我尝试使用GridSearchCV和KMeans聚类来探索最佳的聚类数量，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_facesfrom sklearn.model_selection import StratifiedShuffleSplit, GridSearchCVfrom sklearn.linear_model import LogisticRegressionfrom sklearn.cluster import KMeansfrom sklearn.pipeline import Pipelinefaces = fetch_olivetti_faces()X_data, y_data = faces.data, faces.targetlog_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)以下是完整的错误追踪:
-------------------------------------------------------------------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()`.
我完全不知道发生了什么…我不知道如何解读这个错误信息，但我觉得我的参数网格设置看起来没有问题。请帮帮我！
回答：
当你使用pipeline时，你需要按照以下方式设置参数:
cluster_grid = { 'kmeans__n_clusters': range(2,100)}# 添加n_jobs以并行运行grid = GridSearchCV(pipeline, cluster_grid, n_jobs=-1)其中kmeans来自('kmeans', KMeans())
所以，你的代码应该看起来如下:
pipeline = Pipeline([ ('kmeans', KMeans(), ('log_reg', LogisticRegression())])cluster_grid = { 'kmeans__n_clusters': range(2,100)}# 添加n_jobs以并行运行grid = GridSearchCV(pipeline, cluster_grid, n_jobs=-1)