def test_minmax_scaler_clip(setup, feature_range):
# test behaviour of the parameter 'clip' in MinMaxScaler
X = iris
scaler = MinMaxScaler(feature_range=feature_range, clip=True).fit(X)
X_min, X_max = mt.min(X, axis=0), mt.max(X, axis=0)
X_test = [mt.r_[X_min[:2] - 10, X_max[2:] + 10]]
X_transformed = scaler.transform(X_test)
assert_allclose(X_transformed, [[
feature_range[0], feature_range[0], feature_range[1], feature_range[1]
]])
def testMinmaxScalerClip(self):
for feature_range in [(0, 1), (-10, 10)]:
# test behaviour of the paramter 'clip' in MinMaxScaler
X = self.iris
scaler = MinMaxScaler(feature_range=feature_range,
clip=True).fit(X)
X_min, X_max = mt.min(X, axis=0), mt.max(X, axis=0)
X_test = [mt.r_[X_min[:2] - 10, X_max[2:] + 10]]
X_transformed = scaler.transform(X_test)
assert_allclose(X_transformed, [[
feature_range[0], feature_range[0], feature_range[1],
feature_range[1]
]])
def test_min_max_scaler_zero_variance_features(setup):
# Check min max scaler on toy data with zero variance features
X = [[0., 1., +0.5], [0., 1., -0.1], [0., 1., +1.1]]
X_new = [[+0., 2., 0.5], [-1., 1., 0.0], [+0., 1., 1.5]]
# default params
scaler = MinMaxScaler()
X_trans = scaler.fit_transform(X)
X_expected_0_1 = [[0., 0., 0.5], [0., 0., 0.0], [0., 0., 1.0]]
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
X_trans_new = scaler.transform(X_new)
X_expected_0_1_new = [[+0., 1., 0.500], [-1., 0., 0.083], [+0., 0., 1.333]]
assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)
# not default params
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
X_expected_1_2 = [[1., 1., 1.5], [1., 1., 1.0], [1., 1., 2.0]]
assert_array_almost_equal(X_trans, X_expected_1_2)
# function interface
X_trans = minmax_scale(X)
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans = minmax_scale(X, feature_range=(1, 2))
assert_array_almost_equal(X_trans, X_expected_1_2)
def test_min_max_scaler1d(setup):
X_list_1row = X_1row.to_numpy().tolist()
X_list_1col = X_1col.to_numpy().tolist()
# Test scaling of dataset along single axis
for X in [X_1row, X_1col, X_list_1row, X_list_1col]:
scaler = MinMaxScaler(copy=True)
X_scaled = scaler.fit(X).transform(X)
if isinstance(X, list):
X = mt.array(X) # cast only after scaling done
if _check_dim_1axis(X) == 1:
assert_array_almost_equal(X_scaled.min(axis=0),
mt.zeros(n_features))
assert_array_almost_equal(X_scaled.max(axis=0),
mt.zeros(n_features))
else:
assert_array_almost_equal(X_scaled.min(axis=0), .0)
assert_array_almost_equal(X_scaled.max(axis=0), 1.)
assert scaler.n_samples_seen_ == X.shape[0]
# check inverse transform
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_array_almost_equal(X_scaled_back, X)
# Constant feature
X = mt.ones((5, 1))
scaler = MinMaxScaler()
X_scaled = scaler.fit(X).transform(X)
assert X_scaled.min().to_numpy() >= 0.
assert X_scaled.max().to_numpy() <= 1.
assert scaler.n_samples_seen_ == X.shape[0]
# Function interface
X_1d = X_1row.ravel()
min_ = X_1d.min()
max_ = X_1d.max()
assert_array_almost_equal((X_1d - min_) / (max_ - min_),
minmax_scale(X_1d, copy=True))
def test_min_max_scaler_iris(setup):
X = iris
scaler = MinMaxScaler()
# default params
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), 0)
assert_array_almost_equal(X_trans.max(axis=0), 1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# not default params: min=1, max=2
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), 1)
assert_array_almost_equal(X_trans.max(axis=0), 2)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# min=-.5, max=.6
scaler = MinMaxScaler(feature_range=(-.5, .6))
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), -.5)
assert_array_almost_equal(X_trans.max(axis=0), .6)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# raises on invalid range
scaler = MinMaxScaler(feature_range=(2, 1))
with pytest.raises(ValueError):
scaler.fit(X)
def test_min_max_scaler_partial_fit(setup, chunk_size):
# Test if partial_fit run over many batches of size 1 and 50
# gives the same results as fit
X = X_2d
n = X.shape[0]
# Test mean at the end of the process
scaler_batch = MinMaxScaler().fit(X)
scaler_incr = MinMaxScaler()
for batch in gen_batches(n_samples, chunk_size):
scaler_incr = scaler_incr.partial_fit(X[batch])
assert_array_almost_equal(scaler_batch.data_min_, scaler_incr.data_min_)
assert_array_almost_equal(scaler_batch.data_max_, scaler_incr.data_max_)
assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_
assert_array_almost_equal(scaler_batch.data_range_,
scaler_incr.data_range_)
assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
assert_array_almost_equal(scaler_batch.min_, scaler_incr.min_)
# Test std after 1 step
batch0 = slice(0, chunk_size)
scaler_batch = MinMaxScaler().fit(X[batch0])
scaler_incr = MinMaxScaler().partial_fit(X[batch0])
assert_array_almost_equal(scaler_batch.data_min_, scaler_incr.data_min_)
assert_array_almost_equal(scaler_batch.data_max_, scaler_incr.data_max_)
assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_
assert_array_almost_equal(scaler_batch.data_range_,
scaler_incr.data_range_)
assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
assert_array_almost_equal(scaler_batch.min_, scaler_incr.min_)
# Test std until the end of partial fits, and
_ = MinMaxScaler().fit(X)
scaler_incr = MinMaxScaler() # Clean estimator
for i, batch in enumerate(gen_batches(n_samples, chunk_size)):
scaler_incr = scaler_incr.partial_fit(X[batch])
assert_correct_incr(i,
batch_start=batch.start,
batch_stop=batch.stop,
n=n,
chunk_size=chunk_size,
n_samples_seen=scaler_incr.n_samples_seen_)