_trees=[0, 1, 2, 3, 4],      log_confusion_matrix=True,      y_pred=y_pred,      y_true=y_test,  )

Neptune.combune.xgbost导入NeptuneCallrederors

nepuneCallCrune研究=opuna

kedronetune init

def报表-aclice-napune.run.Handler-uneneptune_run['ndes/report/aclicity''s]s

def创建水管线(**kwargss):返回管道线node(                  report_accuracy,                  ["example_predictions", "example_test_y","neptune_run"],                  None,                  name="report",              ),          ]      )