def test_estimators_news_3cl(self):
graphs, Gs, info = Datasets().news_3cl_1
(A, gt), G = graphs[0], Gs[0]
K = logComm_K(A).get_K(0.5)
for estimator in tqdm(self.estimators):
km = estimator(n_clusters=3)
km.predict(K, G=G)
def test_compare_CT_and_Resistance(self):
graphs, Gs, info = Datasets().news_2cl_1
A, y_true = graphs[0]
D_CT = CT_D(A).get_D(-1)
D_R = H_to_D(CT_H(A).get_K(-1))
D_CT /= np.average(D_CT)
D_R /= np.average(D_R)
self.assertTrue(np.allclose(D_CT, D_R))
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.etalon = {
'news_2cl_1': (.845, .807, .831, .652, .816),
'news_2cl_2': (.587, .587, .588, .512, .568),
'news_2cl_3': (.810, .811, .750, .859, .796),
'news_3cl_1': (.766, .762, .754, .742, .773),
'news_3cl_2': (.770, .783, .755, .626, .730),
'news_3cl_3': (.765, .770, .744, .715, .759),
'news_5cl_1': (.696, .690, .604, .681, .668),
'news_5cl_2': (.640, .646, .587, .596, .604),
'news_5cl_3': (.612, .616, .573, .478, .573),
}
self.datasets = Datasets()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.datasets = Datasets()
self.estimators = [
# KKMeans,
KKMeans,
KKMeans_iterative,
# KKMeans_kernlab,
KWard,
SpectralClustering_rubanov,
# SpectralClustering_sklearn,
# SpectralClustering_kernlab,
KMeans_sklearn,
Ward_sklearn,
]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
util.configure_logging()
self.etalon = { # CCT, FE, logFor, RSP, SCT, SP
'football': (.7928, .9061, .9028, .9092, .8115, .8575),
'karate': (1., 1., 1., 1., 1., 1.),
'polblogs': (.5525, .5813, .5811, .5815, .5757, .5605),
'news_2cl_1': (.7944, .8050, .8381, .7966, .8174, .6540),
'news_2cl_2': (.5819, .5909, .5844, .5797, .5523, .5159),
'news_2cl_3': (.7577, .8107, .7482, .7962, .7857, .8592),
'news_3cl_1': (.7785, .7810, .7530, .7810, .7730, .7426),
'news_3cl_2': (.7616, .7968, .7585, .7761, .7282, .6246),
'news_3cl_3': (.7455, .7707, .7487, .7300, .7627, .7203),
'news_5cl_1': (.6701, .6922, .6143, .7078, .6658, .6815),
'news_5cl_2': (.6177, .6401, .5977, .6243, .6154, .5970),
'news_5cl_3': (.6269, .6065, .5729, .5750, .5712, .4801)
}
self.datasets = Datasets()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
util.configure_logging()
self.etalon = { # SCT CCT FE RSP SP Com For Heat Walk lCom lFor lHeat lWalk LVN
'football': (0.908, 0.889, 0.884, 0.884, 0.812, 0.545, 0.293,
0.492, 0.749, 0.598, 0.424, 0.583, 0.249, 0.698),
'lfr1': (0.976, 0.990, 0.981, 0.983, 0.890, 0.052, 0.027, 0.051,
0.003, 0.475, 0.500, 0.511, 0.485, 0.962),
'lfr2': (1.000, 1.000, 1.000, 1.000, 0.986, 0.147, 0.164, 0.266,
0.694, 0.481, 0.478, 0.449, 0.682, 0.823),
'lfr3': (1.000, 0.997, 1.000, 1.000, 0.989, 0.087, 0.130, 0.159,
0.502, 0.520, 0.379, 0.376, 0.729, 0.827),
'news_2cl_1': (0.608, 0.638, 0.584, 0.581, 0.434, 0.018, 0.032,
0.032, 0.249, 0.291, 0.243, 0.253, 0.462, 0.573),
'news_2cl_2': (0.356, 0.396, 0.359, 0.346, 0.296, 0.008, 0.027,
0.100, 0.394, 0.584, 0.123, 0.207, 0.233, 0.432),
'news_2cl_3': (0.579, 0.584, 0.590, 0.598, 0.616, 0.333, 0.053,
0.117, 0.357, 0.682, 0.553, 0.611, 0.496, 0.586),
'news_3cl_1': (0.697, 0.706, 0.702, 0.696, 0.660, 0.096, 0.032,
0.125, 0.245, 0.489, 0.471, 0.478, 0.748, 0.699),
'news_3cl_2': (0.656, 0.689, 0.711, 0.706, 0.572, 0.068, 0.035,
0.040, 0.294, 0.345, 0.343, 0.281, 0.572, 0.661),
'news_3cl_3': (0.642, 0.605, 0.681, 0.678, 0.720, 0.067, 0.031,
0.033, 0.380, 0.295, 0.255, 0.248, 0.423, 0.673),
'news_5cl_1': (0.641, 0.643, 0.648, 0.651, 0.614, 0.087, 0.019,
0.032, 0.284, 0.259, 0.273, 0.291, 0.504, 0.684),
'news_5cl_2': (0.616, 0.630, 0.643, 0.633, 0.596, 0.185, 0.026,
0.024, 0.291, 0.345, 0.214, 0.234, 0.374, 0.634),
'news_5cl_3': (0.573, 0.624, 0.615, 0.571, 0.480, 0.034, 0.021,
0.021, 0.369, 0.300, 0.337, 0.281, 0.383, 0.572),
'polblogs': (0.556, 0.556, 0.567, 0.568, 0.549, 0.423, 0.322,
0.641, 0.298, 0.511, 0.550, 0.527, 0.577, 0.597),
'karate': (0.832, 0.724, 0.838, 0.832, 1.000, 0.262, 0.395, 1.000,
0.875, 1.000, 0.866, 1.000, 0.697, 0.982)
}
self.datasets = Datasets()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.datasets = Datasets()
def perform():
for dataset_name in dataset_names:
graphs, info = Datasets()[dataset_name]
perform_column(dataset_name, graphs, info['k'])
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
util.configure_logging()
graph, _, info = Datasets().karate
self.graph, self.y_true = graph[0]