1071 lines
38 KiB
Python
1071 lines
38 KiB
Python
import warnings
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import numpy as np
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import numpy.linalg as la
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from scipy import sparse
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from distutils.version import LooseVersion
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from sklearn.utils.testing import assert_almost_equal, clean_warning_registry
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_greater_equal
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from sklearn.utils.testing import assert_less_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_raises_regex
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_false
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from sklearn.utils.testing import assert_warns_message
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from sklearn.utils.testing import assert_no_warnings
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from sklearn.utils.sparsefuncs import mean_variance_axis
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from sklearn.preprocessing.data import _transform_selected
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from sklearn.preprocessing.data import Binarizer
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from sklearn.preprocessing.data import KernelCenterer
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from sklearn.preprocessing.data import Normalizer
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from sklearn.preprocessing.data import normalize
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from sklearn.preprocessing.data import OneHotEncoder
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from sklearn.preprocessing.data import StandardScaler
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from sklearn.preprocessing.data import scale
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from sklearn.preprocessing.data import MinMaxScaler
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from sklearn.preprocessing.data import minmax_scale
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from sklearn.preprocessing.data import MaxAbsScaler
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from sklearn.preprocessing.data import maxabs_scale
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from sklearn.preprocessing.data import RobustScaler
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from sklearn.preprocessing.data import robust_scale
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from sklearn.preprocessing.data import add_dummy_feature
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from sklearn.preprocessing.data import PolynomialFeatures
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from sklearn.utils.validation import DataConversionWarning
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from sklearn import datasets
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iris = datasets.load_iris()
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def toarray(a):
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if hasattr(a, "toarray"):
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a = a.toarray()
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return a
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def test_polynomial_features():
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# Test Polynomial Features
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X1 = np.arange(6)[:, np.newaxis]
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P1 = np.hstack([np.ones_like(X1),
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X1, X1 ** 2, X1 ** 3])
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deg1 = 3
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X2 = np.arange(6).reshape((3, 2))
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x1 = X2[:, :1]
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x2 = X2[:, 1:]
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P2 = np.hstack([x1 ** 0 * x2 ** 0,
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x1 ** 1 * x2 ** 0,
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x1 ** 0 * x2 ** 1,
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x1 ** 2 * x2 ** 0,
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x1 ** 1 * x2 ** 1,
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x1 ** 0 * x2 ** 2])
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deg2 = 2
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for (deg, X, P) in [(deg1, X1, P1), (deg2, X2, P2)]:
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P_test = PolynomialFeatures(deg, include_bias=True).fit_transform(X)
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assert_array_almost_equal(P_test, P)
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P_test = PolynomialFeatures(deg, include_bias=False).fit_transform(X)
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assert_array_almost_equal(P_test, P[:, 1:])
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interact = PolynomialFeatures(2, interaction_only=True, include_bias=True)
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X_poly = interact.fit_transform(X)
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assert_array_almost_equal(X_poly, P2[:, [0, 1, 2, 4]])
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def test_scaler_1d():
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# Test scaling of dataset along single axis
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rng = np.random.RandomState(0)
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X = rng.randn(5)
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X_orig_copy = X.copy()
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scaler = StandardScaler()
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X_scaled = scaler.fit(X).transform(X, copy=False)
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assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
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# check inverse transform
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X_scaled_back = scaler.inverse_transform(X_scaled)
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assert_array_almost_equal(X_scaled_back, X_orig_copy)
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# Test with 1D list
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X = [0., 1., 2, 0.4, 1.]
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scaler = StandardScaler()
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X_scaled = scaler.fit(X).transform(X, copy=False)
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assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
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X_scaled = scale(X)
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assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
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X = np.ones(5)
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assert_array_equal(scale(X, with_mean=False), X)
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def test_standard_scaler_numerical_stability():
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"""Test numerical stability of scaling"""
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# np.log(1e-5) is taken because of its floating point representation
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# was empirically found to cause numerical problems with np.mean & np.std.
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x = np.zeros(8, dtype=np.float64) + np.log(1e-5, dtype=np.float64)
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if LooseVersion(np.__version__) >= LooseVersion('1.9'):
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# This does not raise a warning as the number of samples is too low
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# to trigger the problem in recent numpy
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x_scaled = assert_no_warnings(scale, x)
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assert_array_almost_equal(scale(x), np.zeros(8))
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else:
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w = "standard deviation of the data is probably very close to 0"
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x_scaled = assert_warns_message(UserWarning, w, scale, x)
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assert_array_almost_equal(x_scaled, np.zeros(8))
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# with 2 more samples, the std computation run into numerical issues:
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x = np.zeros(10, dtype=np.float64) + np.log(1e-5, dtype=np.float64)
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w = "standard deviation of the data is probably very close to 0"
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x_scaled = assert_warns_message(UserWarning, w, scale, x)
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assert_array_almost_equal(x_scaled, np.zeros(10))
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x = np.ones(10, dtype=np.float64) * 1e-100
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x_small_scaled = assert_no_warnings(scale, x)
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assert_array_almost_equal(x_small_scaled, np.zeros(10))
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# Large values can cause (often recoverable) numerical stability issues:
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x_big = np.ones(10, dtype=np.float64) * 1e100
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w = "Dataset may contain too large values"
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x_big_scaled = assert_warns_message(UserWarning, w, scale, x_big)
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assert_array_almost_equal(x_big_scaled, np.zeros(10))
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assert_array_almost_equal(x_big_scaled, x_small_scaled)
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x_big_centered = assert_warns_message(UserWarning, w, scale, x_big,
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with_std=False)
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assert_array_almost_equal(x_big_centered, np.zeros(10))
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assert_array_almost_equal(x_big_centered, x_small_scaled)
