continue
id = file.split('.')[0][-2:]
# if id in REMOVE_SUBJECTS:
# continue
print '----------'
print 'id: ' + id
seqs = get_seqs(id)
#print seqs
#print len(seqs)
result = get_len_var(seqs)
print result
id_feature[id] = result
return id_feature
if __name__ == '__main__':
id_feature = get_feature()
#pp.pprint(id_feature)
#write_feature_to_csv(id_feature, 'len_var', False)
#pp.pprint(get_seqs('46'))
write_feature_to_csv(id_feature, 'len_var')
from util import write_feature_to_csv
def get_feature():
fr = open(r'dataset\education\grades.csv', 'rU')
cols = fr.readline()
id_feature = {}
for line in fr.readlines():
items = line.split(',')
id = items[0][1:]
grade = float(items[3])
id_feature[id] = grade
return id_feature
if __name__ == '__main__':
id_feature = get_feature()
write_feature_to_csv(id_feature, 'grade', False)
id_feature[id] = result
return id_feature
if __name__ == '__main__':
# id_feature = get_feature(get_start_var)
# write_feature_to_csv(id_feature, 'start_time_var')
# id_feature = get_feature(get_start_var_offcampus)
# write_feature_to_csv(id_feature, 'start_time_var_offcampus')
# id_feature = get_feature(get_end_var)
# write_feature_to_csv(id_feature, 'end_time_var')
# id_feature = get_feature(get_end_var_offcampus)
# write_feature_to_csv(id_feature, 'end_time_var_offcampus')
# id = '01'
# in_loc_duration = get_in_loc_duration(id)
# get_end_time_test(in_loc_duration, id)
id_feature = get_feature_start_var()
write_feature_to_csv(id_feature, 'start_time_var')
# id_feature = get_feature_end_var()
# write_feature_to_csv(id_feature, 'end_time_var')
def get_freq_pattern(min_support, typed, normalize):
### get seqs
ids = []
all_seqs = [] # all seqs of all subjects
seqs_by_subject = [] # n subjects, length n
for file in os.listdir(wifi_dir):
if not file.endswith(".csv") or file.endswith("datetime.csv"):
continue
id = file.split(".")[0][-2:]
ids.append(id)
seqs = get_seqs(id)
if typed:
type_seqs = to_loc_type(seqs)
all_seqs.extend(type_seqs)
seqs_by_subject.append(type_seqs)
else:
all_seqs.extend(seqs)
seqs_by_subject.append(seqs)
### get freq_patterns from all_seqs
freq_patterns = []
level = 1
max_pattern_len = 6
prev_level_freq = []
while level <= max_pattern_len:
freq = gsp(all_seqs, level, prev_level_freq, min_support, typed)
freq_patterns.extend(freq)
prev_level_freq = freq
level += 1
pp.pprint(freq_patterns)
print len(freq_patterns)
### compute frequency of freq_patterns for each subject
n = len(ids) # n subjects
m = len(freq_patterns) # m frequent patterns
count = np.zeros((n, m)) # n x m matrix
for i in range(n):
for seq in seqs_by_subject[i]:
for j, pat in enumerate(freq_patterns):
pat_str = ",".join(pat)
if pat_str in ",".join(seq):
count[i, j] += 1
print np.sum(count, axis=0)
### use frequency as feature, write to csv
for j, pat in enumerate(freq_patterns):
id_feature = {}
feature = count[:, j]
# print feature
for i, id in enumerate(ids):
id_feature[id] = feature[i]
feature_name = "fp_" + ";".join(pat)
if typed:
write_feature_to_csv(
id_feature, feature_name, os.path.join("freq_pat", "typed", "support%d" % min_support), False
)
elif normalize:
write_feature_to_csv(
id_feature, feature_name, os.path.join("freq_pat", "normalized", "support%d" % min_support), True
)
else:
write_feature_to_csv(id_feature, feature_name, os.path.join("freq_pat", "support%d" % min_support), False)
