def __init__(self, ks, base=None, R=None):
if base is None:
base = defaultlearner()
self.ks = ks
self.R = R
self.base = base
self.normalise = True
def __init__(self, ks, base=None, R=None):
if base is None:
base = defaultlearner()
self.ks = ks
self.R = R
self.base = base
self.normalise = True
def test_empty_input():
learn = milk.defaultlearner()
X = np.random.rand(60, 3)
X[:32] += .52
y = np.arange(60) > 35
model = learn.train(X, y)
preds = model.apply_many([])
assert len(preds) == 0
def test_empty_input():
learn = milk.defaultlearner()
X = np.random.rand(60, 3)
X[:32] += .52
y = np.arange(60) > 35
model = learn.train(X, y)
preds = model.apply_many([])
assert len(preds) == 0
def test_expandend():
np.random.seed(23232432)
X = np.random.rand(100,10)
labels = np.zeros(100)
X[50:] += .5
labels[50:] = 1
learners = milk.defaultlearner(expanded=True)
for learner in learners:
model = learner.train(X, labels)
test = [model.apply(x) for x in X]
test = np.array(test)
assert set(test) == set(labels)
def train(self, features, labels, **kwargs):
from milk.supervised.gridsearch import gridminimise
from milk.supervised import svm
c_features = np.concatenate([f for f,_ in features if f.size])
c_features = c_features[::self.sample]
learner = milk.defaultlearner()
k = (self.k if self.k is not None else len(features)//self.kfrac)
_,codebook = milk.kmeans(c_features, k=k, R=123)
features = project.f(features, codebook)
model = learner.train(features, labels)
return codebook_model(codebook, model)
def test_expandend():
np.random.seed(23232432)
X = np.random.rand(100, 10)
labels = np.zeros(100)
X[50:] += .5
labels[50:] = 1
learners = milk.defaultlearner(expanded=True)
for learner in learners:
model = learner.train(X, labels)
test = [model.apply(x) for x in X]
test = np.array(test)
assert set(test) == set(labels)
def train(self, features, labels, R=134, **kwargs):
from milk.supervised.gridsearch import gridminimise
from milk.supervised import svm
c_features = np.concatenate([f for f, _ in features if f.size])
c_features = c_features[::self.sample]
learner = milk.defaultlearner()
k = (self.k if self.k is not None else len(features) // self.kfrac)
_, codebook = milk.kmeans(c_features, k=k, R=R)
features = project.f(features, codebook)
model = learner.train(features, labels)
return codebook_model(codebook, model)
def test_pickle_learner():
learner = milk.defaultlearner()
assert len(pickle.dumps(learner))
import pickle
import sys
import milk
import milk.supervised
import milk.supervised.adaboost
import milk.supervised.multi
assert(sys.argv[1])
features = pickle.load(open(sys.argv[1], 'r'))
#learner = milk.supervised.tree_learner()
#learner = milk.supervised.adaboost.boost_learner(weak)
#learner = milk.supervised.multi.one_against_one(learner)
learner = milk.defaultlearner(mode='really-slow')
model = learner.train(*features)
pickle.dump(model, open('trainer.pik', 'w'))
cmat,names, preds = milk.nfoldcrossvalidation(*features,
classifier=learner,
return_predictions=1)
print cmat
print names
print preds
def test_extra_arg():
from milksets.wine import load
features, labels = load()
learner = milk.defaultlearner()
model = learner.train(features[::2], labels[::2], extra_arg=5)
assert model.apply(features[1]) < 12.
def __init__(self, ks, base=None):
if base is None:
base = defaultlearner()
self.ks = ks
self.base = base
def __init__(self, ks, base=None):
if base is None:
base = defaultlearner()
self.ks = ks
self.base = base
def test_extra_arg():
from milksets.wine import load
features,labels = load()
learner = milk.defaultlearner()
model = learner.train(features[::2],labels[::2], extra_arg=5)
assert model.apply(features[1]) < 12.
def test_pickle_learner():
learner = milk.defaultlearner()
assert len(pickle.dumps(learner))