1392 lines
46 KiB
Python
1392 lines
46 KiB
Python
"""
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Testing for Support Vector Machine module (sklearn.svm)
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TODO: remove hard coded numerical results when possible
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"""
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import warnings
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import re
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import numpy as np
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import pytest
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from numpy.testing import assert_array_equal, assert_array_almost_equal
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from numpy.testing import assert_almost_equal
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from numpy.testing import assert_allclose
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from scipy import sparse
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from sklearn import svm, linear_model, datasets, metrics, base
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from sklearn.svm import LinearSVC, OneClassSVM, SVR, NuSVR, LinearSVR
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import make_classification, make_blobs
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from sklearn.metrics import f1_score
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from sklearn.metrics.pairwise import rbf_kernel
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import ignore_warnings
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from sklearn.utils.validation import _num_samples
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from sklearn.utils import shuffle
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.exceptions import NotFittedError, UndefinedMetricWarning
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from sklearn.multiclass import OneVsRestClassifier
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# mypy error: Module 'sklearn.svm' has no attribute '_libsvm'
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from sklearn.svm import _libsvm # type: ignore
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# toy sample
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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Y = [1, 1, 1, 2, 2, 2]
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T = [[-1, -1], [2, 2], [3, 2]]
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true_result = [1, 2, 2]
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# also load the iris dataset
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iris = datasets.load_iris()
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rng = check_random_state(42)
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perm = rng.permutation(iris.target.size)
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iris.data = iris.data[perm]
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iris.target = iris.target[perm]
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def test_libsvm_parameters():
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# Test parameters on classes that make use of libsvm.
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clf = svm.SVC(kernel="linear").fit(X, Y)
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assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
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assert_array_equal(clf.support_, [1, 3])
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assert_array_equal(clf.support_vectors_, (X[1], X[3]))
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assert_array_equal(clf.intercept_, [0.0])
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assert_array_equal(clf.predict(X), Y)
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def test_libsvm_iris():
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# Check consistency on dataset iris.
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# shuffle the dataset so that labels are not ordered
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for k in ("linear", "rbf"):
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clf = svm.SVC(kernel=k).fit(iris.data, iris.target)
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assert np.mean(clf.predict(iris.data) == iris.target) > 0.9
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assert hasattr(clf, "coef_") == (k == "linear")
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assert_array_equal(clf.classes_, np.sort(clf.classes_))
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# check also the low-level API
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# We unpack the values to create a dictionary with some of the return values
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# from Libsvm's fit.
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(
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libsvm_support,
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libsvm_support_vectors,
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libsvm_n_class_SV,
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libsvm_sv_coef,
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libsvm_intercept,
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libsvm_probA,
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libsvm_probB,
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# libsvm_fit_status and libsvm_n_iter won't be used below.
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libsvm_fit_status,
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libsvm_n_iter,
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) = _libsvm.fit(iris.data, iris.target.astype(np.float64))
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model_params = {
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"support": libsvm_support,
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"SV": libsvm_support_vectors,
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"nSV": libsvm_n_class_SV,
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"sv_coef": libsvm_sv_coef,
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"intercept": libsvm_intercept,
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"probA": libsvm_probA,
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"probB": libsvm_probB,
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}
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pred = _libsvm.predict(iris.data, **model_params)
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assert np.mean(pred == iris.target) > 0.95
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# We unpack the values to create a dictionary with some of the return values
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# from Libsvm's fit.
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(
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libsvm_support,
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libsvm_support_vectors,
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libsvm_n_class_SV,
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libsvm_sv_coef,
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libsvm_intercept,
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libsvm_probA,
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libsvm_probB,
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# libsvm_fit_status and libsvm_n_iter won't be used below.
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libsvm_fit_status,
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libsvm_n_iter,
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) = _libsvm.fit(iris.data, iris.target.astype(np.float64), kernel="linear")
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model_params = {
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"support": libsvm_support,
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"SV": libsvm_support_vectors,
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"nSV": libsvm_n_class_SV,
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"sv_coef": libsvm_sv_coef,
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"intercept": libsvm_intercept,
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"probA": libsvm_probA,
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"probB": libsvm_probB,
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}
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pred = _libsvm.predict(iris.data, **model_params, kernel="linear")
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assert np.mean(pred == iris.target) > 0.95
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pred = _libsvm.cross_validation(
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iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
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)
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assert np.mean(pred == iris.target) > 0.95
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# If random_seed >= 0, the libsvm rng is seeded (by calling `srand`), hence
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# we should get deterministic results (assuming that there is no other
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# thread calling this wrapper calling `srand` concurrently).
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pred2 = _libsvm.cross_validation(
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iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
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)
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assert_array_equal(pred, pred2)
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def test_precomputed():
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# SVC with a precomputed kernel.
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# We test it with a toy dataset and with iris.
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clf = svm.SVC(kernel="precomputed")
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# Gram matrix for train data (square matrix)
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# (we use just a linear kernel)
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K = np.dot(X, np.array(X).T)
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clf.fit(K, Y)
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# Gram matrix for test data (rectangular matrix)
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KT = np.dot(T, np.array(X).T)
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pred = clf.predict(KT)
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with pytest.raises(ValueError):
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clf.predict(KT.T)
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assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
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assert_array_equal(clf.support_, [1, 3])
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assert_array_equal(clf.intercept_, [0])
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assert_array_almost_equal(clf.support_, [1, 3])
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assert_array_equal(pred, true_result)
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# Gram matrix for test data but compute KT[i,j]
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# for support vectors j only.
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KT = np.zeros_like(KT)
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for i in range(len(T)):
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for j in clf.support_:
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KT[i, j] = np.dot(T[i], X[j])
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pred = clf.predict(KT)
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assert_array_equal(pred, true_result)
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# same as before, but using a callable function instead of the kernel
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# matrix. kernel is just a linear kernel
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def kfunc(x, y):
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return np.dot(x, y.T)
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clf = svm.SVC(kernel=kfunc)
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clf.fit(np.array(X), Y)
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pred = clf.predict(T)
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assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
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assert_array_equal(clf.intercept_, [0])
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assert_array_almost_equal(clf.support_, [1, 3])
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assert_array_equal(pred, true_result)
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# test a precomputed kernel with the iris dataset
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# and check parameters against a linear SVC
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clf = svm.SVC(kernel="precomputed")
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clf2 = svm.SVC(kernel="linear")
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K = np.dot(iris.data, iris.data.T)
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clf.fit(K, iris.target)
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clf2.fit(iris.data, iris.target)
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pred = clf.predict(K)
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assert_array_almost_equal(clf.support_, clf2.support_)
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assert_array_almost_equal(clf.dual_coef_, clf2.dual_coef_)
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assert_array_almost_equal(clf.intercept_, clf2.intercept_)
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assert_almost_equal(np.mean(pred == iris.target), 0.99, decimal=2)
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# Gram matrix for test data but compute KT[i,j]
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# for support vectors j only.
