def fi(data):
print("Using apriori for fim : ")
freq_list = fim.apriori(tracts=data, supp=5)
print("The frequent item list is : ")
print(freq_list)
rules = fim.apriori(tracts=data, target='r', eval='c', report='c')
print("The rules are : ")
print(rules)
rules = fim.apriori(tracts=data, target='r', eval='l', report='l')
print("The rules are (evaluated with lift): ")
print(rules)
print("lfi using apriori : ")
lfi(freq_list)
print("Using fp-growth for fim : ")
freq_list = fim.fpgrowth(tracts=data, supp=5)
print("The frequent item list is : ")
print(freq_list)
rules = fim.fpgrowth(tracts=data,
target='r',
eval='c',
report='c',
conf=60)
print("The rules are (evaluated with confidence): ")
print(rules)
rules = fim.fpgrowth(tracts=data,
target='r',
eval='l',
report='l',
conf=60)
print("The rules are (evaluated with lift): ")
print(rules)
print("lfi using fpgrowth is : ")
lfi(freq_list)
def generate_associations(transactions, min_sup, problems, methods, datasets):
apriori_patterns = apriori(transactions, supp=-min_sup)
print '-------- Apriori --------'
output = []
for (pattern, support) in sorted(apriori_patterns, key=lambda x: -x[1]):
print pattern, support
print 'Number of patterns:', len(apriori_patterns)
rules = apriori(transactions, target='r', supp=-5, conf=90, report='sc')
print '-------- One-to-Many Association Rules --------'
counter = 0
for (ruleleft, ruleright, support,
confidence) in sorted(rules, key=lambda x: x[0]):
if ruleleft in datasets:
for rule in ruleright:
if rule not in datasets:
counter += 1
print ruleleft, '-->', ruleright, support, confidence
elif ruleleft in problems:
for rule in ruleright:
if rule not in problems:
counter += 1
print ruleleft, '-->', ruleright, support, confidence
elif ruleleft in methods:
for rule in ruleright:
if rule not in methods:
counter += 1
print ruleleft, '-->', ruleright, support, confidence
#print ruleleft,'-->',ruleright,support,confidence
print 'Number of rules:', len(rules)
def main():
###############################################################################
# Some basic data analysis
###############################################################################
data = read_file("/Users/zxj/cs535/data/marketing.data",
lambda x: x.split(","))
frequent_itemset = apriori(data, supp=-3, zmin=2, target='s', report='a')
rules = apriori(data, supp=-3, zmin=2, target='r', report='rCL')
print("Frequent itemsets are: ")
print(frequent_itemset)
print("Rules are:")
print(rules)
def mine_frequent_itemsets(self, pandas_df, minsup):
txns_classless = TransactionDB.from_DataFrame(pandas_df.iloc[:, :-1])
frequent_itemsets = fim.apriori(txns_classless.string_representation,
supp=minsup * 100,
report="s")
return frequent_itemsets
def get_onetomany_rules(transactions, output_file):
# One-to-Many Association Rules (ResponseBot 8)
rules = apriori(transactions, target='r', supp=-1000, conf=70, report='sc')
#output
for (ruleleft, ruleright, support,
confidence) in sorted(rules, key=lambda x: x[0]):
#p = ','.join(pattern)
output_file.write('{}' + '-->' + ' {} {} {}\n'.format(
ruleleft, ruleright, str(support), str(confidence)))
def run_fim_apriori(df, minsup):
print("running fim apriori function")
processed_df = process_dataset(df)
print("dataset processed")
result_raw = fim.apriori(processed_df, supp=(minsup * 100))
print("apriori runned")
result = list(map(lambda i: list(i[0]), result_raw))
print("apriori results processed")
return result
def Apriori_main(data_fname, minSupport, out_fname='Apriori_results.txt'):
lines, tid = readDataset(data_fname)
t1 = clock()
temp_freq = apriori(tid,
target='s',
supp=float(minSupport * 100),
conf=100)
CPU_time = clock() - t1
freq_items = convert2dic(temp_freq, lines)
printResults(data_fname, minSupport, CPU_time, freq_items, out_fname)
return (freq_items, CPU_time)
def do_apriori(transactions, output_file):
print("Preforming Apriori...")
