def showResult():
"""Get final result from getResultOfArimaMethod()
and then plot it using pyplot.
Input:
None.
Output:
None.
"""
logging.info('I am in showResult......')
result = getResultOfArimaMethod() # get the result
length = range(5,51,5)
#plt imgs of result
for index in range(5):
indexName = util.getIndexName(index) # get index name
plt.figure(index)
plt.plot(length,result[index])
plt.title("%s of MTSA with Different Window Sizes of Session" % indexName)
plt.xlabel("window size of session")
plt.ylabel(indexName)
plt.legend()
plt.savefig("../img/arima_trend_%s.png" % indexName)
plt.show()
logging.info('I am out showResult......')
def showResult():
"""Using pyplot to plot the experiment results.
Input:
None.
Output:
None.
"""
logging.info('I am in showResult......')
filename = "../txt/%s_testall_%d_%d.txt" % (const.DATASET_NAME,const.TOPIC_NUM,const.TOP_N)
x = range(1,const.TOP_N,1)
result = [[[] for i in range(5)] for i in range(3)]
#read result from file to result
if os.path.exists(filename):
print '%s is existing......' % filename
rFile = open(filename,"r")
lines = rFile.readlines()
for line in lines:
line = line.rstrip('\n')
items = line.split("INFO:")
line = items[1]
items = line.split(":")
ids = items[0]
values = items[1]
idItems = ids.split(">")
mid = int(idItems[0])
topN = int(idItems[1])
valueItems = values.split()
result[mid][0].append(float(valueItems[0]))
result[mid][1].append(float(valueItems[1]))
result[mid][2].append(float(valueItems[2]))
result[mid][3].append(float(valueItems[3]))
result[mid][4].append(float(valueItems[4]))
rFile.close()
else:
rFile = open(filename,"w")
rFile.close()
#if some method is not in file, recreate it
for mid in range(3):
if len(result[mid][0]) == 0:
recalls,precisions,f1s,maes,rmses = getErrorOfRecMethod(mid)
result[mid][0] = recalls
result[mid][1] = precisions
result[mid][2] = f1s
result[mid][3] = maes
result[mid][4] = rmses
#plt img of comparing with pure method
for index in range(5):
plt.figure(index)
indexName = util.getIndexName(index)
mids = [const.ARIMA,const.SIMILAR,const.AVG]
marker = ['k','k-.','k:']
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[20:],result[mid][index][20:],marker[markerIndex],label=util.getMethodName(mid))
else:
plt.plot(x,result[mid][index],marker[markerIndex],label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/pure_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
#plt.show()
logging.info('I am out showResult......')
def showResult():
"""Using pyplot to plot the experiment results.
Input:
None.
Output:
None.
"""
logging.info('I am in showResult......')
filename = "../txt/%s_testall_%d_%d.txt" % (const.DATASET_NAME,const.TOPIC_NUM,const.TOP_N)
x = range(1,const.TOP_N,1)
result = [[[] for i in range(5)] for i in range(const.METHOD_SIZE)]
#read result from file to result
if os.path.exists(filename):
print '%s is existing......' % filename
rFile = open(filename,"r")
lines = rFile.readlines()
for line in lines:
line = line.rstrip('\n')
items = line.split("INFO:")
line = items[1]
items = line.split(":")
ids = items[0]
values = items[1]
idItems = ids.split(">")
mid = int(idItems[0])
topN = int(idItems[1])
valueItems = values.split()
result[mid][0].append(float(valueItems[0]))
result[mid][1].append(float(valueItems[1]))
result[mid][2].append(float(valueItems[2]))
result[mid][3].append(float(valueItems[3]))
result[mid][4].append(float(valueItems[4]))
rFile.close()
else:
rFile = open(filename,"w")
rFile.close()
#if some method is not in file, recreate it
for mid in range(const.METHOD_SIZE):
if len(result[mid][0]) == 0:
recalls,precisions,f1s,maes,rmses = getErrorOfRecMethod(mid)
result[mid][0] = recalls
result[mid][1] = precisions
result[mid][2] = f1s
result[mid][3] = maes
result[mid][4] = rmses
#plt img of comparing with pure method
for index in range(5):
plt.figure(index)
indexName = util.getIndexName(index)
