Python sklearn.metrics.pairwise.check_pairwise_arrays() Examples

The following are 15 code examples of sklearn.metrics.pairwise.check_pairwise_arrays(). 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.metrics.pairwise , or try the search function .
Example #1
Source Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_pairwise.py    License: MIT License 6 votes vote down vote up
def test_check_sparse_arrays():
    # Ensures that checks return valid sparse matrices.
    rng = np.random.RandomState(0)
    XA = rng.random_sample((5, 4))
    XA_sparse = csr_matrix(XA)
    XB = rng.random_sample((5, 4))
    XB_sparse = csr_matrix(XB)
    XA_checked, XB_checked = check_pairwise_arrays(XA_sparse, XB_sparse)
    # compare their difference because testing csr matrices for
    # equality with '==' does not work as expected.
    assert issparse(XA_checked)
    assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
    assert issparse(XB_checked)
    assert_equal(abs(XB_sparse - XB_checked).sum(), 0)

    XA_checked, XA_2_checked = check_pairwise_arrays(XA_sparse, XA_sparse)
    assert issparse(XA_checked)
    assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
    assert issparse(XA_2_checked)
    assert_equal(abs(XA_2_checked - XA_checked).sum(), 0) 
Example #2
Source Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_pairwise.py    License: MIT License 6 votes vote down vote up
def test_check_preserve_type():
    # Ensures that type float32 is preserved.
    XA = np.resize(np.arange(40), (5, 8)).astype(np.float32)
    XB = np.resize(np.arange(40), (5, 8)).astype(np.float32)

    XA_checked, XB_checked = check_pairwise_arrays(XA, None)
    assert_equal(XA_checked.dtype, np.float32)

    # both float32
    XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
    assert_equal(XA_checked.dtype, np.float32)
    assert_equal(XB_checked.dtype, np.float32)

    # mismatched A
    XA_checked, XB_checked = check_pairwise_arrays(XA.astype(np.float),
                                                   XB)
    assert_equal(XA_checked.dtype, np.float)
    assert_equal(XB_checked.dtype, np.float)

    # mismatched B
    XA_checked, XB_checked = check_pairwise_arrays(XA,
                                                   XB.astype(np.float))
    assert_equal(XA_checked.dtype, np.float)
    assert_equal(XB_checked.dtype, np.float) 
Example #3
Source Project: twitter-stock-recommendation   Author: alvarobartt   File: test_pairwise.py    License: MIT License 6 votes vote down vote up
def test_check_sparse_arrays():
    # Ensures that checks return valid sparse matrices.
    rng = np.random.RandomState(0)
    XA = rng.random_sample((5, 4))
    XA_sparse = csr_matrix(XA)
    XB = rng.random_sample((5, 4))
    XB_sparse = csr_matrix(XB)
    XA_checked, XB_checked = check_pairwise_arrays(XA_sparse, XB_sparse)
    # compare their difference because testing csr matrices for
    # equality with '==' does not work as expected.
    assert_true(issparse(XA_checked))
    assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
    assert_true(issparse(XB_checked))
    assert_equal(abs(XB_sparse - XB_checked).sum(), 0)

    XA_checked, XA_2_checked = check_pairwise_arrays(XA_sparse, XA_sparse)
    assert_true(issparse(XA_checked))
    assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
    assert_true(issparse(XA_2_checked))
    assert_equal(abs(XA_2_checked - XA_checked).sum(), 0) 
Example #4
Source Project: twitter-stock-recommendation   Author: alvarobartt   File: test_pairwise.py    License: MIT License 6 votes vote down vote up
def test_check_preserve_type():
    # Ensures that type float32 is preserved.
    XA = np.resize(np.arange(40), (5, 8)).astype(np.float32)
    XB = np.resize(np.arange(40), (5, 8)).astype(np.float32)

    XA_checked, XB_checked = check_pairwise_arrays(XA, None)
    assert_equal(XA_checked.dtype, np.float32)

    # both float32
    XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
    assert_equal(XA_checked.dtype, np.float32)
    assert_equal(XB_checked.dtype, np.float32)

    # mismatched A
    XA_checked, XB_checked = check_pairwise_arrays(XA.astype(np.float),
                                                   XB)
    assert_equal(XA_checked.dtype, np.float)
    assert_equal(XB_checked.dtype, np.float)