# http://www.borgelt.net/pyfim.html
patterns = apriori(transactions,
supp=-1000) # +: percentage -: absolute number
for (pattern, support) in sorted(patterns, key=lambda x: -x[1]):
#pattern is a tuple
if len(set(pattern)) <= 1: continue
p = ','.join(pattern)
output_file.write('{} {} \n'.format(p, str(support)))
print('Number of patterns: {}'.format(len(patterns)))
def run_apriori(transactions, min_sup, author_number):
apriori_patterns = apriori(transactions, supp=-min_sup)
print '-------- Apriori --------'
output = []
for (pattern, support) in sorted(apriori_patterns, key=lambda x: -x[1]):
if len(pattern) < author_number:
continue
print pattern, support
output.append([pattern, support])
print 'Number of patterns:', len(apriori_patterns)
return output
def find_by_year(years):
print 'For year %s:' %years
t_years = get_transactions('%s.csv' %years)
report_years = fim.apriori(t_years,supp=0,conf=0,zmax=4)
result_years = {}
for i in range(1,5):
result_years[i] = {}
for words,counts in report_years:
result_years[len(words)][words] = counts[0]
for i in result_years:
print 'Top 20 most frequent patterns with length %d' %i
print_top_n(result_years[i],20)
def _generate_rules_for_user(recs, settings):
tracts = []
for rec in recs:
if rec.selected and rec.forecast:
tracts.append(_process_recommendations(rec, settings))
metadata = {'total_trips': len(tracts)}
if not tracts:
return None, metadata
return [listify(rule) for rule in apriori(tracts, supp=-1)], metadata
def pass_one(inputfile, support):
result = []
p = 0.05 # 0.6
baskets = generate_baskets(inputfile)
sample_baskets = sampling(baskets, p)
result.append(len(sample_baskets)*1./len(baskets))
adjusted_support = 0.9 * p * support # use lower threshold
frequent_items = [x[0] for x in fim.apriori(sample_baskets, supp=adjusted_support)]
result.append(frequent_items)
print frequent_items
negative_border_items = generate_negative_border(frequent_items, sample_baskets)
print negative_border_items
result.append(negative_border_items)
return result
def mine(self, df, supp, zmin, zmax, is_count=False):
"""return association rules from netflow df
Arguments:
df {dataframe} -- netflow dataframe
supp {number} -- [description]
zmin {number} -- minimum number in itemset
zmax {number} -- max number in itemset
Keyword Arguments:
is_count {bool} -- true if minsup is absolute number, else percentage (default: {False})
"""
transacts = self.netflow_to_transc(df)
supp = -supp if is_count else supp # (positive: percentage, negative: absolute number)
return apriori(transacts, target='m', supp=supp, zmin=zmin, zmax=zmax)
def run_fim_apriori(df: pd.DataFrame, min_suppport_thr: float) -> List[Transaction]:
try:
import fim
except Exception as e:
raise e
print("running fim apriori function")
dataset_transactions: List[Transaction] = dataframe_to_list_of_transactions(df)
print("dataset processed")
frequent_itemsets_raw = fim.apriori(dataset_transactions, supp=(min_suppport_thr*100)) # List[Tuple[]]
print("apriori runned")
frequent_itemsets: List[Transaction] = list(map(lambda i: list(i[0]), frequent_itemsets_raw)) # Li
print("apriori results processed")
return frequent_itemsets
def pass_one(transactions, support, epsilon, delta, mu):
result = []
n = calculate_bound(epsilon, delta)
adjusted_support = support - lower_by(n, mu)
sampled_transactions = random.sample(transactions, n)
# TODO why?
result.append(len(sampled_transactions)*1./len(transactions))
frequent_items =\
[x[0] for x in\
apriori(sampled_transactions, supp=adjusted_support*100)]
result.append(frequent_items)
negative_border_items = generate_negative_border(frequent_items, sampled_transactions)
def generateCARs(transactionDB: TransactionDB,
support: float = 1,
confidence: float = 50,
maxlen: int = 10,
**kwargs):
"""Function for generating ClassAssociationRules from a TransactionDB
Parameters
----------
:param transactionDB : TransactionDB
support : float
minimum support in percents if positive
absolute minimum support if negative
confidence : float
minimum confidence in percents if positive
absolute minimum confidence if negative
maxlen : int
maximum length of mined rules
**kwargs :
arbitrary number of arguments that will be
provided to the fim.apriori function
Returns
-------
list of CARs
"""
appear = transactionDB.appeardict
rules = fim.apriori(transactionDB.string_representation,
supp=support,
conf=confidence,
mode="o",
target="r",
report="sc",
appear=appear,
**kwargs,
zmax=maxlen)
return createCARs(rules)
tid = int(argv[1])
if tid < -2:
print(fpgrowth.__doc__)
elif tid < -1:
print(eclat.__doc__)
elif tid < 0:
print(apriori.__doc__)
else:
tracts = [[1, 2, 3], [1, 4, 5], [2, 3, 4], [1, 2, 3, 4], [2, 3], [1, 2, 4],
[4, 5], [1, 2, 3, 4], [3, 4, 5], [1, 2, 3]]
print('transactions:')
for t in tracts:
print(t)
if tid < 1:
print('apriori(tracts, supp=-3, zmin=2):')
for r in apriori(tracts, supp=-3, zmin=2):
print r
elif tid < 2:
print('eclat(tracts, supp=-3, zmin=2):')
