.. _model-surrogate-global-experimental_python-class_path_and_name:

"""""""""""""""""""
class_path_and_name
"""""""""""""""""""


Specify the module and class names of the python surrogate



.. toctree::
   :hidden:
   :maxdepth: 1



**Specification**

- *Alias:* None

- *Arguments:* STRING


**Description**


Specify the module and class names of the python surrogate.  At minimum,
the class must contain the methods 'construct' and 'predict'.  Dakota
passes to the former a matrix (num samples x num variables) of variable
values and a matrix (num samples x num components) of responses to use
to train the surrogate, where num components is 1 for scalar surrogates.
The `predict` method receives a matrix (num samples x num variables)
and expects a matrix (num samples x num components) of predicted values
to be returned.  Optional 'gradient' and 'hessian' methods are supported
as well.




**Examples**



Use of python-based surrogates in Dakota is enabled with the model
block via:

.. code-block::

  model,
      id_model = 'SURR'
      surrogate global,
      dace_method_pointer = 'DACE'
      experimental_python
          class_path_and_name = "surrogate_polynomial.Surrogate"

An example python class that implements a linear regression surrogate
that works for both scalar and vector-valued responses and that includes
gradient and (trivial) hessian values is the following:

.. code-block:: python

  class Surrogate:

      def __init__(self):
          self.coeffs = None
  
      def construct(self, var, resp):
          var2 = np.hstack((np.ones((var.shape[0], 1)), var))
          self.coeffs = np.zeros(var.shape[1])
          z = np.linalg.inv(np.dot(var2.T, var2))
          self.coeffs = np.dot(z, np.dot(var2.T, resp))
          return
  
      def predict(self, pts):
          return self.coeffs[0]+pts.dot(self.coeffs[1:])

      def gradient(self, pts):
          grad = self.coeffs[1:]
          return grad.T
  
      def hessian(self, pts):
          num_vals = pts.shape[1]
          hess = np.zeros(shape=(num_vals, num_vals))
          return hess