Car Evaluation Dataset

In [1]:

    

from sklearn import svm
from sklearn import cross_validation as cv
import pandas as pd

plt.style.use('ggplot')

from kcat.datasets import CarEvaluation
from kcat.kernels import helpers as kh
from kcat.kernels import search as ks


    

In [2]:

    

# Load the dataset
dataset = CarEvaluation()


    

In [3]:

    

data = {'Kernel': [], 'Test Error': []}
for i in range(5):
    print("Iteration {}".format(i))
    # Split train and test
    X_train, X_test, y_train, y_test = dataset.train_test_split(train_size=500, test_size=1000, random_state=i)
    # Cross-validation
    cvf = cv.StratifiedKFold(y_train, 10)
    # Evaluate models
    for model_class in kh.RBF, kh.K0, kh.K1, kh.M3, kh.M4, kh.M5, kh.M6, kh.M7, kh.M9, kh.MC, kh.MD, kh.ME:
        model = model_class()
        print(model.name, end=', ')
        best_fit = model.train(cvf, X_train, y_train)
        results = model.test(best_fit, X_test, y_test)
        data['Kernel'].append(model.name)
        data['Test Error'].append(1 - results['test_score'])
    print()


    

    




Iteration 0
RBF, K0, K1, M3, M4, M5, M6, M7, M9, MC, MD, ME, 
Iteration 1
RBF, K0, K1, M3, M4, M5, M6, M7, M9, MC, MD, ME, 
Iteration 2
RBF, K0, K1, M3, M4, M5, M6, M7, M9, MC, MD, ME, 
Iteration 3
RBF, K0, K1, M3, M4, M5, M6, M7, M9, MC, MD, ME, 
Iteration 4
RBF, K0, K1, M3, M4, M5, M6, M7, M9, MC, MD, ME, 

In [4]:

    

df = pd.DataFrame(data=data)
df.groupby('Kernel')['Test Error'].mean()


    

    
Out[4]:





Kernel
K0        0.0416
K1        0.0436
M3        0.0418
M4        0.0408
M5        0.0452
M6        0.0404
M7        0.0442
M9        0.0444
MC        0.0444
MD        0.0422
ME        0.0402
RBF       0.0416
Name: Test Error, dtype: float64

In [5]:

    

df.boxplot(by='Kernel');