494 lines
15 KiB
Python
494 lines
15 KiB
Python
import numpy as np
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import pytest
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from scipy import sparse
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from sklearn import base, datasets, linear_model, svm
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from sklearn.datasets import load_digits, make_blobs, make_classification
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.svm.tests import test_svm
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from sklearn.utils._testing import (
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assert_allclose,
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assert_array_almost_equal,
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assert_array_equal,
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ignore_warnings,
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skip_if_32bit,
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)
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from sklearn.utils.extmath import safe_sparse_dot
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from sklearn.utils.fixes import (
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CSR_CONTAINERS,
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DOK_CONTAINERS,
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LIL_CONTAINERS,
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)
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# test sample 1
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X = np.array([[-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 = np.array([[-1, -1], [2, 2], [3, 2]])
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true_result = [1, 2, 2]
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# test sample 2
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X2 = np.array(
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[
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[0, 0, 0],
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[1, 1, 1],
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[2, 0, 0],
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[0, 0, 2],
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[3, 3, 3],
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]
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)
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Y2 = [1, 2, 2, 2, 3]
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T2 = np.array([[-1, -1, -1], [1, 1, 1], [2, 2, 2]])
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true_result2 = [1, 2, 3]
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iris = datasets.load_iris()
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rng = np.random.RandomState(0)
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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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X_blobs, y_blobs = make_blobs(n_samples=100, centers=10, random_state=0)
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def check_svm_model_equal(dense_svm, X_train, y_train, X_test):
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# Use the original svm model for dense fit and clone an exactly same
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# svm model for sparse fit
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sparse_svm = base.clone(dense_svm)
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dense_svm.fit(X_train.toarray(), y_train)
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if sparse.issparse(X_test):
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X_test_dense = X_test.toarray()
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else:
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X_test_dense = X_test
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sparse_svm.fit(X_train, y_train)
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assert sparse.issparse(sparse_svm.support_vectors_)
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assert sparse.issparse(sparse_svm.dual_coef_)
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assert_allclose(dense_svm.support_vectors_, sparse_svm.support_vectors_.toarray())
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assert_allclose(dense_svm.dual_coef_, sparse_svm.dual_coef_.toarray())
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if dense_svm.kernel == "linear":
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assert sparse.issparse(sparse_svm.coef_)
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assert_array_almost_equal(dense_svm.coef_, sparse_svm.coef_.toarray())
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assert_allclose(dense_svm.support_, sparse_svm.support_)
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assert_allclose(dense_svm.predict(X_test_dense), sparse_svm.predict(X_test))
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assert_array_almost_equal(
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dense_svm.decision_function(X_test_dense), sparse_svm.decision_function(X_test)
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)
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assert_array_almost_equal(
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dense_svm.decision_function(X_test_dense),
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sparse_svm.decision_function(X_test_dense),
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)
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if isinstance(dense_svm, svm.OneClassSVM):
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msg = "cannot use sparse input in 'OneClassSVM' trained on dense data"
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else:
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assert_array_almost_equal(
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dense_svm.predict_proba(X_test_dense),
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sparse_svm.predict_proba(X_test),
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decimal=4,
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)
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msg = "cannot use sparse input in 'SVC' trained on dense data"
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if sparse.issparse(X_test):
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with pytest.raises(ValueError, match=msg):
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dense_svm.predict(X_test)
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@skip_if_32bit
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@pytest.mark.parametrize(
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"X_train, y_train, X_test",
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[
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[X, Y, T],
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[X2, Y2, T2],
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[X_blobs[:80], y_blobs[:80], X_blobs[80:]],
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[iris.data, iris.target, iris.data],
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],
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)
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@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
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@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
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def test_svc(X_train, y_train, X_test, kernel, sparse_container):
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"""Check that sparse SVC gives the same result as SVC."""
