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from nose.tools import assert_equal
import numpy as np
from sklearn.utils import testing
from sklearn.preprocessing import FunctionTransformer
def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
def _func(X, *args, **kwargs):
args_store.append(X)
args_store.extend(args)
kwargs_store.update(kwargs)
return func(X)
return _func
def test_delegate_to_func():
# (args|kwargs)_store will hold the positional and keyword arguments
# passed to the function inside the FunctionTransformer.
args_store = []
kwargs_store = {}
X = np.arange(10).reshape((5, 2))
testing.assert_array_equal(
FunctionTransformer(_make_func(args_store, kwargs_store)).transform(X),
X,
'transform should have returned X unchanged',
)
# The function should only have received X.
assert_equal(
args_store,
[X],
'Incorrect positional arguments passed to func: {args}'.format(
args=args_store,
),
)
assert_equal(
kwargs_store,
{},
'Unexpected keyword arguments passed to func: {args}'.format(
args=kwargs_store,
),
)
# reset the argument stores.
args_store[:] = [] # python2 compatible inplace list clear.
kwargs_store.clear()
y = object()
testing.assert_array_equal(
FunctionTransformer(
_make_func(args_store, kwargs_store),
pass_y=True,
).transform(X, y),
X,
'transform should have returned X unchanged',
)
# The function should have received X and y.
assert_equal(
args_store,
[X, y],
'Incorrect positional arguments passed to func: {args}'.format(
args=args_store,
),
)
assert_equal(
kwargs_store,
{},
'Unexpected keyword arguments passed to func: {args}'.format(
args=kwargs_store,
),
)
def test_np_log():
X = np.arange(10).reshape((5, 2))
# Test that the numpy.log example still works.
testing.assert_array_equal(
FunctionTransformer(np.log1p).transform(X),
np.log1p(X),
)
def test_kw_arg():
X = np.linspace(0, 1, num=10).reshape((5, 2))
F = FunctionTransformer(np.around, kw_args=dict(decimals=3))
# Test that rounding is correct
testing.assert_array_equal(F.transform(X),
np.around(X, decimals=3))
def test_kw_arg_update():
X = np.linspace(0, 1, num=10).reshape((5, 2))
F = FunctionTransformer(np.around, kw_args=dict(decimals=3))
F.kw_args['decimals'] = 1
# Test that rounding is correct
testing.assert_array_equal(F.transform(X),
np.around(X, decimals=1))
def test_kw_arg_reset():
X = np.linspace(0, 1, num=10).reshape((5, 2))
F = FunctionTransformer(np.around, kw_args=dict(decimals=3))
F.kw_args = dict(decimals=1)
# Test that rounding is correct
testing.assert_array_equal(F.transform(X),
np.around(X, decimals=1))
def test_inverse_transform():
X = np.array([1, 4, 9, 16]).reshape((2, 2))
# Test that inverse_transform works correctly
F = FunctionTransformer(
func=np.sqrt,
inverse_func=np.around, inv_kw_args=dict(decimals=3))
testing.assert_array_equal(
F.inverse_transform(F.transform(X)),
np.around(np.sqrt(X), decimals=3))
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