main.py

print('Go!',end='...')


from collaborative_filtering.read_data import split_data
from collaborative_filtering.scale_data import scale_data

from collaborative_filtering.generate_model import generate_model

from content_based.bios_to_table import bios_to_table as read_bios
from content_based.generate_tfIdfRecommender import generate_tfIdfRecommender

from get_dataset import getDataset as get_dataset
from evaluate import evaluate

from data_visualization.view_plays_freq import view_plays_freq 
from data_visualization.view_metrics import view_metrics


fakeDataset = True
datasetPath = './fake_dataset/' if fakeDataset else './dataset/'
resultsPath = './fake_results/' if fakeDataset else './results/'

kk = [5 ,10 ,100,200,500]

metrics = {'map': True, 'diversity': True, 'ndcg': True,'mrr': True,'rnd':  True,'ub': True}
methods = {'cf': True, 'cb': True, 'hb': True}



if not fakeDataset: get_dataset()


split_data(datasetPath)

if args.visualize:
    view_plays_freq(datasetPath,True)
    view_plays_freq(datasetPath)

scale_data(datasetPath)
read_bios(datasetPath)
generate_model(datasetPath)
generate_tfIdfRecommender(datasetPath)

print('evaluate!')
evaluate(datasetPath,resultsPath,kk=kk, metrics=metrics, methods=methods)

view_metrics(resultsPath, kk=kk, metrics=metrics,methods=methods, showPlots=True)