def evaluate(
src_cfg, tr_norms, te_norms, analytical_fim, pi_derivatives, sqrt_nr_descs,
do_plot=False, verbose=0):
outfile = os.path.join(
CACHE_PATH, "%s_%s_afim_%s_pi_%s_sqrt_nr_descs_%s.dat" % (
src_cfg, "%s", analytical_fim, pi_derivatives, sqrt_nr_descs))
dataset = Dataset(CFG[src_cfg]['dataset_name'], **CFG[src_cfg]['dataset_params'])
(tr_kernel, tr_labels,
te_kernel, te_labels) = load_kernels(
dataset, tr_norms=tr_norms, te_norms=te_norms,
analytical_fim=analytical_fim, pi_derivatives=pi_derivatives,
sqrt_nr_descs=sqrt_nr_descs, outfile=outfile, do_plot=do_plot,
verbose=verbose)
eval = Evaluation(CFG[src_cfg]['eval_name'], **CFG[src_cfg]['eval_params'])
eval.fit(tr_kernel, tr_labels)
scores = eval.score(te_kernel, te_labels)
if verbose > 0:
print 'Train normalizations:', ', '.join(map(str, tr_norms))
print 'Test normalizations:', ', '.join(map(str, te_norms))
if CFG[src_cfg]['metric'] == 'average_precision':
print_scores(scores)
if CFG[src_cfg]['metric'] == 'average_precision':
print "%.2f" % np.mean(scores)
elif CFG[src_cfg]['metric'] == 'accuracy':
print "%.2f" % scores
def evaluate(src_cfg, tr_kernel, tr_labels, te_kernel, te_labels):
eval = Evaluation(CFG[src_cfg]['eval_name'], **CFG[src_cfg]['eval_params'])
eval.fit(tr_kernel, tr_labels)
scores = eval.score(te_kernel, te_labels)
if CFG[src_cfg]['metric'] == 'average_precision':
print_scores(scores)
if CFG[src_cfg]['metric'] == 'average_precision':
print "%.2f" % np.mean(scores)
elif CFG[src_cfg]['metric'] == 'accuracy':
print "%.2f" % scores
def evaluate(src_cfg, tr_kernel, tr_labels, te_kernel, te_labels):
eval = Evaluation(CFG[src_cfg]['eval_name'], **CFG[src_cfg]['eval_params'])
eval.fit(tr_kernel, tr_labels)
scores = eval.score(te_kernel, te_labels)
if CFG[src_cfg]['metric'] == 'average_precision':
print_scores(scores)
if CFG[src_cfg]['metric'] == 'average_precision':
print "%.2f" % np.mean(scores)
elif CFG[src_cfg]['metric'] == 'accuracy':
print "%.2f" % scores
def evaluate(src_cfg,
tr_norms,
te_norms,
analytical_fim,
pi_derivatives,
sqrt_nr_descs,
do_plot=False,
verbose=0):
outfile = os.path.join(
CACHE_PATH, "%s_%s_afim_%s_pi_%s_sqrt_nr_descs_%s.dat" %
(src_cfg, "%s", analytical_fim, pi_derivatives, sqrt_nr_descs))
dataset = Dataset(CFG[src_cfg]['dataset_name'],
**CFG[src_cfg]['dataset_params'])
(tr_kernel, tr_labels, te_kernel,
te_labels) = load_kernels(dataset,
tr_norms=tr_norms,
te_norms=te_norms,
analytical_fim=analytical_fim,
pi_derivatives=pi_derivatives,
sqrt_nr_descs=sqrt_nr_descs,
outfile=outfile,
do_plot=do_plot,
verbose=verbose)
eval = Evaluation(CFG[src_cfg]['eval_name'], **CFG[src_cfg]['eval_params'])
eval.fit(tr_kernel, tr_labels)
scores = eval.score(te_kernel, te_labels)
if verbose > 0:
print 'Train normalizations:', ', '.join(map(str, tr_norms))
print 'Test normalizations:', ', '.join(map(str, te_norms))
if CFG[src_cfg]['metric'] == 'average_precision':
print_scores(scores)
if CFG[src_cfg]['metric'] == 'average_precision':
print "%.2f" % np.mean(scores)
elif CFG[src_cfg]['metric'] == 'accuracy':
print "%.2f" % scores