Building Machine Learning Systems with Python - Richert, Coelho

Chapter 12
Jug is also specially optimized to work with numpy arrays. So, whenever your tasks
return or receive numpy arrays, you are taking advantage of this optimization. Jug is
another piece of this ecosystem where everything works together.
We will now look back at Chapter 10, Computer Vision–Pattern Recognition Finding
Related Posts. We learned how to compute features on images. Remember that we
were loading image files, computing features, combining these, normalizing them,
and finally learning how to create a classifier. We are going to redo that exercise but
this time with the use of jug. The advantage of this version is that it is now possible
to add a new feature without having to recompute all of the previous versions.
We start with a few imports as follows:
from jug import TaskGenerator
Now we define the first task generator, the feature computation:
@TaskGenerator
def hfeatures(fname):
import mahotas as mh
import numpy as np
im = mh.imread(fname, as_grey=1)
im = mh.stretch(im)
h = mh.features.haralick(im)
return np.hstack([h.ptp(0), h.mean(0)])
Note how we only imported numpy and mahotas inside the function. This is a small
optimization; this way, only if the task is run are the modules loaded. Now we set
up the image filenames as follows:
filenames = glob('dataset/*.jpg')
We can use TaskGenerator on any function, even on the ones that we did not write,
such as numpy.array:
import numpy as np
as_array = TaskGenerator(np.array)
# compute all features:
features = as_array([hfeature(f) for f in filenames])
# get labels as an array as well
labels = map(label_for, f)
res = perform_cross_validation(features, labels)
@TaskGenerator
[ 247 ]