Efficient pairwise DTW calculation using numpy or cython

Question

I am trying to calculate the pairwise distances between multiple time-series contained in a numpy array. Please see the code below

print(type(sales))
print(sales         


        

Answer 1

TL;DR

Your fastdtw falled to install the fast cpp-version and falls back silently to a pure-python version, which is slow.

You need to fix the installation of the fastdtw-package.

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The whole calculation is done in fastdtw, so you cannot really speed it up from the outside. And parallelization and python is not such an easy thing (yet?).

The fastdtw documentation says it needs about O(n) operations for a comparison, so for your whole test-set it will need about order of magnitude of 10^9 operations, which should be finished in about some seconds, if programmed in, for example, C. The performance you see is nowhere near it.

If we look at the code of fastdtw we see, that there are two versions: the cython/cpp-version which is fast and imported via cython and a slow fall back pure-python-version. If the fast version isn't preset, the slow python version is silently used.

So run your calculation, interrupt it with Ctr+C and you will see, that you are somewhere in python-code. You can also go to your lib-folder and see, that there is only the pure-python version inside.

So your installation of the fast fastdtw version failed. Actually, I think the wheel-package is botched, at least for my version there is only the pure python code present.

What to do?

1. Get the source code, e.g. via git clone https://github.com/slaypni/fastdtw
2. go into fstdtw folder and run python setup.py build
3. watch out for errors. Mine was

fatal error: numpy/npy_math.h: No such file or directory

4. fix it.

For me, the fix was to change the following lines in setup.py:

import numpy # THIS ADDED
extensions = [Extension(
        'fastdtw._fastdtw',
        [os.path.join('fastdtw', '_fastdtw' + ext)],
        language="c++",
        include_dirs=[numpy.get_include()], # AND ADDED numpy.get_include()
        libraries=["stdc++"]
    )]

5. repeat 3.+4. until successful
6. run python setup.py install

Now your program should be about 100 times faster. `