Python: using scikit-learn to predict, gives blank predictions

The problem is with your tags_train variable. According to the OneVsRestClassifier documentation, the targets need to be "a sequence of sequences of labels", and your targets are lists of one element.

Below is an edited, self-contained and working version of your code. Note the change in tags_train, in particular the fact the the tags_train is a one-element tuple.

import numpy as np
import scipy
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC


# We have lists called tags_train, descs_train, tags_test, descs_test with the test and train data
tags_train = [('label', ), ('international' ,'solved'), ('international','open')]
descs_train = ['description of ticket one', 'some other ticket two', 'label']

X_train = np.array(descs_train)
y_train = tags_train
X_test = np.array(descs_train)  

classifier = Pipeline([
    ('vectorizer', CountVectorizer()),
    ('tfidf', TfidfTransformer()),
    ('clf', OneVsRestClassifier(LinearSVC(class_weight='auto')))])

classifier = classifier.fit(X_train, y_train)
predicted = classifier.predict(X_test)

print predicted

The output is

[('international',), ('international',), ('international', 'open')]