def draw():
d2wl = get_health_data_2d(0, 2, "mining-data/Health Data.csv")
# frequent
transactions = [round(x[1] * 1) / 1 for x in d2wl[1]]
transactions = zip(transactions)
relim_input = itemmining.get_relim_input(transactions)
report = itemmining.relim(relim_input, min_support=2)
report_keys = report.keys()
report_values = report.values()
joint_sort_report = zip(report_values, report_keys)
joint_sort_report.sort(reverse=True)
print('Health analyse - walk&run:relim frequence:')
f = open('result/Health analyse - walk&run - frequent(relim method).txt',
'w')
print >> f, joint_sort_report
f.close()
ks.draw_2d(d2wl[1],
5,
'Health analyse - walk&run',
d2wl[0][0],
d2wl[0][1],
is_time_sequence=True,
marker='x',
save_path='result/walk&run')
d2wl = get_health_data_2d(0, 2, "mining-data/Sleep Analysis.csv")
# frequent
transactions = [round(x[1] * 1) / 1 for x in d2wl[1]]
transactions = zip(transactions)
relim_input = itemmining.get_relim_input(transactions)
report = itemmining.relim(relim_input, min_support=2)
report_keys = report.keys()
a = []
for x in report_keys:
for i in x:
a.append(i)
a.sort()
report_keys = [frozenset([x]) for x in a]
report_sort = [{k: report[k]} for k in report_keys]
print('Health analyse - sleep:relim frequence:')
f = open('result/Health analyse - sleep - frequent(relim method).txt', 'w')
print >> f, report_sort
f.close()
ks.draw_2d(d2wl[1],
5,
'Health analyse - sleep',
d2wl[0][0],
d2wl[0][1],
is_time_sequence=True,
marker='x',
save_path='result/sleep')
def test_relim(self):
ts1 = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(ts1)
report = itemmining.relim(relim_input, 2)
self.assertEqual(17, len(report))
self.assertEqual(6, report[frozenset(['b', 'd'])])
ts2 = perftesting.get_default_transactions_alt()
relim_input = itemmining.get_relim_input(ts2)
report = itemmining.relim(relim_input, 2)
self.assertEqual(19, len(report))
self.assertEqual(5, report[frozenset(['a', 'b'])])
def test_relim(self):
ts1 = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(ts1)
report = itemmining.relim(relim_input, 2)
self.assertEqual(17, len(report))
self.assertEqual(6, report[frozenset(['b', 'd'])])
ts2 = perftesting.get_default_transactions_alt()
relim_input = itemmining.get_relim_input(ts2)
report = itemmining.relim(relim_input, 2)
self.assertEqual(19, len(report))
self.assertEqual(5, report[frozenset(['a', 'b'])])
def associationRules(transactions, userid, followed=(), c_userid=None):
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=2)
rules = assocrules.mine_assoc_rules(item_sets,
min_support=2,
min_confidence=0.5)
recom_user = {}
for rule_user in rules:
if userid in rule_user[0] and not any(
map(rule_user[1].__contains__,
followed)) and not c_userid in rule_user[1]:
# 支持度
support = rule_user[2] / len(transactions)
# リフト値 1より大きい場合は、Aが発生するとBが発生しやすくなると解釈できる
lift = (rule_user[3] / support, )
if lift[0] <= 1:
continue
rule_user += lift
recom_user[rule_user[1]] = rule_user[4]
recom_user_sorted = sorted(recom_user.items(),
key=lambda x: x[1],
reverse=True)
print("*" * 100)
print("ユーザーレコメンド(バスケット分析)")
print(recom_user_sorted)
print("*" * 100)
rcom_userid_list = set()
for rcom_userid in recom_user_sorted:
rcom_userid_list = rcom_userid_list.union(rcom_userid[0])
return list(rcom_userid_list)
def getFrequentItems(transactions): #function to get frequent itemsets based on given transactions
relim_input = itemmining.get_relim_input(transactions) #restructure transactions into relim input
item_sets = itemmining.relim(relim_input, MINSUP) #get itemsets with minimum support
results = []
for k, v in item_sets.items(): #return results
results.append(list(k))
return results
def printPyminingResult(transactions, support, confidence):
print '\n\nPymining algorithm:'
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=support * 196)
print 'Frequent item set:( size:', len(item_sets), ')'
for key in item_sets:
print '[',
for keys in key:
print keys, ',',
print '], ',
rules = assocrules.mine_assoc_rules(item_sets,
min_support=support * 196,
min_confidence=confidence)
print '\n\nRules:'
for rule in rules:
print '[',
for _ in rule[0]:
print _,
if (len(rule[0]) > 1):
print ',',
print '->',
for _ in rule[1]:
print _,
if (len(rule[1]) > 1):
print ',',
print '], confidence:', rule[3], ', support:', rule[2] / float(196)
def getCooccur(ts,groups,reverse,min_s=2,min_c=0.5):
crules=dict()
result=dict()
relim_input=itemmining.get_relim_input(ts)
itemset=itemmining.relim(relim_input,min_support=min_s)
rules=assocrules.mine_assoc_rules(itemset,min_support=min_s\
,min_confidence=min_c)
# Now calculate the best rule for each cis
# Clean the rules
for rule in rules:
if len(rule[0])>1:
continue
else:
if not rule[0] in crules.keys():
crules[rule[0]]=dict()
for elem in rule[1]:
crules[rule[0]][elem]=rule[3]
for x in reverse.keys():
if not frozenset({x}) in crules.keys():
continue
for y in reverse.keys():
if not y in crules[frozenset({x})].keys():
continue
result[(x,y)]=crules[frozenset({x})][y]
return result
def freqPaatterns(transactions, len, outfName):
print "fp mining"
relim_input = itemmining.get_relim_input(transactions)
report = itemmining.relim(relim_input, min_support = len * 0.01)
with open(outfName, "w") as fout:
for fp in report.items():
#print "%s\t%f\n" % (";".join(fp[0]), fp[1] / float(len))
fout.write("%s\t%f\n" % (";".join(fp[0]), fp[1] / float(len)))
def find_frequent_artists(sample_clusters):
"""
Finds frequent artists from a sample cluster object of users, cluster labels, and artist data
"""
print("Finding frequent item sets")
print(sample_clusters)
# sample cluster data on 5000 random american users, k = 10 for k means, and top 5 artists
frequent_artist_dict = {}
for cluster, user_data in sample_clusters:
print("\nFinding frequent artists for cluster {}".format(cluster))
num_users = len(user_data.user_id)
