Python CDClassifier.score方法代码示例

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_warm_start_l2r():
    clf = CDClassifier(warm_start=True, random_state=0, penalty="l2")

    clf.C = 0.1
    clf.fit(bin_dense, bin_target)
    assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)

    clf.C = 0.2
    clf.fit(bin_dense, bin_target)
    assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_empty_model():
    clf = CDClassifier(C=1e-5, penalty="l1")
    clf.fit(bin_dense, bin_target)
    assert_equal(clf.n_nonzero(), 0)
    acc = clf.score(bin_dense, bin_target)
    assert_equal(acc, 0.5)

    clf = CDClassifier(C=1e-5, penalty="l1", debiasing=True)
    clf.fit(bin_dense, bin_target)
    assert_equal(clf.n_nonzero(), 0)
    acc = clf.score(bin_dense, bin_target)
    assert_equal(acc, 0.5)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_debiasing_warm_start():
    clf = CDClassifier(penalty="l1", max_iter=10,
                       warm_start=True, random_state=0)
    clf.C = 0.5
    clf.fit(bin_dense, bin_target)
    assert_equal(clf.n_nonzero(), 74)
    assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)

    clf.C = 1.0
    clf.fit(bin_dense, bin_target)
    # FIXME: not the same sparsity as without warm start...
    assert_equal(clf.n_nonzero(), 77)
    assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_l1l2_multi_task_log_loss():
    clf = CDClassifier(penalty="l1/l2", loss="log",
                       multiclass=False,
                       max_steps=30,
                       max_iter=20, C=5.0, random_state=0)
    clf.fit(mult_dense, mult_target)
    assert_almost_equal(clf.score(mult_dense, mult_target), 0.8633, 3)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_debiasing_l1():
    for warm_debiasing in (True, False):
        clf = CDClassifier(penalty="l1", debiasing=True,
                           warm_debiasing=warm_debiasing,
                           C=0.05, Cd=1.0, max_iter=10, random_state=0)
        clf.fit(bin_dense, bin_target)
        assert_equal(clf.n_nonzero(), 22)
        assert_almost_equal(clf.score(bin_dense, bin_target), 0.955, 3)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_fit_linear_binary_l1r():
    clf = CDClassifier(C=1.0, random_state=0, penalty="l1")
    clf.fit(bin_dense, bin_target)
    acc = clf.score(bin_dense, bin_target)
    assert_almost_equal(acc, 1.0)
    n_nz = clf.n_nonzero()
    perc = clf.n_nonzero(percentage=True)
    assert_equal(perc, float(n_nz) / bin_dense.shape[1])

    clf = CDClassifier(C=0.1, random_state=0, penalty="l1")
    clf.fit(bin_dense, bin_target)
    acc = clf.score(bin_dense, bin_target)
    assert_almost_equal(acc, 0.97)
    n_nz2 = clf.n_nonzero()
    perc2 = clf.n_nonzero(percentage=True)
    assert_equal(perc2, float(n_nz2) / bin_dense.shape[1])

    assert_true(n_nz > n_nz2)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_fit_squared_loss():
    clf = CDClassifier(C=1.0, random_state=0, penalty="l2",
                       loss="squared", max_iter=100)
    clf.fit(bin_dense, bin_target)
    assert_almost_equal(clf.score(bin_dense, bin_target), 0.99)
    y = bin_target.copy()
    y[y == 0] = -1
    assert_array_almost_equal(np.dot(bin_dense, clf.coef_.ravel()) - y,
                              clf.errors_.ravel())

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_l1l2_multiclass_log_loss_no_linesearch():
    data = mult_csc
    clf = CDClassifier(penalty="l1/l2", loss="log", multiclass=True,
                       selection="uniform", max_steps=0,
                       max_iter=30, C=1.0, random_state=0)
    clf.fit(data, mult_target)
    assert_almost_equal(clf.score(data, mult_target), 0.88, 3)
    nz = np.sum(clf.coef_ != 0)
    assert_equal(nz, 297)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_l1l2_multi_task_squared_hinge_loss():
    Y = LabelBinarizer(neg_label=-1).fit_transform(mult_target)
    clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
                       multiclass=False,
                       max_iter=20, C=5.0, random_state=0)
    clf.fit(mult_dense, mult_target)
    df = clf.decision_function(mult_dense)
    assert_array_almost_equal(clf.errors_.T, 1 - Y * df)
    assert_almost_equal(clf.score(mult_dense, mult_target), 0.8633, 3)
    nz = np.sum(clf.coef_ != 0)
    assert_equal(nz, 300)

    clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
                       multiclass=False,
                       max_iter=20, C=0.05, random_state=0)
    clf.fit(mult_dense, mult_target)
    assert_almost_equal(clf.score(mult_dense, mult_target), 0.8266, 3)
    nz = np.sum(clf.coef_ != 0)
    assert_equal(nz, 231)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_debiasing_l1l2():
    for warm_debiasing in (True, False):
        clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
                           multiclass=False,
                           debiasing=True,
                           warm_debiasing=warm_debiasing,
                           max_iter=20, C=0.01, random_state=0)
        clf.fit(mult_csc, mult_target)
        assert_greater(clf.score(mult_csc, mult_target), 0.75)
        assert_equal(clf.n_nonzero(percentage=True), 0.08)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_fit_squared_loss_l1():
    clf = CDClassifier(C=0.5, random_state=0, penalty="l1",
                       loss="squared", max_iter=100, shrinking=False)
    clf.fit(bin_dense, bin_target)
    assert_almost_equal(clf.score(bin_dense, bin_target), 0.985, 3)
    y = bin_target.copy()
    y[y == 0] = -1
    assert_array_almost_equal(np.dot(bin_dense, clf.coef_.ravel()) - y,
                              clf.errors_.ravel())
    n_nz = clf.n_nonzero()
    assert_equal(n_nz, 89)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_l1l2_multiclass_log_loss():
    for data in (mult_dense, mult_csc):
        clf = CDClassifier(penalty="l1/l2", loss="log", multiclass=True,
                           max_steps=30, max_iter=5, C=1.0, random_state=0)
        clf.fit(data, mult_target)
        assert_almost_equal(clf.score(data, mult_target), 0.8766, 3)
        df = clf.decision_function(data)
        sel = np.array([df[i, int(mult_target[i])] for i in xrange(df.shape[0])])
        df -= sel[:, np.newaxis]
        df = np.exp(df)
        assert_array_almost_equal(clf.errors_, df.T)
        for i in xrange(data.shape[0]):
            assert_almost_equal(clf.errors_[mult_target[i], i], 1.0)
        nz = np.sum(clf.coef_ != 0)
        assert_equal(nz, 297)

        clf = CDClassifier(penalty="l1/l2", loss="log", multiclass=True,
                           max_steps=30, max_iter=5, C=0.3, random_state=0)
        clf.fit(data, mult_target)
        assert_almost_equal(clf.score(data, mult_target), 0.8566, 3)
        nz = np.sum(clf.coef_ != 0)
        assert_equal(nz, 213)
        assert_true(nz % 3 == 0) # should be a multiple of n_classes

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_l1l2_multiclass_squared_hinge_loss_no_linesearch():
    data = mult_csc
    clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
                       multiclass=True, shrinking=False, selection="uniform",
                       max_steps=0, max_iter=200, C=1.0, random_state=0)
    clf.fit(data, mult_target)
    assert_almost_equal(clf.score(data, mult_target), 0.9166, 3)
    df = clf.decision_function(data)
    n_samples, n_vectors = df.shape
    diff = np.zeros_like(clf.errors_)
    for i in xrange(n_samples):
        for k in xrange(n_vectors):
            diff[k, i] = 1 - (df[i, mult_target[i]] - df[i, k])
    assert_array_almost_equal(clf.errors_, diff)
    assert_equal(np.sum(clf.coef_ != 0), 300)

    clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
                       multiclass=True,
                       max_iter=20, C=0.05, random_state=0)
    clf.fit(data, mult_target)
    assert_almost_equal(clf.score(data, mult_target), 0.83, 3)
    nz = np.sum(clf.coef_ != 0)
    assert_equal(nz, 207)
    assert_true(nz % 3 == 0) # should be a multiple of n_classes

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_fit_linear_binary_l1r_log_loss():
    clf = CDClassifier(C=1.0, random_state=0, penalty="l1", loss="log")
    clf.fit(bin_dense, bin_target)
    acc = clf.score(bin_dense, bin_target)
    assert_almost_equal(acc, 0.995)

# 需要导入模块: from lightning.primal_cd import CDClassifier [as 别名]
# 或者: from lightning.primal_cd.CDClassifier import score [as 别名]
def test_l1r_shrinking():
    for shrinking in (True, False):
        clf = CDClassifier(C=0.5, penalty="l1", random_state=0,
                           shrinking=shrinking)
        clf.fit(bin_dense, bin_target)
        assert_equal(clf.score(bin_dense, bin_target), 1.0)