def binary_cp(n_train=100,
C=1,
dataset=1,
max_iter=1000,
n_inference_iter=5,
check_dual_every=10,
test_samples=100,
inference_method='gco'):
# save parameters as meta
meta_data = locals()
logger = logging.getLogger(__name__)
crf = HCRF(n_states=2,
n_features=10,
n_edge_features=1,
alpha=1,
inference_method=inference_method,
n_iter=n_inference_iter)
clf = OneSlackSSVM(crf, verbose=2, n_jobs=4, max_iter=max_iter, C=C)
x_train, y_train, x_test, y_test = load_binary_syntetic(dataset, n_train)
logger.info('start training')
start = time()
clf.fit(x_train,
y_train,
train_scorer=lambda w: compute_score(
crf, w, x_train, y_train, invert=False),
test_scorer=lambda w: compute_score(
crf, w, x_test, y_test, invert=False))
stop = time()
train_score = clf.score(x_train, y_train)
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('Elapsed time: %f s', time_elapsed)
exp_data = {}
exp_data['train_scores'] = clf.train_scores
exp_data['test_scores'] = clf.test_scores
meta_data['dataset_name'] = 'syntetic binary'
meta_data['annotation_type'] = 'full'
meta_data['label_type'] = 'full'
meta_data['trainer'] = 'cutting plane'
meta_data['train_score'] = train_score
meta_data['test_score'] = test_score
meta_data['time_elapsed'] = time_elapsed
return ExperimentResult(exp_data, meta_data)
def binary_over(n_train=100, C=1, dataset=1,
max_iter=100, verbose=1,
test_samples=100, check_every=10,
test_method='gco', test_n_iter=5, relaxed_test=False):
# save parameters as meta
meta_data = locals()
logger = logging.getLogger(__name__)
crf = HCRF(n_states=2, n_features=10, n_edge_features=1, alpha=1,
inference_method=test_method, n_iter=test_n_iter)
trainer = Over(n_states=2, n_features=10, n_edge_features=1,
C=C, max_iter=max_iter, verbose=verbose, check_every=check_every)
x_train, y_train, x_test, y_test = \
load_binary_syntetic(dataset, n_train)
x_test = x_test[:test_samples]
y_test = y_test[:test_samples]
logger.info('start training')
start = time()
trainer.fit(x_train, y_train,
train_scorer=lambda w: compute_score(crf, w, x_train, y_train, invert=True, relaxed=relaxed_test),
test_scorer=lambda w: compute_score(crf, w, x_test, y_test, invert=True, relaxed=relaxed_test))
stop = time()
time_elapsed = stop - start
logger.info('testing')
test_score = compute_score(crf, trainer.w, x_test, y_test, invert=True, relaxed=relaxed_test)
train_score = compute_score(crf, trainer.w, x_train, y_train, invert=True, relaxed=relaxed_test)
logger.info('========================================')
logger.info('train score: %f', train_score)
logger.info('test score: %f', test_score)
exp_data = {}
exp_data['timestamps'] = trainer.timestamps
exp_data['objective'] = trainer.objective_curve
exp_data['w'] = trainer.w
exp_data['train_scores'] = trainer.train_score
exp_data['test_scores'] = trainer.test_score
exp_data['w_history'] = trainer.w_history
meta_data['dataset_name'] = 'syntetic binary'
meta_data['annotation_type'] = 'full'
meta_data['label_type'] = 'full'
meta_data['trainer'] = 'komodakis'
meta_data['train_score'] = train_score
meta_data['test_score'] = test_score
meta_data['time_elapsed'] = time_elapsed
return ExperimentResult(exp_data, meta_data)
def binary_full_fw(n_train=100, C=1, dataset=1,
max_iter=1000, n_inference_iter=5,
check_dual_every=10, test_samples=100,
inference_method='gco'):
# save parameters as meta
meta_data = locals()
logger = logging.getLogger(__name__)
crf = HCRF(n_states=2, n_features=10, n_edge_features=1, alpha=1,
inference_method=inference_method, n_iter=n_inference_iter)
clf = FrankWolfeSSVM(crf, verbose=2, n_jobs=1, check_dual_every=check_dual_every,
max_iter=max_iter, C=C)
x_train, y_train, x_test, y_test = \
load_binary_syntetic(dataset, n_train)
logger.info('start training')
start = time()
clf.fit(x_train, y_train, Xtest=x_test[:test_samples], Ytest=y_test[:test_samples])
stop = time()
train_score = clf.score(x_train, y_train)
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('Elapsed time: %f s', time_elapsed)
exp_data = {}
exp_data['timestamps'] = clf.timestamps_
exp_data['primal_objective'] = clf.primal_objective_curve_
exp_data['objective'] = clf.objective_curve_
exp_data['w_history'] = clf.w_history
exp_data['test_scores'] = clf.test_scores
exp_data['train_scores'] = clf.train_scores
exp_data['w'] = clf.w
meta_data['dataset_name'] = 'syntetic binary'
meta_data['annotation_type'] = 'full'
meta_data['label_type'] = 'full'
meta_data['trainer'] = 'frank-wolfe'
meta_data['train_score'] = train_score
meta_data['test_score'] = test_score
meta_data['time_elapsed'] = time_elapsed
return ExperimentResult(exp_data, meta_data)
