SciPy (website) is a popular tool set that includes NumPy, Pandas, and SymPy. SciKit Learn (website) is also supported. For evaluating exercises written using SciPy we're using the NumPy testing and unittest frameworks.

Let's start with a Hello Pi exercise to explain the basics of evaluation in SciPy. Here is a solution file:

**exercise.py**

import numpy as npprint("Hello ", np.pi)

Evaluation of this simple "Hello Pi" exercise is also very simple. All we have to do is to create a class extending unittest's `TestCase`

class and declare a test method inside. The test method will get the string printed in stdout and compare it with expected output ("Hello World").

**evaluate.py**

import sysfrom unittest import TestCasefrom numpy.testing import assert_equalclass Evaluate(TestCase):def test_hello_pi(self):import exercise # Imports and runs student's solutionoutput = sys.stdout.getvalue() # Returns output since this function startedassert_equal(output, 'Hello 3.141592653589793\n')

Let's see what we did in the above solution code.

1st line, the

`sys`

module is imported which will help us get the output of student's code2nd line, TestCase class is imported to define our test class

`Evaluate`

and it is defined on the 4th line5th line, our first test function is defined

6th line, the exercise module that is implemented by students is imported. Since the solution code is not in a function, it is enough to import the module to run the student's code. We have to import it inside the test function to be able to get the output.

7th line, we get the output of exercise module.

`sys.stdout.getvalue()`

does exactly what we want here. It will only get the strings written to stdout since the test function has started. So you cannot get any outputs written before the`test_hello_world()`

has begun.8th line asserts whether the output is correct and returns feedback if it's not.

SciPy is based on Python and the documentation for Python covers a number of useful test scenarios. Please review those before continuing.

**exercise.py**

import numpy as npa = np.array([1,2,3,4])b = a.reshape(2,2)

**evaluate.py**

from unittest import TestCasefrom numpy.testing import assert_equalclass Evaluate(TestCase):def test_array(self):import exerciseassert_equal(exercise.a, [1, 2, 3, 4])assert_equal(exercise.b, [[1, 2], [3, 4]])

**exercise.py**

import pandas as pddates = pd.date_range('20130101', periods=6)

**evaluate.py**

import datetimefrom unittest import TestCasefrom numpy.testing import assert_equalclass Evaluate(TestCase):def test_array(self):import exerciseassert_equal(exercise.dates[0].date(), datetime.date(2013, 1, 1))assert_equal(exercise.dates[-1].date(), datetime.date(2013, 1, 6))

**exercise.py**

import pandas as pddata = pd.read_csv("data.csv", header=None)

**data.csv**

3,2,1

**evaluate.py**

from unittest import TestCasefrom numpy.testing import assert_equalclass Evaluate(TestCase):def test_array(self):import exerciseassert_equal(exercise.data.to_numpy().ravel(), [3,2,1])

**exercise.py**

import pandas as pddata = pd.read_csv('https://storage.googleapis.com/applied-dl/heart.csv')

**evaluate.py**

from unittest import TestCaseclass Evaluate(TestCase):def test_array(self):import exerciseself.assertTrue(exercise.data.to_numpy().size > 0)

**exercise.py**

from sympy.physics.units import mass, accelerationfrom sympy.physics.units.systems.si import dimsys_SIF = mass * accelerationdeps = dimsys_SI.get_dimensional_dependencies(F)

**evaluate.py**

from unittest import TestCasefrom numpy.testing import assert_equalclass Evaluate(TestCase):def test_array(self):import exerciseassert_equal(exercise.deps, {'length': 1, 'mass': 1, 'time': -2})

**exercise.py**

from sklearn import datasetsfrom sklearn import svmdigits = datasets.load_digits()clf = svm.SVC(gamma=0.001, C=100.)clf.fit(digits.data[:-1], digits.target[:-1])results = clf.predict(digits.data[-1:])

**evaluate.py**

from unittest import TestCasefrom numpy.testing import assert_array_equalclass Evaluate(TestCase):def test_array(self):import exerciseassert_array_equal(exercise.results, [8])