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K = np.zeros_like(K)
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for i in range(len(iris.data)):
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for j in clf.support_:
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K[i, j] = np.dot(iris.data[i], iris.data[j])
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pred = clf.predict(K)
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assert_almost_equal(np.mean(pred == iris.target), 0.99, decimal=2)
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clf = svm.SVC(kernel=kfunc)
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clf.fit(iris.data, iris.target)
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assert_almost_equal(np.mean(pred == iris.target), 0.99, decimal=2)
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def test_svr():
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# Test Support Vector Regression
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diabetes = datasets.load_diabetes()
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for clf in (
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svm.NuSVR(kernel="linear", nu=0.4, C=1.0),
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svm.NuSVR(kernel="linear", nu=0.4, C=10.0),
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svm.SVR(kernel="linear", C=10.0),
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svm.LinearSVR(C=10.0),
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svm.LinearSVR(C=10.0),
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):
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clf.fit(diabetes.data, diabetes.target)
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assert clf.score(diabetes.data, diabetes.target) > 0.02
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# non-regression test; previously, BaseLibSVM would check that
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# len(np.unique(y)) < 2, which must only be done for SVC
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svm.SVR().fit(diabetes.data, np.ones(len(diabetes.data)))
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svm.LinearSVR().fit(diabetes.data, np.ones(len(diabetes.data)))
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def test_linearsvr():
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# check that SVR(kernel='linear') and LinearSVC() give
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# comparable results
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diabetes = datasets.load_diabetes()
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lsvr = svm.LinearSVR(C=1e3).fit(diabetes.data, diabetes.target)
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score1 = lsvr.score(diabetes.data, diabetes.target)
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svr = svm.SVR(kernel="linear", C=1e3).fit(diabetes.data, diabetes.target)
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score2 = svr.score(diabetes.data, diabetes.target)
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assert_allclose(np.linalg.norm(lsvr.coef_), np.linalg.norm(svr.coef_), 1, 0.0001)
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assert_almost_equal(score1, score2, 2)
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def test_linearsvr_fit_sampleweight():
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# check correct result when sample_weight is 1
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# check that SVR(kernel='linear') and LinearSVC() give
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# comparable results
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diabetes = datasets.load_diabetes()
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n_samples = len(diabetes.target)
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unit_weight = np.ones(n_samples)
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lsvr = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(
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diabetes.data, diabetes.target, sample_weight=unit_weight
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)
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score1 = lsvr.score(diabetes.data, diabetes.target)
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lsvr_no_weight = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(
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diabetes.data, diabetes.target
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)
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score2 = lsvr_no_weight.score(diabetes.data, diabetes.target)
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assert_allclose(
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np.linalg.norm(lsvr.coef_), np.linalg.norm(lsvr_no_weight.coef_), 1, 0.0001
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)
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assert_almost_equal(score1, score2, 2)
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# check that fit(X) = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
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# X = X1 repeated n1 times, X2 repeated n2 times and so forth
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random_state = check_random_state(0)
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random_weight = random_state.randint(0, 10, n_samples)
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lsvr_unflat = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(
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diabetes.data, diabetes.target, sample_weight=random_weight
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)
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score3 = lsvr_unflat.score(
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diabetes.data, diabetes.target, sample_weight=random_weight
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)
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X_flat = np.repeat(diabetes.data, random_weight, axis=0)
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y_flat = np.repeat(diabetes.target, random_weight, axis=0)
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lsvr_flat = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(X_flat, y_flat)
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score4 = lsvr_flat.score(X_flat, y_flat)
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assert_almost_equal(score3, score4, 2)
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def test_svr_errors():
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X = [[0.0], [1.0]]
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y = [0.0, 0.5]
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# Bad kernel
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clf = svm.SVR(kernel=lambda x, y: np.array([[1.0]]))
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clf.fit(X, y)
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with pytest.raises(ValueError):
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clf.predict(X)
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def test_oneclass():
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# Test OneClassSVM
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clf = svm.OneClassSVM()
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clf.fit(X)
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pred = clf.predict(T)
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assert_array_equal(pred, [1, -1, -1])
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assert pred.dtype == np.dtype("intp")
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assert_array_almost_equal(clf.intercept_, [-1.218], decimal=3)
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assert_array_almost_equal(clf.dual_coef_, [[0.750, 0.750, 0.750, 0.750]], decimal=3)
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with pytest.raises(AttributeError):
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(lambda: clf.coef_)()
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def test_oneclass_decision_function():
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# Test OneClassSVM decision function
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clf = svm.OneClassSVM()
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rnd = check_random_state(2)
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# Generate train data
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X = 0.3 * rnd.randn(100, 2)
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X_train = np.r_[X + 2, X - 2]
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# Generate some regular novel observations
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X = 0.3 * rnd.randn(20, 2)
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X_test = np.r_[X + 2, X - 2]
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# Generate some abnormal novel observations
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X_outliers = rnd.uniform(low=-4, high=4, size=(20, 2))
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# fit the model
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clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
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clf.fit(X_train)
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# predict things
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y_pred_test = clf.predict(X_test)
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assert np.mean(y_pred_test == 1) > 0.9
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y_pred_outliers = clf.predict(X_outliers)
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assert np.mean(y_pred_outliers == -1) > 0.9
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dec_func_test = clf.decision_function(X_test)
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assert_array_equal((dec_func_test > 0).ravel(), y_pred_test == 1)
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dec_func_outliers = clf.decision_function(X_outliers)
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assert_array_equal((dec_func_outliers > 0).ravel(), y_pred_outliers == 1)
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def test_oneclass_score_samples():
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X_train = [[1, 1], [1, 2], [2, 1]]
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clf = svm.OneClassSVM(gamma=1).fit(X_train)
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assert_array_equal(
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clf.score_samples([[2.0, 2.0]]),
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clf.decision_function([[2.0, 2.0]]) + clf.offset_,
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)
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def test_tweak_params():
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# Make sure some tweaking of parameters works.
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# We change clf.dual_coef_ at run time and expect .predict() to change
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# accordingly. Notice that this is not trivial since it involves a lot
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# of C/Python copying in the libsvm bindings.
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# The success of this test ensures that the mapping between libsvm and
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# the python classifier is complete.
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clf = svm.SVC(kernel="linear", C=1.0)
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clf.fit(X, Y)
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assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
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assert_array_equal(clf.predict([[-0.1, -0.1]]), [1])
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clf._dual_coef_ = np.array([[0.0, 1.0]])
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assert_array_equal(clf.predict([[-0.1, -0.1]]), [2])
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def test_probability():
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# Predict probabilities using SVC
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# This uses cross validation, so we use a slightly bigger testing set.
