def Apriori_LERS(FILENAME, iter1, iter2, minsup, minconf) :
          
    # rule induction
    fullpath_filename = '/data/uci/'+FILENAME+'/apriori/'+'rules_'+str(iter1)+'-'+str(iter2)+'-'+str(minsup)+'-'+str(minconf)+'.pkl'
    rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(fullpath_filename) else apriori.getRulesByApriori(FILENAME, iter1, iter2, minsup, minconf) 

    # rule save
    if not os.path.isfile(fullpath_filename): mlem2.savePickleRules(rules, fullpath_filename) 

    # test data setup
    filepath = '/data/uci/'+FILENAME+'/'+FILENAME+'-test'+str(iter1)+'-'+str(iter2)+'.tsv'
    decision_table_test = mlem2.getDecisionTable(filepath)
    decision_table_test = decision_table_test.dropna()
    decision_class = decision_table_test[decision_table_test.columns[-1]].values.tolist()

    filepath = '/data/uci/'+FILENAME+'/'+FILENAME+'.nominal'
    list_nominal = mlem2.getNominalList(filepath)
    list_judgeNominal = mlem2.getJudgeNominal(decision_table_test, list_nominal)
    
    # predict by LERS
    predictions = LERS.predictByLERS(rules, decision_table_test, list_judgeNominal)
    
    # 正答率を求める
    accuracy = accuracy_score(list(map(str,decision_class)), predictions)
    
    #print('{FILENAME} : {iter1} {iter2}'.format(FILENAME=FILENAME,iter1=iter1,iter2=iter2))    
    logging.basicConfig(filename=os.path.dirname(os.path.abspath("__file__"))+'/'+FILENAME+'.log',format='%(asctime)s,%(message)s',level=logging.DEBUG)
    logging.info('Apriori_LERS,{FILENAME},{iter1},{iter2},{acc},{minsup},{minconf}'.format(FILENAME=FILENAME,iter1=iter1,iter2=iter2,acc=accuracy,minsup=minsup,minconf=minconf))
    
    return(accuracy)
def MLEM2_LERS(FILENAME, iter1, iter2) :

    # 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)

    # test data setup
    filepath = DIR_UCI+'/'+FILENAME+'/'+FILENAME+'-test'+str(iter1)+'-'+str(iter2)+'.tsv'
    decision_table_test = mlem2.getDecisionTable(filepath)
    decision_table_test = decision_table_test.dropna()
    decision_class = decision_table_test[decision_table_test.columns[-1]].values.tolist()

    filepath = DIR_UCI+'/'+FILENAME+'/'+FILENAME+'.nominal'
    list_nominal = mlem2.getNominalList(filepath)
    list_judgeNominal = mlem2.getJudgeNominal(decision_table_test, list_nominal)

    # predict by LERS
    predictions = LERS.predictByLERS(rules, decision_table_test, list_judgeNominal)

    # 正答率を求める
    accuracy = accuracy_score(decision_class, predictions)

    #print('{FILENAME} : {iter1} {iter2}'.format(FILENAME=FILENAME,iter1=iter1,iter2=iter2))
    #logging.info('MLEM2_LERS,1,{FILENAME},{iter1},{iter2},{acc}'.format(FILENAME=FILENAME,iter1=iter1,iter2=iter2,acc=accuracy))
    savepath = DIR_UCI+'/'+FILENAME+'/MLEM2_LERS.csv'
    with open(savepath, "a") as f :
        f.writelines('MLEM2_LERS,1,{FILENAME},{iter1},{iter2},{acc}'.format(FILENAME=FILENAME,iter1=iter1,iter2=iter2,acc=accuracy)+"\n")

    return(accuracy)
def MLEM2_LERS(FILENAME, iter1, iter2):
    # rule induction and rule save
    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)
    )
    if not os.path.isfile(fullpath_filename):
        mlem2.savePickleRules(rules, fullpath_filename)

    # test data setup
    decision_table_test, decision_class = getData(FILENAME, iter1, iter2, T="test")
    list_judgeNominal = getJudgeNominal(decision_table_test, FILENAME)

