def test_train_learning_rate_less_than_zero_should_raise_value_error(self):
data = pd.read_csv('../data/test_data.csv').drop(['Class'], axis=1)
som = StandardSOM((50, 50), 5)
with self.assertRaises(ValueError):
som.train(data, alpha=-0.01)
self.assertFalse(som.trained)
self.assertIsNone(som.codebook)
def test_hexagonal(self):
data = pd.read_csv('../../data/test_data.csv').drop(['Class'], axis=1)
som = StandardSOM((50, 50), 5, "hexagonal")
som.train(data)
qe_m(som)
mqe_m(som)
mqe(som)
mmqe(som)
def test_rectangular(self):
data = pd.read_csv('../../data/test_data.csv').drop(['Class'], axis=1)
som = StandardSOM((50, 50), 5)
som.train(data)
qe_m(som)
mqe_m(som)
mqe(som)
mmqe(som)
def test_train_hexagonal(self):
data = pd.read_csv('../data/test_data.csv').drop(['Class'], axis=1)
som = StandardSOM((50, 50), 5, "hexagonal")
som.train(data)
self.assertTrue(som.trained)
self.assertIsNotNone(som.codebook)
self.assertIsNotNone(som.bmu_indices)
self.assertIsNotNone(som.bmu_distances)
self.assertIsNotNone(som.get_first_bmus())
self.assertIsNotNone(som.get_second_bmus())
def test_rectangular(self):
data = pd.read_csv('../../../data/test_data.csv').drop(['Class'],
axis=1)
som = StandardSOM((50, 50), 5)
som.train(data)
self.assertTrue(som.trained)
self.assertIsNotNone(som.codebook)
self.assertIsNotNone(som.bmu_indices)
self.assertIsNotNone(som.bmu_distances)
self.assertIsNotNone(som.get_first_bmus())
self.assertIsNotNone(som.get_second_bmus())
topographic_error(som)
def test_hexagonal(self):
data = pd.read_csv('../../data/test_data.csv').drop(['Class'], axis=1)
som = StandardSOM((50, 50), 5, "hexagonal")
som.train(data)
topographic_error(som)
def __standard_som_hit_histogram(som: StandardSOM, cmap: str):
"""
Plot the hit histogram for a standard SOM. Plot depends on the topology of the neighborhood (hexagonal or
rectangular).
Parameters:
-----------
som: StandardSOM
The SOM for which the hit histogram should be plotted.
cmap: str
The matplotlib color map for the map.
Returns:
--------
None
"""
if som.codebook is not None and som.trained:
first_bmus = som.get_first_bmus()
# init hit result array
hits = np.zeros(len(som.positions))
# aggregate for each position with hits
agg = {k: len(v) for k, v in first_bmus.items()}
# fill up values at the resp. positions
hits[list(agg.keys())] = list(agg.values())
# define normalizer for matplotlib colors
normalized = Normalize(vmin=np.min(hits), vmax=np.max(hits))
# plot
fig, ax = plt.subplots(1)
ax.set_aspect("equal")
plt.axis('off')
cmap = cm.get_cmap(cmap)
if som.topology == "rectangular":
# axis limits
ax.set_xlim(-1, som.map_size[1])
ax.set_ylim(-1, som.map_size[0])
for pos, hit in zip(som.positions, hits):
# noinspection PyTypeChecker
rect = RegularPolygon((pos[1], pos[0]),
numVertices=4,
radius=np.sqrt(0.5),
orientation=np.radians(45),
edgecolor='k',
facecolor=cmap(normalized(hit)),
alpha=0.2)
ax.add_patch(rect)
else:
# hexagonal topology
# Horizontal cartesian coords
hcoord = [c[0] for c in som.positions]
# Vertical cartersian coords
vcoord = [
2. * np.sin(np.radians(60)) * (c[1] - c[2]) / 3.
for c in som.positions
]
# axis limits
ax.set_xlim(np.min(hcoord) - 2, np.max(hcoord) + 2)
ax.set_ylim(np.min(vcoord) - 2, np.max(vcoord) + 2)
for x, y, hit in zip(hcoord, vcoord, hits):
# noinspection PyTypeChecker
rect = RegularPolygon((x, y),
numVertices=6,
radius=2. / 3.,
orientation=np.radians(30),
edgecolor='k',
facecolor=cmap(normalized(hit)),
alpha=0.2)
ax.add_patch(rect)
# add colorbar
plt.colorbar(cm.ScalarMappable(norm=normalized, cmap=cmap), ax=ax)
# show
plt.show()
def test_train_iterations_less_than_zero_should_raise_value_error(self):
data = pd.read_csv('../data/test_data.csv').drop(['Class'], axis=1)
som = StandardSOM((50, 50), 5)
with self.assertRaises(ValueError):
som.train(data, iterations=-1)
def test_train_data_none_should_raise_value_error(self):
som = StandardSOM((50, 50), 5)
with self.assertRaises(ValueError):
som.train(None)
def test_distance_measure_not_supported_should_raise_value_error(self):
with self.assertRaises(ValueError):
StandardSOM((1, 1), 1, distance_measure="test")
def test_neighborhood_type_not_supported_should_raise_value_error(self):
with self.assertRaises(ValueError):
StandardSOM((1, 1), 1, neighborhood_type="test")
def test_map_size_not_int_tuple_should_raise_value_error(self):
with self.assertRaises(ValueError):
StandardSOM((1.0, 1.0), 1)
def test_neighborhood_radius_equal_zero_should_raise_value_error(self):
with self.assertRaises(ValueError):
StandardSOM((1, 1), 0)
def test_init(self):
StandardSOM((1, 1), 1)