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_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))
