Python sklearn.datasets.load_linnerud() Examples

The following are 12 code examples of sklearn.datasets.load_linnerud(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.datasets , or try the search function .
Example #1
Source File: test_pls.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_predict_transform_copy():
    # check that the "copy" keyword works
    d = load_linnerud()
    X = d.data
    Y = d.target
    clf = pls_.PLSCanonical()
    X_copy = X.copy()
    Y_copy = Y.copy()
    clf.fit(X, Y)
    # check that results are identical with copy
    assert_array_almost_equal(clf.predict(X), clf.predict(X.copy(), copy=False))
    assert_array_almost_equal(clf.transform(X), clf.transform(X.copy(), copy=False))

    # check also if passing Y
    assert_array_almost_equal(clf.transform(X, Y),
                              clf.transform(X.copy(), Y.copy(), copy=False))
    # check that copy doesn't destroy
    # we do want to check exact equality here
    assert_array_equal(X_copy, X)
    assert_array_equal(Y_copy, Y)
    # also check that mean wasn't zero before (to make sure we didn't touch it)
    assert np.all(X.mean(axis=0) != 0)
Example #2
Source File: test_pls.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_predict_transform_copy():
    # check that the "copy" keyword works
    d = load_linnerud()
    X = d.data
    Y = d.target
    clf = pls_.PLSCanonical()
    X_copy = X.copy()
    Y_copy = Y.copy()
    clf.fit(X, Y)
    # check that results are identical with copy
    assert_array_almost_equal(clf.predict(X), clf.predict(X.copy(), copy=False))
    assert_array_almost_equal(clf.transform(X), clf.transform(X.copy(), copy=False))

    # check also if passing Y
    assert_array_almost_equal(clf.transform(X, Y),
                              clf.transform(X.copy(), Y.copy(), copy=False))
    # check that copy doesn't destroy
    # we do want to check exact equality here
    assert_array_equal(X_copy, X)
    assert_array_equal(Y_copy, Y)
    # also check that mean wasn't zero before (to make sure we didn't touch it)
    assert_true(np.all(X.mean(axis=0) != 0))
Example #3
Source File: test_pls.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_convergence_fail():
    d = load_linnerud()
    X = d.data
    Y = d.target
    pls_bynipals = pls_.PLSCanonical(n_components=X.shape[1],
                                     max_iter=2, tol=1e-10)
    assert_warns(ConvergenceWarning, pls_bynipals.fit, X, Y)
Example #4
Source File: test_pls.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_PLSSVD():
    # Let's check the PLSSVD doesn't return all possible component but just
    # the specified number
    d = load_linnerud()
    X = d.data
    Y = d.target
    n_components = 2
    for clf in [pls_.PLSSVD, pls_.PLSRegression, pls_.PLSCanonical]:
        pls = clf(n_components=n_components)
        pls.fit(X, Y)
        assert_equal(n_components, pls.y_scores_.shape[1])
Example #5
Source File: test_pls.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_univariate_pls_regression():
    # Ensure 1d Y is correctly interpreted
    d = load_linnerud()
    X = d.data
    Y = d.target

    clf = pls_.PLSRegression()
    # Compare 1d to column vector
    model1 = clf.fit(X, Y[:, 0]).coef_
    model2 = clf.fit(X, Y[:, :1]).coef_
    assert_array_almost_equal(model1, model2)
Example #6
Source File: test_base.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_load_linnerud():
    res = load_linnerud()
    assert_equal(res.data.shape, (20, 3))
    assert_equal(res.target.shape, (20, 3))
    assert_equal(len(res.target_names), 3)
    assert res.DESCR
    assert os.path.exists(res.data_filename)
    assert os.path.exists(res.target_filename)

    # test return_X_y option
    check_return_X_y(res, partial(load_linnerud))
Example #7
Source File: test_datasets.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_linnerud(self):
        return
        # data must be 1-dimensional

        # data = datasets.load_linnerud()
        # df = pdml.ModelFrame(data)
        # self.assertEqual(df.shape, (150, 5))
Example #8
Source File: test_pls.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_PLSSVD():
    # Let's check the PLSSVD doesn't return all possible component but just
    # the specified number
    d = load_linnerud()
    X = d.data
    Y = d.target
    n_components = 2
    for clf in [pls_.PLSSVD, pls_.PLSRegression, pls_.PLSCanonical]:
        pls = clf(n_components=n_components)
        pls.fit(X, Y)
        assert_equal(n_components, pls.y_scores_.shape[1])
Example #9
Source File: test_pls.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_univariate_pls_regression():
    # Ensure 1d Y is correctly interpreted
    d = load_linnerud()
    X = d.data
    Y = d.target

