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Simplify code of gallery example comparing different manifold methods #15949

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Merged
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Dec 26, 2019
Merged

Simplify code of gallery example comparing different manifold methods #15949

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Simplify code of gallery example comparing different manifold learnin…
DavidBreuer Dec 22, 2019
444fa1e
Use OrderedDict to ensure ordering of methods in manifold example and…
DavidBreuer Dec 23, 2019
4afdac2
Undo switch to new matplotlib axes for consistency between 2d and 3d …
DavidBreuer Dec 24, 2019
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94 changes: 29 additions & 65 deletions examples/manifold/plot_compare_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,8 @@

print(__doc__)

from collections import OrderedDict
from functools import partial
from time import time

import matplotlib.pyplot as plt
Expand All @@ -39,81 +41,43 @@
n_neighbors = 10
n_components = 2

# Create figure
fig = plt.figure(figsize=(15, 8))
plt.suptitle("Manifold Learning with %i points, %i neighbors"
fig.suptitle("Manifold Learning with %i points, %i neighbors"
% (1000, n_neighbors), fontsize=14)


# Add 3d scatter plot
ax = fig.add_subplot(251, projection='3d')
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
ax.view_init(4, -72)

methods = ['standard', 'ltsa', 'hessian', 'modified']
labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']

for i, method in enumerate(methods):
# Set-up manifold methods
LLE = partial(manifold.LocallyLinearEmbedding,
n_neighbors, n_components, eigen_solver='auto')

methods = OrderedDict()
methods['LLE'] = LLE(method='standard')
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This dict will only be iterated in deterministic order in cython >= 3.6. I'm not certain if we are ready to rely upon that yet.

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Right, do you think the collections.OrderedDict I added is fine here?

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Yes, that seems a reasonable solution for now!

methods['LTSA'] = LLE(method='ltsa')
methods['Hessian LLE'] = LLE(method='hessian')
methods['Modified LLE'] = LLE(method='modified')
methods['Isomap'] = manifold.Isomap(n_neighbors, n_components)
methods['MDS'] = manifold.MDS(n_components, max_iter=100, n_init=1)
methods['SE'] = manifold.SpectralEmbedding(n_components=n_components,
n_neighbors=n_neighbors)
methods['t-SNE'] = manifold.TSNE(n_components=n_components, init='pca',
random_state=0)

# Plot results
for i, (label, method) in enumerate(methods.items()):
t0 = time()
Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components,
eigen_solver='auto',
method=method).fit_transform(X)
Y = method.fit_transform(X)
t1 = time()
print("%s: %.2g sec" % (methods[i], t1 - t0))

ax = fig.add_subplot(252 + i)
plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
plt.title("%s (%.2g sec)" % (labels[i], t1 - t0))
print("%s: %.2g sec" % (label, t1 - t0))
ax = fig.add_subplot(2, 5, 2 + i + (i > 3))
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I think we should move to the more modern plt.subplots and iterate over the array of axes while we are cleaning up.

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Yes, I agree and adjusted the axes accordingly. Since the figure includes one 3d axis I had to remove and re-add that axis or is there another solution?

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Forgot about this. Don't know, sorry

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Okay, no problem, then let's keep the former axes definitions.

ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
ax.set_title("%s (%.2g sec)" % (label, t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')

t0 = time()
Y = manifold.Isomap(n_neighbors, n_components).fit_transform(X)
t1 = time()
print("Isomap: %.2g sec" % (t1 - t0))
ax = fig.add_subplot(257)
plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
plt.title("Isomap (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')


t0 = time()
mds = manifold.MDS(n_components, max_iter=100, n_init=1)
Y = mds.fit_transform(X)
t1 = time()
print("MDS: %.2g sec" % (t1 - t0))
ax = fig.add_subplot(258)
plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
plt.title("MDS (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')


t0 = time()
se = manifold.SpectralEmbedding(n_components=n_components,
n_neighbors=n_neighbors)
Y = se.fit_transform(X)
t1 = time()
print("SpectralEmbedding: %.2g sec" % (t1 - t0))
ax = fig.add_subplot(259)
plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
plt.title("SpectralEmbedding (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')

t0 = time()
tsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0)
Y = tsne.fit_transform(X)
t1 = time()
print("t-SNE: %.2g sec" % (t1 - t0))
ax = fig.add_subplot(2, 5, 10)
plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
plt.title("t-SNE (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')
ax.axis('tight')

plt.show()