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for clf in (
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svm.SVC(probability=True, random_state=0, C=1.0),
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svm.NuSVC(probability=True, random_state=0),
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):
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clf.fit(iris.data, iris.target)
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prob_predict = clf.predict_proba(iris.data)
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assert_array_almost_equal(np.sum(prob_predict, 1), np.ones(iris.data.shape[0]))
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assert np.mean(np.argmax(prob_predict, 1) == clf.predict(iris.data)) > 0.9
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assert_almost_equal(
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clf.predict_proba(iris.data), np.exp(clf.predict_log_proba(iris.data)), 8
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)
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def test_decision_function():
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# Test decision_function
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# Sanity check, test that decision_function implemented in python
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# returns the same as the one in libsvm
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# multi class:
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clf = svm.SVC(kernel="linear", C=0.1, decision_function_shape="ovo").fit(
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iris.data, iris.target
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)
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dec = np.dot(iris.data, clf.coef_.T) + clf.intercept_
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assert_array_almost_equal(dec, clf.decision_function(iris.data))
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# binary:
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clf.fit(X, Y)
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dec = np.dot(X, clf.coef_.T) + clf.intercept_
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prediction = clf.predict(X)
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assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
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assert_array_almost_equal(
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prediction, clf.classes_[(clf.decision_function(X) > 0).astype(int)]
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)
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expected = np.array([-1.0, -0.66, -1.0, 0.66, 1.0, 1.0])
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assert_array_almost_equal(clf.decision_function(X), expected, 2)
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# kernel binary:
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clf = svm.SVC(kernel="rbf", gamma=1, decision_function_shape="ovo")
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clf.fit(X, Y)
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rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma)
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dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_
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assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
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@pytest.mark.parametrize("SVM", (svm.SVC, svm.NuSVC))
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def test_decision_function_shape(SVM):
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# check that decision_function_shape='ovr' or 'ovo' gives
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# correct shape and is consistent with predict
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clf = SVM(kernel="linear", decision_function_shape="ovr").fit(
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iris.data, iris.target
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)
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dec = clf.decision_function(iris.data)
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assert dec.shape == (len(iris.data), 3)
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assert_array_equal(clf.predict(iris.data), np.argmax(dec, axis=1))
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# with five classes:
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X, y = make_blobs(n_samples=80, centers=5, random_state=0)
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|
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
|
|
|
|
clf = SVM(kernel="linear", decision_function_shape="ovr").fit(X_train, y_train)
|
|
dec = clf.decision_function(X_test)
|
|
assert dec.shape == (len(X_test), 5)
|
|
assert_array_equal(clf.predict(X_test), np.argmax(dec, axis=1))
|
|
|
|
# check shape of ovo_decition_function=True
|
|
clf = SVM(kernel="linear", decision_function_shape="ovo").fit(X_train, y_train)
|
|
dec = clf.decision_function(X_train)
|
|
assert dec.shape == (len(X_train), 10)
|
|
|
|
|
|
def test_svr_predict():
|
|
# Test SVR's decision_function
|
|
# Sanity check, test that predict implemented in python
|
|
# returns the same as the one in libsvm
|
|
|
|
X = iris.data
|
|
y = iris.target
|
|
|
|
# linear kernel
|
|
reg = svm.SVR(kernel="linear", C=0.1).fit(X, y)
|
|
|
|
dec = np.dot(X, reg.coef_.T) + reg.intercept_
|
|
assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())
|
|
|
|
# rbf kernel
|
|
reg = svm.SVR(kernel="rbf", gamma=1).fit(X, y)
|
|
|
|
rbfs = rbf_kernel(X, reg.support_vectors_, gamma=reg.gamma)
|
|
dec = np.dot(rbfs, reg.dual_coef_.T) + reg.intercept_
|
|
assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())
|
|
|
|
|
|
def test_weight():
|
|
# Test class weights
|
|
clf = svm.SVC(class_weight={1: 0.1})
|
|
# we give a small weights to class 1
|
|
clf.fit(X, Y)
|
|
# so all predicted values belong to class 2
|
|
assert_array_almost_equal(clf.predict(X), [2] * 6)
|
|
|
|
X_, y_ = make_classification(
|
|
n_samples=200, n_features=10, weights=[0.833, 0.167], random_state=2
|
|
)
|
|
|
|
for clf in (
|
|
linear_model.LogisticRegression(),
|
|
svm.LinearSVC(random_state=0),
|
|
svm.SVC(),
|
|
):
|
|
clf.set_params(class_weight={0: 0.1, 1: 10})
|
|
clf.fit(X_[:100], y_[:100])
|
|
y_pred = clf.predict(X_[100:])
|
|
assert f1_score(y_[100:], y_pred) > 0.3
|
|
|
|
|
|
@pytest.mark.parametrize("estimator", [svm.SVC(C=1e-2), svm.NuSVC()])
|
|
def test_svm_classifier_sided_sample_weight(estimator):
|
|
# fit a linear SVM and check that giving more weight to opposed samples
|