    clf = pls_.PLSRegression()
    # Compare 1d to column vector
    model1 = clf.fit(X, Y[:, 0]).coef_
    model2 = clf.fit(X, Y[:, :1]).coef_
    assert_array_almost_equal(model1, model2)
Example #10
Source File: test_pls.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_pls_errors():
    d = load_linnerud()
    X = d.data
    Y = d.target
    for clf in [pls_.PLSCanonical(), pls_.PLSRegression(),
                pls_.PLSSVD()]:
        clf.n_components = 4
        assert_raise_message(ValueError, "Invalid number of components",
                             clf.fit, X, Y)
Example #11
Source File: test_pls.py    From Mastering-Elasticsearch-7.0 with MIT License 4 votes vote down vote up 
def test_scale_and_stability():
    # We test scale=True parameter
    # This allows to check numerical stability over platforms as well

    d = load_linnerud()
    X1 = d.data
    Y1 = d.target
    # causes X[:, -1].std() to be zero
    X1[:, -1] = 1.0

    # From bug #2821
    # Test with X2, T2 s.t. clf.x_score[:, 1] == 0, clf.y_score[:, 1] == 0
    # This test robustness of algorithm when dealing with value close to 0
    X2 = np.array([[0., 0., 1.],
                   [1., 0., 0.],
                   [2., 2., 2.],
                   [3., 5., 4.]])
    Y2 = np.array([[0.1, -0.2],
                   [0.9, 1.1],
                   [6.2, 5.9],
                   [11.9, 12.3]])

    for (X, Y) in [(X1, Y1), (X2, Y2)]:
        X_std = X.std(axis=0, ddof=1)
        X_std[X_std == 0] = 1
        Y_std = Y.std(axis=0, ddof=1)
        Y_std[Y_std == 0] = 1

        X_s = (X - X.mean(axis=0)) / X_std
        Y_s = (Y - Y.mean(axis=0)) / Y_std

        for clf in [CCA(), pls_.PLSCanonical(), pls_.PLSRegression(),
                    pls_.PLSSVD()]:
            clf.set_params(scale=True)
            X_score, Y_score = clf.fit_transform(X, Y)
            clf.set_params(scale=False)
            X_s_score, Y_s_score = clf.fit_transform(X_s, Y_s)
            assert_array_almost_equal(X_s_score, X_score, decimal=4)
            assert_array_almost_equal(Y_s_score, Y_score, decimal=4)
            # Scaling should be idempotent
            clf.set_params(scale=True)
            X_score, Y_score = clf.fit_transform(X_s, Y_s)
            assert_array_almost_equal(X_s_score, X_score, decimal=4)
            assert_array_almost_equal(Y_s_score, Y_score, decimal=4)
Example #12
Source File: test_pls.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
def test_scale_and_stability():
    # We test scale=True parameter
    # This allows to check numerical stability over platforms as well

    d = load_linnerud()
    X1 = d.data
    Y1 = d.target
    # causes X[:, -1].std() to be zero
    X1[:, -1] = 1.0

    # From bug #2821
    # Test with X2, T2 s.t. clf.x_score[:, 1] == 0, clf.y_score[:, 1] == 0
    # This test robustness of algorithm when dealing with value close to 0
    X2 = np.array([[0., 0., 1.],
                   [1., 0., 0.],
                   [2., 2., 2.],
                   [3., 5., 4.]])
    Y2 = np.array([[0.1, -0.2],
                   [0.9, 1.1],
                   [6.2, 5.9],
                   [11.9, 12.3]])

    for (X, Y) in [(X1, Y1), (X2, Y2)]:
        X_std = X.std(axis=0, ddof=1)
        X_std[X_std == 0] = 1
        Y_std = Y.std(axis=0, ddof=1)
        Y_std[Y_std == 0] = 1

        X_s = (X - X.mean(axis=0)) / X_std
        Y_s = (Y - Y.mean(axis=0)) / Y_std

        for clf in [CCA(), pls_.PLSCanonical(), pls_.PLSRegression(),
                    pls_.PLSSVD()]:
            clf.set_params(scale=True)
            X_score, Y_score = clf.fit_transform(X, Y)
            clf.set_params(scale=False)
            X_s_score, Y_s_score = clf.fit_transform(X_s, Y_s)
            assert_array_almost_equal(X_s_score, X_score)
            assert_array_almost_equal(Y_s_score, Y_score)
            # Scaling should be idempotent
            clf.set_params(scale=True)
            X_score, Y_score = clf.fit_transform(X_s, Y_s)
            assert_array_almost_equal(X_s_score, X_score)
            assert_array_almost_equal(Y_s_score, Y_score)