我是python中H2O的新手。我正在尝试使用来自H2O网站的示例代码的集合模型来建模我的数据。 (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html)
我已经将GBM和RF应用为基本模型。然后使用堆叠,我试图在整体模型中合并它们。另外,在我的训练数据中,我创建了另一个名为'fold'的列,用于fold_column = "fold"
我应用10倍cv,我观察到我收到了cv1的结果。然而,所有来自其他9个cvs的预测都是空的。我在这里错过了什么?
这是我的示例数据:
码:
import h2o
from h2o.estimators.random_forest import H2ORandomForestEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
from h2o.grid.grid_search import H2OGridSearch
from __future__ import print_function
h2o.init(port=23, nthreads=6)
train = h2o.H2OFrame(ens_df)
test = h2o.H2OFrame(test_ens_eq)
x = train.drop(['Date','EQUITY','fold'],axis=1).columns
y = 'EQUITY'
cat_cols = ['A','B','C','D']
train[cat_cols] = train[cat_cols].asfactor()
test[cat_cols] = test[cat_cols].asfactor()
my_gbm = H2OGradientBoostingEstimator(distribution="gaussian",
ntrees=10,
max_depth=3,
min_rows=2,
learn_rate=0.2,
keep_cross_validation_predictions=True,
seed=1)
my_gbm.train(x=x, y=y, training_frame=train, fold_column = "fold")
然后当我检查cv结果时
my_gbm.cross_validation_predictions():
另外,当我在测试集中尝试合奏时,我会收到以下警告:
# Train a stacked ensemble using the GBM and GLM above
ensemble = H2OStackedEnsembleEstimator(model_id="mlee_ensemble",
base_models=[my_gbm, my_rf])
ensemble.train(x=x, y=y, training_frame=train)
# Eval ensemble performance on the test data
perf_stack_test = ensemble.model_performance(test)
pred = ensemble.predict(test)
pred
/mgmt/data/conda/envs/python3.6_4.4/lib/python3.6/site-packages/h2o/job.py:69: UserWarning: Test/Validation dataset is missing column 'fold': substituting in a column of NaN
warnings.warn(w)
我错过了关于fold_column的一些内容吗?
以下是如何使用自定义折叠列(从列表创建)的示例。这是H2O用户指南中Stacked Ensemble页面中example Python code的修改版本。
import h2o
from h2o.estimators.random_forest import H2ORandomForestEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
from h2o.grid.grid_search import H2OGridSearch
from __future__ import print_function
h2o.init()
# Import a sample binary outcome training set into H2O
train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
# Identify predictors and response
x = train.columns
y = "response"
x.remove(y)
# For binary classification, response should be a factor
train[y] = train[y].asfactor()
# Add a fold column, generate from a list
# The list has 10 unique values, so there will be 10 folds
fold_list = list(range(10)) * 1000
train['fold_id'] = h2o.H2OFrame(fold_list)
# Train and cross-validate a GBM
my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=10,
keep_cross_validation_predictions=True,
seed=1)
my_gbm.train(x=x, y=y, training_frame=train, fold_column="fold_id")
# Train and cross-validate a RF
my_rf = H2ORandomForestEstimator(ntrees=50,
keep_cross_validation_predictions=True,
seed=1)
my_rf.train(x=x, y=y, training_frame=train, fold_column="fold_id")
# Train a stacked ensemble using the GBM and RF above
ensemble = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf])
ensemble.train(x=x, y=y, training_frame=train)
回答关于如何在模型中查看交叉验证预测的第二个问题。它们存储在两个地方,但是,您可能想要使用的方法是:.cross_validation_holdout_predictions()
此方法以训练观察的原始顺序返回交叉验证预测的单个H2OFrame:
In [11]: my_gbm.cross_validation_holdout_predictions()
Out[11]:
predict p0 p1
--------- -------- --------
1 0.323155 0.676845
1 0.248131 0.751869
1 0.288241 0.711759
1 0.407768 0.592232
1 0.507294 0.492706
0 0.6417 0.3583
1 0.253329 0.746671
1 0.289916 0.710084
1 0.524328 0.475672
1 0.252006 0.747994
[10000 rows x 3 columns]
第二种方法.cross_validation_predictions()
是一个列表,它存储H2OFrame中每个折叠的预测,它与原始训练帧具有相同的行数,但在该折叠中不活动的行的值为零。这通常不是人们认为最有用的格式,因此我建议使用其他方法。
In [13]: type(my_gbm.cross_validation_predictions())
Out[13]: list
In [14]: len(my_gbm.cross_validation_predictions())
Out[14]: 10
In [15]: my_gbm.cross_validation_predictions()[0]
Out[15]:
predict p0 p1
--------- -------- --------
1 0.323155 0.676845
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
[10000 rows x 3 columns]