def test_gridminimise():
features = np.arange(100)
labels = np.tile((0, 1), 50)
paramspace = {
'a': np.arange(4),
'b': np.arange(-3, 3),
'c': np.linspace(2., 10)
}
best, value = gridminimise(simple_learner(),
features,
labels,
paramspace,
measure=(lambda _, p: p[0]),
return_value=True)
best = dict(best)
val = f(best['a'], best['b'], best['c'])
assert value == val * 100
for a in np.arange(4):
for b in np.arange(-3, 3):
for c in np.linspace(2., 10):
assert val <= f(a, b, c)
gs = gridsearch(simple_learner(),
paramspace,
measure=(lambda _, p: p[0]),
annotate=True)
model = gs.train(features, labels)
assert model.value == value
assert model.arguments == val
def m_svm():
if ARGV.one_vs:
svm_model = svm.svm_to_binary(svm.svm_raw())
else:
svm_model = multi.one_against_one(svm.svm_to_binary(svm.svm_raw()))
# return milk.defaultclassifier(mode='slow', multi_strategy='1-vs-1')
learner = milk.supervised.classifier.ctransforms(
# remove nans
supervised.normalise.chkfinite(),
# normalize to [-1,1]
supervised.normalise.interval_normalise(),
# feature selection
featureselection.featureselector(
featureselection.linear_independent_features),
# sda filter
featureselection.sda_filter(),
# same parameter range as 'medium'
supervised.gridsearch(
svm_model,
params = {
'C': 2.0 ** np.arange(-2, 4),
'kernel': [ svm.rbf_kernel(2.0 ** i) for i in xrange(-4, 4) ]
}
)
)
return learner
def test_gridminimise():
features = np.arange(100)
labels = np.tile((0,1), 50)
paramspace = { 'a': np.arange(4), 'b' : np.arange(-3,3), 'c' : np.linspace(2., 10) }
best,value = gridminimise(simple_learner(), features, labels, paramspace, measure=(lambda _, p: p[0]), return_value=True)
best = dict(best)
val = f(best['a'], best['b'], best['c'])
assert value == val*100
for a in np.arange(4):
for b in np.arange(-3,3):
for c in np.linspace(2., 10):
assert val <= f(a,b,c)
gs = gridsearch(simple_learner(), paramspace, measure=(lambda _, p: p[0]), annotate=True)
model = gs.train(features, labels)
assert model.value == value
assert model.arguments == val
def m_svm():
if ARGV.one_vs:
svm_model = svm.svm_to_binary(svm.svm_raw())
else:
svm_model = multi.one_against_one(svm.svm_to_binary(svm.svm_raw()))
# return milk.defaultclassifier(mode='slow', multi_strategy='1-vs-1')
learner = milk.supervised.classifier.ctransforms(
# remove nans
supervised.normalise.chkfinite(),
# normalize to [-1,1]
supervised.normalise.interval_normalise(),
# feature selection
featureselection.featureselector(
featureselection.linear_independent_features),
# sda filter
featureselection.sda_filter(),
# same parameter range as 'medium'
supervised.gridsearch(
svm_model,
params={
'C': 2.0**np.arange(-2, 4),
'kernel': [svm.rbf_kernel(2.0**i) for i in xrange(-4, 4)]
}))
return learner
