def create_model(result):
meta = result.meta
alpha = meta['alpha']
n_inference_iter = meta['n_inference_iter']
max_iter = meta['max_iter']
C = meta['C']
inner_tol = meta['inner_tol']
inactive_window = meta['inactive_window']
inactive_threshold = meta['inactive_threshold']
latent_iter = meta['latent_iter']
outer_tol = meta['outer_tol']
min_changes = meta['min_changes']
try:
inference_cache = meta['inference_cache']
except:
inference_cache = 0
crf = None
if meta['dataset_name'] == 'syntetic':
crf = HCRF(n_states=10, n_features=10, n_edge_features=2, alpha=alpha,
inference_method='gco', n_iter=n_inference_iter)
elif meta['dataset_name'] == 'msrc':
crf = HCRF(n_states=24, n_features=2028, n_edge_features=4, alpha=alpha,
inference_method='gco', n_iter=n_inference_iter)
base_clf = OneSlackSSVM(crf, max_iter=max_iter, C=C, verbose=0,
tol=inner_tol, n_jobs=4, inference_cache=inference_cache,
inactive_window=inactive_window,
inactive_threshold=inactive_threshold)
clf = LatentSSVM(base_clf, latent_iter=latent_iter, verbose=2,
tol=outer_tol, min_changes=min_changes, n_jobs=4)
return clf
def syntetic_weak(n_full=10,
n_train=200,
C=0.1,
dataset=1,
latent_iter=15,
max_iter=500,
inner_tol=0.001,
outer_tol=0.01,
min_changes=0,
initialize=True,
alpha=0.1,
n_inference_iter=5,
inactive_window=50,
inactive_threshold=1e-5,
warm_start=False,
inference_cache=0,
save_inner_w=False,
inference_method='gco'):
# save parameters as meta
meta_data = locals()
logger = logging.getLogger(__name__)
crf = HCRF(n_states=10,
n_features=10,
n_edge_features=2,
alpha=alpha,
inference_method=inference_method,
n_iter=n_inference_iter)
base_clf = OneSlackSSVM(crf,
verbose=2,
n_jobs=4,
max_iter=max_iter,
tol=inner_tol,
C=C,
inference_cache=inference_cache,
inactive_window=inactive_window,
inactive_threshold=inactive_threshold)
clf = LatentSSVM(base_clf,
latent_iter=latent_iter,
verbose=2,
tol=outer_tol,
min_changes=min_changes,
n_jobs=4)
x_train, y_train, y_train_full, x_test, y_test = \
load_syntetic(dataset, n_full, n_train)
start = time()
clf.fit(x_train,
y_train,
initialize=initialize,
warm_start=warm_start,
save_inner_w=save_inner_w)
stop = time()
train_score = clf.score(x_train, y_train_full)
test_score = clf.score(x_test, y_test)
time_elapsed = stop - start
logger.info('============================================================')
logger.info('Score on train set: %f', train_score)
logger.info('Score on test set: %f', test_score)
logger.info('Norm of weight vector: |w|=%f', np.linalg.norm(clf.w))
logger.info('Elapsed time: %f s', time_elapsed)
test_scores = []
for score in clf.staged_score(x_test, y_test):
test_scores.append(score)
train_scores = []
for score in clf.staged_score(x_train, y_train_full):
train_scores.append(score)
raw_scores = []
for score in clf.staged_score2(x_train, y_train):
raw_scores.append(score)
exp_data = clf._get_data()
exp_data['test_scores'] = np.array(test_scores)
exp_data['train_scores'] = np.array(train_scores)
exp_data['raw_scores'] = np.array(raw_scores)
meta_data['dataset_name'] = 'syntetic'
meta_data['annotation_type'] = 'image-level labelling'
meta_data['label_type'] = 'full+weak'
meta_data['train_score'] = train_score
meta_data['test_score'] = test_score
meta_data['time_elapsed'] = time_elapsed
meta_data['iter_done'] = clf.iter_done
return ExperimentResult(exp_data, meta_data)