for r in eclat(tracts, supp=-3, zmin=2):
print r
elif tid < 3:
print('fpgrowth(tracts, supp=-3, zmin=2):')
for r in fpgrowth(tracts, supp=-3, zmin=2):
print r
else:
print('fim(tracts, supp=-3, zmin=2, report=\'#\'):')
for r in fim(tracts, supp=-3, zmin=2, report='#'):
print r
#SUPPORT = -3
#ZMIN = 2
SUPPORT = -0.01 * len(data)
ZMIN = 1
#ZMAX = 5
CONF = 80
LIFT = 1.03
ITEMSET_REPORT = 'a'
#RULES_REPORT='rCL'
RULES_REPORT = 'C'
#frequent_itemset = apriori(data, supp=SUPPORT, zmin=ZMIN, conf=CONF, eval='l', thresh=LIFT, target='s', report=ITEMSET_REPORT)
frequent_itemset = apriori(data,
supp=SUPPORT,
zmin=ZMIN,
target='s',
report=ITEMSET_REPORT)
#rules = apriori(data, supp=SUPPORT, zmin=ZMIN, conf=CONF, eval='l', thresh=LIFT, target='r', report=RULES_REPORT)
rules = apriori(data, supp=SUPPORT, zmin=ZMIN, target='r', report=RULES_REPORT)
#for item in data:
# print(item)
print('========================')
#for itemset in frequent_itemset: print(itemset)
for i in range(5):
print(random.choice(frequent_itemset))
print('------------------------')
#for rule in rules: print(rule)
for i in range(5):
transactions.append([x for x in line.rstrip('\n').split(',') if filter_fun_str(x) ])
trcount = len(transactions)
print(trcount)
transactions = filter(lambda x: len(x) >= 1, transactions)
print(len(transactions))
def ele_to_str(ele):
global db
return util.ele_to_str(db, ele)
sets = map(set, transactions)
print('running algorithm', file=sys.stderr)
before = time.time()
if algo == "apriori":
s = fim.apriori(transactions, supp=2)
s = sorted(s, key=lambda x:x[1])
for items,supp in s:
items = map(ele_to_str, items)
print(u"{} items: {}".format(supp/float(trcount), "|".join(items)).encode('utf-8'))
if algo == "eclat":
s = fim.eclat(transactions, supp=2)
s = sorted(s, key=lambda x:x[1])
for items,supp in s:
items = map(ele_to_str, items)
print(u"{} items: {}".format(supp, "|".join(items)).encode('utf-8'))
elif algo == "eclat-rules":
rules = fim.eclat(transactions, target='r', report='aC')
rules = sorted(rules, key = lambda x: x[3])
for consequence, condition, support_count, confidence_percent in rules:
condition = map(ele_to_str, condition)
__author__ = 'ssatpati'
import re
from fim import apriori
baskets = []
with open('ProductPurchaseData.txt', 'r') as f:
for line in f:
items = re.split(r'\s', line)
items.sort()
baskets.append(items)
for r in apriori(baskets, target='r', zmax=2, supp= -100, report='c', eval='c', conf=90):
if r[0]:
print '%s\t%s\t%s' %(r[0],r[1],r[2])
import fim
import sys
from data import load_transactions
if __name__ == '__main__':
filename = sys.argv[1]
threshold = int(sys.argv[2])
transactions = load_transactions(filename)
fis = fim.apriori(transactions)
print fis
__author__ = 'Daniel Bernardes, Mamadou Diaby, Raphael Fournier, Francoise Fogelman Soulie and Emmanuel Viennet'
__version__ = '1.0'
import sys, fim
if __name__ == "__main__":
if len(sys.argv) > 1:
threshold = 100 * float(sys.argv[1])
else:
threshold = 1 # default: 1%
fin = sys.stdin
profiles = []
for line in fin:
tokens = line.split()
profiles.append(map(int, tokens[1:]))
fin.close()
confidence = fim.apriori(profiles,
max=2,
supp=threshold,
report='e',
eval='c',
thresh=threshold)
for triplet in confidence:
print '{0:d} {1:d} {2:.5f}'.format(triplet[0][0], triplet[0][1],
triplet[1][0])
# edges
pos={}
pos.update(pos_a)
pos.update(pos_b)
nx.draw_networkx_edges(g,pos,edgelist=nx.edges(g),width=1,alpha=0.8,edge_color='g')
nx.draw_networkx_labels(g,pos,font_size=10,font_family='sans-serif')
plt.title('Graph representation')
plt.show()
###############################################################################
# Now do rule finding
###############################################################################
frequent_itemset = apriori(data, supp=-3, zmin=2, target='s', report='a')
rules = apriori(data, supp=-3, zmin=2, target='r', report='rCL')
print(frequent_itemset)
print(rules)
frequent_itemset_1=[]
frequent_itemset_2=[]
frequent_itemset_3=[]
'''
original apriori, generate 1 item
'''
def apriori(data,supp=3,zmin,target,report):
frequent_itemset=[]
transaction.add(words[i] + '_' + words[i + 1])
i += 2
continue
#if words[i] in stopwords or len(words[i]) == 1 or words[i].isdigit() or not words[i].isalnum() or len(words[i]) > 25:
# i += 1
# continue
if not check_string_guality(words[i]):
i += 1
continue
transaction.add(words[i])
i += 1
if not transaction: continue
author_transactions.append(list(transaction))
# output
#for transaction in author_transactions:
# print transaction
print "Done with author transactions"
#patterns = apriori(author_lists, supp=-12)
patterns = apriori(author_transactions, supp=-10)
print '-------- Author Affiliation Apriori --------'
#for (pattern,support) in sorted(patterns,key=lambda x:-x[1]):
# if len(pattern) <= 1: continue
# print pattern,support
for (pattern, support) in sorted(patterns, key=lambda x: -x[1]):
#pattern is a tuple
if len(pattern) <= 1: continue
output_file.write('{} {} \n'.format(pattern, str(support)))
