2466 lines
92 KiB
Python
2466 lines
92 KiB
Python
# Authors:
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#
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# Giorgio Patrini
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#
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# License: BSD 3 clause
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from __future__ import division
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import warnings
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import itertools
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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, stats
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from scipy.sparse import random as sparse_random
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import pytest
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from sklearn.utils import gen_batches
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from sklearn.utils.testing import assert_raise_message
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import 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_array_less
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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_warns_message
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from sklearn.utils.testing import assert_no_warnings
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from sklearn.utils.testing import assert_allclose
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from sklearn.utils.testing import assert_allclose_dense_sparse
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from sklearn.utils.testing import skip_if_32bit
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from sklearn.utils.sparsefuncs import mean_variance_axis
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from sklearn.preprocessing.data import _handle_zeros_in_scale
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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 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 QuantileTransformer
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from sklearn.preprocessing.data import quantile_transform
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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.preprocessing.data import PowerTransformer
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from sklearn.preprocessing.data import power_transform
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from sklearn.exceptions import DataConversionWarning, NotFittedError
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from sklearn.base import clone
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import cross_val_predict
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from sklearn.svm import SVR
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from sklearn.utils import shuffle
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from sklearn import datasets
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iris = datasets.load_iris()
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# Make some data to be used many times
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rng = np.random.RandomState(0)
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n_features = 30
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n_samples = 1000
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offsets = rng.uniform(-1, 1, size=n_features)
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scales = rng.uniform(1, 10, size=n_features)
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X_2d = rng.randn(n_samples, n_features) * scales + offsets
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X_1row = X_2d[0, :].reshape(1, n_features)
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X_1col = X_2d[:, 0].reshape(n_samples, 1)
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X_list_1row = X_1row.tolist()
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X_list_1col = X_1col.tolist()
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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 _check_dim_1axis(a):
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if isinstance(a, list):
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return np.array(a).shape[0]
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return a.shape[0]
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def assert_correct_incr(i, batch_start, batch_stop, n, chunk_size,
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n_samples_seen):
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if batch_stop != n:
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assert_equal((i + 1) * chunk_size, n_samples_seen)
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else:
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assert_equal(i * chunk_size + (batch_stop - batch_start),
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n_samples_seen)
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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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assert_equal(interact.powers_.shape, (interact.n_output_features_,
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interact.n_input_features_))
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def test_polynomial_feature_names():
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X = np.arange(30).reshape(10, 3)
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poly = PolynomialFeatures(degree=2, include_bias=True).fit(X)
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feature_names = poly.get_feature_names()
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assert_array_equal(['1', 'x0', 'x1', 'x2', 'x0^2', 'x0 x1',
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'x0 x2', 'x1^2', 'x1 x2', 'x2^2'],
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feature_names)
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poly = PolynomialFeatures(degree=3, include_bias=False).fit(X)
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feature_names = poly.get_feature_names(["a", "b", "c"])
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assert_array_equal(['a', 'b', 'c', 'a^2', 'a b', 'a c', 'b^2',
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'b c', 'c^2', 'a^3', 'a^2 b', 'a^2 c',
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'a b^2', 'a b c', 'a c^2', 'b^3', 'b^2 c',
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'b c^2', 'c^3'], feature_names)
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# test some unicode
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poly = PolynomialFeatures(degree=1, include_bias=True).fit(X)
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feature_names = poly.get_feature_names(
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[u"\u0001F40D", u"\u262E", u"\u05D0"])
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assert_array_equal([u"1", u"\u0001F40D", u"\u262E", u"\u05D0"],
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feature_names)
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def test_polynomial_feature_array_order():
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X = np.arange(10).reshape(5, 2)
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def is_c_contiguous(a):
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return np.isfortran(a.T)
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assert is_c_contiguous(PolynomialFeatures().fit_transform(X))
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assert is_c_contiguous(PolynomialFeatures(order='C').fit_transform(X))
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assert np.isfortran(PolynomialFeatures(order='F').fit_transform(X))
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@pytest.mark.parametrize(['deg', 'include_bias', 'interaction_only', 'dtype'],
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[(1, True, False, int),
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(2, True, False, int),
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(2, True, False, np.float32),
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(2, True, False, np.float64),
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(3, False, False, np.float64),
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(3, False, True, np.float64),
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(4, False, False, np.float64),
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(4, False, True, np.float64)])
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def test_polynomial_features_csc_X(deg, include_bias, interaction_only, dtype):
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rng = np.random.RandomState(0)
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X = rng.randint(0, 2, (100, 2))
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X_csc = sparse.csc_matrix(X)
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est = PolynomialFeatures(deg, include_bias=include_bias,
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interaction_only=interaction_only)
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Xt_csc = est.fit_transform(X_csc.astype(dtype))
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Xt_dense = est.fit_transform(X.astype(dtype))
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assert isinstance(Xt_csc, sparse.csc_matrix)
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assert Xt_csc.dtype == Xt_dense.dtype
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assert_array_almost_equal(Xt_csc.A, Xt_dense)
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@pytest.mark.parametrize(['deg', 'include_bias', 'interaction_only', 'dtype'],
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[(1, True, False, int),
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(2, True, False, int),
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(2, True, False, np.float32),
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(2, True, False, np.float64),
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(3, False, False, np.float64),
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(3, False, True, np.float64)])
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def test_polynomial_features_csr_X(deg, include_bias, interaction_only, dtype):
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rng = np.random.RandomState(0)
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X = rng.randint(0, 2, (100, 2))
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X_csr = sparse.csr_matrix(X)
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est = PolynomialFeatures(deg, include_bias=include_bias,
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interaction_only=interaction_only)
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Xt_csr = est.fit_transform(X_csr.astype(dtype))
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Xt_dense = est.fit_transform(X.astype(dtype))
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assert isinstance(Xt_csr, sparse.csr_matrix)
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assert Xt_csr.dtype == Xt_dense.dtype
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assert_array_almost_equal(Xt_csr.A, Xt_dense)
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@pytest.mark.parametrize(['deg', 'include_bias', 'interaction_only', 'dtype'],
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[(2, True, False, np.float32),
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(2, True, False, np.float64),
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(3, False, False, np.float64),
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(3, False, True, np.float64)])
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def test_polynomial_features_csr_X_floats(deg, include_bias,
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interaction_only, dtype):
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X_csr = sparse_random(1000, 10, 0.5, random_state=0).tocsr()
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X = X_csr.toarray()
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est = PolynomialFeatures(deg, include_bias=include_bias,
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interaction_only=interaction_only)
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Xt_csr = est.fit_transform(X_csr.astype(dtype))
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Xt_dense = est.fit_transform(X.astype(dtype))
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assert isinstance(Xt_csr, sparse.csr_matrix)
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assert Xt_csr.dtype == Xt_dense.dtype
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assert_array_almost_equal(Xt_csr.A, Xt_dense)
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@pytest.mark.parametrize(['zero_row_index', 'deg', 'interaction_only'],
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[(0, 2, True), (1, 2, True), (2, 2, True),
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(0, 3, True), (1, 3, True), (2, 3, True),
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(0, 2, False), (1, 2, False), (2, 2, False),
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(0, 3, False), (1, 3, False), (2, 3, False)])
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def test_polynomial_features_csr_X_zero_row(zero_row_index, deg,
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interaction_only):
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X_csr = sparse_random(3, 10, 1.0, random_state=0).tocsr()
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X_csr[zero_row_index, :] = 0.0
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X = X_csr.toarray()
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est = PolynomialFeatures(deg, include_bias=False,
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interaction_only=interaction_only)
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Xt_csr = est.fit_transform(X_csr)
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Xt_dense = est.fit_transform(X)
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assert isinstance(Xt_csr, sparse.csr_matrix)
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assert Xt_csr.dtype == Xt_dense.dtype
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assert_array_almost_equal(Xt_csr.A, Xt_dense)
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# This degree should always be one more than the highest degree supported by
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# _csr_expansion.
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@pytest.mark.parametrize(['include_bias', 'interaction_only'],
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[(True, True), (True, False),
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(False, True), (False, False)])
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def test_polynomial_features_csr_X_degree_4(include_bias, interaction_only):
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X_csr = sparse_random(1000, 10, 0.5, random_state=0).tocsr()
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X = X_csr.toarray()
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est = PolynomialFeatures(4, include_bias=include_bias,
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interaction_only=interaction_only)
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Xt_csr = est.fit_transform(X_csr)
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Xt_dense = est.fit_transform(X)
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assert isinstance(Xt_csr, sparse.csr_matrix)
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assert Xt_csr.dtype == Xt_dense.dtype
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assert_array_almost_equal(Xt_csr.A, Xt_dense)
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@pytest.mark.parametrize(['deg', 'dim', 'interaction_only'],
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[(2, 1, True),
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(2, 2, True),
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(3, 1, True),
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(3, 2, True),
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(3, 3, True),
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(2, 1, False),
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(2, 2, False),
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(3, 1, False),
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(3, 2, False),
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(3, 3, False)])
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def test_polynomial_features_csr_X_dim_edges(deg, dim, interaction_only):
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X_csr = sparse_random(1000, dim, 0.5, random_state=0).tocsr()
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X = X_csr.toarray()
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est = PolynomialFeatures(deg, interaction_only=interaction_only)
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Xt_csr = est.fit_transform(X_csr)
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Xt_dense = est.fit_transform(X)
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assert isinstance(Xt_csr, sparse.csr_matrix)
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assert Xt_csr.dtype == Xt_dense.dtype
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assert_array_almost_equal(Xt_csr.A, Xt_dense)
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def test_standard_scaler_1d():
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# Test scaling of dataset along single axis
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for X in [X_1row, X_1col, X_list_1row, X_list_1row]:
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scaler = StandardScaler()
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X_scaled = scaler.fit(X).transform(X, copy=True)
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if isinstance(X, list):
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X = np.array(X) # cast only after scaling done
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if _check_dim_1axis(X) == 1:
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assert_almost_equal(scaler.mean_, X.ravel())
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assert_almost_equal(scaler.scale_, np.ones(n_features))
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assert_array_almost_equal(X_scaled.mean(axis=0),
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np.zeros_like(n_features))
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assert_array_almost_equal(X_scaled.std(axis=0),
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np.zeros_like(n_features))
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else:
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assert_almost_equal(scaler.mean_, X.mean())
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assert_almost_equal(scaler.scale_, X.std())
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assert_array_almost_equal(X_scaled.mean(axis=0),
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np.zeros_like(n_features))
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assert_array_almost_equal(X_scaled.mean(axis=0), .0)
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assert_array_almost_equal(X_scaled.std(axis=0), 1.)
