RunAll.py

#!python
# All script can be called at once from here

import multiprocessing
import Networks
import Modes

# Running options
cpu = max(multiprocessing.cpu_count() - 1, 1)  # The number of cpu the scripts will use. I always leave 1 free so I can still use my computer
# I recommend to leave at least 1 cpu free so you can still work on your computer
shuffling_files = 89  # prime number to maximize spreading. Take a prime higher to the number of data files to limit the file size
keep_for_testing = 7  # Number of files that will be kept for testing only. Increase this number as you get more files (10% is standard)
# Keep in mind testing on games that were not used for training is the only we can be sure the neural network is not recognizing games but has
# actually learned to predict the winner.
restore = False  # leave this to False, or your model will overfit the data (it will recognize the game and not learn why the game is won/loss)

# Mode and Network
# Look at Modes.py and Networks.py to see the list of available modes/networks
# Feel free to build/tune your own networks
# BUT, keep in mind that more complex networks require more data and take more time to train.
m = Modes.ABR_TJMCS_Mode(['9.6','9.7','9.8'])
n = Networks.DenseUniform(mode=m, n_hidden_layers=5, NN=1024, dropout=0.2, batch_size=1000, report=1)


# Scripts to execute, comment useless ones
# In particular, if you just want to run the app, comment all but 'BestPicks'
to_execute = [
    'PlayersListing',
    'DataDownloader',  # runs on multiple cpu
    'DataExtractor',  # runs on multiple cpu
    'RoleUpdater',
    'DataProcessing',  # runs on multiple cpu
    'DataShuffling',  # runs on multiple cpu
    'Learner',  # runs on gpu
    'BestPicks',
]

if __name__ == '__main__':
    if 'PlayersListing' in to_execute:
        import PlayersListing
        PlayersListing.run(m)
    if 'DataDownloader' in to_execute:
        import DataDownloader
        DataDownloader.run(m)
    if 'DataExtractor' in to_execute:
        import DataExtractor
        DataExtractor.run(m, cpu)
    if 'RoleUpdater' in to_execute:
        import RoleUpdater
        RoleUpdater.run(m)
    if 'DataProcessing' in to_execute:
        import DataProcessing
        DataProcessing.run(m, cpu)
    if 'DataShuffling' in to_execute:
        import DataShuffling
        DataShuffling.run(m, shuffling_files, keep_for_testing, cpu)
    if 'Learner' in to_execute:
        import Learner
        Learner.run(m, n, restore)
    if 'BestPicks' in to_execute:
        import BestPicks
        BestPicks.run(m, n)