316 lines
10 KiB
Python
316 lines
10 KiB
Python
import numpy as np
|
|
import numpy.linalg as la
|
|
import scipy.sparse as sp
|
|
|
|
from numpy.testing import assert_almost_equal
|
|
from numpy.testing import assert_array_almost_equal
|
|
from numpy.testing import assert_array_equal
|
|
from numpy.testing import assert_equal
|
|
|
|
from nose.tools import assert_raises
|
|
|
|
from scikits.learn.preprocessing import Binarizer
|
|
from scikits.learn.preprocessing import KernelCenterer
|
|
from scikits.learn.preprocessing import LabelBinarizer
|
|
from scikits.learn.preprocessing import Normalizer
|
|
from scikits.learn.preprocessing import normalize
|
|
from scikits.learn.preprocessing import Scaler
|
|
from scikits.learn.preprocessing import scale
|
|
|
|
from scikits.learn import datasets
|
|
from scikits.learn.linear_model.stochastic_gradient import SGDClassifier
|
|
|
|
np.random.seed(0)
|
|
|
|
iris = datasets.load_iris()
|
|
|
|
|
|
def toarray(a):
|
|
if hasattr(a, "toarray"):
|
|
a = a.toarray()
|
|
return a
|
|
|
|
|
|
def test_scaler():
|
|
"""Test scaling of dataset along all axis"""
|
|
# First test with 1D data
|
|
X = np.random.randn(5)
|
|
|
|
scaler = Scaler()
|
|
X_scaled = scaler.fit(X).transform(X, copy=False)
|
|
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
|
|
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
|
|
|
|
# Test with 1D list
|
|
X = [0., 1., 2, 0.4, 1.]
|
|
scaler = Scaler()
|
|
X_scaled = scaler.fit(X).transform(X, copy=False)
|
|
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
|
|
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
|
|
|
|
X_scaled = scale(X)
|
|
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
|
|
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
|
|
|
|
# Test with 2D data
|
|
X = np.random.randn(4, 5)
|
|
X[:, 0] = 0.0 # first feature is always of zero
|
|
|
|
scaler = Scaler()
|
|
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), 5 * [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
|
|
|
|
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), 4 * [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), 4 * [0.0])
|
|
assert_array_almost_equal(X_scaled.std(axis=1), 4 * [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), 5 * [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 = np.random.randn(4, 5)
|
|
X[:, 0] = 1.0 # first feature is a constant, non zero feature
|
|
scaler = Scaler()
|
|
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), 5 * [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_without_centering():
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randn(4, 5)
|
|
X[:, 0] = 0.0 # first feature is always of zero
|
|
|
|
scaler = Scaler(with_mean=False)
|
|
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), [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_scaled = scale(X, with_mean=False)
|
|
assert not np.any(np.isnan(X_scaled))
|
|
|
|
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
|
|
|
|
|
|
def test_normalizer_l1():
|
|
np.random.seed(0)
|
|
X_orig = np.random.randn(4, 5)
|
|
X_orig[3, :] = 0.0
|
|
|
|
# check inputs that support the no-copy optim
|
|
for init in (np.array, sp.csr_matrix):
|
|
|
|
X = init(X_orig.copy())
|
|
|
|
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 (sp.coo_matrix, sp.csc_matrix, sp.lil_matrix):
|
|
X = init(X_orig.copy())
|
|
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
|
|
|
|
assert X_norm is not X
|
|
assert isinstance(X_norm, sp.csr_matrix)
