def run_test(username_='tathagata', samplesize=.10, K=5):
"""
username_ - user in smartplayer.user_rating db table
samplesize_ - what percentage will be the cutoff for cross validation;
example:
.10 - 10% will be test and 90% will be train
The sample set is changed per each interation of the KNN algorithm;
Therefore, for a samplesize of .10 the algorithm will run
10 times while changing the subset which will be the testing set
and which subset will be the training set.
RETURN:
Return values are the RMSE, correct value, incorrect value , respectively.
"""
print '--------- Running Tests and cross validation ----------'
print 'Size of k = {0}'.format(K)
print 'Cross validating with partition size: {0}'.format(samplesize)
pred_d = dict()
actual_d = dict()
actual_d = get_data.get_user_rat(username_)
video_list = glob.glob(videosloc_ + '*.wav')
testnum_ = len(video_list) * samplesize
correct = 0
incorrect = 0
RMSE_val = 0
while(len(video_list) != 0):
test_l = []
for i in xrange(int(floor(testnum_))):
test_l.append(video_list.pop().split('/')[-1].replace('.wav', ''))
if(len(video_list) == 0):
break
# run tests
pred_d = run_knn.run_all(username_, test_l, K)
for song, pred in pred_d.iteritems():
print 'Actual {0}: Predicted: {1}'.format(actual_d[song], pred)
if(int(actual_d[song]) == int(pred)):
correct += 1
else:
incorrect += 1
RMSE_val += (int(pred) - int(actual_d[song]))**2
del test_l #empty list
RMSE = sqrt((RMSE_val)/len(actual_d))
return RMSE, correct, incorrect
def create_mf(userid_, querysongs_, dir_):
"""
userid_ - userid for user in smartplayer database
querysongs_ - list of song(s) that need ratings
dir - dir is a directory that contains all of the songs/videos
in .wav format
Create mf file that contain these songs with either
a like or a dislike with them.
This what an mf file should look like:
/User/Rob/files/rap1.wav 1
/User/Rob/files/rap2.wav 1
/User/Rob/files/rap3.wav 1
/User/Rob/files/rap4.wav 0
Returns - name of file written to
"""
new_filename = userid_ + '.mf'
if dir_.endswith('/') == False:
dir_ += '/'
mffile_ = open(new_filename, 'w')
"""
query database for all songs that the user liked or
disliked and put then in a list
"""
D_videos = get_data.get_user_rat(userid_)
for song in querysongs_:
songpath = dir_ + song + '.wav'
if os.path.isfile(songpath):
mffile_.write(songpath + '\t0\n')
D_videos[song] = 0 # test song has rating 0
for song, rating in D_videos.iteritems():
if song in querysongs_: # don't include test songs
continue
songpath = dir_ + song + '.wav'
if os.path.isfile(songpath):
mffile_.write(songpath + '\t' + str(rating) + '\n')
return new_filename, D_videos
def run_all_ks(username_='tathagata', samplesize_=0.1):
"""
Run for k | k = 1 to sqrt(n)
"""
k_d = {}
corr_d = {}
incorr_d = {}
actual_d = get_data.get_user_rat(username_)
upto = int(sqrt(len(actual_d)))
for k in xrange(1, upto):
k_d[k], corr_d[k], incorr_d[k] = run_test(username_, samplesize_, k)
if(k == 1):
bestk = k
low_incorr_num = incorr_d[k]
elif(incorr_d[k] < low_incorr_num):
bestk = k
print 'for k = {0} the RMSE was {1}'.format(str(k), str(k_d[k]))
print 'for k = {0} the correct was {1}'.format(str(k), str(corr_d[k]))
print 'for k = {0} the incorrect was {1}'.format(str(k), str(incorr_d[k]))
print 'Best value of k to use is {0}'.format(str(bestk))
def run_all_ks(username_="tathagata", samplesize_=0.1):
"""
Run for k | k = 1 to sqrt(n)
"""
k_d = {}
corr_d = {}
incorr_d = {}
actual_d = get_data.get_user_rat(username_)
upto = int(sqrt(len(actual_d)))
for k in xrange(1, upto):
k_d[k], corr_d[k], incorr_d[k] = run_test(username_, samplesize_, k)
if k == 1 or incorr_d[k] < low_incorr_num:
bestk = k
low_incorr_num = incorr_d[k]
low_RMSE = k_d[k]
print "for k = {0} the RMSE was {1}".format(str(k), str(k_d[k]))
print "for k = {0} the correct was {1}".format(str(k), str(corr_d[k]))
print "for k = {0} the incorrect was {1}".format(str(k), str(incorr_d[k]))
print "Best value of k to use is {0}".format(str(bestk))
print "Number of incorrect for this k was {0}".format(str(low_incorr_num))
print "Lowest RMSE was {0}".format(str(low_RMSE))
