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Document Classification with scikit-learn
import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score
NEWLINE = '\n'
HAM = 'ham'
SPAM = 'spam'
('data/spam', SPAM),
('data/easy_ham', HAM),
('data/hard_ham', HAM),
('data/beck-s', HAM),
('data/farmer-d', HAM),
('data/kaminski-v', HAM),
('data/kitchen-l', HAM),
('data/lokay-m', HAM),
('data/williams-w3', HAM),
('data/BG', SPAM),
('data/GP', SPAM),
('data/SH', SPAM)
SKIP_FILES = {'cmds'}
def read_files(path):
for root, dir_names, file_names in os.walk(path):
for path in dir_names:
read_files(os.path.join(root, path))
for file_name in file_names:
if file_name not in SKIP_FILES:
file_path = os.path.join(root, file_name)
if os.path.isfile(file_path):
past_header, lines = False, []
f = open(file_path, encoding="latin-1")
for line in f:
if past_header:
elif line == NEWLINE:
past_header = True
content = NEWLINE.join(lines)
yield file_path, content
def build_data_frame(path, classification):
rows = []
index = []
for file_name, text in read_files(path):
rows.append({'text': text, 'class': classification})
data_frame = DataFrame(rows, index=index)
return data_frame
data = DataFrame({'text': [], 'class': []})
for path, classification in SOURCES:
data = data.append(build_data_frame(path, classification))
data = data.reindex(numpy.random.permutation(data.index))
pipeline = Pipeline([
('count_vectorizer', CountVectorizer(ngram_range=(1, 2))),
('classifier', MultinomialNB())
k_fold = KFold(n=len(data), n_folds=6)
scores = []
confusion = numpy.array([[0, 0], [0, 0]])
for train_indices, test_indices in k_fold:
train_text = data.iloc[train_indices]['text'].values
train_y = data.iloc[train_indices]['class'].values.astype(str)
test_text = data.iloc[test_indices]['text'].values
test_y = data.iloc[test_indices]['class'].values.astype(str), train_y)
predictions = pipeline.predict(test_text)
confusion += confusion_matrix(test_y, predictions)
score = f1_score(test_y, predictions, pos_label=SPAM)
print('Total emails classified:', len(data))
print('Score:', sum(scores)/len(scores))
print('Confusion matrix:')

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I finally got around to finishing this tutorial and put it on my blog. Please enjoy the finished version here.

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NagabhushanS commented Apr 2, 2017

@zurez...Pickle the pipeline (model) object to a file..Later you can load the pickled object.

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