|
|
|
|
X_norm = toarray(X_norm)
|
|
for i in xrange(3):
|
|
assert_almost_equal(row_sums[i], 1.0)
|
|
assert_almost_equal(la.norm(X_norm[3]), 0.0)
|
|
|
|
|
|
def test_normalizer_l2():
|
|
np.random.seed(0)
|
|
X_orig = np.random.randn(4, 5)
|
|
X_orig[3, :] = 0.0
|
|
|
|
# check inputs that support the no-copy optim
|
|
for init in (np.array, sp.csr_matrix):
|
|
|
|
X = init(X_orig.copy())
|
|
|
|
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 xrange(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 (sp.coo_matrix, sp.csc_matrix, sp.lil_matrix):
|
|
X = init(X_orig.copy())
|
|
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
|
|
|
|
assert X_norm is not X
|
|
assert isinstance(X_norm, sp.csr_matrix)
|
|
|
|
X_norm = toarray(X_norm)
|
|
for i in xrange(3):
|
|
assert_almost_equal(la.norm(X_norm[i]), 1.0)
|
|
assert_almost_equal(la.norm(X_norm[3]), 0.0)
|
|
|
|
|
|
def test_normalize_errors():
|
|
"""Check that invalid arguments yield ValueError"""
|
|
assert_raises(ValueError, normalize, [[0]], axis=2)
|
|
assert_raises(ValueError, normalize, [[0]], norm='l3')
|
|
|
|
|
|
def test_binarizer():
|
|
X_ = np.array([[1, 0, 5], [2, 3, 0]])
|
|
|
|
for init in (np.array, sp.csr_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)
|
|
|
|
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)
|
|
assert X_bin is X
|
|
X_bin = toarray(X_bin)
|
|
assert_equal(np.sum(X_bin == 0), 2)
|
|
assert_equal(np.sum(X_bin == 1), 4)
|
|
|
|
|
|
def test_label_binarizer():
|
|
lb = LabelBinarizer()
|
|
|
|
# two-class case
|
|
inp = np.array([0, 1, 1, 0])
|
|
expected = np.array([[0, 1, 1, 0]]).T
|
|
got = lb.fit_transform(inp)
|
|
assert_array_equal(expected, got)
|
|
assert_array_equal(lb.inverse_transform(got), inp)
|
|
|
|
# multi-class case
|
|
inp = np.array([3, 2, 1, 2, 0])
|
|
expected = np.array([[0, 0, 0, 1],
|
|
[0, 0, 1, 0],
|
|
[0, 1, 0, 0],
|
|
[0, 0, 1, 0],
|
|
[1, 0, 0, 0]])
|
|
got = lb.fit_transform(inp)
|
|
assert_array_equal(expected, got)
|
|
assert_array_equal(lb.inverse_transform(got), inp)
|
|
|
|
|
|
def test_label_binarizer_multilabel():
|
|
lb = LabelBinarizer()
|
|
|
|
inp = [(2, 3), (1,), (1, 2)]
|
|
expected = np.array([[0, 1, 1],
|
|
[1, 0, 0],
|
|
[1, 1, 0]])
|
|
got = lb.fit_transform(inp)
|
|
assert_array_equal(expected, got)
|
|
assert_equal(lb.inverse_transform(got), inp)
|
|
|
|
|
|
def test_label_binarizer_errors():
|
|
"""Check that invalid arguments yield ValueError"""
|
|
one_class = np.array([0, 0, 0, 0])
|
|
lb = LabelBinarizer().fit(one_class)
|
|
|
|
multi_label = [(2, 3), (0,), (0, 2)]
|
|
assert_raises(ValueError, lb.transform, multi_label)
|
|
|
|
|
|
def test_label_binarizer_iris():
|
|
lb = LabelBinarizer()
|
|
Y = lb.fit_transform(iris.target)
|
|
clfs = [SGDClassifier().fit(iris.data, Y[:, k])
|
|
for k in range(len(lb.classes_))]
|
|
Y_pred = np.array([clf.decision_function(iris.data) for clf in clfs]).T
|
|
y_pred = lb.inverse_transform(Y_pred)
|
|
accuracy = np.mean(iris.target == y_pred)
|
|
y_pred2 = SGDClassifier().fit(iris.data, iris.target).predict(iris.data)
|
|
accuracy2 = np.mean(iris.target == y_pred2)
|
|
assert_almost_equal(accuracy, accuracy2)
|
|
|
|
|
|
def test_center_kernel():
|
|
"""Test that KernelCenterer is equivalent to Scaler in feature space"""
|
|
X_fit = np.random.random((5, 4))
|
|
scaler = Scaler(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 = np.random.random((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)
|