def test_sparse():
copy = CopyTransformer()
tfidf = TfidfTransformer()
X_t = tfidf.fit_transform([[1, 2, 3]])
assert issparse(X_t)
X_dense = copy.transform(X_t).toarray()
expect = np.array([[0.26726124, 0.53452248, 0.80178373]])
assert np.allclose(X_dense, expect)
def test_sparse():
copy = CopyTransformer()
tfidf = TfidfTransformer()
X_t = tfidf.fit_transform([[1, 2, 3]])
assert issparse(X_t)
X_dense = copy.transform(X_t).toarray()
expect = np.array([[0.26726124, 0.53452248, 0.80178373]])
assert np.allclose(X_dense, expect)
def test_pipeline():
param_grid = [{'logisticregression__C': [1, 0.1, 10]}]
pipe = make_pipeline(StandardScaler(),
CopyTransformer(),
LogisticRegression())
grid = GridSearchCV(pipe, param_grid, cv=3, n_jobs=1)
grid.fit(X, y)
def test_pipeline():
param_grid = [{'logisticregression__C': [1, 0.1, 10]}]
pipe = make_pipeline(
StandardScaler(), CopyTransformer(),
LogisticRegression(solver='liblinear', multi_class='ovr'))
grid = GridSearchCV(pipe, param_grid, cv=3, n_jobs=1, iid=False)
grid.fit(X, y)
def test_copy_failtype():
copy = CopyTransformer()
expect = ("X must be a list or NumPy array or SciPy sparse array."
" Found <class 'int'>")
if sys.version_info < (3, 0):
expect = expect.replace('class', 'type')
assert_raises(ValueError, expect, copy.transform, 1)
def test_copy():
copy = CopyTransformer()
np.testing.assert_array_equal(X, copy.transform(X))
def test_copy():
copy = CopyTransformer()
np.testing.assert_array_equal(X, copy.transform(X))
from mlxtend.preprocessing import CopyTransformer
trans = CopyTransformer()
meta = {
'id': 'raw1',
'name': 'Raw Features',
'description': (
"Just copy raw features to comply with "
"this preprocessing framewortk."),
'keywords': ['CopyTransformer', 'mlxtend'],
'feature_names_prefix': 'raw'
}