print 'Number of patterns:', len(patterns)
plt.subplot(121)
nx.draw_networkx_nodes(g,pos_a,nodelist=a,node_color='r',node_size=300,alpha=0.8)
nx.draw_networkx_nodes(g,pos_b,nodelist=b,node_color='b',node_size=300,alpha=0.8)
# edges
pos={}
pos.update(pos_a)
pos.update(pos_b)
nx.draw_networkx_edges(g,pos,edgelist=nx.edges(g),width=1,alpha=0.8,edge_color='g')
nx.draw_networkx_labels(g,pos,font_size=10,font_family='sans-serif')
plt.title('Graph representation')
plt.show()
'''
###############################################################################
# Now do rule finding
###############################################################################
#frequent_itemset = apriori(data, supp=10, zmin=2, target='s', report='a')
rules = apriori(data, supp=10, zmin=2, zmax=5, target='r', report='SCl')
#print(frequent_itemset)
#print(rules)
###############################################################################
#sort the result
r = sorted(rules, reverse=True, key=lambda x: x[4])
print(r)
print(len(r))
def build(self):
"""
Takes variables from constructor and outputs
anomaly scores for each row/observation as a pandas data frame
"""
# create variables which hold number of rows and columns
rows = len(self.data.index)
cols = len(self.data.columns)
# default value of mlen parameter is equal to number of columns
if self.mlen == 0.5:
self.mlen = cols
# adding column name to each row
data2 = pd.DataFrame({col: str(col) + '='
for col in self.data},
index=self.data.index) + self.data.astype(str)
# transforming dataset to list of lists
records = []
for i in range(0, rows):
records.append([str(data2.values[i, j]) for j in range(0, cols)])
# creating transaction dataset
print("Creating transactions from a dataset")
t = time.process_time()
te = TransactionEncoder()
oht_ary = te.fit(records).transform(records, sparse=True)
elapsed_time = time.process_time() - t
print("Transactions created in: " + str(elapsed_time))
# creating sparse data frame from transaction encoder
sparse_df = pd.SparseDataFrame(oht_ary,
columns=te.columns_,
default_fill_value=False)
# using apriori to find frequent itemsets
supp = self.support / 100
print("Running apriori with settings: support={}, maxlen={}".format(
supp, self.mlen))
t = time.process_time()
apr = fim.apriori(records,
target="s",
supp=self.support,
zmax=self.mlen,
report="s")
elapsed_time = time.process_time() - t
print("Apriory finished in: " + str(elapsed_time))
# adding new column length of the rule
frequent_itemsets = pd.DataFrame(apr)
frequent_itemsets['length'] = frequent_itemsets[0].apply(
lambda x: len(x))
print(frequent_itemsets.index)
# creating a numpy array of lengths and qualities so operation such as multiplication can be done
fiLenghts = np.array([frequent_itemsets['length']], np.int8)
fiQualities = np.array([frequent_itemsets[1]], np.float16)
# converting itemsets to frozensets so subsetting can be done
print("Converting to datasets frozensets and computing coverages")
t = time.process_time()
items_list = []
fi = frequent_itemsets[0]
for i in fi:
items_frozen = frozenset(i)
items_list.append(items_frozen)
# converting transactions to frozensets
transactions = []
for i in records:
i = frozenset(i)
transactions.append(i)
# list that will temporarily store coverages
tmp = []
print("Computing coverages")
# comparing each transaction with itemsets
for i in items_list:
for i2 in transactions:
if i.issubset(i2):
tmp.append(1)
else:
tmp.append(0)
# converting coverages to numpy array
coverages = np.array([tmp])
elapsed_time = time.process_time() - t
print("Computing coverages finished in: " + str(elapsed_time))
# converting coverages to valid shape and creating transpose matrix
fiCoverages = coverages.reshape(len(frequent_itemsets), rows)
fiCoveragesT = np.array(np.transpose(fiCoverages))
fiQualitiesT = np.transpose(fiQualities)
# compute basic score for each coverage
t = time.process_time()
print("Computing results for each coverage")
result = np.array(1 / (fiLenghts * np.transpose(fiQualities)),
dtype=np.float16)
print(result)
elapsed_time = time.process_time() - t
print("Computing results finished in: " + str(elapsed_time))
# create matrix with results on diagonal
result2 = np.diagonal(result)
shape = (len(frequent_itemsets), len(frequent_itemsets))
# it was necessary to create matrix with zeros to have matrix with particular shape with values only on the diagonal
diagonalHelper = np.zeros(shape)
np.fill_diagonal(diagonalHelper, result2)
# matrix multiplication
print("Computing individual scores")
scores = np.array(np.matmul(fiCoveragesT, diagonalHelper))
print("Done")
# prepare items for subsetting
data_items = sparse_df.columns.values.tolist()
dataItems = pd.DataFrame(data_items)
# coverage of each data item
dataItemsList = []
# converting to frozenset so subsetting can be done
for i in range(0, len(dataItems.values)):
dataItemsList.append(
frozenset([str(dataItems.values[i, j]) for j in range(0, 1)]))