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assert_equal(scaler.n_samples_seen_, X.shape[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)
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# Constant feature
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X = np.ones((5, 1))
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scaler = StandardScaler()
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X_scaled = scaler.fit(X).transform(X, copy=True)
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assert_almost_equal(scaler.mean_, 1.)
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assert_almost_equal(scaler.scale_, 1.)
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assert_array_almost_equal(X_scaled.mean(axis=0), .0)
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assert_array_almost_equal(X_scaled.std(axis=0), .0)
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assert_equal(scaler.n_samples_seen_, X.shape[0])
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def test_standard_scaler_dtype():
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# Ensure scaling does not affect dtype
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rng = np.random.RandomState(0)
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n_samples = 10
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n_features = 3
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for dtype in [np.float16, np.float32, np.float64]:
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X = rng.randn(n_samples, n_features).astype(dtype)
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scaler = StandardScaler()
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X_scaled = scaler.fit(X).transform(X)
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assert X.dtype == X_scaled.dtype
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assert scaler.mean_.dtype == np.float64
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assert scaler.scale_.dtype == np.float64
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def test_scale_1d():
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# 1-d inputs
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X_list = [1., 3., 5., 0.]
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X_arr = np.array(X_list)
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for X in [X_list, X_arr]:
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X_scaled = scale(X)
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assert_array_almost_equal(X_scaled.mean(), 0.0)
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assert_array_almost_equal(X_scaled.std(), 1.0)
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assert_array_equal(scale(X, with_mean=False, with_std=False), X)
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@skip_if_32bit
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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.full(8, np.log(1e-5), dtype=np.float64)
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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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# with 2 more samples, the std computation run into numerical issues:
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x = np.full(10, 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.full(10, 1e-100, dtype=np.float64)
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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.full(10, 1e100, dtype=np.float64)
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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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n_features = 5
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n_samples = 4
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X = rng.randn(n_samples, n_features)
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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 not np.any(np.isnan(X_scaled))
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assert_equal(scaler.n_samples_seen_, n_samples)
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assert_array_almost_equal(X_scaled.mean(axis=0), n_features * [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 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 X_scaled_back is not X
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assert X_scaled_back is not X_scaled
|
|
assert_array_almost_equal(X_scaled_back, X)
|
|
|
|
X_scaled = scale(X, axis=1, with_std=False)
|
|
assert not np.any(np.isnan(X_scaled))
|
|
assert_array_almost_equal(X_scaled.mean(axis=1), n_samples * [0.0])
|
|
X_scaled = scale(X, axis=1, with_std=True)
|
|
assert not np.any(np.isnan(X_scaled))
|
|
assert_array_almost_equal(X_scaled.mean(axis=1), n_samples * [0.0])
|
|
assert_array_almost_equal(X_scaled.std(axis=1), n_samples * [1.0])
|
|
# Check that the data hasn't been modified
|
|
assert X_scaled is not X
|
|
|
|
X_scaled = scaler.fit(X).transform(X, copy=False)
|
|
assert not np.any(np.isnan(X_scaled))
|
|
assert_array_almost_equal(X_scaled.mean(axis=0), n_features * [0.0])
|
|
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
|
|
# Check that X has not been copied
|
|
assert X_scaled is X
|
|
|
|
X = rng.randn(4, 5)
|
|
X[:, 0] = 1.0 # first feature is a constant, non zero feature
|
|
scaler = StandardScaler()
|
|
X_scaled = scaler.fit(X).transform(X, copy=True)
|
|
assert not np.any(np.isnan(X_scaled))
|
|
assert_array_almost_equal(X_scaled.mean(axis=0), n_features * [0.0])
|
|
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
|
|
# Check that X has not been copied
|
|
assert X_scaled is not X
|
|
|
|
|
|
def test_scaler_float16_overflow():
|
|
# Test if the scaler will not overflow on float16 numpy arrays
|
|
rng = np.random.RandomState(0)
|
|
# float16 has a maximum of 65500.0. On the worst case 5 * 200000 is 100000
|
|
# which is enough to overflow the data type
|
|
X = rng.uniform(5, 10, [200000, 1]).astype(np.float16)
|
|
|
|
with np.errstate(over='raise'):
|
|
scaler = StandardScaler().fit(X)
|
|
X_scaled = scaler.transform(X)
|
|
|
|
# Calculate the float64 equivalent to verify result
|
|
X_scaled_f64 = StandardScaler().fit_transform(X.astype(np.float64))
|
|
|
|
# Overflow calculations may cause -inf, inf, or nan. Since there is no nan
|
|
# input, all of the outputs should be finite. This may be redundant since a
|
|
# FloatingPointError exception will be thrown on overflow above.
|
|
assert np.all(np.isfinite(X_scaled))
|
|
|
|
# The normal distribution is very unlikely to go above 4. At 4.0-8.0 the
|
|
# float16 precision is 2^-8 which is around 0.004. Thus only 2 decimals are
|
|
# checked to account for precision differences.
|
|
assert_array_almost_equal(X_scaled, X_scaled_f64, decimal=2)
|
|
|
|
|
|
def test_handle_zeros_in_scale():
|
|
s1 = np.array([0, 1, 2, 3])
|
|
s2 = _handle_zeros_in_scale(s1, copy=True)
|
|
|
|
assert not s1[0] == s2[0]
|
|
assert_array_equal(s1, np.array([0, 1, 2, 3]))
|
|
assert_array_equal(s2, np.array([1, 1, 2, 3]))
|
|
|
|
|
|
def test_minmax_scaler_partial_fit():
|
|
# 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]
|
|
|
|
for chunk_size in [1, 2, 50, n, n + 42]:
|
|
# 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_equal(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_equal(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
|
|
scaler_batch = 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_)
|
|
|
|
|
|
def test_standard_scaler_partial_fit():
|
|
# 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]
|
|
|
|
for chunk_size in [1, 2, 50, n, n + 42]:
|
|
# Test mean at the end of the process
|
|
scaler_batch = StandardScaler(with_std=False).fit(X)
|
|
|
|
scaler_incr = StandardScaler(with_std=False)
|
|
for batch in gen_batches(n_samples, chunk_size):
|
|
scaler_incr = scaler_incr.partial_fit(X[batch])
|
|
|
|
assert_array_almost_equal(scaler_batch.mean_, scaler_incr.mean_)
|
|
assert_equal(scaler_batch.var_, scaler_incr.var_) # Nones
|
|
assert_equal(scaler_batch.n_samples_seen_, scaler_incr.n_samples_seen_)
|
|
|
|
# Test std after 1 step
|
|
batch0 = slice(0, chunk_size)
|
|
scaler_incr = StandardScaler().partial_fit(X[batch0])
|
|
if chunk_size == 1:
|
|
assert_array_almost_equal(np.zeros(n_features, dtype=np.float64),
|
|
scaler_incr.var_)
|
|
assert_array_almost_equal(np.ones(n_features, dtype=np.float64),
|
|
scaler_incr.scale_)
|
|
else:
|
|
assert_array_almost_equal(np.var(X[batch0], axis=0),
|
|
scaler_incr.var_)
|
|
assert_array_almost_equal(np.std(X[batch0], axis=0),
|
|
scaler_incr.scale_) # no constants
|
|
|
|
# Test std until the end of partial fits, and
|
|
scaler_batch = StandardScaler().fit(X)
|
|
scaler_incr = StandardScaler() # 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_)
|
|
|
|
assert_array_almost_equal(scaler_batch.var_, scaler_incr.var_)
|
|
assert_equal(scaler_batch.n_samples_seen_, scaler_incr.n_samples_seen_)
|
|
|
|
|
|
def test_standard_scaler_partial_fit_numerical_stability():
|
|
# Test if the incremental computation introduces significative errors
|
|
# for large datasets with values of large magniture
|
|
rng = np.random.RandomState(0)
|
|
n_features = 2
|
|
n_samples = 100
|
|
offsets = rng.uniform(-1e15, 1e15, size=n_features)
|
|
scales = rng.uniform(1e3, 1e6, size=n_features)
|
|
X = rng.randn(n_samples, n_features) * scales + offsets
|
|
|
|
scaler_batch = StandardScaler().fit(X)
|
|
scaler_incr = StandardScaler()
|
|
for chunk in X:
|
|
scaler_incr = scaler_incr.partial_fit(chunk.reshape(1, n_features))
|
|
|
|
# Regardless of abs values, they must not be more diff 6 significant digits
|
|
tol = 10 ** (-6)
|
|
assert_allclose(scaler_incr.mean_, scaler_batch.mean_, rtol=tol)
|
|
assert_allclose(scaler_incr.var_, scaler_batch.var_, rtol=tol)
|
|
assert_allclose(scaler_incr.scale_, scaler_batch.scale_, rtol=tol)
|
|
# NOTE Be aware that for much larger offsets std is very unstable (last
|
|
# assert) while mean is OK.
|
|
|
|
# Sparse input
|
|
size = (100, 3)
|
|
scale = 1e20
|
|
X = rng.randint(0, 2, size).astype(np.float64) * scale
|
|
X_csr = sparse.csr_matrix(X)
|
|
X_csc = sparse.csc_matrix(X)
|
|
|
|
for X in [X_csr, X_csc]:
|
|
# with_mean=False is required with sparse input
|
|
scaler = StandardScaler(with_mean=False).fit(X)
|
|
scaler_incr = StandardScaler(with_mean=False)
|
|
|
|
for chunk in X:
|
|
# chunk = sparse.csr_matrix(data_chunks)
|
|
scaler_incr = scaler_incr.partial_fit(chunk)
|
|
|
|
# Regardless of magnitude, they must not differ more than of 6 digits
|
|
tol = 10 ** (-6)
|
|
assert scaler.mean_ is not None
|
|
assert_allclose(scaler_incr.var_, scaler.var_, rtol=tol)
|
|
assert_allclose(scaler_incr.scale_, scaler.scale_, rtol=tol)
|
|
|
|
|
|
def test_partial_fit_sparse_input():
|
|
# Check that sparsity is not destroyed
|
|
X = np.array([[1.], [0.], [0.], [5.]])
