Python/Sklearn - 值错误 : could not convert string to float

Question

我正在尝试使用 10 倍 CV 在我的数据集中运行 kNN 分类器。我对 WEKA 中的模型有一些经验，但很难将其转移到 Sklearn。

下面是我的代码

filename = 'train4.csv'
names = ['attribute names are here']

df = pandas.read_csv(filename, names=names)

num_folds = 10
kfold = KFold(n_splits=10, random_state=7)
model = KNeighborsClassifier()
results = cross_val_score(model, df.drop('mix1_instrument', axis=1), df['mix1_instrument'], cv=kfold)
print(results.mean())

我收到这个错误

 ValueError: could not convert string to float: ''

如何转换此属性？这包含对我的实例进行分类的有用信息，转换会对此产生影响吗？

我认为有两个“对象”属性需要转换名为“class1”和“class2”

下面的示例数据...

{
    'temporalCentroid': {
        0: 'temporalCentroid',
        1: '1.67324',
        2: '1.330722',
        3: '0.786984',
        4: '1.850129'
    },
    'LogSpecCentroid': {
        0: 'LogSpecCentroid',
        1: '-1.043802',
        2: '-0.82943',
        3: '-2.441297',
        4: '-0.837145'
    },
    'LogSpecSpread': {
        0: 'LogSpecSpread',
        1: '0.747558',
        2: '1.378373',
        3: '0.667634',
        4: '1.238404'
    },
    'MFCC1': {
        0: 'MFCC1',
        1: '3.502117',
        2: '6.697601',
        3: '4.011488',
        4: '0.823614'
    },
    'MFCC2': {
        0: 'MFCC2',
        1: '-9.208897',
        2: '-9.741549',
        3: '15.27665',
        4: '-15.22256'
    },
    'MFCC3': {
        0: 'MFCC3',
        1: '-2.334097',
        2: '-9.868089',
        3: '0.802509',
        4: '-4.978688'
    },
    'MFCC4': {
        0: 'MFCC4',
        1: '-9.013086',
        2: '0.609091',
        3: '2.50685',
        4: '-2.489553'
    },
    'MFCC5': {
        0: 'MFCC5',
        1: '4.847481',
        2: '1.733307',
        3: '0.10459',
        4: '1.066615'
    },
    'MFCC6': {
        0: 'MFCC6',
        1: '-4.770421',
        2: '-5.381835',
        3: '-0.260118',
        4: '-1.020861'
    },
    'MFCC7': {
        0: 'MFCC7',
        1: '-3.362488',
        2: '-1.261088',
        3: '0.593255',
        4: '-2.007349'
    },
    'MFCC8': {
        0: 'MFCC8',
        1: '-9.527529',
        2: '-3.809237',
        3: '-0.362287',
        4: '-8.938164'
    },
    'MFCC9': {
        0: 'MFCC9',
        1: '-9.629579',
        2: '1.486923',
        3: '-2.957592',
        4: '-2.324424'
    },
    'MFCC10': {
        0: 'MFCC10',
        1: '1.848685',
        2: '-3.938455',
        3: '-1.884439',
        4: '-2.535579'
    },
    'MFCC11': {
        0: 'MFCC11',
        1: '-2.311295',
        2: '-2.159865',
        3: '-0.827179',
        4: '0.638553'
    },
    'MFCC12': {
        0: 'MFCC12',
        1: '-7.696675',
        2: '-3.138412',
        3: '-0.605056',
        4: '-1.116259'
    },
    'MFCC13': {
        0: 'MFCC13',
        1: '10.35572',
        2: '9.095669',
        3: '6.426399',
        4: '15.04535'
    },
    'MFCCMin': {
        0: 'MFCCMin',
        1: '-9.629579',
        2: '-9.868089',
        3: '-2.957592',
        4: '-15.22256'
    },
    'MFCCMax': {
        0: 'MFCCMax',
        1: '10.35572',
        2: '9.095669',
        3: '15.27665',
        4: '15.04535'
    },
    'MFCCSum': {
        0: 'MFCCSum',
        1: '-37.300064',
        2: '-19.675939',
        3: '22.82507',
        4: '-23.059305'
    },
    'MFCCAvg': {
        0: 'MFCCAvg',
        1: '-2.869235692',
        2: '-1.513533769',
        3: '1.755774615',
        4: '-1.773792692'
    },
    'MFCCStd': {
        0: 'MFCCStd',
        1: '6.409842944',
        2: '5.558499123',
        3: '4.756836281',
        4: '6.76039911'
    },
    'Energy': {
        0: 'Energy',
        1: '-2.96148',
        2: '-3.522993',
        3: '-3.409359',
        4: '-2.235853'
    },
    'ZeroCrossings': {
        0: 'ZeroCrossings',
        1: '128',
        2: '188',
        3: '43',
        4: '288'
    },
    'SpecCentroid': {
        0: 'SpecCentroid',
        1: '284.0513',
        2: '414.8489',
        3: '102.2096',
        4: '405.1262'
    },
    'SpecSpread': {
        0: 'SpecSpread',
        1: '207.5526',
        2: '350.7937',
        3: '53.52178',
        4: '360.0353'
    },
    'Rolloff': {
        0: 'Rolloff',
        1: '263.7817',
        2: '783.2703',
        3: '129.1992',
        4: '912.4695'
    },
    'Flux': {
        0: 'Flux',
        1: '0',
        2: '0',
        3: '0',
        4: '0'
    },
    'bandsCoefMin': {
        0: 'bandsCoefMin',
        1: '-0.224957',
        2: '-0.247903',
        3: '-0.22283',
        4: '-0.232534'
    },
    'bandsCoefMax': {
        0: 'bandsCoefMax',
        1: '-0.074945',
        2: '-0.113654',
        3: '-0.062254',
        4: '-0.080883'
    },
    'bandsCoefSum1': {
        0: 'bandsCoefSum1',
        1: '-5.575428',
        2: '-5.524777',
        3: '-5.511125',
        4: '-5.532536'
    },
    'bandsCoefAvg': {
        0: 'bandsCoefAvg',
        1: '-0.168952364',
        2: '-0.167417485',
        3: '-0.167003788',
        4: '-0.167652606'
    },
    'bandsCoefStd': {
        0: 'bandsCoefStd',
        1: '0.042580181',
        2: '0.048429973',
        3: '0.049881374',
        4: '0.0475839'
    },
    'bandsCoefSum': {
        0: 'bandsCoefSum',
        1: '382.5963',
        2: '360.9232',
        3: '384.3541',
        4: '368.9903'
    },
    'prjmin': {
        0: 'prjmin',
        1: '-0.999362',
        2: '-0.999719',
        3: '-0.988315',
        4: '-0.999421'
    },
    'prjmax': {
        0: 'prjmax',
        1: '0.023797',
        2: '0.009596',
        3: '0.028112',
        4: '0.024612'
    },
    'prjSum': {
        0: 'prjSum',
        1: '-0.99911',
        2: '-1.006792',
        3: '-1.084054',
        4: '-1.002478'
    },
    'prjAvg': {
        0: 'prjAvg',
        1: '-0.030276061',
        2: '-0.030508848',
        3: '-0.032850121',
        4: '-0.030378121'
    },
    'prjStd': {
        0: 'prjStd',
        1: '0.174082468',
        2: '0.174040569',
        3: '0.173600498',
        4: '0.174064118'
    },
    'LogAttackTime': {
        0: 'LogAttackTime',
        1: '0.365883',
        2: '-0.35427',
        3: '-0.669283',
        4: '-0.026181'
    },
    'HamoPkMin': {
        0: 'HamoPkMin',
        1: '0',
        2: '0',
        3: '0',
        4: '0'
    },
    'HamoPkMax': {
        0: 'HamoPkMax',
        1: '1.025473',
        2: '1.05761',
        3: '0.986766',
        4: '0.957316'
    },
    'HamoPkSum': {
        0: 'HamoPkSum',
        1: '14.391206',
        2: '20.306125',
        3: '9.727358',
        4: '14.772449'
    },
    'HamoPkAvg': {
        0: 'HamoPkAvg',
        1: '0.513971643',
        2: '0.72521875',
        3: '0.347405643',
        4: '0.527587464'
    },
    'HamoPkStd': {
        0: 'HamoPkStd',
        1: '0.376622124',
        2: '0.325929503',
        3: '0.388971641',
        4: '0.381693476'
    },
    'class1': {
        0: 'class1',
        1: 'aerophone',
        2: 'aerophone',
        3: 'chordophone',
        4: 'aerophone'
    },
    'class2': {
        0: 'class2',
        1: 'aero_single-reed',
        2: 'aero_lip-vibrated',
        3: 'chrd_simple',
        4: 'aero_single-reed'
    },
    'mix1_instrument': {
        0: 'mix1_instrument',
        1: 'Saxophone',
        2: 'Trumpet',
        3: 'Piano',
        4: 'Clarinet'
    }
}

