Counterctorizer中的TypeError scikit-learn：預期的字符串或緩衝區

Question

我正在嘗試解決分類問題。當我喂文本CountVectorizer提示錯誤：

expected string or buffer.

什麼不對的，因爲它包含數和文字，甚至特殊字符的消息混合物，也是我的信息數據集。

樣品怎樣消息看起來是以下幾點：

0   I have not received my gifts which I ordered ok 
1     hth her wells idyll McGill kooky bbc.co 
2         test test test 1 test 
3             test 
4       hello where is my reward points 
5  hi, can you get koovs coupons or vouchers here... 

這裏是我以前做的代碼分類：

import pandas as pd 
from sklearn.feature_extraction.text import CountVectorizer 
df = pd.read_excel('training_data.xlsx') 
X_train = df.message 
print X_train.shape 
map_class_label = {'checkin':0, 'greeting':1,'more reward options':2,'noclass':3, 'other':4,'points':5, 
          'referral points':6,'snapbill':7, 'thanks':8,'voucher not working':9,'voucher':10} 
df['label_num'] = df['Final Category'].map(map_class_label) 
y_train = df.label_num 
vectorizer = CountVectorizer(lowercase=False,decode_error='ignore') 
X_train_dtm = vectorizer.fit_transform(X_train) 

Answer 1

您需要通過astype將列message到string，因爲在數據是一些數字值：

df = pd.read_excel('training_data.xlsx') 
df['message'] = df['message'].values.astype('unicode') 
... 
...