|
|
X_csr = sparse.csr_matrix(X)
|
|
X_csc = sparse.csc_matrix(X)
|
|
|
|
null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
|
|
for X in [X_csr, X_csc]:
|
|
|
|
X_null = null_transform.partial_fit(X).transform(X)
|
|
assert_array_equal(X_null.data, X.data)
|
|
X_orig = null_transform.inverse_transform(X_null)
|
|
assert_array_equal(X_orig.data, X_null.data)
|
|
assert_array_equal(X_orig.data, X.data)
|
|
|
|
|
|
def test_standard_scaler_trasform_with_partial_fit():
|
|
# Check some postconditions after applying partial_fit and transform
|
|
X = X_2d[:100, :]
|
|
|
|
scaler_incr = StandardScaler()
|
|
for i, batch in enumerate(gen_batches(X.shape[0], 1)):
|
|
|
|
X_sofar = X[:(i + 1), :]
|
|
chunks_copy = X_sofar.copy()
|
|
scaled_batch = StandardScaler().fit_transform(X_sofar)
|
|
|
|
scaler_incr = scaler_incr.partial_fit(X[batch])
|
|
scaled_incr = scaler_incr.transform(X_sofar)
|
|
|
|
assert_array_almost_equal(scaled_batch, scaled_incr)
|
|
assert_array_almost_equal(X_sofar, chunks_copy) # No change
|
|
right_input = scaler_incr.inverse_transform(scaled_incr)
|
|
assert_array_almost_equal(X_sofar, right_input)
|
|
|
|
zero = np.zeros(X.shape[1])
|
|
epsilon = np.finfo(float).eps
|
|
assert_array_less(zero, scaler_incr.var_ + epsilon) # as less or equal
|
|
assert_array_less(zero, scaler_incr.scale_ + epsilon)
|
|
# (i+1) because the Scaler has been already fitted
|
|
assert_equal((i + 1), scaler_incr.n_samples_seen_)
|
|
|
|
|
|
def test_min_max_scaler_iris():
|
|
X = iris.data
|
|
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))
|
|
assert_raises(ValueError, scaler.fit, X)
|
|
|
|
|
|
def test_min_max_scaler_zero_variance_features():
|
|
# 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_minmax_scale_axis1():
|
|
X = iris.data
|
|
X_trans = minmax_scale(X, axis=1)
|
|
assert_array_almost_equal(np.min(X_trans, axis=1), 0)
|
|
assert_array_almost_equal(np.max(X_trans, axis=1), 1)
|
|
|
|
|
|
def test_min_max_scaler_1d():
|
|
# Test scaling of dataset along single axis
|
|
for X in [X_1row, X_1col, X_list_1row, X_list_1row]:
|
|
|
|
scaler = MinMaxScaler(copy=True)
|
|
X_scaled = scaler.fit(X).transform(X)
|
|
|
|
if isinstance(X, list):
|
|
X = np.array(X) # cast only after scaling done
|
|
|
|
if _check_dim_1axis(X) == 1:
|
|
assert_array_almost_equal(X_scaled.min(axis=0),
|
|
np.zeros(n_features))
|
|
assert_array_almost_equal(X_scaled.max(axis=0),
|
|
np.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_equal(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 = np.ones((5, 1))
|
|
scaler = MinMaxScaler()
|
|
X_scaled = scaler.fit(X).transform(X)
|
|
assert_greater_equal(X_scaled.min(), 0.)
|
|
assert_less_equal(X_scaled.max(), 1.)
|
|
assert_equal(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_scaler_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_csc = sparse.csc_matrix(X)
|
|
|
|
assert_raises(ValueError, StandardScaler().fit, X_csr)
|
|
assert_raises(ValueError, StandardScaler().fit, X_csc)
|
|
|
|
null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
|
|
X_null = null_transform.fit_transform(X_csr)
|
|
assert_array_equal(X_null.data, X_csr.data)
|
|
X_orig = null_transform.inverse_transform(X_null)
|
|
assert_array_equal(X_orig.data, X_csr.data)
|
|
|
|
scaler = StandardScaler(with_mean=False).fit(X)
|
|
X_scaled = scaler.transform(X, copy=True)
|
|
assert not np.any(np.isnan(X_scaled))
|
|
|
|
scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
|
|
X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
|
|
assert not np.any(np.isnan(X_csr_scaled.data))
|
|
|
|
scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
|
|
X_csc_scaled = scaler_csc.transform(X_csc, copy=True)
|
|
assert not np.any(np.isnan(X_csc_scaled.data))
|
|
|
|
assert_array_almost_equal(scaler.mean_, scaler_csr.mean_)
|
|
assert_array_almost_equal(scaler.var_, scaler_csr.var_)
|
|
assert_array_almost_equal(scaler.scale_, scaler_csr.scale_)
|
|
|
|
assert_array_almost_equal(scaler.mean_, scaler_csc.mean_)
|
|
assert_array_almost_equal(scaler.var_, scaler_csc.var_)
|
|
assert_array_almost_equal(scaler.scale_, scaler_csc.scale_)
|
|
|
|
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.])
|
|
|
|
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))
|
|
|
|
# Check that X has not been modified (copy)
|
|
assert X_scaled is not X
|
|
assert X_csr_scaled is not X_csr
|
|
|
|
X_scaled_back = scaler.inverse_transform(X_scaled)
|
|
assert X_scaled_back is not X
|
|
assert 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 X_csr_scaled_back is not X_csr
|
|
assert 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 X_csc_scaled_back is not X_csc
|
|
assert X_csc_scaled_back is not X_csc_scaled
|
|
assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
|
|
|
|
|
|
@pytest.mark.parametrize("with_mean", [True, False])
|
|
@pytest.mark.parametrize("with_std", [True, False])
|
|
@pytest.mark.parametrize("array_constructor",
|
|
[np.asarray, sparse.csc_matrix, sparse.csr_matrix])
|
|
def test_scaler_n_samples_seen_with_nan(with_mean, with_std,
|
|
array_constructor):
|
|
X = np.array([[0, 1, 3],
|
|
[np.nan, 6, 10],
|
|
[5, 4, np.nan],
|
|
[8, 0, np.nan]],
|
|
dtype=np.float64)
|
|
X = array_constructor(X)
|
|
|
|
if sparse.issparse(X) and with_mean:
|
|
pytest.skip("'with_mean=True' cannot be used with sparse matrix.")
