GridSearchCV参数

Question

我尝试使用GridSearchCV和KMeans集群来探索要使用的集群的最佳数目，以便在分类问题上获得最佳结果。

我有以下代码：

from sklearn.datasets import fetch_olivetti_faces
from sklearn.model_selection import StratifiedShuffleSplit, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.cluster import KMeans
from sklearn.pipeline import Pipeline

faces = fetch_olivetti_faces()

X_data, y_data = faces.data, faces.target
log_reg = LogisticRegression()

split = StratifiedShuffleSplit(n_splits = 1, test_size=.2, random_state=42)

for train_index, test_index in split.split(X_train, y_train):
    X_train_set , y_train_set = X_data[train_index,], y_data[train_index,]
    X_test_set, y_test_set = X_data[test_index,], y_data[test_index, ]

pipeline = Pipeline([
        ('kmeans', KMeans(n_clusters = 30)),
        ('log_reg', LogisticRegression())
    ])

cluster_grid = dict(n_clusters=range(2,100))
grid = GridSearchCV(pipeline, cluster_grid)

grid.fit(X_train_set, y_train_set, cv=5, verbose=2)

以下是所有的回溯：

   -------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-42-80e6a3932897> in <module>
----> 1 grid.fit(X_train_set, y_train_set, cv=5, verbose=2)

~/opt/anaconda3/lib/python3.7/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

~/opt/anaconda3/lib/python3.7/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 

~/opt/anaconda3/lib/python3.7/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:

~/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py in __call__(self, iterable)
    919             # remaining jobs.
    920             self._iterating = False
--> 921             if self.dispatch_one_batch(iterator):
    922                 self._iterating = self._original_iterator is not None
    923 

~/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
    757                 return False
    758             else:
--> 759                 self._dispatch(tasks)
    760                 return True
    761 

~/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py in _dispatch(self, batch)
    714         with self._lock:
    715             job_idx = len(self._jobs)
--> 716             job = self._backend.apply_async(batch, callback=cb)
    717             # A job can complete so quickly than its callback is
    718             # called before we get here, causing self._jobs to

~/opt/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
    180     def apply_async(self, func, callback=None):
    181         """Schedule a func to be run"""
--> 182         result = ImmediateResult(func)
    183         if callback:
    184             callback(result)

~/opt/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
    547         # Don't delay the application, to avoid keeping the input
    548         # arguments in memory
--> 549         self.results = batch()
    550 
    551     def get(self):

~/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py in __call__(self)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~/opt/anaconda3/lib/python3.7/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()

~/opt/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py in set_params(self, **kwargs)
    162         self
    163         """
--> 164         self._set_params('steps', **kwargs)
    165         return self
    166 

~/opt/anaconda3/lib/python3.7/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 

~/opt/anaconda3/lib/python3.7/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_clusters for estimator Pipeline(memory=None,
         steps=[('kmeans',
                 KMeans(algorithm='auto', copy_x=True, init='k-means++',
                        max_iter=300, n_clusters=30, n_init=10, n_jobs=None,
                        precompute_distances='auto', random_state=None,
                        tol=0.0001, verbose=0)),
                ('log_reg',
                 LogisticRegression(C=1.0, class_weight=None, dual=False,
                                    fit_intercept=True, intercept_scaling=1,
                                    l1_ratio=None, max_iter=100,
                                    multi_class='warn', n_jobs=None,
                                    penalty='l2', random_state=None,
                                    solver='warn', tol=0.0001, verbose=0,
                                    warm_start=False))],
         verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.

​

我不知道到底是怎么回事.我不知道如何解释这个错误信息，但我的参数网格似乎并没有失控。请帮帮忙！

Answer 1

当您使用pipeline时，您需要给出如下参数：

cluster_grid = {
    'kmeans__n_clusters': range(2,100)
}
# adding n_jobs to run in parallel
grid = GridSearchCV(pipeline, cluster_grid, n_jobs=-1)

kmeans是从('kmeans', KMeans())中提取的

因此，您的代码应该如下所示：

pipeline = Pipeline([
    ('kmeans', KMeans(),
    ('log_reg', LogisticRegression())
])
cluster_grid = {
    'kmeans__n_clusters': range(2,100)
}
# adding n_jobs to run in parallel
grid = GridSearchCV(pipeline, cluster_grid, n_jobs=-1)

Answer 2

参数n_clusters只适用于KMeans而不适用于LogisticRegression。

在cluster_grid中指定网格参数仅用于KMeans

# Parameters of pipelines can be set using ‘__’ separated parameter names:

cluster_grid = dict(kmeans__n_clusters=range(2,100))

参考资料：https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html