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def test_scaler_2d_arrays():
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# Test scaling of 2d array along first axis
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rng = np.random.RandomState(0)
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X = rng.randn(4, 5)
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X[:, 0] = 0.0 # first feature is always of zero
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scaler = StandardScaler()
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X_scaled = scaler.fit(X).transform(X, copy=True)
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assert_false(np.any(np.isnan(X_scaled)))
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assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has been copied
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assert_true(X_scaled is not X)
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# check inverse transform
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X_scaled_back = scaler.inverse_transform(X_scaled)
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assert_true(X_scaled_back is not X)
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assert_true(X_scaled_back is not X_scaled)
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assert_array_almost_equal(X_scaled_back, X)
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X_scaled = scale(X, axis=1, with_std=False)
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assert_false(np.any(np.isnan(X_scaled)))
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assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
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X_scaled = scale(X, axis=1, with_std=True)
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assert_false(np.any(np.isnan(X_scaled)))
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assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=1), 4 * [1.0])
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# Check that the data hasn't been modified
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assert_true(X_scaled is not X)
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X_scaled = scaler.fit(X).transform(X, copy=False)
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assert_false(np.any(np.isnan(X_scaled)))
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assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has not been copied
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assert_true(X_scaled is X)
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X = rng.randn(4, 5)
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X[:, 0] = 1.0 # first feature is a constant, non zero feature
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scaler = StandardScaler()
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X_scaled = scaler.fit(X).transform(X, copy=True)
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assert_false(np.any(np.isnan(X_scaled)))
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assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has not been copied
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assert_true(X_scaled is not X)
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def test_min_max_scaler_iris():
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X = iris.data
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scaler = MinMaxScaler()
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# default params
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X_trans = scaler.fit_transform(X)
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assert_array_almost_equal(X_trans.min(axis=0), 0)
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assert_array_almost_equal(X_trans.min(axis=0), 0)
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assert_array_almost_equal(X_trans.max(axis=0), 1)
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X_trans_inv = scaler.inverse_transform(X_trans)
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assert_array_almost_equal(X, X_trans_inv)
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# not default params: min=1, max=2
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scaler = MinMaxScaler(feature_range=(1, 2))
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X_trans = scaler.fit_transform(X)
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assert_array_almost_equal(X_trans.min(axis=0), 1)
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assert_array_almost_equal(X_trans.max(axis=0), 2)
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X_trans_inv = scaler.inverse_transform(X_trans)
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assert_array_almost_equal(X, X_trans_inv)
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# min=-.5, max=.6
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scaler = MinMaxScaler(feature_range=(-.5, .6))
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X_trans = scaler.fit_transform(X)
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assert_array_almost_equal(X_trans.min(axis=0), -.5)
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assert_array_almost_equal(X_trans.max(axis=0), .6)
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X_trans_inv = scaler.inverse_transform(X_trans)
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assert_array_almost_equal(X, X_trans_inv)
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# raises on invalid range
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scaler = MinMaxScaler(feature_range=(2, 1))
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assert_raises(ValueError, scaler.fit, X)
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def test_min_max_scaler_zero_variance_features():
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# Check min max scaler on toy data with zero variance features
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X = [[0., 1., +0.5],
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[0., 1., -0.1],
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[0., 1., +1.1]]
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X_new = [[+0., 2., 0.5],
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[-1., 1., 0.0],
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[+0., 1., 1.5]]
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# default params
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scaler = MinMaxScaler()
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X_trans = scaler.fit_transform(X)
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X_expected_0_1 = [[0., 0., 0.5],
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[0., 0., 0.0],
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[0., 0., 1.0]]
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assert_array_almost_equal(X_trans, X_expected_0_1)
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X_trans_inv = scaler.inverse_transform(X_trans)
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assert_array_almost_equal(X, X_trans_inv)
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X_trans_new = scaler.transform(X_new)
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X_expected_0_1_new = [[+0., 1., 0.500],
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[-1., 0., 0.083],
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[+0., 0., 1.333]]
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assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)
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# not default params
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scaler = MinMaxScaler(feature_range=(1, 2))
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X_trans = scaler.fit_transform(X)
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X_expected_1_2 = [[1., 1., 1.5],
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[1., 1., 1.0],
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[1., 1., 2.0]]
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assert_array_almost_equal(X_trans, X_expected_1_2)
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# function interface
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X_trans = minmax_scale(X)
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assert_array_almost_equal(X_trans, X_expected_0_1)
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X_trans = minmax_scale(X, feature_range=(1, 2))
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assert_array_almost_equal(X_trans, X_expected_1_2)
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def test_minmax_scale_axis1():
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X = iris.data
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X_trans = minmax_scale(X, axis=1)
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assert_array_almost_equal(np.min(X_trans, axis=1), 0)
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assert_array_almost_equal(np.max(X_trans, axis=1), 1)
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def test_min_max_scaler_1d():
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# Test scaling of dataset along single axis
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rng = np.random.RandomState(0)
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X = rng.randn(5)
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X_orig_copy = X.copy()
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scaler = MinMaxScaler()
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X_scaled = scaler.fit(X).transform(X)
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assert_array_almost_equal(X_scaled.min(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.max(axis=0), 1.0)
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# check inverse transform
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X_scaled_back = scaler.inverse_transform(X_scaled)
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assert_array_almost_equal(X_scaled_back, X_orig_copy)
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# Test with 1D list
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X = [0., 1., 2, 0.4, 1.]
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scaler = MinMaxScaler()
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X_scaled = scaler.fit(X).transform(X)
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assert_array_almost_equal(X_scaled.min(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.max(axis=0), 1.0)
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# Constant feature.
