362 lines
12 KiB
Python
362 lines
12 KiB
Python
import numpy as np
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import numpy.linalg as la
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import scipy.sparse as sp
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from numpy.testing import assert_almost_equal
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from numpy.testing import assert_array_almost_equal
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from numpy.testing import assert_array_equal
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from numpy.testing import assert_equal
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from nose.tools import assert_raises
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from sklearn.preprocessing import Binarizer
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from sklearn.preprocessing import KernelCenterer
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from sklearn.preprocessing import LabelBinarizer
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from sklearn.preprocessing import Normalizer
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from sklearn.preprocessing import normalize
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from sklearn.preprocessing import Scaler
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from sklearn.preprocessing import scale
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from sklearn import datasets
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from sklearn.linear_model.stochastic_gradient import SGDClassifier
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np.random.seed(0)
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iris = datasets.load_iris()
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def toarray(a):
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if hasattr(a, "toarray"):
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a = a.toarray()
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return a
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def test_scaler():
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"""Test scaling of dataset along all axis"""
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# First test with 1D data
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X = np.random.randn(5)
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X_orig_copy = X.copy()
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scaler = Scaler()
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X_scaled = scaler.fit(X).transform(X, copy=False)
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assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
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# check inverse transform
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X_scaled_back = scaler.inverse_transform(X_scaled)
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assert_array_almost_equal(X_scaled_back, X_orig_copy)
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# Test with 1D list
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X = [0., 1., 2, 0.4, 1.]
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scaler = Scaler()
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X_scaled = scaler.fit(X).transform(X, copy=False)
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assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
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X_scaled = scale(X)
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assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
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assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
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# Test with 2D data
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X = np.random.randn(4, 5)
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X[:, 0] = 0.0 # first feature is always of zero
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scaler = Scaler()
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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_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has not been copied
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assert 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
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assert_array_almost_equal(X_scaled_back, X)
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X_scaled = scale(X, axis=1, with_std=False)
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assert not np.any(np.isnan(X_scaled))
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assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
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X_scaled = scale(X, axis=1, with_std=True)
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assert not np.any(np.isnan(X_scaled))
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assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=1), 4 * [1.0])
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# Check that the data hasn't been modified
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assert X_scaled is not X
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X_scaled = scaler.fit(X).transform(X, copy=False)
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assert not np.any(np.isnan(X_scaled))
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assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has not been copied
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assert X_scaled is X
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X = np.random.randn(4, 5)
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X[:, 0] = 1.0 # first feature is a constant, non zero feature
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scaler = Scaler()
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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_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has not been copied
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assert X_scaled is not X
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def test_scaler_without_centering():
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rng = np.random.RandomState(42)
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X = rng.randn(4, 5)
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X[:, 0] = 0.0 # first feature is always of zero
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scaler = Scaler(with_mean=False)
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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_array_almost_equal(
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X_scaled.mean(axis=0), [0., -0.01, 2.24, -0.35, -0.78], 2)
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has not been copied
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assert X_scaled is not X
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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
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assert_array_almost_equal(X_scaled_back, X)
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X_scaled = scale(X, with_mean=False)
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assert not np.any(np.isnan(X_scaled))
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assert_array_almost_equal(
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X_scaled.mean(axis=0), [0., -0.01, 2.24, -0.35, -0.78], 2)
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assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
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# Check that X has not been copied
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assert X_scaled is not X