    # mismatched B
    XA_checked, XB_checked = check_pairwise_arrays(XA,
                                                   XB.astype(np.float))
    assert_equal(XA_checked.dtype, np.float)
    assert_equal(XB_checked.dtype, np.float) 
Example #5
Source Project: MKLpy   Author: IvanoLauriola   File: boolean.py    License: GNU General Public License v3.0 5 votes vote down vote up
def monotone_dnf_kernel(X,Z=None,d=2,c=2):
    X, Z = check_pairwise_arrays(X, Z)
    n = X.shape[1]
    n_c = binom(n,c)
    XX = np.dot(X.sum(axis=1).reshape(X.shape[0],1), np.ones((1,Z.shape[0])))
    ZZ = np.dot(T.sum(axis=1).reshape(Z.shape[0],1), np.ones((1,X.shape[0])))
    XXc = binom(XX,c)
    ZZc = binom(ZZ,c)
    return binom(n_c,d) - binom(n_c - XXc, d) - binom(n_c - ZZc.T, d) + binom(my_mdk(X,Z,c),d) 
Example #6
Source Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_dense_matrices():
    # Ensure that pairwise array check works for dense matrices.
    # Check that if XB is None, XB is returned as reference to XA
    XA = np.resize(np.arange(40), (5, 8))
    XA_checked, XB_checked = check_pairwise_arrays(XA, None)
    assert XA_checked is XB_checked
    assert_array_equal(XA, XA_checked) 
Example #7
Source Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_XB_returned():
    # Ensure that if XA and XB are given correctly, they return as equal.
    # Check that if XB is not None, it is returned equal.
    # Note that the second dimension of XB is the same as XA.
    XA = np.resize(np.arange(40), (5, 8))
    XB = np.resize(np.arange(32), (4, 8))
    XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
    assert_array_equal(XA, XA_checked)
    assert_array_equal(XB, XB_checked)

    XB = np.resize(np.arange(40), (5, 8))
    XA_checked, XB_checked = check_paired_arrays(XA, XB)
    assert_array_equal(XA, XA_checked)
    assert_array_equal(XB, XB_checked) 
Example #8
Source Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_different_dimensions():
    # Ensure an error is raised if the dimensions are different.
    XA = np.resize(np.arange(45), (5, 9))
    XB = np.resize(np.arange(32), (4, 8))
    assert_raises(ValueError, check_pairwise_arrays, XA, XB)

    XB = np.resize(np.arange(4 * 9), (4, 9))
    assert_raises(ValueError, check_paired_arrays, XA, XB) 
Example #9
Source Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_invalid_dimensions():
    # Ensure an error is raised on 1D input arrays.
    # The modified tests are not 1D. In the old test, the array was internally
    # converted to 2D anyways
    XA = np.arange(45).reshape(9, 5)
    XB = np.arange(32).reshape(4, 8)
    assert_raises(ValueError, check_pairwise_arrays, XA, XB)
    XA = np.arange(45).reshape(9, 5)
    XB = np.arange(32).reshape(4, 8)
    assert_raises(ValueError, check_pairwise_arrays, XA, XB) 
Example #10
Source Project: twitter-stock-recommendation   Author: alvarobartt   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_dense_matrices():
    # Ensure that pairwise array check works for dense matrices.
    # Check that if XB is None, XB is returned as reference to XA
    XA = np.resize(np.arange(40), (5, 8))
    XA_checked, XB_checked = check_pairwise_arrays(XA, None)
    assert_true(XA_checked is XB_checked)
    assert_array_equal(XA, XA_checked) 
Example #11
Source Project: twitter-stock-recommendation   Author: alvarobartt   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_XB_returned():
    # Ensure that if XA and XB are given correctly, they return as equal.
    # Check that if XB is not None, it is returned equal.
    # Note that the second dimension of XB is the same as XA.
    XA = np.resize(np.arange(40), (5, 8))
    XB = np.resize(np.arange(32), (4, 8))
    XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
    assert_array_equal(XA, XA_checked)
    assert_array_equal(XB, XB_checked)

    XB = np.resize(np.arange(40), (5, 8))
    XA_checked, XB_checked = check_paired_arrays(XA, XB)
    assert_array_equal(XA, XA_checked)
    assert_array_equal(XB, XB_checked) 
Example #12
Source Project: twitter-stock-recommendation   Author: alvarobartt   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_different_dimensions():
    # Ensure an error is raised if the dimensions are different.
    XA = np.resize(np.arange(45), (5, 9))
    XB = np.resize(np.arange(32), (4, 8))
    assert_raises(ValueError, check_pairwise_arrays, XA, XB)

