Thoughtful Machine Learning with Python: A Test-Driven Approach by Matthew Kirk

Author:Matthew Kirk [Kirk, Matthew]
Language: eng
Format: azw3, mobi, pdf
Publisher: O'Reilly Media
Published: 2017-01-16T05:00:00+00:00

from collections import defaultdict dictionary = {'a': 'b'} dictionary['b'] # Yields KeyError default_dictionary = defaultdict(lambda: 0, dictionary) default_dictionary['b'] == 0

You’ll notice that we’re doing a bit of error handling for the case when zeros happen, because we will throw a divide-by-zero error. Next, we need to address the probability of word tag combinations, which we can do by introducing the following to our existing test:

import StringIO class TestPOSTagger(unittest.TestCase): def setUp(): self.stream = StringIO("A/B C/D C/D A/D A/B ./.") self.pos_tagger = POSTagger([StringIO.StringIO(self.stream)]) self.pos_tagger.train() def it_calculates_probability_of_word_and_tag(self): self.assertEqual(self.pos_tagger.word_tag_probability("Z", "Z"), 0) # A and B happens 2 times, count of b happens twice therefore 100% self.assertEqual(self.pos_tagger.word_tag_probability("A", "B"), 1) # A and D happens 1 time, count of D happens 3 times so 1/3 self.assertEqual(self.pos_tagger.word_tag_probability("A", "D"), 1.0/3.0) # START and START happens 1, time, count of start happens 1 so 1 self.assertEqual(self.pos_tagger.word_tag_probability("START", "START"), 1) self.assertEqual(self.pos_tagger.word_tag_probability(".", "."), 1)

To make this work in the POSTagger class, we need to write the following:

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