def test_wrong_subject2():
test_util.start('Testing WrongSubject2...')
ep = WrongSubject2()
test_util.assertion(ep.match(NLP('She is a cook')).has_error, True, None)
test_util.assertion(ep.match(NLP('He is a cook')).has_error, False, None)
test_util.assertion(ep.match(NLP('It is a cook.')).has_error, True, None)
test_util.result()
def test_he_is_a_nurse():
test_util.start('Testing HeIsANurse match...')
pattern = HeIsANurse()
test_util.assertion(pattern.match(NLP('He is a nurse')), True, None)
test_util.assertion(pattern.match(NLP('Nurse')), False, None)
test_util.assertion(pattern.match(NLP("A nurse.")), False, None)
test_util.result()
def test_where_are_you_from():
test_util.start('Testing errors1.where_are_you_from...')
test_util.assertion(
where_are_you_from(NLP('I am Taiwan.')).has_error, True, 'I am Taiwan')
test_util.assertion(
where_are_you_from(NLP('I am from Taiwan.')).has_error, False,
'I am from Taiwan')
test_util.result()
def test_he_is_a_job():
test_util.start('Testing HeIsAJob match...')
pattern = HeIsAJob()
test_util.assertion(pattern.match(NLP('He is a cook')), True, None)
test_util.assertion(pattern.match(NLP('He is a taxi driver.')), True, None)
test_util.assertion(pattern.match(NLP('She is a policeman')), False, None)
test_util.assertion(pattern.match(NLP('He is a Cook')), False, None)
test_util.result()
def test_no_he_is_not():
test_util.start('Testing NoHeIsNot match...')
pattern = NoHeIsNot()
test_util.assertion(pattern.match(NLP('No')), True, None)
test_util.assertion(pattern.match(NLP('No, he is not.')), True, None)
test_util.assertion(pattern.match(NLP("No, he isn't")), True, None)
test_util.assertion(pattern.match(NLP('Yes he is.')), False, None)
test_util.result()
def test_capitalized_cook():
test_util.start('Testing CapitalizedCook...')
ep = CapitalizedCook()
test_util.assertion(
ep.match(NLP('He is a cook')).has_error, False, 'He is a cook')
test_util.assertion(
ep.match(NLP('He is a Cook')).has_error, True, 'He is a Cook')
test_util.result()
def test_capitalized_doctor():
test_util.start('Testing CapitalizedDoctor...')
ep = CapitalizedDoctor()
test_util.assertion(
ep.match(NLP('She is a doctor')).has_error, False, 'She is a doctor')
test_util.assertion(
ep.match(NLP('She is a Doctor')).has_error, True, 'She is a Doctor')
test_util.result()
def test_he_is_a_cook():
test_util.start('Testing SheIsADoctor match...')
pattern = HeIsACook()
test_util.assertion(pattern.match(NLP('He is a cook')), True, None)
test_util.assertion(pattern.match(NLP('He is a cook.')), True, None)
test_util.assertion(pattern.match(NLP('She is a cook')), False, None)
test_util.assertion(pattern.match(NLP('He is a Cook')), False, None)
test_util.result()
def test_she_is_a_doctor_match():
test_util.start('Testing SheIsADoctor match...')
pattern = SheIsADoctor()
test_util.assertion(pattern.match(NLP('She is a doctor')), True, None)
test_util.assertion(pattern.match(NLP('She is a doctor.')), True, None)
test_util.assertion(pattern.match(NLP('He is a doctor')), False, None)
test_util.assertion(pattern.match(NLP('She is a Doctor')), False, None)
test_util.result()
def test_polarity_mismatch():
test_util.start('Testing PolarityMismatch...')
ep = PolarityMismatch()
test_util.assertion(ep.match(NLP('Yes, he is')).has_error, False, None)
test_util.assertion(ep.match(NLP('No, he is not')).has_error, False, None)
test_util.assertion(ep.match(NLP("Yes, he isn't")).has_error, True, None)
test_util.assertion(ep.match(NLP('No, he is.')).has_error, True, None)
test_util.result()
def test_wrong_determiner():
test_util.start('Testing WrongDeterminer...')
ep = WrongDeterminer()
test_util.assertion(ep.match(NLP('He is an doctor')).has_error, True, None)
test_util.assertion(ep.match(NLP('He is a apple')).has_error, True, None)
test_util.assertion(ep.match(NLP('He is an apple')).has_error, False, None)
test_util.assertion(ep.match(NLP('He is a doctor')).has_error, False, None)
test_util.result()
def test_sentence_starts_lower():
test_util.start('Testing SentenceStartsLower...')
