def test_multinomial_nb(self):
model = hyperopt_estimator(
classifier=components.multinomial_nb('classifier'),
preprocessing=[],
algo=rand.suggest,
trial_timeout=5.0,
max_evals=5,
)
# Inputs for MultinomialNB must be non-negative
model.fit(np.abs(self.X_train), self.Y_train)
model.score(np.abs(self.X_test), self.Y_test)
def test_multinomial_nb(self):
model = hyperopt_estimator(
classifier=components.multinomial_nb('classifier'),
preprocessing=[],
algo=rand.suggest,
trial_timeout=5.0,
max_evals=5,
)
# Inputs for MultinomialNB must be non-negative
model.fit(np.abs(self.X_train), self.Y_train)
model.score(np.abs(self.X_test), self.Y_test)
def test_one_hot_encoder(self):
# requires a classifier that can handle sparse data
model = hyperopt_estimator(
classifier=components.multinomial_nb('classifier'),
preprocessing=[components.one_hot_encoder('preprocessing')],
algo=rand.suggest,
trial_timeout=5.0,
max_evals=5,
)
# Inputs for one_hot_encoder must be non-negative integers
model.fit(np.abs(np.round(self.X_test).astype(np.int)), self.Y_test)
model.score(np.abs(np.round(self.X_test).astype(np.int)), self.Y_test)
def test_one_hot_encoder(self):
# requires a classifier that can handle sparse data
model = hyperopt_estimator(
classifier=components.multinomial_nb('classifier'),
preprocessing=[components.one_hot_encoder('preprocessing')],
algo=rand.suggest,
trial_timeout=5.0,
max_evals=5,
)
# Inputs for one_hot_encoder must be non-negative integers
model.fit(np.abs(np.round(self.X_test).astype(np.int)), self.Y_test)
model.score(np.abs(np.round(self.X_test).astype(np.int)), self.Y_test)
def test_tfidf(self):
# requires a classifier that can handle sparse data
model = hyperopt_estimator(
classifier=components.multinomial_nb('classifier'),
preprocessing=[components.tfidf('preprocessing')],
algo=rand.suggest,
trial_timeout=5.0,
max_evals=5,
)
X = np.array([
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
])
Y = np.array([0, 1, 2, 0])
model.fit(X, Y)
model.score(X, Y)
def test_tfidf(self):
# requires a classifier that can handle sparse data
model = hyperopt_estimator(
classifier=components.multinomial_nb('classifier'),
preprocessing=[components.tfidf('preprocessing')],
algo=rand.suggest,
trial_timeout=5.0,
max_evals=5,
)
X = np.array([
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
])
Y = np.array([0, 1, 2, 0])
model.fit(X, Y)
model.score(X, Y)