|
# in the space will flip the decision toward these samples.
|
|
X = [[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 0]]
|
|
estimator.set_params(kernel="linear")
|
|
|
|
# check that with unit weights, a sample is supposed to be predicted on
|
|
# the boundary
|
|
sample_weight = [1] * 6
|
|
estimator.fit(X, Y, sample_weight=sample_weight)
|
|
y_pred = estimator.decision_function([[-1.0, 1.0]])
|
|
assert y_pred == pytest.approx(0)
|
|
|
|
# give more weights to opposed samples
|
|
sample_weight = [10.0, 0.1, 0.1, 0.1, 0.1, 10]
|
|
estimator.fit(X, Y, sample_weight=sample_weight)
|
|
y_pred = estimator.decision_function([[-1.0, 1.0]])
|
|
assert y_pred < 0
|
|
|
|
sample_weight = [1.0, 0.1, 10.0, 10.0, 0.1, 0.1]
|
|
estimator.fit(X, Y, sample_weight=sample_weight)
|
|
y_pred = estimator.decision_function([[-1.0, 1.0]])
|
|
assert y_pred > 0
|
|
|
|
|
|
@pytest.mark.parametrize("estimator", [svm.SVR(C=1e-2), svm.NuSVR(C=1e-2)])
|
|
def test_svm_regressor_sided_sample_weight(estimator):
|
|
# similar test to test_svm_classifier_sided_sample_weight but for
|
|
# SVM regressors
|
|
X = [[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 0]]
|
|
estimator.set_params(kernel="linear")
|
|
|
|
# check that with unit weights, a sample is supposed to be predicted on
|
|
# the boundary
|
|
sample_weight = [1] * 6
|
|
estimator.fit(X, Y, sample_weight=sample_weight)
|
|
y_pred = estimator.predict([[-1.0, 1.0]])
|
|
assert y_pred == pytest.approx(1.5)
|
|
|
|
# give more weights to opposed samples
|
|
sample_weight = [10.0, 0.1, 0.1, 0.1, 0.1, 10]
|
|
estimator.fit(X, Y, sample_weight=sample_weight)
|
|
y_pred = estimator.predict([[-1.0, 1.0]])
|
|
assert y_pred < 1.5
|
|
|
|
sample_weight = [1.0, 0.1, 10.0, 10.0, 0.1, 0.1]
|
|
estimator.fit(X, Y, sample_weight=sample_weight)
|
|
y_pred = estimator.predict([[-1.0, 1.0]])
|
|
assert y_pred > 1.5
|
|
|
|
|
|
def test_svm_equivalence_sample_weight_C():
|
|
# test that rescaling all samples is the same as changing C
|
|
clf = svm.SVC()
|
|
clf.fit(X, Y)
|
|
dual_coef_no_weight = clf.dual_coef_
|
|
clf.set_params(C=100)
|
|
clf.fit(X, Y, sample_weight=np.repeat(0.01, len(X)))
|
|
assert_allclose(dual_coef_no_weight, clf.dual_coef_)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Estimator, err_msg",
|
|
[
|
|
(svm.SVC, "Invalid input - all samples have zero or negative weights."),
|
|
(svm.NuSVC, "(negative dimensions are not allowed|nu is infeasible)"),
|
|
(svm.SVR, "Invalid input - all samples have zero or negative weights."),
|
|
(svm.NuSVR, "Invalid input - all samples have zero or negative weights."),
|
|
(svm.OneClassSVM, "Invalid input - all samples have zero or negative weights."),
|
|
],
|
|
ids=["SVC", "NuSVC", "SVR", "NuSVR", "OneClassSVM"],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"sample_weight",
|
|
[[0] * len(Y), [-0.3] * len(Y)],
|
|
ids=["weights-are-zero", "weights-are-negative"],
|
|
)
|
|
def test_negative_sample_weights_mask_all_samples(Estimator, err_msg, sample_weight):
|
|
est = Estimator(kernel="linear")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
est.fit(X, Y, sample_weight=sample_weight)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Classifier, err_msg",
|
|
[
|
|
(
|
|
svm.SVC,
|
|
"Invalid input - all samples with positive weights belong to the same"
|
|
" class",
|
|
),
|
|
(svm.NuSVC, "specified nu is infeasible"),
|
|
],
|
|
ids=["SVC", "NuSVC"],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"sample_weight",
|
|
[[0, -0.5, 0, 1, 1, 1], [1, 1, 1, 0, -0.1, -0.3]],
|
|
ids=["mask-label-1", "mask-label-2"],
|
|
)
|
|
def test_negative_weights_svc_leave_just_one_label(Classifier, err_msg, sample_weight):
|
|
clf = Classifier(kernel="linear")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
clf.fit(X, Y, sample_weight=sample_weight)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Classifier, model",
|
|
[
|
|
(svm.SVC, {"when-left": [0.3998, 0.4], "when-right": [0.4, 0.3999]}),
|
|
(svm.NuSVC, {"when-left": [0.3333, 0.3333], "when-right": [0.3333, 0.3333]}),
|
|
],
|
|
ids=["SVC", "NuSVC"],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"sample_weight, mask_side",
|
|
[([1, -0.5, 1, 1, 1, 1], "when-left"), ([1, 1, 1, 0, 1, 1], "when-right")],
|
|
ids=["partial-mask-label-1", "partial-mask-label-2"],
|
|
)
|
|
def test_negative_weights_svc_leave_two_labels(
|
|
Classifier, model, sample_weight, mask_side
|
|
):
|
|
clf = Classifier(kernel="linear")
|
|
clf.fit(X, Y, sample_weight=sample_weight)
|
|
assert_allclose(clf.coef_, [model[mask_side]], rtol=1e-3)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Estimator", [svm.SVC, svm.NuSVC, svm.NuSVR], ids=["SVC", "NuSVC", "NuSVR"]
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"sample_weight",
|
|
[[1, -0.5, 1, 1, 1, 1], [1, 1, 1, 0, 1, 1]],
|
|
ids=["partial-mask-label-1", "partial-mask-label-2"],
|
|
)
|
|
def test_negative_weight_equal_coeffs(Estimator, sample_weight):
|
|
# model generates equal coefficients
|
|
est = Estimator(kernel="linear")
|
|
est.fit(X, Y, sample_weight=sample_weight)
|
|
coef = np.abs(est.coef_).ravel()
|
|
assert coef[0] == pytest.approx(coef[1], rel=1e-3)
|
|
|
|
|
|
@ignore_warnings(category=UndefinedMetricWarning)
|
|
def test_auto_weight():
|
|
# Test class weights for imbalanced data
|
|
from sklearn.linear_model import LogisticRegression
|
|
|
|
# We take as dataset the two-dimensional projection of iris so
|
|
# that it is not separable and remove half of predictors from
|
|
# class 1.
|
|
# We add one to the targets as a non-regression test:
|
|
# class_weight="balanced"
|
|
# used to work only when the labels where a range [0..K).
|
|
from sklearn.utils import compute_class_weight
|
|
|
|
X, y = iris.data[:, :2], iris.target + 1
|
|
unbalanced = np.delete(np.arange(y.size), np.where(y > 2)[0][::2])
|
|
|
|
classes = np.unique(y[unbalanced])
|
|
class_weights = compute_class_weight("balanced", classes=classes, y=y[unbalanced])
|
|
assert np.argmax(class_weights) == 2
|
|
|
|
for clf in (
|
|
svm.SVC(kernel="linear"),
|
|
svm.LinearSVC(random_state=0),
|
|
LogisticRegression(),
|
|
):
|
|
# check that score is better when class='balanced' is set.
|
|
y_pred = clf.fit(X[unbalanced], y[unbalanced]).predict(X)
|
|
clf.set_params(class_weight="balanced")
|
|
y_pred_balanced = clf.fit(
|
|
X[unbalanced],
|
|
y[unbalanced],
|
|
).predict(X)
|
|
assert metrics.f1_score(y, y_pred, average="macro") <= metrics.f1_score(
|
|
y, y_pred_balanced, average="macro"
|
|
)
|
|
|
|
|
|
def test_bad_input():
|
|
# Test dimensions for labels
|
|
Y2 = Y[:-1] # wrong dimensions for labels
|
|
with pytest.raises(ValueError):
|
|
