131 lines
3.6 KiB
Python
131 lines
3.6 KiB
Python
"""
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===========================================================
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Hierarchical clustering: structured vs unstructured ward
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===========================================================
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Example builds a swiss roll dataset and runs
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hierarchical clustering on their position.
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For more information, see :ref:`hierarchical_clustering`.
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In a first step, the hierarchical clustering is performed without connectivity
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constraints on the structure and is solely based on distance, whereas in
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a second step the clustering is restricted to the k-Nearest Neighbors
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graph: it's a hierarchical clustering with structure prior.
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Some of the clusters learned without connectivity constraints do not
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respect the structure of the swiss roll and extend across different folds of
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the manifolds. On the opposite, when opposing connectivity constraints,
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the clusters form a nice parcellation of the swiss roll.
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"""
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# Authors : Vincent Michel, 2010
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# Alexandre Gramfort, 2010
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# Gael Varoquaux, 2010
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# License: BSD 3 clause
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import time as time
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# The following import is required
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# for 3D projection to work with matplotlib < 3.2
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import mpl_toolkits.mplot3d # noqa: F401
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import numpy as np
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# %%
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# Generate data
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# -------------
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#
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# We start by generating the Swiss Roll dataset.
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from sklearn.datasets import make_swiss_roll
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n_samples = 1500
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noise = 0.05
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X, _ = make_swiss_roll(n_samples, noise=noise)
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# Make it thinner
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X[:, 1] *= 0.5
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# %%
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# Compute clustering
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# ------------------
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#
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# We perform AgglomerativeClustering which comes under Hierarchical Clustering
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# without any connectivity constraints.
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from sklearn.cluster import AgglomerativeClustering
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print("Compute unstructured hierarchical clustering...")
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st = time.time()
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ward = AgglomerativeClustering(n_clusters=6, linkage="ward").fit(X)
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elapsed_time = time.time() - st
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label = ward.labels_
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print(f"Elapsed time: {elapsed_time:.2f}s")
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print(f"Number of points: {label.size}")
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# %%
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# Plot result
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# -----------
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# Plotting the unstructured hierarchical clusters.
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import matplotlib.pyplot as plt
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fig1 = plt.figure()
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ax1 = fig1.add_subplot(111, projection="3d", elev=7, azim=-80)
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ax1.set_position([0, 0, 0.95, 1])
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for l in np.unique(label):
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ax1.scatter(
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X[label == l, 0],
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X[label == l, 1],
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X[label == l, 2],
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color=plt.cm.jet(float(l) / np.max(label + 1)),
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s=20,
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edgecolor="k",
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)
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_ = fig1.suptitle(f"Without connectivity constraints (time {elapsed_time:.2f}s)")
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# %%
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# We are defining k-Nearest Neighbors with 10 neighbors
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# -----------------------------------------------------
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from sklearn.neighbors import kneighbors_graph
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connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)
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# %%
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# Compute clustering
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# ------------------
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#
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# We perform AgglomerativeClustering again with connectivity constraints.
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print("Compute structured hierarchical clustering...")
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st = time.time()
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ward = AgglomerativeClustering(
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n_clusters=6, connectivity=connectivity, linkage="ward"
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).fit(X)
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elapsed_time = time.time() - st
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label = ward.labels_
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print(f"Elapsed time: {elapsed_time:.2f}s")
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print(f"Number of points: {label.size}")
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# %%
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# Plot result
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# -----------
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#
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# Plotting the structured hierarchical clusters.
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fig2 = plt.figure()
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ax2 = fig2.add_subplot(121, projection="3d", elev=7, azim=-80)
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ax2.set_position([0, 0, 0.95, 1])
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for l in np.unique(label):
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ax2.scatter(
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X[label == l, 0],
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X[label == l, 1],
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X[label == l, 2],
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color=plt.cm.jet(float(l) / np.max(label + 1)),
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s=20,
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edgecolor="k",
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)
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fig2.suptitle(f"With connectivity constraints (time {elapsed_time:.2f}s)")
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plt.show()
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