def test_one_hot(self):
enc = encoders.OneHotEncoder(verbose=1, return_df=False)
enc.fit(X)
self.assertEqual(
enc.transform(X_t).shape[1],
enc.transform(X).shape[1],
'We have to get the same count of columns despite the presence of a new value'
)
enc = encoders.OneHotEncoder(verbose=1,
return_df=True,
handle_unknown='indicator')
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('extra_-1', out.columns.values)
enc = encoders.OneHotEncoder(verbose=1,
return_df=True,
handle_unknown='return_nan')
enc.fit(X)
out = enc.transform(X_t)
self.assertEqual(
len([x for x in out.columns.values
if str(x).startswith('extra_')]), 3)
enc = encoders.OneHotEncoder(verbose=1,
return_df=True,
handle_unknown='error')
# The exception is already raised in fit() because transform() is called there to get
# feature_names right.
enc.fit(X)
with self.assertRaises(ValueError):
enc.transform(X_t)
enc = encoders.OneHotEncoder(verbose=1,
return_df=True,
handle_unknown='return_nan',
use_cat_names=True)
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('extra_A', out.columns.values)
enc = encoders.OneHotEncoder(verbose=1,
return_df=True,
use_cat_names=True,
handle_unknown='indicator')
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('extra_-1', out.columns.values)
# test inverse_transform
X_i = th.create_dataset(n_rows=100, has_missing=False)
X_i_t = th.create_dataset(n_rows=50, has_missing=False)
cols = ['underscore', 'none', 'extra', 321, 'categorical']
enc = encoders.OneHotEncoder(verbose=1, use_cat_names=True, cols=cols)
enc.fit(X_i)
obtained = enc.inverse_transform(enc.transform(X_i_t))
th.verify_inverse_transform(X_i_t, obtained)
def test_inverse_transform(self):
# we do not allow None in these data (but "none" column without any None is ok)
X = th.create_dataset(n_rows=100, has_none=False)
X_t = th.create_dataset(n_rows=50, has_none=False)
X_t_extra = th.create_dataset(n_rows=50, extras=True, has_none=False)
cols = ['underscore', 'none', 'extra', 321, 'categorical']
for encoder_name in ['BaseNEncoder', 'BinaryEncoder', 'OneHotEncoder', 'OrdinalEncoder']:
with self.subTest(encoder_name=encoder_name):
# simple run
enc = getattr(encoders, encoder_name)(verbose=1, cols=cols)
enc.fit(X)
th.verify_inverse_transform(X_t, enc.inverse_transform(enc.transform(X_t)))