# calculates the minimum support of artists according to some proportion of users
# ex: pass in 10, so min support is num users / 10, or 10% of users
# for some reason we can't import this number as a parameter...?
min_sup = math.floor(num_users / 5)
if min_sup == 0:
min_sup = 1
# this is for humongous clusters where a large minimum support ( > 300 ) doesn't really make sense
# for the Last.fm data set
# if num_users > 1000:
# min_sup = num_users/20
# print("min sup: ", min_sup)
# print("number of users: {}".format(num_users))
# create a list of "transactions" for frequent mining from the top artists for the current user
transactions = (list(user_data.top_artists))
relim_input = itemmining.get_relim_input(transactions)
# the report stores each frequent item as a dictionary of the form:
# frozenset(artist id, artist name) : count
report = itemmining.relim(relim_input, min_support=min_sup)
# each frequent item is stored as a frozen set
# process each frozen set item by converting it into a list and accessing the data
# (through the 0th index, because it's a list with just 1 element)
# then grabbing just the artist name through the 1st index
# (because it is the 2nd item in the (artist ID, artist name) tuple for each frozen set
report = [(list(item)[0][1], report[item]) for item in report
if len(item) == 1]
# sort the report object in reverse order so the highest played artists are first
report = sorted(report, key=lambda tup: tup[1], reverse=True)
# print(report)
# store the report list for the cluster number in the frequent artist dictionary
frequent_artist_dict[cluster] = report
return frequent_artist_dict
def frequent_itemsets(self, min_support=2):
"""Mine frequent item sets (FIM) using the RELIM algorithm.
:param min_support: Minimum count of occurences an itemset must have to be returned.
:returns: A mapping of itemsets to their supportcount.
:rtype: dict(set=int)
"""
relim_input = itemmining.get_relim_input(list(self.groups()))
return itemmining.relim(relim_input, min_support=min_support)
def freq_items(self):
#Give this function a list of transactions and a key function, returns a data
#structure used as the input of the relim algorithm.
relim_input = itemmining.get_relim_input(self.transactions)
#use the input from get_relim_input() and minimum support
#to return frequent item sets of items appearing in a list of transactions
#based on Recursive Elimination
item_sets = itemmining.relim(relim_input, self.min_sup)
return item_sets
def testDefaultSupportConf(self):
ts1 = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(ts1)
report = itemmining.relim(relim_input, 2)
rules = assocrules.mine_assoc_rules(report, min_support=2)
self.assertEqual(23, len(rules))
a_rule = (frozenset(['b', 'e']), frozenset(['d']), 2, 1.0)
self.assertTrue(a_rule in rules)
ts2 = perftesting.get_default_transactions_alt()
relim_input = itemmining.get_relim_input(ts2)
report = itemmining.relim(relim_input, 2)
rules = assocrules.mine_assoc_rules(report, min_support=2)
self.assertEqual(29, len(rules))
a_rule = (frozenset(['e']), frozenset(['a', 'd']), 2, 2.0/3.0)
self.assertTrue(a_rule in rules)
def testDefaultSupportConf(self):
ts1 = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(ts1)
report = itemmining.relim(relim_input, 2)
rules = assocrules.mine_assoc_rules(report, min_support=2)
self.assertEqual(20, len(rules))
a_rule = (frozenset(['b', 'e']), frozenset(['d']), 2, 1.0)
self.assertTrue(a_rule in rules)
ts2 = perftesting.get_default_transactions_alt()
relim_input = itemmining.get_relim_input(ts2)
report = itemmining.relim(relim_input, 2)
rules = assocrules.mine_assoc_rules(report, min_support=2)
self.assertEqual(20, len(rules))
a_rule = (frozenset(['e']), frozenset(['a', 'd']), 2, 2.0/3.0)
self.assertTrue(a_rule in rules)
def testSupport5(self):
ts1 = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(ts1)
report = itemmining.relim(relim_input, 5)
rules = assocrules.mine_assoc_rules(report, min_support=5)
self.assertEqual(2, len(rules))
a_rule = (frozenset(['d']), frozenset(['b']), 6, 0.75)
self.assertTrue(a_rule in rules)
def testConfidence075(self):
ts1 = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(ts1)
report = itemmining.relim(relim_input, 2)
rules = assocrules.mine_assoc_rules(report, min_support=2, min_confidence=0.75)
self.assertEqual(8, len(rules))
a_rule = (frozenset(["b"]), frozenset(["d"]), 6, 0.75)
self.assertTrue(a_rule in rules)
def testSupport5(self):
ts1 = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(ts1)
report = itemmining.relim(relim_input, 5)
rules = assocrules.mine_assoc_rules(report, min_support=5)
self.assertEqual(2, len(rules))
a_rule = (frozenset(['d']), frozenset(['b']), 6, 0.75)
self.assertTrue(a_rule in rules)
def freq_items(self):
"""
Import the itemming tools and perform relim mining
Returns:
item_sets: frequent item sets
"""
relim_input = itemmining.get_relim_input(self.transactions)
item_sets = itemmining.relim(relim_input, self.min_sup)
return item_sets
def get_association_rules(seqs, min_support=2):
transactions = list(seqs)
# print transactions
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=min_support)
rules = assocrules.mine_assoc_rules(item_sets, min_support=min_support, min_confidence=0.5)
# print(rules)
return rules
def find_freq_sets():
"""Match"""
print "\n", my.TS_START, my.TS_WINDOW, "\n"
SQL = """SELECT timestamp, text\
FROM {rel_tweet} \
WHERE timestamp BETWEEN '{ts_start}'
AND timestamp '{ts_start}' + INTERVAL '{window} days'
""".format(
rel_tweet=my.REL_TWEET, ts_start=my.TS_START, window=my.TS_WINDOW
)
print "Querying DB..."
