“随机搜索”中的示例；使用Python和H2O进行机器学习；手动不工作
我有点困惑，因为我没有从工作中得到最后一个例子（第36页） 代码如下：“随机搜索”中的示例；使用Python和H2O进行机器学习；手动不工作,python,scikit-learn,h2o,valueerror,Python,Scikit Learn,H2o,Valueerror,我有点困惑，因为我没有从工作中得到最后一个例子（第36页） 代码如下： import h2o h2o.init() from h2o.estimators.gbm import H2OGradientBoostingEstimator from h2o.transforms.preprocessing import H2OScaler from h2o.cross_validation import H2OKFold from h2o.model.regression import h2o_r
import h2o
h2o.init()
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.transforms.preprocessing import H2OScaler
from h2o.cross_validation import H2OKFold
from h2o.model.regression import h2o_r2_score
from sklearn.pipeline import Pipeline
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics.scorer import make_scorer
h2o.__PROGRESS_BAR__=False
h2o.no_progress()
iris_data_path = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv"# load demonstration data1819In [5]:
iris_df = h2o.import_file(path=iris_data_path)
params = {"standardize__center": [True, False],
"standardize__scale": [True, False],
"gbm__ntrees": [10,20],
"gbm__max_depth": [1,2,3],
"gbm__learn_rate": [0.1,0.2]}
custom_cv = H2OKFold(iris_df, n_folds=5, seed=42)
pipeline = Pipeline([("standardize", H2OScaler()),
("gbm", H2OGradientBoostingEstimator(distribution="gaussian"))])
random_search = RandomizedSearchCV(pipeline, params, n_iter=5, scoring=make_scorer(h2o_r2_score),
cv=custom_cv, random_state=42, n_jobs=1)
random_search.fit(iris_df[1:], iris_df[0])
Traceback (most recent call last):
File "untitled-Copy1.py", line 34, in <module>
random_search.fit(iris_df[1:], iris_df[0])
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 710, in fit
self._run_search(evaluate_candidates)
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 1484, in _run_search
random_state=self.random_state))
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 689, in evaluate_candidates
cv.split(X, y, groups)))
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py", line 1004, in __call__
if self.dispatch_one_batch(iterator):
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py", line 835, in dispatch_one_batch
self._dispatch(tasks)
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py", line 754, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/_parallel_backends.py", line 209, in apply_async
result = ImmediateResult(func)
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/_parallel_backends.py", line 590, in __init__
self.results = batch()
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py", line 256, in __call__
for func, args, kwargs in self.items]
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py", line 256, in <listcomp>
for func, args, kwargs in self.items]
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 508, in _fit_and_score
X_train, y_train = _safe_split(estimator, X, y, train)
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/utils/metaestimators.py", line 201, in _safe_split
X_subset = _safe_indexing(X, indices)
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/utils/__init__.py", line 390, in _safe_indexing
indices_dtype = _determine_key_type(indices)
File "/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/utils/__init__.py", line 288, in _determine_key_type
raise ValueError(err_msg)
ValueError: No valid specification of the columns. Only a scalar, list or slice of all integers or all strings, or boolean mask is allowed
Closing connection _sid_b8c1 at exit
H2O session _sid_b8c1 closed.
回溯（最近一次呼叫最后一次）：
文件“untitled-Copy1.py”，第34行，在
随机搜索.fit（iris_-df[1:]，iris_-df[0]）
文件“/department/jupyter dev/anaconda3/envs/python36/lib/python3.6/site packages/sklearn/model_selection/_search.py”，第710行
自我评估。运行搜索（评估候选人）
文件“/department/jupyter dev/anaconda3/envs/python36/lib/python3.6/site packages/sklearn/model\u selection/\u search.py”，第1484行，在运行搜索中
随机状态=自身。随机状态）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/model\u-selection/\u-search.py”，第689行，在评估候选者中
cv.分割（X、y、组）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py”，第1004行，在调用中__
如果self.dispatch\u一批（迭代器）：
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py”，第835行，一批发送
自我分配（任务）
文件“/department/jupyter dev/anaconda3/envs/python36/lib/python3.6/site packages/joblib/parallel.py”，第754行，在
作业=self.\u后端.apply\u异步（批处理，回调=cb）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblb/_-parallel_-backends.py”，第209行，在apply_-async中
结果=立即结果（func）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/_-parallel\u-backends.py”，第590行，在uu-init中__
self.results=batch（）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py”，第256行，在调用__
对于self.items中的func、args、kwargs]
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/joblib/parallel.py”，第256行，在
对于self.items中的func、args、kwargs]
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/model\u-selection/\u-validation.py”，第508行，在“fit”和“score”中
X\u序列，y\u序列=\u安全分割（估计量，X，y，序列）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/utils/metaestimators.py”，第201行，分拆
X_子集=_安全索引（X，索引）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/utils/_-init__.py”，第390行，在安全索引中
索引\u数据类型=\u确定\u键\u类型（索引）
文件“/department/jupyter-dev/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/utils/_______________.py”，第288行，以确定键类型
提升值错误（错误消息）
ValueError:没有有效的列规范。只允许所有整数或字符串的标量、列表或切片，或布尔掩码
关闭出口处的连接_sid_b8c1
H2O会话_sid_b8c1已关闭。