Learning Data Mining with Python

Extracting Features with Transformers
We then create our transformer instance and fit it using this test data:
mean_discrete = MeanDiscrete()
mean_discrete.fit(X_test)
Next, we check whether the internal mean parameter was correctly set by comparing
it with our independently verified result:
assert_array_equal(mean_discrete.mean, np.array([13.5, 15.5]))
We then run the transform to create the transformed dataset. We also create an
(independently computed) array with the expected values for the output:
X_transformed = mean_discrete.transform(X_test)
X_expected = np.array([[ 0, 0],
[ 0, 0],
[ 0, 0],
[ 0, 0],
[ 0, 0],
[ 1, 1],
[ 1, 1],
[ 1, 1],
[ 1, 1],
[ 1, 1]])
Finally, we test that our returned result is indeed what we expected:
assert_array_equal(X_transformed, X_expected)
We can run the test by simply running the function itself:
test_meandiscrete()
If there was no error, then the test ran without an issue! You can verify this by
changing some of the tests to deliberately incorrect values, and seeing that the test
fails. Remember to change them back so that the test passes.
If we had multiple tests, it would be worth using a testing framework called nose to
run our tests.
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