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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
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assert_array_almost_equal(X_scaled_back, X)
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def test_normalizer_l1():
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rng = np.random.RandomState(0)
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X_dense = rng.randn(4, 5)
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X_sparse_unpruned = sp.csr_matrix(X_dense)
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# set the row number 3 to zero
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X_dense[3, :] = 0.0
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# set the row number 3 to zero without pruning (can happen in real life)
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indptr_3 = X_sparse_unpruned.indptr[3]
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indptr_4 = X_sparse_unpruned.indptr[4]
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X_sparse_unpruned.data[indptr_3:indptr_4] = 0.0
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# build the pruned variant using the regular constructor
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X_sparse_pruned = sp.csr_matrix(X_dense)
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# check inputs that support the no-copy optim
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for X in (X_dense, X_sparse_pruned, X_sparse_unpruned):
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normalizer = Normalizer(norm='l1', copy=True)
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X_norm = normalizer.transform(X)
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assert X_norm is not X
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X_norm1 = toarray(X_norm)
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normalizer = Normalizer(norm='l1', copy=False)
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X_norm = normalizer.transform(X)
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assert X_norm is X
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X_norm2 = toarray(X_norm)
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for X_norm in (X_norm1, X_norm2):
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row_sums = np.abs(X_norm).sum(axis=1)
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for i in range(3):
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assert_almost_equal(row_sums[i], 1.0)
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assert_almost_equal(row_sums[3], 0.0)
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# check input for which copy=False won't prevent a copy
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for init in (sp.coo_matrix, sp.csc_matrix, sp.lil_matrix):
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X = init(X_dense)
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X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
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assert X_norm is not X
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assert isinstance(X_norm, sp.csr_matrix)
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X_norm = toarray(X_norm)
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for i in xrange(3):
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assert_almost_equal(row_sums[i], 1.0)
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assert_almost_equal(la.norm(X_norm[3]), 0.0)
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def test_normalizer_l2():
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rng = np.random.RandomState(0)
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X_dense = rng.randn(4, 5)
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X_sparse_unpruned = sp.csr_matrix(X_dense)
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# set the row number 3 to zero
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X_dense[3, :] = 0.0
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# set the row number 3 to zero without pruning (can happen in real life)
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indptr_3 = X_sparse_unpruned.indptr[3]
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indptr_4 = X_sparse_unpruned.indptr[4]
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X_sparse_unpruned.data[indptr_3:indptr_4] = 0.0
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# build the pruned variant using the regular constructor
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X_sparse_pruned = sp.csr_matrix(X_dense)
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# check inputs that support the no-copy optim
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for X in (X_dense, X_sparse_pruned, X_sparse_unpruned):
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normalizer = Normalizer(norm='l2', copy=True)
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X_norm1 = normalizer.transform(X)
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assert X_norm1 is not X
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X_norm1 = toarray(X_norm1)
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normalizer = Normalizer(norm='l2', copy=False)
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X_norm2 = normalizer.transform(X)
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assert X_norm2 is X
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X_norm2 = toarray(X_norm2)
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for X_norm in (X_norm1, X_norm2):
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for i in xrange(3):
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assert_almost_equal(la.norm(X_norm[i]), 1.0)
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assert_almost_equal(la.norm(X_norm[3]), 0.0)
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# check input for which copy=False won't prevent a copy
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for init in (sp.coo_matrix, sp.csc_matrix, sp.lil_matrix):
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X = init(X_dense)
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X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
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assert X_norm is not X
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assert isinstance(X_norm, sp.csr_matrix)
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X_norm = toarray(X_norm)
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for i in xrange(3):
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assert_almost_equal(la.norm(X_norm[i]), 1.0)
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assert_almost_equal(la.norm(X_norm[3]), 0.0)
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def test_normalize_errors():
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"""Check that invalid arguments yield ValueError"""
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assert_raises(ValueError, normalize, [[0]], axis=2)
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assert_raises(ValueError, normalize, [[0]], norm='l3')
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def test_binarizer():
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X_ = np.array([[1, 0, 5], [2, 3, 0]])
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for init in (np.array, sp.csr_matrix):
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X = init(X_.copy())
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binarizer = Binarizer(threshold=2.0, copy=True)
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X_bin = toarray(binarizer.transform(X))
