def test_compare_score_should_return_highest_score_list():
reporter = DocReporter()
input = [('word1', 'doc1', 5), ('word2', 'doc1', 3), ('word1', 'doc2', 6),
('word2', 'doc2', 8)]
expected_output = {'word1': ('doc2', 6), 'word2': ('doc2', 8)}
output = reporter.compare_score(input)
assert output == expected_output
def test_compare_score_should_return_first_document_for_same_score():
reporter = DocReporter()
input = [('word1', 'doc1', 5), ('word2', 'doc1', 5), ('word1', 'doc2', 5),
('word2', 'doc2', 5)]
expected_output = {'word1': ('doc1', 5), 'word2': ('doc1', 5)}
output = reporter.compare_score(input)
assert output == expected_output
def test_calculate_tf_score_should_return_the_correct_score_list_of_one_word():
reporter = DocReporter()
doc = 'doc1'
input = 'Once upon a time, far, far away' # length = 7
words = ['time']
expected_output = [('time', 'doc1', 1.0 / 7.0)]
output = reporter.calculate_tf_score(doc, input, words)
assert output == expected_output
def test_calculate_tf_score_should_return_the_correct_score_for_list_of_words(
):
reporter = DocReporter()
doc = 'doc1'
input = 'Once upon a time, far, far away' # length = 7
words = ['time', 'far']
expected_outputs = [('time', 'doc1', 1.0 / 7.0),
('far', 'doc1', 2.0 / 7.0)]
outputs = reporter.calculate_tf_score(doc, input, words)
for expected in expected_outputs:
for output in outputs:
if output[0] == expected[0]:
assert output[2] == expected[2]
def test_compare_score_should_return_highest_score_when_score_is_close_to_zero(
):
reporter = DocReporter()
input = [('word1', 'doc1', 0.0), ('word2', 'doc1', 0.000001),
('word3', 'doc1', 0.0), ('word1', 'doc2', 0.0),
('word2', 'doc2', 0.0), ('word3', 'doc2', 0.000001)]
expected_output = {
'word1': ('doc1', 0.0),
'word2': ('doc1', 0.000001),
'word3': ('doc2', 0.000001)
}
output = reporter.compare_score(input)
assert output == expected_output
def test_calculate_tf_score_should_handle_uppercase_search_words():
count = 0
reporter = DocReporter()
doc = 'doc1'
input = 'Once upon a time, far, far away, I found myself at NASA' # Length is 12
words = ['I', 'NASA']
expected_outputs = [('i', 'doc1', 1.0 / 12.0),
('nasa', 'doc1', 1.0 / 12.0)]
outputs = reporter.calculate_tf_score(doc, input, words)
for expected in expected_outputs:
for output in outputs:
if output[0] == expected[0]:
assert output[2] == expected[2]
count += 1
if count != len(words):
assert False
def test_calculate_tf_score_should_score_all_words():
count = 0
reporter = DocReporter()
doc = 'doc1'
input = 'Once upon a time, far, far away' # Length is 7
words = ['once', 'upon', 'a', 'time', 'far', 'away']
expected_outputs = [
('once', 'doc1', 1.0 / 7.0), ('upon', 'doc1', 1.0 / 7.0),
('a', 'doc1', 1.0 / 7.0), ('time', 'doc1', 1.0 / 7.0),
('far', 'doc1', 2.0 / 7.0), ('away', 'doc1', 1.0 / 7.0)
]
outputs = reporter.calculate_tf_score(doc, input, words)
for expected in expected_outputs:
for output in outputs:
if output[0] == expected[0]:
count += 1
if count != len(words):
assert False
from doc_reporter import DocReporter
reporter = DocReporter()
docs = [
'mobydick-chapter1.txt', 'mobydick-chapter2.txt', 'mobydick-chapter3.txt',
'mobydick-chapter4.txt', 'mobydick-chapter5.txt'
]
words = ['queequeg', 'whale', 'sea']
reporter.create_report_from_local_documents(docs, words)