ep = SentenceStartsLower()
test_util.assertion(ep.match(NLP('hi')).has_error, True, 'hi')
test_util.assertion(ep.match(NLP('Hi')).has_error, False, 'Hi')
test_util.assertion(ep.match(NLP('i am Tim')).has_error, True, 'i am Tim')
test_util.assertion(
ep.match(NLP('She is a cook')).has_error, False, 'She is a cook')
test_util.result()
def test_missing_determiner():
test_util.start('Testing MissingDeterminer...')
ep = MissingDeterminer()
test_util.assertion(ep.match(NLP('He is doctor')).has_error, True, None)
test_util.assertion(
ep.match(NLP('I have cold')).has_error, True, 'I have cold')
test_util.assertion(ep.match(NLP('He is a doctor')).has_error, False, None)
test_util.assertion(ep.match(NLP('Yes, he is')).has_error, False, None)
test_util.result()
def test_match_what_grade_are_you_in_response():
matches = [
(NLP('I am in the 7th grade.'), '7th'),
(NLP("I'm in the eighth grade."), 'eighth'),
(NLP('The Ninth grade.'), 'Ninth'),
]
non_matches = [NLP('I am in the eight grade.')]
test_util.test_matches(matches, non_matches,
match_what_grade_are_you_in_response)
def test_wrong_job_2():
test_util.start('Testing WrongJob2...')
ep = WrongJob('cook')
test_util.assertion(
ep.match(NLP('He is a driver')).has_error, True, 'driver')
test_util.assertion(ep.match(NLP('He is a cook')).has_error, False, 'cook')
test_util.assertion(
ep.match(NLP('He is a teacher')).has_error, True, 'teacher')
test_util.result()
def test_missing_verb():
test_util.start('Testing MissingVerb...')
ep = MissingVerb()
test_util.assertion(ep.match(NLP('He a doctor')).has_error, True, None)
test_util.assertion(ep.match(NLP('Happy')).has_error, False, None)
test_util.assertion(ep.match(NLP('I happy')).has_error, True, None)
test_util.assertion(
ep.match(NLP('She is a doctor')).has_error, False, None)
test_util.assertion(ep.match(NLP('She a doctor.')).has_error, True, None)
test_util.result()
def test_capitalized_father():
test_util.start('Testing CapitalizedFather...')
ep = CapitalizedFather()
test_util.assertion(
ep.match(NLP('My father is a cook')).has_error, False,
'My father is a cook')
test_util.assertion(
ep.match(NLP('My Father is a cook')).has_error, True,
'My Father is a cook')
test_util.result()
def test_wrong_job_1():
test_util.start('Testing WrongJob1...')
ep = WrongJob('doctor')
test_util.assertion(
ep.match(NLP('She is a driver')).has_error, True, 'driver')
test_util.assertion(
ep.match(NLP('She is a doctor')).has_error, False, 'doctor')
test_util.assertion(
ep.match(NLP('She is a teacher')).has_error, True, 'teacher')
test_util.result()
def test_my_mother_is_a_job():
test_util.start('Testing MyMotherIsAJob match...')
pattern = MyMotherIsAJob()
test_util.assertion(pattern.match(NLP('My mother is a cook')), True,
'My mother is a cook')
test_util.assertion(pattern.match(NLP('My mother is a taxi driver.')),
True, 'My mother is a taxi driver.')
test_util.assertion(pattern.match(NLP('My Mother is a policeman')), False,
'My Mother is a policeman')
test_util.assertion(pattern.match(NLP('My mother is a Cook')), False,
'My mother is a Cook')
test_util.result()
def test_wrong_subject_is_3():
test_util.start('Testing WrongSubjectIs3...')
ep = WrongSubjectIs('She')
test_util.assertion(
ep.match(NLP('He is a nurse')).has_error, True, 'He is a nurse')
test_util.assertion(
ep.match(NLP('It is a nurse')).has_error, True, 'It is a nurse')
test_util.assertion(
ep.match(NLP('This is a nurse')).has_error, True, 'This is a nurse')
test_util.assertion(
ep.match(NLP('That is a nurse')).has_error, True, 'That is a nurse')
test_util.assertion(
ep.match(NLP('She is a nurse')).has_error, False, 'She is a nurse')
test_util.result()
def match_how_are_you_response(user_input):
"""Match user input pattern for task 1.3.
PATTERN MATCHED:
BOT: How are you today?
USR: [options]
I am {state}(, thank you|thanks)
I'm {state}(, thank you|thanks)
{State}(, thank you|thanks)
I have a cold
Not bad
Not too bad
I feel happy
[/options]
INFO RETURNED:
The user's state.
ISSUES:
- There is a lot of potential variation in the state.
Therefore make this a regex group, extract it, and process
it separately.
Args:
user_input: the user input (SpaCy doc).
Returns:
models.Match object.