con = psycopg2.connect(my.DB_CONN_STRING)
cur = con.cursor()
cur.execute(SQL)
recs = cur.fetchall()
con.close()
print "{count} records retrieved.".format(count=len(recs))
global sw
sw = stopwords.words("english")
sw.extend(my.STOPWORDS)
sw = list(set(sw))
global tokens
with open("data/" + my.DATA_FOLDER + "tokens.json", "rb") as fp:
tokens = sorted(anyjson.loads(fp.read()))
print len(tokens)
pool = Pool(processes=my.PROCESSES)
tweets = pool.map(_trim_tweet, recs)
tweets = filter(None, tweets)
tweets_len = len(tweets)
recs = None
print "{count} tokenized tweets to be processed.".format(count=len(tweets))
# Frequent itemset mining
relim_input = itemmining.get_relim_input(tweets)
tweets = None
print "Generated Relim input."
sets = itemmining.relim(relim_input, min_support=int(math.sqrt(tweets_len)))
relim_input = None
print len(sets), min(sets.values()), max(sets.values())
sets = sorted(sets.items(), key=lambda x: x[1], reverse=True)
texts = []
for s, f in sets[: my.K]:
txt = " ".join(tokens[i] for i in list(s))
texts.append(txt)
filename = "data/" + my.DATA_FOLDER + "frequent_sets"
with open(filename + "_all" + ".pickle", "wb") as fp:
pickle.dump(sets, fp)
with open(filename + ".pickle", "wb") as fp:
pickle.dump(sets[: my.K], fp)
with open(filename + ".txt", "wb") as fp:
fp.write("\n".join(texts))
def testConfidenceForComplexRules(self):
transab = (("a", "b"),) * 1000
transac = (("a", "c"),) * 1000
transabc = (("a", "b", "c"),) * 5
trans = transab + transac + transabc
relim_input = itemmining.get_relim_input(trans)
report = itemmining.relim(relim_input, 5)
rules = assocrules.mine_assoc_rules(report, min_support=5, min_confidence=0.9)
self.assertEqual(3, len(rules))
a_rule = (frozenset(["b", "c"]), frozenset(["a"]), 5, 1.0)
self.assertTrue(a_rule in rules)
def testConfidenceForComplexRules(self):
transab = (('a', 'b'), ) * 1000
transac = (('a', 'c'), ) * 1000
transabc = (('a', 'b', 'c'), ) * 5
trans = transab + transac + transabc
relim_input = itemmining.get_relim_input(trans)
report = itemmining.relim(relim_input, 5)
rules = assocrules.mine_assoc_rules(
report, min_support=5, min_confidence=0.9)
self.assertEqual(3, len(rules))
a_rule = (frozenset(['b', 'c']), frozenset(['a']), 5, 1.0)
self.assertTrue(a_rule in rules)
def frequentSet(input):
relim_input = itemmining.get_relim_input(input)
report = itemmining.relim(relim_input, min_support=MIN_SUPPORT)
return report
# by pass filtering parameters
filteredResult = []
for item in report:
for key in item:
if validDependent(list(key)):
filteredResult.append(item)
return (0,filteredResult)
def _freq_phrases(id):
'''Map function'''
with open('data/' + my.DATA_FOLDER + 'sets.json', 'rb') as fp:
sets = anyjson.loads(fp.read())
keywords = tuple(itertools.chain(*sets[str(id)]))
data_path = 'data/' + my.DATA_FOLDER + 'data/'
with open(data_path + str(id) + '.txt', 'rb') as fp:
cr = csv.reader(fp, delimiter=',')
tweets = [row[1].split() for row in cr]
tweets_len = len(tweets)
print 'Tweets:', tweets_len
if tweets_len < 5000: return
fd = FreqDist(tuple(itertools.chain(*tweets)))
vocab = fd.keys()[:-fd.Nr(1)]
for w in keywords:
if w in vocab: vocab.remove(w)
print 'Tokens:', fd.N(), ',', fd.B(), '-', fd.Nr(1), '=', len(vocab)
path = 'data/' + my.DATA_FOLDER + 'frequent_tokens/'
if not os.path.exists(path): os.makedirs(path)
with open(path + str(id) + '.txt', 'wb') as fp:
fp.write('\n'.join(vocab))
words = dict((w, vocab.index(w)) for w in vocab)
tweets = tuple(tuple(words[w] for w in tw if w in words) \
for tw in tweets)
relim_input = itemmining.get_relim_input(tweets)
print 'Generated Relim input... Min support:', math.sqrt(tweets_len)
sets = itemmining.relim(relim_input,
min_support=int(math.sqrt(tweets_len)))
print len(sets), min(sets.values()), max(sets.values())
