.csv至.arff函数python

Question

我正在尝试从CSV到ARFF，现在我有：

def csv2arff(csv_path, arff_path=None):
    with open(csv_path, 'r') as fr:
        attributes = []
        
        if arff_path is None:
            arff_path = csv_path[:-4] + '_prueba.arff'  # *.arff -> *.csv
            
        write_sw = False
        with open(arff_path, 'w') as fw:
            fw.write('@relation base_datos_modelo_3_limpia \n')
            firstline = fr.readlines()[0].rstrip()
            fw.write(firstline)

@RELATION

BASE_DATOS_MODELO_3_LIMPIA

DVJ_VALGUS_VALGUS_KNEEMEDIALLDISPLACEMENT_D_D_D_D_D_DISCR fppa_nd_discr，asym_slcmjlanding-pvgrf（ 10 percent）_discr，asym-rom-phir（≥8）_discr，asym_tj_valgus_fppa（10percent）_discr，tj_valgus_fppa_nd_discr，asym-phf-phf-ke（a asym-phf-ke（> 8）百分比）_discr，asym_ybtpl（10％） _discr,Position,Asym-ROM-PADF-KE(≥8º)_discr,DVJ_Valgus_KneeMedialDisplacement_ND_discr,DVJ_Valgus_Knee-to-ankle-ratio_discr,Asym-ROM-PKF(≥8)_discr,Asym-ROM-PHABD(≥8)_discr,Asym -ROM-PHF-KF（≥8）_DISCR，ASYM-ROM-PHER（≥8）_DISCR，ASMYYBTANTERIOR10PERCENTDISCR，ASYM-ROM-pHABD-HF（≥8）_DISCR（≥8）_DISCR，ANSYM-PHE（ANSYM-PHE（ANSYM-PHE（≥8）_DISCR） （＆gt; 4cm）-dvj_valgus_knee; edialldisplacement_discr，asym_slcmjtakeoff-pvgrf（10percent）_discr，asym-rom-phadd（≥8）_discr，asym-ybtcomposite（asym-ybtcomposite（10percent）_discrth，assim_discr， pm（10％）_discr，asym_dvj_valgus_fppa (10percent)_discr,Asym_SLCMJ-pLFT(10percent)_discr,DominantLeg,Asym-ROM-PADF-KF(≥8)_discr,ROM-PHER_ND,CPRDmentalskills,POMStension,STAI-R,ROM-PHER_D,ROM-PHIR_D,ROM- PADF-KF_ND,ROM-PADF-KF_D,Age_at_PHV,ROM-PHIR_ND,CPRDtcohesion,Eperience,ROM-PHABD-HF_D,MaturityOffset,Weight,ROM-PHADD_ND,Height,ROM-PHADD_D,Age,POMSdepressio,ROM-PADF-KE_ND, pomsanger，ybtanterior_dnorm，ybtanterior_ndnorm，pomsvigour，soft-tissue_injury_≥4天，

所以我想在每个属性之前将“ @attribute”放在“ @attribute”之前，然后更改“，”，“”为“ \ n”。但是不知道该怎么做，我试图做出一个函数来改变“”，但没有起作用，有什么想法吗？

谢谢你们。

Answer 1

尝试 liac-arff 库。

这是将UCI IRIS数据集从ARFF转换为CSV的示例，然后返回ARFF：

import csv
import arff

# arff -> csv
content = arff.load(open('./iris.arff', 'r'))
with open('./out.csv', 'w') as fp:
    writer = csv.writer(fp)
    header = []
    for n, t in content['attributes']:
        header.append(n)
    writer.writerow(header)
    writer.writerows(content['data'])

# csv -> arff
with open('./out.csv', 'r') as fp:
    reader = csv.reader(fp)
    header = None
    data = []
    for row in reader:
        if header is None:
            header = row
        else:
            data.append(row)

content = {}
content['relation'] = "from my csv file"
content['attributes'] = []
for n in header:
    if n == "class":
        content['attributes'].append((n, ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']))
    else:
        content['attributes'].append((n, 'NUMERIC'))
content['data'] = data
with open('./out.arff', 'w') as fp:
    arff.dump(content, fp)

nb：在最后阶段，我们需要指定名义类值，您可以通过扫描扫描来确定它们数据。