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assert_equal(np.sum(X_bin == 0), 4)
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assert_equal(np.sum(X_bin == 1), 2)
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binarizer = Binarizer(copy=True).fit(X)
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X_bin = toarray(binarizer.transform(X))
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assert X_bin is not X
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assert_equal(np.sum(X_bin == 0), 2)
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assert_equal(np.sum(X_bin == 1), 4)
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binarizer = Binarizer(copy=True)
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X_bin = binarizer.transform(X)
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assert X_bin is not X
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X_bin = toarray(X_bin)
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assert_equal(np.sum(X_bin == 0), 2)
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assert_equal(np.sum(X_bin == 1), 4)
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binarizer = Binarizer(copy=False)
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X_bin = binarizer.transform(X)
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assert X_bin is X
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X_bin = toarray(X_bin)
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assert_equal(np.sum(X_bin == 0), 2)
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assert_equal(np.sum(X_bin == 1), 4)
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def test_label_binarizer():
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lb = LabelBinarizer()
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# two-class case
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inp = np.array([0, 1, 1, 0])
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expected = np.array([[0, 1, 1, 0]]).T
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got = lb.fit_transform(inp)
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assert_array_equal(expected, got)
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assert_array_equal(lb.inverse_transform(got), inp)
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# multi-class case
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inp = np.array([3, 2, 1, 2, 0])
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expected = np.array([[0, 0, 0, 1],
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[0, 0, 1, 0],
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[0, 1, 0, 0],
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[0, 0, 1, 0],
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[1, 0, 0, 0]])
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got = lb.fit_transform(inp)
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assert_array_equal(expected, got)
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assert_array_equal(lb.inverse_transform(got), inp)
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def test_label_binarizer_multilabel():
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lb = LabelBinarizer()
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inp = [(2, 3), (1,), (1, 2)]
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expected = np.array([[0, 1, 1],
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[1, 0, 0],
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[1, 1, 0]])
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got = lb.fit_transform(inp)
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assert_array_equal(expected, got)
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assert_equal(lb.inverse_transform(got), inp)
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def test_label_binarizer_errors():
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"""Check that invalid arguments yield ValueError"""
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one_class = np.array([0, 0, 0, 0])
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lb = LabelBinarizer().fit(one_class)
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multi_label = [(2, 3), (0,), (0, 2)]
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assert_raises(ValueError, lb.transform, multi_label)
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def test_label_binarizer_iris():
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lb = LabelBinarizer()
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Y = lb.fit_transform(iris.target)
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clfs = [SGDClassifier().fit(iris.data, Y[:, k])
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for k in range(len(lb.classes_))]
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Y_pred = np.array([clf.decision_function(iris.data) for clf in clfs]).T
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y_pred = lb.inverse_transform(Y_pred)
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accuracy = np.mean(iris.target == y_pred)
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y_pred2 = SGDClassifier().fit(iris.data, iris.target).predict(iris.data)
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accuracy2 = np.mean(iris.target == y_pred2)
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assert_almost_equal(accuracy, accuracy2)
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def test_center_kernel():
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"""Test that KernelCenterer is equivalent to Scaler in feature space"""
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X_fit = np.random.random((5, 4))
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scaler = Scaler(with_std=False)
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scaler.fit(X_fit)
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X_fit_centered = scaler.transform(X_fit)
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K_fit = np.dot(X_fit, X_fit.T)
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# center fit time matrix
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centerer = KernelCenterer()
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K_fit_centered = np.dot(X_fit_centered, X_fit_centered.T)
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K_fit_centered2 = centerer.fit_transform(K_fit)
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assert_array_almost_equal(K_fit_centered, K_fit_centered2)
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# center predict time matrix
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X_pred = np.random.random((2, 4))
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K_pred = np.dot(X_pred, X_fit.T)
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X_pred_centered = scaler.transform(X_pred)
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K_pred_centered = np.dot(X_pred_centered, X_fit_centered.T)
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K_pred_centered2 = centerer.transform(K_pred)
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assert_array_almost_equal(K_pred_centered, K_pred_centered2)
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def test_fit_transform():
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X = np.random.random((5, 4))
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for obj in ((Scaler(), Normalizer(), Binarizer())):
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X_transformed = obj.fit(X).transform(X)
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X_transformed2 = obj.fit_transform(X)
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assert_array_equal(X_transformed, X_transformed2)
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