svm.SVC().fit(X, Y2)
|
|
|
|
# Test with arrays that are non-contiguous.
|
|
for clf in (svm.SVC(), svm.LinearSVC(random_state=0)):
|
|
Xf = np.asfortranarray(X)
|
|
assert not Xf.flags["C_CONTIGUOUS"]
|
|
yf = np.ascontiguousarray(np.tile(Y, (2, 1)).T)
|
|
yf = yf[:, -1]
|
|
assert not yf.flags["F_CONTIGUOUS"]
|
|
assert not yf.flags["C_CONTIGUOUS"]
|
|
clf.fit(Xf, yf)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
|
|
# error for precomputed kernelsx
|
|
clf = svm.SVC(kernel="precomputed")
|
|
with pytest.raises(ValueError):
|
|
clf.fit(X, Y)
|
|
|
|
# predict with sparse input when trained with dense
|
|
clf = svm.SVC().fit(X, Y)
|
|
with pytest.raises(ValueError):
|
|
clf.predict(sparse.lil_matrix(X))
|
|
|
|
Xt = np.array(X).T
|
|
clf.fit(np.dot(X, Xt), Y)
|
|
with pytest.raises(ValueError):
|
|
clf.predict(X)
|
|
|
|
clf = svm.SVC()
|
|
clf.fit(X, Y)
|
|
with pytest.raises(ValueError):
|
|
clf.predict(Xt)
|
|
|
|
|
|
def test_svc_nonfinite_params():
|
|
# Check SVC throws ValueError when dealing with non-finite parameter values
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 10
|
|
fmax = np.finfo(np.float64).max
|
|
X = fmax * rng.uniform(size=(n_samples, 2))
|
|
y = rng.randint(0, 2, size=n_samples)
|
|
|
|
clf = svm.SVC()
|
|
msg = "The dual coefficients or intercepts are not finite"
|
|
with pytest.raises(ValueError, match=msg):
|
|
clf.fit(X, y)
|
|
|
|
|
|
def test_unicode_kernel():
|
|
# Test that a unicode kernel name does not cause a TypeError
|
|
clf = svm.SVC(kernel="linear", probability=True)
|
|
clf.fit(X, Y)
|
|
clf.predict_proba(T)
|
|
_libsvm.cross_validation(
|
|
iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
|
|
)
|
|
|
|
|
|
def test_sparse_precomputed():
|
|
clf = svm.SVC(kernel="precomputed")
|
|
sparse_gram = sparse.csr_matrix([[1, 0], [0, 1]])
|
|
with pytest.raises(TypeError, match="Sparse precomputed"):
|
|
clf.fit(sparse_gram, [0, 1])
|
|
|
|
|
|
def test_sparse_fit_support_vectors_empty():
|
|
# Regression test for #14893
|
|
X_train = sparse.csr_matrix(
|
|
[[0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]]
|
|
)
|
|
y_train = np.array([0.04, 0.04, 0.10, 0.16])
|
|
model = svm.SVR(kernel="linear")
|
|
model.fit(X_train, y_train)
|
|
assert not model.support_vectors_.data.size
|
|
assert not model.dual_coef_.data.size
|
|
|
|
|
|
@pytest.mark.parametrize("loss", ["hinge", "squared_hinge"])
|
|
@pytest.mark.parametrize("penalty", ["l1", "l2"])
|
|
@pytest.mark.parametrize("dual", [True, False])
|
|
def test_linearsvc_parameters(loss, penalty, dual):
|
|
# Test possible parameter combinations in LinearSVC
|
|
# Generate list of possible parameter combinations
|
|
X, y = make_classification(n_samples=5, n_features=5, random_state=0)
|
|
|
|
clf = svm.LinearSVC(penalty=penalty, loss=loss, dual=dual, random_state=0)
|
|
if (
|
|
(loss, penalty) == ("hinge", "l1")
|
|
or (loss, penalty, dual) == ("hinge", "l2", False)
|
|
or (penalty, dual) == ("l1", True)
|
|
):
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="Unsupported set of arguments.*penalty='%s.*loss='%s.*dual=%s"
|
|
% (penalty, loss, dual),
|
|
):
|
|
clf.fit(X, y)
|
|
else:
|
|
clf.fit(X, y)
|
|
|
|
|
|
def test_linearsvc():
|
|
# Test basic routines using LinearSVC
|
|
clf = svm.LinearSVC(random_state=0).fit(X, Y)
|
|
|
|
# by default should have intercept
|
|
assert clf.fit_intercept
|
|
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert_array_almost_equal(clf.intercept_, [0], decimal=3)
|
|
|
|
# the same with l1 penalty
|
|
clf = svm.LinearSVC(
|
|
penalty="l1", loss="squared_hinge", dual=False, random_state=0
|
|
).fit(X, Y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
|
|
# l2 penalty with dual formulation
|
|
clf = svm.LinearSVC(penalty="l2", dual=True, random_state=0).fit(X, Y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
|
|
# l2 penalty, l1 loss
|
|
clf = svm.LinearSVC(penalty="l2", loss="hinge", dual=True, random_state=0)
|
|
clf.fit(X, Y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
|
|
# test also decision function
|
|
dec = clf.decision_function(T)
|
|
res = (dec > 0).astype(int) + 1
|
|
assert_array_equal(res, true_result)
|
|
|
|
|
|
def test_linearsvc_crammer_singer():
|
|
# Test LinearSVC with crammer_singer multi-class svm
|
|
ovr_clf = svm.LinearSVC(random_state=0).fit(iris.data, iris.target)
|
|
cs_clf = svm.LinearSVC(multi_class="crammer_singer", random_state=0)
|
|
cs_clf.fit(iris.data, iris.target)
|
|
|
|
# similar prediction for ovr and crammer-singer:
|
|
assert (ovr_clf.predict(iris.data) == cs_clf.predict(iris.data)).mean() > 0.9
|
|
|
|
# classifiers shouldn't be the same
|
|
assert (ovr_clf.coef_ != cs_clf.coef_).all()
|
|
|
|
# test decision function
|
|
assert_array_equal(
|
|
cs_clf.predict(iris.data),
|
|
np.argmax(cs_clf.decision_function(iris.data), axis=1),
|
|
)
|
|
dec_func = np.dot(iris.data, cs_clf.coef_.T) + cs_clf.intercept_
|
|
assert_array_almost_equal(dec_func, cs_clf.decision_function(iris.data))
|
|
|
|
|
|
def test_linearsvc_fit_sampleweight():
|
|
# check correct result when sample_weight is 1
|
|
n_samples = len(X)
|
|
unit_weight = np.ones(n_samples)
|
|
clf = svm.LinearSVC(random_state=0).fit(X, Y)
|
|
clf_unitweight = svm.LinearSVC(random_state=0, tol=1e-12, max_iter=1000).fit(
|
|
X, Y, sample_weight=unit_weight
|
|
)
|
|
|
|
# check if same as sample_weight=None
|
|
assert_array_equal(clf_unitweight.predict(T), clf.predict(T))
|
|
assert_allclose(clf.coef_, clf_unitweight.coef_, 1, 0.0001)
|
|
|
|
# check that fit(X) = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
|
|
# X = X1 repeated n1 times, X2 repeated n2 times and so forth
|
|
|
|
random_state = check_random_state(0)
|
|
random_weight = random_state.randint(0, 10, n_samples)
|
|
lsvc_unflat = svm.LinearSVC(random_state=0, tol=1e-12, max_iter=1000).fit(
|
|
X, Y, sample_weight=random_weight
|
|
)
|
|
pred1 = lsvc_unflat.predict(T)
|
|
|
|
X_flat = np.repeat(X, random_weight, axis=0)
|
|
y_flat = np.repeat(Y, random_weight, axis=0)
|
|
lsvc_flat = svm.LinearSVC(random_state=0, tol=1e-12, max_iter=1000).fit(
|
|
X_flat, y_flat
|
|
)
|
|
pred2 = lsvc_flat.predict(T)
|
|
|
|
assert_array_equal(pred1, pred2)
|
|
assert_allclose(lsvc_unflat.coef_, lsvc_flat.coef_, 1, 0.0001)
|
|
|
|
|
|
def test_crammer_singer_binary():
|
|
# Test Crammer-Singer formulation in the binary case
|
|
X, y = make_classification(n_classes=2, random_state=0)
|
|
|
|
for fit_intercept in (True, False):
|
|
acc = (
|
|
svm.LinearSVC(
|
|
fit_intercept=fit_intercept,
|
|
multi_class="crammer_singer",
|
|
random_state=0,
|
|
)
|
|
.fit(X, y)
|
|
.score(X, y)
|
|
)
|
|
assert acc > 0.9
|
|
|
|
|
|
def test_linearsvc_iris():
