def test_manhattan_distances_execution(setup, x, y, is_sparse): if is_sparse: rx, ry = raw_sparse_x, raw_sparse_y else: rx, ry = raw_x, raw_y sv = [True, False] if not is_sparse else [True] for sum_over_features in sv: d = manhattan_distances(x, y, sum_over_features) result = d.execute().fetch() expected = sk_manhattan_distances(rx, ry, sum_over_features=sum_over_features) np.testing.assert_almost_equal(result, expected) d = manhattan_distances(x, sum_over_features=sum_over_features) result = d.execute().fetch() expected = sk_manhattan_distances(rx, sum_over_features=sum_over_features) np.testing.assert_almost_equal(result, expected)
def testManhattanDistancesExecution(self): raw_x = np.random.rand(20, 5) raw_y = np.random.rand(21, 5) x1 = mt.tensor(raw_x, chunk_size=30) y1 = mt.tensor(raw_y, chunk_size=30) x2 = mt.tensor(raw_x, chunk_size=11) y2 = mt.tensor(raw_y, chunk_size=12) raw_sparse_x = sps.random(20, 5, density=0.4, format='csr', random_state=0) raw_sparse_y = sps.random(21, 5, density=0.3, format='csr', random_state=0) x3 = mt.tensor(raw_sparse_x, chunk_size=30) y3 = mt.tensor(raw_sparse_y, chunk_size=30) x4 = mt.tensor(raw_sparse_x, chunk_size=11) y4 = mt.tensor(raw_sparse_y, chunk_size=12) for x, y, is_sparse in [(x1, y1, False), (x2, y2, False), (x3, y3, True), (x4, y4, True)]: if is_sparse: rx, ry = raw_sparse_x, raw_sparse_y else: rx, ry = raw_x, raw_y sv = [True, False] if not is_sparse else [True] for sum_over_features in sv: d = manhattan_distances(x, y, sum_over_features) result = self.executor.execute_tensor(d, concat=True)[0] expected = sk_manhattan_distances(rx, ry, sum_over_features) np.testing.assert_almost_equal(result, expected) d = manhattan_distances(x, sum_over_features=sum_over_features) result = self.executor.execute_tensor(d, concat=True)[0] expected = sk_manhattan_distances( rx, sum_over_features=sum_over_features) np.testing.assert_almost_equal(result, expected)