    # predict by LERS
    predictions = LERS.predictByLERS(rules, decision_table_test, list_judgeNominal)

    # 正答率を求める
    accuracy = accuracy_score(decision_class, predictions)
    # rules の数を求める
    num = len(rules)
    # 各クラスのrulesの数を求める
    num_class = strNumClassRules(rules)
    # 平均の長さを求める
    mean_length = mlem2.getMeanLength(rules)
    # 平均支持度と平均確信度を求める
    decision_table_train, decision_class = getData(FILENAME, iter1, iter2, T="train")
    list_judgeNominal = getJudgeNominal(decision_table_train, FILENAME)
    mean_support, mean_conf = LERS.getSupportConfidenceRules(rules, decision_table_train, list_judgeNominal)
    # AccとRecallを求める
    acc_recall = LERS.getAccurayRecall(rules, decision_table_train, list_judgeNominal)

    # ファイルにsave
    savepath = DIR_UCI + "/" + FILENAME + "/fairness/00_normal/MLEM2_LERS.csv"
    with open(savepath, "a") as f:
        f.writelines(
            "MLEM2_LERS,{FILENAME},{iter1},{iter2},{acc},{num},{num_class},{mean_length},{mean_support},{mean_conf},{acc_recall}".format(
                FILENAME=FILENAME,
                iter1=iter1,
                iter2=iter2,
                acc=accuracy,
                num=num,
                num_class=num_class,
                mean_length=mean_length,
                mean_support=mean_support,
                mean_conf=mean_conf,
                acc_recall=strAccRecall(rules, acc_recall),
            )
            + "\n"
        )
    return 0
def MLEM2_RuleClusteringByConsistentTimesSimExceptMRule_LERS(FILENAME, iter1, iter2, k, m) :
    # 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_times_sim_except_mrule/'+'rules-'+str(k)+'_'+str(iter1)+'-'+str(iter2)+'.pkl'
    rules = mlem2.loadPickleRules(fullpath_filename) if os.path.isfile(fullpath_filename) else clustering.getRuleClusteringByConsistentTimesSimilarityExceptMRule(rules, colnames, list_judgeNominal, k=k, m=m)

    # rule save
    if not os.path.isfile(fullpath_filename): mlem2.savePickleRules(rules, fullpath_filename) 

    # test data setup
    filepath = DIR_UCI+'/'+FILENAME+'/'+FILENAME+'-test'+str(iter1)+'-'+str(iter2)+'.tsv'
    decision_table_test = mlem2.getDecisionTable(filepath)
    decision_table_test = decision_table_test.dropna()
    decision_class = decision_table_test[decision_table_test.columns[-1]].values.tolist()

    filepath = DIR_UCI+'/'+FILENAME+'/'+FILENAME+'.nominal'
    list_nominal = mlem2.getNominalList(filepath)
    list_judgeNominal = mlem2.getJudgeNominal(decision_table_test, list_nominal)
    
    # predict by LERS
    predictions = LERS.predictByLERS(rules, decision_table_test, list_judgeNominal)
    
    # 正答率を求める
    accuracy = accuracy_score(decision_class, predictions)
    
    #print('{FILENAME} : {iter1} {iter2}'.format(FILENAME=FILENAME,iter1=iter1,iter2=iter2))    
    #logging.info('MLEM2_RuleClusteringByConsistentSimExceptMRule_LERS,{k},{FILENAME},{iter1},{iter2},{acc}'.format(FILENAME=FILENAME,k=k,iter1=iter1,iter2=iter2,acc=accuracy))
    savepath = DIR_UCI+'/'+FILENAME+'/MLEM2_RuleClusteringByConsistentTimesSimExceptMRule_LERS.csv'
    with open(savepath, "a") as f :
        f.writelines('MLEM2_RuleClusteringByConsistentTimesSimExceptMRule_LERS,{k},{FILENAME},{iter1},{iter2},{acc}'.format(FILENAME=FILENAME,k=k,iter1=iter1,iter2=iter2,acc=accuracy)+"\n")
    
    return(accuracy)
def MLEM2_delEAlphaRule_LERS(FILENAME, iter1, iter2, DELFUN, CLASS, ATTRIBUTE_VALUE, alpha):
    print(
        datetime.now().strftime("%Y/%m/%d %H:%M:%S")
        + " "
        + FILENAME
        + " "
        + str(iter1)
        + " "
        + str(iter2)
        + " "
        + DELFUN.__name__
        + " "
        + strAttributeValue(ATTRIBUTE_VALUE)
        + " "
        + str(alpha)
        + " "
        + "START"
    )