sets = sorted(sets.items(), key=lambda x:x[1], reverse=True)
texts = []
for s, f in sets[:1000]:
txt = ' '.join(vocab[i] for i in tuple(s))
texts.append(txt)
path = 'data/' + my.DATA_FOLDER + 'frequent_phrases/'
if not os.path.exists(path): os.makedirs(path)
with open(path + str(id) + '_all' + '.pickle', 'wb') as fp:
pickle.dump(sets, fp)
with open(path + str(id) + '.pickle', 'wb') as fp:
pickle.dump(sets[:my.K], fp)
with open(path + str(id) + '.txt', 'wb') as fp:
fp.write('\n'.join(texts))
def mine_rules_relim(self, baskets):
print "preparing itemset"
relim_input = itemmining.get_relim_input(baskets)
print "finding frequent itemsets"
self.item_sets = itemmining.relim(relim_input, min_support = len(baskets) * self.min_support)
print "finding association rules"
self.rules = assocrules.mine_assoc_rules(self.item_sets, len(baskets),
min_support = self.min_support, min_confidence = self.min_confidence,
min_lift = self.min_lift)
# sort by support
self.nonmax_suppression()
self.rules = sorted(self.rules, key = lambda x: -x[2])
def __findFS(df, sup):
"""
This function is used to find the "frequent" itemsets
for the database
"""
if type(df) == pd.core.frame.DataFrame:
L = df.shape[0]
tss = []
for i in range(L):
tss.append(df.columns[df.iloc[i] > 0].tolist())
relim = itemmining.get_relim_input(tss)
else:
relim = itemmining.get_relim_input(df)
report = itemmining.relim(relim, min_support=sup)
return report
def fun1():
transactions = (('a', 'b', 'c'), ('b'), ('a'), ('a', 'c', 'd'), ('b', 'c'),
('b', 'c'))
relim_input = itemmining.get_relim_input(transactions)
report = itemmining.relim(relim_input, min_support=2)
print("Here is the default transactions data.\n{}".format(transactions))
time.sleep(0.5)
print("See the default frequent itemsets?[y/n]")
p1 = input()
if p1 == 'y':
print(report)
input("Press any button to return to CONTENT")
else:
input("Thank you for visiting, press Any button to return to CONTENT")
def getAssoc(transactions,min_s=2,min_c=0.5):
'''
getAssoc will return the association rule in the following
format
'''
result=dict()
for key in transactions.keys():
relim_input=itemmining.get_relim_input(\
transactions[key])
itemset=itemmining.relim(relim_input\
,min_support=min_s)
result[key]=assocrules.mine_assoc_rules(\
itemset,min_support=min_s,min_confidence=min_c)
return result
def execute(self,data_source):
import csv
transactions = []
with open(data_source, 'r') as f:
reader = csv.reader(f)
transactions = list(reader)
# print(transactions)
# transactions = [['a', 'b', 'c'], ['b'], ['a'], ['a', 'c', 'd'], ['b', 'c'], ['b', 'c']]
# print(type(transactions))
relim_input = itemmining.get_relim_input(transactions)
report = itemmining.relim(relim_input, min_support = self.support.get())
result = ""
for itemset, count in report.items():
result = result + ", ".join(itemset) + ": " + str(count) + "\n"
# print(report)
return result
def find_similarity_sets(segmented_df, minimal_support):
global TRANSACTIONS, ITEMSETS, FINAL_ROWS
TRANSACTIONS = []
FINAL_ROWS = {}
print("Getting going")
segmented_df = segmented_df.groupby("time_group").apply(squash)
print("FIM...")
transactions = tuple(TRANSACTIONS) #perftesting.get_default_transactions()
print("Got " + str(len(transactions)) + " transactions.")
relim_input = itemmining.get_relim_input(transactions)
ITEMSETS = itemmining.relim(relim_input, min_support=minimal_support)
# clean for closed frequent patterns
itemsets1 = []
print("Closing the patterns...")
for s in ITEMSETS:
can_add = True
for j in range(len(itemsets1)):
if set(s).issubset(itemsets1[j]):
can_add = False
break
if set(s).issuperset(itemsets1[j]):
itemsets1[j] = set(s)
can_add = False
break
if can_add:
itemsets1.append(s)
ITEMSETS = itemsets1
# per itemset determine rows
print("Per Window go...")
