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# scipy.special.pseudo_huber

scipy.special.pseudo_huber(delta, r, out=None) = <ufunc 'pseudo_huber'>

  Pseudo-Huber loss function.

  \[\mathrm{pseudo\_huber}(\delta, r) = \delta^2 \left( \sqrt{ 1 + \left( \frac{r}{\delta} \right)^2 } - 1 \right)\]
  Parameters:
    delta array_like

      Input array, indicating the soft quadratic vs. linear loss changepoint.

    r array_like

      Input array, possibly representing residuals.

    out ndarray, optional

      Optional output array for the function results

  Returns:
    res scalar or ndarray

      The computed Pseudo-Huber loss function values.

  See also

  `huber`

    Similar function which this function approximates

  Notes

  Like `huber`, `pseudo_huber` often serves as a robust loss function in statistics or machine learning to reduce the influence of outliers. Unlike `huber`, `pseudo_huber` is smooth.

  Typically, r represents residuals, the difference between a model prediction and data. Then, for \(|r|\leq\delta\), `pseudo_huber` resembles the squared error and for \(|r|>\delta\) the absolute error. This way, the Pseudo-Huber loss often achieves a fast convergence in model fitting for small residuals like the squared error loss function and still reduces the influence of outliers (\(|r|>\delta\)) like the absolute error loss. As \(\delta\) is the cutoff between squared and absolute error regimes, it has to be tuned carefully for each problem. `pseudo_huber` is also convex, making it suitable for gradient based optimization. [1] [2]

  Added in version 0.15.0.

  References

  [1]

  Hartley, Zisserman, “Multiple View Geometry in Computer Vision”. 2003. Cambridge University Press. p. 619

  [2]

  Charbonnier et al. “Deterministic edge-preserving regularization in computed imaging”. 1997. IEEE Trans. Image Processing. 6 (2): 298 - 311.

  Examples

  Import all necessary modules.

      >>> import numpy as np
      >>> from scipy.special import pseudo_huber, huber
      >>> import matplotlib.pyplot as plt

  Calculate the function for `delta=1` at `r=2`.

      >>> pseudo_huber(1., 2.)
      1.2360679774997898

  Calculate the function at `r=2` for different delta by providing a list or NumPy array for delta.

      >>> pseudo_huber([1., 2., 4.], 3.)
      array([2.16227766, 3.21110255, 4.        ])

  Calculate the function for `delta=1` at several points by providing a list or NumPy array for r.

      >>> pseudo_huber(2., np.array([1., 1.5, 3., 4.]))
      array([0.47213595, 1.        , 3.21110255, 4.94427191])

  The function can be calculated for different delta and r by providing arrays for both with compatible shapes for broadcasting.

      >>> r = np.array([1., 2.5, 8., 10.])
      >>> deltas = np.array([[1.], [5.], [9.]])
      >>> print(r.shape, deltas.shape)
      (4,) (3, 1)

      >>> pseudo_huber(deltas, r)
      array([[ 0.41421356,  1.6925824 ,  7.06225775,  9.04987562],
             [ 0.49509757,  2.95084972, 22.16990566, 30.90169944],
             [ 0.49846624,  3.06693762, 27.37435121, 40.08261642]])

  Plot the function for different delta.

      >>> x = np.linspace(-4, 4, 500)
      >>> deltas = [1, 2, 3]
      >>> linestyles = ["dashed", "dotted", "dashdot"]
      >>> fig, ax = plt.subplots()
      >>> combined_plot_parameters = list(zip(deltas, linestyles))
      >>> for delta, style in combined_plot_parameters:
      ...     ax.plot(x, pseudo_huber(delta, x), label=rf"$\delta={delta}$",
      ...             ls=style)
      >>> ax.legend(loc="upper center")
      >>> ax.set_xlabel("$x$")
      >>> ax.set_title(r"Pseudo-Huber loss function $h_{\delta}(x)$")
      >>> ax.set_xlim(-4, 4)
      >>> ax.set_ylim(0, 8)
      >>> plt.show()

  Finally, illustrate the difference between `huber` and `pseudo_huber` by plotting them and their gradients with respect to r. The plot shows that `pseudo_huber` is continuously differentiable while `huber` is not at the points \(\pm\delta\).

      >>> def huber_grad(delta, x):
      ...     grad = np.copy(x)
      ...     linear_area = np.argwhere(np.abs(x) > delta)
      ...     grad[linear_area]=delta*np.sign(x[linear_area])
      ...     return grad
      >>> def pseudo_huber_grad(delta, x):
      ...     return x* (1+(x/delta)**2)**(-0.5)
      >>> x=np.linspace(-3, 3, 500)
      >>> delta = 1.
      >>> fig, ax = plt.subplots(figsize=(7, 7))
      >>> ax.plot(x, huber(delta, x), label="Huber", ls="dashed")
      >>> ax.plot(x, huber_grad(delta, x), label="Huber Gradient", ls="dashdot")
      >>> ax.plot(x, pseudo_huber(delta, x), label="Pseudo-Huber", ls="dotted")
      >>> ax.plot(x, pseudo_huber_grad(delta, x), label="Pseudo-Huber Gradient",
      ...         ls="solid")
      >>> ax.legend(loc="upper center")
      >>> plt.show()