"""
match = False
info = None
r1 = r"^(I'm |I am )?" \
r"(very |not )?" \
r"(?P<state>([A-Z]{1}[a-z]*)|[a-z-]*)" \
r"(, thank you|, thanks)?(.)?$"
r2 = r'^I have (?P<state>(a cold|the flu))(.)?$'
cold_flu = ['a cold', 'the flu']
pattern_match1 = re.match(r1, user_input.text)
pattern_match2 = re.match(r2, user_input.text)
if pattern_match1:
modifier = pattern_match1.group(2)
state = pattern_match1.group(3)
# state should be an adjective
info_tok = NLP(state)[0]
if info_tok.tag_ in pos.ADJECTIVES:
match = True
info = state
if modifier:
info = modifier + state
elif pattern_match2:
state = pattern_match2.group(1)
if state in cold_flu:
match = True
info = state
# if we don't have a match thus far... try some more complicated methods.
if not match:
match, info = how_are_you_dep_match(user_input)
# NOTE: these nested if blocks suck. Wrap these as subfunctions and
# return for failing conditions. Cleaner that way...
return models.Match(user_input, match, info)
def test_what_is_your_name():
test_util.start('Testing errors1.what_is_your_name...')
assert not what_is_your_name(NLP('I am Tim')).has_error
assert what_is_your_name(NLP('I am tim')).has_error
assert what_is_your_name(NLP('He is Tim')).has_error
test_util.assertion(what_is_your_name(NLP('Hi')).has_error, True, 'Hi')
test_util.assertion(what_is_your_name(NLP('hi')).has_error, True, 'hi')
test_util.assertion(what_is_your_name(NLP('Tim')).has_error, False, 'Tim')
test_util.assertion(what_is_your_name(NLP('tim')).has_error, True, 'tim')
test_util.result()
def test_match_how_old_are_you_response():
matches = [
(NLP('I am eight years old'), 'eight'), (NLP("I'm 9 years old"), '9'),
(NLP('I am thirteen'), 'thirteen'), (NLP("I'm twenty"), 'twenty'),
(NLP('Seven years old'), 'Seven'), (NLP('11'), '11')
]
non_matches = []
test_util.test_matches(matches, non_matches,
match_how_old_are_you_response)
def test_match_where_are_your_from_response():
matches = [(NLP('I am from New Zealand'), 'New Zealand'),
(NLP("I'm from Taiwan."), 'Taiwan'), (NLP('Iran.'), 'Iran')]
non_matches = [
NLP('I am an elephant'),
NLP('I am Taiwan.'),
NLP('I am from taiwan')
]
test_util.test_matches(matches, non_matches,
match_where_are_you_from_response)
def what_is_your_name(user_input):
"""
Error set:
1: He is Sina. [wrong subject]
2: invalid name
3: I am tim. [Non-capital name]
"""
# 1
incorrect_subjects = ['You', 'He', 'She', 'This']
head = next(t for t in user_input if t.head == t)
if user_input[0].text in incorrect_subjects:
return models.ErrorResult(
True, 'You must give me YOUR name. Start with "I"...')
# 2
if user_input.text in common.INVALID_NAMES:
return models.ErrorResult(True, 'That is not a name.')
# 3
if len(user_input) == 1 and user_input[0].is_lower:
return models.ErrorResult(
True, 'Your name must start with a big letter: '
'"Steve" and not "steve".')
if head.lemma_ == 'be':
right_children = list(head.rights)
if len(right_children) >= 1:
name = right_children[0] # first is all we want
# 2
if name.text in common.INVALID_NAMES:
return models.ErrorResult(True, 'That is not a name.')
# 3
if name.is_lower:
if name.pos_ == 'NOUN':
return models.ErrorResult(
True, 'Your name must start with a big letter: '
'"Steve" and not "steve".')
if name.pos_ == 'ADJ':
temp = NLP(name.text)
if temp[0].pos_ == 'NOUN':
return models.ErrorResult(
True, 'Your name must start with a big letter: '
'"Steve" and not "steve".')
return models.ErrorResult(False)
def match_how_old_are_you_response(user_input):
"""Match user input pattern for task 1.5.
PATTERN MATCHES:
BOT: How old are you?
USR: [options]
I am {age} years old
I'm {age} years old
I am {age}
I'm {age}
{age} years old
{age}
[/options]
INFO RETURNED:
The user's age.
ISSUES:
- The age could be given as text or a number.
Args:
user_input: the user input (SpaCy doc).
Returns:
models.Match object.
"""
r = r'^' \
r"(I am |I'm )?" \
r"%s" % common_regex.NUMBER + \
r"( years old)?" \
r'(.)?' \
r'$'
match = False
info = None
pattern_match = re.match(r, user_input.text)
if pattern_match:
info = pattern_match.group('number')
age_match = common_regex.match_number(NLP(info))
match = age_match.match
return models.Match(user_input, match, info)
