def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
doc = nlp(ngrams.text())
entities = set()
for ent in doc.ents:
if not args or ent.label_.lower() in {x.lower() for x in args}:
entities.add(ent.text)
return entities
def test_parameterized_constructor():
ng = Ngrams("this is a test", 3)
grams1 = {"this", "is", "a", "test"}
grams2 = {"this is", "is a", "a test"}
grams3 = {"this is a", "is a test"}
assert ng == set.union(grams1, grams2, grams3)
assert ng[1] == grams1
assert ng[2] == grams2
assert ng[3] == grams3
assert ng.text() == "this is a test"
def test_parameterized_constructor():
ng = Ngrams('this is a test', 3)
grams1 = {'this', 'is', 'a', 'test'}
grams2 = {'this is', 'is a', 'a test'}
grams3 = {'this is a', 'is a test'}
assert ng == set.union(grams1, grams2, grams3)
assert ng[1] == grams1
assert ng[2] == grams2
assert ng[3] == grams3
assert ng.text() == 'this is a test'
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
results = self.analyzer.polarity_scores(ngrams.text())
print(results)
if "pos" in args[0].lower():
return results["pos"] > results["neu"] and results["pos"] > results["neg"]
elif "neg" in args[0].lower():
return results["neg"] > results["neu"] and results["neg"] > results["pos"]
elif "neu" in args[0].lower():
return results["neu"] > results["pos"] and results["neu"] > results["neg"]
else:
return results["pos"] > results["neu"] or results["neg"] > results["neu"]
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
results = self.analyzer.polarity_scores(ngrams.text())
print(results)
if 'pos' in args[0].lower():
return results['pos'] > results['neu'] and results['pos'] > results['neg']
elif 'neg' in args[0].lower():
return results['neg'] > results['neu'] and results['neg'] > results['pos']
elif 'neu' in args[0].lower():
return results['neu'] > results['pos'] and results['neu'] > results['neg']
else:
return results['pos'] > results['neu'] or results['neg'] > results['neu']
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
statement_only = 's' in args or 'state' in args or 'statement' in args
vars['__score__'] = 0.0
if '__previous_unx_response__' not in vars:
vars['__previous_unx_response__'] = 'Gotcha.'
if '__previous_unx_answer__' not in vars:
vars['__previous_unx_answer__'] = 'None'
is_question = self.question_natex.match(ngrams.text())
if is_question and statement_only:
return False
elif is_question:
if '_explained_stupidity_' in vars and vars[
'_explained_stupidity_'] == 'True':
options = {
'I\'m not sure.', 'I don\'t know.',
'I\'m not sure about that.', ''
} - {vars['__previous_unx_response__']}
question_response = random.choice(list(options))
vars['__previous_unx_answer__'] = question_response
vars['__response_prefix__'] = question_response
else:
vars['_explained_stupidity_'] = 'True'
vars['__response_prefix__'] = 'I\'m not sure.'
elif len(ngrams.text().split()) < 3 and len(args) == 0:
vars['__response_prefix__'] = ''
return True
else:
options = {'Yeah.', 'For sure.', 'Right.', 'Uh-huh.'
} - {vars['__previous_unx_response__']}
statement_response = random.choice(list(options))
if len(args) > 0:
statement_response = ', '.join([
arg
for arg in args if arg not in {'s', 'state', 'statement'}
]) + ', '
if args[0] == 'None':
statement_response = ''
vars['__previous_unx_response__'] = statement_response
vars['__response_prefix__'] = statement_response
return True
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
statement_only = "s" in args or "state" in args or "statement" in args
vars["__score__"] = 0.0
if "__previous_unx_response__" not in vars:
vars["__previous_unx_response__"] = "Gotcha."
if "__previous_unx_answer__" not in vars:
vars["__previous_unx_answer__"] = "None"
is_question = self.question_natex.match(ngrams.text())
if is_question and statement_only:
return False
elif is_question:
if "_explained_stupidity_" in vars and vars[
"_explained_stupidity_"] == "True":
options = {
"I'm not sure.", "I don't know.",
"I'm not sure about that.", ""
} - {vars["__previous_unx_response__"]}
question_response = random.choice(list(options))
vars["__previous_unx_answer__"] = question_response
vars["__response_prefix__"] = question_response
else:
vars["_explained_stupidity_"] = "True"
vars["__response_prefix__"] = "I'm not sure."
elif len(ngrams.text().split()) < 3 and len(args) == 0:
vars["__response_prefix__"] = ""
return True
else:
options = {"Yeah.", "For sure.", "Right.", "Uh-huh."
} - {vars["__previous_unx_response__"]}
statement_response = random.choice(list(options))
if len(args) > 0:
statement_response = ", ".join([
arg
for arg in args if arg not in {"s", "state", "statement"}
]) + ", "
if args[0] == "None":
statement_response = ""
vars["__previous_unx_response__"] = statement_response
vars["__response_prefix__"] = statement_response
return True
def test_default_constructor():
ng = Ngrams("this is a test sentence")
grams1 = {"this", "is", "a", "test", "sentence"}
grams2 = {"this is", "is a", "a test", "test sentence"}
grams3 = {"this is a", "is a test", "a test sentence"}
grams4 = {"this is a test", "is a test sentence"}
grams5 = {"this is a test sentence"}
assert ng == set.union(grams1, grams2, grams3, grams4, grams5)
assert ng[1] == grams1
assert ng[2] == grams2
assert ng[3] == grams3
assert ng[4] == grams4
assert ng[5] == grams5
assert ng.text() == "this is a test sentence"
def test_default_constructor():
ng = Ngrams("this is a test sentence")
grams1 = {'this', 'is', 'a', 'test', 'sentence'}
grams2 = {'this is', 'is a', 'a test', 'test sentence'}
grams3 = {'this is a', 'is a test', 'a test sentence'}
grams4 = {'this is a test', 'is a test sentence'}
grams5 = {'this is a test sentence'}
assert ng == set.union(grams1, grams2, grams3, grams4, grams5)
assert ng[1] == grams1
assert ng[2] == grams2
assert ng[3] == grams3
assert ng[4] == grams4
assert ng[5] == grams5
assert ng.text() == 'this is a test sentence'
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
for i, arg in enumerate(args):
if isinstance(arg, str) and arg.isnumeric():
threshold = float(arg)
del args[i]
break
else:
threshold = 0.0
user = nlp(ngrams.text())
dev = [nlp(arg) for arg in args]
similarity = max([self._similarity(user, x) for x in dev])
vars["__score__"] = similarity
if similarity < threshold:
return False
else:
return True
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
match = self.natex.match(ngrams.text(), vars=vars)
return bool(match)
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
if ngrams:
if len(ngrams.text().split()) <= int(args[0]):
return True
else:
return False
def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
doc = nlp(ngrams.text())
return {token.text for token in doc if token.pos_.lower() in {x.lower() for x in args}}
