def ibm_Apiori_hashtree(min_support, min_confidence):
path_ibm = './dataset/IBM-Quest-Data-Generator.exe/ttt.data.txt'
member_group = DataReader.readDataIBM(path_ibm)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
for i in range(len(L1)):
if str(L1[0][i]) == 'None':
L1 = L1.drop([i], axis=0)
L1 = L1.reset_index(drop=True)
Ln = L1
Ls = []
Ls.append(L1)
while len(Ln) > 1:
print(len(Ls))
C = apriori.generate_cand_itemset(Ln)
# print(C)
Lnn = apriori_byTree.generate_L(C, member_group, min_support)
Ln = Lnn
Ls.append(Ln)
print('freq itemsets:')
freq_itemset = []
freq_itemset_count = []
for i in range(1, len(Ls)):
temp = (np.array(Ls[i])).tolist()
for j in range(len(temp)):
freq_itemset_count.append(temp[j][0])
freq_itemset.append(list(temp[j][1:]))
print(temp[j][0], list(temp[j][1:]))
#生成rules
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)
def kaggle_Apiori_bruteForce(min_support, min_confidence):
path_kaggle = './dataset/BreadBasket_DMS.csv'
member_group = DataReader.readDataKaggle(path_kaggle)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
Ln = L1
Ls = []
Ls.append(L1)
while len(Ln) > 1:
print(len(Ls))
Lnn = apriori.cal_support_and_generate_L(
apriori.generate_cand_itemset(Ln), result_df, min_support)
Ln = Lnn
Ls.append(Ln)
print('freq itemsets:')
freq_itemset = []
freq_itemset_count = []
for i in range(1, len(Ls)):
temp = (np.array(Ls[i])).tolist()
for j in range(len(temp)):
freq_itemset_count.append(temp[j][0])
freq_itemset.append(list(temp[j][1:]))
print(temp[j][0], list(temp[j][1:]))
#生成rules
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)
def test_Apiori_hashtree(min_support, min_confidence):
path = './dataset/Test.csv'
df = pd.read_csv(path)
# print(df)
# 將每個組合分開
member = []
member_group = []
# group_id=[]
for i in range(len(df)):
if i == 0:
# member.append(df['tid'][i])
member.append(df['Item'][i])
elif df['TID'][i] == df['TID'][i - 1]:
member.append((df['Item'][i]))
else:
member_group.append(list(member))
# group_id.append(df['t_id'][i-1])
member = []
# member.append(df['tid'][i])
member.append(df['Item'][i])
member_group.append(list(member))
# member_group = DataReader.readDataKaggle(path_kaggle)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
for i in range(len(L1)):
if str(L1[0][i]) == 'None':
L1 = L1.drop([i], axis=0)
L1 = L1.reset_index(drop=True)
Ln = L1
Ls = []
Ls.append(L1)
while len(Ln) > 1:
print(len(Ls))
C = apriori.generate_cand_itemset(Ln)
# print(C)
Lnn = apriori_byTree.generate_L_kaggle(C, member_group, min_support)
Ln = Lnn
if len(Ln) == 0:
break
Ls.append(Ln)
print('freq itemsets:')
freq_itemset = []
freq_itemset_count = []
for i in range(1, len(Ls)):
temp = (np.array(Ls[i])).tolist()
for j in range(len(temp)):
freq_itemset_count.append(temp[j][0])
freq_itemset.append(list(temp[j][1:]))
print(temp[j][0], list(temp[j][1:]))
#生成rules
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)
def test_Apiori_bruteForce(min_support, min_confidence):
path = './dataset/Test.csv'
df = pd.read_csv(path)
# print(df)
# 將每個組合分開
member = []
member_group = []
# group_id=[]
for i in range(len(df)):
if i == 0:
# member.append(df['tid'][i])
member.append(df['Item'][i])
elif df['TID'][i] == df['TID'][i - 1]:
member.append((df['Item'][i]))
else:
member_group.append(list(member))
# group_id.append(df['t_id'][i-1])
member = []
# member.append(df['tid'][i])
member.append(df['Item'][i])
member_group.append(list(member))
# member_group = DataReader.readDataKaggle(path_kaggle)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
Ln = L1
Ls = []
Ls.append(L1)
while len(Ln) > 1:
print(len(Ls))
Lnn = apriori.cal_support_and_generate_L(
apriori.generate_cand_itemset(Ln), result_df, min_support)
Ln = Lnn
Ls.append(Ln)
print('freq itemsets:')
freq_itemset = []
freq_itemset_count = []
for i in range(1, len(Ls)):
temp = (np.array(Ls[i])).tolist()
for j in range(len(temp)):
freq_itemset_count.append(temp[j][0])
freq_itemset.append(list(temp[j][1:]))
print(temp[j][0], list(temp[j][1:]))
#生成rules
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)
def ibm_fptree(min_support, min_confidence):
path_ibm = './dataset/IBM-Quest-Data-Generator.exe/ttt.data.txt'
member_group = DataReader.readDataIBM(path_ibm)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
for i in range(len(L1)):
if str(L1[0][i]) == 'None':
L1 = L1.drop([i], axis=0)
L1 = L1.reset_index(drop=True)
l1_sort = L1.sort_values(['count'], ascending=False)
l1_sort = l1_sort.reset_index(drop=True)
# member_group = (np.array(result_df)).tolist()
new_member_group = []
