stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
algorithms.eaSimple(pop,
toolbox,
0.7,
0.3,
150,
stats=stats,
halloffame=hof)
return pop, stats, hof
if __name__ == "__main__":
pop, stats, hof = main()
print('track names in hof individual set size', SET_SIZE)
for h in hof:
for i in h:
print(trackNames[i])
bestSetURIs = [trackURIs[i] for i in h]
eval = Evaluation()
print(" hof set scores ",
eval.exactSetMatches(ugpTrackURIs, bestSetURIs))
print(' === ')
pdb.set_trace()
def WeightedSum(ideal, trackFeatures, weights):
'''
keyOrder = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', \
'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature', 'popularity', 'release_date']
'''
trackDiffs = []
wSum = sum(weights)
for track in tqdm(trackFeatures):
trackDiffSum = 0
for cIdx, cIdeal in enumerate(ideal):
trackDiffSum += (weights[cIdx] / wSum) * abs(cIdeal - track[cIdx])
trackDiffs.append(trackDiffSum / len(cons))
return trackDiffs
trackDiffs = UnweightedSum(ideal, trackFeatures)
print('sorting')
trackDiffs, trackURIs, trackNames, trackFeatures = zip(
*sorted(zip(trackDiffs, trackURIs, trackNames, trackFeatures)))
#for i in range(20):
#print(trackDiffs[i], trackNames[i])
eval = Evaluation()
print(" set scores ", eval.exactSetMatches(testTrackURIs,
trackURIs[:SET_SIZE]))
pdb.set_trace()