dataItemsCoverage = []
# subsetting columns with items
for i in dataItemsList:
for i2 in items_list:
if i2.issubset(i):
dataItemsCoverage.append(1)
else:
dataItemsCoverage.append(0)
# converting coverages to numpy array
dataItemsCoverageArr = np.array([dataItemsCoverage])
tmp4 = dataItemsCoverageArr.reshape(len(dataItems.values),
len(frequent_itemsets))
# variable that stores sum of columns
print("Computing penalizations")
t = time.process_time()
colSums = np.array(self.data.count(axis=1))
# variable that stores sum of rows
rowSums = np.array([fiCoveragesT.sum(axis=1)])
# preparing parts of the equation
part1 = np.matmul(fiCoveragesT, np.transpose(tmp4))
part2 = part1.sum(axis=1)
# compute how many items of each transaction is not covered by appropriate frequent itemsets
fiC = colSums - part2
elapsed_time = time.process_time() - t
print("Computing penalizations finished in: " + str(elapsed_time))
# compute final score as a mean value of scores and penalizations: (sum of scores + penalization*number of transactions)/(number of scores + penalization)
print("Computing scores for each row")
t = time.process_time()
scorings = (scores.sum(axis=1) + fiC * rows) / (rowSums + fiC)
elapsed_time = time.process_time() - t
print("Computing final scores finished in: " + str(elapsed_time))
# creating pandas data frame with Scores column
columnOutput = ["Scores"]
output = pd.DataFrame(data=np.transpose(scorings),
index=data2.values,
columns=columnOutput,
dtype=object)
# print anomaly scores for each row/observation
print(output)
# returns maximum value of anomaly scores
print(output[output['Scores'] == output['Scores'].max()])
print(fiC)
return output
hotel = json.load(jsonHotel)
hotelAddress = hotel.get('HotelInfo').get('Address').encode('ascii', 'ignore').lower()
hotelAddress = re.split(r'<.[^>]+>([^<]*)<.[^>]+>', hotelAddress)
hotelName = hotel.get('HotelInfo').get('Name').encode('ascii','ignore').lower()
nameTokens = tokenizer.tokenize(hotelName)
stopset = set().union(stopset, nameTokens)
stopset = set().union(stopset, hotelAddress)
#tokens = word_tokenize(hotel.get('Reviews')[0].get('Content').encode('ascii', 'ignore'))
#print removeStopWords(tokens)
test = [('l1', 'l2', 'l5'), ('l2', 'l4'), ('l2', 'l3'), ('l1', 'l2', 'l4'), ('l1', 'l3'), ('l2', 'l3'), ('l1', 'l3'), ('l1', 'l2', 'l3', 'l5'),('l1', 'l2', 'l3')]
#trans = []
#
#for review in hotel.get('Reviews'):
# content = review.get('Content').encode('ascii', 'ignore')
# allTokens = tokenizer.tokenize(content.lower())
# strippedTokens = removeStopWords(allTokens)
# trans += [(strippedTokens)]
setmax = 2
support = 35
confidence = 50
#feats = apriori(trans, zmax= setmax,supp=support, conf = confidence, target='r', report = 'CS')
testFeats = apriori(test, zmin=2, zmax=setmax, supp=support, conf = confidence, target = 'r', report = 'Cabh')
#file = open('Hotel'+fileNum+'_z'+str(setmax)+'_s'+str(support)+'_c'+str(confidence)+'.txt', 'w')
#file.write(str(feats))
#file.close()
print testFeats
if words[i] in stopwordset or len(words[i]) == 1 or words[i].isdigit():
i += 1
continue
transaction.add(words[i])
i += 1
transactions.append(list(transaction))
# output
for transaction in transactions:
print transaction
# In[5]:
# http://www.borgelt.net/pyfim.html
from fim import apriori, fpgrowth
patterns = apriori(transactions, supp=-3) # +: percentage -: absolute number
# output
print '-------- Apriori --------'
for (pattern, support) in sorted(patterns, key=lambda x: -x[1]):
print pattern, support
print 'Number of patterns:', len(patterns)
# In[6]:
patterns = fpgrowth(transactions, supp=-3)
# output
print '-------- FP-Growth --------'
for (pattern, support) in sorted(patterns, key=lambda x: -x[1]):
print pattern, support
print 'Number of patterns:', len(patterns)
trcount = len(transactions)
print(trcount)
transactions = filter(lambda x: len(x) >= 1, transactions)
print(len(transactions))
def ele_to_str(ele):
global db
return util.ele_to_str(db, ele)
sets = map(set, transactions)
print('running algorithm', file=sys.stderr)
before = time.time()
if algo == "apriori":
s = fim.apriori(transactions, supp=2)
s = sorted(s, key=lambda x: x[1])
for items, supp in s:
items = map(ele_to_str, items)
print(u"{} items: {}".format(supp / float(trcount),
"|".join(items)).encode('utf-8'))
if algo == "eclat":
s = fim.eclat(transactions, supp=2)
s = sorted(s, key=lambda x: x[1])
for items, supp in s:
items = map(ele_to_str, items)
print(u"{} items: {}".format(supp, "|".join(items)).encode('utf-8'))
elif algo == "eclat-rules":
rules = fim.eclat(transactions, target='r', report='aC')
rules = sorted(rules, key=lambda x: x[3])
for consequence, condition, support_count, confidence_percent in rules:
__author__ = 'chengmin'
import fim,os
#with open(os.getcwd()+'/APRIORIREADME.TXT',"w")as fout:
#fout.write(fim.apriori.__doc__)
#fout.write(str(help(fim.apriori)))
#fout.write(fim.apriori)
fin=open("FIMtest").read()
print(fim.apriori(fin))
df[i] = i + " " + df[i].astype(str)
df["credit_default"] = "default " + df["credit_default"].astype(str)
baskets = df.values.tolist()
'''
itemsets = apriori(baskets, supp=10, zmin=2, target='m')
print (len(itemsets))
foo = open("itemsets", "w")
for itemset in itemsets:
foo.write(""+str(itemset))
foo.write("\n\n")
'''
rules = apriori(baskets, supp=10, zmin=1, target='r', conf=40, report='ascl')
f = open("rules", "w")
count = 0
lista = list()
for rule in rules:
if rule[5] > 2 and "female" in rule[1]:
count += 1
f.write("" + str(rule))
f.write("\n\n")
lista.append(rule[0])
print(count)
print(set(lista))
'''
#FREQUENT ITEMSET
def mbasket(self,
data_p,
support_par,
confidence_par,
method='apriori',
lift_par=1.2):
"""
:param
:return:
"""
start0 = time()
## Apriori analysis + association rules creation
# find association rules with default settings
rules = pd.DataFrame()
if method == 'fpgrowth':
start = time()
frequent_itemsets = pd.DataFrame(fpgrowth(data_p[1],
supp=support_par * 100,
zmin=1,
target='s',
report='s',
mode='o'),
columns=['itemsets', 'support'])
run_time = round(time() - start, 3)
print("fpgrowth() -", run_time, "s")
elif method == 'eclat':
start = time()
frequent_itemsets = pd.DataFrame(eclat(data_p[1],
supp=support_par * 100,
zmin=1,
target='s',
report='s',
mode='o'),
columns=['itemsets', 'support'])
run_time = round(time() - start, 3)
print("eclat() -", run_time, "s")
elif method == 'relim':
start = time()
frequent_itemsets = pd.DataFrame(relim(data_p[1],
supp=support_par * 100,
zmin=1,
target='s',
report='s'),
columns=['itemsets', 'support'])
run_time = round(time() - start, 3)
print("relim() -", run_time, "s")
elif method == 'sam':
start = time()
frequent_itemsets = pd.DataFrame(sam(data_p[1],
supp=support_par * 100,
zmin=1,
target='s',
report='s'),
columns=['itemsets', 'support'])
run_time = round(time() - start, 3)
print("sam() -", run_time, "s")
elif method == 'ista':
start = time()
frequent_itemsets = pd.DataFrame(ista(data_p[1],
supp=support_par * 100,
zmin=1,
report='s'),
columns=['itemsets', 'support'])
run_time = round(time() - start, 3)
print("ista() -", run_time, "s")
else:
start = time()
frequent_itemsets = pd.DataFrame(apriori(data_p[1],
supp=support_par * 100,
zmin=1,
target='s',
report='s',
mode='o'),
columns=['itemsets', 'support'])
run_time = round(time() - start, 3)
rules = self.find_rules(frequent_itemsets, lift_par,
confidence_par)
print("apriori() -", run_time, "s")
# users with antedecents from the rules calculated above
if rules.shape[0] > 0:
pivot_binary_tr = data_p[0].transpose()
recom = {}
pb = {}
rules['antecedents'] = rules['antecedents'].apply(
lambda x: frozenset(x))
for user in pivot_binary_tr.columns:
products_bought = pivot_binary_tr.index[pivot_binary_tr[user]
== 1]
pb[user] = products_bought
suitable_rules = []
for ante in rules['antecedents'].iteritems():
if ante[1].issubset(products_bought): # do poprawy
suitable_rules.append(ante[0])
recom[user] = suitable_rules
recom = pd.DataFrame.from_dict(recom,
orient='index').stack().reset_index(
level=1,
drop=True).reset_index()
recom.columns = ['review_profilename', 'Rule']
# products bought - zeby wykluczyc te produkty z rekomendacji
pb2 = pd.DataFrame.from_dict(pb,
orient='index').stack().reset_index(
level=1, drop=True).reset_index()
pb2.columns = ['review_profilename', 'antecedents1']
rule_cons = rules[['antecedents', 'consequents']].reset_index()
rule_cons['consequents'] = [
i for i, *_ in rule_cons['consequents']
] # change format from frozensets to normal
rule_cons['antecedents'] = [
list(i) for i in rule_cons['antecedents']
]
rule_cons.columns = ['Rule', 'antecedents', 'consequents']
recom = recom.merge(rule_cons, on='Rule')
recom.drop_duplicates(['review_profilename', 'consequents'],
keep='first',
inplace=True)
# exclude from recommendations products already bought
recom_already_satisfied = pb2.merge(
recom,
left_on=['review_profilename', 'antecedents1'],
right_on=['review_profilename', 'consequents'])
recom_already_satisfied['beer_already_known'] = 1
sum_recom_already_satisfied = recom_already_satisfied[
'beer_already_known'].sum()
recom_new = recom.merge(
recom_already_satisfied[[
'review_profilename', 'Rule', 'consequents',
'beer_already_known'
]],
on=['review_profilename', 'Rule', 'consequents'],
how='left')
recom_new = recom_new[recom_new['beer_already_known'] != 1][[
'review_profilename', 'Rule', 'antecedents', 'consequents'
]]
else:
rule_cons = 0
recom_new = 0
sum_recom_already_satisfied = 0
mba_time = round(time() - start0, 2)
print("mbasket() -", mba_time, "s")
return [
rule_cons, recom_new, mba_time, sum_recom_already_satisfied,
run_time
]
item_counts = {}
frequencies = []
rules = []
# read input file by line and split to
# store each line as list of items
# fim apriori expects this data structure as input
baskets = [
line.split()
for line in open('ProductPurchaseData.txt').read().strip().split('\n')
]