mids = [const.ARIMA,const.SIMILAR,const.AVG]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))
else:
plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms(Pure)" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/pure_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
#plt.show()
#plt img of comparing with hybrid method
for index in range(5):
plt.figure(index+5)
indexName = util.getIndexName(index)
mids = [const.ARIMA,const.ARIMA_SIMILAR,const.ARIMA_AVG,const.ALL_HYBRID]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))
else:
plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms(Hybrid)" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/hybrid_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
#plt.show()
#plt img of comparing with sequential method
for index in range(5):
plt.figure(index+10)
indexName = util.getIndexName(index)
mids = [const.ARIMA,const.KNN,const.PATTERN,const.MARKOV,const.MARKOV_3]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))
else:
plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Methods" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/seq_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
#plt.show()
plt.figure(30)
plt.plot(x,result[const.ARIMA_SIMILAR][3],'k-.',label=util.getMethodName(const.ARIMA_SIMILAR))
plt.plot(x,result[const.ARIMA_AVG][3],'k+',label=util.getMethodName(const.ARIMA_AVG))
plt.plot(x,result[const.ALL_HYBRID][3],'k',label=util.getMethodName(const.ALL_HYBRID))
plt.title("MAE of Hybrid Music Recommendation Methods")
plt.xlabel("Number of recommendations")
plt.ylabel("MAE")
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/local_global_%s_%s_%d_%d.png" % (const.DATASET_NAME,"MAE",const.TOPIC_NUM,const.TOP_N))
plt.figure(31)
plt.plot(x,result[const.ARIMA_SIMILAR][4],'k-.',label=util.getMethodName(const.ARIMA_SIMILAR))
plt.plot(x,result[const.ARIMA_AVG][4],'k+',label=util.getMethodName(const.ARIMA_AVG))
plt.plot(x,result[const.ALL_HYBRID][4],'k',label=util.getMethodName(const.ALL_HYBRID))
plt.title("RMSE of Hybrid Music Recommendation Methods")
plt.xlabel("Number of recommendations")
plt.ylabel("RMSE")
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/local_global_%s_%s_%d_%d.png" % (const.DATASET_NAME,"RMSE",const.TOPIC_NUM,const.TOP_N))
plt.figure(19)
improvement = []
for i in range(len(result[const.ARIMA][1])):
improvement.append((result[const.ARIMA][1][i]-result[const.KNN][1][i]) / result[const.KNN][1][i])
plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')
plt.title('Average Precision Improvement over UserKNN Recommender')
plt.xlabel('Number of recommendations')
plt.ylabel('Improvement in Average Precision (times)')
plt.legend()
indexName = util.getIndexName(1)
plt.savefig("../img/improvement_knn_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
plt.figure(20)
improvement = []
for i in range(len(result[const.ARIMA][1])):
improvement.append((result[const.ARIMA][1][i]-result[const.KNN][1][i]) / result[const.KNN][1][i])
plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')
plt.title('Average Precision Improvement over UserKNN Recommender')
plt.xlabel('Number of recommendations')
plt.ylabel('Improvement in Average Precision (times)')
plt.legend()
indexName = util.getIndexName(1)
plt.savefig("../img/improvement_knn_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
plt.figure(21)
improvement = []
for i in range(len(result[const.ARIMA][2])):
improvement.append((result[const.ARIMA][2][i]-result[const.KNN][2][i]) / result[const.KNN][2][i])
plt.plot(x[10:],improvement[10:],'k',label='Improvement over UserKNN Recommender')
plt.title('Average F1-Score Improvement over UserKNN Recommender')
plt.xlabel('Number of recommendations')
plt.ylabel('Improvement in Average F1-Score (times)')
plt.legend()
indexName = util.getIndexName(2)
plt.savefig("../img/improvement_knn_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
#plt.show()
logging.info('I am out showResult......')