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X = np.zeros(5)
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scaler = MinMaxScaler()
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X_scaled = scaler.fit(X).transform(X)
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assert_greater_equal(X_scaled.min(), 0.)
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assert_less_equal(X_scaled.max(), 1.)
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def test_scaler_without_centering():
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rng = np.random.RandomState(42)
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X = rng.randn(4, 5)
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X[:, 0] = 0.0 # first feature is always of zero
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X_csr = sparse.csr_matrix(X)
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X_csc = sparse.csc_matrix(X)
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assert_raises(ValueError, StandardScaler().fit, X_csr)
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null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
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X_null = null_transform.fit_transform(X_csr)
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assert_array_equal(X_null.data, X_csr.data)
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X_orig = null_transform.inverse_transform(X_null)
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assert_array_equal(X_orig.data, X_csr.data)
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scaler = StandardScaler(with_mean=False).fit(X)
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X_scaled = scaler.transform(X, copy=True)
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assert_false(np.any(np.isnan(X_scaled)))
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scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
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X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
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assert_false(np.any(np.isnan(X_csr_scaled.data)))
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scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
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X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
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assert_false(np.any(np.isnan(X_csc_scaled.data)))
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assert_equal(scaler.mean_, scaler_csr.mean_)
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assert_array_almost_equal(scaler.std_, scaler_csr.std_)
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assert_equal(scaler.mean_, scaler_csc.mean_)
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assert_array_almost_equal(scaler.std_, scaler_csc.std_)
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assert_array_almost_equal(
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X_scaled.mean(axis=0), [0., -0.01, 2.24, -0.35, -0.78], 2)
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis(X_csr_scaled, 0)
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assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
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assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
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# Check that X has not been modified (copy)
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assert_true(X_scaled is not X)
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assert_true(X_csr_scaled is not X_csr)
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X_scaled_back = scaler.inverse_transform(X_scaled)
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assert_true(X_scaled_back is not X)
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assert_true(X_scaled_back is not X_scaled)
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assert_array_almost_equal(X_scaled_back, X)
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X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
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assert_true(X_csr_scaled_back is not X_csr)
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assert_true(X_csr_scaled_back is not X_csr_scaled)
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assert_array_almost_equal(X_csr_scaled_back.toarray(), X)
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X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
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assert_true(X_csc_scaled_back is not X_csc)
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assert_true(X_csc_scaled_back is not X_csc_scaled)
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assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
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def test_scaler_int():
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# test that scaler converts integer input to floating
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# for both sparse and dense matrices
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rng = np.random.RandomState(42)
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X = rng.randint(20, size=(4, 5))
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X[:, 0] = 0 # first feature is always of zero
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X_csr = sparse.csr_matrix(X)
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X_csc = sparse.csc_matrix(X)
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null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
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clean_warning_registry()
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with warnings.catch_warnings(record=True):
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X_null = null_transform.fit_transform(X_csr)
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assert_array_equal(X_null.data, X_csr.data)
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X_orig = null_transform.inverse_transform(X_null)
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assert_array_equal(X_orig.data, X_csr.data)
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clean_warning_registry()
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with warnings.catch_warnings(record=True):
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scaler = StandardScaler(with_mean=False).fit(X)
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X_scaled = scaler.transform(X, copy=True)