|
|
|
|
transformer = StandardScaler(with_mean=with_mean, with_std=with_std)
|
|
transformer.fit(X)
|
|
|
|
assert_array_equal(transformer.n_samples_seen_, np.array([3, 4, 2]))
|
|
|
|
|
|
def _check_identity_scalers_attributes(scaler_1, scaler_2):
|
|
assert scaler_1.mean_ is scaler_2.mean_ is None
|
|
assert scaler_1.var_ is scaler_2.var_ is None
|
|
assert scaler_1.scale_ is scaler_2.scale_ is None
|
|
assert scaler_1.n_samples_seen_ == scaler_2.n_samples_seen_
|
|
|
|
|
|
def test_scaler_return_identity():
|
|
# test that the scaler return identity when with_mean and with_std are
|
|
# False
|
|
X_dense = np.array([[0, 1, 3],
|
|
[5, 6, 0],
|
|
[8, 0, 10]],
|
|
dtype=np.float64)
|
|
X_csr = sparse.csr_matrix(X_dense)
|
|
X_csc = X_csr.tocsc()
|
|
|
|
transformer_dense = StandardScaler(with_mean=False, with_std=False)
|
|
X_trans_dense = transformer_dense.fit_transform(X_dense)
|
|
|
|
transformer_csr = clone(transformer_dense)
|
|
X_trans_csr = transformer_csr.fit_transform(X_csr)
|
|
|
|
transformer_csc = clone(transformer_dense)
|
|
X_trans_csc = transformer_csc.fit_transform(X_csc)
|
|
|
|
assert_allclose_dense_sparse(X_trans_csr, X_csr)
|
|
assert_allclose_dense_sparse(X_trans_csc, X_csc)
|
|
assert_allclose(X_trans_dense, X_dense)
|
|
|
|
for trans_1, trans_2 in itertools.combinations([transformer_dense,
|
|
transformer_csr,
|
|
transformer_csc],
|
|
2):
|
|
_check_identity_scalers_attributes(trans_1, trans_2)
|
|
|
|
transformer_dense.partial_fit(X_dense)
|
|
transformer_csr.partial_fit(X_csr)
|
|
transformer_csc.partial_fit(X_csc)
|
|
|
|
for trans_1, trans_2 in itertools.combinations([transformer_dense,
|
|
transformer_csr,
|
|
transformer_csc],
|
|
2):
|
|
_check_identity_scalers_attributes(trans_1, trans_2)
|
|
|
|
transformer_dense.fit(X_dense)
|
|
transformer_csr.fit(X_csr)
|
|
transformer_csc.fit(X_csc)
|
|
|
|
for trans_1, trans_2 in itertools.combinations([transformer_dense,
|
|
transformer_csr,
|
|
transformer_csc],
|
|
2):
|
|
_check_identity_scalers_attributes(trans_1, trans_2)
|
|
|
|
|
|
def test_scaler_int():
|
|
# test that scaler converts integer input to floating
|
|
# for both sparse and dense matrices
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randint(20, size=(4, 5))
|
|
X[:, 0] = 0 # first feature is always of zero
|
|
X_csr = sparse.csr_matrix(X)
|
|
X_csc = sparse.csc_matrix(X)
|
|
|
|
null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
|
|
clean_warning_registry()
|
|
with warnings.catch_warnings(record=True):
|
|
X_null = null_transform.fit_transform(X_csr)
|
|
assert_array_equal(X_null.data, X_csr.data)
|
|
X_orig = null_transform.inverse_transform(X_null)
|
|
assert_array_equal(X_orig.data, X_csr.data)
|
|
|
|
clean_warning_registry()
|
|
with warnings.catch_warnings(record=True):
|
|
scaler = StandardScaler(with_mean=False).fit(X)
|
|
X_scaled = scaler.transform(X, copy=True)
|
|
assert not np.any(np.isnan(X_scaled))
|
|
|
|
clean_warning_registry()
|
|
with warnings.catch_warnings(record=True):
|
|
scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
|
|
X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
|
|
assert not np.any(np.isnan(X_csr_scaled.data))
|
|
|
|
clean_warning_registry()
|
|
with warnings.catch_warnings(record=True):
|
|
scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
|
|
X_csc_scaled = scaler_csc.transform(X_csc, copy=True)
|
|
assert not np.any(np.isnan(X_csc_scaled.data))
|
|
|
|
assert_array_almost_equal(scaler.mean_, scaler_csr.mean_)
|
|
assert_array_almost_equal(scaler.var_, scaler_csr.var_)
|
|
assert_array_almost_equal(scaler.scale_, scaler_csr.scale_)
|
|
|
|
assert_array_almost_equal(scaler.mean_, scaler_csc.mean_)
|
|
assert_array_almost_equal(scaler.var_, scaler_csc.var_)
|
|
assert_array_almost_equal(scaler.scale_, scaler_csc.scale_)
|
|
|
|
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 X_scaled is not X
|
|
assert X_csr_scaled is not X_csr
|
|
|
|
X_scaled_back = scaler.inverse_transform(X_scaled)
|
|
assert X_scaled_back is not X
|
|
assert 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 X_csr_scaled_back is not X_csr
|
|
assert 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 X_csc_scaled_back is not X_csc
|
|
assert 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_csc = sparse.csc_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())
|
|
|
|
X_csc_copy = X_csc.copy()
|
|
StandardScaler(with_mean=False, copy=False).fit(X_csc)
|
|
assert_array_equal(X_csc.toarray(), X_csc_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)
|
|
X_csc = sparse.csc_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)
|
|
|
|
assert_raises(ValueError, scale, X_csc, with_mean=True)
|
|
assert_raises(ValueError, StandardScaler(with_mean=True).fit, X_csc)
|
|
|
|
# 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)
|
|
assert_raises(ValueError, scaler.transform, X_csc)
|
|
|
|
X_transformed_csr = sparse.csr_matrix(scaler.transform(X))
|
|
assert_raises(ValueError, scaler.inverse_transform, X_transformed_csr)
|
|
|
|
X_transformed_csc = sparse.csc_matrix(scaler.transform(X))
|
|
assert_raises(ValueError, scaler.inverse_transform, X_transformed_csc)
|
|
|
|
|
|
def test_scale_input_finiteness_validation():
|
|
# Check if non finite inputs raise ValueError
|
|
X = [[np.inf, 5, 6, 7, 8]]
|
|
assert_raises_regex(ValueError,
|
|
"Input contains infinity or a value too large",
|
|
scale, X)
|
|
|
|
|
|
def test_robust_scaler_error_sparse():
|
|
X_sparse = sparse.rand(1000, 10)
|
|
scaler = RobustScaler(with_centering=True)
|
|
err_msg = "Cannot center sparse matrices"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
scaler.fit(X_sparse)
|
|
|
|
|
|
@pytest.mark.parametrize("with_centering", [True, False])
|
|
@pytest.mark.parametrize("with_scaling", [True, False])
|
|
@pytest.mark.parametrize("X", [np.random.randn(10, 3),
|
|
sparse.rand(10, 3, density=0.5)])
|
|
def test_robust_scaler_attributes(X, with_centering, with_scaling):
|
|
# check consistent type of attributes
|
|
if with_centering and sparse.issparse(X):
|
|
pytest.skip("RobustScaler cannot center sparse matrix")
|
|
|
|
scaler = RobustScaler(with_centering=with_centering,
|
|
with_scaling=with_scaling)
|
|
scaler.fit(X)
|
|
|
|
if with_centering:
|
|
assert isinstance(scaler.center_, np.ndarray)
|
|
else:
|
|
assert scaler.center_ is None
|
|
if with_scaling:
|
|
assert isinstance(scaler.scale_, np.ndarray)
|
|
else:
|
|
assert scaler.scale_ is None
|
|
|
|
|
|
def test_robust_scaler_col_zero_sparse():
|
|
# check that the scaler is working when there is not data materialized in a
|
|
# column of a sparse matrix
|
|
X = np.random.randn(10, 5)
|
|
X[:, 0] = 0
|
|
X = sparse.csr_matrix(X)
|
|
|
|
scaler = RobustScaler(with_centering=False)
|
|
scaler.fit(X)
|
|
assert scaler.scale_[0] == pytest.approx(1)
|
|
|
|
X_trans = scaler.transform(X)
|
|
assert_allclose(X[:, 0].toarray(), X_trans[:, 0].toarray())
|
|
|
|
|
|
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)
|
|
|
|
|
|
@pytest.mark.parametrize("density", [0, 0.05, 0.1, 0.5, 1])
|
|
@pytest.mark.parametrize("strictly_signed",
|
|
['positive', 'negative', 'zeros', None])
|
|
def test_robust_scaler_equivalence_dense_sparse(density, strictly_signed):
|
|
# Check the equivalence of the fitting with dense and sparse matrices
|
|
X_sparse = sparse.rand(1000, 5, density=density).tocsc()
|
|
if strictly_signed == 'positive':
|
|
X_sparse.data = np.abs(X_sparse.data)
|
|
elif strictly_signed == 'negative':
|
|
X_sparse.data = - np.abs(X_sparse.data)
|
|
elif strictly_signed == 'zeros':
|
|
X_sparse.data = np.zeros(X_sparse.data.shape, dtype=np.float64)
|
|
X_dense = X_sparse.toarray()
|
|
|
|
scaler_sparse = RobustScaler(with_centering=False)
|
|
scaler_dense = RobustScaler(with_centering=False)
|
|
|
|
scaler_sparse.fit(X_sparse)
|
|
scaler_dense.fit(X_dense)
|
|
|
|
assert_allclose(scaler_sparse.scale_, scaler_dense.scale_)
|
|
|
|
|
|
def test_robust_scaler_transform_one_row_csr():
|
|
# Check RobustScaler on transforming csr matrix with one row
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(4, 5)
|
|
single_row = np.array([[0.1, 1., 2., 0., -1.]])
