[BUG] Sporadic SGD test failures

[JohnZed]

Reproduction is very inconsistent, but occasionally we see: ```

=================================== FAILURES =================================== ____ testsgd[dataframe-log-l1-float64-invscaling] ____

dtype = <class 'numpy.float64'>, lrate = 'invscaling', penalty = 'l1' loss = 'log', datatype = 'dataframe'

@pytest.mark.parametrize('lrate', ['constant', 'invscaling', 'adaptive'])
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
@pytest.mark.parametrize('penalty', ['none', 'l1', 'l2', 'elasticnet'])
@pytest.mark.parametrize('loss', ['hinge', 'log', 'squared_loss'])
@pytest.mark.parametrize('datatype', ["dataframe", "numpy"])
def test_sgd(dtype, lrate, penalty, loss, datatype):

    X, y = make_blobs(n_samples=100,
                      n_features=3,
                      centers=2,
                      random_state=0)
    X = X.astype(dtype)
    y = y.astype(dtype)

    if loss == "hinge" or loss == "squared_loss":
        y[y == 0] = -1

    X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                        train_size=0.8)

    if datatype == "dataframe":
        X_train = cudf.DataFrame(X_train)
        X_test = cudf.DataFrame(X_test)
        y_train = cudf.Series(y_train)

    cu_sgd = cumlSGD(learning_rate=lrate, eta0=0.005, epochs=2000,
                     fit_intercept=True, batch_size=4096,
                     tol=0.0, penalty=penalty, loss=loss)

    cu_sgd.fit(X_train, y_train)
    cu_pred = cu_sgd.predict(X_test)

    if datatype == "dataframe":
        assert isinstance(cu_pred, cudf.Series)
        cu_pred = cu_pred.to_array()

    else:
        assert isinstance(cu_pred, np.ndarray)

    if loss == "log":
        cu_pred[cu_pred < 0.5] = 0
        cu_pred[cu_pred >= 0.5] = 1
    elif loss == "squared_loss":
        cu_pred[cu_pred < 0] = -1
        cu_pred[cu_pred >= 0] = 1

    # Adjust for squared loss (we don't need to test for high accuracy,
    # just that the loss function tended towards the expected classes.

  assert np.array_equal(cu_pred, y_test)

E assert False E + where False = <function array_equal at 0x7f85083d24c0>(array([1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0.,\n 1., 1., 1.]), array([1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0.,\n 1., 1., 1.])) E + where <function array_equal at 0x7f85083d24c0> = np.array_equal

cuml/test/test_sgd.py:75: AssertionError

e.g. in this pipeline: https://gpuci.gpuopenanalytics.com/blue/rest/organizations/jenkins/pipelines/rapidsai/pipelines/gpuci/pipelines/cuml/pipelines/prb/pipelines/cuml-gpu-build/runs/15441/log/?start=0

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