Python sklearn.metrics.pairwise.pairwise_distances_argmin() Examples

The following are 1 code examples of sklearn.metrics.pairwise.pairwise_distances_argmin(). 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: twitter-stock-recommendation   Author: alvarobartt   File: test_pairwise.py    License: MIT License 4 votes vote down vote up
def test_pairwise_distances_argmin_min():
    # Check pairwise minimum distances computation for any metric
    X = [[0], [1]]
    Y = [[-1], [2]]

    Xsp = dok_matrix(X)
    Ysp = csr_matrix(Y, dtype=np.float32)

    # euclidean metric
    D, E = pairwise_distances_argmin_min(X, Y, metric="euclidean")
    D2 = pairwise_distances_argmin(X, Y, metric="euclidean")
    assert_array_almost_equal(D, [0, 1])
    assert_array_almost_equal(D2, [0, 1])
    assert_array_almost_equal(D, [0, 1])
    assert_array_almost_equal(E, [1., 1.])

    # sparse matrix case
    Dsp, Esp = pairwise_distances_argmin_min(Xsp, Ysp, metric="euclidean")
    assert_array_equal(Dsp, D)
    assert_array_equal(Esp, E)
    # We don't want np.matrix here
    assert_equal(type(Dsp), np.ndarray)
    assert_equal(type(Esp), np.ndarray)

    # Non-euclidean scikit-learn metric
    D, E = pairwise_distances_argmin_min(X, Y, metric="manhattan")
    D2 = pairwise_distances_argmin(X, Y, metric="manhattan")
    assert_array_almost_equal(D, [0, 1])
    assert_array_almost_equal(D2, [0, 1])
    assert_array_almost_equal(E, [1., 1.])
    D, E = pairwise_distances_argmin_min(Xsp, Ysp, metric="manhattan")
    D2 = pairwise_distances_argmin(Xsp, Ysp, metric="manhattan")
    assert_array_almost_equal(D, [0, 1])
    assert_array_almost_equal(E, [1., 1.])

    # Non-euclidean Scipy distance (callable)
    D, E = pairwise_distances_argmin_min(X, Y, metric=minkowski,
                                         metric_kwargs={"p": 2})
    assert_array_almost_equal(D, [0, 1])
    assert_array_almost_equal(E, [1., 1.])

    # Non-euclidean Scipy distance (string)
    D, E = pairwise_distances_argmin_min(X, Y, metric="minkowski",
                                         metric_kwargs={"p": 2})
    assert_array_almost_equal(D, [0, 1])
    assert_array_almost_equal(E, [1., 1.])

    # Compare with naive implementation
    rng = np.random.RandomState(0)
    X = rng.randn(97, 149)
    Y = rng.randn(111, 149)

    dist = pairwise_distances(X, Y, metric="manhattan")
    dist_orig_ind = dist.argmin(axis=0)
    dist_orig_val = dist[dist_orig_ind, range(len(dist_orig_ind))]

    dist_chunked_ind, dist_chunked_val = pairwise_distances_argmin_min(
        X, Y, axis=0, metric="manhattan", batch_size=50)
    np.testing.assert_almost_equal(dist_orig_ind, dist_chunked_ind, decimal=7)
    np.testing.assert_almost_equal(dist_orig_val, dist_chunked_val, decimal=7)