def test_bow(self):
model = Model('bow', self.gmm)
model.compute_kernels([self.tr_fn], [self.te_fn])
Kxx, Kyx = model.get_kernels()
evaluation = Evaluation(self.dataset.DATASET)
score = evaluation.fit(Kxx, self.cx).score(Kyx, self.cy)
assert_allclose(score, self.expected_scores['bow'], rtol=1e-4)
def test_bow(self):
model = Model('bow', self.gmm)
model.compute_kernels([self.tr_fn], [self.te_fn])
Kxx, Kyx = model.get_kernels()
evaluation = Evaluation(self.dataset.DATASET)
score = evaluation.fit(Kxx, self.cx).score(Kyx, self.cy)
assert_allclose(score, self.expected_scores['bow'], rtol=1e-4)
def evaluate_given_dataset(dataset, **kwargs):
model_type = kwargs.get('model_type', 'fv')
sstats_folder = dataset.SSTATS_DIR
tr_fn = os.path.join(sstats_folder, 'train.dat')
tr_labels_fn = os.path.join(sstats_folder, 'labels_train.info')
te_fn = os.path.join(sstats_folder, 'test.dat')
te_labels_fn = os.path.join(sstats_folder, 'labels_test.info')
gmm = load_gmm(dataset.GMM)
tr_labels = pickle.load(open(tr_labels_fn, 'r'))
te_labels = pickle.load(open(te_labels_fn, 'r'))
model = Model(model_type, gmm)
model.compute_kernels([tr_fn], [te_fn])
Kxx, Kyx = model.get_kernels()
evaluation = Evaluation(dataset.DATASET, **kwargs)
print evaluation.fit(Kxx, tr_labels).score(Kyx, te_labels)
def evaluate_given_dataset(dataset, **kwargs):
model_type = kwargs.get('model_type', 'fv')
sstats_folder = dataset.SSTATS_DIR
tr_fn = os.path.join(sstats_folder, 'train.dat')
tr_labels_fn = os.path.join(sstats_folder, 'labels_train.info')
te_fn = os.path.join(sstats_folder, 'test.dat')
te_labels_fn = os.path.join(sstats_folder, 'labels_test.info')
gmm = load_gmm(dataset.GMM)
tr_labels = pickle.load(open(tr_labels_fn, 'r'))
te_labels = pickle.load(open(te_labels_fn, 'r'))
model = Model(model_type, gmm)
model.compute_kernels([tr_fn], [te_fn])
Kxx, Kyx = model.get_kernels()
evaluation = Evaluation(dataset.DATASET, **kwargs)
print evaluation.fit(Kxx, tr_labels).score(Kyx, te_labels)
