transformer test

    fit_transformer = dc.trans.CoulombFitTransformer(dataset)

    X_t = fit_transformer.X_transform(dataset.X)

    assert len(X_t.shape) == 2

  def test_IRV_transformer(self):

    n_features = 128

    n_samples = 20

    test_samples = 5

    n_tasks = 2

    X = np.random.randint(2, size=(n_samples, n_features))

    y = np.zeros((n_samples, n_tasks))

    w = np.ones((n_samples, n_tasks))

    dataset = dc.data.NumpyDataset(X, y, w, ids=None)

    X_test = np.random.randint(2, size=(test_samples, n_features))

    y_test = np.zeros((test_samples, n_tasks))

    w_test = np.ones((test_samples, n_tasks))

    test_dataset = dc.data.NumpyDataset(X_test, y_test, w_test, ids=None)

    sims = np.sum(X_test[0,:]*X, axis=1, dtype=float)/np.sum(np.sign(X_test[0,:] + X), axis=1, dtype=float)

    sims = sorted(sims, reverse=True)

    IRV_transformer = dc.trans.IRVTransformer(10, n_tasks, dataset)

    test_dataset_trans = IRV_transformer.transform(test_dataset)

    assert test_dataset_trans.X.shape == (test_samples, 20*n_tasks)

    assert np.allclose(test_dataset_trans.X[0,:10], sims[:10])

    assert np.allclose(test_dataset_trans.X[0,10:20], [0]*10)