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ValueError：マルチクラス形式はサポートされていません、xgboost

私の最初のマルチクラスの古典。 XとYの値があります。Yには5つの値[0,1,2,3,4]があります。しかし、私はこの「マルチクラス形式はサポートされていません」を取得します。 xgb_paramsにnum_classが必要であることを理解しますが、 'num_class'：range（0,5,1）を使用する場合は、推定量XGBClassifierの無効なパラメーターnum_classを取得します。

xgb_model = xgb.XGBClassifier(objective='multi:softmax')

xgb_params  = [
{
"n_estimators": range(50, 501, 50),
}
]
cv = cross_validation.StratifiedShuffleSplit(y_train, n_iter=5, 
test_size=0.3, random_state=42)

xgb_grid = grid_search.GridSearchCV(xgb_model, xgb_params, scoring='roc_auc', cv=cv, n_jobs=-1, verbose=3)
xgb_grid.fit(X_train, y_train)

この値の例：

                          X                                     Y
-1.35173485 1.50224188  2.04951167  0.43759658  0.24381777      2
2.81047260  1.31259056  1.39265240  0.16384002  0.65438366      3
2.32878809  -1.92845940 -2.06453246 0.73132270  0.11771229      2
-0.12810555 -2.07268765 -2.40760215 0.97855042  0.11144164      1
1.88682063  0.75792329  -0.09754671 0.46571931  0.62111648      2
-1.09361266 1.74758304  2.49960891  0.36679883  0.88895562      2
0.71760095  -1.30711698 -2.15681966 0.33700593  0.07171119      2
4.60060308  -1.60544855 -1.88996123 0.94500124  0.63776116      4
-0.84223064 2.78233537  3.07299711  0.31470071  0.34424704      1
-0.71236435 0.53140549  0.46677096  0.12320728  0.58829090      2
-0.35333909 1.12463059  1.70104349  0.89084673  0.16585229      2
3.04322100  -1.36878116 -2.31056167 0.81178387  0.04095645      1
-1.04088918 -1.97497570 -1.93285343 0.54101882  0.02528487      1
-0.41624939 0.54592833  0.95458283  0.40004902  0.55062705      2
-1.77706795 0.29061278  0.68186697  0.17430716  0.75095729      0

コードエラー：

 Fitting 5 folds for each of 10 candidates, totalling 50 fits
[CV] n_estimators=50 .................................................
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-213-43ea40d77391> in <module>()
     10 
     11 xgb_grid = grid_search.GridSearchCV(xgb_model, xgb_params, scoring='roc_auc', cv=cv, n_jobs=-1, verbose=3)
---> 12 xgb_grid.fit(X_train, y_train)

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
    827 
    828         """
--> 829         return self._fit(X, y, ParameterGrid(self.param_grid))
    830 
    831 

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
    571                                     self.fit_params, return_parameters=True,
    572                                     error_score=self.error_score)
--> 573                 for parameters in parameter_iterable
    574                 for train, test in cv)
    575 

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    756             # was dispatched. In particular this covers the Edge
    757             # case of Parallel used with an exhausted iterator.
--> 758             while self.dispatch_one_batch(iterator):
    759                 self._iterating = True
    760             else:

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
    606                 return False
    607             else:
--> 608                 self._dispatch(tasks)
    609                 return True
    610 

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
    569         dispatch_timestamp = time.time()
    570         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571         job = self._backend.apply_async(batch, callback=cb)
    572         self._jobs.append(job)
    573 

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in apply_async(self, func, callback)
    107     def apply_async(self, func, callback=None):
    108         """Schedule a func to be run"""
--> 109         result = ImmediateResult(func)
    110         if callback:
    111             callback(result)

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in __init__(self, batch)
    324         # Don't delay the application, to avoid keeping the input
    325         # arguments in memory
--> 326         self.results = batch()
    327 
    328     def get(self):

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc 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):

/home/rudolf/anaconda2/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)
   1682 
   1683     else:
-> 1684         test_score = _score(estimator, X_test, y_test, scorer)
   1685         if return_train_score:
   1686             train_score = _score(estimator, X_train, y_train, scorer)

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _score(estimator, X_test, y_test, scorer)
   1739         score = scorer(estimator, X_test)
   1740     else:
-> 1741         score = scorer(estimator, X_test, y_test)
   1742     if hasattr(score, 'item'):
   1743         try:

/home/rudolf/anaconda2/lib/python2.7/site-packages/sklearn/metrics/scorer.pyc in __call__(self, clf, X, y, sample_weight)
    169         y_type = type_of_target(y)
    170         if y_type not in ("binary", "multilabel-indicator"):
--> 171             raise ValueError("{0} format is not supported".format(y_type))
    172 
    173         if is_regressor(clf):

ValueError: multiclass format is not supported
pythonxgboost
6
Rudolf Khmelenko

パラメータを削除した後、同じエラーが発生します：scoring='roc_auc'、 できます！多分roc_aucはバイナリクラスにのみ使用されます

5
deepx

roc_aucは二項分類問題に制限されています。

予測スコアから受信者動作特性曲線（ROC AUC）の下の面積を計算します。

注：この実装は、ラベルインジケーター形式のバイナリ分類タスクまたはマルチラベル分類タスクに制限されています。

参照： http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html

2
makis

「rocauc」スコアリングメカニズムを使用する代わりに、「accuracy」を使用したのと同じ問題が発生し、機能しました。

sklearn.metricsからインポートaccuracy_scoreスコア= accuracy_score（y_test、preds）

0
Saurabh Kumar