为训练和测试数据集做CV时出错

Question

我试图为我的培训和测试数据集做简历。我正在使用LinearRegressor。但是，当我运行代码时，我会得到下面的错误。怎么解决这个问题？我的简历部分代码正确吗？谢谢你的help.......................................................

简历代码参考：scikit-learn cross_validation over-fitting or under-fitting

    X_normalized, y_for_normalized = scaled_df[[ "Part's Z-Height (mm)","Part's Solid Volume (cm^3)","Layer Height (mm)","Printing/Scanning Speed (mm/s)","Part's Orientation (Support's volume) (cm^3)"]], scaled_df [["Climate change (kg CO2 eq.)","Climate change, incl biogenic carbon (kg CO2 eq.)","Fine Particulate Matter Formation (kg PM2.5 eq.)","Fossil depletion (kg oil eq.)","Freshwater Consumption (m^3)","Freshwater ecotoxicity (kg 1,4-DB eq.)","Freshwater Eutrophication (kg P eq.)","Human toxicity, cancer (kg 1,4-DB eq.)","Human toxicity, non-cancer (kg 1,4-DB eq.)","Ionizing Radiation (Bq. C-60 eq. to air)","Land use (Annual crop eq. yr)","Marine ecotoxicity (kg 1,4-DB eq.)","Marine Eutrophication (kg N eq.)","Metal depletion (kg Cu eq.)","Photochemical Ozone Formation, Ecosystem (kg NOx eq.)","Photochemical Ozone Formation, Human Health (kg NOx eq.)","Stratospheric Ozone Depletion (kg CFC-11 eq.)","Terrestrial Acidification (kg SO2 eq.)","Terrestrial ecotoxicity (kg 1,4-DB eq.)"]]. 

Part's Z-Height (mm)    Part's Solid Volume (cm^3)  Layer Height (mm)   Printing/Scanning Speed (mm/s)  Part's Orientation (Support's volume) (cm^3)    Climate change (kg CO2 eq.) Climate change, incl biogenic carbon (kg CO2 eq.)   Fine Particulate Matter Formation (kg PM2.5 eq.)    Fossil depletion (kg oil eq.)   Freshwater Consumption (m^3)    Freshwater ecotoxicity (kg 1,4-DB eq.)  Freshwater Eutrophication (kg P eq.)    Human toxicity, cancer (kg 1,4-DB eq.)  Human toxicity, non-cancer (kg 1,4-DB eq.)  Ionizing Radiation (Bq. C-60 eq. to air)    Land use (Annual crop eq. yr)   Marine ecotoxicity (kg 1,4-DB eq.)  Marine Eutrophication (kg N eq.)    Metal depletion (kg Cu eq.) Photochemical Ozone Formation, Ecosystem (kg NOx eq.)   Photochemical Ozone Formation, Human Health (kg NOx eq.)    Stratospheric Ozone Depletion (kg CFC-11 eq.)   Terrestrial Acidification (kg SO2 eq.)  Terrestrial ecotoxicity (kg 1,4-DB eq.)
0   0.258287    0.005030    0.0 0.666667    0.040088    0.069825    0.056976    0.083205    0.010373    0.113808    0.104798    0.086400    0.110358    0.012836    0.091120    0.108676    0.090401    0.087426    0.125608    0.079028    0.080495    0.078380    0.082404    0.045040
1   0.258287    0.005030    0.2 0.666667    0.036597    0.041682    0.022880    0.074884    0.004841    0.045640    0.102285    0.082884    0.044202    0.005414    0.086700    0.105749    0.087161    0.084130    0.060373    0.072878    0.073529    0.074829    0.075438    0.018122
2   0.258287    0.009557    0.4 0.666667    0.031013    0.033310    0.012113    0.073035    0.003458    0.023401    0.102914    0.082494    0.022690    0.003231    0.086279    0.105749    0.086937    0.084130    0.039708    0.071341    0.071981    0.074698    0.073447    0.009856
3   0.258287    0.009054    0.6 0.666667    0.031013    0.029213    0.006954    0.072111    0.002766    0.012936    0.102914    0.082103    0.012524    0.001921    0.086069    0.105423    0.086602    0.084130    0.029579    0.070572    0.071207    0.074435    0.072452    0.005723
4   0.258287    0.010060    1.0 0.666667    0.031711    0.025650    0.001795    0.071803    0.003458    0.002180    0.103542    0.082884    0.002063    0.001048    0.086490    0.106074    0.087049    0.084542    0.019449    0.070572    0.071207    0.074961    0.072452    0.001908
5   0.258287    0.005030    0.0 0.000000    0.040088    0.074279    0.062360    0.084129    0.011065    0.125000    0.104798    0.086790    0.121114    0.014146    0.091330    0.108676    0.091519    0.087426    0.136143    0.080566    0.081269    0.078511    0.083400    0.049385
6   0.258287    0.038226    0.0 0.666667    0.040088    0.097791    0.074249    0.109091    0.038036    0.135174    0.129299    0.111788    0.132164    0.024625    0.116582    0.133725    0.116102    0.112970    0.154781    0.105166    0.106037    0.104419    0.108280    0.064222
7   0.137212    0.004527    0.0 0.666667    0.030314    0.058247    0.046433    0.076117    0.003458    0.095349    0.099144    0.080150    0.092382    0.008907    0.084806    0.102821    0.084702    0.081246    0.106159    0.072878    0.073529    0.072199    0.075438    0.035608
8   0.137212    0.004527    0.2 0.666667    0.029616    0.035269    0.017721    0.069954    0.000000    0.037355    0.098516    0.078197    0.036246    0.002794    0.082281    0.101520    0.082803    0.080010    0.051053    0.068266    0.068885    0.070489    0.070462    0.013247
9   0.137212    0.010060    0.4 0.666667    0.028918    0.031706    0.010543    0.072111    0.002766    0.020494    0.102285    0.081712    0.019891    0.002358    0.085438    0.104773    0.086043    0.083306    0.036467    0.070572    0.071207    0.073908    0.072452    0.008372
10  0.137212    0.010060    0.6 0.666667    0.028220    0.027431    0.005384    0.070878    0.001383    0.010320    0.101657    0.080931    0.010019    0.001484    0.084806    0.104448    0.085373    0.082894    0.026742    0.069803    0.070433    0.073251    0.071457    0.004345
11  0.137212    0.009557    1.0 0.666667    0.027522    0.022800    0.000000    0.069029    0.000000    0.000000    0.101029    0.080150    0.000000    0.000000    0.083754    0.103472    0.084367    0.081658    0.016613    0.068266    0.068885    0.072330    0.070462    0.000000
12  0.137212    0.004527    0.0 0.000000    0.030314    0.062879    0.052266    0.077042    0.004149    0.107122    0.099144    0.080541    0.103875    0.010217    0.085227    0.102821    0.085037    0.081658    0.117099    0.073647    0.074303    0.072462    0.076433    0.040165
13  0.137212    0.037723    0.0 0.666667    0.030314    0.085857    0.063257    0.102003    0.031120    0.116134    0.123645    0.105929    0.112568    0.020695    0.110269    0.127544    0.110515    0.106790    0.134522    0.098247    0.099071    0.097843    0.101314    0.053624
14  0.077118    0.004527    0.0 0.666667    0.054050    0.080335    0.064827    0.091217    0.018672    0.126453    0.111709    0.093821    0.122145    0.016766    0.098485    0.115833    0.098223    0.094842    0.139789    0.087485    0.088235    0.085876    0.090366    0.052777
15  0.077118    0.004527    0.0 0.000000    0.054050    0.085144    0.070884    0.092450    0.019364    0.138081    0.111709    0.094211    0.133638    0.018075    0.099116    0.116158    0.098223    0.094842    0.151135    0.088253    0.089009    0.086139    0.091361    0.057864
16  0.077118    0.004527    0.0 0.333333    0.054050    0.082472    0.067519    0.091834    0.019364    0.132267    0.111709    0.094211    0.127744    0.017639    0.098695    0.116158    0.098223    0.094842    0.144652    0.087485    0.088235    0.086007    0.091361    0.054684

