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from __future__ import division,print_function, absolute_import | |
from sklearn.datasets import fetch_20newsgroups #built-in dataset | |
from sklearn.feature_extraction.text import CountVectorizer | |
from sklearn.feature_extraction.text import TfidfTransformer | |
from sklearn.naive_bayes import MultinomialNB | |
import pickle | |
from kafka import KafkaConsumer | |
#Defining model and training it | |
categories = ["talk.politics.misc","misc.forsale","rec.motorcycles",\ | |
"comp.sys.mac.hardware","sci.med","talk.religion.misc"] #http://qwone.com/~jason/20Newsgroups/ for reference | |
def fetch_train_dataset(categories): | |
twenty_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42) | |
return twenty_train | |
def bag_of_words(categories): | |
count_vect = CountVectorizer() | |
X_train_counts = count_vect.fit_transform(fetch_train_dataset(categories).data) | |
pickle.dump(count_vect.vocabulary_, open("vocab.pickle", 'wb')) | |
return X_train_counts | |
def tf_idf(categories): | |
tf_transformer = TfidfTransformer() | |
return (tf_transformer,tf_transformer.fit_transform(bag_of_words(categories))) | |
def model(categories): | |
clf = MultinomialNB().fit(tf_idf(categories)[1], fetch_train_dataset(categories).target) | |
return clf | |
model = model(categories) | |
pickle.dump(model,open("model.pickle", 'wb')) | |
print("Training Finished!") | |
#Training Finished Here |
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