Naive_bayes.py

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import fetch_20newsgroups

data =fetch_20newsgroups()
print(data.target_names)

categories =['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc',
             'comp.sys.ibm.pc.hardware','comp.sys.mac.hardware', 
             'comp.windows.x', 'misc.forsale', 'rec.autos', 
             'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 
             'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 
             'soc.religion.christian', 'talk.politics.guns', 
             'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']

train = fetch_20newsgroups(subset='train', categories= categories)
test = fetch_20newsgroups(subset='test', categories= categories)

# print(train.data[10])

print(len(train.data))
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline

model = make_pipeline(TfidfVectorizer(), MultinomialNB())

model.fit(train.data, train.target)
labels = model.predict(test.data)

from sklearn.metrics import confusion_matrix
mat = confusion_matrix(test.target, labels)
sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
                xticklabels=train.target_names, yticklabels=train.target_names)
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.show()


def predict_category(s, train=train, model=model):
    pred = model.predict([s])
    return train.target_names[pred[0]]

predict_category('Jesus Christ')
# predict_category('Sending load to International Space Station ISS')
# predict_category('Audi is better than BMW')
# predict_category('President of India')