def run():
mse_hist = []
mde_hist = []
samples = 100
config_dict = yaml.load(open(sys.argv[1], 'r'))
uniq = config_dict['uniq_map_file']
runiq = config_dict['runiq_map_file']
path = config_dict['pickle_dir']
rec = Recommender(path, uniq, runiq)
for sample in range(samples):
ch = []
tags_in_agg1 = []
tags_in_agg2 = []
user = rec.generate_user()
ch = rec.recommend(user, n=5)
for fname in user:
prettyprint_song(config_dict, fname)
print user, ' --> ', ch
print '-->'
for fname in ch:
prettyprint_song(config_dict, fname)
agg1 = aggregate_tags(config_dict, user)
agg2 = aggregate_tags(config_dict, ch)
tags_in_agg1.append(len(agg1))
tags_in_agg2.append(len(agg2))
mse, mde = compare_aggregators(agg1, agg2)
mde_hist.append(mde)
mse_hist.append(mse)
print "*************** SAMPLE %d" % (sample)
_, (mse_plot, mde_plot) = plt.subplots(2)
print float(sum(tags_in_agg1)) / len(tags_in_agg1)
print float(sum(tags_in_agg2)) / len(tags_in_agg2)
mse_plot.set_title("Mean Squared Error / Tags Hist")
mde_plot.set_title("Manhattan Dist Error / Tags Hist")
mse_plot.hist(mse_hist, bins=100)
mde_plot.hist(mde_hist, bins=100)
plt.savefig(config_dict['hist_path'])