    # rule induction and rule save
    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)
    )
    if not os.path.isfile(fullpath_filename):
        mlem2.savePickleRules(rules, fullpath_filename)

    # train data setup
    decision_table_train, decision_class = getData(FILENAME, iter1, iter2, T="train")
    list_judgeNominal = getJudgeNominal(decision_table_train, FILENAME)

    # alpha差別的なルールの基本条件削除 or ルールを削除
    if CLASS != "ALL":
        rules_target = mlem2.getRulesClass(rules, CLASS)
        rules_nontarget = mlem2.getRulesClass(rules, CLASS, judge=False)
        for attr in ATTRIBUTE_VALUE:
            for e in ATTRIBUTE_VALUE[attr]:
                rules_target = DELFUN(rules_target, attr, e, decision_table_train, list_judgeNominal, alpha)
        rules_target.extend(rules_nontarget)
        rules = rules_target
    else:
        for attr in ATTRIBUTE_VALUE:
            for e in ATTRIBUTE_VALUE[attr]:
                rules = DELFUN(rules, attr, e, decision_table_train, list_judgeNominal, alpha)

    print(
        datetime.now().strftime("%Y/%m/%d %H:%M:%S")
        + " "
        + FILENAME
        + " "
        + str(iter1)
        + " "
        + str(iter2)
        + " "
        + DELFUN.__name__
        + " "
        + strAttributeValue(ATTRIBUTE_VALUE)
        + " "
        + str(alpha)
        + " "
        + "RULES"
    )

    # test data setup
    decision_table_test, decision_class = getData(FILENAME, iter1, iter2, T="test")
    list_judgeNominal = getJudgeNominal(decision_table_test, FILENAME)

    # predict by LERS
    predictions = LERS.predictByLERS(rules, decision_table_test, list_judgeNominal)

    # 正答率を求める
    accuracy = accuracy_score(decision_class, predictions)
    # rules の数を求める
    num = len(rules)
    # 各クラスのrulesの数を求める
    num_class = strNumClassRules(rules)
    # 平均の長さを求める
    mean_length = mlem2.getMeanLength(rules)
    # 平均支持度と平均確信度を求める
    list_judgeNominal = getJudgeNominal(decision_table_train, FILENAME)
    mean_support, mean_conf = LERS.getSupportConfidenceRules(rules, decision_table_train, list_judgeNominal)
    # AccとRecallを求める
    acc_recall = LERS.getAccurayRecall(rules, decision_table_train, list_judgeNominal)

    # ファイルにsave
    savepath = DIR_UCI + "/" + FILENAME + "/fairness/02_alpha_preserve/MLEM2_delEAlphaRule_LERS.csv"
    with open(savepath, "a") as f:
        f.writelines(
            "MLEM2_delEAlphaRule_LERS,{DELFUN},{CLASS},{FILENAME},{ATTRIBUTE_VALUE},{alpha},{iter1},{iter2},{acc},{num},{num_class},{mean_length},{mean_support},{mean_conf},{acc_recall}".format(
                DELFUN=DELFUN.__name__,
                CLASS=CLASS,
                FILENAME=FILENAME,
                ATTRIBUTE_VALUE=strAttributeValue(ATTRIBUTE_VALUE),
                alpha=alpha,
                iter1=iter1,
                iter2=iter2,
                acc=accuracy,
                num=num,
                num_class=num_class,
                mean_length=mean_length,
                mean_support=mean_support,
                mean_conf=mean_conf,
                acc_recall=strAccRecall(rules, acc_recall),
            )
            + "\n"
        )
    print(
        datetime.now().strftime("%Y/%m/%d %H:%M:%S")
        + " "
        + FILENAME
        + " "
        + str(iter1)
        + " "
        + str(iter2)
        + " "
        + DELFUN.__name__
        + " "
        + strAttributeValue(ATTRIBUTE_VALUE)
        + " "
        + str(alpha)
        + " "
        + "END"
    )

    return 0