segmented_df.index = range(len(segmented_df))
max_idx = segmented_df["time_group"].max()
segmented_df = segmented_df.groupby("time_group").apply(
lambda x: put_to_final(x, max_idx))
# write result
res_df = pandas.DataFrame(
[[str(a) for a in r[:-2]] + [r[-2], r[-1]]
for r in list(FINAL_ROWS.values())],
columns=["indices", "values", "length_indices", "length_content"])
res_df = res_df[res_df["length_content"] >= minimal_support]
return res_df.groupby("indices").apply(lambda x: x.iloc[-1])
def mine_patterns(data, MINING_METHOD, CONFUSION_MATRIX):
if (MINING_METHOD == 'seq_mining'):
mined_patterns = {
KEY: sorted(seqmining.freq_seq_enum([data[KEY][trace_id] for trace_id in data[KEY]], min_support=2))
for KEY in CONFUSION_MATRIX
}
if (MINING_METHOD == 'item_mining'):
mined_patterns_to_be_preprocessed = {
KEY: itemmining.relim(itemmining.get_relim_input([data[KEY][trace_id] for trace_id in data[KEY]]), min_support=2)
for KEY in CONFUSION_MATRIX
}
mined_patterns = {
KEY: [
(tuple(element), mined_patterns_to_be_preprocessed[KEY][element])
for element in mined_patterns_to_be_preprocessed[KEY]]
for KEY in CONFUSION_MATRIX
}
return mined_patterns
def execute(self,data_source):
import csv
transactions = []
with open(data_source, 'r') as f:
reader = csv.reader(f)
transactions = list(reader)
# print(transactions)
# transactions = [['a', 'b', 'c'], ['b'], ['a'], ['a', 'c', 'd'], ['b', 'c'], ['b', 'c']]
# print(type(transactions))
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support = self.support.get())
rules = assocrules.mine_assoc_rules(item_sets, min_support=self.support.get(), min_confidence=self.confidence.get_float())
result = ""
for rule in rules:
print(rule[0])
result = result + ", ".join(rule[0]) + " => " + ", ".join(rule[1]) + "\n"
# result = result + ", ".join(rule[0]) + " => " + ", ".join(rule[1]) + ": " + str(rule[2]) + ", " + str(rule[3]) + "\n"
# print(report)
return result
def association_rules(data, min_support, min_confidence):
"""
Generates association rules from crawled data
"""
badges = data['badges']
transactions = data['transactions']
# pymining only works, if the identifiers are one character strings :(
transactions = tuple(tuple(chr(b) for b in t) for t in transactions)
# pymining dance
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=min_support)
rules = assocrules.mine_assoc_rules(item_sets, min_support=min_support,
min_confidence=min_confidence)
# translate identifiers back to badge names
rules = [[frozenset(badges[ord(b)] for b in r[0]),
frozenset(badges[ord(b)] for b in r[1]),
r[2], r[3]] for r in rules]
return rules
def fun2():
transactions = (('a', 'b', 'c'), ('b'), ('a'), ('a', 'c', 'd'), ('b', 'c'),
('b', 'c'))
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=2)
rules = assocrules.mine_assoc_rules(item_sets,
min_support=2,
min_confidence=0.5)
print("The default transactions data is:")
print(transactions)
time.sleep(0.5)
input("Press any button to continue...")
print(
"Here is the association rules we have mined. Frozenset means the pattern in the transactions"
)
time.sleep(1)
print(rules)
print(
"\nNote:(frozenset({e'}), frozenset({'b', 'd'}), 2, 1.0) means:\n # e -> b, d with support 2 and confidence 0.66"
)
input("Press Any button to return to CONTENT")
def getAssoc2(ts,groups,reverse,min_s=2,min_c=0.5):
crules=dict()
result=dict()
relim_input=itemmining.get_relim_input(ts)
itemset=itemmining.relim(relim_input,min_support=min_s)
rules=assocrules.mine_assoc_rules(itemset,min_support=min_s\
,min_confidence=min_c)
# Now calculate the best rule for each cis
# Clean the rules
for rule in rules:
if len(rule[0])>1:
continue
else:
if rule[0] in crules.keys():
if (len(rule[1])+1)*rule[3]<=crules[rule[0]]:
continue
crules[rule[0]]=(len(rule[1])+1)*rule[3]
for cis in groups.keys():
key=frozenset({groups[cis]})
if key in crules.keys():
result[cis]=crules[key]
return result
def _build_phrase(self):
input_list = {}
for post in self.valid:
for cate in post["category"]:
if cate not in input_list:
input_list[cate] = []
input_list[cate].append(post["title"] + post["content"])
phrases = {}
for cate, posts in input_list.items():
relim_input = itemmining.get_relim_input(posts)
fis = itemmining.relim(relim_input, min_support=2)
phrases[cate] = {}
for phrase, count in fis.items():
new_p = list(phrase)
if len(new_p) >= 2:
phrases[cate]['_'.join(new_p)] = count
print(cate)
print(phrases[cate])
phrase_json = open(os.path.join("../data/sto/phrase_dict.json"), 'w')
json.dump(phrase_json, phrases)
phrase_json.close()
return phrases
print("---------------------------")
print("---------------------------")
print("Eigenvector centrality (a measure of the influence of a node in a network)")
print(sorted(list(nx.eigenvector_centrality(G).items()),key=operator.itemgetter(1),reverse=True))
print("---------------------------")
print("---------------------------")
print("Katz centrality (relative influence of a node)")
print(sorted(list(nx.katz_centrality_numpy(G).items()),key=operator.itemgetter(1),reverse=True))
print("---------------------------")
def sumupRelationship(relationship_mat,subject):
relationship_mat.drop([subject],inplace=True).sort_values(inplace=True)
friends = []
addAllNodes(nodes)
buildAllGroupLink(groups)
#drawNetwork()
relations = buildRelationshipMat(plot=False,half=False)
sumupRelationship(relations,"Micoud")
#displayDegrees()
#clustering()
relim_input = itemmining.get_relim_input(groups_tuples)
report = itemmining.relim(relim_input, 2)
#http://www.cl.cam.ac.uk/~cm542/teaching/2011/stna-pdfs/stna-lecture11.pdf
top_rules = sorted_rules[:10]
def get_product_names(list_ids):
return tuple([df_fruits.loc[id, 'product_name'] for id in list_ids])
top_rules_names = [tuple(list(map(get_product_names, rule[:2]))+ [rule[2]]) for rule in top_rules]
#%% Fast implemented
#This takes long
from pymining import itemmining, assocrules
transactions=aisle_orders['products']
item_sets = itemmining.get_relim_input(transactions)
#%%
min_supp= SUPPORT_THRESHOLD * NUMBER_ORDERS_AISLE
item_sets = itemmining.relim(item_sets, min_support=min_supp)
#%%
thresholds = [0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45]
times = []
max_lengths = []
numbers = []
for t in thresholds:
start = time.time()
rules = assocrules.mine_assoc_rules(item_sets, min_support=min_supp, min_confidence=t)
execution_time = time.time() - start
times.append(execution_time)
max_lengths.append(max([len(i[1]) for i in rules]))
numbers.append(len(rules))
d_freq_itemsets_out_control = dict( (fs, l_all_itemsets_out_control_AS_FROZENSETS.count(fs)) for fs in l_all_itemsets_in_control_AS_FROZENSETS)
temp_copy = d_freq_itemsets_out_control.copy()
for fs in temp_copy:
if temp_copy[fs] < 2:
d_freq_itemsets_out_control.pop(fs, None)
