def ezensemble(X_train, y_train):
a = list(X_train)
ee = EasyEnsemble(random_state=0, n_subsets=10)
ee.fit(X_train, y_train)
X_resampled, y_resampled = ee.fit_sample(X_train, y_train)
X_resampled = pd.DataFrame(X_resampled[1], columns=a)
y_resampled = pd.DataFrame(y_resampled[1], columns=['Target'])
return X_resampled, y_resampled
def test_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
ee = EasyEnsemble(random_state=RND_SEED)
ee.fit(X, Y)
assert_raises(RuntimeError, ee.sample, np.random.random((100, 40)),
np.array([0] * 50 + [1] * 50))
def test_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
ee = EasyEnsemble(random_state=RND_SEED)
ee.fit(X, Y)
assert_raises(RuntimeError, ee.sample, np.random.random((100, 40)),
np.array([0] * 50 + [1] * 50))
def test_ee_fit():
"""Test the fitting method"""
# Define the parameter for the under-sampling
ratio = 'auto'
# Create the object
ee = EasyEnsemble(ratio=ratio, random_state=RND_SEED)
# Fit the data
ee.fit(X, Y)
# Check if the data information have been computed
assert_equal(ee.min_c_, 0)
assert_equal(ee.maj_c_, 1)
assert_equal(ee.stats_c_[0], 500)
assert_equal(ee.stats_c_[1], 4500)
def test_ee_fit():
"""Test the fitting method"""
# Define the parameter for the under-sampling
ratio = 'auto'
# Create the object
ee = EasyEnsemble(ratio=ratio, random_state=RND_SEED)
# Fit the data
ee.fit(X, Y)
# Check if the data information have been computed
assert_equal(ee.min_c_, 0)
assert_equal(ee.maj_c_, 1)
assert_equal(ee.stats_c_[0], 500)
assert_equal(ee.stats_c_[1], 4500)
def get_batch(self, tokenized_samples, labels):
e = EasyEnsemble(random_state=0, n_subsets=1)
e.fit(tokenized_samples, labels)
X_resampled, y_resampled = e.sample(tokenized_samples, labels)
X = X_resampled[0]
y = y_resampled[0]
targets = np.zeros(shape=(len(X), self._num_labels))
samples = np.zeros(shape=(len(X), self._max_document_length))
for sample_ix, sample in enumerate(X):
label = y[sample_ix]
targets[sample_ix, label] = 1
samples[sample_ix, :sample.shape[0]] = \
sample[:self._max_document_length]
return samples, targets