# target = 's' -> frequent item sets
# supp = negative -> minimum support of an item set
# zmax = number -> maximum number of items per item set
item_sets = fim.apriori(baskets, target='s', supp=-100, zmax=2)
for r in item_sets:
# apriori reports in the format ((itemset), support)
item_set, item_count = r
# k = 1
if len(item_set) == 1:
item_counts[item_set[0]] = item_count
# k = 2
elif len(item_set) == 2:
item1, item2 = item_set
# lexicographial ordering of the rules
# report the rule a->b but not the rule b->a
if item1 < item2:
frequencies.append(((item1, item2), float(item_count)))
import sqlite3
from fim import apriori, fpgrowth
def extract_data(db):
command = 'SELECT keyword, paper_id from Keywords'
output = db.execute(command)
transactions = {}
for t in output:
if t[0] not in transactions:
transactions[t[1]] = [t[0]]
else:
transactions[t[1]].append(t[0])
list_tran = []
for t in transactions:
list_tran.append(transactions[t])
return list_tran
if __name__ == '__main__':
conn = sqlite3.connect('data/database.db')
c = conn.cursor()
transactions = extract_data(c)
print 'loaded data'
apriori_patterns = apriori(transactions, supp=-7)
print '-------- Apriori --------'
output = []
for (pattern,support) in sorted(apriori_patterns,key=lambda x:-x[1]):
if len(pattern) > 1:
print pattern,support
print 'Number of patterns:',len(apriori_patterns)
def BBMax_Accuracy_main(fname1, fname2, fname3, sup, m_time):
global tid
global lines
change_raw_data = 0
lines,tid = readDataset(fname3)
abs_supp = ceil(sup*lines-0.5)
F = readLargeData(fname1)
S = minSet(readSensitiveSet(fname2))
SS = supersets(S, F.keys())
Rev_Fd = list(set(F) - SS)
start_time = clock()
Rev_pos_bord = convert2frozen_m(apriori(Rev_Fd, target = 'm', supp = float(0.0), conf=100))
sens_ind = []
for i in xrange(lines):
flag = True
for itemset in S:
if itemset.issubset(tid[i]):
sens_ind.append(i)
flag = False
break
if flag:
for itemset in Rev_pos_bord:
if itemset.issubset(tid[i]):
sens_ind.append(i)
break
sens_ind = list(set(sens_ind))
N = len(sens_ind)
cpx = cplex.Cplex()
cpx.set_results_stream(None)
cpx.objective.set_sense(cpx.objective.sense.minimize)
cpx.variables.add(obj = (1,)*N + (lines,)*len(Rev_pos_bord), lb =(0,)*(N+len(Rev_pos_bord)),
ub=(1,)*N+(cplex.infinity,)*len(Rev_pos_bord),
types=(cpx.variables.type.integer,)*(N+len(Rev_pos_bord)))
for itemset in S:
ind = []
cur_supp = 0
for i in xrange(N):
if itemset.issubset(tid[sens_ind[i]]):
ind.append(i)
cur_supp += 1
## print(ind)
## print(itemset)
## print("GreaterEq than ",cur_supp - abs_supp + 1)
cpx.linear_constraints.add(lin_expr = [SparsePair(ind = ind, val=(1,)*len(ind))],
senses=["G"], rhs=[cur_supp - abs_supp + 1])
rpb_c = 0
for itemset in Rev_pos_bord:
ind = []
cur_supp = 0
for i in range(N):
if itemset.issubset(tid[sens_ind[i]]):
ind.append(i)
cur_supp += 1
ind.append(N+rpb_c)
rpb_c += 1
## print(ind)
## print(itemset)
## print("LessEq than ",cur_supp - abs_supp)
cpx.linear_constraints.add(lin_expr = [SparsePair(ind = ind, val=(1,)*(len(ind)-1)+(-1,))],
senses=["L"], rhs=[cur_supp - abs_supp])
cpx.parameters.mip.pool.relgap.set(0)
## cpx.parameters.preprocessing.presolve.set(cpx.parameters.preprocessing.presolve.values.off)
## cpx.populate_solution_pool()
cpx.solve()
if any([i for i in map(int, cpx.solution.get_values())[lines:(lines+len(Rev_pos_bord))]]):
print("System would be infeasible!!")
print("Number of solutions: ", cpx.solution.pool.get_num())
## print(map(int, cpx.solution.get_values()))
## print("Objective: ", cpx.solution.get_objective_value())
for i in get_indices(map(int, cpx.solution.get_values())[0:N], 1):
temp_set = set()
for itemset in S:
if itemset.issubset(tid[sens_ind[i]]):
temp_set.add(itemset)
while len(temp_set) > 0:
item_dic = {}
for itemset in temp_set:
for item in itemset:
if item not in item_dic:
item_dic[item] = 0
item_dic[item] += 1
max_val = 0
for item, freq in item_dic.items():
if max_val < freq:
max_val = freq
element = frozenset([item])
if item_dic.values().count(max_val) > 1:
candidates = [frozenset([item]) for item, freq in item_dic.items() if freq==max_val]
element = candidates[randrange(0, len(candidates))]
tid[sens_ind[i]] = tid[sens_ind[i]] - element
change_raw_data += 1
for itemset in temp_set:
if element.issubset(itemset):
temp_set = temp_set - set([itemset])
exec_time=((clock()-start_time))
total_time = exec_time + m_time,"sec"
exec_time = exec_time,"sec"
cpx = None
######----create out files-----######
out_file = open('BBMax_Accuracy_results.txt', 'w')
out_file2 = open('BBMax_Accuracy_visible.txt','w')
print('Border-Based Max-Accuracy Results\n---------------\n',file = out_file2)
print('\nThe Sanitized DB is:\n',file = out_file2)
for i in xrange(lines):
k = ' '.join(sorted(tid[i]))
z = '{'+ k + '}'
print(k, file = out_file)
print(z, file = out_file2)
out_file.close()
print(file = out_file2)
m_time = m_time, "sec"
print('changes in raw data:', change_raw_data, file = out_file2)
print('data min. alg. time = ', m_time, file = out_file2)
