In [1]:

    

import time

from sklearn import svm
from sklearn import cross_validation as cv

from kcat import datasets as ds
from kcat.kernels import functions as fn
from kcat.utils import pgen


    

In [2]:

    

X, Y, y = ds.Synthetic(1000, 25).data_arrays
Xpgen = pgen(X)
Xp = Xpgen(X)


    

In [3]:

    

# Test kernel k0:
clf = svm.SVC(kernel='precomputed')
t0 = time.time()
gram = fn.k0(Y, Y)
print("Execution time: {:0.3f} s".format(time.time() - t0))
scores = cv.cross_val_score(clf, gram, y, cv=2)
print("Mean score: {:0.3f}".format(scores.mean()))


    

    




Execution time: 0.402 s
Mean score: 0.956

In [5]:

    

# Test kernel k1:
clf = svm.SVC(kernel='precomputed')
t0 = time.time()
gram = fn.k1(X, X, Xp, Xp)
print("Execution time: {:0.3f} s".format(time.time() - t0))
scores = cv.cross_val_score(clf, gram, y, cv=2)
print("Mean score: {:0.3f}".format(scores.mean()))


    

    




Execution time: 0.124 s
Mean score: 0.966

In [6]:

    

# Test kernel k2:
clf = svm.SVC(kernel='precomputed')
t0 = time.time()
gram = fn.k2(X, X, Xp, Xp)
print("Execution time: {:0.3f} s".format(time.time() - t0))
scores = cv.cross_val_score(clf, gram, y, cv=2)
print("Mean score: {:0.3f}".format(scores.mean()))


    

    




Execution time: 0.168 s
Mean score: 0.972

In [8]:

    

# Test kernel m1:
clf = svm.SVC(kernel='precomputed')
t0 = time.time()
gram = fn.m3(X, X, Xp, Xp, alpha=1.5**-4, prev='f1', post='ident', gamma=2**-2)
print("Execution time: {:0.3f} s".format(time.time() - t0))
scores = cv.cross_val_score(clf, gram, y, cv=2)
print("Mean score: {:0.3f}".format(scores.mean()))


    

    




Execution time: 1.359 s
Mean score: 0.381

In [6]: