test_sklearn.py

import csv, random

data = [(d['text'], d['label']) for d in csv.DictReader(open('issues2.csv'))]
random.shuffle(data)

train_data = data[:1000]
test_data = data[1000:]

train_texts = [t for (t,i) in train_data]
train_labels = [i for (t,i) in train_data]
test_texts = [t for (t,i) in test_data]
test_labels = [i for (t,i) in test_data]

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.linear_model import SGDClassifier

text_clf = Pipeline([('vect', CountVectorizer()),
                     ('tfidf', TfidfTransformer()),
                     ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
                                               alpha=1e-3, max_iter=10, random_state=42)),
])

text_clf = text_clf.fit(train_texts, train_labels)

predicted = text_clf.predict(train_texts)
print("Accuracy on training set:", np.mean(predicted == train_labels))

predicted = text_clf.predict(test_texts)
print("Accuracy on test set:", np.mean(predicted == test_labels))

test_sklearn.session

$ env/bin/python test_sklearn.py 
Accuracy on training set: 0.684
Accuracy on test set: 0.4605809128630705