def mine_string_patterns(doc):
id, lines = doc
docs = []
for i, line in enumerate(lines):
lr = []
line = re.sub(r'\d+', '', line)
toks = line.strip().split(' ')
for t in toks:
if t:
lr.append(t)
docs.append(lr)
wordmap = {} # type: Dict[str, int] #problematic!
idx = 0
for doc in docs:
for tok in doc:
if tok not in wordmap:
wordmap[tok] = idx
idx += 1
doc_vecs = []
for doc in docs:
doc_vec = []
for tok in doc:
doc_vec.append(wordmap[tok])
doc_vecs.append(doc_vec)
db = doc_vecs
ps = PrefixSpan(db)
invwordmap = invert(wordmap)
func = ps.frequent
# lambda function for sorting
key = None
# upper bound
bound = None
# filter lambda function
filter = None
threshold = 2
closed = True
generator = False
ps.minlen = 2
ps.maxlen = 10
results = []
for freq, patt in func(threshold,
closed=closed,
generator=generator,
key=key,
bound=bound,
filter=filter):
pattern = ' '.join((invwordmap[i] for i in patt))
results.append([pattern, freq])
return id, results
def one_stacking_period():
dict = {}
with open(FILE) as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='"')
next(spamreader, None)
curr_usr = "******"
temp = []
curr_time = 0
aversion = "c"
for row in spamreader:
if not curr_usr == row[2]:
curr_usr = row[2]
dict[curr_usr] = temp
temp = []
if row[2] == "":
continue
curr_time += int(row[3])
if row[6] == "0":
if aversion == "c":
temp.append(aversion)
aversion = "a"
temp.append(aversion)
aversion = "c"
curr_time = 0
if curr_time > PERIOD * 1000:
temp.append(aversion)
curr_time = curr_time - (PERIOD * 1000)
aversion = "c"
for i in list(dict.values()):
print(" -1 ".join(i) + " -2")
# print(dict.values())
ps = PrefixSpan(list(dict.values()))
print("one stacking period \n\n")
ps.minlen = 3
ps.maxlen = 8
for i in ps.topk(20):
print(i)
print("\n")
for i in ps.topk(20, closed=True):
print(i)
print("\n")
for i in ps.topk(20, generator=True):
print(i)
print("\n")
# for i in ps.frequent(2):
# print(i)
print("\n\n\n")
def find_clusters_names(labels, features):
groups = [[] for i in range(0, max(labels)+1)]
for i in range(0, max(labels)+1):
groups[i] = features[features['labels'] == i].index
groups[i] = groups[i].tolist()
for group in groups:
for i in range(0, len(group)):
group[i] = group[i].split("::")
group[i] = group[i] + group[i][len(group[i])-1].split(" ")
res= []
for group in groups :
prefix = PrefixSpan(group)
prefix.maxlen = 4
prefix.minlen = 4
res.append(prefix.topk(5, filter = lambda patt, matches : diversity_score(patt) >= len(patt)))
return [create_str(res[i][0][1]) for i in range(0, len(res))]
def raw_data():
dict = {}
with open(FILE) as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='"')
next(spamreader, None)
curr_usr = "******"
temp = []
for row in spamreader:
if not curr_usr == row[2]:
curr_usr = row[2]
dict[curr_usr] = temp
temp = []
if row[2] == "":
continue
if row[6] == "0":
temp.append("a")
else:
temp.append("c")
for i in list(dict.values()):
print(" -1 ".join(i) + " -2")
# print(dict.values())
ps = PrefixSpan(list(dict.values()))
print("raw data \n\n")
ps.minlen = 3
ps.maxlen = 8
for i in ps.topk(20):
print(i)
print("\n")
for i in ps.topk(20, closed=True):
print(i)
print("\n")
for i in ps.topk(20, generator=True):
print(i)
print("\n")
# for i in ps.frequent(2):
# print(i)
print("\n\n\n")
def aversion_direction_one_stacking_period():
dict = {}
with open(FILE) as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='"')
next(spamreader, None)
curr_usr = "******"
avg = []
curr_time = 0
aversion = [0.0, 0.0, "1"]
temp = []
for row in spamreader:
if not curr_usr == row[2]:
mean = np.average(avg, axis=0)
t = []
for i in temp:
res = "c"
if i[2] == "0":
diff = [np.abs(a - b) for a, b in zip(i[0:2], mean)]
# if np.abs((diff[0] / mean[0]) - (diff[1] / mean[1])) < treshold:
if (np.abs(((diff[0] + mean[0]) / mean[0]) - ((diff[1] + mean[1]) / mean[1])) < treshold) or \
(((diff[0] + mean[0]) / mean[0]) > treshold2 and (
((diff[1] + mean[1]) / mean[1]) > treshold2)):
res = "f"
elif diff[0] - diff[1] > 0:
if i[0] < mean[0]:
res = "l"
if i[0] > mean[0]:
res = "r"
else:
if i[1] < mean[1]:
res = "u"
if i[1] > mean[1]:
res = "d"
t.append(res)
dict[curr_usr] = t
curr_usr = row[2]
temp = []
avg = []
if row[2] == "":
continue
if row[6] == "1":
avg.append([
float(row[4].replace(",", ".")),
float(row[5].replace(",", "."))
])
curr_time += int(row[3])
if row[6] == "0":
if aversion[2] == "1":
temp.append(aversion)
aversion = [
float(row[4].replace(",", ".")),
float(row[5].replace(",", ".")), row[6]
]
temp.append(aversion)
aversion = [0.0, 0.0, "1"]
curr_time = 0
if curr_time > PERIOD * 1000:
temp.append(aversion)
curr_time = curr_time - (PERIOD * 1000)
aversion = [0.0, 0.0, "1"]
for i in list(dict.values()):
print(" -1 ".join(i) + " -2")
# print(dict.values())
ps = PrefixSpan(list(dict.values()))
print("aversion direction one stacking period \n\n")
ps.minlen = 3
ps.maxlen = 8
for i in ps.topk(20):
print(i)
print("\n")
for i in ps.topk(20, closed=True):
print(i)
print("\n")
for i in ps.topk(20, generator=True):
print(i)
print("\n")
# for i in ps.frequent(2):
# print(i)
print("\n\n\n")
discrete_time = []
with open(filename, "r", encoding="utf-8") as weights_file:
print(f"Reading file {filename}")
for i, weights_triple in enumerate(weights_file):
current_weights = weights_triple.replace(",", ".").split("\t")
weights.append(int(current_weights[1]))
discrete_time_base = int(current_weights[0].strip())
discrete_time.append(discrete_time_base)
curr_frequency = int(current_weights[3].strip())
frequency.append(curr_frequency)
for k in range(0, curr_frequency):
weights.append(int(current_weights[1]))
discrete_time_base += 1
discrete_time.append(discrete_time_base)
if limit is not None and (i == limit or discrete_time_base >= limit):
print("Limit reached")
break
return discrete_time, weights
if __name__ == '__main__':
basedir = "C:/Users/havar/Home/cache_simulation_results/"
_t, _w = _read_db(basedir + "scaled_w_01.csv")
data = list(chunks(_w, 1000))
ps = PrefixSpan(data)
ps.minlen = 5
ps.maxlen = 100
print(ps.frequent(5, closed=True))
