def test_similar_graphics(self):
"""Tests if Displayer class is presenting a similar graphic from the one printed
by the hard-coded lines bellow (manual checking).
"""
points = 1000
data, color = datasets.make_swiss_roll(points, random_state=0)
neighbors = 10
to_dimension = 2
result = manifold.Isomap(neighbors, to_dimension).fit_transform(data)
# Expected printing...
Axes3D
fig = plt.figure(figsize=(15, 8))
plt.suptitle("Expected image", fontsize=14)
ax = fig.add_subplot(121, projection='3d')
ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=color, cmap=plt.cm.Spectral)
ax.view_init(4, -72)
ax = fig.add_subplot(122)
plt.scatter(result[:, 0], result[:, 1], c=color, cmap=plt.cm.Spectral)
plt.title("SKLearn's Isomap")
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')
# Actual printing...
Displayer(title="Actual image", points=points, neighbors=neighbors) \
.load(data, color, title='Graphic I') \
.load(result, color, title='SKLearn\'s Isomap') \
.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_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_swiss_roll(self):
samples = 1000
neighbors = 10
n_components = 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, n_components).fit_transform(data)
elapsed = time() - start
displayer \
.load(
title="SKLearn's Isomap with %i neighbors, taking %.1fs." % (neighbors, elapsed),
data=result,
color=c)
start = time()
result = Isomap(k=neighbors, n_components=n_components).transform(data)
elapsed = time() - start
displayer \
.load(
title="Isomap with %i neighbors, taking %.1fs" % (neighbors, elapsed),
data=result,
color=c)
displayer.show()
def test_rendering(self):
i = algorithms.Isomap(self.test_data, n_components=2)
result = i.run()
Displayer() \
.load(result, np.random.rand(4), title='K-Isomap') \
.show()
def test_random_generation(self):
"""Tests if the Displayer class is presenting 4 graphics correctly (manual checking).
"""
points = 1000
data, color = datasets.make_swiss_roll(points, random_state=0)
neighbors = 10
d = Displayer(points=points, neighbors=neighbors)
d \
.load(data, color, title='Graphic I') \
.load(data, color, title='Graphic II') \
.load(data, color, title='Graphic III') \
.load(data, color, title='Graphic IV') \
.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 displayer(self):
self._displayer = self._displayer or Displayer(plotting=self.plotting)
return self._displayer
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()