## loop thru dict of [list of itemsets] for each patient
### use the PYMINING module!
#key = pt id
#value = [list of itemsets]
d_d_freqitemsets_by_pt = dict()
for key in d_itemsets_per_pt:
transactions = d_itemsets_per_pt[key]
relim_input = itemmining.get_relim_input(transactions)
report = itemmining.relim(relim_input, min_support=2)
d_freq_itemsets_this_pt = report
report_keys = report.keys()
for fs_itemset in report_keys: #loop thru dict of counts
if (fs_itemset not in d_freq_itemsets_across_all_pts):#if itemset is a not a freq pattern across all patients
d_freq_itemsets_this_pt.pop(fs_itemset, None) #then remove the key (itemset) from the dict and dont return an error
d_d_freqitemsets_by_pt[key] = d_freq_itemsets_this_pt
## plot!
if bool_plot:
for key in d_d_freqitemsets_by_pt:
report = d_d_freqitemsets_by_pt[key]
xlabels = []
plot_dict = {}
for k in report:
s = str(list(k))
def get_frequent_itemsets_with_support(self,support=2):
return itemmining.relim(self.relim_input,min_support=support)
def Apriori_three(data_tuple):
transactions = perftesting.get_default_transactions()
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=50)#////最小关联度
rules = assocrules.mine_assoc_rules(item_sets, min_support=2, min_confidence=0.5)
print(rules)
def _execute(self):
self.transactions = mongoComputeHashTagItemSets(self.name)
relim_input = itemmining.get_relim_input(self.transactions)
self.item_sets = itemmining.relim(relim_input, self.min_support)
self.rules = assocrules.mine_assoc_rules(self.item_sets, self.min_support, self.min_confidence)
def freq_items(self):
relim_input = itemmining.get_relim_input(self.transactions)
item_sets = itemmining.relim(relim_input, self.min_sup)
return item_sets
# #transactions = genNewActDietTypeDataSetForLessSleep() # #transactions = genNewActDietTypeDataSetForMoreSleep() # # # print transactions # # relim_input = itemmining.get_relim_input(transactions) # # print 0.1*i # item_sets = itemmining.relim(relim_input, min_support=int(len(transactions)*0.1*3)) ## print len(item_sets) # rules = assocrules.mine_assoc_rules(item_sets, min_support=int(len(transactions)*0.1*3), min_confidence=0.1*i) # print len(rules) transactions = genOriginalActDietTypeDataSetForMoreSleep() relim_input = itemmining.get_relim_input(transactions) item_sets = itemmining.relim(relim_input, min_support=int(len(transactions)*0.1*3)) #print item_sets rules = assocrules.mine_assoc_rules(item_sets, min_support=len(transactions)*0.3, min_confidence=0.70) print rules #df = newDataProcess.newFeatureFrame() #df = df[df['label']==1] #print df.shape[0] #for factor in ['bike','leisure','starchyP','fruitP']: # df_temp1 = df[df[factor]>0] # #print df_temp1.shape[0] # df_temp2 = df[df['workStudy']>0] # #print df_temp2.shape[0] # df_temp3 = df_temp1[df_temp1['workStudy']>0] # #print df_temp3.shape[0]
result = finder.compute(args.min_support,
args.max_length_sequence,
args.iterative)
result[1].sort()
print(count_occurences(result[1], args.max_length_sequence))
print(result[1])
else:
result = profile.run(
"finder.compute(args.min_support, args.max_length_sequence, args.iterative)",
"prefix_span.stat")
p = pstats.Stats('prefix_span.stat')
p.strip_dirs().sort_stats(-1).print_stats()
else:
print("[*] Execution Frequent Itemset algorithm.")
