Question

我正在尝试根据许多GLM，GBM和深度学习模型在H2O中创建一个集成模型。

这是我到目前为止所做的。

导入相关库：

import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
from h2o.grid.grid_search import H2OGridSearch

可以从here下载数据。导入：

airlines = h2o.import_file(path = "/Users/alexwoolford/h2o/allyears2k.csv", destination_frame = "airlines.hex")

分为训练/测试集：

airlines_80,airlines_20 = airlines.split_frame(ratios=[.8], destination_frames=["airlines_80.hex", "airlines_20.hex"])

定义变量（将y预测为x中所有列的函数）

x= airlines.columns
y= "ArrDelay"
x.remove(y)

设置公共属性：

folds=5
assignment_type="Modulo"
search_criteria={'strategy': 'RandomDiscrete', 'max_models': 5, 'seed': 1}

使用H2O的网格搜索来创建各种模型：

# GLM
glm_params = {"alpha": [0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.],
              "lambda": [0, 1e-7, 1e-5, 1e-3, 1e-1]}

glm_grid = H2OGridSearch(model=H2OGeneralizedLinearEstimator(fold_assignment=assignment_type, nfolds=folds),
                         grid_id='glm_grid',
                         hyper_params=glm_params,
                         search_criteria=search_criteria)
glm_grid.train(x=x,
               y=y,
               training_frame=airlines_80,
               validation_frame=airlines_20)

# GBM
gbm_params = {'learn_rate': [0.01, 0.03],
              'max_depth': [3, 4, 5, 6, 9],
              'sample_rate': [0.7, 0.8, 0.9, 1],
              'col_sample_rate': [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]}

gbm_grid = H2OGridSearch(model=H2OGradientBoostingEstimator(fold_assignment=assignment_type, nfolds=folds),
                         grid_id='gbm_grid',
                         hyper_params=gbm_params,
                         search_criteria=search_criteria)
gbm_grid.train(x=x,
               y=y,
               training_frame=airlines_80,
               validation_frame=airlines_20)

# Deep learning
dl_params = {'activation': ['rectifier', 'rectifier_with_dropout'],
             'hidden': [[10,10], [20,15], [50,50,50]],
             'l1': [0, 1e-3, 1e-5],
             'l2': [0, 1e-3, 1e-5]}

dl_grid = H2OGridSearch(model=H2ODeepLearningEstimator(fold_assignment=assignment_type, nfolds=folds),
                        grid_id='dl_grid',
                        hyper_params=dl_params,
                        search_criteria=search_criteria)

dl_grid.train(x=x,
              y=y,
              training_frame=airlines_80,
              validation_frame=airlines_20)

获取所有model_id的列表：

all_model_ids = glm_grid.model_ids + gbm_grid.model_ids + dl_grid.model_ids

我尝试创建整体的地方：

ensemble = H2OStackedEnsembleEstimator(base_models=all_model_ids)
ensemble.train(x=x, y=y, training_frame=airlines_80, validation_frame=airlines_20)

...引发以下错误：

stackedensemble Model Build progress: | (failed)
---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-26-bc7b6094816f> in <module>()
      1 ensemble = H2OStackedEnsembleEstimator(base_models=all_model_ids)
----> 2 ensemble.train(x=x, y=y, training_frame=airlines_80, validation_frame=airlines_20)

/anaconda3/lib/python3.6/site-packages/h2o/estimators/estimator_base.py in train(self, x, y, training_frame, offset_column, fold_column, weights_column, validation_frame, max_runtime_secs, ignored_columns, model_id, verbose)
    235             return
    236 
--> 237         model.poll(verbose_model_scoring_history=verbose)
    238         model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
    239         self._resolve_model(model.dest_key, model_json)

/anaconda3/lib/python3.6/site-packages/h2o/job.py in poll(self, verbose_model_scoring_history)
     75             if (isinstance(self.job, dict)) and ("stacktrace" in list(self.job)):
     76                 raise EnvironmentError("Job with key {} failed with an exception: {}\nstacktrace: "
---> 77                                        "\n{}".format(self.job_key, self.exception, self.job["stacktrace"]))
     78             else:
     79                 raise EnvironmentError("Job with key %s failed with an exception: %s" % (self.job_key, self.exception))

OSError: Job with key $03017f00000132d4ffffffff$_a2359a38ec8d31316aee91398f0249f8 failed with an exception: water.exceptions.H2OIllegalArgumentException: Base model does not keep cross-validation predictions: 5
stacktrace: 
water.exceptions.H2OIllegalArgumentException: Base model does not keep cross-validation predictions: 5
    at hex.StackedEnsembleModel.checkAndInheritModelProperties(StackedEnsembleModel.java:382)
    at hex.ensemble.StackedEnsemble$StackedEnsembleDriver.computeImpl(StackedEnsemble.java:234)
    at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:218)
    at water.H2O$H2OCountedCompleter.compute(H2O.java:1395)
    at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
    at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
    at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
    at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
    at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)

你能看出我在做什么吗？

Answer 1

您似乎在每个模型中都缺少参数keep_cross_validation_predictions=True。例如，您可能要对GLM执行以下操作，然后对要堆叠的其他模型执行以下操作：

glm_grid = H2OGridSearch(model=H2OGeneralizedLinearEstimator(fold_assignment=assignment_type, nfolds=folds,
    keep_cross_validation_predictions=True),
                                 grid_id='glm_grid',
                                 hyper_params=glm_params,
                                 search_criteria=search_criteria)

h2o合奏引发错误：“基本模型未保留交叉验证预测”

1 个答案: