Hi,
I am using the below to pickle CountVectorizer
vectorizer =
CountVectorizer(tokenizer=extract_features_sk,lowercase=self.lowercase,binary=self.is_binary)
output = open(self.fn_vec , 'wb')
pickle.dump(vectorizer, output)
output.close()
If I load the pickle in the same app, all works. If I load in a
different app, I get the below even all required libraries and
functions are included in the new app.
So...does it make sense to pickel CountVectorizer? I just did not
want to fit CountVectorizer every time I wanted to score a svm model.
Traceback (most recent call last):
File "/home/ubuntu/workspace/tango/solariat_nlp/src/solariat_nlp/__init__.py",
line 12, in <module>
from solariat_nlp.train import extract_features, create_vector, predict
File "/home/ubuntu/workspace/tango/solariat_nlp/src/solariat_nlp/__init__.py",
line 183, in <module>
cf.load_vector()
File
"/home/ubuntu/workspace/tango/solariat_nlp/src/solariat_nlp/scikitssvm/svm_class.py",
line 177, in load_vector
self.vectorizer = pickle.load(pkl_file)
File "/usr/lib/python2.6/pickle.py", line 1370, in load
return Unpickler(file).load()
File "/usr/lib/python2.6/pickle.py", line 858, in load
dispatch[key](self)
File "/usr/lib/python2.6/pickle.py", line 1090, in load_global
klass = self.find_class(module, name)
File "/usr/lib/python2.6/pickle.py", line 1126, in find_class
klass = getattr(mod, name)
AttributeError: 'module' object has no attribute 'extract_features_sk'
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