Numpy 训练模型时发生不兼容行维度的值错误

我在一个平台上实现了一个决策树。在此之前，我想用CountVectorizer转换特定列。为此，我使用管道使其更简单

但存在不兼容行维度的错误

代码 错误

---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在（）
---->1.管道安装（x_系列、y_系列）
7帧
/bmat中的usr/local/lib/python3.6/dist-packages/scipy/sparse/construct.py（块、格式、数据类型）
584 exp=眉毛长度[i]，
585 got=A.shape[0]））
-->586提升值错误（msg）
587
588如果bcol_长度[j]==0：
ValueError:块[0，：]的行维度不兼容。已获取块[0,1]。形状[0]==2205，应为1。

问题

不兼容行维度的错误是如何形成的

如何解决

---

尝试将所需列传递给ohe as list，同时将简单字符串传递给cv

from sklearn.feature_extraction.text import CountVectorizer as cv
from sklearn.preprocessing import OneHotEncoder as ohe
from sklearn.compose import ColumnTransformer as ct
from sklearn.pipeline import make_pipeline as mp
from sklearn.tree import DecisionTreeClassifier as dtc

data = pd.DataFrame({'rating':np.random.randint(0,10,6),'variation':['a','b','c','a','b','c'],
                   'verified_reviews':['adnf asd','sdf dsa','das j s','asd jd s','sad jds a','sajd'],
                   'feedback':np.random.randint(0,2,6)})

transformer=ct(transformers=[('review_counts',cv(),'verified_reviews'),
                             ('variation_dummies', ohe(),['variation'])],
               remainder='passthrough')

pipe= mp(transformer, dtc(random_state=42))

x= data[['rating','variation','verified_reviews']].copy()
y= data.feedback

pipe.fit(x,y)

根据，每当转换器需要1D数组作为输入时，列都被指定为字符串（“xxx”）。对于需要2D数据的转换器，我们需要将列指定为字符串列表（[“xxx”]）

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-79-a981c354b190> in <module>()
----> 1 pipe.fit(x_train,y_train)

7 frames
/usr/local/lib/python3.6/dist-packages/scipy/sparse/construct.py in bmat(blocks, format, dtype)
    584                                                     exp=brow_lengths[i],
    585                                                     got=A.shape[0]))
--> 586                     raise ValueError(msg)
    587 
    588                 if bcol_lengths[j] == 0:

ValueError: blocks[0,:] has incompatible row dimensions. Got blocks[0,1].shape[0] == 2205, expected 1.

from sklearn.feature_extraction.text import CountVectorizer as cv
from sklearn.preprocessing import OneHotEncoder as ohe
from sklearn.compose import ColumnTransformer as ct
from sklearn.pipeline import make_pipeline as mp
from sklearn.tree import DecisionTreeClassifier as dtc

data = pd.DataFrame({'rating':np.random.randint(0,10,6),'variation':['a','b','c','a','b','c'],
                   'verified_reviews':['adnf asd','sdf dsa','das j s','asd jd s','sad jds a','sajd'],
                   'feedback':np.random.randint(0,2,6)})

transformer=ct(transformers=[('review_counts',cv(),'verified_reviews'),
                             ('variation_dummies', ohe(),['variation'])],
               remainder='passthrough')

pipe= mp(transformer, dtc(random_state=42))

x= data[['rating','variation','verified_reviews']].copy()
y= data.feedback

pipe.fit(x,y)