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assert_false(np.any(np.isnan(X_scaled)))
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clean_warning_registry()
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with warnings.catch_warnings(record=True):
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scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
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X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
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assert_false(np.any(np.isnan(X_csr_scaled.data)))
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clean_warning_registry()
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with warnings.catch_warnings(record=True):
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scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
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X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
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assert_false(np.any(np.isnan(X_csc_scaled.data)))
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assert_equal(scaler.mean_, scaler_csr.mean_)
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assert_array_almost_equal(scaler.std_, scaler_csr.std_)
|
|
|
|
assert_equal(scaler.mean_, scaler_csc.mean_)
|
|
assert_array_almost_equal(scaler.std_, scaler_csc.std_)
|
|
|
|
assert_array_almost_equal(
|
|
X_scaled.mean(axis=0),
|
|
[0., 1.109, 1.856, 21., 1.559], 2)
|
|
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
|
|
|
|
X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis(
|
|
X_csr_scaled.astype(np.float), 0)
|
|
assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
|
|
assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
|
|
|
|
# Check that X has not been modified (copy)
|
|
assert_true(X_scaled is not X)
|
|
assert_true(X_csr_scaled is not X_csr)
|
|
|
|
X_scaled_back = scaler.inverse_transform(X_scaled)
|
|
assert_true(X_scaled_back is not X)
|
|
assert_true(X_scaled_back is not X_scaled)
|
|
assert_array_almost_equal(X_scaled_back, X)
|
|
|
|
X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
|
|
assert_true(X_csr_scaled_back is not X_csr)
|
|
assert_true(X_csr_scaled_back is not X_csr_scaled)
|
|
assert_array_almost_equal(X_csr_scaled_back.toarray(), X)
|
|
|
|
X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
|
|
assert_true(X_csc_scaled_back is not X_csc)
|
|
assert_true(X_csc_scaled_back is not X_csc_scaled)
|
|
assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
|
|
|
|
|
|
def test_scaler_without_copy():
|
|
# Check that StandardScaler.fit does not change input
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randn(4, 5)
|
|
X[:, 0] = 0.0 # first feature is always of zero
|
|
X_csr = sparse.csr_matrix(X)
|
|
|
|
X_copy = X.copy()
|
|
StandardScaler(copy=False).fit(X)
|
|
assert_array_equal(X, X_copy)
|
|
|
|
X_csr_copy = X_csr.copy()
|
|
StandardScaler(with_mean=False, copy=False).fit(X_csr)
|
|
assert_array_equal(X_csr.toarray(), X_csr_copy.toarray())
|
|
|
|
|
|
def test_scale_sparse_with_mean_raise_exception():
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randn(4, 5)
|
|
X_csr = sparse.csr_matrix(X)
|
|
|
|
# check scaling and fit with direct calls on sparse data
|
|
assert_raises(ValueError, scale, X_csr, with_mean=True)
|
|
assert_raises(ValueError, StandardScaler(with_mean=True).fit, X_csr)
|
|
|
|
# check transform and inverse_transform after a fit on a dense array
|
|
scaler = StandardScaler(with_mean=True).fit(X)
|
|
assert_raises(ValueError, scaler.transform, X_csr)
|
|
|
|
X_transformed_csr = sparse.csr_matrix(scaler.transform(X))
|
|
assert_raises(ValueError, scaler.inverse_transform, X_transformed_csr)
|
|
|
|
|
|
def test_scale_input_finiteness_validation():
|
|
# Check if non finite inputs raise ValueError
|
|
X = [np.nan, 5, 6, 7, 8]
|
|
assert_raises_regex(ValueError,
|
|
"Input contains NaN, infinity or a value too large",
|
|
scale, X)
|
|
|
|
X = [np.inf, 5, 6, 7, 8]
|
|
assert_raises_regex(ValueError,
|
|
"Input contains NaN, infinity or a value too large",
|
|
scale, X)
|
|
|
|
|
|
def test_scale_function_without_centering():
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randn(4, 5)
|
|
X[:, 0] = 0.0 # first feature is always of zero
|
|
X_csr = sparse.csr_matrix(X)
|
|
|
|
X_scaled = scale(X, with_mean=False)
|
|
assert_false(np.any(np.isnan(X_scaled)))
|
|
|
|
X_csr_scaled = scale(X_csr, with_mean=False)
|
|
assert_false(np.any(np.isnan(X_csr_scaled.data)))
|
|
|
|
# test csc has same outcome
|
|
X_csc_scaled = scale(X_csr.tocsc(), with_mean=False)
|
|
assert_array_almost_equal(X_scaled, X_csc_scaled.toarray())
|
|
|
|
# raises value error on axis != 0
|
|
assert_raises(ValueError, scale, X_csr, with_mean=False, axis=1)
|
|
|
|
assert_array_almost_equal(X_scaled.mean(axis=0),
|
|
[0., -0.01, 2.24, -0.35, -0.78], 2)
|
|
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
|
|
# Check that X has not been copied
|
|
assert_true(X_scaled is not X)
|
|
|
|
X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis(X_csr_scaled, 0)
|
|
assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
|
|
assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
|
|
|
|
|
|
def test_robust_scaler_2d_arrays():
|
|
"""Test robust scaling of 2d array along first axis"""
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(4, 5)
|
|
X[:, 0] = 0.0 # first feature is always of zero
|
|
|
|
scaler = RobustScaler()
|
|
X_scaled = scaler.fit(X).transform(X)
|
|
|
|
assert_array_almost_equal(np.median(X_scaled, axis=0), 5 * [0.0])
|
|
assert_array_almost_equal(X_scaled.std(axis=0)[0], 0)
|
|
|
|
|
|
def test_robust_scaler_iris():
|
|
X = iris.data
|
|
scaler = RobustScaler()
|
|
X_trans = scaler.fit_transform(X)
|
|
assert_array_almost_equal(np.median(X_trans, axis=0), 0)
|
|
X_trans_inv = scaler.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv)
|
|
q = np.percentile(X_trans, q=(25, 75), axis=0)
|
|
iqr = q[1] - q[0]
|
|
assert_array_almost_equal(iqr, 1)
|
|
|
|
|
|
def test_robust_scale_axis1():
|
|
X = iris.data
|
|
X_trans = robust_scale(X, axis=1)
|
|
assert_array_almost_equal(np.median(X_trans, axis=1), 0)
|
|
q = np.percentile(X_trans, q=(25, 75), axis=1)
|
|
iqr = q[1] - q[0]
|
|
assert_array_almost_equal(iqr, 1)
|
|
|
|
|
|
def test_robust_scaler_zero_variance_features():
|
|
"""Check RobustScaler on toy data with zero variance features"""
|
|
X = [[0., 1., +0.5],
|
|
[0., 1., -0.1],
|
|
[0., 1., +1.1]]
|
|
|
|
scaler = RobustScaler()
|
|
X_trans = scaler.fit_transform(X)