|
|
scaler = RobustScaler(with_centering=False)
|
|
scaler = scaler.fit(X)
|
|
row_trans = scaler.transform(sparse.csr_matrix(single_row))
|
|
row_expected = single_row / scaler.scale_
|
|
assert_array_almost_equal(row_trans.toarray(), row_expected)
|
|
row_scaled_back = scaler.inverse_transform(row_trans)
|
|
assert_array_almost_equal(single_row, row_scaled_back.toarray())
|
|
|
|
|
|
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_scaler_iris_quantiles():
|
|
X = iris.data
|
|
scaler = RobustScaler(quantile_range=(10, 90))
|
|
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=(10, 90), axis=0)
|
|
q_range = q[1] - q[0]
|
|
assert_array_almost_equal(q_range, 1)
|
|
|
|
|
|
def test_quantile_transform_iris():
|
|
X = iris.data
|
|
# uniform output distribution
|
|
transformer = QuantileTransformer(n_quantiles=30)
|
|
X_trans = transformer.fit_transform(X)
|
|
X_trans_inv = transformer.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv)
|
|
# normal output distribution
|
|
transformer = QuantileTransformer(n_quantiles=30,
|
|
output_distribution='normal')
|
|
X_trans = transformer.fit_transform(X)
|
|
X_trans_inv = transformer.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv)
|
|
# make sure it is possible to take the inverse of a sparse matrix
|
|
# which contain negative value; this is the case in the iris dataset
|
|
X_sparse = sparse.csc_matrix(X)
|
|
X_sparse_tran = transformer.fit_transform(X_sparse)
|
|
X_sparse_tran_inv = transformer.inverse_transform(X_sparse_tran)
|
|
assert_array_almost_equal(X_sparse.A, X_sparse_tran_inv.A)
|
|
|
|
|
|
def test_quantile_transform_check_error():
|
|
X = np.transpose([[0, 25, 50, 0, 0, 0, 75, 0, 0, 100],
|
|
[2, 4, 0, 0, 6, 8, 0, 10, 0, 0],
|
|
[0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]])
|
|
X = sparse.csc_matrix(X)
|
|
X_neg = np.transpose([[0, 25, 50, 0, 0, 0, 75, 0, 0, 100],
|
|
[-2, 4, 0, 0, 6, 8, 0, 10, 0, 0],
|
|
[0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]])
|
|
X_neg = sparse.csc_matrix(X_neg)
|
|
|
|
assert_raises_regex(ValueError, "Invalid value for 'n_quantiles': 0.",
|
|
QuantileTransformer(n_quantiles=0).fit, X)
|
|
assert_raises_regex(ValueError, "Invalid value for 'subsample': 0.",
|
|
QuantileTransformer(subsample=0).fit, X)
|
|
assert_raises_regex(ValueError, "The number of quantiles cannot be"
|
|
" greater than the number of samples used. Got"
|
|
" 1000 quantiles and 10 samples.",
|
|
QuantileTransformer(subsample=10).fit, X)
|
|
|
|
transformer = QuantileTransformer(n_quantiles=10)
|
|
assert_raises_regex(ValueError, "QuantileTransformer only accepts "
|
|
"non-negative sparse matrices.",
|
|
transformer.fit, X_neg)
|
|
transformer.fit(X)
|
|
assert_raises_regex(ValueError, "QuantileTransformer only accepts "
|
|
"non-negative sparse matrices.",
|
|
transformer.transform, X_neg)
|
|
|
|
X_bad_feat = np.transpose([[0, 25, 50, 0, 0, 0, 75, 0, 0, 100],
|
|
[0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]])
|
|
assert_raises_regex(ValueError, "X does not have the same number of "
|
|
"features as the previously fitted data. Got 2"
|
|
" instead of 3.",
|
|
transformer.transform, X_bad_feat)
|
|
assert_raises_regex(ValueError, "X does not have the same number of "
|
|
"features as the previously fitted data. Got 2"
|
|
" instead of 3.",
|
|
transformer.inverse_transform, X_bad_feat)
|
|
|
|
transformer = QuantileTransformer(n_quantiles=10,
|
|
output_distribution='rnd')
|
|
# check that an error is raised at fit time
|
|
assert_raises_regex(ValueError, "'output_distribution' has to be either"
|
|
" 'normal' or 'uniform'. Got 'rnd' instead.",
|
|
transformer.fit, X)
|
|
# check that an error is raised at transform time
|
|
transformer.output_distribution = 'uniform'
|
|
transformer.fit(X)
|
|
X_tran = transformer.transform(X)
|
|
transformer.output_distribution = 'rnd'
|
|
assert_raises_regex(ValueError, "'output_distribution' has to be either"
|
|
" 'normal' or 'uniform'. Got 'rnd' instead.",
|
|
transformer.transform, X)
|
|
# check that an error is raised at inverse_transform time
|
|
assert_raises_regex(ValueError, "'output_distribution' has to be either"
|
|
" 'normal' or 'uniform'. Got 'rnd' instead.",
|
|
transformer.inverse_transform, X_tran)
|
|
# check that an error is raised if input is scalar
|
|
assert_raise_message(ValueError,
|
|
'Expected 2D array, got scalar array instead',
|
|
transformer.transform, 10)
|
|
|
|
|
|
def test_quantile_transform_sparse_ignore_zeros():
|
|
X = np.array([[0, 1],
|
|
[0, 0],
|
|
[0, 2],
|
|
[0, 2],
|
|
[0, 1]])
|
|
X_sparse = sparse.csc_matrix(X)
|
|
transformer = QuantileTransformer(ignore_implicit_zeros=True,
|
|
n_quantiles=5)
|
|
|
|
# dense case -> warning raise
|
|
assert_warns_message(UserWarning, "'ignore_implicit_zeros' takes effect"
|
|
" only with sparse matrix. This parameter has no"
|
|
" effect.", transformer.fit, X)
|
|
|
|
X_expected = np.array([[0, 0],
|
|
[0, 0],
|
|
[0, 1],
|
|
[0, 1],
|
|
[0, 0]])
|
|
X_trans = transformer.fit_transform(X_sparse)
|
|
assert_almost_equal(X_expected, X_trans.A)
|
|
|
|
# consider the case where sparse entries are missing values and user-given
|
|
# zeros are to be considered
|
|
X_data = np.array([0, 0, 1, 0, 2, 2, 1, 0, 1, 2, 0])
|
|
X_col = np.array([0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1])
|
|
X_row = np.array([0, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8])
|
|
X_sparse = sparse.csc_matrix((X_data, (X_row, X_col)))
|
|
X_trans = transformer.fit_transform(X_sparse)
|
|
X_expected = np.array([[0., 0.5],
|
|
[0., 0.],
|
|
[0., 1.],
|
|
[0., 1.],
|
|
[0., 0.5],
|
|
[0., 0.],
|
|
[0., 0.5],
|
|
[0., 1.],
|
|
[0., 0.]])
|
|
assert_almost_equal(X_expected, X_trans.A)
|
|
|
|
transformer = QuantileTransformer(ignore_implicit_zeros=True,
|
|
n_quantiles=5)
|
|
X_data = np.array([-1, -1, 1, 0, 0, 0, 1, -1, 1])
|
|
X_col = np.array([0, 0, 1, 1, 1, 1, 1, 1, 1])
|
|
X_row = np.array([0, 4, 0, 1, 2, 3, 4, 5, 6])
|
|
X_sparse = sparse.csc_matrix((X_data, (X_row, X_col)))
|
|
X_trans = transformer.fit_transform(X_sparse)
|
|
X_expected = np.array([[0, 1],
|
|
[0, 0.375],
|
|
[0, 0.375],
|
|
[0, 0.375],
|
|
[0, 1],
|
|
[0, 0],
|
|
[0, 1]])
|
|
assert_almost_equal(X_expected, X_trans.A)
|
|
assert_almost_equal(X_sparse.A, transformer.inverse_transform(X_trans).A)
|
|
|
|
# check in conjunction with subsampling
|
|
transformer = QuantileTransformer(ignore_implicit_zeros=True,
|
|
n_quantiles=5,
|
|
subsample=8,
|
|
random_state=0)
|
|
X_trans = transformer.fit_transform(X_sparse)
|
|
assert_almost_equal(X_expected, X_trans.A)
|
|
assert_almost_equal(X_sparse.A, transformer.inverse_transform(X_trans).A)
|
|
|
|
|
|
def test_quantile_transform_dense_toy():
|
|
X = np.array([[0, 2, 2.6],
|
|
[25, 4, 4.1],
|
|
[50, 6, 2.3],
|
|
[75, 8, 9.5],
|
|
[100, 10, 0.1]])
|
|
|
|
transformer = QuantileTransformer(n_quantiles=5)
|
|
transformer.fit(X)
|
|
|
|
# using the a uniform output, each entry of X should be map between 0 and 1
|
|
# and equally spaced
|
|
X_trans = transformer.fit_transform(X)
|
|
X_expected = np.tile(np.linspace(0, 1, num=5), (3, 1)).T
|
|
assert_almost_equal(np.sort(X_trans, axis=0), X_expected)
|
|
|
|
X_test = np.array([
|
|
[-1, 1, 0],
|
|
[101, 11, 10],
|
|
])
|
|
X_expected = np.array([
|
|
[0, 0, 0],
|
|
[1, 1, 1],
|
|
])
|
|
assert_array_almost_equal(transformer.transform(X_test), X_expected)
|
|
|
|
X_trans_inv = transformer.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv)
|
|
|
|
|
|
def test_quantile_transform_subsampling():
|
|
# Test that subsampling the input yield to a consistent results We check
|
|
# that the computed quantiles are almost mapped to a [0, 1] vector where
|
|
# values are equally spaced. The infinite norm is checked to be smaller
|
|
# than a given threshold. This is repeated 5 times.
|
|
|
|
# dense support
|
|
n_samples = 1000000
|
|
n_quantiles = 1000
|
|
X = np.sort(np.random.sample((n_samples, 1)), axis=0)
|
|
ROUND = 5
|
|
inf_norm_arr = []
|
|
for random_state in range(ROUND):
|
|
transformer = QuantileTransformer(random_state=random_state,
|
|
n_quantiles=n_quantiles,
|
|
subsample=n_samples // 10)
|
|
transformer.fit(X)
|
|
diff = (np.linspace(0, 1, n_quantiles) -
|
|
np.ravel(transformer.quantiles_))
|
|
inf_norm = np.max(np.abs(diff))
|
|
assert inf_norm < 1e-2
|
|
inf_norm_arr.append(inf_norm)
|
|
# each random subsampling yield a unique approximation to the expected
|
|
# linspace CDF
|
|
assert_equal(len(np.unique(inf_norm_arr)), len(inf_norm_arr))
|
|
|
|
# sparse support
|
|
|
|
X = sparse.rand(n_samples, 1, density=.99, format='csc', random_state=0)
|
|
inf_norm_arr = []
|
|
for random_state in range(ROUND):
|
|
transformer = QuantileTransformer(random_state=random_state,
|
|
n_quantiles=n_quantiles,
|
|
subsample=n_samples // 10)
|
|
transformer.fit(X)
|
|
diff = (np.linspace(0, 1, n_quantiles) -
|
|
np.ravel(transformer.quantiles_))
|
|
inf_norm = np.max(np.abs(diff))
|
|
assert inf_norm < 1e-1
|
|
inf_norm_arr.append(inf_norm)
|
|
# each random subsampling yield a unique approximation to the expected
|
|
# linspace CDF
|
|
assert_equal(len(np.unique(inf_norm_arr)), len(inf_norm_arr))
|
|
|
|
|
|
def test_quantile_transform_sparse_toy():
|
|
X = np.array([[0., 2., 0.],
|
|
[25., 4., 0.],
|
|
[50., 0., 2.6],
|
|
[0., 0., 4.1],
|
|
[0., 6., 0.],
|
|
[0., 8., 0.],
|
|
[75., 0., 2.3],
|
|
[0., 10., 0.],
|
|
[0., 0., 9.5],
|
|
[100., 0., 0.1]])
|
|
|
|
X = sparse.csc_matrix(X)
|
|
|
|
transformer = QuantileTransformer(n_quantiles=10)
|
|
transformer.fit(X)
|
|
|
|
X_trans = transformer.fit_transform(X)
|
|
assert_array_almost_equal(np.min(X_trans.toarray(), axis=0), 0.)
|
|
assert_array_almost_equal(np.max(X_trans.toarray(), axis=0), 1.)
