我正在尝试准备数据以输入决策树和多项朴素贝叶斯分类器。
这就是我的数据的样子(pandas 数据框)
Label Feat1 Feat2 Feat3 Feat4
0 1 3 2 1
1 0 1 1 2
2 2 2 1 1
3 3 3 2 3
我已将数据拆分为 dataLabel 和 dataFeatures。
使用 dataLabel.ravel()
我需要离散化特征,以便分类器将它们视为类别而不是数字。
我正在尝试使用 OneHotEncoder
来执行此操作
enc = OneHotEncoder()
enc.fit(dataFeatures)
chk = enc.transform(dataFeatures)
from sklearn.naive_bayes import MultinomialNB
mnb = MultinomialNB()
from sklearn import metrics
from sklearn.cross_validation import cross_val_score
scores = cross_val_score(mnb, Y, chk, cv=10, scoring='accuracy')
我收到此错误 - 输入形状错误 (64, 16)
这是标签和输入的形状
dataLabel.shape = 72
chk.shape = 72,16
为什么分类器不接受 onehotencoded 特征?
编辑 - 整个堆栈跟踪代码
/root/anaconda2/lib/python2.7/site-packages/sklearn/utils /validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
DeprecationWarning)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda2/lib/python2.7/site-packages/sklearn /cross_validation.py", line 1433, in cross_val_score
for train, test in cv)
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 800, in __call__
while self.dispatch_one_batch(iterator):
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 658, in dispatch_one_batch
self._dispatch(tasks)
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 566, in _dispatch
job = ImmediateComputeBatch(batch)
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 180, in __init__
self.results = batch()
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 72, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py", line 1531, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 527, in fit
X, y = check_X_y(X, y, 'csr')
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py", line 515, in check_X_y
y = column_or_1d(y, warn=True)
File "/root/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py", line 551, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError:错误的输入形状 (64, 16)
最佳答案
首先,您必须交换 chk
和 Y
考虑 cross_val_score
文档。接下来,您没有指定什么是 Y
所以我希望它是一个一维数组。最后一个不是单独使用,最好使用 Pipeline
将所有变压器组合在一个分类器中。 。像这样:
from sklearn import metrics
from sklearn.cross_validation import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
clf = Pipeline([
('transformer', OneHotEncoder()),
('estimator', MultinomialNB()),
])
scores = cross_val_score(clf, dataFeatures.values, Y, cv=10, scoring='accuracy')
关于python - OneHotEncoded 功能在输入到分类器时导致错误,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38574222/