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X_train = sparse_container(X_train)
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clf = svm.SVC(
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gamma=1,
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kernel=kernel,
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probability=True,
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random_state=0,
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decision_function_shape="ovo",
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)
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check_svm_model_equal(clf, X_train, y_train, X_test)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_unsorted_indices(csr_container):
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# test that the result with sorted and unsorted indices in csr is the same
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# we use a subset of digits as iris, blobs or make_classification didn't
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# show the problem
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X, y = load_digits(return_X_y=True)
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X_test = csr_container(X[50:100])
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X, y = X[:50], y[:50]
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X_sparse = csr_container(X)
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coef_dense = (
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svm.SVC(kernel="linear", probability=True, random_state=0).fit(X, y).coef_
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)
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sparse_svc = svm.SVC(kernel="linear", probability=True, random_state=0).fit(
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X_sparse, y
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)
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coef_sorted = sparse_svc.coef_
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# make sure dense and sparse SVM give the same result
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assert_allclose(coef_dense, coef_sorted.toarray())
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# reverse each row's indices
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def scramble_indices(X):
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new_data = []
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new_indices = []
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for i in range(1, len(X.indptr)):
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row_slice = slice(*X.indptr[i - 1 : i + 1])
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new_data.extend(X.data[row_slice][::-1])
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new_indices.extend(X.indices[row_slice][::-1])
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return csr_container((new_data, new_indices, X.indptr), shape=X.shape)
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X_sparse_unsorted = scramble_indices(X_sparse)
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X_test_unsorted = scramble_indices(X_test)
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assert not X_sparse_unsorted.has_sorted_indices
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assert not X_test_unsorted.has_sorted_indices
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unsorted_svc = svm.SVC(kernel="linear", probability=True, random_state=0).fit(
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X_sparse_unsorted, y
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)
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coef_unsorted = unsorted_svc.coef_
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# make sure unsorted indices give same result
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assert_allclose(coef_unsorted.toarray(), coef_sorted.toarray())
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assert_allclose(
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sparse_svc.predict_proba(X_test_unsorted), sparse_svc.predict_proba(X_test)
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)
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@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
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def test_svc_with_custom_kernel(lil_container):
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def kfunc(x, y):
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return safe_sparse_dot(x, y.T)
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X_sp = lil_container(X)
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clf_lin = svm.SVC(kernel="linear").fit(X_sp, Y)
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clf_mylin = svm.SVC(kernel=kfunc).fit(X_sp, Y)
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assert_array_equal(clf_lin.predict(X_sp), clf_mylin.predict(X_sp))
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@skip_if_32bit
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf"])
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def test_svc_iris(csr_container, kernel):
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# Test the sparse SVC with the iris dataset
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iris_data_sp = csr_container(iris.data)
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sp_clf = svm.SVC(kernel=kernel).fit(iris_data_sp, iris.target)
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clf = svm.SVC(kernel=kernel).fit(iris.data, iris.target)
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assert_allclose(clf.support_vectors_, sp_clf.support_vectors_.toarray())
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assert_allclose(clf.dual_coef_, sp_clf.dual_coef_.toarray())
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assert_allclose(clf.predict(iris.data), sp_clf.predict(iris_data_sp))
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if kernel == "linear":
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assert_allclose(clf.coef_, sp_clf.coef_.toarray())
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_sparse_decision_function(csr_container):
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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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iris_data_sp = csr_container(iris.data)
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svc = svm.SVC(kernel="linear", C=0.1, decision_function_shape="ovo")
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clf = svc.fit(iris_data_sp, iris.target)
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dec = safe_sparse_dot(iris_data_sp, clf.coef_.T) + clf.intercept_
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assert_allclose(dec, clf.decision_function(iris_data_sp))
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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_allclose(dec.ravel(), clf.decision_function(X))
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assert_allclose(
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prediction, clf.classes_[(clf.decision_function(X) > 0).astype(int).ravel()]
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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, decimal=2)
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@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
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def test_error(lil_container):
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# Test that it gives proper exception on deficient input
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clf = svm.SVC()
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X_sp = lil_container(X)
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Y2 = Y[:-1] # wrong dimensions for labels
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with pytest.raises(ValueError):