     lin_regressor = LinearRegression()
    
    # pass the order of your polynomial here  
    poly = PolynomialFeatures(1)
    
    # convert to be used further to linear regression
    X_transform = poly.fit_transform(x_train)
    
    # fit this to Linear Regressor
    linear_regg=lin_regressor.fit(X_transform,y_train).                                               

     import numpy as np
from sklearn.metrics import SCORERS
from sklearn.model_selection import KFold

scorer = SCORERS['r2']

cv = KFold(n_splits=5, random_state=0,shuffle=True)
train_scores, test_scores = [], []

for train, test in cv.split(X_normalized):
    X_transform2 = poly.fit_transform(X_normalized)
    OL=lin_regressor.fit(X_transform2.iloc[train], y_for_normalized.iloc[train])
    tr_21 = OL.score(X_train, y_train)
    ts_21 = OL.score(X_test, y_test)
    print ("Train score:", tr_21) # from documentation .score returns r^2
    print ("Test score:", ts_21)   # from documentation .score returns r^2
    
    train_scores.append(tr_21)
    test_scores.append(ts_21)



print ("The Mean for Train scores is:",(np.mean(train_scores)))
    
print ("The Mean for Test scores is:",(np.mean(test_scores)))

“”“

    --------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/var/folders/mm/r4gnnwl948zclfyx12w803040000gn/T/ipykernel_73165/2276765730.py in <module>
     10 for train, test in cv.split(X_normalized):
     11     X_transform2 = poly.fit_transform(X_normalized)
---> 12     OL=lin_regressor.fit(X_transform2.iloc[train], y_for_normalized.iloc[train])
     13     tr_21 = OL.score(X_train, y_train)
     14     ts_21 = OL.score(X_test, y_test)

AttributeError: 'numpy.ndarray' object has no attribute 'iloc'

“”“

Answer 1

尝尝这个

from sklearn.model_selection import KFold
model=LinearRegression()
kfold_validation=KFold(10)

import numpy as np
from sklearn.model_selection import cross_val_score

results=cross_val_score(model,X,y,cv=kfold_validation)
print(results)
print(np.mean(results))

Answer 2

以下是已编辑的代码

import numpy as np
import pandas as pd
from sklearn.metrics import SCORERS
from sklearn.model_selection import KFold

scorer = SCORERS['r2']

cv = KFold(n_splits=5, random_state=0,shuffle=True)
train_scores, test_scores = [], []

for train, test in cv.split(X_normalized):
    X_transform2 = poly.fit_transform(X_normalized)
    OL=lin_regressor.fit(X_transform2, y_for_normalized)
    tr_21 = OL.score(X_train, y_train)
    ts_21 = OL.score(X_test, y_test)
    print ("Train score:", tr_21) # from documentation .score returns r^2
    print ("Test score:", ts_21)   # from documentation .score returns r^2
    
    train_scores.append(tr_21)
    test_scores.append(ts_21)



print ("The Mean for Train scores is:",(np.mean(train_scores)))
    
print ("The Mean for Test scores is:",(np.mean(test_scores)))

注意-我有些担心你的y_for_normalise是什么样子。因为我认为您的代码本身是正确的，您现在正在正确地可视化数据。检查这段代码。