59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
import bonobo
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from fs.tarfs import TarFS
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import os
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def cleanse_sms(row):
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if row['category'] == 'spam':
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row['sms_clean'] = '**MARKED AS SPAM** ' + row['sms'][0:50] + ('...' if len(row['sms']) > 50 else '')
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else:
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row['sms_clean'] = row['sms']
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return row['sms_clean']
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graph = bonobo.Graph(
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bonobo.PickleReader('spam.pkl'), # spam.pkl is within the gzipped tarball
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cleanse_sms,
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print
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)
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if __name__ == '__main__':
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'''
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This example shows how a different file system service can be injected
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into a transformation (as compressing pickled objects often makes sense
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anyways). The pickle itself contains a list of lists as follows:
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```
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[
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['category', 'sms'],
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['ham', 'Go until jurong point, crazy..'],
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['ham', 'Ok lar... Joking wif u oni...'],
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['spam', 'Free entry in 2 a wkly comp to win...'],
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['ham', 'U dun say so early hor... U c already then say...'],
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['ham', 'Nah I don't think he goes to usf, he lives around here though'],
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['spam', 'FreeMsg Hey there darling it's been 3 week's now...'],
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...
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]
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```
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where the first column categorizes and sms as "ham" or "spam". The second
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column contains the sms itself.
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Data set taken from:
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https://www.kaggle.com/uciml/sms-spam-collection-dataset/downloads/sms-spam-collection-dataset.zip
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The transformation (1) reads the pickled data, (2) marks and shortens
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messages categorized as spam, and (3) prints the output.
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'''
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services = {
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'fs': TarFS(
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os.path.join(bonobo.get_examples_path(), 'datasets', 'spam.tgz')
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)
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}
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bonobo.run(graph, services=services)
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