def test_entries(self):
dictogram = Dictogram(self.fish_words)
# Verify histogram as dictionary of entries like {word: count}
assert len(dictogram) == 5
self.assertCountEqual(dictogram, self.fish_dict) # Ignore item order
# Verify histogram as list of entries like [(word, count)]
listogram = dictogram.items()
assert len(listogram) == 5
self.assertCountEqual(listogram, self.fish_list) # Ignore item order
def test_entries():
fish_words = ['one', 'fish', 'two', 'fish', 'red', 'fish', 'blue', 'fish']
fish_list = [('one', 1), ('fish', 4), ('two', 1), ('red', 1), ('blue', 1)]
fish_dict = {'one': 1, 'fish': 4, 'two': 1, 'red': 1, 'blue': 1}
case = unittest.TestCase()
dictogram = Dictogram(fish_words)
# Verify histogram as dictionary of entries like {word: count}
assert len(dictogram) == 5
case.assertCountEqual(dictogram, fish_dict) # Ignore item order
# Verify histogram as list of entries like [(word, count)]
listogram = dictogram.items()
assert len(listogram) == 5
case.assertCountEqual(listogram, fish_list) # Ignore item order
def test_sample(self):
histogram = Dictogram(self.fish_words)
# Create a list of 10,000 word samples from histogram
samples_list = [histogram.sample() for _ in range(10000)]
# Create a histogram to count frequency of each word
samples_hist = Dictogram(samples_list)
# Check each word in original histogram
for word, count in histogram.items():
# Calculate word's observed frequency
observed_freq = count / histogram.tokens
# Calculate word's sampled frequency
samples = samples_hist.frequency(word)
sampled_freq = samples / samples_hist.tokens
# Verify word's sampled frequency is close to observed frequency
lower_bound = observed_freq * 0.9 # 10% below = 90% = 0.9
upper_bound = observed_freq * 1.1 # 10% above = 110% = 1.1
assert lower_bound <= sampled_freq <= upper_bound