|
|
|
|
# NOTE: for such a small sample size, what we expect in the third column
|
|
# depends HEAVILY on the method used to calculate quantiles. The values
|
|
# here were calculated to fit the quantiles produces by np.percentile
|
|
# using numpy 1.9 Calculating quantiles with
|
|
# scipy.stats.mstats.scoreatquantile or scipy.stats.mstats.mquantiles
|
|
# would yield very different results!
|
|
X_expected = [[0., 0., +0.0],
|
|
[0., 0., -1.0],
|
|
[0., 0., +1.0]]
|
|
assert_array_almost_equal(X_trans, X_expected)
|
|
X_trans_inv = scaler.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv)
|
|
|
|
# make sure new data gets transformed correctly
|
|
X_new = [[+0., 2., 0.5],
|
|
[-1., 1., 0.0],
|
|
[+0., 1., 1.5]]
|
|
X_trans_new = scaler.transform(X_new)
|
|
X_expected_new = [[+0., 1., +0.],
|
|
[-1., 0., -0.83333],
|
|
[+0., 0., +1.66667]]
|
|
assert_array_almost_equal(X_trans_new, X_expected_new, decimal=3)
|
|
|
|
|
|
def test_maxabs_scaler_zero_variance_features():
|
|
"""Check MaxAbsScaler on toy data with zero variance features"""
|
|
X = [[0., 1., +0.5],
|
|
[0., 1., -0.3],
|
|
[0., 1., +1.5],
|
|
[0., 0., +0.0]]
|
|
|
|
scaler = MaxAbsScaler()
|
|
X_trans = scaler.fit_transform(X)
|
|
X_expected = [[0., 1., 1.0 / 3.0],
|
|
[0., 1., -0.2],
|
|
[0., 1., 1.0],
|
|
[0., 0., 0.0]]
|
|
assert_array_almost_equal(X_trans, X_expected)
|
|
X_trans_inv = scaler.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv)
|
|
|
|
# make sure new data gets transformed correctly
|
|
X_new = [[+0., 2., 0.5],
|
|
[-1., 1., 0.0],
|
|
[+0., 1., 1.5]]
|
|
X_trans_new = scaler.transform(X_new)
|
|
X_expected_new = [[+0., 2.0, 1.0 / 3.0],
|
|
[-1., 1.0, 0.0],
|
|
[+0., 1.0, 1.0]]
|
|
|
|
assert_array_almost_equal(X_trans_new, X_expected_new, decimal=2)
|
|
|
|
# sparse data
|
|
X_csr = sparse.csr_matrix(X)
|
|
X_trans = scaler.fit_transform(X_csr)
|
|
X_expected = [[0., 1., 1.0 / 3.0],
|
|
[0., 1., -0.2],
|
|
[0., 1., 1.0],
|
|
[0., 0., 0.0]]
|
|
assert_array_almost_equal(X_trans.A, X_expected)
|
|
X_trans_inv = scaler.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv.A)
|
|
|
|
|
|
def test_maxabs_scaler_large_negative_value():
|
|
"""Check MaxAbsScaler on toy data with a large negative value"""
|
|
X = [[0., 1., +0.5, -1.0],
|
|
[0., 1., -0.3, -0.5],
|
|
[0., 1., -100.0, 0.0],
|
|
[0., 0., +0.0, -2.0]]
|
|
|
|
scaler = MaxAbsScaler()
|
|
X_trans = scaler.fit_transform(X)
|
|
X_expected = [[0., 1., 0.005, -0.5],
|
|
[0., 1., -0.003, -0.25],
|
|
[0., 1., -1.0, 0.0],
|
|
[0., 0., 0.0, -1.0]]
|
|
assert_array_almost_equal(X_trans, X_expected)
|
|
|
|
|
|
def test_warning_scaling_integers():
|
|
# Check warning when scaling integer data
|
|
X = np.array([[1, 2, 0],
|
|
[0, 0, 0]], dtype=np.uint8)
|
|
|
|
w = "Data with input dtype uint8 was converted to float64"
|
|
|
|
clean_warning_registry()
|
|
assert_warns_message(DataConversionWarning, w, scale, X)
|
|
assert_warns_message(DataConversionWarning, w, StandardScaler().fit, X)
|
|
assert_warns_message(DataConversionWarning, w, MinMaxScaler().fit, X)
|
|
|
|
|
|
def test_normalizer_l1():
|
|
rng = np.random.RandomState(0)
|
|
X_dense = rng.randn(4, 5)
|
|
X_sparse_unpruned = sparse.csr_matrix(X_dense)
|
|
|
|
# set the row number 3 to zero
|
|
X_dense[3, :] = 0.0
|
|
|
|
# set the row number 3 to zero without pruning (can happen in real life)
|
|
indptr_3 = X_sparse_unpruned.indptr[3]
|
|
indptr_4 = X_sparse_unpruned.indptr[4]
|
|
X_sparse_unpruned.data[indptr_3:indptr_4] = 0.0
|
|
|
|
# build the pruned variant using the regular constructor
|
|
X_sparse_pruned = sparse.csr_matrix(X_dense)
|
|
|
|
# check inputs that support the no-copy optim
|
|
for X in (X_dense, X_sparse_pruned, X_sparse_unpruned):
|
|
|
|
normalizer = Normalizer(norm='l1', copy=True)
|
|
X_norm = normalizer.transform(X)
|
|
assert_true(X_norm is not X)
|
|
X_norm1 = toarray(X_norm)
|
|
|
|
normalizer = Normalizer(norm='l1', copy=False)
|
|
X_norm = normalizer.transform(X)
|
|