|
|
# Test that LinearSVC gives plausible predictions on the iris dataset
|
|
# Also, test symbolic class names (classes_).
|
|
target = iris.target_names[iris.target]
|
|
clf = svm.LinearSVC(random_state=0).fit(iris.data, target)
|
|
assert set(clf.classes_) == set(iris.target_names)
|
|
assert np.mean(clf.predict(iris.data) == target) > 0.8
|
|
|
|
dec = clf.decision_function(iris.data)
|
|
pred = iris.target_names[np.argmax(dec, 1)]
|
|
assert_array_equal(pred, clf.predict(iris.data))
|
|
|
|
|
|
def test_dense_liblinear_intercept_handling(classifier=svm.LinearSVC):
|
|
# Test that dense liblinear honours intercept_scaling param
|
|
X = [[2, 1], [3, 1], [1, 3], [2, 3]]
|
|
y = [0, 0, 1, 1]
|
|
clf = classifier(
|
|
fit_intercept=True,
|
|
penalty="l1",
|
|
loss="squared_hinge",
|
|
dual=False,
|
|
C=4,
|
|
tol=1e-7,
|
|
random_state=0,
|
|
)
|
|
assert clf.intercept_scaling == 1, clf.intercept_scaling
|
|
assert clf.fit_intercept
|
|
|
|
# when intercept_scaling is low the intercept value is highly "penalized"
|
|
# by regularization
|
|
clf.intercept_scaling = 1
|
|
clf.fit(X, y)
|
|
assert_almost_equal(clf.intercept_, 0, decimal=5)
|
|
|
|
# when intercept_scaling is sufficiently high, the intercept value
|
|
# is not affected by regularization
|
|
clf.intercept_scaling = 100
|
|
clf.fit(X, y)
|
|
intercept1 = clf.intercept_
|
|
assert intercept1 < -1
|
|
|
|
# when intercept_scaling is sufficiently high, the intercept value
|
|
# doesn't depend on intercept_scaling value
|
|
clf.intercept_scaling = 1000
|
|
clf.fit(X, y)
|
|
intercept2 = clf.intercept_
|
|
assert_array_almost_equal(intercept1, intercept2, decimal=2)
|
|
|
|
|
|
def test_liblinear_set_coef():
|
|
# multi-class case
|
|
clf = svm.LinearSVC().fit(iris.data, iris.target)
|
|
values = clf.decision_function(iris.data)
|
|
clf.coef_ = clf.coef_.copy()
|
|
clf.intercept_ = clf.intercept_.copy()
|
|
values2 = clf.decision_function(iris.data)
|
|
assert_array_almost_equal(values, values2)
|
|
|
|
# binary-class case
|
|
X = [[2, 1], [3, 1], [1, 3], [2, 3]]
|
|
y = [0, 0, 1, 1]
|
|
|
|
clf = svm.LinearSVC().fit(X, y)
|
|
values = clf.decision_function(X)
|
|
clf.coef_ = clf.coef_.copy()
|
|
clf.intercept_ = clf.intercept_.copy()
|
|
values2 = clf.decision_function(X)
|
|
assert_array_equal(values, values2)
|
|
|
|
|
|
def test_immutable_coef_property():
|
|
# Check that primal coef modification are not silently ignored
|
|
svms = [
|
|
svm.SVC(kernel="linear").fit(iris.data, iris.target),
|
|
svm.NuSVC(kernel="linear").fit(iris.data, iris.target),
|
|
svm.SVR(kernel="linear").fit(iris.data, iris.target),
|
|
svm.NuSVR(kernel="linear").fit(iris.data, iris.target),
|
|
svm.OneClassSVM(kernel="linear").fit(iris.data),
|
|
]
|
|
for clf in svms:
|
|
with pytest.raises(AttributeError):
|
|
clf.__setattr__("coef_", np.arange(3))
|
|
with pytest.raises((RuntimeError, ValueError)):
|
|
clf.coef_.__setitem__((0, 0), 0)
|
|
|
|
|
|
def test_linearsvc_verbose():
|
|
# stdout: redirect
|
|
import os
|
|
|
|
stdout = os.dup(1) # save original stdout
|
|
os.dup2(os.pipe()[1], 1) # replace it
|
|
|
|
# actual call
|
|
clf = svm.LinearSVC(verbose=1)
|
|
clf.fit(X, Y)
|
|
|
|
# stdout: restore
|
|
os.dup2(stdout, 1) # restore original stdout
|
|
|
|
|
|
def test_svc_clone_with_callable_kernel():
|
|
# create SVM with callable linear kernel, check that results are the same
|
|
# as with built-in linear kernel
|
|
svm_callable = svm.SVC(
|
|
kernel=lambda x, y: np.dot(x, y.T),
|
|
probability=True,
|
|
random_state=0,
|
|
decision_function_shape="ovr",
|
|
)
|
|
# clone for checking clonability with lambda functions..
|
|
svm_cloned = base.clone(svm_callable)
|
|
svm_cloned.fit(iris.data, iris.target)
|
|
|
|
svm_builtin = svm.SVC(
|
|
kernel="linear", probability=True, random_state=0, decision_function_shape="ovr"
|
|
)
|
|
svm_builtin.fit(iris.data, iris.target)
|
|
|
|
assert_array_almost_equal(svm_cloned.dual_coef_, svm_builtin.dual_coef_)
|
|
assert_array_almost_equal(svm_cloned.intercept_, svm_builtin.intercept_)
|
|
assert_array_equal(svm_cloned.predict(iris.data), svm_builtin.predict(iris.data))
|
|
|
|
assert_array_almost_equal(
|
|
svm_cloned.predict_proba(iris.data),
|
|
svm_builtin.predict_proba(iris.data),
|
|
decimal=4,
|
|
)
|
|
assert_array_almost_equal(
|
|
svm_cloned.decision_function(iris.data),
|
|
svm_builtin.decision_function(iris.data),
|
|
)
|
|
|
|
|
|
def test_svc_bad_kernel():
|
|
svc = svm.SVC(kernel=lambda x, y: x)
|
|
with pytest.raises(ValueError):
|
|
svc.fit(X, Y)
|
|
|
|
|
|
def test_libsvm_convergence_warnings():
|
|
a = svm.SVC(
|
|
kernel=lambda x, y: np.dot(x, y.T), probability=True, random_state=0, max_iter=2
|
|
)
|
|
warning_msg = (
|
|
r"Solver terminated early \(max_iter=2\). Consider pre-processing "
|
|
r"your data with StandardScaler or MinMaxScaler."
|
|
)
|
|
with pytest.warns(ConvergenceWarning, match=warning_msg):
|
|
a.fit(np.array(X), Y)
|
|
assert np.all(a.n_iter_ == 2)
|
|
|
|
|
|
def test_unfitted():
|
|
X = "foo!" # input validation not required when SVM not fitted
|
|
|
|
clf = svm.SVC()
|
|
with pytest.raises(Exception, match=r".*\bSVC\b.*\bnot\b.*\bfitted\b"):
|
|
clf.predict(X)
|
|
|
|
clf = svm.NuSVR()
|
|
with pytest.raises(Exception, match=r".*\bNuSVR\b.*\bnot\b.*\bfitted\b"):
|
|
clf.predict(X)
|
|
|
|
|
|
# ignore convergence warnings from max_iter=1
|
|
@ignore_warnings
|
|
def test_consistent_proba():
|
|
a = svm.SVC(probability=True, max_iter=1, random_state=0)
|
|
proba_1 = a.fit(X, Y).predict_proba(X)
|
|
a = svm.SVC(probability=True, max_iter=1, random_state=0)
|
|
proba_2 = a.fit(X, Y).predict_proba(X)
|
|
assert_array_almost_equal(proba_1, proba_2)
|
|
|
|
|
|
def test_linear_svm_convergence_warnings():
|
|
# Test that warnings are raised if model does not converge
|
|
|
|
lsvc = svm.LinearSVC(random_state=0, max_iter=2)
|
|
warning_msg = "Liblinear failed to converge, increase the number of iterations."
