Python/Sklearn - Wert Fehler: Konnte Zeichenfolge nicht in Gleitkomma konvertieren

Question

Ich versuche, einen kNN-Klassifikator über meine Datenmenge mit 10-fach CV auszuführen. Ich habe einige Erfahrungen mit Modellen in WEKA, aber ich habe Mühe, dies auf Sklearn zu übertragen.

Unten ist mein Code

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()) 

Ich erhalte diesen Fehler

ValueError: could not convert string to float: '' 

Wie kann ich dieses Attribut konvertieren? Und das enthält nützliche Informationen zum Klassifizieren meiner Instanzen, würde eine Konvertierung dies beeinflussen?

Es gibt zwei Attribute, die 'Objekt' sind, die ich brauche Umwandlung namens 'class1' glauben und klasse2'

Beispieldaten unter ...

{ 
    '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' 
    } 
} 

Dank

Answer 1

Hier a kleine Demo:

Quelle 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 

Labels kodiert:

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 -- 

Ergebnis - alle Text/String-Spalten wurden in Zahlen umgewandelt, so dass wir es über Neuronale Netze speisen:

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 

Inverse transfomration:

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)