detection_error_tradeoff()

audplot.detection_error_tradeoff(x, y, *, error_rates=False, xlim=[0.001, 0.5], ylim=[0.001, 0.5], label=None, ax=None)

Detection error tradeoff curve.

A detection error tradeoff (DET) curve is a graphical plot of error rates for binary classification systems, plotting the false non-match rate (FNMR) against the false match rate (FMR).

You can provide truth and prediction values as input or you can directly provide FMR and FNMR, which can be calculated using audmetric.detection_error_tradeoff().

The axes of the plot are scaled non-linearly by their standard normal deviates. This means you have to scale every value by this transformation when you would like to change ticks positions or axis limits afterwards. The scaling is performed by scipy.special.ndtri() if scipy is installed, otherwise audmath.inverse_normal_distribution() is used, which is slower for large input arrays.

Parameters

• x (Union[Sequence, Series]) – truth values or false match rate (FMR)

• y (Union[Sequence, Series]) – predicted values or false non-match rate (FNMR)

• error_rates (bool) – if False it expects truth values as x, and prediction values as y. If True it expects FMR as x, and FNMR as y

• xlim (Sequence) – x-axis limits with \(x \in ]0, 1[\)

• ylim (Sequence) – y-axis limits with \(y \in ]0, 1[\)

• label (Optional[str]) – label to be shown in the legend. The legend will not be shown automatically

• ax (Optional[Axes]) – pre-existing axes for the plot. Otherwise, calls matplotlib.pyplot.gca() internally

Return type

Callable

Returns

function to transform input values to standard normal derivate scale

Examples

>>> truth = np.array([1] * 1000 + [0] * 1000)
>>> # Random prediction
>>> pred1 = np.random.random_sample(2000)
>>> # Better than random prediction
>>> pred2 = np.zeros(
...     2000,
... )
>>> pred2[:1000] = np.random.normal(loc=0.6, scale=0.1, size=1000)
>>> pred2[1000:] = np.random.normal(loc=0.4, scale=0.1, size=1000)
>>> pred2 = np.clip(pred2, 0, 1)
>>> transform = detection_error_tradeoff(
...     truth,
...     pred1,
...     xlim=[0.01, 0.99],  # use large limits for random
...     ylim=[0.01, 0.99],
...     label="pred1",
... )
>>> # Add pred2 to plot using transformed FMR and FNMR values
>>> import audmetric
>>> fmr, fnmr, _ = audmetric.detection_error_tradeoff(truth, pred2)
>>> _ = plt.plot(transform(fmr), transform(fnmr), label="pred2")
>>> _ = plt.legend()
>>> plt.tight_layout()