Building Machine Learning Systems with Python - Third Edition

Increasing experimentation agility

Before we dive into classifier training, let's think about experimentation agility. Although we have the word "fast" in FFT, it is much slower than the creation of the features in our text-based chapters. And because we are still in an experimentation phase, we might want to think about how we could speed up the whole feature-creation process.

Of course, the creation of the FFT per file will be the same each time we run the classifier. We could, therefore, cache it and read the cached FFT representation instead of the complete WAV file. We do this with the create_fft() function, which, in turn, uses scipy.fft() to create the FFT. For the sake of simplicity (and speed!), let's fix the number of FFT components ...