8000 DOC: Fixing notebook-style formatting on plot_segmentation_toy.py by RenTrieu · Pull Request #23140 · scikit-learn/scikit-learn · GitHub
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DOC: Fixing notebook-style formatting on plot_segmentation_toy.py #23140

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Merged
merged 9 commits into from
Apr 27, 2022
Merged

DOC: Fixing notebook-style formatting on plot_segmentation_toy.py #23140

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6b29d33
Fixing notebook-style formatting on plot_segmentation_toy.py
RenTrieu Apr 14, 2022
4c8c703
Merge remote-tracking branch 'upstream/main' into fix_notebook_style
RenTrieu Apr 16, 2022
766979a
Update examples/cluster/plot_segmentation_toy.py
RenTrieu Apr 16, 2022
799ed0b
Update examples/cluster/plot_segmentation_toy.py
RenTrieu Apr 16, 2022
f9f1175
Update examples/cluster/plot_segmentation_toy.py
RenTrieu Apr 16, 2022
c4e184b
Merge branch 'fix_notebook_style' of github.com:RenTrieu/scikit-learn…
RenTrieu Apr 16, 2022
8c68368
Implementing jsilike's style suggestions
RenTrieu Apr 16, 2022
da4c690
Reformatted code with black
RenTrieu Apr 16, 2022
e660a1a
Moving sklearn imports to the cells in which they are first used and …
RenTrieu Apr 16, 2022
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49 changes: 33 additions & 16 deletions examples/cluster/plot_segmentation_toy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,11 +30,10 @@
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause

# %%
# Generate the data
# -----------------
import numpy as np
import matplotlib.pyplot as plt

from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering

l = 100
x, y = np.indices((l, l))
Expand All @@ -51,8 +50,9 @@
circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3**2
circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4**2

# #############################################################################
# 4 circles
# %%
# Plotting four circles
# ---------------------
img = circle1 + circle2 + circle3 + circle4

# We use a mask that limits to the foreground: the problem that we are
Expand All @@ -63,25 +63,41 @@
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)

# %%
# Convert the image into a graph with the value of the gradient on the
# edges.
from sklearn.feature_extraction import image

graph = image.img_to_graph(img, mask=mask)

# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
# %%
# Take a decreasing function of the gradient resulting in a segmentation
# that is close to a Voronoi partition
graph.data = np.exp(-graph.data / graph.data.std())

# Force the solver to be arpack, since amg is numerically
# unstable on this example
# %%
# Here we perform spectral clustering using the arpack solver since amg is
# numerically unstable on this example. We then plot the results.
from sklearn.cluster import spectral_clustering
import matplotlib.pyplot as plt

labels = spectral_clustering(graph, n_clusters=4, eigen_solver="arpack")
label_im = np.full(mask.shape, -1.0)
label_im[mask] = labels

plt.matshow(img)
plt.matshow(label_im)
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))
axs[0].matshow(img)
axs[1].matshow(label_im)

plt.show()

# %%
# Plotting two circles
# --------------------
# Here we repeat the above process but only consider the first two circles
# we generated. Note that this results in a cleaner separation between the
# circles as the region sizes are easier to balance in this case.

# #############################################################################
# 2 circles
img = circle1 + circle2
mask = img.astype(bool)
img = img.astype(float)
Expand All @@ -95,7 +111,8 @@
label_im = np.full(mask.shape, -1.0)
label_im[mask] = labels

plt.matshow(img)
plt.matshow(label_im)
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))
axs[0].matshow(img)
axs[1].matshow(label_im)

plt.show()
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