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Machine Learning workflow automatization

Project description

Python machine learning infrastructure project. The idea of MLizard is to make it easy to run lots of different experiments on lots of different options, constantly changing or exchanging parts of the process, without loosing track of what you did, when you did it, how you did it, what came out of it, which files are connected to it and so on. So this is how it looks like:

# for this demo we use the docstring as config
alpha = 0.7
beta = 7
gamma = "Foo"
# we import the experiment factory
from mlizard.experiment import createExperiment

# at the beginning of the file we create an experiment
ex = createExperiment("Demo", config_string=__doc__)

def part0(rnd):
   return rnd.randint(10)

def part1(X, alpha, beta, logger):
   X -= alpha
   X *= beta"multiplied by %f and added %f", alpha, beta)
   return X

def mainFunction():
   # this is the main method, here we put everything together
   X = part0() # note that we do not need to pass rnd
   X = part1(X) # and no alpha, beta, and logger

So we have to create an experiment and decorate all of our functions. But what do we get for this? - automatic option passing (alpha, beta) - a logger - a random number generator that is seeded by the experiment - automatic caching of intermediate results

More to come (Roadmap): - easy option sweeps - report file - online results view - database of runs/options/results - git integration (track version of code for every result)

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