.. _unittest:

============
Unit Testing
============

.. warning::
   This document is very outdated.

Aesara relies heavily on unit testing. Its importance cannot be
stressed enough!

Unit Testing revolves around the following principles:

* ensuring correctness: making sure that your :class:`Op`, :class:`Type` or
  optimization works in the way you intended it to work. It is important for
  this testing to be as thorough as possible: test not only the obvious cases,
  but more importantly the corner cases which are more likely to trigger bugs
  down the line.

* test all possible failure paths. This means testing that your code
  fails in the appropriate manner, by raising the correct errors when
  in certain situations.

* sanity check: making sure that everything still runs after you've
  done your modification. If your changes cause unit tests to start
  failing, it could be that you've changed an API on which other users
  rely on. It is therefore your responsibility to either a) provide
  the fix or b) inform the author of your changes and coordinate with
  that person to produce a fix. If this sounds like too much of a
  burden... then good! APIs aren't meant to be changed on a whim!


We use `pytest <https://docs.pytest.org>`_.  New tests should
generally take the form of a test function, and each check within a test should
involve an assertion of some kind.

.. note::

  Tests that check for a lack of failures (e.g. that ``Exception``\s aren't
  raised) are generally *not* good tests.  Instead, assert something more
  relevant and explicit about the expected outputs or side-effects of the code
  being tested.


How to Run Unit Tests
---------------------

Mostly ``pytest aesara/``

Folder Layout
-------------

Files containing unit tests should be prefixed with the word "test".

Ideally, every python module should have a unittest file associated
with it, as shown below. Unit tests that test functionality of module
``<module>.py`` should therefore be stored in
``tests/<sub-package>/test_<module>.py``::

    Aesara/aesara/tensor/basic.py
    Aesara/tests/tensor/test_basic.py

    Aesara/aesara/tensor/elemwise.py
    Aesara/tests/tensor/test_elemwise.py


How to Write a Unit Test
========================

Test Cases and Methods
----------------------

Unit tests should be grouped "logically" into test cases, which are
meant to group all unit tests operating on the same element and/or
concept.

Test cases should be functions or classes prefixed with the word "test".

Test methods should be as specific as possible and cover a particular
aspect of the problem. For example, when testing the :class:`Dot` :class:`Op`, one
test method could check for validity, while another could verify that
the proper errors are raised when inputs have invalid dimensions.

Test method names should be as explicit as possible, so that users can
see at first glance, what functionality is being tested and what tests
need to be added.

Checking for correctness
------------------------

When checking for correctness of mathematical expressions, the user
should preferably compare aesara's output to the equivalent NumPy
implementation.

Example:

.. code-block:: python

    def test_dot_validity():
        a = aet.dmatrix('a')
        b = aet.dmatrix('b')
        c = aet.dot(a, b)
        f = aesara.function([a, b], [c])
        assert np.array_equal(f(self.avals, self.bvals), numpy.dot(self.avals, self.bvals))


Creating an :class:`Op` Unit Test
=================================

A few tools have been developed to help automate the development of
unit tests for Aesara :class:`Op`\s.


.. _validating_grad:

Validating the Gradient
-----------------------

The :func:`verify_grad` function can be used to validate that the :meth:`Op.grad`
method of your :class:`Op` is properly implemented. :func:`verify_grad` is based
on the Finite Difference Method where the derivative of function ``f``
at point ``x`` is approximated as:

.. math::

   \frac{\partial{f}}{\partial{x}} = lim_{\Delta \rightarrow 0} \frac {f(x+\Delta) - f(x-\Delta)} {2\Delta}

``verify_grad`` performs the following steps:

* approximates the gradient numerically using the Finite Difference Method

* calculate the gradient using the symbolic expression provided in the
  ``grad`` function

* compares the two values. The tests passes if they are equal to
  within a certain tolerance.

Here is the prototype for the :func:`verify_grad` function.

.. code-block:: python

    def verify_grad(fun, pt, n_tests=2, rng=None, eps=1.0e-7, abs_tol=0.0001, rel_tol=0.0001):

:func:`verify_grad` raises an ``Exception`` if the difference between the analytic gradient and
numerical gradient (computed through the Finite Difference Method) of a random
projection of the fun's output to a scalar exceeds both the given absolute and
relative tolerances.

The parameters are as follows:

* ``fun``: a Python function that takes Aesara variables as inputs,
  and returns an Aesara variable.
  For instance, an :class:`Op` instance with a single output is such a function.
  It can also be a Python function that calls an op with some of its
  inputs being fixed to specific values, or that combine multiple :class:`Op`\s.

