.. _unittest:

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

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 Op, 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!

This page is in no way meant to replace tutorials on Python's unittest
module, for this we refer the reader to the `official documentation
<http://docs.python.org/library/unittest.html>`_.  We will however
adress certain specificities about how unittests relate to aesara.

PyTest Primer
===============

We use pytest now! New tests should mostly be functions, with assertions

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

Mostly `pytest aesara/`

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

"tests" directories are scattered throughout aesara. Each tests
subfolder is meant to contain the unittests which validate the .py
files in the parent folder.

Files containing unittests should be prefixed with the word "test".

Optimally every python module should have a unittest file associated
with it, as shown below. Unittests testing functionality of module
<module>.py should therefore be stored in tests/test_<module>.py::

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


How to Write a Unittest
=======================

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

Unittests should be grouped "logically" into test cases, which are
meant to group all unittests operating on the same element and/or
concept. Test cases are implemented as Python classes which inherit
from unittest.TestCase

Test cases contain multiple test methods. These should be 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 TensorDot 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.

Example:

.. code-block:: python

    import unittest

    class TestTensorDot(unittest.TestCase):
        def test_validity(self):
            # do stuff
            ...
        def test_invalid_dims(self):
            # do more stuff
            ...


Test cases can define a special setUp method, which will get called
before each test method is executed. This is a good place to put
functionality which is shared amongst all test methods in the test
case (i.e initializing data, parameters, seeding random number
generators -- more on this later)

.. testcode:: writeUnitest

    import unittest

    class TestTensorDot(unittest.TestCase):
        def setUp(self):
            # data which will be used in various test methods
            self.avals = numpy.array([[1,5,3],[2,4,1]])
            self.bvals = numpy.array([[2,3,1,8],[4,2,1,1],[1,4,8,5]])

Similarly, test cases can define a tearDown method, which will be
implicitely called at the end of each test method.


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

    class TestTensorDot(unittest.TestCase):
        def setUp(self):
            ...

        def test_validity(self):
            a = T.dmatrix('a')
            b = T.dmatrix('b')
            c = T.dot(a, b)
            f = aesara.function([a, b], [c])
            cmp = f(self.avals, self.bvals) == numpy.dot(self.avals, self.bvals)
            self.assertTrue(numpy.all(cmp))

Avoid hard-coding variables, as in the following case:

.. code-block:: python

    self.assertTrue(numpy.all(f(self.avals, self.bvals) == numpy.array([[25, 25, 30, 28], [21, 18, 14, 25]])))

This makes the test case less manageable and forces the user to update
the variables each time the input is changed or possibly when the
module being tested changes (after a bug fix for example). It also
constrains the test case to specific input/output data pairs. The
section on random values covers why this might not be such a good
idea.

Here is a list of useful functions, as defined by TestCase:

* checking the state of boolean variables: assert,
  assertTrue, assertFalse

* checking for (in)equality constraints: assertEqual,
  assertNotEqual

* checking for (in)equality constraints up to a given precision (very
  useful in aesara): assertAlmostEqual,
  assertNotAlmostEqual


Checking for errors
-------------------

On top of verifying that your code provides the correct output, it is
equally important to test that it fails in the appropriate manner,
raising the appropriate exceptions, etc. Silent failures are deadly,
as they can go unnoticed for a long time and a hard to detect
"after-the-fact".

Example:

.. code-block:: python

    import unittest

    class TestTensorDot(unittest.TestCase):
        ...
        def test_3D_dot_fail(self):
            def func():
                a = TensorType('float64', (False,False,False)) # create 3d tensor
                b = dmatrix()
                c = dot(a,b) # we expect this to fail
            # above should fail as dot operates on 2D tensors only
            self.assertRaises(TypeError, func)

Useful function, as defined by TestCase:

* assertRaises


Test Cases and Aesara Modes
---------------------------

When compiling aesara functions or modules, a mode parameter can be
given to specify which linker and optimizer to use.

Example:

.. code-block:: python

    from aesara import function

    f = function([a,b],[c],mode='FAST_RUN')

Whenever possible, unit tests should omit this parameter. Leaving
out the mode will ensure that unit tests use the default mode.
This default mode is set to
the configuration variable :attr:`config.mode`, which defaults to
'FAST_RUN', and can be set by various mechanisms (see :mod:`config`).

In particular, the enviromnment variable :envvar:`AESARA_FLAGS`
allows the user to easily switch the mode in which unittests are
run. For example to run all tests in all modes from a BASH script,
type this:

.. code-block:: bash

    AESARA_FLAGS='mode=FAST_COMPILE' pytest
    AESARA_FLAGS='mode=FAST_RUN' pytest
    AESARA_FLAGS='mode=DebugMode' pytest

.. _random_value_in_tests:

Using Random Values in Test Cases
---------------------------------

``numpy.random`` is often used in unit tests to initialize large data
structures, for use as inputs to the function or module being
tested. When doing this, it is imperative that the random number
generator be seeded at the be beginning of each unit test. This will
ensure that unittest behaviour is consistent from one execution to
another (i.e., always pass or always fail).

Instead of using ``numpy.random.seed`` to do this, we encourage users to
do the following:

.. testcode::

    from tests import unittest_tools

    class TestTensorDot(unittest.TestCase):
        def setUp(self):
            unittest_tools.seed_rng()
            # OR ... call with an explicit seed
            unittest_tools.seed_rng(234234)  # use only if really necessary!

The behaviour of ``seed_rng`` is as follows:

* If an explicit seed is given, it will be used for seeding numpy's rng.

* If not, it will use ``config.unittests__rseed`` (its default value is ``666``).

* If ``config.unittests__rseed`` is set to ``"random"``, it will seed the rng with
  None, which is equivalent to seeding with a random seed.


The main advantage of using ``unittest_tools.seed_rng`` is that it allows
us to change the seed used in the unitests, without having to manually
edit all the files. For example, this allows the nightly build to run
``pytest`` repeatedly, changing the seed on every run (hence achieving
a higher confidence that the variables are correct), while still
making sure unittests are deterministic.

Users who prefer their unittests to be random (when run on their local
machine) can simply set ``config.unittests__rseed`` to ``'random'`` (see
:mod:`config`).

Similarly, to provide a seed to ``numpy.random.RandomState``, simply use:

.. testcode::

    import numpy

    rng = numpy.random.RandomState(unittest_tools.fetch_seed())
    # OR providing an explicit seed
    rng = numpy.random.RandomState(unittest_tools.fetch_seed(1231))  # again not recommended

Note that the ability to change the seed from one test to another,
is incompatible with the method of hard-coding the baseline variables
(against which we compare the aesara outputs). These must then be
determined "algorithmically". Although this represents more work, the
test suite will be better because of it.

To help you check that the boundaries provided to ``numpy.random`` are
correct and your tests will pass those corner cases, you can check
``utt.MockRandomState``. Code using ``utt.MockRandomState`` should not
be committed, it is just a tool to help adjust the sampling range.


Creating an Op UnitTest
=======================

A few tools have been developed to help automate the development of
unitests for Aesara Ops.


.. _validating_grad:

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

The ``verify_grad`` function can be used to validate that the ``grad``
function of your Op is properly implemented. ``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 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):

``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 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 ops.

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

Here is an example of ``makeTester`` generating testcases for the Dot
product 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 op we
want to test. We then provide in the ``expected`` field, a function
which ``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
  op-input/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. Unittest 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 ``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
  ``perform`` should fail). Fails unless an Exception is raised.

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


``makeBroadcastTester`` is a wrapper function for 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 Op's perform function has
been applied.
