def test_display_dimensions(self):
data_dir = 'datasets/'
data_set = 'glass/glass.data'
file = os.path.join(data_dir, data_set)
print('Displaying data set {%s} in the Rn' % file)
glass = Retriever(file, delimiter=',')
# Glass has the samples' ids in the first column.
glass.split_column(0)
# Additionally, its last column represents the target feature.
glass.split_target()
data, c = glass.retrieve()
reduced_data = algorithms.Isomap(data, e=20).run()
d = Displayer(title=data_set)
# Scatter all dimensions (3-by-3), using as many graphs as necessary.
for begin in range(0, glass.features_count, 3):
end = min(glass.features_count, begin + 3)
d.load(data[:, begin:end],
color=c,
title='Dimensions: d e [%i, %i]' % (begin + 1, end))
d \
.load('Reduced glass data-set', reduced_data, c) \
.show()
def test_random_matrix(self):
data = np.trunc(5 + 10 * np.random.rand(10, 3))
neighbors = 2
epsilon = 5.
to_dimension = 2
d = Displayer(title="Isomap algorithms comparison") \
.load(title="Canonical data set.", data=data)
start = time()
result = manifold.Isomap(neighbors, to_dimension).fit_transform(data)
elapsed = time() - start
d.load(title="SKLearn's Isomap with %i neighbors, taking %.1fs." % (neighbors, elapsed),
data=result)
start = time()
result = algorithms.Isomap(nearest_method='e', e=epsilon, n_components=to_dimension) \
.transform(data)
elapsed = time() - start
d.load(title="My E-Isomap with epsilon %i, taking %.1fs." % (epsilon, elapsed),
data=result)
start = time()
result = algorithms.Isomap(k=neighbors, n_components=to_dimension) \
.transform(data)
elapsed = time() - start
d.load(title="My K-Isomap with %i neighbors, taking %.1fs." % (neighbors, elapsed),
data=result)
d.show()
def test_canonical_data(self):
data = np.array([
[0, 0, 0],
[1, 0, 0],
[1, 1, 0],
[1, 1, 1],
])
c = np.array([0, 0, 1, 1])
neighbors = 2
epsilon = 1.5
to_dimension = 2
d = Displayer(title="Isomap algorithms comparison") \
.load(title="Canonical data set.", data=data, color=c)
start = time()
result = manifold.Isomap(neighbors, to_dimension).fit_transform(data)
elapsed = time() - start
d.load(title="SKLearn's Isomap with %i neighbors, taking %.1fs." % (neighbors, elapsed),
data=result,
color=c)
start = time()
result = algorithms.Isomap(nearest_method='e', e=epsilon, n_components=to_dimension) \
.transform(data)
elapsed = time() - start
d.load(title="My E-Isomap with epsilon %i, taking %.1fs." % (epsilon, elapsed),
data=result,
color=c)
start = time()
result = algorithms.Isomap(k=neighbors, n_components=to_dimension) \
.transform(data)
elapsed = time() - start
d.load(title="My K-Isomap with %i neighbors, taking %.1fs." % (neighbors, elapsed),
data=result,
color=c)
d.show()
def test_swiss_roll(self):
samples = 1000
neighbors = 10
epsilon = 5
to_dimension = 2
data, c = datasets.make_swiss_roll(n_samples=samples, random_state=0)
displayer = Displayer(title="Isomap algorithms comparison") \
.load(title="Swiss roll from %i samples." % (samples,), data=data, color=c)
start = time()
result = manifold.Isomap(neighbors, to_dimension,
path_method='D').fit_transform(data)
elapsed = time() - start
displayer \
.load(
title="SKLearn's Isomap (%i neighbors, dijkstra, taking %.1fs)" % (neighbors, elapsed),
data=result,
color=c)
start = time()
result = manifold.Isomap(neighbors, to_dimension,
path_method='FW').fit_transform(data)
elapsed = time() - start
displayer \
.load(
title="SKLearn's Isomap (%i neighbors, floyd-warshall, taking %.1fs)" % (neighbors, elapsed),
data=result,
color=c)
start = time()
result = algorithms \
.Isomap(nearest_method='e', e=epsilon, n_components=to_dimension) \
.transform(data)
elapsed = time() - start
displayer.load(title="E-Isomap (epsilon=%i, dijkstra, %.1fs)" %
(epsilon, elapsed),
data=result,
color=c)
start = time()
result = algorithms \
.Isomap(nearest_method='e', shortest_path_method='fw', e=epsilon, n_components=to_dimension) \
.transform(data)
elapsed = time() - start
displayer.load(title="E-Isomap (epsilon=%i, floyd-warshall, %.1fs)" %
(epsilon, elapsed),
data=result,
color=c)
start = time()
result = algorithms \
.Isomap(k=neighbors, n_components=to_dimension) \
.transform(data)
elapsed = time() - start
displayer.load(title="K-Isomap (%i neighbors, dijkstra, %.1fs)" %
(neighbors, elapsed),
data=result,
color=c)
start = time()
result = algorithms \
.Isomap(k=neighbors, n_components=to_dimension, shortest_path_method='fw') \
.transform(data)
elapsed = time() - start
displayer.load(title="K-Isomap (%i neighbors, floyd-warshall, %.1fs)" %
(neighbors, elapsed),
data=result,
color=c)
displayer.show()