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clf.fit(X_sp, Y2)
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clf.fit(X_sp, Y)
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assert_array_equal(clf.predict(T), true_result)
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@pytest.mark.parametrize(
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"lil_container, dok_container", zip(LIL_CONTAINERS, DOK_CONTAINERS)
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)
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def test_linearsvc(lil_container, dok_container):
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# Similar to test_SVC
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X_sp = lil_container(X)
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X2_sp = dok_container(X2)
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clf = svm.LinearSVC(dual="auto", random_state=0).fit(X, Y)
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sp_clf = svm.LinearSVC(dual="auto", random_state=0).fit(X_sp, Y)
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assert sp_clf.fit_intercept
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assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
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assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)
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assert_allclose(clf.predict(X), sp_clf.predict(X_sp))
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clf.fit(X2, Y2)
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sp_clf.fit(X2_sp, Y2)
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assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
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assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_linearsvc_iris(csr_container):
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# Test the sparse LinearSVC with the iris dataset
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iris_data_sp = csr_container(iris.data)
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sp_clf = svm.LinearSVC(dual="auto", random_state=0).fit(iris_data_sp, iris.target)
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clf = svm.LinearSVC(dual="auto", random_state=0).fit(iris.data, iris.target)
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assert clf.fit_intercept == sp_clf.fit_intercept
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assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=1)
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assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=1)
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assert_allclose(clf.predict(iris.data), sp_clf.predict(iris_data_sp))
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# check decision_function
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pred = np.argmax(sp_clf.decision_function(iris_data_sp), axis=1)
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assert_allclose(pred, clf.predict(iris.data))
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# sparsify the coefficients on both models and check that they still
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# produce the same results
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clf.sparsify()
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assert_array_equal(pred, clf.predict(iris_data_sp))
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sp_clf.sparsify()
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assert_array_equal(pred, sp_clf.predict(iris_data_sp))
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_weight(csr_container):
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# Test class weights
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X_, y_ = make_classification(
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n_samples=200, n_features=100, weights=[0.833, 0.167], random_state=0
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)
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X_ = csr_container(X_)
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for clf in (
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linear_model.LogisticRegression(),
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svm.LinearSVC(dual="auto", random_state=0),
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svm.SVC(),
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):
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clf.set_params(class_weight={0: 5})
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clf.fit(X_[:180], y_[:180])
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y_pred = clf.predict(X_[180:])
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assert np.sum(y_pred == y_[180:]) >= 11
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@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
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def test_sample_weights(lil_container):
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# Test weights on individual samples
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X_sp = lil_container(X)
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clf = svm.SVC()
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clf.fit(X_sp, Y)
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assert_array_equal(clf.predict([X[2]]), [1.0])
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sample_weight = [0.1] * 3 + [10] * 3
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clf.fit(X_sp, Y, sample_weight=sample_weight)
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assert_array_equal(clf.predict([X[2]]), [2.0])
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def test_sparse_liblinear_intercept_handling():
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# Test that sparse liblinear honours intercept_scaling param
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test_svm.test_dense_liblinear_intercept_handling(svm.LinearSVC)
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@pytest.mark.parametrize(
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"X_train, y_train, X_test",
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[
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[X, None, T],
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[X2, None, T2],
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[X_blobs[:80], None, X_blobs[80:]],
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[iris.data, None, iris.data],
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],
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)
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@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
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@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
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@skip_if_32bit
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def test_sparse_oneclasssvm(X_train, y_train, X_test, kernel, sparse_container):
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# Check that sparse OneClassSVM gives the same result as dense OneClassSVM
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X_train = sparse_container(X_train)
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clf = svm.OneClassSVM(gamma=1, kernel=kernel)
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check_svm_model_equal(clf, X_train, y_train, X_test)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_sparse_realdata(csr_container):
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# Test on a subset from the 20newsgroups dataset.
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# This catches some bugs if input is not correctly converted into
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# sparse format or weights are not correctly initialized.
