Storing TfidfVectorizer for future use
-1
I need to store a TfidfVectorizer for future use. Following this post, I did below -
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
Then on a separate flask service, I do below
@app.route('/cuisine/api/json',methods=['POST'])
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
But I am getting below error
sklearn.exceptions.NotFittedError: TfidfVectorizer - Vocabulary wasn't fitted.
Not sure what m I missing. Can anyone please suggest?
python scikit-learn
asked Nov 25 '18 at 6:12
nadnad
60731427
add a comment |
-1
I need to store a TfidfVectorizer for future use. Following this post, I did below -
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
Then on a separate flask service, I do below
@app.route('/cuisine/api/json',methods=['POST'])
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
But I am getting below error
sklearn.exceptions.NotFittedError: TfidfVectorizer - Vocabulary wasn't fitted.
Not sure what m I missing. Can anyone please suggest?
python scikit-learn
asked Nov 25 '18 at 6:12
nadnad
60731427
add a comment |
-1
-1
-1
I need to store a TfidfVectorizer for future use. Following this post, I did below -
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
Then on a separate flask service, I do below
@app.route('/cuisine/api/json',methods=['POST'])
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
But I am getting below error
sklearn.exceptions.NotFittedError: TfidfVectorizer - Vocabulary wasn't fitted.
Not sure what m I missing. Can anyone please suggest?
python scikit-learn
asked Nov 25 '18 at 6:12
nadnad
60731427
I need to store a TfidfVectorizer for future use. Following this post, I did below -
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
Then on a separate flask service, I do below
@app.route('/cuisine/api/json',methods=['POST'])
def getCuisine():
content=jsonify(request.json)
test = pd.io.json.json_normalize(request.json)
tfidf_vect = pickle.load(open("vectorizer.pickle", "rb"))
test['ingredients'] = [str(map(makeString, x)) for x in test['ingredients']]
test_transform = tfidf_vect.transform(test['ingredients'].values)
le = preprocessing.LabelEncoder()
X_test = test_transform
y_test = le.fit_transform(test['cuisine'].values)
But I am getting below error
sklearn.exceptions.NotFittedError: TfidfVectorizer - Vocabulary wasn't fitted.
Not sure what m I missing. Can anyone please suggest?
python scikit-learn
python scikit-learn
asked Nov 25 '18 at 6:12
nadnad
60731427
asked Nov 25 '18 at 6:12
nadnad
60731427
asked Nov 25 '18 at 6:12
nadnad
60731427
asked Nov 25 '18 at 6:12
nadnad
60731427
asked Nov 25 '18 at 6:12
nadnad
60731427
60731427
add a comment |
add a comment |
1 Answer
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1
You forgot to fit your model first:
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
tfidf_vect.fit() <-- pass your training data!
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
You forgot to fit your model first:
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
tfidf_vect.fit() <-- pass your training data!
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
add a comment |
1
You forgot to fit your model first:
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
tfidf_vect.fit() <-- pass your training data!
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
add a comment |
1
1
1
You forgot to fit your model first:
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
tfidf_vect.fit() <-- pass your training data!
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
You forgot to fit your model first:
tfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'w{1,}', max_features=5000)
tfidf_vect.fit() <-- pass your training data!
pickle.dump(tfidf_vect, open("vectorizer.pickle", "wb"))
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
answered Nov 25 '18 at 8:35
Matthieu BrucherMatthieu Brucher
16.3k32143
16.3k32143
add a comment |
add a comment |
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