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import numpy as np
from scipy import sparse
from sklearn import datasets, svm, linear_model, base
from numpy.testing import assert_array_almost_equal, \
assert_array_equal, assert_equal
from nose.tools import assert_raises, assert_true
from sklearn.datasets.samples_generator import make_classification
from sklearn.svm.tests import test_svm
# test sample 1
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
X_sp = sparse.lil_matrix(X)
Y = [1, 1, 1, 2, 2, 2]
T = np.array([[-1, -1], [2, 2], [3, 2]])
true_result = [1, 2, 2]
# test sample 2
X2 = np.array([[0, 0, 0], [1, 1, 1], [2, 0, 0, ],
[0, 0, 2], [3, 3, 3]])
X2_sp = sparse.dok_matrix(X2)
Y2 = [1, 2, 2, 2, 3]
T2 = np.array([[-1, -1, -1], [1, 1, 1], [2, 2, 2]])
true_result2 = [1, 2, 3]
iris = datasets.load_iris()
# permute
rng = np.random.RandomState(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
# sparsify
iris.data = sparse.csr_matrix(iris.data)
def test_svc():
"""Check that sparse SVC gives the same result as SVC"""
clf = svm.SVC(kernel='linear').fit(X, Y)
sp_clf = svm.SVC(kernel='linear').fit(X_sp, Y)
assert_array_equal(sp_clf.predict(T), true_result)
assert_true(sparse.issparse(sp_clf.support_vectors_))
assert_array_almost_equal(clf.support_vectors_,
sp_clf.support_vectors_.todense())
assert_true(sparse.issparse(sp_clf.dual_coef_))
assert_array_almost_equal(clf.dual_coef_, sp_clf.dual_coef_.todense())
assert_true(sparse.issparse(sp_clf.coef_))
assert_array_almost_equal(clf.coef_, sp_clf.coef_.todense())
assert_array_almost_equal(clf.predict(T), sp_clf.predict(T))
# refit with a different dataset
clf.fit(X2, Y2)
sp_clf.fit(X2_sp, Y2)
assert_array_almost_equal(clf.support_vectors_,
sp_clf.support_vectors_.todense())
assert_array_almost_equal(clf.dual_coef_, sp_clf.dual_coef_.todense())
assert_array_almost_equal(clf.coef_, sp_clf.coef_.todense())
assert_array_almost_equal(clf.predict(T2), sp_clf.predict(T2))
def test_svc_iris():
"""Test the sparse SVC with the iris dataset"""
for k in ('linear', 'poly', 'rbf'):
sp_clf = svm.SVC(kernel=k).fit(iris.data, iris.target)
clf = svm.SVC(kernel=k).fit(iris.data.todense(), iris.target)
assert_array_almost_equal(clf.support_vectors_,
sp_clf.support_vectors_.todense())
assert_array_almost_equal(clf.dual_coef_, sp_clf.dual_coef_.todense())
assert_array_almost_equal(
clf.predict(iris.data.todense()), sp_clf.predict(iris.data))
if k == 'linear':
assert_array_almost_equal(clf.coef_, sp_clf.coef_.todense())
def test_error():
"""
Test that it gives proper exception on deficient input
"""
# impossible value of C
assert_raises(ValueError, svm.SVC(C=-1).fit, X, Y)
# impossible value of nu
clf = svm.NuSVC(nu=0.0)
assert_raises(ValueError, clf.fit, X_sp, Y)
Y2 = Y[:-1] # wrong dimensions for labels
assert_raises(ValueError, clf.fit, X_sp, Y2)
clf = svm.SVC()
clf.fit(X_sp, Y)
assert_array_equal(clf.predict(T), true_result)
def test_linearsvc():
"""
Similar to test_SVC
"""
clf = svm.LinearSVC().fit(X, Y)
sp_clf = svm.LinearSVC().fit(X_sp, Y)
assert_true(sp_clf.fit_intercept)