#plt img of comparing with pure method
for index in range(5):
plt.figure(index+50)
indexName = util.getIndexName(index)
print indexName
mids = [const.ARIMA,const.SIMILAR,const.KNN,const.AVG,const.PATTERN,const.MARKOV]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],result[mid][index][10:],const.marker1[markerIndex],label=util.getMethodName(mid))
else:
plt.plot(x,result[mid][index],const.marker1[markerIndex],label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/all_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
#plt img of comparing with hybrid method
for index in range(5):
plt.figure(index+75)
indexName = util.getIndexName(index)
mids = [const.ARIMA,const.ALL_HYBRID]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],result[mid][index][10:],const.marker[markerIndex],label=util.getMethodName(mid))
else:
plt.plot(x,result[mid][index],const.marker[markerIndex],label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1,160)
plt.savefig("../img/hybrid_only_%s_%s_%d_%d.png" % (const.DATASET_NAME,indexName,const.TOPIC_NUM,const.TOP_N))
def showResult():
"""Using pyplot to plot the experiment results.
Input:
None.
Output:
None.
"""
logging.info('I am in showResult......')
filename = "../txt/%s_testall_%d_%d.txt" % (const.DATASET_NAME,
const.TOPIC_NUM, const.TOP_N)
x = range(1, const.TOP_N, 1)
result = [[[] for i in range(5)] for i in range(const.METHOD_SIZE)]
#read result from file to result
if os.path.exists(filename):
print '%s is existing......' % filename
rFile = open(filename, "r")
lines = rFile.readlines()
for line in lines:
line = line.rstrip('\n')
items = line.split("INFO:")
line = items[1]
items = line.split(":")
ids = items[0]
values = items[1]
idItems = ids.split(">")
mid = int(idItems[0])
topN = int(idItems[1])
valueItems = values.split()
result[mid][0].append(float(valueItems[0]))
result[mid][1].append(float(valueItems[1]))
result[mid][2].append(float(valueItems[2]))
result[mid][3].append(float(valueItems[3]))
result[mid][4].append(float(valueItems[4]))
rFile.close()
else:
rFile = open(filename, "w")
rFile.close()
#if some method is not in file, recreate it
for mid in range(const.METHOD_SIZE):
if len(result[mid][0]) == 0:
recalls, precisions, f1s, maes, rmses = getErrorOfRecMethod(mid)
result[mid][0] = recalls
result[mid][1] = precisions
result[mid][2] = f1s
result[mid][3] = maes
result[mid][4] = rmses
#plt img of comparing with pure method
for index in range(5):
plt.figure(index)
indexName = util.getIndexName(index)
mids = [const.ARIMA, const.SIMILAR, const.AVG]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],
result[mid][index][10:],
const.marker[markerIndex],
label=util.getMethodName(mid))
else:
plt.plot(x,
result[mid][index],
const.marker[markerIndex],
label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms(Pure)" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1, 160)
plt.savefig(
"../img/pure_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))
#plt.show()
#plt img of comparing with hybrid method
for index in range(5):
plt.figure(index + 5)
indexName = util.getIndexName(index)
mids = [
const.ARIMA, const.ARIMA_SIMILAR, const.ARIMA_AVG, const.ALL_HYBRID
]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],
result[mid][index][10:],
const.marker[markerIndex],
label=util.getMethodName(mid))
else:
plt.plot(x,
result[mid][index],
const.marker[markerIndex],
label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms(Hybrid)" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1, 160)
plt.savefig(
"../img/hybrid_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))
#plt.show()
#plt img of comparing with sequential method
for index in range(5):
plt.figure(index + 10)
indexName = util.getIndexName(index)
mids = [
const.ARIMA, const.KNN, const.PATTERN, const.MARKOV, const.MARKOV_3
]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],
result[mid][index][10:],
const.marker[markerIndex],
label=util.getMethodName(mid))
else:
plt.plot(x,
result[mid][index],
const.marker[markerIndex],
label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Methods" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1, 160)
plt.savefig(
