Building Machine Learning Systems with Python - Richert, Coelho

Getting Started with Python Machine Learning
>>> c[~np.isnan(c)]
array([ 1., 2., 3., 4.])
>>> np.mean(c[~np.isnan(c)])
2.5
Comparing runtime behaviors
Let us compare the runtime behavior of NumPy with normal Python lists. In the
following code, we will calculate the sum of all squared numbers of 1 to 1000 and
see how much time the calculation will take. We do it 10000 times and report the
total time so that our measurement is accurate enough.
import timeit
normal_py_sec = timeit.timeit('sum(x*x for x in xrange(1000))',
number=10000)
naive_np_sec = timeit.timeit('sum(na*na)',
setup="import numpy as np; na=np.
arange(1000)",
number=10000)
good_np_sec = timeit.timeit('na.dot(na)',
setup="import numpy as np; na=np.
arange(1000)",
number=10000)
print("Normal Python: %f sec"%normal_py_sec)
print("Naive NumPy: %f sec"%naive_np_sec)
print("Good NumPy: %f sec"%good_np_sec)
Normal Python: 1.157467 sec
Naive NumPy: 4.061293 sec
Good NumPy: 0.033419 sec
We make two interesting observations. First, just using NumPy as data storage
(Naive NumPy) takes 3.5 times longer, which is surprising since we believe it must
be much faster as it is written as a C extension. One reason for this is that the access of
individual elements from Python itself is rather costly. Only when we are able to apply
algorithms inside the optimized extension code do we get speed improvements, and
tremendous ones at that: using the dot() function of NumPy, we are more than 25
times faster. In summary, in every algorithm we are about to implement, we should
always look at how we can move loops over individual elements from Python to some
of the highly optimized NumPy or SciPy extension functions.
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