python - 如何调整 BaggingClassifier 中 SVM 的参数

Question

我正在尝试使用 C 支持向量分类对电影评论进行情感分析。下面的代码返回错误

AttributeError: Invalid parameter C for estimator BaggingClassifier.

据我所知，尝试进行网格搜索时会出现错误。我尝试过提取可用参数，但是当我使用那里的某些参数时，我收到了相同的错误。当代码运行时，它似乎在遇到问题之前打印了一些作业，但对我来说，有些尝试有效而其他尝试无效，这对我来说没有意义。我到处寻找，虽然已经回答了一些类似的问题，但我无法将他们的解决方案应用于我的问题。我没看到什么？

token = RegexpTokenizer(r'[a-zA-Z0-9]+')
en_stopwords = set(stopwords.words("english"))


tf = TfidfVectorizer(lowercase=True,
                     tokenizer=token.tokenize,
                     analyzer='word',
                     stop_words=en_stopwords,
                     ngram_range=(1, 1))


# text_tf = tf.fit_transform(data['Phrase'])

X_train, X_test, y_train, y_test = train_test_split(data['Phrase'], data['Sentiment'], test_size=0.3, random_state=1)

kfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=1)

np.random.seed(1)

n_estimators=10
svc = BaggingClassifier(SVC(probability=True, kernel="linear", class_weight="balanced"),
                        max_samples=1.0 / n_estimators, n_estimators=n_estimators)


pipeline_svm = Pipeline([('tf', tf), ('svc', svc)])


grid_svc = GridSearchCV(pipeline_svm,
                        param_grid={'svc__C': [1,10]},
                        cv=kfolds,
                        scoring='roc_auc',
                        verbose=50,
                        n_jobs=-1)


grid_svc.fit(X_train, y_train)
grid_svc.score(X_test, y_test)






sklearn.externals.joblib.externals.loky.process_executor._RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\externals\loky\process_executor.py", line 418, in _process_worker
    r = call_item()
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\externals\loky\process_executor.py", line 272, in __call__
    return self.fn(*self.args, **self.kwargs)
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 567, in __call__
    return self.func(*args, **kwargs)
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in __call__
    for func, args, kwargs in self.items]
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in <listcomp>
    for func, args, kwargs in self.items]
  File "C:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 514, in _fit_and_score
    estimator.set_params(**parameters)
  File "C:\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 147, in set_params
    self._set_params('steps', **kwargs)
  File "C:\Anaconda3\lib\site-packages\sklearn\utils\metaestimators.py", line 52, in _set_params
    super(_BaseComposition, self).set_params(**params)
  File "C:\Anaconda3\lib\site-packages\sklearn\base.py", line 222, in set_params
    valid_params[key].set_params(**sub_params)
  File "C:\Anaconda3\lib\site-packages\sklearn\base.py", line 213, in set_params
    (key, self))
ValueError: Invalid parameter C for estimator BaggingClassifier(base_estimator=SVC(C=1.0, cache_size=200, class_weight='balanced', coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='linear', max_iter=-1, probability=True, random_state=None,
  shrinking=True, tol=0.001, verbose=False),
         bootstrap=True, bootstrap_features=False, max_features=1.0,
         max_samples=0.1, n_estimators=10, n_jobs=None, oob_score=False,
         random_state=None, verbose=0, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3267, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-3-5e5bd8e0d8dd>", line 1, in <module>
    grid_svm.fit(X_train, y_train)
  File "C:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 722, in fit
    self._run_search(evaluate_candidates)
  File "C:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 1191, in _run_search
    evaluate_candidates(ParameterGrid(self.param_grid))
  File "C:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 711, in evaluate_candidates
    cv.split(X, y, groups)))
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 930, in __call__
    self.retrieve()
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 833, in retrieve
    self._output.extend(job.get(timeout=self.timeout))
  File "C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 521, in wrap_future_result
    return future.result(timeout=timeout)
  File "C:\Anaconda3\lib\concurrent\futures\_base.py", line 432, in result
    return self.__get_result()
  File "C:\Anaconda3\lib\concurrent\futures\_base.py", line 384, in __get_result
    raise self._exception
ValueError: Invalid parameter C for estimator BaggingClassifier(base_estimator=SVC(C=1.0, cache_size=200, class_weight='balanced', coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='linear', max_iter=-1, probability=True, random_state=None,
  shrinking=True, tol=0.001, verbose=False),
         bootstrap=True, bootstrap_features=False, max_features=1.0,
         max_samples=0.1, n_estimators=10, n_jobs=None, oob_score=False,
         random_state=None, verbose=0, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.

Answer 1

svc__C 将不起作用；虽然没有记录，但您应该将 param_grid 定义更改为:

param_grid={'base_estimator__C': [1,10]}

更多详情，请参阅 Tuning parameters of the classifier used by BaggingClassifier和 Grid search on parameters inside the parameters of a BaggingClassifier .