我正在为我的多项朴素贝叶斯模型寻找超参数α的最优值,该模型使用交叉验证和负对数损失作为度量。我写了代码:
alphas = list(range(1, 500))
#perform k fold cross validation for different metrics
def cross_val(metric):
MSE = []
cv_scores = []
training_scores = []
for alpha in alphas:
naive_bayes = MultinomialNB(alpha=alpha)
scores = cross_val_score(naive_bayes, x_train_counts, y_train, cv=20, scoring='neg_log_loss')
#score() returns the mean accuracy on the given test data and labels
scores_training = naive_bayes.fit(x_train_counts, y_train).score(x_train_counts, y_train)
cv_scores.append(scores.mean())
training_scores.append(scores_training)
#changing to misclassification error
MSE = [1 - x for x in cv_scores]
#determining best alpha
optimal_alpha = alphas[MSE.index(min(MSE))]
print('\nThe optimal value of alpha for %s is %f' % (metric, optimal_alpha))
return optimal_alpha
optimal_alpha = cross_val('neg_log_loss')
上面的代码最初是有效的。现在它抛出以下错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-43-facbaa3537ca> in <module>()
----> 1 optimal_alpha = cross_val('neg_log_loss')
2 prediction(optimal_alpha, 'neg_log_loss')
<ipython-input-41-ff0a9191d45c> in cross_val(metric)
13 for alpha in alphas:
14 naive_bayes = MultinomialNB(alpha=alpha)
---> 15 scores = cross_val_score(naive_bayes, x_train_counts, y_train, cv=20, scoring='neg_log_loss')
16
17 #score() returns the mean accuracy on the given test data and labels
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/cross_validation.py in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
1579 train, test, verbose, None,
1580 fit_params)
-> 1581 for train, test in cv)
1582 return np.array(scores)[:, 0]
1583
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
777 # was dispatched. In particular this covers the edge
778 # case of Parallel used with an exhausted iterator.
--> 779 while self.dispatch_one_batch(iterator):
780 self._iterating = True
781 else:
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
623 return False
624 else:
--> 625 self._dispatch(tasks)
626 return True
627
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
586 dispatch_timestamp = time.time()
587 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588 job = self._backend.apply_async(batch, callback=cb)
589 self._jobs.append(job)
590
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
109 def apply_async(self, func, callback=None):
110 """Schedule a func to be run"""
--> 111 result = ImmediateResult(func)
112 if callback:
113 callback(result)
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
330 # Don't delay the application, to avoid keeping the input
331 # arguments in memory
--> 332 self.results = batch()
333
334 def get(self):
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/cross_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)
1692
1693 else:
-> 1694 test_score = _score(estimator, X_test, y_test, scorer)
1695 if return_train_score:
1696 train_score = _score(estimator, X_train, y_train, scorer)
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/cross_validation.py in _score(estimator, X_test, y_test, scorer)
1749 score = scorer(estimator, X_test)
1750 else:
-> 1751 score = scorer(estimator, X_test, y_test)
1752 if hasattr(score, 'item'):
1753 try:
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/metrics/scorer.py in __call__(self, clf, X, y, sample_weight)
142 **self._kwargs)
143 else:
--> 144 return self._sign * self._score_func(y, y_pred, **self._kwargs)
145
146 def _factory_args(self):
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/sklearn/metrics/classification.py in log_loss(y_true, y_pred, eps, normalize, sample_weight, labels)
1684 "y_true: {2}".format(transformed_labels.shape[1],
1685 y_pred.shape[1],
-> 1686 lb.classes_))
1687 else:
1688 raise ValueError('The number of classes in labels is different '
ValueError: y_true and y_pred contain different number of classes 26, 27. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [ 2 4 5 6 7 8 9 10 11 12 14 15 16 17 19 21 22 23 24 27 29 30 31 32
33 35]
这段代码最初工作过几次。突然,它停止了工作。我怎样才能成功?