    XB = np.resize(np.arange(4 * 9), (4, 9))
    assert_raises(ValueError, check_paired_arrays, XA, XB) 
Example #13
Source Project: twitter-stock-recommendation   Author: alvarobartt   File: test_pairwise.py    License: MIT License 5 votes vote down vote up
def test_check_invalid_dimensions():
    # Ensure an error is raised on 1D input arrays.
    # The modified tests are not 1D. In the old test, the array was internally
    # converted to 2D anyways
    XA = np.arange(45).reshape(9, 5)
    XB = np.arange(32).reshape(4, 8)
    assert_raises(ValueError, check_pairwise_arrays, XA, XB)
    XA = np.arange(45).reshape(9, 5)
    XB = np.arange(32).reshape(4, 8)
    assert_raises(ValueError, check_pairwise_arrays, XA, XB) 
Example #14
Source Project: alphacsc   Author: alphacsc   File: custom_distances.py    License: BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up
def roll_invariant_euclidean_distances(X, Y=None, squared=False):
    """
    Considering the rows of X (and Y=X) as vectors, compute the
    distance matrix between each pair of vectors.
    The distance is the minimum of the euclidean distance over all rolls:

        dist(x, y) = min_\tau(||x(t) - y(t - \tau)||^2)

    Parameters
    ----------
    X : array, shape (n_samples_1, n_features)

    Y : array, shape (n_samples_2, n_features)

    squared : boolean
        Not used. Only for API compatibility.

    Returns
    -------
    distances : array, shape (n_samples_1, n_samples_2)

    """
    X = np.atleast_2d(X)
    if Y is not None:
        Y = np.atleast_2d(Y)
    X, Y = check_pairwise_arrays(X, Y)
    n_samples_1, n_features = X.shape
    n_samples_2, n_features = Y.shape

    X_norm = np.power(np.linalg.norm(X, axis=1), 2)
    Y_norm = np.power(np.linalg.norm(Y, axis=1), 2)

    # n_pads = 0
    # n_fft = next_fast_len(n_features + n_pads)
    n_fft = n_features  # not fast but otherwise the distance is wrong
    X_hat = rfft(X, n_fft, axis=1)
    Y_hat = rfft(Y, n_fft, axis=1).conj()

    # # broadcasting can have a huge memory cost
    # XY_hat = X_hat[:, None, :] * Y_hat[None, :, :]
    # XY = irfft(XY_hat, n_fft, axis=2).max(axis=2)
    # distances = X_norm[:, None] + Y_norm[None, :] - 2 * XY

    distances = np.zeros((n_samples_1, n_samples_2))
    if n_samples_2 > 1:
        print('RIED on %s samples, this might be slow' % (distances.shape, ))
    for ii in range(n_samples_1):
        for jj in range(n_samples_2):
            XY = irfft(X_hat[ii] * Y_hat[jj], n_fft).max()
            distances[ii, jj] = X_norm[ii] + Y_norm[jj] - 2 * XY

    distances += 1e-12

    return distances 
Example #15
Source Project: alphacsc   Author: alphacsc   File: custom_distances.py    License: BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up
def translation_invariant_euclidean_distances(X, Y=None, squared=False,
                                              symmetric=False):
    """
    Considering the rows of X (and Y=X) as vectors, compute the
    distance matrix between each pair of vectors.
    The distance is the minimum of the euclidean distance over a set of
    translations:

        dist(x, y) = min_{i, j}(||x(i:i+T) - y(j:j+T)||^2)

    where T = n_features / 2, and 1 <= i, j <= n_features / 2

    Parameters
    ----------
    X : array, shape (n_samples_1, n_features)

    Y : array, shape (n_samples_2, n_features)

    squared : boolean
        Not used. Only for API compatibility.

    symmetric : boolean
        If False, the distance is not symmetric anymore, since we keep indice
        j fixed at `n_features / 4`.

    Returns
    -------
    distances : array, shape (n_samples_1, n_samples_2)

    """
    X = np.atleast_2d(X)
    if Y is not None:
        Y = np.atleast_2d(Y)
    X, Y = check_pairwise_arrays(X, Y)
    n_samples_1, n_features = X.shape
    n_samples_2, n_features = Y.shape

    distances = np.zeros((n_samples_1, n_samples_2))
    # if n_samples_2 > 1:
    #     print('TIED on %s samples, this might be slow' % (distances.shape, ))
    for nn in range(n_samples_1):
        for mm in range(n_samples_2):
            XY = (X[nn, :, None] - Y[mm, None, :]) ** 2

            if symmetric:
                jj_range = np.arange(n_features // 2)
            else:
                jj_range = [n_features // 4]

            dist = np.zeros((n_features // 2, len(jj_range)))
            for ii in range(n_features // 2):
                for jj, kk in enumerate(jj_range):
                    xy = XY[ii:ii + n_features // 2, kk:kk + n_features // 2]
                    dist[ii, jj] = xy.trace(axis1=0, axis2=1)
            distances[nn, mm] = dist.min()

    return distances