assert_true(X_norm is X)
|
|
X_norm2 = toarray(X_norm)
|
|
|
|
for X_norm in (X_norm1, X_norm2):
|
|
row_sums = np.abs(X_norm).sum(axis=1)
|
|
for i in range(3):
|
|
assert_almost_equal(row_sums[i], 1.0)
|
|
assert_almost_equal(row_sums[3], 0.0)
|
|
|
|
# check input for which copy=False won't prevent a copy
|
|
for init in (sparse.coo_matrix, sparse.csc_matrix, sparse.lil_matrix):
|
|
X = init(X_dense)
|
|
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
|
|
|
|
assert_true(X_norm is not X)
|
|
assert_true(isinstance(X_norm, sparse.csr_matrix))
|
|
|
|
X_norm = toarray(X_norm)
|
|
for i in range(3):
|
|
assert_almost_equal(row_sums[i], 1.0)
|
|
assert_almost_equal(la.norm(X_norm[3]), 0.0)
|
|
|
|
|
|
def test_normalizer_l2():
|
|
rng = np.random.RandomState(0)
|
|
X_dense = rng.randn(4, 5)
|
|
X_sparse_unpruned = sparse.csr_matrix(X_dense)
|
|
|
|
# set the row number 3 to zero
|
|
X_dense[3, :] = 0.0
|
|
|
|
# set the row number 3 to zero without pruning (can happen in real life)
|
|
indptr_3 = X_sparse_unpruned.indptr[3]
|
|
indptr_4 = X_sparse_unpruned.indptr[4]
|
|
X_sparse_unpruned.data[indptr_3:indptr_4] = 0.0
|
|
|
|
# build the pruned variant using the regular constructor
|
|
X_sparse_pruned = sparse.csr_matrix(X_dense)
|
|
|
|
# check inputs that support the no-copy optim
|
|
for X in (X_dense, X_sparse_pruned, X_sparse_unpruned):
|
|
|
|
normalizer = Normalizer(norm='l2', copy=True)
|
|
X_norm1 = normalizer.transform(X)
|
|
assert_true(X_norm1 is not X)
|
|
X_norm1 = toarray(X_norm1)
|
|
|
|
normalizer = Normalizer(norm='l2', copy=False)
|
|
X_norm2 = normalizer.transform(X)
|
|
assert_true(X_norm2 is X)
|
|
X_norm2 = toarray(X_norm2)
|
|
|
|
for X_norm in (X_norm1, X_norm2):
|
|
for i in range(3):
|
|
assert_almost_equal(la.norm(X_norm[i]), 1.0)
|
|
assert_almost_equal(la.norm(X_norm[3]), 0.0)
|
|
|
|
# check input for which copy=False won't prevent a copy
|
|
for init in (sparse.coo_matrix, sparse.csc_matrix, sparse.lil_matrix):
|
|
X = init(X_dense)
|
|
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
|
|
|
|
assert_true(X_norm is not X)
|
|
assert_true(isinstance(X_norm, sparse.csr_matrix))
|
|
|
|
X_norm = toarray(X_norm)
|
|
for i in range(3):
|
|
assert_almost_equal(la.norm(X_norm[i]), 1.0)
|
|
assert_almost_equal(la.norm(X_norm[3]), 0.0)
|
|
|
|
|
|
def test_normalizer_max():
|
|
rng = np.random.RandomState(0)
|
|
X_dense = rng.randn(4, 5)
|
|
X_sparse_unpruned = sparse.csr_matrix(X_dense)
|
|
|
|
# set the row number 3 to zero
|
|
X_dense[3, :] = 0.0
|
|
|
|
# set the row number 3 to zero without pruning (can happen in real life)
|
|
indptr_3 = X_sparse_unpruned.indptr[3]
|
|
indptr_4 = X_sparse_unpruned.indptr[4]
|
|
X_sparse_unpruned.data[indptr_3:indptr_4] = 0.0
|
|
|
|
# build the pruned variant using the regular constructor
|
|
X_sparse_pruned = sparse.csr_matrix(X_dense)
|
|
|
|
# check inputs that support the no-copy optim
|
|
for X in (X_dense, X_sparse_pruned, X_sparse_unpruned):
|
|
|
|
normalizer = Normalizer(norm='max', copy=True)
|
|
X_norm1 = normalizer.transform(X)
|
|
assert_true(X_norm1 is not X)
|
|
X_norm1 = toarray(X_norm1)
|
|
|
|
normalizer = Normalizer(norm='max', copy=False)
|
|
X_norm2 = normalizer.transform(X)
|
|
assert_true(X_norm2 is X)
|
|
X_norm2 = toarray(X_norm2)
|
|
|
|
for X_norm in (X_norm1, X_norm2):
|
|
row_maxs = X_norm.max(axis=1)
|
|
for i in range(3):
|
|
assert_almost_equal(row_maxs[i], 1.0)
|
|
assert_almost_equal(row_maxs[3], 0.0)
|
|
|
|
# check input for which copy=False won't prevent a copy
|
|
for init in (sparse.coo_matrix, sparse.csc_matrix, sparse.lil_matrix):
|
|
X = init(X_dense)
|
|
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
|
|
|
|
assert_true(X_norm is not X)
|
|
assert_true(isinstance(X_norm, sparse.csr_matrix))
|
|
|
|
X_norm = toarray(X_norm)
|
|
for i in range(3):
|
|
assert_almost_equal(row_maxs[i], 1.0)
|
|
assert_almost_equal(la.norm(X_norm[3]), 0.0)
|
|
|
|
|
|
def test_normalize():
