def test_compute_CD(self):
avranks = [1.9, 3.2, 2.8, 3.3]
cd = scoring.compute_CD(avranks, 30)
np.testing.assert_almost_equal(cd, 0.856344)
cd = scoring.compute_CD(avranks, 30, test="bonferroni-dunn")
np.testing.assert_almost_equal(cd, 0.798)
@contextlib.contextmanager
def mock_module(name):
if not name in sys.modules:
try:
sys.modules[name] = Mock()
yield
finally:
del sys.modules[name]
else:
yield
# Do what you will, just don't crash
with mock_module("matplotlib"), \
mock_module("matplotlib.pyplot"), \
mock_module("matplotlib.backends.backend_agg"):
scoring.graph_ranks(avranks, "abcd", cd)
scoring.graph_ranks(avranks, "abcd", cd, cdmethod=0)
def test_compute_CD(self):
avranks = [1.9, 3.2, 2.8, 3.3]
cd = scoring.compute_CD(avranks, 30)
np.testing.assert_almost_equal(cd, 0.856344)
cd = scoring.compute_CD(avranks, 30, test="bonferroni-dunn")
np.testing.assert_almost_equal(cd, 0.798)
@contextlib.contextmanager
def mock_module(name):
if not name in sys.modules:
try:
sys.modules[name] = Mock()
yield
finally:
del sys.modules[name]
else:
yield
# Do what you will, just don't crash
with mock_module("matplotlib"), \
mock_module("matplotlib.pyplot"), \
mock_module("matplotlib.backends.backend_agg"):
scoring.graph_ranks(avranks, "abcd", cd)
scoring.graph_ranks(avranks, "abcd", cd, cdmethod=0)
def test_compute_CD(self):
avranks = [1.9, 3.2, 2.8, 3.3]
cd = scoring.compute_CD(avranks, 30)
np.testing.assert_almost_equal(cd, 0.856344)
cd = scoring.compute_CD(avranks, 30, test="bonferroni-dunn")
np.testing.assert_almost_equal(cd, 0.798)
# Do what you will, just don't crash
scoring.graph_ranks(avranks, "abcd", cd)
scoring.graph_ranks(avranks, "abcd", cd, cdmethod=0)
def test_compute_CD(self):
avranks = [1.9, 3.2, 2.8, 3.3]
cd = scoring.compute_CD(avranks, 30)
np.testing.assert_almost_equal(cd, 0.856344)
cd = scoring.compute_CD(avranks, 30, test="bonferroni-dunn")
np.testing.assert_almost_equal(cd, 0.798)
# Do what you will, just don't crash
scoring.graph_ranks(avranks, "abcd", cd)
scoring.graph_ranks(avranks, "abcd", cd, cdmethod=0)
# EIther aggregate it specific to each evaluator, and then average the ranks of the 4 to 6 evaluators
# Or, rank irrespective of evaluator and aggregate across 60 x (4 to 6) evaluators. Hansen et al. talk about this kind of evaluation.
use_approach = 0
if use_approach == 0:
mat = scipy.io.loadmat("./utils/data_from_util_scripts/n_means.mat")
arr = mat['n_means']
arr = arr[0]
for cnt, i in enumerate(arr):
print cnt
num_datasets = i[0][0]
avranks = i[0][1:]
algs = fn_names[cnt]
cd = compute_CD(avranks, num_datasets)
print cd
graph_ranks("cd_plt"+str(cnt)+".pdf", avranks, algs, cd=cd, width=6)
else:
arr = []
mat = scipy.io.loadmat("./utils/data_from_util_scripts/friedman_raw_input.mat")
arr1 = mat['all_friedman_input']
for i in arr1[0]:
j = np.apply_along_axis(rankdata, 1, i)
x = np.mean(j, axis=0)
x = np.hstack((j.shape[0], x))
arr.append(x)
for cnt, i in enumerate(arr):
print cnt
num_datasets = i[0]
avranks = i[1:]
def rank_plot():
from Orange.evaluation.scoring import graph_ranks
avgranks = np.mean(np.argsort(np.argsort(results)) + 1, axis=0)
cd = compute_CD(avgranks, len(results))
print(avgranks, cd)
graph_ranks('ranks.pdf', avgranks, ["kmeans", 'gmm', 'lac'], cd=cd)