print('hiding alg. time = ', exec_time, file = out_file2)
print('total execution time = ', total_time, file = out_file2)
out_file2.close()
return(tid, change_raw_data, Rev_Fd)
for sentences in content:
for word, pos in sentences:
if word.isalpha() and word.lower() not in stopset:
trimmedTokens += [(word.lower(), pos)]
trans += [lemmatize.getLemmas(trimmedTokens)]
trimmedTokens = []
lemmatize.saveLemmaDict()
freqDict = cityFreq(city, corpus_path, files)
TotalHotels = freqDict['TotalHotels']
TotalReviews = freqDict['TotalReviews']
support = ceil(0.01*TotalReviews)/len(trans)*25
feats = apriori(trans, zmin = setmin, zmax= setmax,supp=support, conf = confidence, target='r', report = 'CS')
for rule in feats:
ruleSupp = rule[2][1]
ruleConf = rule[2][0]
word1 = rule[0]
word2 = rule[1][0]
if not wordLookup.has_key(word1):
addWord(word1, i)
i+=1
if not wordLookup.has_key(word2):
addWord(word2, i)
i+=1
if len(rule[1])==2:
word3 = rule[1][1]
if not wordLookup.has_key(word3):
def associationMining(papers):
textFolder = 'data/text/'
support = 9
confidence = 10
rules = {}
# Create stopwords list
stopwordsFile = open('stopwords.txt', 'r')
stopwords = set()
for line in stopwordsFile:
word = line.strip('\r\n').lower()
stopwords.add(word)
stopwordsFile.close()
transactions = []
for key, value in papers.items():
if 'folder' in papers[key] and 'filename' in papers[key]:
# Get candidates
candidates = []
dataFile = open(textFolder + papers[key]['folder'] +
papers[key]['filename'])
for line in dataFile:
text = line.strip('\r\n')
words = easy_tokenizer(text)
candidates.append(words)
dataFile.close()
# Compute words dict
wordDict = {}
for words in candidates:
for word in words:
if word in stopwords or len(word) == 1 or word.isdigit():
continue
if word not in wordDict:
wordDict[word] = 0
wordDict[word] += 1
# Compute bigrams
bigrams = {}
L = 0
for words in candidates:
n = len(words)
L += n
for i in range(0, n - 1):
if words[i] in wordDict and words[i + 1] in wordDict:
bigram = words[i] + '_' + words[i + 1]
if bigram not in bigrams:
# bigram's count, first word's count, second word's count, significance score
bigrams[bigram] = [
0, wordDict[words[i]], wordDict[words[i + 1]],
0.0
]
bigrams[bigram][0] += 1
# Readjust bigrams scores
for bigram in bigrams:
bigrams[bigram][3] = (1.0 * bigrams[bigram][0] - \
1.0 * bigrams[bigram][1] * bigrams[bigram][2]/L) / \
((1.0 * bigrams[bigram][0])**0.5)
# Compute transactions
bigramDict = {}
for bigram in bigrams:
if bigrams[bigram][0] > 1:
first, second = bigram.split('_')
if first not in bigramDict:
bigramDict[first] = set()
bigramDict[first].add(second)
# Compute quality entities
transactions = []
for words in candidates:
transaction = set() # set of words/bigrams
n = len(words)
i = 0
while i < n:
if words[i] in bigramDict and i + 1 < n and words[
i + 1] in bigramDict[words[i]]:
transaction.add(words[i] + '_' + words[i + 1])
i += 2
continue
if words[i] in stopwords or len(
words[i]) == 1 or words[i].isdigit():
i += 1
continue
transaction.add(words[i])
i += 1
transactions.append(list(transaction))
rules = apriori(transactions,
target='r',
supp=support,
conf=confidence,
report='sc')
print '--------- One-to-Many Assocation Rules ------------'
for left, right, support, confidence in sorted(rules, key=lambda x: x[0]):
print left, '-->', right, support, confidence
print 'Number of rules: ', len(rules)
print(fpgrowth.__doc__)
elif tid < -1:
print(eclat.__doc__)
elif tid < 0:
print(apriori.__doc__)
else:
tracts = [ [ 1, 2, 3 ],
[ 1, 4, 5 ],
[ 2, 3, 4 ],
[ 1, 2, 3, 4 ],
[ 2, 3 ],
[ 1, 2, 4 ],
[ 4, 5 ],
[ 1, 2, 3, 4 ],
[ 3, 4, 5 ],
[ 1, 2, 3 ] ]
print('transactions:')
for t in tracts: print(t)
if tid < 1:
print ('apriori(tracts, supp=-3, zmin=2):')
for r in apriori(tracts, supp=-3, zmin=2): print r
elif tid < 2:
print ('eclat(tracts, supp=-3, zmin=2):')
for r in eclat(tracts, supp=-3, zmin=2): print r
elif tid < 3:
print ('fpgrowth(tracts, supp=-3, zmin=2):')
for r in fpgrowth(tracts, supp=-3, zmin=2): print r
else:
print ('fim(tracts, supp=-3, zmin=2, report=\'#\'):')
for r in fim(tracts, supp=-3, zmin=2, report='#'): print r
import fim
try:
item_counts = {}
frequencies = []
rules = []
# read input file by line and split to
# store each line as list of items
# fim apriori expects this data structure as input
baskets = [ line.split() for line in open('ProductPurchaseData.txt').read().strip().split('\n')]
# target = 's' -> frequent item sets
# supp = negative -> minimum support of an item set
# zmax = number -> maximum number of items per item set
item_sets = fim.apriori(baskets, target='s', supp=-100, zmax=2)
for r in item_sets:
# apriori reports in the format ((itemset), support)
item_set, item_count = r
# k = 1
if len(item_set) == 1:
item_counts[item_set[0]] = item_count
# k = 2
elif len(item_set) == 2:
item1, item2 = item_set
# lexicographial ordering of the rules
# report the rule a->b but not the rule b->a
if item1 < item2:
frequencies.append(((item1, item2), float(item_count)))