assert_array_almost_equal(clf.raw_coef_, sp_clf.raw_coef_, decimal=4)
assert_array_almost_equal(clf.predict(X), sp_clf.predict(X_sp))
clf.fit(X2, Y2)
sp_clf.fit(X2_sp, Y2)
assert_array_almost_equal(clf.raw_coef_, sp_clf.raw_coef_, decimal=4)
def test_linearsvc_iris():
"""Test the sparse LinearSVC with the iris dataset"""
sp_clf = svm.LinearSVC().fit(iris.data, iris.target)
clf = svm.LinearSVC().fit(iris.data.todense(), iris.target)
assert_array_almost_equal(clf.label_, sp_clf.label_)
assert_equal(clf.fit_intercept, sp_clf.fit_intercept)
assert_array_almost_equal(clf.raw_coef_, sp_clf.raw_coef_, decimal=1)
assert_array_almost_equal(
clf.predict(iris.data.todense()), sp_clf.predict(iris.data))
# check decision_function
pred = np.argmax(sp_clf.decision_function(iris.data), 1)
assert_array_almost_equal(pred, clf.predict(iris.data.todense()))
def test_weight():
"""
Test class weights
"""
X_, y_ = make_classification(n_samples=200, n_features=100,
weights=[0.833, 0.167], random_state=0)
X_ = sparse.csr_matrix(X_)
for clf in (linear_model.LogisticRegression(),
svm.LinearSVC(),
svm.SVC()):
clf.set_params(class_weight={0: 5})
clf.fit(X_[:180], y_[:180])
y_pred = clf.predict(X_[180:])
assert_true(np.sum(y_pred == y_[180:]) >= 11)
def test_sample_weights():
"""
Test weights on individual samples
"""
clf = svm.SVC()
clf.fit(X_sp, Y)
assert_array_equal(clf.predict(X[2]), [1.])
sample_weight = [.1] * 3 + [10] * 3
clf.fit(X_sp, Y, sample_weight=sample_weight)
assert_array_equal(clf.predict(X[2]), [2.])
def test_sparse_liblinear_intercept_handling():
"""
Test that sparse liblinear honours intercept_scaling param
"""
test_svm.test_dense_liblinear_intercept_handling(svm.LinearSVC)
def test_sparse_realdata():
"""
Test on a subset from the 20newsgroups dataset.
This catchs some bugs if input is not correctly converted into
sparse format or weights are not correctly initialized.
"""
data = np.array([0.03771744, 0.1003567, 0.01174647, 0.027069])
indices = np.array([6, 5, 35, 31])
indptr = np.array(
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4])
X = sparse.csr_matrix((data, indices, indptr))
y = np.array(
[1., 0., 2., 2., 1., 1., 1., 2., 2., 0., 1., 2., 2.,
0., 2., 0., 3., 0., 3., 0., 1., 1., 3., 2., 3., 2.,
0., 3., 1., 0., 2., 1., 2., 0., 1., 0., 2., 3., 1.,
3., 0., 1., 0., 0., 2., 0., 1., 2., 2., 2., 3., 2.,
0., 3., 2., 1., 2., 3., 2., 2., 0., 1., 0., 1., 2.,
3., 0., 0., 2., 2., 1., 3., 1., 1., 0., 1., 2., 1.,
1., 3.])
clf = svm.SVC(kernel='linear').fit(X.todense(), y)
sp_clf = svm.SVC(kernel='linear').fit(sparse.coo_matrix(X), y)
assert_array_equal(clf.support_vectors_, sp_clf.support_vectors_.todense())
assert_array_equal(clf.dual_coef_, sp_clf.dual_coef_.todense())
def test_sparse_svc_clone_with_callable_kernel():
a = svm.SVC(C=1, kernel=lambda x, y: x * y.T, probability=True)
b = base.clone(a)
b.fit(X_sp, Y)
b.predict(X_sp)
b.predict_proba(X_sp)
# b.decision_function(X_sp) # XXX : should be supported
if __name__ == '__main__':
import nose
nose.runmodule()
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