def make_s_curve(**kargs):
n_points = 1000
X, color = datasets.samples_generator.make_s_curve(n_points, random_state=0) # [log] dim > X: (1000, 3), color: (1000,)
n_neighbors = 10
n_components = 2
outputdir = kargs.get('outputdir', os.path.join(os.getcwd(), 'plot'))
if not os.path.exists(outputdir): os.makedirs(outputdir) # base directory
fig = plt.figure(figsize=(15, 8))
plt.suptitle("Manifold Learning with %i points, %i neighbors"
% (1000, n_neighbors), fontsize=14)
try:
# compatibility matplotlib < 1.0
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)
except:
ax = fig.add_subplot(251, projection='3d')
plt.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)
ext = 'tif'
fpath = os.path.join(TestDir, name_image_file(descriptor='scurve', **kargs))
plt.savefig(fpath, dpi=300)
plt.close()
return (X, color)
def isomap(X, **kargs):
# import matplotlib.cm as cm
n_neighbors = 10
n_components = 2
y = kargs.get('y', None)
# ulabels = labels = y = kargs.get('y', None)
# n_labels = X.shape[0]
# if labels is not None:
# ulabels = set(labels)
# n_labels = len(ulabels)
t0 = time()
X_proj = Y = manifold.Isomap(n_neighbors, n_components).fit_transform(X)
t1 = time()
print("Isomap: %.2g sec" % (t1 - t0))
outputdir = kargs.get('outputdir', os.path.join(os.getcwd(), 'plot'))
if not os.path.exists(outputdir): os.makedirs(outputdir) # base directory
plot_mode = kargs.get('graph_mode', 'seaborn')
fpath = os.path.join(TestDir, name_image_file(
descriptor='isomap', **kargs)) # kargs: seq_ptype, d2v_method
if plot_mode.startswith('s'):
scatter2(X_proj, y)
plt.savefig(fpath, dpi=300)
else:
# [params] colors
n_points = X.shape[0]
colors = itertools.cycle(cm.rainbow(np.linspace(0, 1, n_points)))
# for i, c in enumerate(itertools.cycle(cm.rainbow(np.linspace(0, 1, n_points)))):
# for i, c in enumerate(itertools.cycle(["r", "b", "g"])):
lcmap = {
0: 'g',
1: 'b',
2: 'r',
}
colors = (lcmap[l] for l in y) # loop through y
fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(257)
plt.scatter(X_proj[:, 0],
X_proj[:, 1],
c=next(colors),
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')
plt.savefig(fpath, dpi=300)
plt.close()
return X_proj
def mds(X, **kargs):
# import matplotlib.cm as cm
# n_neighbors = 10
n_components = 2
y = kargs.get('y', None)
t0 = time()
mds = manifold.MDS(n_components, max_iter=100, n_init=1)
X_proj = Y = mds.fit_transform(X)
t1 = time()
print("MDS: %.2g sec" % (t1 - t0))
outputdir = kargs.get('outputdir', os.path.join(os.getcwd(), 'plot'))
if not os.path.exists(outputdir): os.makedirs(outputdir) # base directory
plot_mode = kargs.get('graph_mode', 'seaborn')
fpath = os.path.join(TestDir, name_image_file(
descriptor='mds', **kargs)) # kargs: seq_ptype, d2v_method
if plot_mode.startswith('s'):
scatter2(X_proj, y)
plt.savefig(fpath, dpi=300)
else:
# [params] colors
n_points = X_proj.shape[0]
# colors = itertools.cycle(cm.rainbow(np.linspace(0, 1, n_points)))
lcmap = {
0: 'g',
1: 'b',
2: 'r',
}
colors = (lcmap[l] for l in y) # loop through y
fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(258)
plt.scatter(X_proj[:, 0],
X_proj[:, 1],
c=next(colors),
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')
plt.savefig(fpath, dpi=300)
plt.close()
return Y
def locally_linear(X, **kargs):
# import matplotlib.cm as cm
n_neighbors = 10
n_components = 2
y = kargs.get('y', None)
methods = ['standard', 'ltsa', 'hessian', 'modified']
labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']
plt.clf()
fig = plt.figure(figsize=(15, 8))
n_points = X.shape[0]
outputdir = kargs.get('outputdir', os.path.join(os.getcwd(), 'plot'))
if not os.path.exists(outputdir): os.makedirs(outputdir) # base directory
# configure color(s)
colors = itertools.cycle(cm.rainbow(np.linspace(0, 1, n_points)))
for i, method in enumerate(methods):
t0 = time()
Y = manifold.LocallyLinearEmbedding(n_neighbors,
n_components,
eigen_solver='auto',
method=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=next(colors), cmap=plt.cm.Spectral)
plt.title("%s (%.2g sec)" % (labels[i], t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')
# scatter2(X_proj, y)
fpath = os.path.join(
TestDir, name_image_file(descriptor=method,
**kargs)) # kargs: seq_ptype, d2v_method
plt.savefig(fpath, dpi=300)
plt.close()
return