|
|
with pytest.warns(ConvergenceWarning, match=warning_msg):
|
|
lsvc.fit(X, Y)
|
|
# Check that we have an n_iter_ attribute with int type as opposed to a
|
|
# numpy array or an np.int32 so as to match the docstring.
|
|
assert isinstance(lsvc.n_iter_, int)
|
|
assert lsvc.n_iter_ == 2
|
|
|
|
lsvr = svm.LinearSVR(random_state=0, max_iter=2)
|
|
with pytest.warns(ConvergenceWarning, match=warning_msg):
|
|
lsvr.fit(iris.data, iris.target)
|
|
assert isinstance(lsvr.n_iter_, int)
|
|
assert lsvr.n_iter_ == 2
|
|
|
|
|
|
def test_svr_coef_sign():
|
|
# Test that SVR(kernel="linear") has coef_ with the right sign.
|
|
# Non-regression test for #2933.
|
|
X = np.random.RandomState(21).randn(10, 3)
|
|
y = np.random.RandomState(12).randn(10)
|
|
|
|
for svr in [svm.SVR(kernel="linear"), svm.NuSVR(kernel="linear"), svm.LinearSVR()]:
|
|
svr.fit(X, y)
|
|
assert_array_almost_equal(
|
|
svr.predict(X), np.dot(X, svr.coef_.ravel()) + svr.intercept_
|
|
)
|
|
|
|
|
|
def test_lsvc_intercept_scaling_zero():
|
|
# Test that intercept_scaling is ignored when fit_intercept is False
|
|
|
|
lsvc = svm.LinearSVC(fit_intercept=False)
|
|
lsvc.fit(X, Y)
|
|
assert lsvc.intercept_ == 0.0
|
|
|
|
|
|
def test_hasattr_predict_proba():
|
|
# Method must be (un)available before or after fit, switched by
|
|
# `probability` param
|
|
|
|
G = svm.SVC(probability=True)
|
|
assert hasattr(G, "predict_proba")
|
|
G.fit(iris.data, iris.target)
|
|
assert hasattr(G, "predict_proba")
|
|
|
|
G = svm.SVC(probability=False)
|
|
assert not hasattr(G, "predict_proba")
|
|
G.fit(iris.data, iris.target)
|
|
assert not hasattr(G, "predict_proba")
|
|
|
|
# Switching to `probability=True` after fitting should make
|
|
# predict_proba available, but calling it must not work:
|
|
G.probability = True
|
|
assert hasattr(G, "predict_proba")
|
|
msg = "predict_proba is not available when fitted with probability=False"
|
|
|
|
with pytest.raises(NotFittedError, match=msg):
|
|
G.predict_proba(iris.data)
|
|
|
|
|
|
def test_decision_function_shape_two_class():
|
|
for n_classes in [2, 3]:
|
|
X, y = make_blobs(centers=n_classes, random_state=0)
|
|
for estimator in [svm.SVC, svm.NuSVC]:
|
|
clf = OneVsRestClassifier(estimator(decision_function_shape="ovr")).fit(
|
|
X, y
|
|
)
|
|
assert len(clf.predict(X)) == len(y)
|
|
|
|
|
|
def test_ovr_decision_function():
|
|
# One point from each quadrant represents one class
|
|
X_train = np.array([[1, 1], [-1, 1], [-1, -1], [1, -1]])
|
|
y_train = [0, 1, 2, 3]
|
|
|
|
# First point is closer to the decision boundaries than the second point
|
|
base_points = np.array([[5, 5], [10, 10]])
|
|
|
|
# For all the quadrants (classes)
|
|
X_test = np.vstack(
|
|
(
|
|
base_points * [1, 1], # Q1
|
|
base_points * [-1, 1], # Q2
|
|
base_points * [-1, -1], # Q3
|
|
base_points * [1, -1], # Q4
|
|
)
|
|
)
|
|
|
|
y_test = [0] * 2 + [1] * 2 + [2] * 2 + [3] * 2
|
|
|
|
clf = svm.SVC(kernel="linear", decision_function_shape="ovr")
|
|
clf.fit(X_train, y_train)
|
|
|
|
y_pred = clf.predict(X_test)
|
|
|
|
# Test if the prediction is the same as y
|
|
assert_array_equal(y_pred, y_test)
|
|
|
|
deci_val = clf.decision_function(X_test)
|
|
|
|
# Assert that the predicted class has the maximum value
|
|
assert_array_equal(np.argmax(deci_val, axis=1), y_pred)
|
|
|
|
# Get decision value at test points for the predicted class
|
|
pred_class_deci_val = deci_val[range(8), y_pred].reshape((4, 2))
|
|
|
|
# Assert pred_class_deci_val > 0 here
|
|
assert np.min(pred_class_deci_val) > 0.0
|
|
|
|
# Test if the first point has lower decision value on every quadrant
|
|
# compared to the second point
|
|
assert np.all(pred_class_deci_val[:, 0] < pred_class_deci_val[:, 1])
|
|
|
|
|
|
@pytest.mark.parametrize("SVCClass", [svm.SVC, svm.NuSVC])
|
|
def test_svc_invalid_break_ties_param(SVCClass):
|
|
X, y = make_blobs(random_state=42)
|
|
|
|
svm = SVCClass(
|
|
kernel="linear", decision_function_shape="ovo", break_ties=True, random_state=42
|
|
).fit(X, y)
|
|
|
|
with pytest.raises(ValueError, match="break_ties must be False"):
|
|
svm.predict(y)
|
|
|
|
|
|
@pytest.mark.parametrize("SVCClass", [svm.SVC, svm.NuSVC])
|
|
def test_svc_ovr_tie_breaking(SVCClass):
|
|
"""Test if predict breaks ties in OVR mode.