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data = np.array([0.03771744, 0.1003567, 0.01174647, 0.027069])
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# SVC does not support large sparse, so we specify int32 indices
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# In this case, `csr_matrix` automatically uses int32 regardless of the dtypes of
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# `indices` and `indptr` but `csr_array` may or may not use the same dtype as
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# `indices` and `indptr`, which would be int64 if not specified
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indices = np.array([6, 5, 35, 31], dtype=np.int32)
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indptr = np.array([0] * 8 + [1] * 32 + [2] * 38 + [4] * 3, dtype=np.int32)
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X = csr_container((data, indices, indptr))
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y = np.array(
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[
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1.0,
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0.0,
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2.0,
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2.0,
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1.0,
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1.0,
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1.0,
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2.0,
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2.0,
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0.0,
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1.0,
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2.0,
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2.0,
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0.0,
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2.0,
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0.0,
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3.0,
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0.0,
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3.0,
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0.0,
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1.0,
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1.0,
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3.0,
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2.0,
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3.0,
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2.0,
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0.0,
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3.0,
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1.0,
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0.0,
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2.0,
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1.0,
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2.0,
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0.0,
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1.0,
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0.0,
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2.0,
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3.0,
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1.0,
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3.0,
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0.0,
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1.0,
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0.0,
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0.0,
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2.0,
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0.0,
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1.0,
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2.0,
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2.0,
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2.0,
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3.0,
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2.0,
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0.0,
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3.0,
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2.0,
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1.0,
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2.0,
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3.0,
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2.0,
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2.0,
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0.0,
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1.0,
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0.0,
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1.0,
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2.0,
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3.0,
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0.0,
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0.0,
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2.0,
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2.0,
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1.0,
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3.0,
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1.0,
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1.0,
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0.0,
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1.0,
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2.0,
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1.0,
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1.0,
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3.0,
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]
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)
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clf = svm.SVC(kernel="linear").fit(X.toarray(), y)
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sp_clf = svm.SVC(kernel="linear").fit(X.tocoo(), y)
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assert_array_equal(clf.support_vectors_, sp_clf.support_vectors_.toarray())
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assert_array_equal(clf.dual_coef_, sp_clf.dual_coef_.toarray())
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@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
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def test_sparse_svc_clone_with_callable_kernel(lil_container):
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# Test that the "dense_fit" is called even though we use sparse input
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# meaning that everything works fine.
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a = svm.SVC(C=1, kernel=lambda x, y: x @ y.T, probability=True, random_state=0)
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b = base.clone(a)
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|
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X_sp = lil_container(X)
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b.fit(X_sp, Y)
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pred = b.predict(X_sp)
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b.predict_proba(X_sp)
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|
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dense_svm = svm.SVC(
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C=1, kernel=lambda x, y: np.dot(x, y.T), probability=True, random_state=0
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)
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pred_dense = dense_svm.fit(X, Y).predict(X)
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assert_array_equal(pred_dense, pred)
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# b.decision_function(X_sp) # XXX : should be supported
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|
|
|
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@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
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def test_timeout(lil_container):
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sp = svm.SVC(
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C=1, kernel=lambda x, y: x @ y.T, probability=True, random_state=0, max_iter=1
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)
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warning_msg = (
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r"Solver terminated early \(max_iter=1\). Consider pre-processing "
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r"your data with StandardScaler or MinMaxScaler."
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|
)
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with pytest.warns(ConvergenceWarning, match=warning_msg):
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sp.fit(lil_container(X), Y)
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|
|
|
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def test_consistent_proba():
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a = svm.SVC(probability=True, max_iter=1, random_state=0)
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with ignore_warnings(category=ConvergenceWarning):
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proba_1 = a.fit(X, Y).predict_proba(X)
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a = svm.SVC(probability=True, max_iter=1, random_state=0)
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with ignore_warnings(category=ConvergenceWarning):
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proba_2 = a.fit(X, Y).predict_proba(X)
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|
assert_allclose(proba_1, proba_2)
|