谢谢

Answer 1

这是一个小演示:

来源 DF:

In [43]: df
Out[43]:
     Energy  HamoPkStd       class1             class2 mix1_instrument
0 -2.961480  14.391206    aerophone   aero_single-reed       Saxophone
1 -3.522993  20.306125  chordophone  aero_lip-vibrated         Trumpet
2 -3.409359   9.727358    aerophone        chrd_simple           Piano

标签编码:

In [44]: %paste
from sklearn.preprocessing import LabelBinarizer, LabelEncoder

str_cols = df.columns[df.columns.str.contains('(?:class|instrument)')]
clfs = {c:LabelEncoder() for c in str_cols}

for col, clf in clfs.items():
    df[col] = clfs[col].fit_transform(df[col])
## -- End pasted text --

结果 - 所有文本/字符串列都已转换为数字，因此我们可以将其提供给神经网络:

In [45]: df
Out[45]:
     Energy  HamoPkStd  class1  class2  mix1_instrument
0 -2.961480  14.391206       0       1                1
1 -3.522993  20.306125       1       0                2
2 -3.409359   9.727358       0       2                0

逆变换:

In [48]: clfs['class1'].inverse_transform(df['class1'])
Out[48]: array(['aerophone', 'chordophone', 'aerophone'], dtype=object)

In [49]: clfs['mix1_instrument'].inverse_transform(df['mix1_instrument'])
Out[49]: array(['Saxophone', 'Trumpet', 'Piano'], dtype=object)