"../img/seq_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))
#plt.show()
plt.figure(30)
plt.plot(x,
result[const.ARIMA_SIMILAR][3],
'k-.',
label=util.getMethodName(const.ARIMA_SIMILAR))
plt.plot(x,
result[const.ARIMA_AVG][3],
'k+',
label=util.getMethodName(const.ARIMA_AVG))
plt.plot(x,
result[const.ALL_HYBRID][3],
'k',
label=util.getMethodName(const.ALL_HYBRID))
plt.title("MAE of Hybrid Music Recommendation Methods")
plt.xlabel("Number of recommendations")
plt.ylabel("MAE")
plt.legend()
plt.xlim(1, 160)
plt.savefig("../img/local_global_%s_%s_%d_%d.png" %
(const.DATASET_NAME, "MAE", const.TOPIC_NUM, const.TOP_N))
plt.figure(31)
plt.plot(x,
result[const.ARIMA_SIMILAR][4],
'k-.',
label=util.getMethodName(const.ARIMA_SIMILAR))
plt.plot(x,
result[const.ARIMA_AVG][4],
'k+',
label=util.getMethodName(const.ARIMA_AVG))
plt.plot(x,
result[const.ALL_HYBRID][4],
'k',
label=util.getMethodName(const.ALL_HYBRID))
plt.title("RMSE of Hybrid Music Recommendation Methods")
plt.xlabel("Number of recommendations")
plt.ylabel("RMSE")
plt.legend()
plt.xlim(1, 160)
plt.savefig("../img/local_global_%s_%s_%d_%d.png" %
(const.DATASET_NAME, "RMSE", const.TOPIC_NUM, const.TOP_N))
plt.figure(19)
improvement = []
for i in range(len(result[const.ARIMA][1])):
improvement.append(
(result[const.ARIMA][1][i] - result[const.KNN][1][i]) /
result[const.KNN][1][i])
plt.plot(x[10:],
improvement[10:],
'k',
label='Improvement over UserKNN Recommender')
plt.title('Average Precision Improvement over UserKNN Recommender')
plt.xlabel('Number of recommendations')
plt.ylabel('Improvement in Average Precision (times)')
plt.legend()
indexName = util.getIndexName(1)
plt.savefig("../img/improvement_knn_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))
plt.figure(20)
improvement = []
for i in range(len(result[const.ARIMA][1])):
improvement.append(
(result[const.ARIMA][1][i] - result[const.KNN][1][i]) /
result[const.KNN][1][i])
plt.plot(x[10:],
improvement[10:],
'k',
label='Improvement over UserKNN Recommender')
plt.title('Average Precision Improvement over UserKNN Recommender')
plt.xlabel('Number of recommendations')
plt.ylabel('Improvement in Average Precision (times)')
plt.legend()
indexName = util.getIndexName(1)
plt.savefig("../img/improvement_knn_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))
plt.figure(21)
improvement = []
for i in range(len(result[const.ARIMA][2])):
improvement.append(
(result[const.ARIMA][2][i] - result[const.KNN][2][i]) /
result[const.KNN][2][i])
plt.plot(x[10:],
improvement[10:],
'k',
label='Improvement over UserKNN Recommender')
plt.title('Average F1-Score Improvement over UserKNN Recommender')
plt.xlabel('Number of recommendations')
plt.ylabel('Improvement in Average F1-Score (times)')
plt.legend()
indexName = util.getIndexName(2)
plt.savefig("../img/improvement_knn_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))
#plt.show()
logging.info('I am out showResult......')
#plt img of comparing with pure method
for index in range(5):
plt.figure(index + 50)
indexName = util.getIndexName(index)
print indexName
mids = [
const.ARIMA, const.SIMILAR, const.KNN, const.AVG, const.PATTERN,
const.MARKOV
]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],
result[mid][index][10:],
const.marker1[markerIndex],
label=util.getMethodName(mid))
else:
plt.plot(x,
result[mid][index],
const.marker1[markerIndex],
label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1, 160)
plt.savefig(
"../img/all_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))
#plt img of comparing with hybrid method
for index in range(5):
plt.figure(index + 75)
indexName = util.getIndexName(index)
mids = [const.ARIMA, const.ALL_HYBRID]
markerIndex = 0
for mid in mids:
if index == 1 or index == 2:
plt.plot(x[10:],
result[mid][index][10:],
const.marker[markerIndex],
label=util.getMethodName(mid))
else:
plt.plot(x,
result[mid][index],
const.marker[markerIndex],
label=util.getMethodName(mid))
markerIndex += 1
plt.title("%s of Different Recommend Algorithms" % indexName)
plt.xlabel("Number of recommendations")
plt.ylabel(indexName)
plt.legend()
plt.xlim(1, 160)
plt.savefig(
"../img/hybrid_only_%s_%s_%d_%d.png" %
(const.DATASET_NAME, indexName, const.TOPIC_NUM, const.TOP_N))