def test_categories_reducer(self):
"""Tests join_reviews_with_categories_reducer with null data and some
static data."""
job = ReviewAutoPilot()
VALUES = (("business", {"categories": CATEGORY}), ("review", TEXT))
category_results = list(job.join_reviews_with_categories_reducer(BIZ, VALUES))
results = [(CATEGORY, TEXT)]
self.assertEqual(category_results, results)
def test_categories_reducer(self):
"""Tests join_reviews_with_categories_reducer with null data and some
static data."""
job = ReviewAutoPilot()
VALUES = (('business', {'categories': CATEGORY}), ('review', TEXT))
category_results = list(
job.join_reviews_with_categories_reducer(BIZ, VALUES))
results = [(CATEGORY, TEXT)]
self.assertEqual(category_results, results)
def test_business_mapper(self):
"""tests the individual mappers of ReviewAutoPilot"""
job = ReviewAutoPilot()
biz_results = list(job.business_join_mapper(None, DATA[0]))
review_results = list(job.business_join_mapper(None, DATA[1]))
biz_after_results = [(ID, ("business", DATA[0]))]
review_after_results = [(ID, ("review", DATA[1]["text"]))]
self.assertEqual(biz_results, biz_after_results)
self.assertEqual(review_results, review_after_results)
def test_business_mapper(self):
"""tests the individual mappers of ReviewAutoPilot"""
job = ReviewAutoPilot()
biz_results = list(job.business_join_mapper(None, DATA[0]))
review_results = list(job.business_join_mapper(None, DATA[1]))
biz_after_results = [(ID, ('business', DATA[0]))]
review_after_results = [(ID, ('review', DATA[1]['text']))]
self.assertEqual(biz_results, biz_after_results)
self.assertEqual(review_results, review_after_results)
def test_smoke(self):
"""Uses small, static dataset possible on local, since a full run takes
too long."""
# Random data to feed into the markov model.
# I use long runs of foo to get through the threshold filters.
text = ('foo bar foo baz foo car foo daz ' + ('foo ' * 10) +
'foofoo yelp'
'foo yar foo foo bar bar dar')
single_review = REVIEW_TEMPLATE % (text, BIZ)
business = BUSINESS_TEMPLATE % (CATEGORY, BIZ)
static_stdin = StringIO(single_review + business)
job = ReviewAutoPilot(['-r', 'inline', '--no-conf', '-'])
job.sandbox(stdin=static_stdin)
results = []
with job.make_runner() as runner:
runner.run()
for line in runner.stream_output():
key, value = job.parse_output_line(line)
results.append(value)
# Normal output to compare
result = {'foo': 0.99009900990099009, '<end>': 0.0099009900990099011}
self.assertEqual(results[0], result)
def test_smoke(self):
"""Uses small, static dataset possible on local, since a full run takes
too long."""
# Random data to feed into the markov model.
# I use long runs of foo to get through the threshold filters.
text = "foo bar foo baz foo car foo daz " + ("foo " * 10) + "foofoo yelp" "foo yar foo foo bar bar dar"
single_review = REVIEW_TEMPLATE % (text, BIZ)
business = BUSINESS_TEMPLATE % (CATEGORY, BIZ)
static_stdin = StringIO(single_review + business)
job = ReviewAutoPilot(["-r", "inline", "--no-conf", "-"])
job.sandbox(stdin=static_stdin)
results = []
with job.make_runner() as runner:
runner.run()
for line in runner.stream_output():
key, value = job.parse_output_line(line)
results.append(value)
# Normal output to compare
result = {"foo": 0.99009900990099009, "<end>": 0.0099009900990099011}
self.assertEqual(results[0], result)
def test_split_mapper(self):
"""Tests split_mapper reducer in autopilot"""
job = ReviewAutoPilot()
TEST_RETURN = (("hello", "C"), ("<end>", 1))
self.assertEqual(job.review_split_mapper(CATEGORY, TEXT).next(), TEST_RETURN)
def test_split_mapper(self):
"""Tests split_mapper reducer in autopilot"""
job = ReviewAutoPilot()
TEST_RETURN = (('hello', 'C'), ('<end>', 1))
self.assertEqual(
job.review_split_mapper(CATEGORY, TEXT).next(), TEST_RETURN)