8000 DOC: use notebook-style for plot_ward_structured_vs_unstructured.py by krishnakoumar27 · Pull Request #23228 · scikit-learn/scikit-learn · GitHub
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DOC: use notebook-style for plot_ward_structured_vs_unstructured.py #23228

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Merged
merged 3 commits into from
May 1, 2022
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DOC: use notebook-style for plot_ward_structured_vs_unstructured.py #23228

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2997350
fixing the notebook style format.
krishnakoumar27 Apr 27, 2022
61774f4
Updated title visibility in the graph
krishnakoumar27 Apr 29, 2022
dbdc8dd
Update examples/cluster/plot_ward_structured_vs_unstructured.py
glemaitre May 1, 2022
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75 changes: 50 additions & 25 deletions examples/cluster/plot_ward_structured_vs_unstructured.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,83 +27,108 @@

import time as time

import matplotlib.pyplot as plt

# The following import is required
# for 3D projection to work with matplotlib < 3.2

import mpl_toolkits.mplot3d # noqa: F401

import numpy as np

from sklearn.cluster import AgglomerativeClustering

# %%
# Generate data
# -------------
#
# We start by generating the Swiss Roll dataset.

from sklearn.datasets import make_swiss_roll

# #############################################################################
# Generate data (swiss roll dataset)
n_samples = 1500
noise = 0.05
X, _ = make_swiss_roll(n_samples, noise=noise)
# Make it thinner
X[:, 1] *= 0.5

# #############################################################################
# %%
# Compute clustering
# ------------------
#
# We perform AgglomerativeClustering which comes under Hierarchical Clustering
# without any connectivity constraints.

from sklearn.cluster import AgglomerativeClustering

print("Compute unstructured hierarchical clustering...")
st = time.time()
ward = AgglomerativeClustering(n_clusters=6, linkage="ward").fit(X)
elapsed_time = time.time() - st
label = ward.labels_
print("Elapsed time: %.2fs" % elapsed_time)
print("Number of points: %i" % label.size)
print(f"Elapsed time: {elapsed_time:.2f}s")
print(f"Number of points: {label.size}")

# #############################################################################
# %%
# Plot result
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d", elev=7, azim=-80)
ax.set_position([0, 0, 0.95, 1])
# -----------
# Plotting the unstructured hierarchical clusters.

import matplotlib.pyplot as plt

fig1 = plt.figure()
ax1 = fig1.add_subplot(111, projection="3d", elev=7, azim=-80)
ax1.set_position([0, 0, 0.95, 1])
for l in np.unique(label):
ax.scatter(
ax1.scatter(
X[label == l, 0],
X[label == l, 1],
X[label == l, 2],
color=plt.cm.jet(float(l) / np.max(label + 1)),
s=20,
edgecolor="k",
)
plt.title("Without connectivity constraints (time %.2fs)" % elapsed_time)
_ = fig1.suptitle(f"Without connectivity constraints (time {elapsed_time:.2f}s)")

# %%
# We are defining k-Nearest Neighbors with 10 neighbors
# -----------------------------------------------------

# #############################################################################
# Define the structure A of the data. Here a 10 nearest neighbors
from sklearn.neighbors import kneighbors_graph

connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)

# #############################################################################
# %%
# Compute clustering
# ------------------
#
# We perform AgglomerativeClustering again with connectivity constraints.

print("Compute structured hierarchical clustering...")
st = time.time()
ward = AgglomerativeClustering(
n_clusters=6, connectivity=connectivity, linkage="ward"
).fit(X)
elapsed_time = time.time() - st
label = ward.labels_
print("Elapsed time: %.2fs" % elapsed_time)
print("Number of points: %i" % label.size)
print(f"Elapsed time: {elapsed_time:.2f}s")
print(f"Number of points: {label.size}")

# #############################################################################
# %%
# Plot result
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d", elev=7, azim=-80)
ax.set_position([0, 0, 0.95, 1])
# -----------
#
# Plotting the structured hierarchical clusters.

fig2 = plt.figure()
ax2 = fig2.add_subplot(121, projection="3d", elev=7, azim=-80)
ax2.set_position([0, 0, 0.95, 1])
for l in np.unique(label):
ax.scatter(
ax2.scatter(
X[label == l, 0],
X[label == l, 1],
X[label == l, 2],
color=plt.cm.jet(float(l) / np.max(label + 1)),
s=20,
edgecolor="k",
)
plt.title("With connectivity constraints (time %.2fs)" % elapsed_time)
fig2.suptitle(f"With connectivity constraints (time {elapsed_time:.2f}s)")

plt.show()
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