relim_input = itemmining.get_relim_input(dataset)
result = itemmining.relim(relim_input,
min_support=args.min_support)
print(result[1])
# Save the file to disk
with open(args.output_dir + output_result, "wb") as f:
pickle.dump(result[1], f)
else:
logger = logging.getLogger("performance_logger")
file_handler = logging.FileHandler("./experiments/performance_5.csv")
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
exp_parser = ExperimentParser(dict(), "./experiments.conf")
args_e = exp_parser.parse()
def twitter_analysis(path,files):
tweets=[]
words=[]
bigrams=[]
hashtags=[]
mentions=[]
twitterpic=[]
instagrampic=[]
otherUrls=[]
positive_terms=[]
negative_terms=[]
usefulwords=[]
#read input files and save tweets#
f=open(str(path)+'/tweets/'+str(files)+'.txt_parsed.txt_final.txt')
for line in f:
if line.startswith('@@@'):
try:
tweet=line.strip().lower().split('\t')[3]
except IndexError:
tweet=" "
tweets.append(tweet)
#words#
terms=tweet.split()
words.append(terms)
##useful words##
usefulword=[]
for term in terms:
if term in english_stopwords: continue
else: usefulword.append(term)
usefulwordt=tuple(usefulword)
usefulwords.append(usefulwordt)
usefulwordst=tuple(usefulwords)
#two grams#
twograms=ngrams(terms, 2)
tgs=[]
for twogram in twograms:
joined='_'.join(twogram)
tgs.append(joined) ##the original code will return genrator so I changed##
bigrams.append(tgs)
#hash tags#
myHashtags=re.findall('#[^ ]+',tweet)
hashtags.append(myHashtags)
#mentions#
myMentions=re.findall('@[^ ]+',tweet)
mentions.append(myMentions)
#twitter pic#
myTp=re.findall('pic.twitter.com/[^ ]+',tweet)
twitterpic.append(myTp)
#instagram pic#
myIp=re.findall('http://instagram.com/p/[^ ]+',tweet)
instagrampic.append(myIp)
#other Url#
otherUrl=re.findall('http://[^ ]+',tweet)
other=[]
for Url in otherUrl:
if "http://instagram.com/p/" not in Url:
other.append(Url)
otherUrls.append(other)
#positive words#
myPos=[]
for term in terms:
if term in english_stopwords: continue
if term in pos:myPos.append(term)
positive_terms.append(myPos)
#negative words#
myNeg=[]
for term in terms:
if term in english_stopwords: continue
if term in neg:myNeg.append(term)
negative_terms.append(myNeg)
##twitter_analysis('/Users/yuehan/Desktop/twitter','tial.txt_parsed.txt_final.txt')##This is for demo analysis
##save csv files##
newpath = str(path)+'/text_results/'
if not os.path.exists(newpath): os.makedirs(newpath)
with open(str(path)+'/text_results/'+str(files)+'_textresults.csv','wb') as f1:
w=csv.writer(f1)
row1=['tweets','words','bigrams','hashtags','mentions','twitterpic','instagrampic','otherUrls','positive_terms','negative_terms']
w.writerow(row1)
for v in range(0,len(tweets)):
tweetss=tweets[v]
wordss=words[v]
wordss=','.join(wordss)
bigramss=bigrams[v]
bigramss=','.join(bigramss)
hashtagss=hashtags[v]
hashtagss=','.join(hashtagss)
mentionss=mentions[v]
mentionss=','.join(mentionss)
twitterpics=twitterpic[v]
twitterpics=','.join(twitterpics)
instagrampics=instagrampic[v]
instagrampics=','.join(instagrampics)
otherUrlss=otherUrls[v]
otherUrlss=','.join(otherUrlss)
positive_termss=positive_terms[v]
positive_termss=','.join(positive_termss)
negative_termss=negative_terms[v]
negative_termss=','.join(negative_termss)
w.writerow([tweetss,wordss,bigramss,hashtagss,mentionss,twitterpics,instagrampics,otherUrlss,positive_termss,negative_termss])
##find frequent Item Sets (which shows up more than 3 times, I tried with 5 and some will have blank sets)
relim_input = itemmining.get_relim_input(usefulwordst)
report = itemmining.relim(relim_input, min_support=3)
##print report.keys()
newpath = str(path)+'/frequentsets/'
if not os.path.exists(newpath): os.makedirs(newpath)
writer=csv.writer(open(str(path)+'/frequentsets/'+str(files)+'_frequentsets.csv','wb'))
for key,value in report.items():
if "', '" in str(key):
key=str(key)
key=key.replace("frozenset(['","")
key=key.replace("'])","")
key=key.replace('frozenset(["','')
key=key.replace('"])','')
key=key.replace("',",",")
key=key.replace('",',',')
key=key.replace(', "',', ')
key=key.replace(", '",", ")
writer.writerow([key,value])
else:
pass
def Apriori_one(data_tuple):
relim = itemmining.get_relim_input(data_tuple)
report = itemmining.relim(relim, min_support=100)# //最小关联度
print(report)
def frequent_itemset(input, min_supp):
return itemmining.relim(input, min_support=min_supp)
def Apriori_tow(data_tuple):
transactions = data_tuple
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=100)
rules = assocrules.mine_assoc_rules(item_sets, min_support=100, min_confidence=0.5)
print(rules)
import pandas as pd
import numpy as np
from pymining import seqmining, itemmining, assocrules, perftesting
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()
studydf = pd.read_csv("studydf.csv")
violationdf = studydf[['INSPECTION DATE','VIOLATION CODE']].reset_index()
violationdf['VIOLATION CODE'] = violationdf['VIOLATION CODE'].astype('str')
plotseries = violationdf['VIOLATION CODE'].value_counts().iloc[0:20]
ax = sns.barplot(y=plotseries.index, x=plotseries.values, palette="Blues_d")
testdf = violationdf.groupby(['CAMIS'])['VIOLATION CODE'].apply(list)
minelist = testdf.tolist()[0:10]
minelist = tuple(tuple(x) for x in minelist)
relim_input = itemmining.get_relim_input(minelist)
item_sets = itemmining.relim(relim_input, min_support=2)
rules = assocrules.mine_assoc_rules(item_sets, min_support=2, min_confidence=0.5)
print rules
freq_seqs = seqmining.freq_seq_enum(minelist, 2)
print freq_seqs
rules2 = assocrules.mine_assoc_rules(item_sets, min_support=1, min_confidence=0.5)
