def MLEM2_OnlyK_Identified(FILENAME, iter1, iter2, k, p):
# rule induction
fullpath_filename = DIR_UCI + '/' + FILENAME + '/rules/' + 'rules_' + str(
iter1) + '-' + str(iter2) + '.pkl'
rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(
fullpath_filename) else mlem2.getRulesByMLEM2(FILENAME, iter1, iter2)
# rule save
if not os.path.isfile(fullpath_filename):
mlem2.savePickleRules(rules, fullpath_filename)
# only-k rule filter
fullpath_filename = DIR_UCI + '/' + FILENAME + '/rules_onlyK/' + 'rules-' + str(
k) + '_' + str(iter1) + '-' + str(iter2) + '.pkl'
rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(
fullpath_filename) else [r for r in rules if len(r.getSupport()) >= k]
# rule save
if not os.path.isfile(fullpath_filename):
mlem2.savePickleRules(rules, fullpath_filename)
# PerIdentifiedClass を求める
ans = mlem2.getPerIdentifiedClass(rules, p)
# save
savepath = DIR_UCI + '/' + FILENAME + '/Identify_MLEM2_OnlyK.csv'
with open(savepath, "a") as f:
f.writelines(
'Identify_MLEM2_OnlyK,{k},{p},{FILENAME},{iter1},{iter2},{ans}'.
format(
FILENAME=FILENAME, k=k, p=p, iter1=iter1, iter2=iter2,
ans=ans) + "\n")
return (ans)
def MLEM2_RuleClusteringByConsistentSim_Identified(FILENAME, iter1, iter2, k,
p):
# rule induction
fullpath_filename = DIR_UCI + '/' + FILENAME + '/rules/' + 'rules_' + str(
iter1) + '-' + str(iter2) + '.pkl'
rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(
fullpath_filename) else mlem2.getRulesByMLEM2(FILENAME, iter1, iter2)
# rule save
if not os.path.isfile(fullpath_filename):
mlem2.savePickleRules(rules, fullpath_filename)
# rule clustering
filepath = DIR_UCI + '/' + FILENAME + '/' + FILENAME + '-train' + str(
iter1) + '-' + str(iter2) + '.tsv'
decision_table = mlem2.getDecisionTable(filepath)
colnames = mlem2.getColNames(decision_table)
filepath = DIR_UCI + '/' + FILENAME + '/' + FILENAME + '.nominal'
list_nominal = mlem2.getNominalList(filepath)
list_judgeNominal = mlem2.getJudgeNominal(decision_table, list_nominal)
fullpath_filename = DIR_UCI + '/' + FILENAME + '/rules_cluster_consistent_sim/' + 'rules-' + str(
k) + '_' + str(iter1) + '-' + str(iter2) + '.pkl'
rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(
fullpath_filename
) else clustering.getRuleClusteringByConsistentSimilarity(
rules, colnames, list_judgeNominal, k=k)
# rule save
if not os.path.isfile(fullpath_filename):
mlem2.savePickleRules(rules, fullpath_filename)
# PerIdentifiedClass を求める
ans = mlem2.getPerIdentifiedClass(rules, p)
# save
savepath = DIR_UCI + '/' + FILENAME + '/Identify_MLEM2_RuleClusteringByConsistentSim.csv'
with open(savepath, "a") as f:
f.writelines(
'Identify_MLEM2_RuleClusteringByConsistentSim,{k},{p},{FILENAME},{iter1},{iter2},{ans}'
.format(
FILENAME=FILENAME, k=k, p=p, iter1=iter1, iter2=iter2,
ans=ans) + "\n")
return (ans)
def MLEM2_RuleClusteringBySameCondition_Identified(FILENAME, iter1, iter2, k,
p):
# rule induction
fullpath_filename = DIR_UCI + '/' + FILENAME + '/rules/' + 'rules_' + str(
iter1) + '-' + str(iter2) + '.pkl'
rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(
fullpath_filename) else mlem2.getRulesByMLEM2(FILENAME, iter1, iter2)
# rule save
if not os.path.isfile(fullpath_filename):
mlem2.savePickleRules(rules, fullpath_filename)
# rule clustering
fullpath_filename = DIR_UCI + '/' + FILENAME + '/rules_cluster_same_condition/' + 'rules-' + str(
k) + '_' + str(iter1) + '-' + str(iter2) + '.pkl'
rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(
fullpath_filename) else clustering.getRuleClusteringBySameCondition(
rules, k=k)
# rule save
if not os.path.isfile(fullpath_filename):
mlem2.savePickleRules(rules, fullpath_filename)
# PerIdentifiedClass を求める
ans = mlem2.getPerIdentifiedClass(rules, p)
# save
savepath = DIR_UCI + '/' + FILENAME + '/Identify_MLEM2_SameCondition.csv'
with open(savepath, "a") as f:
f.writelines(
'Identify_MLEM2_SameCondition,{k},{p},{FILENAME},{iter1},{iter2},{ans}'
.format(
FILENAME=FILENAME, k=k, p=p, iter1=iter1, iter2=iter2,
ans=ans) + "\n")
return (ans)