Esempio n. 1
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def test_fit_reg_squared_l1():
    clf = CDRegressor(C=1.0, random_state=0, penalty="l1",
                      loss="squared", max_iter=100)
    clf.fit(digit.data, digit.target)
    y_pred = (clf.predict(digit.data) > 0.5).astype(int)
    acc = np.mean(digit.target == y_pred)
    assert_almost_equal(acc, 1.0, 3)
Esempio n. 2
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def test_fit_reg_squared_l1():
    clf = CDRegressor(C=1.0, random_state=0, penalty="l1",
                      loss="squared", max_iter=100)
    clf.fit(digit.data, digit.target)
    y_pred = (clf.predict(digit.data) > 0.5).astype(int)
    acc = np.mean(digit.target == y_pred)
    assert_almost_equal(acc, 1.0, 3)
Esempio n. 3
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def test_fit_reg_squared_loss_nn_l2():
    K = pairwise_kernels(digit.data, metric="poly", degree=4)
    clf = CDRegressor(C=1, random_state=0, penalty="nnl2",
                      loss="squared", max_iter=100)
    clf.fit(K, digit.target)
    y_pred = (clf.predict(K) > 0.5).astype(int)
    acc = np.mean(digit.target == y_pred)
    assert_almost_equal(acc, 0.9444, 3)
Esempio n. 4
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def test_fit_reg_squared_multiple_outputs():
    reg = CDRegressor(C=0.05, random_state=0, penalty="l1/l2",
                      loss="squared", max_iter=100)
    lb = LabelBinarizer()
    Y = lb.fit_transform(mult_target)
    reg.fit(mult_dense, Y)
    y_pred = lb.inverse_transform(reg.predict(mult_dense))
    assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
    assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)
Esempio n. 5
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def test_fit_reg_squared_multiple_outputs():
    reg = CDRegressor(C=1.0, random_state=0, penalty="l2",
                      loss="squared", max_iter=100)
    Y = np.zeros((len(digit.target), 2))
    Y[:, 0] = digit.target
    Y[:, 1] = digit.target
    reg.fit(digit.data, Y)
    y_pred = reg.predict(digit.data)
    assert_equal(y_pred.shape[0], len(digit.target))
    assert_equal(y_pred.shape[1], 2)
Esempio n. 6
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def test_fit_reg_squared_multiple_outputs():
    reg = CDRegressor(C=1.0, random_state=0, penalty="l2",
                      loss="squared", max_iter=100)
    Y = np.zeros((len(digit.target), 2))
    Y[:, 0] = digit.target
    Y[:, 1] = digit.target
    reg.fit(digit.data, Y)
    y_pred = reg.predict(digit.data)
    assert_equal(y_pred.shape[0], len(digit.target))
    assert_equal(y_pred.shape[1], 2)
Esempio n. 7
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def test_fit_reg_squared_l1(train_data):
    X, y = train_data
    clf = CDRegressor(C=1.0,
                      random_state=0,
                      penalty="l1",
                      loss="squared",
                      max_iter=100)
    clf.fit(X, y)
    y_pred = (clf.predict(X) > 0.5).astype(int)
    acc = np.mean(y == y_pred)
    np.testing.assert_almost_equal(acc, 1.0, 3)
Esempio n. 8
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def test_fit_reg_squared_loss_nn_l2():
    K = pairwise_kernels(digit.data, metric="poly", degree=4)
    clf = CDRegressor(C=1,
                      random_state=0,
                      penalty="nnl2",
                      loss="squared",
                      max_iter=100)
    clf.fit(K, digit.target)
    y_pred = (clf.predict(K) > 0.5).astype(int)
    acc = np.mean(digit.target == y_pred)
    assert_almost_equal(acc, 0.9444, 3)
Esempio n. 9
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def test_warm_start_l1r_regression():
    clf = CDRegressor(warm_start=True, random_state=0, penalty="l1")

    clf.C = 0.1
    clf.fit(bin_dense, bin_target)
    n_nz = clf.n_nonzero()

    clf.C = 0.2
    clf.fit(bin_dense, bin_target)
    n_nz2 = clf.n_nonzero()

    assert_true(n_nz < n_nz2)
Esempio n. 10
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def test_fit_reg_squared_multiple_outputs(train_data):
    X, y = train_data
    reg = CDRegressor(C=1.0,
                      random_state=0,
                      penalty="l2",
                      loss="squared",
                      max_iter=100)
    Y = np.zeros((len(y), 2))
    Y[:, 0] = y
    Y[:, 1] = y
    reg.fit(X, Y)
    y_pred = reg.predict(X)
    assert y_pred.shape[0] == len(y)
    assert y_pred.shape[1] == 2
Esempio n. 11
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def test_warm_start_l1r_regression():
    clf = CDRegressor(warm_start=True, random_state=0, penalty="l1")

    clf.C = 0.1
    clf.fit(bin_dense, bin_target)
    n_nz = clf.n_nonzero()

    clf.C = 0.2
    clf.fit(bin_dense, bin_target)
    n_nz2 = clf.n_nonzero()

    assert_true(n_nz < n_nz2)
Esempio n. 12
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def test_fit_reg_squared_multiple_outputs():
    reg = CDRegressor(C=0.05, random_state=0, penalty="l1/l2",
                      loss="squared", max_iter=100)
    lb = LabelBinarizer()
    Y = lb.fit_transform(mult_target)
    reg.fit(mult_dense, Y)
    y_pred = lb.inverse_transform(reg.predict(mult_dense))
    assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
    assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)