标签：python scikit-learn grid-search
想要使用Gridsearch查找最佳参数并使用f1作为评分指标.
如果我删除评分功能,一切正常,我没有错误.
这是我的代码：
from sklearn import grid_search
parameters = {'n_neighbors':(1,3,5,10,15),'weights':('uniform','distance'),'algorithm':('ball_tree','kd_tree','brute'),'leaf_size':(5,10,20,30,50)}
reg = grid_search.GridSearchCV(estimator=neigh,param_grid=parameters,scoring="f1")
train_classifier(reg, X_train, y_train)
train_f1_score = predict_labels(reg, X_train, y_train)
print reg.best_params_
print "F1 score for training set: {}".format(train_f1_score)
print "F1 score for test set: {}".format(predict_labels(reg, X_test, y_test))
当我执行我得到页面上的页面为错误,我不能做它的正面或反面:(
ValueError Traceback (most recent call last)
<ipython-input-17-3083ff8a20ea> in <module>()
3 parameters = {'n_neighbors':(1,3,5,10,15),'weights':('uniform','distance'),'algorithm':('ball_tree','kd_tree','brute'),'leaf_size':(5,10,20,30,50)}
4 reg = grid_search.GridSearchCV(estimator=neigh,param_grid=parameters,scoring="f1")
----> 5 train_classifier(reg, X_train, y_train)
6 train_f1_score = predict_labels(reg, X_train, y_train)
7 print reg.best_params_
<ipython-input-9-b56ce25fd90b> in train_classifier(clf, X_train, y_train)
5 print "Training {}...".format(clf.__class__.__name__)
6 start = time.time()
----> 7 clf.fit(X_train, y_train)
8 end = time.time()
9 print "Done!\nTraining time (secs): {:.3f}".format(end - start)
//anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
802
803 """
--> 804 return self._fit(X, y, ParameterGrid(self.param_grid))
805
806
//anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
551 self.fit_params, return_parameters=True,
552 error_score=self.error_score)
--> 553 for parameters in parameter_iterable
554 for train, test in cv)
555
//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
802 self._iterating = True
803
--> 804 while self.dispatch_one_batch(iterator):
805 pass
806
//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
660 return False
661 else:
--> 662 self._dispatch(tasks)
663 return True
664
//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
568
569 if self._pool is None:
--> 570 job = ImmediateComputeBatch(batch)
571 self._jobs.append(job)
572 self.n_dispatched_batches += 1
//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, batch)
181 # Don't delay the application, to avoid keeping the input
182 # arguments in memory
--> 183 self.results = batch()
184
185 def get(self):
//anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self)
70
71 def __call__(self):
---> 72 return [func(*args, **kwargs) for func, args, kwargs in self.items]
73
74 def __len__(self):
//anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)
1548
1549 else:
-> 1550 test_score = _score(estimator, X_test, y_test, scorer)
1551 if return_train_score:
1552 train_score = _score(estimator, X_train, y_train, scorer)
//anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _score(estimator, X_test, y_test, scorer)
1604 score = scorer(estimator, X_test)
1605 else:
-> 1606 score = scorer(estimator, X_test, y_test)
1607 if not isinstance(score, numbers.Number):
1608 raise ValueError("scoring must return a number, got %s (%s) instead."
//anaconda/lib/python2.7/site-packages/sklearn/metrics/scorer.pyc in __call__(self, estimator, X, y_true, sample_weight)
88 else:
89 return self._sign * self._score_func(y_true, y_pred,
---> 90 **self._kwargs)
91
92
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight)
637 return fbeta_score(y_true, y_pred, 1, labels=labels,
638 pos_label=pos_label, average=average,
--> 639 sample_weight=sample_weight)
640
641
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight)
754 average=average,
755 warn_for=('f-score',),
--> 756 sample_weight=sample_weight)
757 return f
758
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight)
982 else:
983 raise ValueError("pos_label=%r is not a valid label: %r" %
--> 984 (pos_label, present_labels))
985 labels = [pos_label]
986 if labels is None:
ValueError: pos_label=1 is not a valid label: array(['no', 'yes'],
dtype='|S3')
解决方法:
看起来您的标签数组的值为“no”和“yes”,您应该将它们转换为二进制1-0数字表示,因为您的错误表明评分函数无法理解标签数组中0和1的位置.
在不修改标签数组的情况下解决此问题的其他方法：
from sklearn.metrics import f1_score
from sklearn.metrics import make_scorer
f1_scorer = make_scorer(f1_score, pos_label="yes")
reg = grid_search.GridSearchCV(estimator=neigh,param_grid=parameters,scoring=f1_scorer)
标签：python,scikit-learn,grid-search 来源： https://codeday.me/bug/20190717/1488620.html
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。