* ``pt``: the list of numpy.ndarrays to use as input values

* ``n_tests``: number of times to run the test

* ``rng``: random number generator used to generate a random vector u,
  we check the gradient of sum(u*fn) at pt

* ``eps``: stepsize used in the Finite Difference Method

* ``abs_tol``: absolute tolerance used as threshold for gradient comparison

* ``rel_tol``: relative tolerance used as threshold for gradient comparison

In the general case, you can define ``fun`` as you want, as long as it
takes as inputs Aesara symbolic variables and returns a sinble Aesara
symbolic variable:

.. testcode::

    def test_verify_exprgrad():
        def fun(x,y,z):
            return (x + tensor.cos(y)) / (4 * z)**2

        x_val = numpy.asarray([[1], [1.1], [1.2]])
        y_val = numpy.asarray([0.1, 0.2])
        z_val = numpy.asarray(2)
        rng = numpy.random.RandomState(42)

        aesara.gradient.verify_grad(fun, [x_val, y_val, z_val], rng=rng)

Here is an example showing how to use :func:`verify_grad` on an :class:`Op` instance:

.. testcode::

    def test_flatten_outdimNone():
        # Testing gradient w.r.t. all inputs of an op (in this example the op
        # being used is Flatten(), which takes a single input).
        a_val = numpy.asarray([[0,1,2],[3,4,5]], dtype='float64')
        rng = numpy.random.RandomState(42)
        aesara.gradient.verify_grad(tensor.Flatten(), [a_val], rng=rng)

Here is another example, showing how to verify the gradient w.r.t. a subset of
an :class:`Op`'s inputs. This is useful in particular when the gradient w.r.t. some of
the inputs cannot be computed by finite difference (e.g. for discrete inputs),
which would cause :func:`verify_grad` to crash.

.. testcode::

    def test_crossentropy_softmax_grad():
        op = tensor.nnet.crossentropy_softmax_argmax_1hot_with_bias
        def op_with_fixed_y_idx(x, b):
            # Input `y_idx` of this Op takes integer values, so we fix them
            # to some constant array.
            # Although this op has multiple outputs, we can return only one.
            # Here, we return the first output only.
            return op(x, b, y_idx=numpy.asarray([0, 2]))[0]

        x_val = numpy.asarray([[-1, 0, 1], [3, 2, 1]], dtype='float64')
        b_val = numpy.asarray([1, 2, 3], dtype='float64')
        rng = numpy.random.RandomState(42)

        aesara.gradient.verify_grad(op_with_fixed_y_idx, [x_val, b_val], rng=rng)

.. note::

    Although ``verify_grad`` is defined in ``aesara.tensor.basic``, unittests
    should use the version of ``verify_grad`` defined in ``tests.unittest_tools``.
    This is simply a wrapper function which takes care of seeding the random
    number generator appropriately before calling ``aesara.gradient.verify_grad``

makeTester and makeBroadcastTester
==================================

Most :class:`Op` unittests perform the same function. All such tests must
verify that the :class:`Op` generates the proper output, that the gradient is
valid, that the :class:`Op` fails in known/expected ways. Because so much of
this is common, two helper functions exists to make your lives easier:
:func:`makeTester` and :func:`makeBroadcastTester` (defined in module
:mod:`tests.tensor.utils`).

Here is an example of ``makeTester`` generating testcases for the dot
product :class:`Op`:

.. testcode::

    from numpy import dot
    from numpy.random import rand

    from tests.tensor.utils import makeTester

    TestDot = makeTester(name = 'DotTester',
                         op = dot,
                         expected = lambda x, y: numpy.dot(x, y),
                         checks = {},
                         good = dict(correct1 = (rand(5, 7), rand(7, 5)),
                                     correct2 = (rand(5, 7), rand(7, 9)),
                                     correct3 = (rand(5, 7), rand(7))),
                         bad_build = dict(),
                         bad_runtime = dict(bad1 = (rand(5, 7), rand(5, 7)),
                                           bad2 = (rand(5, 7), rand(8,3))),
                         grad = dict())

In the above example, we provide a name and a reference to the :class:`Op` we
want to test. We then provide in the ``expected`` field, a function
which :func:`makeTester` can use to compute the correct values. The
following five parameters are dictionaries which contain:

* checks: dictionary of validation functions (dictionary key is a
  description of what each function does). Each function accepts two
  parameters and performs some sort of validation check on each
  :class:`Op`-input/:class:`Op`-output value pairs.  If the function returns ``False``, an
  ``Exception`` is raised containing the check's description.

* good: contains valid input values, for which the output should match
  the expected output. Unit tests will fail if this is not the case.

* bad_build: invalid parameters which should generate an ``Exception``
  when attempting to build the graph (call to :meth:`Op.make_node` should
  fail).  Fails unless an ``Exception`` is raised.

* bad_runtime: invalid parameters which should generate an ``Exception``
  at runtime, when trying to compute the actual output values (call to
  :meth:`Op.perform` should fail). Fails unless an ``Exception`` is raised.

* grad: dictionary containing input values which will be used in the
  call to :func:`verify_grad`


:func:`makeBroadcastTester` is a wrapper function for :func:`makeTester`.  If an
``inplace=True`` parameter is passed to it, it will take care of
adding an entry to the ``checks`` dictionary. This check will ensure
that inputs and outputs are equal, after the :class:`Op`'s perform function has
been applied.
