In [1]:

    

""" The goal is to classify dataset, based on a set of labels.
    Bayesian statistics are used to do this,
    along with various "naive" assumptions about how
    the data is distributed.
"""
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')


    

In [2]:

    

""" Gaussian Naive Bayes
"""
from sklearn.datasets import make_blobs
X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')


    

    
Out[2]:





<matplotlib.collections.PathCollection at 0x7fa8eaafc898>






    

In [3]:

    

""" Use the `GaussianNB` estimator
"""
from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(X, y)

""" Now let's generate some new data and predict the label:
"""
rng = np.random.RandomState(0)
Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)
ynew = model.predict(Xnew)

""" Now we can plot this new data to get an idea of where the decision boundary is:
"""
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')
lim = plt.axis()
plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)
plt.axis(lim)


    

    
Out[3]:





(-6.0, 8.0, -14.0, 4.0)






    

In [4]:

    

""" But Bayesian statistics also allow for probabilistic labeling:
"""
yprob = model.predict_proba(Xnew)
yprob[-8:].round(2)


    

    
Out[4]:





array([[ 0.89,  0.11],
       [ 1.  ,  0.  ],
       [ 1.  ,  0.  ],
       [ 1.  ,  0.  ],
       [ 1.  ,  0.  ],
       [ 1.  ,  0.  ],
       [ 0.  ,  1.  ],
       [ 0.15,  0.85]])

In [5]:

    

""" Multinomial Naive Bayes
    This will just be a quick whirl wind example in:
    Text Classification
"""
from sklearn.datasets import fetch_20newsgroups

data = fetch_20newsgroups()
data.target_names


    

    




Downloading dataset from http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz (14 MB)






    
Out[5]:





['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']

In [6]:

    

categories = ['talk.religion.misc', 'soc.religion.christian',
              'sci.space', 'comp.graphics']
train = fetch_20newsgroups(subset='train', categories=categories)
test = fetch_20newsgroups(subset='test', categories=categories)
print(train.data[5])


    

    




From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)
Subject: Federal Hearing
Originator: dmcgee@uluhe
Organization: School of Ocean and Earth Science and Technology
Distribution: usa
Lines: 10


Fact or rumor....?  Madalyn Murray O'Hare an atheist who eliminated the
use of the bible reading and prayer in public schools 15 years ago is now
going to appear before the FCC with a petition to stop the reading of the
Gospel on the airways of America.  And she is also campaigning to remove
Christmas programs, songs, etc from the public schools.  If it is true
then mail to Federal Communications Commission 1919 H Street Washington DC
20054 expressing your opposition to her request.  Reference Petition number

2493.

In [10]:

    

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)

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

print(predict_category('sending a payload to the ISS'))
print(predict_category('discussing islam vs atheism'))
print(predict_category('determining the screen resolution'))


    

    




sci.space
soc.religion.christian
comp.graphics

In [11]:

    

""" When to use Naive Bayes

    Naive Bayes is a very simple classification option.

    * When the naive assumptions actually match the data (very rare in practice)
    * For very well-separated categories, when model complexity is less important
    * For very high-dimensional data, when model complexity is less important
"""
print('')


    

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