numpy concatenate dimension mismatch - numpy

Question

I've been running into an issue with numpy's concatenate that I've been unable to make sense of, and was hoping that someone had encountered and resolved the same problem.I'm attempting to join together two arrays created by SciKit-Learn's TfidfVectorizer and labelencoder, but getting an error message that "arrays must have the same number of dimensions", despite the fact that the inputs are a (77946, 12157) array and (77946, 1000) array, respectively. (As requested in the comments, a reproducible example is at the bottom)
TV=TfidfVectorizer(min_df=1,max_features=1000)
tagvect=preprocessing.LabelBinarizer()
tagvect2=preprocessing.LabelBinarizer()
tagvect2.fit(DS['location2'].tolist())
TV.fit(DS['tweet'])
GBR=GradientBoostingRegressor()
print "creating Xtrain and test"
A=tagvect2.transform(DS['location2'])
B=TV.transform(DS['tweet'])
print A.shape
print B.shape
pdb.set_trace()
Xtrain=np.concatenate([A,B.todense()],axis=1)
I initially thought the fact that B was encoded as a sparse matrix might be causing the issues, but converting it to a dense matrix didn't resolve the issue. I had the same issue using hstack instead.
Even more peculiar is that adding in a third labelencoder matrix results in no error :
TV.fit(DS['tweet'])
tagvect.fit(DS['state'].tolist())
tagvect2.fit(DS['location'].tolist())
GBR=GradientBoostingRegressor()
print "creating Xtrain and test"
Xtrain=pd.DataFrame(np.concatenate([tagvect.transform(DS['state']),tagvect2.transform(DS['location']),TV.transform(DS['tweet'])],axis=1))
Here is the error message:
Traceback (most recent call last):
File "smallerdimensions.py", line 49, in <module>
Xtrain=pd.DataFrame(np.concatenate((A,B.todense()),axis=1))
ValueError: arrays must have same number of dimensions
Thank you for any help you can provide. Here is a reproducible example:
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import preprocessing
import numpy as np
tweets=["Jazz for a Rainy Afternoon","RT: #mention: I love rainy days.", "Good Morning Chicago!"]
location=["Oklahoma", "Oklahoma","Illinois"]
DS=pd.DataFrame({"tweet":tweets,"location":location})
TV=TfidfVectorizer(min_df=1,max_features=1000)
tagvect=preprocessing.LabelBinarizer()
DS['location']=DS['location'].fillna("none")
tagvect.fit(DS['location'].tolist())
TV.fit(DS['tweet'])
print "before problem"
print DS['tweet']
print DS['location']
print tagvect.transform(DS['location'])
print tagvect.transform(DS['location']).shape
print TV.transform(DS['tweet']).shape
print TV.transform(DS['tweet'])
print TV.transform(DS['tweet']).todense()
print np.concatenate([tagvect.transform(DS['location']),TV.transform(DS['tweet'])],axis=1)
Numpy is v 1.6.1, pandas is v 0.12.0, scikit is 0.14.1.