from ijcai15 import PersTour
#algo = PersTour('./Osaka', 'userVisits-Osak.csv')
#algo.recommend(0.0)
#algo.recommend(0.5)
#algo.recommend(0.5, time_based=False)
#algo.recommend(1.0)
#algo.recommend(1.0, time_based=False)
#algo = PersTour('./Edinburgh', 'userVisits-Edin.csv')
#algo.recommend(0.0)
#algo.recommend(0.5)
#algo.recommend(0.5, time_based=False)
#algo.recommend(1.0)
#algo.recommend(1.0, time_based=False)
#algo = PersTour('./Glasgow', 'userVisits-Glas.csv')
#algo.recommend(0.0)
#algo.recommend(0.5)
#algo.recommend(0.5, time_based=False)
#algo.recommend(1.0)
#algo.recommend(1.0, time_based=False)
#algo = PersTour('./Toronto', 'userVisits-Toro.csv')
algo = PersTour('./Toronto2', 'userVisits-Toro.csv')
algo.recommend(0.0)
#algo.recommend(0.5)
#algo.recommend(0.5, time_based=False)
#algo.recommend(1.0)
#algo.recommend(1.0, time_based=False)
# for k in range(weights.shape[0]):
# paramvec[l] = weights[k]
# values[k] = calc_mean_F1score(ptobj, trainset, enumseqs_dict, scorevec_dict, scoremat, paramvec)
# midx = values.argmax()
# print('best F1-score:', values[midx], ', best weight:', weights[midx])
# paramvec[l] = weights[midx]
#plt.scatter(np.linspace(-1, 1, 21), values, marker='+')
plt.show()
input('...')
if __name__ == '__main__':
dirname = './Edinburgh'
basefilename = 'userVisits-Edin.csv'
ptobj = PersTour(dirname, basefilename)
ptobj.calc_metrics({x for x in range(len(ptobj.sequences))})
# train and test sequences
#seqtrainset, seqtestset = gen_train_test_set(ptobj)
#print(len(seqtrainset), len(seqtestset))
fname = 'train_test.set'
seqtrainset, seqtestset = load_train_test_set(fname)
#lengths = []
#for seq in seqtrainset:
# lengths.append(len(ptobj.sequences[seq]))
#plt.hist(lengths, bins=20)
#plt.show()
#input('...')
