def test_min_max_scaler_transform_variable(data):
x, expect_x_scaled = data
xvar = Variable(x)
scaler = MinMaxScaler()
scaler.fit(xvar)
x_scaled = scaler.transform(xvar)
assert isinstance(x_scaled, Variable)
assert numpy.allclose(x_scaled.array, expect_x_scaled)
def test_min_max_scaler_transform_zero_max():
x = numpy.array([[0, 2], [0, 2], [0, 2]], dtype=numpy.float32)
expect_x_scaled = numpy.array([[0, 0], [0, 0], [0, 0]],
dtype=numpy.float32)
scaler = MinMaxScaler()
scaler.fit(x)
x_scaled = scaler.transform(x)
# print('min', scaler.min, 'max', scaler.max)
assert numpy.allclose(x_scaled, expect_x_scaled)
def test_min_max_scaler_inverse_transform(data, indices):
x, expect_x_scaled = data
scaler = MinMaxScaler()
scaler.fit(x, indices=indices)
x_inverse = scaler.inverse_transform(expect_x_scaled)
if indices is None:
indices = numpy.arange(x.shape[1])
for index in range(x.shape[1]):
if index in indices:
assert numpy.allclose(x_inverse[:, index], x[:, index])
else:
assert numpy.allclose(x_inverse[:, index], expect_x_scaled[:,
index])
def test_min_max_scaler_serialize(tmpdir, data, indices):
x, expect_x_scaled = data
scaler = MinMaxScaler()
scaler.fit(x, indices=indices)
scaler_filepath = os.path.join(str(tmpdir), 'scaler.npz')
serializers.save_npz(scaler_filepath, scaler)
scaler2 = MinMaxScaler()
serializers.load_npz(scaler_filepath, scaler2)
# print('scaler2 attribs:', scaler2.min, scaler2.max, scaler2.indices)
assert numpy.allclose(scaler.min, scaler2.min)
assert numpy.allclose(scaler.max, scaler2.max)
assert scaler.indices == scaler2.indices
def test_min_max_scaler_transform(data, indices):
x, expect_x_scaled = data
scaler = MinMaxScaler()
scaler.fit(x, indices=indices)
x_scaled = scaler.transform(x)
if indices is None:
indices = numpy.arange(x.shape[1])
numpy.allclose(scaler.max, numpy.array([0.4, 30, 0.3])[indices])
numpy.allclose(scaler.min, numpy.array([-0.3, -40, 0])[indices])
for index in range(x.shape[1]):
if index in indices:
assert numpy.allclose(x_scaled[:, index], expect_x_scaled[:,
index])
else:
assert numpy.allclose(x_scaled[:, index], x[:, index])
def test_min_max_scaler_transform_gpu(data):
x, expect_x_scaled = data
scaler = MinMaxScaler()
scaler.to_gpu()
x = cuda.to_gpu(x)
scaler.fit(x)
x_scaled = scaler.transform(x)
assert isinstance(x_scaled, cuda.cupy.ndarray)
assert numpy.allclose(cuda.to_cpu(x_scaled), expect_x_scaled)
def test_min_max_scaler_3darray(data, axis):
x, expect_x_scaled = data
s0, s1 = x.shape
if axis == 1:
# feature axis is 1, insert other axis to 2nd axis
x = numpy.broadcast_to(x[:, :, None], (s0, s1, 2))
expect_x_scaled = numpy.broadcast_to(expect_x_scaled[:, :, None],
(s0, s1, 2))
elif axis == 2:
# feature axis is 2, insert other axis to 1st axis
x = numpy.broadcast_to(x[:, None, :], (s0, 3, s1))
expect_x_scaled = numpy.broadcast_to(expect_x_scaled[:, None, :],
(s0, 3, s1))
assert x.ndim == 3
indices = None
scaler = MinMaxScaler()
scaler.fit(x, indices=indices, axis=axis)
x_scaled = scaler.transform(x, axis=axis)
assert x_scaled.shape == expect_x_scaled.shape
assert numpy.allclose(x_scaled, expect_x_scaled, atol=1e-7)
x_inverse = scaler.inverse_transform(expect_x_scaled, axis=axis)
for index in numpy.arange(x.shape[1]):
assert numpy.allclose(x_inverse[:, index], x[:, index], atol=1e-7)
def test_min_max_scaler_assert_raises():
x = numpy.array([[0.1, 0.2, 0.3], [0.5, 0.3, 0.1]], dtype=numpy.float32)
scaler = MinMaxScaler()
# call transform before fit raises error
with pytest.raises(AttributeError):
scaler.transform(x)
with pytest.raises(AttributeError):
scaler.inverse_transform(x)
def test_min_max_scaler_fit_transform(data):
x, expect_x_scaled = data
scaler = MinMaxScaler()
x_scaled = scaler.fit_transform(x)
assert numpy.allclose(x_scaled, expect_x_scaled)
