我正在尝试在模型管道中使用TransformedTargetRegressor，并在其顶部运行GridSearchCV。
这是一个最小的工作示例：
from sklearn.datasets import make_regression
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.compose import TransformedTargetRegressor
X,y = make_regression()
model_pipe = Pipeline([
('model', TransformedTargetRegressor(RandomForestRegressor()))
])
params={'model__n_estimators': [1, 10, 50]}
model = GridSearchCV(model_pipe, param_grid= params)
model.fit(X,y)
此模型导致以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-48-828bdf0e7ede> in <module>
17 model = GridSearchCV(model_pipe, param_grid= params)
18
---> 19 model.fit(X,y)
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
686 return results
687
--> 688 self._run_search(evaluate_candidates)
689
690 # For multi-metric evaluation, store the best_index_, best_params_ and
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates)
1147 def _run_search(self, evaluate_candidates):
1148 """Search all candidates in param_grid"""
-> 1149 evaluate_candidates(ParameterGrid(self.param_grid))
1150
1151
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params)
665 for parameters, (train, test)
666 in product(candidate_params,
--> 667 cv.split(X, y, groups)))
668
669 if len(out) < 1:
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/joblib/parallel.py in __call__(self, iterable)
1001 # remaining jobs.
1002 self._iterating = False
-> 1003 if self.dispatch_one_batch(iterator):
1004 self._iterating = self._original_iterator is not None
1005
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
832 return False
833 else:
--> 834 self._dispatch(tasks)
835 return True
836
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/joblib/parallel.py in _dispatch(self, batch)
751 with self._lock:
752 job_idx = len(self._jobs)
--> 753 job = self._backend.apply_async(batch, callback=cb)
754 # A job can complete so quickly than its callback is
755 # called before we get here, causing self._jobs to
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
199 def apply_async(self, func, callback=None):
200 """Schedule a func to be run"""
--> 201 result = ImmediateResult(func)
202 if callback:
203 callback(result)
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
580 # Don't delay the application, to avoid keeping the input
581 # arguments in memory
--> 582 self.results = batch()
583
584 def get(self):
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/joblib/parallel.py in __call__(self)
254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
255 return [func(*args, **kwargs)
--> 256 for func, args, kwargs in self.items]
257
258 def __len__(self):
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/joblib/parallel.py in <listcomp>(.0)
254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
255 return [func(*args, **kwargs)
--> 256 for func, args, kwargs in self.items]
257
258 def __len__(self):
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
501 train_scores = {}
502 if parameters is not None:
--> 503 estimator.set_params(**parameters)
504
505 start_time = time.time()
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/pipeline.py in set_params(self, **kwargs)
162 self
163 """
--> 164 self._set_params('steps', **kwargs)
165 return self
166
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/utils/metaestimators.py in _set_params(self, attr, **params)
48 self._replace_estimator(attr, name, params.pop(name))
49 # 3. Step parameters and other initialisation arguments
---> 50 super().set_params(**params)
51 return self
52
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/base.py in set_params(self, **params)
231
232 for key, sub_params in nested_params.items():
--> 233 valid_params[key].set_params(**sub_params)
234
235 return self
~/miniconda3/envs/gymbo/lib/python3.6/site-packages/sklearn/base.py in set_params(self, **params)
222 'Check the list of available parameters '
223 'with `estimator.get_params().keys()`.' %
--> 224 (key, self))
225
226 if delim:
ValueError: Invalid parameter n_estimators for estimator TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None,
regressor=RandomForestRegressor(bootstrap=True,
criterion='mse',
max_depth=None,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators='warn',
n_jobs=None,
oob_score=False,
random_state=None,
verbose=0,
warm_start=False),
transformer=None). Check the list of available parameters with `estimator.get_params().keys()`.
当我从管道中删除TransformedTargetRegressor并仅通过随机森林时，此模型将运行。为什么是这样？如上所示，如何在管道中使用TransformedTargetRegressor？
我已经找到答案了。 TransformedTargetregressor需要应用于网格搜索估计器，因此
from sklearn.datasets import make_regression
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.compose import TransformedTargetRegressor
X,y = make_regression()
model_pipe = Pipeline([
('model', RandomForestRegressor())
])
params={'model__n_estimators': [1, 10, 50]}
model = TransformedTargetRegressor(GridSearchCV(model_pipe, param_grid= params), func=np.log, inverse_func=np.exp)
model.fit(X,y)
RandomForestRegressor作为regressor参数存储在TargetTransformedRegressor中。
因此，为params定义GridSearchCV的正确方法是
params={'model__regressor__n_estimators': [1, 10, 50]}