|
|
Related issue: https://github.com/scikit-learn/scikit-learn/issues/8277
|
|
"""
|
|
X, y = make_blobs(random_state=0, n_samples=20, n_features=2)
|
|
|
|
xs = np.linspace(X[:, 0].min(), X[:, 0].max(), 100)
|
|
ys = np.linspace(X[:, 1].min(), X[:, 1].max(), 100)
|
|
xx, yy = np.meshgrid(xs, ys)
|
|
|
|
common_params = dict(
|
|
kernel="rbf", gamma=1e6, random_state=42, decision_function_shape="ovr"
|
|
)
|
|
svm = SVCClass(
|
|
break_ties=False,
|
|
**common_params,
|
|
).fit(X, y)
|
|
pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])
|
|
dv = svm.decision_function(np.c_[xx.ravel(), yy.ravel()])
|
|
assert not np.all(pred == np.argmax(dv, axis=1))
|
|
|
|
svm = SVCClass(
|
|
break_ties=True,
|
|
**common_params,
|
|
).fit(X, y)
|
|
pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])
|
|
dv = svm.decision_function(np.c_[xx.ravel(), yy.ravel()])
|
|
assert np.all(pred == np.argmax(dv, axis=1))
|
|
|
|
|
|
def test_gamma_scale():
|
|
X, y = [[0.0], [1.0]], [0, 1]
|
|
|
|
clf = svm.SVC()
|
|
clf.fit(X, y)
|
|
assert_almost_equal(clf._gamma, 4)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"SVM, params",
|
|
[
|
|
(LinearSVC, {"penalty": "l1", "loss": "squared_hinge", "dual": False}),
|
|
(LinearSVC, {"penalty": "l2", "loss": "squared_hinge", "dual": True}),
|
|
(LinearSVC, {"penalty": "l2", "loss": "squared_hinge", "dual": False}),
|
|
(LinearSVC, {"penalty": "l2", "loss": "hinge", "dual": True}),
|
|
(LinearSVR, {"loss": "epsilon_insensitive", "dual": True}),
|
|
(LinearSVR, {"loss": "squared_epsilon_insensitive", "dual": True}),
|
|
(LinearSVR, {"loss": "squared_epsilon_insensitive", "dual": True}),
|
|
],
|
|
)
|
|
def test_linearsvm_liblinear_sample_weight(SVM, params):
|
|
X = np.array(
|
|
[
|
|
[1, 3],
|
|
[1, 3],
|
|
[1, 3],
|
|
[1, 3],
|
|
[2, 1],
|
|
[2, 1],
|
|
[2, 1],
|
|
[2, 1],
|
|
[3, 3],
|
|
[3, 3],
|
|
[3, 3],
|
|
[3, 3],
|
|
[4, 1],
|
|
[4, 1],
|
|
[4, 1],
|
|
[4, 1],
|
|
],
|
|
dtype=np.dtype("float"),
|
|
)
|
|
y = np.array(
|
|
[1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2], dtype=np.dtype("int")
|
|
)
|
|
|
|
X2 = np.vstack([X, X])
|
|
y2 = np.hstack([y, 3 - y])
|
|
sample_weight = np.ones(shape=len(y) * 2)
|
|
sample_weight[len(y) :] = 0
|
|
X2, y2, sample_weight = shuffle(X2, y2, sample_weight, random_state=0)
|
|
|
|
base_estimator = SVM(random_state=42)
|
|
base_estimator.set_params(**params)
|
|
base_estimator.set_params(tol=1e-12, max_iter=1000)
|
|
est_no_weight = base.clone(base_estimator).fit(X, y)
|
|
est_with_weight = base.clone(base_estimator).fit(
|
|
X2, y2, sample_weight=sample_weight
|
|
)
|
|
|
|
for method in ("predict", "decision_function"):
|
|
if hasattr(base_estimator, method):
|
|
X_est_no_weight = getattr(est_no_weight, method)(X)
|
|
X_est_with_weight = getattr(est_with_weight, method)(X)
|
|
assert_allclose(X_est_no_weight, X_est_with_weight)
|
|
|
|
|
|
@pytest.mark.parametrize("Klass", (OneClassSVM, SVR, NuSVR))
|
|
def test_n_support(Klass):
|
|
# Make n_support is correct for oneclass and SVR (used to be
|
|
# non-initialized)
|
|
# this is a non regression test for issue #14774
|
|
X = np.array([[0], [0.44], [0.45], [0.46], [1]])
|
|
y = np.arange(X.shape[0])
|
|
est = Klass()
|
|
assert not hasattr(est, "n_support_")
|
|
est.fit(X, y)
|
|
assert est.n_support_[0] == est.support_vectors_.shape[0]
|
|
assert est.n_support_.size == 1
|
|
|
|
|
|
@pytest.mark.parametrize("Estimator", [svm.SVC, svm.SVR])
|
|
def test_custom_kernel_not_array_input(Estimator):
|
|
"""Test using a custom kernel that is not fed with array-like for floats"""
|
|
data = ["A A", "A", "B", "B B", "A B"]
|
|
X = np.array([[2, 0], [1, 0], [0, 1], [0, 2], [1, 1]]) # count encoding
|
|
y = np.array([1, 1, 2, 2, 1])
|
|
|
|
def string_kernel(X1, X2):
|
|
assert isinstance(X1[0], str)
|
|
n_samples1 = _num_samples(X1)
|
|
n_samples2 = _num_samples(X2)
|
|
K = np.zeros((n_samples1, n_samples2))
|
|
for ii in range(n_samples1):
|
|
for jj in range(ii, n_samples2):
|
|
K[ii, jj] = X1[ii].count("A") * X2[jj].count("A")
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|
K[ii, jj] += X1[ii].count("B") * X2[jj].count("B")
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|
K[jj, ii] = K[ii, jj]
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|
return K
|
|
|
|
K = string_kernel(data, data)
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|
assert_array_equal(np.dot(X, X.T), K)
|
|
|
|
svc1 = Estimator(kernel=string_kernel).fit(data, y)
|
|
svc2 = Estimator(kernel="linear").fit(X, y)
|
|
svc3 = Estimator(kernel="precomputed").fit(K, y)
|
|
|
|
assert svc1.score(data, y) == svc3.score(K, y)
|
|
assert svc1.score(data, y) == svc2.score(X, y)
|
|
if hasattr(svc1, "decision_function"): # classifier
|
|
assert_allclose(svc1.decision_function(data), svc2.decision_function(X))
|
|
assert_allclose(svc1.decision_function(data), svc3.decision_function(K))
|
|
assert_array_equal(svc1.predict(data), svc2.predict(X))
|
|
assert_array_equal(svc1.predict(data), svc3.predict(K))
|
|
else: # regressor
|
|
assert_allclose(svc1.predict(data), svc2.predict(X))
|
|
assert_allclose(svc1.predict(data), svc3.predict(K))
|
|
|
|
|
|
def test_svc_raises_error_internal_representation():
|
|
"""Check that SVC raises error when internal representation is altered.
|
|
|
|
Non-regression test for #18891 and https://nvd.nist.gov/vuln/detail/CVE-2020-28975
|
|
"""
|
|
clf = svm.SVC(kernel="linear").fit(X, Y)
|
|
clf._n_support[0] = 1000000
|
|
|
|
msg = "The internal representation of SVC was altered"
|
|
with pytest.raises(ValueError, match=msg):
|
|
clf.predict(X)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator, expected_n_iter_type",
|
|
[
|
|
(svm.SVC, np.ndarray),
|
|
(svm.NuSVC, np.ndarray),
|
|
(svm.SVR, int),
|
|
(svm.NuSVR, int),
|
|
(svm.OneClassSVM, int),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"dataset",
|
|
[
|
|
make_classification(n_classes=2, n_informative=2, random_state=0),
|
|
make_classification(n_classes=3, n_informative=3, random_state=0),
|
|
make_classification(n_classes=4, n_informative=4, random_state=0),
|
|
],
|
|
)
|
|
def test_n_iter_libsvm(estimator, expected_n_iter_type, dataset):
|
|
# Check that the type of n_iter_ is correct for the classes that inherit
|
|
# from BaseSVC.
|
|
# Note that for SVC, and NuSVC this is an ndarray; while for SVR, NuSVR, and
|
|
# OneClassSVM, it is an int.
|
|
# For SVC and NuSVC also check the shape of n_iter_.
|
|
X, y = dataset
|
|
n_iter = estimator(kernel="linear").fit(X, y).n_iter_
|
|
assert type(n_iter) == expected_n_iter_type
|
|
if estimator in [svm.SVC, svm.NuSVC]:
|
|
n_classes = len(np.unique(y))
|
|
assert n_iter.shape == (n_classes * (n_classes - 1) // 2,)
|
|
|
|
|
|
# TODO(1.4): Remove
|
|
@pytest.mark.parametrize("Klass", [SVR, NuSVR, OneClassSVM])
|
|
def test_svm_class_weights_deprecation(Klass):
|
|
clf = Klass()
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("error", FutureWarning)
|
|
clf.fit(X, Y)
|
|
msg = (
|
|
"Attribute `class_weight_` was deprecated in version 1.2 and will be removed"
|
|
" in 1.4"
|
|
)
|
|
with pytest.warns(FutureWarning, match=re.escape(msg)):
|
|
getattr(clf, "class_weight_")
|