Value error of incompatible row dimension occurred while training the model

Question

i was implementing a decision tree on a dataset. Before that i wanted to transform a particular column with CountVectorizer. For this, i am using pipeline to make it simpler.

But there is an error of incompatible row dimensions.

code

# Imported the libraries....
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


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

x_train,x_test,y_train,y_test= tts(x,y,test_size=0.3,random_state=42,stratify=y)
print(x_train.shape,y_train.shape)             # ((2205, 3), (2205,))

pipe.fit(x_train,y_train)                       # Error on this line

Error

---------------------------------------------------------------------------
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.

Questions

1. How this error of incompatible row dimension is forming ?
2. How it can be solved?

Answer 1

try to pass the desired column to ohe as list while a simple string to 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)

As per the documentation, whenever the transformer expects a 1D array as input, the columns were specified as a string ("xxx"). For the transformers which expects 2D data, we need to specify the column as a list of strings (["xxx"]).