Processing a simple workflow in Python -

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I am working on a code that takes the dataset and runs some algorithms on it.

User uploads a dataset, and then selects the algorithm to run on this dataset and creates a workflow like this: 

  workflow = {0: {' Dataset ':' some dataset ', 1: {' algorithm1 ': "parameter"}, 2: {' algorithm2 ': "parameter"}, 3: {' algorithm 3 ': "parameter"}}

which means that I will run the workflow [0] as my dataset, and I will run it as algorithm1 . After that, I will take the result and I will run the algorithm2 on this result like my new dataset. And I will take new results and run it on algorithm3 This happens to this last item and there is no limit to this workflow.

I am writing it in Python. Can you recommend some strategy about processing this workflow?

You want to run a pipeline on some datasets. It looks like less operation (fold in some languages). 

  result = less (lambda data, (anonym, p): algo_by_name (anonymous) (p, data), workflow)

This workflow considers (text-oriented so that you can load it with YAML / JSON): 

  workflow = ['data', ('algo0', {}), '(Algo1 ', {' Param ': value}), ...]

And that looks like your algorithm: 

  def algo0 (p , Data): ... return_output_filename

takes algo_by_name and gives you an algo function; For example: 

  def algo_by_name (name): return {'algo0': algo0, 'algo1': algo1,} [name]

( Edit old: If you want a framework to write a pipeline, you can use it as it is like making an instrument, but with progress support and beautiful flow chart.)

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