#compute normalized homogeneous polynomial kernels with degrees 0,1,2,...,10.
print('computing monotone Conjunctive Kernels...', end='')
from MKLpy.metrics import pairwise
from MKLpy.preprocessing import kernel_normalization
#WARNING: the maximum arity of the conjunctive kernel depends on the number of active variables for each example,
# that is 4 in the case of iris dataset binarized
KL = [
kernel_normalization(pairwise.monotone_conjunctive_kernel(Xbin, c=c))
for c in range(5)
]
print('done')
#train/test KL split (N.B. here we split a kernel list directly)
from MKLpy.model_selection import train_test_split
KLtr, KLte, Ytr, Yte = train_test_split(KL, Y, test_size=.3, random_state=42)
#MKL algorithms
from MKLpy.algorithms import EasyMKL, KOMD #KOMD is not a MKL algorithm but a simple kernel machine like the SVM
from MKLpy.model_selection import cross_val_score, cross_val_predict
from sklearn.svm import SVC
import numpy as np
print('tuning lambda for EasyMKL...', end='')
base_learner = SVC(C=10000) #simil hard-margin svm
best_results = {}
for lam in [0, 0.01, 0.1, 0.2, 0.9,
1]: #possible lambda values for the EasyMKL algorithm
#MKLpy.model_selection.cross_val_predict performs the cross validation automatically, it optimizes the accuracy
#the counterpart cross_val_score optimized the roc_auc_score (use score='roc_auc')
#WARNING: these functions will change in the next version
scores = cross_val_predict(KLtr,
binarizer = binarization.AverageBinarizer()
Xbin = binarizer.fit_transform(X,Y)
print ('done')
#compute normalized homogeneous polynomial kernels with degrees 0,1,2,...,10.
print ('computing monotone Conjunctive Kernels...', end='')
from MKLpy.metrics import pairwise
from MKLpy.preprocessing import kernel_normalization
#WARNING: the maximum arity of the conjunctive kernel depends on the number of active variables for each example,
# that is 4 in the case of iris dataset binarized
KL = [kernel_normalization(pairwise.monotone_conjunctive_kernel(Xbin, c=c)) for c in range(5)]
print ('done')
#train/test KL split (N.B. here we split a kernel list directly)
from MKLpy.model_selection import train_test_split
KLtr,KLte,Ytr,Yte = train_test_split(KL, Y, test_size=.3, random_state=42)
#MKL algorithms
from MKLpy.algorithms import EasyMKL, KOMD #KOMD is not a MKL algorithm but a simple kernel machine like the SVM
from MKLpy.model_selection import cross_val_score, cross_val_predict
from sklearn.svm import SVC
import numpy as np
print ('tuning lambda for EasyMKL...', end='')
base_learner = SVC(C=10000) #simil hard-margin svm
best_results = {}
for lam in [0, 0.01, 0.1, 0.2, 0.9, 1]: #possible lambda values for the EasyMKL algorithm
#MKLpy.model_selection.cross_val_predict performs the cross validation automatically, it optimizes the accuracy
#the counterpart cross_val_score optimized the roc_auc_score (use score='roc_auc')
#WARNING: these functions will change in the next version
scores = cross_val_predict(KLtr, Ytr, EasyMKL(estimator=base_learner, lam=lam), n_folds=5, score='accuracy')
acc = np.mean(scores)