print rules2
print("input item counts:"+str(len(inputlist)))
# for inputentry in inputlist:
# print(inputentry)
# print(inputlist)
#20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2,1
# ,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2
for sup in [30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2]:
# print(sup)
relim_input = itemmining.get_relim_input(inputlist)
# print("length relim_input:" + str(len(relim_input)))
# for item in relim_input:
# print("length item:"+str(len(item)))
# print(item)
item_sets = itemmining.relim(relim_input, min_support=sup)
# print("length item_sets:"+str(len(item_sets)))
itempitlens.append(len(item_sets))
# print(item_sets)
rules = assocrules.mine_assoc_rules(item_sets, min_support=sup, min_confidence=0)
# print(len(rules))
# print((rules))
rulepitlens.append(len(rules))
# print(nonsense)
# relim_input = itemmining.get_relim_input(durfam)
# item_sets = itemmining.relim(relim_input, min_support=sup)
# rules = assocrules.mine_assoc_rules(item_sets, min_support=2, min_confidence=0.5)
#with open("Assocpit_{}.txt".format(sup),"w") as assocresults:
#!/usr/bin/env python
from pymining import itemmining, assocrules
data = (('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'), ('a', 'f', 'g'),
('b', 'd', 'e', 'f', 'j'), ('a', 'b', 'd', 'i', 'k'), ('a', 'b', 'e',
'g'))
min_sup = 3
min_conf = 0.5
# get frequent itemsets using pymining
relim_input = itemmining.get_relim_input(data)
frequent_itemsets = itemmining.relim(relim_input, min_sup)
# get association rules using pymining
results = assocrules.mine_assoc_rules(frequent_itemsets, min_sup, min_conf)
for key in frequent_itemsets.keys():
print(str(key) + " : " + str(frequent_itemsets[key]))
for key in results:
print(str(key))
def frequency_item_set(self, columns = None, support = 0.1, rules = False, confidence = 0.8, engine = 'pymining'):
"""
Use frequency item set mining to find subgroups where data goes
missing together.
Parameters:
----------
columns: list, default None
Subset of the columns you want to use.
support: float, default 0.1
Minimum support to use while item set mining. Too small values can break memory.
Support should be greater than zero and less than 1.
rules: bool, default True
Whether association rules should be mined. If True, method returns two_sample
dataframes instead of one.
confidence: float, default
Minimum confidence for rules being mined. Should be between 0 and 1.
engine: {'pymining'}
Only one engine is being supported right now.
Returns:
-------
item_sets_df, rules_df : DataFrame, DataFrame
Tabulated results for itemsets and association rules mined.
"""
from pymining import itemmining, assocrules
if support<=0 or support>1: #support should be between one and zero.
print('Support has to be between 0 and 1')
return
if confidence<0 or confidence>1: #confidence can be zero.
print('Confidence has to be between 0 and 1')
return
mf_ = self._masked_missframe(where = None, columns = columns, how = 'any')
# Converting all missing values to 1, and non-missing to nan.
bench = pd.DataFrame(np.where(mf_, 1, np.nan), columns = mf_.columns)
# Replacing 1's with the index of the column they belong to.
# Converting to numbers instead of column names for supposed performance boost.
bench = bench * list(range(0, mf_.columns.shape[0]))
rows = bench.values
transactions = []
for row in rows:
# Removing the nans in each row and compressing the rows.
# (nan, 1, nan, 3) --> (1, 3)
transactions.append(tuple(row[~np.isnan(row)]))
# Converting float threshold to represent number of rows.
support = int(support*mf_.shape[0])
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=support)
# Converting to DataFrames and getting columns names back.
item_sets_df = pd.DataFrame({'Itemset':list(item_sets.keys()), 'Support': list(item_sets.values())})
item_sets_df.Itemset = item_sets_df.Itemset.apply(lambda x: mf_.columns[list(x)].tolist())
# For now the same supports being used in FIM and Association Rules.
rules = assocrules.mine_assoc_rules(item_sets, min_support=support, min_confidence=confidence)
rules_df = pd.DataFrame(rules, columns = ['X =>', 'Y', 'Support', 'Confidence'])
# Converting rules to DataFrame and getting columns names back.
rules_df['X =>'] = rules_df['X =>'].apply(lambda x: mf_.columns[list(x)].tolist())
rules_df['Y'] = rules_df['Y'].apply(lambda x: mf_.columns[list(x)].tolist())
return item_sets_df, rules_df
if len(value)>1:
# JUST in case ALL elements are same in the list/set - since a "set" does not allow for duplicates, we use this construct in here
if len(set(value))==1:
support = len(value)
#converting to 1st charater in the company name back to upper case
for i in range(0,len(value)):
value[i]=str(value[i][0]).upper()+str(value[i][1:])
company.append(value)
elif len(set(value))>1:
list6=[]
for m in range(0, len(value)):
list6.append(str(value[m]).split())
transactions=list6
support = len(value)
relim_input = itemmining.get_relim_input(transactions)
report = itemmining.relim(relim_input, support)
c=report.keys()
c.sort()
m=0
flag=0
for m in range(0, len(value)):
for n in range(0,len(list(c[-1]))):
if re.search(value[m],list(c[-1])[n]):
flag=1
else :
flag =0
company.append([str(value[m][0]).upper()+value[m][1:]]*support)
break
elif len(value)==1:
try:
company.append([value[0][0].upper()+value[0][1:]])
def find_frequent_itemsets(transactions, support):
relim_input = itemmining.get_relim_input(transactions)
item_sets = itemmining.relim(relim_input, min_support=support)
return item_sets