|
|
|
|
X_trans_inv = transformer.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X.toarray(), X_trans_inv.toarray())
|
|
|
|
transformer_dense = QuantileTransformer(n_quantiles=10).fit(
|
|
X.toarray())
|
|
|
|
X_trans = transformer_dense.transform(X)
|
|
assert_array_almost_equal(np.min(X_trans.toarray(), axis=0), 0.)
|
|
assert_array_almost_equal(np.max(X_trans.toarray(), axis=0), 1.)
|
|
|
|
X_trans_inv = transformer_dense.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X.toarray(), X_trans_inv.toarray())
|
|
|
|
|
|
def test_quantile_transform_axis1():
|
|
X = np.array([[0, 25, 50, 75, 100],
|
|
[2, 4, 6, 8, 10],
|
|
[2.6, 4.1, 2.3, 9.5, 0.1]])
|
|
|
|
X_trans_a0 = quantile_transform(X.T, axis=0, n_quantiles=5)
|
|
X_trans_a1 = quantile_transform(X, axis=1, n_quantiles=5)
|
|
assert_array_almost_equal(X_trans_a0, X_trans_a1.T)
|
|
|
|
|
|
def test_quantile_transform_bounds():
|
|
# Lower and upper bounds are manually mapped. We checked that in the case
|
|
# of a constant feature and binary feature, the bounds are properly mapped.
|
|
X_dense = np.array([[0, 0],
|
|
[0, 0],
|
|
[1, 0]])
|
|
X_sparse = sparse.csc_matrix(X_dense)
|
|
|
|
# check sparse and dense are consistent
|
|
X_trans = QuantileTransformer(n_quantiles=3,
|
|
random_state=0).fit_transform(X_dense)
|
|
assert_array_almost_equal(X_trans, X_dense)
|
|
X_trans_sp = QuantileTransformer(n_quantiles=3,
|
|
random_state=0).fit_transform(X_sparse)
|
|
assert_array_almost_equal(X_trans_sp.A, X_dense)
|
|
assert_array_almost_equal(X_trans, X_trans_sp.A)
|
|
|
|
# check the consistency of the bounds by learning on 1 matrix
|
|
# and transforming another
|
|
X = np.array([[0, 1],
|
|
[0, 0.5],
|
|
[1, 0]])
|
|
X1 = np.array([[0, 0.1],
|
|
[0, 0.5],
|
|
[1, 0.1]])
|
|
transformer = QuantileTransformer(n_quantiles=3).fit(X)
|
|
X_trans = transformer.transform(X1)
|
|
assert_array_almost_equal(X_trans, X1)
|
|
|
|
# check that values outside of the range learned will be mapped properly.
|
|
X = np.random.random((1000, 1))
|
|
transformer = QuantileTransformer()
|
|
transformer.fit(X)
|
|
assert_equal(transformer.transform([[-10]]),
|
|
transformer.transform([[np.min(X)]]))
|
|
assert_equal(transformer.transform([[10]]),
|
|
transformer.transform([[np.max(X)]]))
|
|
assert_equal(transformer.inverse_transform([[-10]]),
|
|
transformer.inverse_transform(
|
|
[[np.min(transformer.references_)]]))
|
|
assert_equal(transformer.inverse_transform([[10]]),
|
|
transformer.inverse_transform(
|
|
[[np.max(transformer.references_)]]))
|
|
|
|
|
|
def test_quantile_transform_and_inverse():
|
|
# iris dataset
|
|
X = iris.data
|
|
transformer = QuantileTransformer(n_quantiles=1000, random_state=0)
|
|
X_trans = transformer.fit_transform(X)
|
|
X_trans_inv = transformer.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X, X_trans_inv)
|
|
|
|
|
|
def test_quantile_transform_nan():
|
|
X = np.array([[np.nan, 0, 0, 1],
|
|
[np.nan, np.nan, 0, 0.5],
|
|
[np.nan, 1, 1, 0]])
|
|
|
|
transformer = QuantileTransformer(n_quantiles=10, random_state=42)
|
|
transformer.fit_transform(X)
|
|
|
|
# check that the quantile of the first column is all NaN
|
|
assert np.isnan(transformer.quantiles_[:, 0]).all()
|
|
# all other column should not contain NaN
|
|
assert not np.isnan(transformer.quantiles_[:, 1:]).any()
|
|
|
|
|
|
def test_robust_scaler_invalid_range():
|
|
for range_ in [
|
|
(-1, 90),
|
|
(-2, -3),
|
|
(10, 101),
|
|
(100.5, 101),
|
|
(90, 50),
|
|
]:
|
|
scaler = RobustScaler(quantile_range=range_)
|
|
|
|
assert_raises_regex(ValueError, r'Invalid quantile range: \(',
|
|
scaler.fit, iris.data)
|
|
|
|
|
|
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 not np.any(np.isnan(X_scaled))
|
|
|
|
X_csr_scaled = scale(X_csr, with_mean=False)
|
|
assert not 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 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))
|
|
|
|
# null scale
|
|
X_csr_scaled = scale(X_csr, with_mean=False, with_std=False, copy=True)
|
|
assert_array_almost_equal(X_csr.toarray(), X_csr_scaled.toarray())
|
|
|
|
|
|
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_scale_1d_array():
|
|
X = iris.data[:, 1]
|
|
X_trans = robust_scale(X)
|
|
assert_array_almost_equal(np.median(X_trans), 0)
|
|
q = np.percentile(X_trans, q=(25, 75))
|
|
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)
|
|
|
|
# function interface
|
|
X_trans = maxabs_scale(X)
|
|
assert_array_almost_equal(X_trans, X_expected)
|
|
|
|
# sparse data
|
|
X_csr = sparse.csr_matrix(X)
|
|
X_csc = sparse.csc_matrix(X)
|
|
X_trans_csr = scaler.fit_transform(X_csr)
|
|
X_trans_csc = scaler.fit_transform(X_csc)
|
|
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_csr.A, X_expected)
|
|
assert_array_almost_equal(X_trans_csc.A, X_expected)
|
|
X_trans_csr_inv = scaler.inverse_transform(X_trans_csr)
|
|
X_trans_csc_inv = scaler.inverse_transform(X_trans_csc)
|
|
assert_array_almost_equal(X, X_trans_csr_inv.A)
|
|
assert_array_almost_equal(X, X_trans_csc_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_maxabs_scaler_transform_one_row_csr():
|
|
# Check MaxAbsScaler on transforming csr matrix with one row
|
|
X = sparse.csr_matrix([[0.5, 1., 1.]])
|
|
scaler = MaxAbsScaler()
|
|
scaler = scaler.fit(X)
|
|
X_trans = scaler.transform(X)
|
|
X_expected = sparse.csr_matrix([[1., 1., 1.]])
|
|
assert_array_almost_equal(X_trans.toarray(), X_expected.toarray())
|
|
X_scaled_back = scaler.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X.toarray(), X_scaled_back.toarray())
|
|
|
|
|
|
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_maxabs_scaler_1d():
|
|
# Test scaling of dataset along single axis
|
|
for X in [X_1row, X_1col, X_list_1row, X_list_1row]:
|
|
|
|
scaler = MaxAbsScaler(copy=True)
|
|
X_scaled = scaler.fit(X).transform(X)
|
|
|
|
if isinstance(X, list):
|
|
X = np.array(X) # cast only after scaling done
|
|
|
|
if _check_dim_1axis(X) == 1:
|
|
assert_array_almost_equal(np.abs(X_scaled.max(axis=0)),
|
|
np.ones(n_features))
|
|
else:
|
|
assert_array_almost_equal(np.abs(X_scaled.max(axis=0)), 1.)
|
|
assert_equal(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 = np.ones((5, 1))
|
|
scaler = MaxAbsScaler()
|
|
X_scaled = scaler.fit(X).transform(X)
|
|
assert_array_almost_equal(np.abs(X_scaled.max(axis=0)), 1.)
