Python sklearn.kernel_approximation.AdditiveChi2Sampler() Examples

The following are 7 code examples of sklearn.kernel_approximation.AdditiveChi2Sampler(). 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.kernel_approximation , or try the search function .
Example #1
Source File: test_kernel_approximation.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_input_validation():
    # Regression test: kernel approx. transformers should work on lists
    # No assertions; the old versions would simply crash
    X = [[1, 2], [3, 4], [5, 6]]
    AdditiveChi2Sampler().fit(X).transform(X)
    SkewedChi2Sampler().fit(X).transform(X)
    RBFSampler().fit(X).transform(X)

    X = csr_matrix(X)
    RBFSampler().fit(X).transform(X)
Example #2
Source File: additive_chi2_sampler.py    From lale with Apache License 2.0 5 votes vote down vote up 
def __init__(self, sample_steps=2, sample_interval=None):
        self._hyperparams = {
            'sample_steps': sample_steps,
            'sample_interval': sample_interval}
        self._wrapped_model = Op(**self._hyperparams)
Example #3
Source File: common.py    From reveal-graph-embedding with Apache License 2.0 5 votes vote down vote up 
def chi_squared_projection(features):
    chi2_feature = AdditiveChi2Sampler()
    X_transformed = chi2_feature.fit_transform(features)
    X_transformed = X_transformed.tocsr()

    return X_transformed
Example #4
Source File: test_kernel_approximation.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.kernel_approximation.AdditiveChi2Sampler,
                      ka.AdditiveChi2Sampler)
        self.assertIs(df.kernel_approximation.Nystroem, ka.Nystroem)
        self.assertIs(df.kernel_approximation.RBFSampler, ka.RBFSampler)
        self.assertIs(df.kernel_approximation.SkewedChi2Sampler,
                      ka.SkewedChi2Sampler)
Example #5
Source File: test_kernel_approximation.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_input_validation():
    # Regression test: kernel approx. transformers should work on lists
    # No assertions; the old versions would simply crash
    X = [[1, 2], [3, 4], [5, 6]]
    AdditiveChi2Sampler().fit(X).transform(X)
    SkewedChi2Sampler().fit(X).transform(X)
    RBFSampler().fit(X).transform(X)

    X = csr_matrix(X)
    RBFSampler().fit(X).transform(X)
Example #6
Source File: test_kernel_approximation.py    From Mastering-Elasticsearch-7.0 with MIT License 4 votes vote down vote up 
def test_additive_chi2_sampler():
    # test that AdditiveChi2Sampler approximates kernel on random data

    # compute exact kernel
    # abbreviations for easier formula
    X_ = X[:, np.newaxis, :]
    Y_ = Y[np.newaxis, :, :]

    large_kernel = 2 * X_ * Y_ / (X_ + Y_)

    # reduce to n_samples_x x n_samples_y by summing over features
    kernel = (large_kernel.sum(axis=2))

    # approximate kernel mapping
    transform = AdditiveChi2Sampler(sample_steps=3)
    X_trans = transform.fit_transform(X)
    Y_trans = transform.transform(Y)

    kernel_approx = np.dot(X_trans, Y_trans.T)

    assert_array_almost_equal(kernel, kernel_approx, 1)

    X_sp_trans = transform.fit_transform(csr_matrix(X))
    Y_sp_trans = transform.transform(csr_matrix(Y))

    assert_array_equal(X_trans, X_sp_trans.A)
    assert_array_equal(Y_trans, Y_sp_trans.A)

    # test error is raised on negative input
    Y_neg = Y.copy()
    Y_neg[0, 0] = -1
    assert_raises(ValueError, transform.transform, Y_neg)

    # test error on invalid sample_steps
    transform = AdditiveChi2Sampler(sample_steps=4)
    assert_raises(ValueError, transform.fit, X)

    # test that the sample interval is set correctly
    sample_steps_available = [1, 2, 3]
    for sample_steps in sample_steps_available:

        # test that the sample_interval is initialized correctly
        transform = AdditiveChi2Sampler(sample_steps=sample_steps)
        assert_equal(transform.sample_interval, None)

        # test that the sample_interval is changed in the fit method
        transform.fit(X)
        assert_not_equal(transform.sample_interval_, None)

    # test that the sample_interval is set correctly
    sample_interval = 0.3
    transform = AdditiveChi2Sampler(sample_steps=4,
                                    sample_interval=sample_interval)
    assert_equal(transform.sample_interval, sample_interval)
    transform.fit(X)
    assert_equal(transform.sample_interval_, sample_interval)
Example #7
Source File: test_kernel_approximation.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
def test_additive_chi2_sampler():
    # test that AdditiveChi2Sampler approximates kernel on random data

    # compute exact kernel
    # abbreviations for easier formula
    X_ = X[:, np.newaxis, :]
    Y_ = Y[np.newaxis, :, :]

    large_kernel = 2 * X_ * Y_ / (X_ + Y_)

    # reduce to n_samples_x x n_samples_y by summing over features
    kernel = (large_kernel.sum(axis=2))

    # approximate kernel mapping
    transform = AdditiveChi2Sampler(sample_steps=3)
    X_trans = transform.fit_transform(X)
    Y_trans = transform.transform(Y)

    kernel_approx = np.dot(X_trans, Y_trans.T)

    assert_array_almost_equal(kernel, kernel_approx, 1)

    X_sp_trans = transform.fit_transform(csr_matrix(X))
    Y_sp_trans = transform.transform(csr_matrix(Y))

    assert_array_equal(X_trans, X_sp_trans.A)
    assert_array_equal(Y_trans, Y_sp_trans.A)

    # test error is raised on negative input
    Y_neg = Y.copy()
    Y_neg[0, 0] = -1
    assert_raises(ValueError, transform.transform, Y_neg)

    # test error on invalid sample_steps
    transform = AdditiveChi2Sampler(sample_steps=4)
    assert_raises(ValueError, transform.fit, X)

    # test that the sample interval is set correctly
    sample_steps_available = [1, 2, 3]
    for sample_steps in sample_steps_available:

        # test that the sample_interval is initialized correctly
        transform = AdditiveChi2Sampler(sample_steps=sample_steps)
        assert_equal(transform.sample_interval, None)

        # test that the sample_interval is changed in the fit method
        transform.fit(X)
        assert_not_equal(transform.sample_interval_, None)

    # test that the sample_interval is set correctly
    sample_interval = 0.3
    transform = AdditiveChi2Sampler(sample_steps=4,
                                    sample_interval=sample_interval)
    assert_equal(transform.sample_interval, sample_interval)
    transform.fit(X)
    assert_equal(transform.sample_interval_, sample_interval)