def problem4():
if check_doctest('analyze_tweet_sentiment', trends):
return True
# Change the representation of sentiments to validate abstraction barrier.
original_make_sentiment = trends.make_sentiment
original_sentiment_value = trends.sentiment_value
original_has_sentiment = trends.has_sentiment
trends.make_sentiment = lambda s: lambda : s
trends.sentiment_value = lambda s: s()
trends.has_sentiment = lambda s: s() != None
sentiment_tests = (
((trends.make_tweet('Help, I\'m trapped in an autograder factory and I can\'t get out!'.lower(), None, 0, 0),), -0.416666667),
((trends.make_tweet('The thing that I love about hating things that I love is that I hate loving that I hate doing it.'.lower(), None, 0, 0),), 0.075),
)
no_sentiment_tests = (
((trends.make_tweet('Peter Piper picked a peck of pickled peppers'.lower(), None, 0, 0),), None),
)
def analyze(tweet):
return trends.sentiment_value(trends.analyze_tweet_sentiment(tweet))
if check_func(analyze, sentiment_tests, comp=comp_float):
return True
if check_func(analyze, no_sentiment_tests):
return True
trends.make_sentiment = original_make_sentiment
trends.sentiment_value = original_sentiment_value
trends.has_sentiment = original_has_sentiment
def problem4():
if check_doctest('analyze_tweet_sentiment', trends):
return True
# Change the representation of sentiments to validate abstraction barrier.
original_make_sentiment = trends.make_sentiment
original_sentiment_value = trends.sentiment_value
original_has_sentiment = trends.has_sentiment
trends.make_sentiment = lambda s: lambda: s
trends.sentiment_value = lambda s: s()
trends.has_sentiment = lambda s: s() != None
sentiment_tests = (
((trends.make_tweet(
'Help, I\'m trapped in an autograder factory and I can\'t get out!'
.lower(), None, 0, 0), ), -0.416666667),
((trends.make_tweet(
'The thing that I love about hating things that I love is that I hate loving that I hate doing it.'
.lower(), None, 0, 0), ), 0.075),
)
no_sentiment_tests = (((trends.make_tweet(
'Peter Piper picked a peck of pickled peppers'.lower(), None, 0,
0), ), None), )
def analyze(tweet):
return trends.sentiment_value(trends.analyze_tweet_sentiment(tweet))
if check_func(analyze, sentiment_tests, comp=comp_float):
return True
if check_func(analyze, no_sentiment_tests):
return True
trends.make_sentiment = original_make_sentiment
trends.sentiment_value = original_sentiment_value
trends.has_sentiment = original_has_sentiment
def test_average():
tweets = pirate_tweets(trends.make_tweet) + (
trends.make_tweet('This tweet is without a sentiment', None, None, None),
trends.make_tweet('This tweet is also without a sentiment', None, None, None),
)
tweets_by_state = {
'MT': [ tweets[1], tweets[5] ],
'MI': [ tweets[0], tweets[4] ],
'FL': [ tweets[3], tweets[7] ],
'ND': [ tweets[2], tweets[6] ],
'AA': [ tweets[8], tweets[9] ],
}
expected = {
'MT': -0.08333333333333333,
'MI': 0.325,
'FL': 0.5,
'ND': 0.020833333333333332
}
tests = ( ((tweets_by_state,),expected) ,)
if check_func(trends.average_sentiments, tests, comp=comp_dict):
return True
def test_average():
tweets = pirate_tweets(trends.make_tweet) + (
trends.make_tweet('This tweet is without a sentiment', None, None,
None),
trends.make_tweet('This tweet is also without a sentiment', None,
None, None),
)
tweets_by_state = {
'MT': [tweets[1], tweets[5]],
'MI': [tweets[0], tweets[4]],
'FL': [tweets[3], tweets[7]],
'ND': [tweets[2], tweets[6]],
'AA': [tweets[8], tweets[9]],
}
expected = {
'MT': -0.08333333333333333,
'MI': 0.325,
'FL': 0.5,
'ND': 0.020833333333333332
}
tests = (((tweets_by_state, ), expected), )
if check_func(trends.average_sentiments, tests, comp=comp_dict):
return True