|
|
assert_equal(scaler.n_samples_seen_, X.shape[0])
|
|
|
|
# function interface
|
|
X_1d = X_1row.ravel()
|
|
max_abs = np.abs(X_1d).max()
|
|
assert_array_almost_equal(X_1d / max_abs, maxabs_scale(X_1d, copy=True))
|
|
|
|
|
|
def test_maxabs_scaler_partial_fit():
|
|
# Test if partial_fit run over many batches of size 1 and 50
|
|
# gives the same results as fit
|
|
X = X_2d[:100, :]
|
|
n = X.shape[0]
|
|
|
|
for chunk_size in [1, 2, 50, n, n + 42]:
|
|
# Test mean at the end of the process
|
|
scaler_batch = MaxAbsScaler().fit(X)
|
|
|
|
scaler_incr = MaxAbsScaler()
|
|
scaler_incr_csr = MaxAbsScaler()
|
|
scaler_incr_csc = MaxAbsScaler()
|
|
for batch in gen_batches(n, chunk_size):
|
|
scaler_incr = scaler_incr.partial_fit(X[batch])
|
|
X_csr = sparse.csr_matrix(X[batch])
|
|
scaler_incr_csr = scaler_incr_csr.partial_fit(X_csr)
|
|
X_csc = sparse.csc_matrix(X[batch])
|
|
scaler_incr_csc = scaler_incr_csc.partial_fit(X_csc)
|
|
|
|
assert_array_almost_equal(scaler_batch.max_abs_, scaler_incr.max_abs_)
|
|
assert_array_almost_equal(scaler_batch.max_abs_,
|
|
scaler_incr_csr.max_abs_)
|
|
assert_array_almost_equal(scaler_batch.max_abs_,
|
|
scaler_incr_csc.max_abs_)
|
|
assert_equal(scaler_batch.n_samples_seen_, scaler_incr.n_samples_seen_)
|
|
assert_equal(scaler_batch.n_samples_seen_,
|
|
scaler_incr_csr.n_samples_seen_)
|
|
assert_equal(scaler_batch.n_samples_seen_,
|
|
scaler_incr_csc.n_samples_seen_)
|
|
assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
|
|
assert_array_almost_equal(scaler_batch.scale_, scaler_incr_csr.scale_)
|
|
assert_array_almost_equal(scaler_batch.scale_, scaler_incr_csc.scale_)
|
|
assert_array_almost_equal(scaler_batch.transform(X),
|
|
scaler_incr.transform(X))
|
|
|
|
# Test std after 1 step
|
|
batch0 = slice(0, chunk_size)
|
|
scaler_batch = MaxAbsScaler().fit(X[batch0])
|
|
scaler_incr = MaxAbsScaler().partial_fit(X[batch0])
|
|
|
|
assert_array_almost_equal(scaler_batch.max_abs_, scaler_incr.max_abs_)
|
|
assert_equal(scaler_batch.n_samples_seen_, scaler_incr.n_samples_seen_)
|
|
assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
|
|
assert_array_almost_equal(scaler_batch.transform(X),
|
|
scaler_incr.transform(X))
|
|
|
|
# Test std until the end of partial fits, and
|
|
scaler_batch = MaxAbsScaler().fit(X)
|
|
scaler_incr = MaxAbsScaler() # Clean estimator
|
|
for i, batch in enumerate(gen_batches(n, 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_)
|
|
|
|
|
|
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 X_norm is not X
|
|
X_norm1 = toarray(X_norm)
|
|
|
|
normalizer = Normalizer(norm='l1', copy=False)
|
|
X_norm = normalizer.transform(X)
|
|
assert 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 X_norm is not X
|
|
assert 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 X_norm1 is not X
|
|
X_norm1 = toarray(X_norm1)
|
|
|
|
normalizer = Normalizer(norm='l2', copy=False)
|
|
X_norm2 = normalizer.transform(X)
|
|
assert 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 X_norm is not X
|
|
assert 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 X_norm1 is not X
|
|
X_norm1 = toarray(X_norm1)
|
|
|
|
normalizer = Normalizer(norm='max', copy=False)
|
|
X_norm2 = normalizer.transform(X)
|
|
assert 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 X_norm is not X
|
|
assert 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')
|
|
|
|
rs = np.random.RandomState(0)
|
|
X_dense = rs.randn(10, 5)
|
|
X_sparse = sparse.csr_matrix(X_dense)
|
|
ones = np.ones((10))
|
|
for X in (X_dense, X_sparse):
|
|
for dtype in (np.float32, np.float64):
|
|
for norm in ('l1', 'l2'):
|
|
X = X.astype(dtype)
|
|
X_norm = normalize(X, norm=norm)
|
|
assert_equal(X_norm.dtype, dtype)
|
|
|
|
X_norm = toarray(X_norm)
|
|
if norm == 'l1':
|
|
row_sums = np.abs(X_norm).sum(axis=1)
|
|
else:
|
|
X_norm_squared = X_norm**2
|
|
row_sums = X_norm_squared.sum(axis=1)
|
|
|
|
assert_array_almost_equal(row_sums, ones)
|
|
|
|
# Test return_norm
|
|
X_dense = np.array([[3.0, 0, 4.0], [1.0, 0.0, 0.0], [2.0, 3.0, 0.0]])
|
|
for norm in ('l1', 'l2', 'max'):
|
|
_, norms = normalize(X_dense, norm=norm, return_norm=True)
|
|
if norm == 'l1':
|
|
assert_array_almost_equal(norms, np.array([7.0, 1.0, 5.0]))
|
|
elif norm == 'l2':
|
|
assert_array_almost_equal(norms, np.array([5.0, 1.0, 3.60555127]))
|
|
else:
|
|
assert_array_almost_equal(norms, np.array([4.0, 1.0, 3.0]))
|
|
|
|
X_sparse = sparse.csr_matrix(X_dense)
|
|
for norm in ('l1', 'l2'):
|
|
assert_raises(NotImplementedError, normalize, X_sparse,
|
|
norm=norm, return_norm=True)
|
|
_, norms = normalize(X_sparse, norm='max', return_norm=True)
|
|
assert_array_almost_equal(norms, np.array([4.0, 1.0, 3.0]))
|
|
|
|
|
|
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 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 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 X_bin is X
|
|
|
|
binarizer = Binarizer(copy=False)
|
|
X_float = np.array([[1, 0, 5], [2, 3, -1]], dtype=np.float64)
|
|
X_bin = binarizer.transform(X_float)
|
|
if init is not list:
|
|
assert X_bin is X_float
|
|
|
|
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)
|
|
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_cv_pipeline_precomputed():
|
|
# Cross-validate a regression on four coplanar points with the same
|
|
# value. Use precomputed kernel to ensure Pipeline with KernelCenterer
|
|
# is treated as a _pairwise operation.
|
|
X = np.array([[3, 0, 0], [0, 3, 0], [0, 0, 3], [1, 1, 1]])
|
|
y_true = np.ones((4,))
|
|
K = X.dot(X.T)
|
|
kcent = KernelCenterer()
|
|
pipeline = Pipeline([("kernel_centerer", kcent), ("svr",
|
|
SVR(gamma='scale'))])
|
|
|
|
# did the pipeline set the _pairwise attribute?
|
|
assert pipeline._pairwise
|
|
|
|
# test cross-validation, score should be almost perfect
|
|
# NB: this test is pretty vacuous -- it's mainly to test integration
|
|
# of Pipeline and KernelCenterer
|
|
y_pred = cross_val_predict(pipeline, K, y_true, cv=2)
|
|
assert_array_almost_equal(y_true, y_pred)
|
|
|
|
|
|
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 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 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 sparse.isspmatrix_csr(X), X
|
|
assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]])
|
|
|
|
|
|
def test_fit_cold_start():
|
|
X = iris.data
|
|
X_2d = X[:, :2]
|
|
|
|
# Scalers that have a partial_fit method
|
|
scalers = [StandardScaler(with_mean=False, with_std=False),
|
|
MinMaxScaler(),
|
|
MaxAbsScaler()]
|
|
|
|
for scaler in scalers:
|
|
scaler.fit_transform(X)
|
|
# with a different shape, this may break the scaler unless the internal
|
|
# state is reset
|
|
scaler.fit_transform(X_2d)
|
|
|
|
|
|
def test_quantile_transform_valid_axis():
|
|
X = np.array([[0, 25, 50, 75, 100],
|
|
[2, 4, 6, 8, 10],
|
|
[2.6, 4.1, 2.3, 9.5, 0.1]])
|
|
|
|
assert_raises_regex(ValueError, "axis should be either equal to 0 or 1"
|
|
". Got axis=2", quantile_transform, X.T, axis=2)
|
|
|
|
|
|
@pytest.mark.parametrize("method", ['box-cox', 'yeo-johnson'])
|
|
def test_power_transformer_notfitted(method):
|
|
pt = PowerTransformer(method=method)
|
|
X = np.abs(X_1col)
|
|
assert_raises(NotFittedError, pt.transform, X)
|
|
assert_raises(NotFittedError, pt.inverse_transform, X)
|
|
|
|
|
|
@pytest.mark.parametrize('method', ['box-cox', 'yeo-johnson'])
|
|
@pytest.mark.parametrize('standardize', [True, False])
|
|
@pytest.mark.parametrize('X', [X_1col, X_2d])
|
|
def test_power_transformer_inverse(method, standardize, X):
|
|
# Make sure we get the original input when applying transform and then
|
|
# inverse transform
|
|
X = np.abs(X) if method == 'box-cox' else X
|
|
pt = PowerTransformer(method=method, standardize=standardize)
|
|
X_trans = pt.fit_transform(X)
|
|
assert_almost_equal(X, pt.inverse_transform(X_trans))
|
|
|
|
|
|
def test_power_transformer_1d():
|
|
X = np.abs(X_1col)
|
|
|
|
for standardize in [True, False]:
|
|
pt = PowerTransformer(method='box-cox', standardize=standardize)
|
|
|
|
X_trans = pt.fit_transform(X)
|
|
X_trans_func = power_transform(
|
|
X, method='box-cox',
|
|
standardize=standardize
|
|
)
|
|
|
|
X_expected, lambda_expected = stats.boxcox(X.flatten())
|
|
|
|
if standardize:
|
|
X_expected = scale(X_expected)
|
|
|
|
assert_almost_equal(X_expected.reshape(-1, 1), X_trans)
|
|
assert_almost_equal(X_expected.reshape(-1, 1), X_trans_func)
|
|
|
|
assert_almost_equal(X, pt.inverse_transform(X_trans))
|
|
assert_almost_equal(lambda_expected, pt.lambdas_[0])
|
|
|
|
assert len(pt.lambdas_) == X.shape[1]
|
|
