def test_scaler_float16_overflow():
    # Test if the scaler will not overflow on float16 numpy arrays
    rng = np.random.RandomState(0)
    # float16 has a maximum of 65500.0. On the worst case 5 * 200000 is 100000
    # which is enough to overflow the data type
    X = rng.uniform(5, 10, [200000, 1]).astype(np.float16)

    with np.errstate(over='raise'):
        scaler = StandardScaler().fit(X)
        X_scaled = scaler.transform(X)

    # Calculate the float64 equivalent to verify result
    X_scaled_f64 = StandardScaler().fit_transform(X.astype(np.float64))

    # Overflow calculations may cause -inf, inf, or nan. Since there is no nan
    # input, all of the outputs should be finite. This may be redundant since a
    # FloatingPointError exception will be thrown on overflow above.
    assert np.all(np.isfinite(X_scaled))

    # The normal distribution is very unlikely to go above 4. At 4.0-8.0 the
    # float16 precision is 2^-8 which is around 0.004. Thus only 2 decimals are
    # checked to account for precision differences.
    assert_array_almost_equal(X_scaled, X_scaled_f64, decimal=2)