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)
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)
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)
# Two ways to aggregate the rank information # 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