|
|
# Test normalize function
|
|
# Only tests functionality not used by the tests for Normalizer.
|
|
X = np.random.RandomState(37).randn(3, 2)
|
|
assert_array_equal(normalize(X, copy=False),
|
|
normalize(X.T, axis=0, copy=False).T)
|
|
assert_raises(ValueError, normalize, [[0]], axis=2)
|
|
assert_raises(ValueError, normalize, [[0]], norm='l3')
|
|
|
|
|
|
def test_binarizer():
|
|
X_ = np.array([[1, 0, 5], [2, 3, -1]])
|
|
|
|
for init in (np.array, list, sparse.csr_matrix, sparse.csc_matrix):
|
|
|
|
X = init(X_.copy())
|
|
|
|
binarizer = Binarizer(threshold=2.0, copy=True)
|
|
X_bin = toarray(binarizer.transform(X))
|
|
assert_equal(np.sum(X_bin == 0), 4)
|
|
assert_equal(np.sum(X_bin == 1), 2)
|
|
X_bin = binarizer.transform(X)
|
|
assert_equal(sparse.issparse(X), sparse.issparse(X_bin))
|
|
|
|
binarizer = Binarizer(copy=True).fit(X)
|
|
X_bin = toarray(binarizer.transform(X))
|
|
assert_true(X_bin is not X)
|
|
assert_equal(np.sum(X_bin == 0), 2)
|
|
assert_equal(np.sum(X_bin == 1), 4)
|
|
|
|
binarizer = Binarizer(copy=True)
|
|
X_bin = binarizer.transform(X)
|
|
assert_true(X_bin is not X)
|
|
X_bin = toarray(X_bin)
|
|
assert_equal(np.sum(X_bin == 0), 2)
|
|
assert_equal(np.sum(X_bin == 1), 4)
|
|
|
|
binarizer = Binarizer(copy=False)
|
|
X_bin = binarizer.transform(X)
|
|
if init is not list:
|
|
assert_true(X_bin is X)
|
|
X_bin = toarray(X_bin)
|
|
assert_equal(np.sum(X_bin == 0), 2)
|
|
assert_equal(np.sum(X_bin == 1), 4)
|
|
|
|
binarizer = Binarizer(threshold=-0.5, copy=True)
|
|
for init in (np.array, list):
|
|
X = init(X_.copy())
|
|
|
|
X_bin = toarray(binarizer.transform(X))
|
|
assert_equal(np.sum(X_bin == 0), 1)
|
|
assert_equal(np.sum(X_bin == 1), 5)
|
|
X_bin = binarizer.transform(X)
|
|
|
|
# Cannot use threshold < 0 for sparse
|
|
assert_raises(ValueError, binarizer.transform, sparse.csc_matrix(X))
|
|
|
|
|
|
def test_center_kernel():
|
|
# Test that KernelCenterer is equivalent to StandardScaler
|
|
# in feature space
|
|
rng = np.random.RandomState(0)
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|
X_fit = rng.random_sample((5, 4))
|
|
scaler = StandardScaler(with_std=False)
|
|
scaler.fit(X_fit)
|
|
X_fit_centered = scaler.transform(X_fit)
|
|
K_fit = np.dot(X_fit, X_fit.T)
|
|
|
|
# center fit time matrix
|
|
centerer = KernelCenterer()
|
|
K_fit_centered = np.dot(X_fit_centered, X_fit_centered.T)
|
|
K_fit_centered2 = centerer.fit_transform(K_fit)
|
|
assert_array_almost_equal(K_fit_centered, K_fit_centered2)
|
|
|
|
# center predict time matrix
|
|
X_pred = rng.random_sample((2, 4))
|
|
K_pred = np.dot(X_pred, X_fit.T)
|
|
X_pred_centered = scaler.transform(X_pred)
|
|
K_pred_centered = np.dot(X_pred_centered, X_fit_centered.T)
|
|
K_pred_centered2 = centerer.transform(K_pred)
|
|
assert_array_almost_equal(K_pred_centered, K_pred_centered2)
|
|
|
|
|
|
def test_fit_transform():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
for obj in ((StandardScaler(), Normalizer(), Binarizer())):
|
|
X_transformed = obj.fit(X).transform(X)
|
|
X_transformed2 = obj.fit_transform(X)
|
|
assert_array_equal(X_transformed, X_transformed2)
|
|
|
|
|
|
def test_add_dummy_feature():
|
|
X = [[1, 0], [0, 1], [0, 1]]
|
|
X = add_dummy_feature(X)
|
|
assert_array_equal(X, [[1, 1, 0], [1, 0, 1], [1, 0, 1]])
|
|
|
|
|
|
def test_add_dummy_feature_coo():
|
|
X = sparse.coo_matrix([[1, 0], [0, 1], [0, 1]])
|
|
X = add_dummy_feature(X)
|
|
assert_true(sparse.isspmatrix_coo(X), X)
|
|
assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]])
|
|
|
|
|
|
def test_add_dummy_feature_csc():
|
|
X = sparse.csc_matrix([[1, 0], [0, 1], [0, 1]])
|
|
X = add_dummy_feature(X)
|
|
assert_true(sparse.isspmatrix_csc(X), X)
|
|
assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]])
|
|
|
|
|
|
def test_add_dummy_feature_csr():
|
|
X = sparse.csr_matrix([[1, 0], [0, 1], [0, 1]])
|
|
X = add_dummy_feature(X)
|
|
assert_true(sparse.isspmatrix_csr(X), X)
|
|
assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]])
|
|
|
|
|
|
def test_one_hot_encoder_sparse():
|
|
# Test OneHotEncoder's fit and transform.
|
|
X = [[3, 2, 1], [0, 1, 1]]
|
|
enc = OneHotEncoder()
|
|
# discover max values automatically
|
|
X_trans = enc.fit_transform(X).toarray()
|
|
assert_equal(X_trans.shape, (2, 5))
|
|
assert_array_equal(enc.active_features_,
|
|
np.where([1, 0, 0, 1, 0, 1, 1, 0, 1])[0])
|
|
assert_array_equal(enc.feature_indices_, [0, 4, 7, 9])
|
|
|
|
# check outcome
|
|
assert_array_equal(X_trans,
|
|
[[0., 1., 0., 1., 1.],
|
|
[1., 0., 1., 0., 1.]])