def binary_cp(n_train=100, C=1, dataset=1,
max_iter=1000, n_inference_iter=5,
check_dual_every=10, test_samples=100,
inference_method='gco'):
# save parameters as meta
meta_data = locals()
logger = logging.getLogger(__name__)
crf = HCRF(n_states=2, n_features=10, n_edge_features=1, alpha=1,
inference_method=inference_method, n_iter=n_inference_iter)
clf = OneSlackSSVM(crf, verbose=2, n_jobs=4,
max_iter=max_iter, C=C)
x_train, y_train, x_test, y_test = load_binary_syntetic(dataset, n_train)
logger.info('start training')
start = time()
clf.fit(x_train, y_train,
train_scorer=lambda w: compute_score(crf, w, x_train, y_train, invert=False),
test_scorer=lambda w: compute_score(crf, w, x_test, y_test, invert=False))
stop = time()
train_score = clf.score(x_train, y_train)
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('Elapsed time: %f s', time_elapsed)
exp_data = {}
exp_data['train_scores'] = clf.train_scores
exp_data['test_scores'] = clf.test_scores
meta_data['dataset_name'] = 'syntetic binary'
meta_data['annotation_type'] = 'full'
meta_data['label_type'] = 'full'
meta_data['trainer'] = 'cutting plane'
meta_data['train_score'] = train_score
meta_data['test_score'] = test_score
meta_data['time_elapsed'] = time_elapsed
return ExperimentResult(exp_data, meta_data)
def binary_over(n_train=100,
C=1,
dataset=1,
max_iter=100,
verbose=1,
test_samples=100,
check_every=10,
test_method='gco',
test_n_iter=5,
relaxed_test=False):
# save parameters as meta
meta_data = locals()
logger = logging.getLogger(__name__)
crf = HCRF(n_states=2,
n_features=10,
n_edge_features=1,
alpha=1,
inference_method=test_method,
n_iter=test_n_iter)
trainer = Over(n_states=2,
n_features=10,
n_edge_features=1,
C=C,
max_iter=max_iter,
verbose=verbose,
check_every=check_every)
x_train, y_train, x_test, y_test = \
load_binary_syntetic(dataset, n_train)
x_test = x_test[:test_samples]
y_test = y_test[:test_samples]
logger.info('start training')
start = time()
trainer.fit(
x_train,
y_train,
train_scorer=lambda w: compute_score(
crf, w, x_train, y_train, invert=True, relaxed=relaxed_test),
test_scorer=lambda w: compute_score(
crf, w, x_test, y_test, invert=True, relaxed=relaxed_test))
stop = time()
time_elapsed = stop - start
logger.info('testing')
test_score = compute_score(crf,
trainer.w,
x_test,
y_test,
invert=True,
relaxed=relaxed_test)
train_score = compute_score(crf,
trainer.w,
x_train,
y_train,
invert=True,
relaxed=relaxed_test)
logger.info('========================================')
logger.info('train score: %f', train_score)
logger.info('test score: %f', test_score)
exp_data = {}
exp_data['timestamps'] = trainer.timestamps
exp_data['objective'] = trainer.objective_curve
exp_data['w'] = trainer.w
exp_data['train_scores'] = trainer.train_score
exp_data['test_scores'] = trainer.test_score
exp_data['w_history'] = trainer.w_history
meta_data['dataset_name'] = 'syntetic binary'
meta_data['annotation_type'] = 'full'
meta_data['label_type'] = 'full'
meta_data['trainer'] = 'komodakis'
meta_data['train_score'] = train_score
meta_data['test_score'] = test_score
meta_data['time_elapsed'] = time_elapsed
return ExperimentResult(exp_data, meta_data)
def binary_full_fw(n_train=100,
C=1,
dataset=1,
max_iter=1000,
n_inference_iter=5,
check_dual_every=10,
test_samples=100,
inference_method='gco'):
# save parameters as meta
meta_data = locals()
logger = logging.getLogger(__name__)
crf = HCRF(n_states=2,
n_features=10,
n_edge_features=1,
alpha=1,
inference_method=inference_method,
n_iter=n_inference_iter)
clf = FrankWolfeSSVM(crf,
verbose=2,
n_jobs=1,
check_dual_every=check_dual_every,
max_iter=max_iter,
C=C)
x_train, y_train, x_test, y_test = \
load_binary_syntetic(dataset, n_train)
logger.info('start training')
start = time()
clf.fit(x_train,
y_train,
Xtest=x_test[:test_samples],
Ytest=y_test[:test_samples])
stop = time()
train_score = clf.score(x_train, y_train)
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('Elapsed time: %f s', time_elapsed)
exp_data = {}
exp_data['timestamps'] = clf.timestamps_
exp_data['primal_objective'] = clf.primal_objective_curve_
exp_data['objective'] = clf.objective_curve_
exp_data['w_history'] = clf.w_history
exp_data['test_scores'] = clf.test_scores
exp_data['train_scores'] = clf.train_scores
exp_data['w'] = clf.w
meta_data['dataset_name'] = 'syntetic binary'
meta_data['annotation_type'] = 'full'
meta_data['label_type'] = 'full'
meta_data['trainer'] = 'frank-wolfe'
meta_data['train_score'] = train_score
meta_data['test_score'] = test_score
meta_data['time_elapsed'] = time_elapsed
return ExperimentResult(exp_data, meta_data)
