def runApriori(file):
minSupport = float(input("Enter the minimum support value :: "))
minConfidence = float(input("Enter the minimum confidence value :: "))
data = apriori.dataFromFile(file)
items, rules = apriori.runApriori(data, minSupport, minConfidence)
apriori.printResults(items, rules)
def main():
import sys
from optparse import OptionParser
from apriori import runApriori, dataFromFile, printResults
optparser = OptionParser()
optparser.add_option('-f', '--inputFile',
dest='input',
help='filename containing csv',
default=None)
optparser.add_option('-s', '--minSupport',
dest='minS',
help='minimum support value',
default=0.4,
type='float')
(options, args) = optparser.parse_args()
inFile = None
if options.input is None:
inFile = sys.stdin
elif options.input is not None:
inFile = dataFromFile(options.input)
else:
print ('No dataset filename specified, system with exit\n')
sys.exit('System will exit')
minSupport = options.minS
items, PBoarder, NBoarder = runApriori(inFile, minSupport)
printResults(items, minSupport, options.input.split("/")[2], PBoarder, NBoarder)
def test_run_apriori_should_get_items_and_rules(self):
data = 'apple,beer,rice,chicken\n'
data += 'apple,beer,rice\n'
data += 'apple,beer\n'
data += 'apple,mango\n'
data += 'milk,beer,rice,chicken\n'
data += 'milk,beer,rice\n'
data += 'milk,beer\n'
data += 'milk,mango'
os.system('echo \'' + data + '\' > test_apriori.csv')
inFile = dataFromFile('test_apriori.csv')
minSupport = 0.5
minConfidence = 0.05
items, rules = runApriori(inFile, minSupport, minConfidence)
expected = [
(('milk',), 0.5),
(('apple',), 0.5),
(('beer',), 0.75),
(('rice',), 0.5),
(('beer', 'rice'), 0.5)
]
self.assertEqual(items, expected)
expected = [
((('beer',), ('rice',)), 0.6666666666666666),
((('rice',), ('beer',)), 1.0)
]
self.assertEqual(rules, expected)
os.system('rm test_apriori.csv')
def test_run_apriori_should_get_items_and_rules(self):
data = 'apple,beer,rice,chicken\n'
data += 'apple,beer,rice\n'
data += 'apple,beer\n'
data += 'apple,mango\n'
data += 'milk,beer,rice,chicken\n'
data += 'milk,beer,rice\n'
data += 'milk,beer\n'
data += 'milk,mango'
os.system('echo \'' + data + '\' > test_apriori.csv')
inFile = dataFromFile('test_apriori.csv')
minSupport = 0.5
minConfidence = 0.05
items, rules = runApriori(inFile, minSupport, minConfidence)
expected = [(('milk', ), 0.5), (('apple', ), 0.5), (('beer', ), 0.75),
(('rice', ), 0.5), (('beer', 'rice'), 0.5)]
self.assertEqual(items, expected)
expected = [((('beer', ), ('rice', )), 0.6666666666666666),
((('rice', ), ('beer', )), 1.0)]
self.assertEqual(rules, expected)
os.system('rm test_apriori.csv')
def test_read_data_from_file(self):
os.system('echo \'apple,beer,rice\' > test_apriori.csv')
result = dataFromFile('test_apriori.csv')
data = [each for each in result]
expected = frozenset(['beer', 'rice', 'apple'])
self.assertEqual(data[0], expected)
os.system('rm test_apriori.csv')
def test_read_data_from_file(self):
os.system('echo \'apple,beer,rice\' > test_apriori.csv')
result = dataFromFile('test_apriori.csv')
data = [each for each in result]
expected = frozenset(['beer', 'rice', 'apple'])
self.assertEqual(data[0], expected)
os.system('rm test_apriori.csv')
def get_all_recommendation( sup, con, subreddit): """ function to save all of the best recommendation result """ temp_file = apriori.dataFromFile(tempFile_path) items, rules = apriori.runApriori(temp_file, sup, con) recommendation_set = list() for rule, confidence in rules: pre, post = rule for item in pre: if item not in recommendation_set and item.lower() != subreddit.lower(): recommendation_set.append(item) os.remove(tempFile_path) return recommendation_set
def test_run_apriori_should_get_items_and_rules(self):
data = 'apple,beer,rice,chicken\n'
data += 'apple,beer,rice\n'
data += 'apple,beer\n'
data += 'apple,mango\n'
data += 'milk,beer,rice,chicken\n'
data += 'milk,beer,rice\n'
data += 'milk,beer\n'
data += 'milk,mango\n'
with open('test_apriori.csv', 'w') as fh:
fh.write(data)
inFile = dataFromFile('test_apriori.csv')
minSupport = 0.5
minConfidence = 0.05
items, rules = runApriori(inFile, minSupport, minConfidence)
## to make the arrangement consistent
items = sorted(items, key=lambda x: (len(x[0]), x[1], x[0]))
items = [(set(a), b) for a,b in items]
expected = [(("apple",), 0.5),
(("milk",), 0.5),
(("rice",), 0.5),
(("beer",), 0.75),
(("beer", "rice"), 0.5)]
expected = [(set(a), b) for a,b in expected]
self.assertEqual(items, expected)
expected = [
((('beer',), ('rice',)), 0.6666666666666666),
((('rice',), ('beer',)), 1.0)
]
self.assertEqual(set(rules), set(expected))
)
st.markdown(
' > Support(A) = (Number of transactions in which A appears)/(Total Number of Transactions'
)
st.markdown(' > Confidence(A->B) = Support(AUB)/Support(A)')
st.markdown('---')
support = st.slider("Enter the Minimum Support Value",
min_value=0.1,
max_value=0.9,
value=0.15)
confidence = st.slider("Enter the Minimum Confidence Value",
min_value=0.1,
max_value=0.9,
value=0.6)
inFile = dataFromFile(default_csv)
items, rules = runApriori(inFile, support, confidence)
i, r = to_str_results(items, rules)
st.markdown("## Results")
st.markdown("### Frequent Itemsets")
st.write(i)
st.markdown("### Frequent Rules")
st.write(r)
if max_col_ind == cut_df or max_col_ind == cut_df - 1:
print('Value for cutting DataFrame should be higher')
for row_ind in range(len(csv_data)):
for col_ind in range(len(csv_data.iloc[0, :])):
if type(csv_data.iloc[row_ind, col_ind]) == float:
csv_data.iloc[row_ind, col_ind] = float('NaN')
csv_data.to_csv('statuscodes_toleranceRange=' + str(toleranceRange) + '.csv',
header=False,
index=False)
#%% _end of making it look nicely
#%% start o the apriori algorythm
inFile = dataFromFile('statuscodes_toleranceRange=' + str(toleranceRange) +
'.csv')
#inFile = dataFromFile('statuscodes.csv')
items, rules = runApriori(inFile, minSupport, minConfidence)
#%% end o the apriori algorythm
#items=tempitems[:64]
#rules=temprules[:70]
#%% saving all the rules to a .txt file
list_of_tuples = rules
f = open(
'rules_toleranceRange=' + str(toleranceRange) + '_MinSupport=' +
str(minSupport) + '_MinConfidence=' + str(minConfidence) + '_maxFailure=' +
str(maxFailure) + '_minFailure=' + str(minFailure) + '.txt', 'w')
for t in list_of_tuples:
