def test_tfidf_with_all_datasets(self):
should_succeed = ["movies"]
should_fail = ["irisArr", "irisDf", "digits", "housing", "creditG", "drugRev"]
for name in should_succeed:
dataset = getattr(self, f"_{name}")
TfidfVectorizer.validate_schema(**dataset)
for name in should_fail:
dataset = getattr(self, f"_{name}")
with self.assertRaises(ValueError):
TfidfVectorizer.validate_schema(**dataset)
def test_more_hyperparam_values(self):
with self.assertRaises(jsonschema.ValidationError):
tf_idf = TfidfVectorizer(max_df=2.5, min_df=2,
max_features=1000,
stop_words='english')
with self.assertRaises(jsonschema.ValidationError):
tf_idf = TfidfVectorizer(max_df=2, min_df=2,
max_features=1000,
stop_words=['I', 'we', 'not', 'this', 'that'],
analyzer = 'char')
def test_tfidf_with_all_datasets(self):
should_succeed = ['movies']
should_fail = ['irisArr', 'irisDf', 'digits', 'housing', 'creditG', 'drugRev']
for name in should_succeed:
dataset = getattr(self, f'_{name}')
TfidfVectorizer.validate_schema(**dataset)
for name in should_fail:
dataset = getattr(self, f'_{name}')
with self.assertRaises(ValueError):
TfidfVectorizer.validate_schema(**dataset)
def test_more_hyperparam_values(self):
with EnableSchemaValidation():
with self.assertRaises(jsonschema.ValidationError):
_ = TfidfVectorizer(
max_df=2.5, min_df=2, max_features=1000, stop_words="english"
)
with self.assertRaises(jsonschema.ValidationError):
_ = TfidfVectorizer(
max_df=2,
min_df=2,
max_features=1000,
stop_words=["I", "we", "not", "this", "that"],
analyzer="char",
)
def test_non_null_tokenizer(self):
# tokenize the doc and lemmatize its tokens
def my_tokenizer():
return 'abc'
with self.assertRaises(jsonschema.ValidationError):
tf_idf = TfidfVectorizer(max_df=2, min_df=2,
max_features=1000,
stop_words='english',
tokenizer = my_tokenizer,
analyzer = 'char')
def test_non_null_tokenizer(self):
# tokenize the doc and lemmatize its tokens
def my_tokenizer():
return "abc"
with EnableSchemaValidation():
with self.assertRaises(jsonschema.ValidationError):
_ = TfidfVectorizer(
max_df=2,
min_df=2,
max_features=1000,
stop_words="english",
tokenizer=my_tokenizer,
analyzer="char",
)