for i in range(len(member_group)):
member = []
for j in range(len(l1_sort)):
if l1_sort[0][j] in member_group[i]:
member.append(l1_sort[0][j])
new_member_group.append(member)
# 建立FP tree
fp_tree_root = fpTree.createTree(new_member_group)
# fp growth
freq_pats = fpTree.fpGrowth(l1_sort, fp_tree_root, min_support)
freq_itemset = []
freq_itemset_count = []
for i in range(len(freq_pats)):
if (freq_pats[i].val in freq_itemset) == False:
freq_itemset.append(freq_pats[i].val)
freq_itemset_count.append(freq_pats[i].count)
df_itemset = pd.DataFrame(freq_itemset)
df_itemset_count = pd.DataFrame(freq_itemset_count, columns=['count'])
df_ans = pd.concat([df_itemset_count, df_itemset], axis=1)
print('freq itemsets:')
print(df_ans)
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)
def kaggle_fptree(min_support, min_confidence):
path_kaggle = './dataset/BreadBasket_DMS.csv'
member_group = DataReader.readDataKaggle(path_kaggle)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
l1_sort = L1.sort_values(['count'], ascending=False)
l1_sort = l1_sort.reset_index(drop=True)
# member_group = (np.array(result_df)).tolist()
new_member_group = []
for i in range(len(member_group)):
member = []
for j in range(len(l1_sort)):
if l1_sort[0][j] in member_group[i]:
member.append(l1_sort[0][j])
new_member_group.append(member)
# 建立FP tree
fp_tree_root = fpTree.createTree(new_member_group)
# fp growth
freq_pats = fpTree.fpGrowth(l1_sort, fp_tree_root, min_support)
freq_itemset = []
freq_itemset_count = []
for i in range(len(freq_pats)):
if (freq_pats[i].val in freq_itemset) == False:
freq_itemset.append(freq_pats[i].val)
freq_itemset_count.append(freq_pats[i].count)
df_itemset = pd.DataFrame(freq_itemset)
df_itemset_count = pd.DataFrame(freq_itemset_count, columns=['count'])
df_ans = pd.concat([df_itemset_count, df_itemset], axis=1)
print('freq itemsets:')
print(df_ans)
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)
def kaggle_Apiori_hashtree(min_support, min_confidence):
path_kaggle = './dataset/BreadBasket_DMS.csv'
member_group = DataReader.readDataKaggle(path_kaggle)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
for i in range(len(L1)):
if str(L1[0][i]) == 'None':
L1 = L1.drop([i], axis=0)
L1 = L1.reset_index(drop=True)
Ln = L1
Ls = []
Ls.append(L1)
while len(Ln) > 1:
print(len(Ls))
C = apriori.generate_cand_itemset(Ln)
# print(C)
Lnn = apriori_byTree.generate_L_kaggle(C, member_group, min_support)
Ln = Lnn
if len(Ln) == 0:
break
Ls.append(Ln)
print('freq itemsets:')
freq_itemset = []
freq_itemset_count = []
for i in range(1, len(Ls)):
temp = (np.array(Ls[i])).tolist()
for j in range(len(temp)):
freq_itemset_count.append(temp[j][0])
freq_itemset.append(list(temp[j][1:]))
print(temp[j][0], list(temp[j][1:]))
#生成rules
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)
def test_fptree(min_support, min_confidence):
path = './dataset/Test.csv'
df = pd.read_csv(path)
# print(df)
# 將每個組合分開
member = []
member_group = []
# group_id=[]
for i in range(len(df)):
if i == 0:
# member.append(df['tid'][i])
member.append(df['Item'][i])
elif df['TID'][i] == df['TID'][i - 1]:
member.append((df['Item'][i]))
else:
member_group.append(list(member))
# group_id.append(df['t_id'][i-1])
member = []
# member.append(df['tid'][i])
member.append(df['Item'][i])
member_group.append(list(member))
# member_group = DataReader.readDataKaggle(path_kaggle)
result_df = pd.DataFrame(member_group)
result_df_T = result_df.T
L1 = apriori.createL1(result_df_T, min_support)
l1_sort = L1.sort_values(['count'], ascending=False)
l1_sort = l1_sort.reset_index(drop=True)
# member_group = (np.array(result_df)).tolist()
new_member_group = []
for i in range(len(member_group)):
member = []
for j in range(len(l1_sort)):
if l1_sort[0][j] in member_group[i]:
member.append(l1_sort[0][j])
new_member_group.append(member)
# 建立FP tree
fp_tree_root = fpTree.createTree(new_member_group)
# fp growth
freq_pats = fpTree.fpGrowth(l1_sort, fp_tree_root, min_support)
freq_itemset = []
freq_itemset_count = []
for i in range(len(freq_pats)):
if (freq_pats[i].val in freq_itemset) == False:
freq_itemset.append(freq_pats[i].val)
freq_itemset_count.append(freq_pats[i].count)
df_itemset = pd.DataFrame(freq_itemset)
df_itemset_count = pd.DataFrame(freq_itemset_count, columns=['count'])
df_ans = pd.concat([df_itemset_count, df_itemset], axis=1)
print('freq itemsets:')
print(df_ans)
generateRule.generateRule(freq_itemset, freq_itemset_count, L1,
min_confidence)