|
|
|
|
# max value given as 3
|
|
enc = OneHotEncoder(n_values=4)
|
|
X_trans = enc.fit_transform(X)
|
|
assert_equal(X_trans.shape, (2, 4 * 3))
|
|
assert_array_equal(enc.feature_indices_, [0, 4, 8, 12])
|
|
|
|
# max value given per feature
|
|
enc = OneHotEncoder(n_values=[3, 2, 2])
|
|
X = [[1, 0, 1], [0, 1, 1]]
|
|
X_trans = enc.fit_transform(X)
|
|
assert_equal(X_trans.shape, (2, 3 + 2 + 2))
|
|
assert_array_equal(enc.n_values_, [3, 2, 2])
|
|
# check that testing with larger feature works:
|
|
X = np.array([[2, 0, 1], [0, 1, 1]])
|
|
enc.transform(X)
|
|
|
|
# test that an error is raised when out of bounds:
|
|
X_too_large = [[0, 2, 1], [0, 1, 1]]
|
|
assert_raises(ValueError, enc.transform, X_too_large)
|
|
assert_raises(ValueError, OneHotEncoder(n_values=2).fit_transform, X)
|
|
|
|
# test that error is raised when wrong number of features
|
|
assert_raises(ValueError, enc.transform, X[:, :-1])
|
|
# test that error is raised when wrong number of features in fit
|
|
# with prespecified n_values
|
|
assert_raises(ValueError, enc.fit, X[:, :-1])
|
|
# test exception on wrong init param
|
|
assert_raises(TypeError, OneHotEncoder(n_values=np.int).fit, X)
|
|
|
|
enc = OneHotEncoder()
|
|
# test negative input to fit
|
|
assert_raises(ValueError, enc.fit, [[0], [-1]])
|
|
|
|
# test negative input to transform
|
|
enc.fit([[0], [1]])
|
|
assert_raises(ValueError, enc.transform, [[0], [-1]])
|
|
|
|
|
|
def test_one_hot_encoder_dense():
|
|
# check for sparse=False
|
|
X = [[3, 2, 1], [0, 1, 1]]
|
|
enc = OneHotEncoder(sparse=False)
|
|
# discover max values automatically
|
|
X_trans = enc.fit_transform(X)
|
|
assert_equal(X_trans.shape, (2, 5))
|
|
assert_array_equal(enc.active_features_,
|
|
np.where([1, 0, 0, 1, 0, 1, 1, 0, 1])[0])
|
|
assert_array_equal(enc.feature_indices_, [0, 4, 7, 9])
|
|
|
|
# check outcome
|
|
assert_array_equal(X_trans,
|
|
np.array([[0., 1., 0., 1., 1.],
|
|
[1., 0., 1., 0., 1.]]))
|
|
|
|
|
|
def _check_transform_selected(X, X_expected, sel):
|
|
for M in (X, sparse.csr_matrix(X)):
|
|
Xtr = _transform_selected(M, Binarizer().transform, sel)
|
|
assert_array_equal(toarray(Xtr), X_expected)
|
|
|
|
|
|
def test_transform_selected():
|
|
X = [[3, 2, 1], [0, 1, 1]]
|
|
|
|
X_expected = [[1, 2, 1], [0, 1, 1]]
|
|
_check_transform_selected(X, X_expected, [0])
|
|
_check_transform_selected(X, X_expected, [True, False, False])
|
|
|
|
X_expected = [[1, 1, 1], [0, 1, 1]]
|
|
_check_transform_selected(X, X_expected, [0, 1, 2])
|
|
_check_transform_selected(X, X_expected, [True, True, True])
|
|
_check_transform_selected(X, X_expected, "all")
|
|
|
|
_check_transform_selected(X, X, [])
|
|
_check_transform_selected(X, X, [False, False, False])
|
|
|
|
|
|
def _run_one_hot(X, X2, cat):
|
|
enc = OneHotEncoder(categorical_features=cat)
|
|
Xtr = enc.fit_transform(X)
|
|
X2tr = enc.transform(X2)
|
|
return Xtr, X2tr
|
|
|
|
|
|
def _check_one_hot(X, X2, cat, n_features):
|
|
ind = np.where(cat)[0]
|
|
# With mask
|
|
A, B = _run_one_hot(X, X2, cat)
|
|
# With indices
|
|
C, D = _run_one_hot(X, X2, ind)
|
|
# Check shape
|
|
assert_equal(A.shape, (2, n_features))
|
|
assert_equal(B.shape, (1, n_features))
|
|
assert_equal(C.shape, (2, n_features))
|
|
assert_equal(D.shape, (1, n_features))
|
|
# Check that mask and indices give the same results
|
|
assert_array_equal(toarray(A), toarray(C))
|
|
assert_array_equal(toarray(B), toarray(D))
|
|
|
|
|
|
def test_one_hot_encoder_categorical_features():
|
|
X = np.array([[3, 2, 1], [0, 1, 1]])
|
|
X2 = np.array([[1, 1, 1]])
|
|
|
|
cat = [True, False, False]
|
|
_check_one_hot(X, X2, cat, 4)
|
|
|
|
# Edge case: all non-categorical
|
|
cat = [False, False, False]
|
|
_check_one_hot(X, X2, cat, 3)
|
|
|
|
# Edge case: all categorical
|
|
cat = [True, True, True]
|
|
_check_one_hot(X, X2, cat, 5)
|
|
|
|
|
|
def test_one_hot_encoder_unknown_transform():
|
|
X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
|
|
y = np.array([[4, 1, 1]])
|
|
|
|
# Test that one hot encoder raises error for unknown features
|
|
# present during transform.
|
|
oh = OneHotEncoder(handle_unknown='error')
|
|
oh.fit(X)
|
|
assert_raises(ValueError, oh.transform, y)
|
|
|
|
# Test the ignore option, ignores unknown features.
|
|
oh = OneHotEncoder(handle_unknown='ignore')
|
|
oh.fit(X)
|
|
assert_array_equal(
|
|
oh.transform(y).toarray(),
|
|
np.array([[0., 0., 0., 0., 1., 0., 0.]])
|
|
)
|
|
|
|
# Raise error if handle_unknown is neither ignore or error.
|
|
oh = OneHotEncoder(handle_unknown='42')
|
|
oh.fit(X)
|
|
assert_raises(ValueError, oh.transform, y)
|