assert isinstance(pt.lambdas_, np.ndarray)
|
|
|
|
|
|
def test_power_transformer_2d():
|
|
X = np.abs(X_2d)
|
|
|
|
for standardize in [True, False]:
|
|
pt = PowerTransformer(method='box-cox', standardize=standardize)
|
|
|
|
X_trans_class = pt.fit_transform(X)
|
|
X_trans_func = power_transform(
|
|
X, method='box-cox',
|
|
standardize=standardize
|
|
)
|
|
|
|
for X_trans in [X_trans_class, X_trans_func]:
|
|
for j in range(X_trans.shape[1]):
|
|
X_expected, lmbda = stats.boxcox(X[:, j].flatten())
|
|
|
|
if standardize:
|
|
X_expected = scale(X_expected)
|
|
|
|
assert_almost_equal(X_trans[:, j], X_expected)
|
|
assert_almost_equal(lmbda, pt.lambdas_[j])
|
|
|
|
# Test inverse transformation
|
|
X_inv = pt.inverse_transform(X_trans)
|
|
assert_array_almost_equal(X_inv, X)
|
|
|
|
assert len(pt.lambdas_) == X.shape[1]
|
|
assert isinstance(pt.lambdas_, np.ndarray)
|
|
|
|
|
|
def test_power_transformer_boxcox_strictly_positive_exception():
|
|
# Exceptions should be raised for negative arrays and zero arrays when
|
|
# method is boxcox
|
|
|
|
pt = PowerTransformer(method='box-cox')
|
|
pt.fit(np.abs(X_2d))
|
|
X_with_negatives = X_2d
|
|
not_positive_message = 'strictly positive'
|
|
|
|
assert_raise_message(ValueError, not_positive_message,
|
|
pt.transform, X_with_negatives)
|
|
|
|
assert_raise_message(ValueError, not_positive_message,
|
|
pt.fit, X_with_negatives)
|
|
|
|
assert_raise_message(ValueError, not_positive_message,
|
|
power_transform, X_with_negatives, 'box-cox')
|
|
|
|
assert_raise_message(ValueError, not_positive_message,
|
|
pt.transform, np.zeros(X_2d.shape))
|
|
|
|
assert_raise_message(ValueError, not_positive_message,
|
|
pt.fit, np.zeros(X_2d.shape))
|
|
|
|
assert_raise_message(ValueError, not_positive_message,
|
|
power_transform, np.zeros(X_2d.shape), 'box-cox')
|
|
|
|
|
|
@pytest.mark.parametrize('X', [X_2d, np.abs(X_2d), -np.abs(X_2d),
|
|
np.zeros(X_2d.shape)])
|
|
def test_power_transformer_yeojohnson_any_input(X):
|
|
# Yeo-Johnson method should support any kind of input
|
|
power_transform(X, method='yeo-johnson')
|
|
|
|
|
|
@pytest.mark.parametrize("method", ['box-cox', 'yeo-johnson'])
|
|
def test_power_transformer_shape_exception(method):
|
|
pt = PowerTransformer(method=method)
|
|
X = np.abs(X_2d)
|
|
pt.fit(X)
|
|
|
|
# Exceptions should be raised for arrays with different num_columns
|
|
# than during fitting
|
|
wrong_shape_message = 'Input data has a different number of features'
|
|
|
|
assert_raise_message(ValueError, wrong_shape_message,
|
|
pt.transform, X[:, 0:1])
|
|
|
|
assert_raise_message(ValueError, wrong_shape_message,
|
|
pt.inverse_transform, X[:, 0:1])
|
|
|
|
|
|
def test_power_transformer_method_exception():
|
|
pt = PowerTransformer(method='monty-python')
|
|
X = np.abs(X_2d)
|
|
|
|
# An exception should be raised if PowerTransformer.method isn't valid
|
|
bad_method_message = "'method' must be one of"
|
|
assert_raise_message(ValueError, bad_method_message,
|
|
pt.fit, X)
|
|
|
|
|
|
def test_power_transformer_lambda_zero():
|
|
pt = PowerTransformer(method='box-cox', standardize=False)
|
|
X = np.abs(X_2d)[:, 0:1]
|
|
|
|
# Test the lambda = 0 case
|
|
pt.lambdas_ = np.array([0])
|
|
X_trans = pt.transform(X)
|
|
assert_array_almost_equal(pt.inverse_transform(X_trans), X)
|
|
|
|
|
|
def test_power_transformer_lambda_one():
|
|
# Make sure lambda = 1 corresponds to the identity for yeo-johnson
|
|
pt = PowerTransformer(method='yeo-johnson', standardize=False)
|
|
X = np.abs(X_2d)[:, 0:1]
|
|
|
|
pt.lambdas_ = np.array([1])
|
|
X_trans = pt.transform(X)
|
|
assert_array_almost_equal(X_trans, X)
|
|
|
|
|
|
@pytest.mark.parametrize("method, lmbda", [('box-cox', .1),
|
|
('box-cox', .5),
|
|
('yeo-johnson', .1),
|
|
('yeo-johnson', .5),
|
|
('yeo-johnson', 1.),
|
|
])
|
|
def test_optimization_power_transformer(method, lmbda):
|
|
# Test the optimization procedure:
|
|
# - set a predefined value for lambda
|
|
# - apply inverse_transform to a normal dist (we get X_inv)
|
|
# - apply fit_transform to X_inv (we get X_inv_trans)
|
|
# - check that X_inv_trans is roughly equal to X
|
|
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 20000
|
|
X = rng.normal(loc=0, scale=1, size=(n_samples, 1))
|
|
|
|
pt = PowerTransformer(method=method, standardize=False)
|
|
pt.lambdas_ = [lmbda]
|
|
X_inv = pt.inverse_transform(X)
|
|
|
|
pt = PowerTransformer(method=method, standardize=False)
|
|
X_inv_trans = pt.fit_transform(X_inv)
|
|
|
|
assert_almost_equal(0, np.linalg.norm(X - X_inv_trans) / n_samples,
|
|
decimal=2)
|
|
assert_almost_equal(0, X_inv_trans.mean(), decimal=1)
|
|
assert_almost_equal(1, X_inv_trans.std(), decimal=1)
|
|
|
|
|
|
def test_yeo_johnson_darwin_example():
|
|
# test from original paper "A new family of power transformations to
|
|
# improve normality or symmetry" by Yeo and Johnson.
|
|
X = [6.1, -8.4, 1.0, 2.0, 0.7, 2.9, 3.5, 5.1, 1.8, 3.6, 7.0, 3.0, 9.3,
|
|
7.5, -6.0]
|
|
X = np.array(X).reshape(-1, 1)
|
|
lmbda = PowerTransformer(method='yeo-johnson').fit(X).lambdas_
|
|
assert np.allclose(lmbda, 1.305, atol=1e-3)
|
|
|
|
|
|
@pytest.mark.parametrize('method', ['box-cox', 'yeo-johnson'])
|
|
def test_power_transformer_nans(method):
|
|
# Make sure lambda estimation is not influenced by NaN values
|
|
# and that transform() supports NaN silently
|
|
|
|
X = np.abs(X_1col)
|
|
pt = PowerTransformer(method=method)
|
|
pt.fit(X)
|
|
lmbda_no_nans = pt.lambdas_[0]
|
|
|
|
# concat nans at the end and check lambda stays the same
|
|
X = np.concatenate([X, np.full_like(X, np.nan)])
|
|
X = shuffle(X, random_state=0)
|
|
|
|
pt.fit(X)
|
|
lmbda_nans = pt.lambdas_[0]
|
|
|
|
assert_almost_equal(lmbda_no_nans, lmbda_nans, decimal=5)
|
|
|
|
X_trans = pt.transform(X)
|
|
assert_array_equal(np.isnan(X_trans), np.isnan(X))
|
|
|
|
|
|
@pytest.mark.parametrize('method', ['box-cox', 'yeo-johnson'])
|
|
@pytest.mark.parametrize('standardize', [True, False])
|
|
def test_power_transformer_fit_transform(method, standardize):
|
|
# check that fit_transform() and fit().transform() return the same values
|
|
X = X_1col
|
|
if method == 'box-cox':
|
|
X = np.abs(X)
|
|
|
|
pt = PowerTransformer(method, standardize)
|
|
assert_array_almost_equal(pt.fit(X).transform(X), pt.fit_transform(X))
|
|
|
|
|
|
@pytest.mark.parametrize('method', ['box-cox', 'yeo-johnson'])
|
|
@pytest.mark.parametrize('standardize', [True, False])
|
|
def test_power_transformer_copy_True(method, standardize):
|
|
# Check that neither fit, transform, fit_transform nor inverse_transform
|
|
# modify X inplace when copy=True
|
|
X = X_1col
|
|
if method == 'box-cox':
|
|
X = np.abs(X)
|
|
|
|
X_original = X.copy()
|
|
assert X is not X_original # sanity checks
|
|
assert_array_almost_equal(X, X_original)
|
|
|
|
pt = PowerTransformer(method, standardize, copy=True)
|
|
|
|
pt.fit(X)
|
|
assert_array_almost_equal(X, X_original)
|
|
X_trans = pt.transform(X)
|
|
assert X_trans is not X
|
|
|
|
X_trans = pt.fit_transform(X)
|
|
assert_array_almost_equal(X, X_original)
|
|
assert X_trans is not X
|
|
|
|
X_inv_trans = pt.inverse_transform(X_trans)
|
|
assert X_trans is not X_inv_trans
|
|
|
|
|
|
@pytest.mark.parametrize('method', ['box-cox', 'yeo-johnson'])
|
|
@pytest.mark.parametrize('standardize', [True, False])
|
|
def test_power_transformer_copy_False(method, standardize):
|
|
# check that when copy=False fit doesn't change X inplace but transform,
|
|
# fit_transform and inverse_transform do.
|
|
X = X_1col
|
|
if method == 'box-cox':
|
|
X = np.abs(X)
|
|
|
|
X_original = X.copy()
|
|
assert X is not X_original # sanity checks
|
|
assert_array_almost_equal(X, X_original)
|
|
|
|
pt = PowerTransformer(method, standardize, copy=False)
|
|
|
|
pt.fit(X)
|
|
assert_array_almost_equal(X, X_original) # fit didn't change X
|
|
|
|
X_trans = pt.transform(X)
|
|
assert X_trans is X
|
|
|
|
if method == 'box-cox':
|
|
X = np.abs(X)
|
|
X_trans = pt.fit_transform(X)
|
|
assert X_trans is X
|
|
|
|
X_inv_trans = pt.inverse_transform(X_trans)
|
|
assert X_trans is X_inv_trans
|
|
|
|
|
|
def test_power_transform_default_method():
|
|
X = np.abs(X_2d)
|
|
|
|
future_warning_message = (
|
|
"The default value of 'method' "
|
|
"will change from 'box-cox'"
|
|
)
|
|
assert_warns_message(FutureWarning, future_warning_message,
|
|
power_transform, X)
|
|
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter('ignore')
|
|
X_trans_default = power_transform(X)
|
|
|
|
X_trans_boxcox = power_transform(X, method='box-cox')
|
|
assert_array_equal(X_trans_boxcox, X_trans_default)
|