Perform Agglomerative Clustering

Task

Swipe to start coding

Your task is to use different linkage types and to look at the performance of agglomerative clustering on moons and circles datasets. You have to:

Import AgglomerativeClustering class from sklearn.cluster module.
Add a parameter with the name linkage as an input of the function.
Add .fit() method of the agglomerative object to train the model.
Use 'single', 'complete', and 'average' as parameters of the function(parameters in the code have to be used in the same order).

Solution

from sklearn.datasets import make_moons, make_circles

import matplotlib.pyplot as plt

import numpy as np

from sklearn.cluster import AgglomerativeClustering

def check_linkage_parameter(X, y, ds_name, linkage):

agglomerative = AgglomerativeClustering(linkage=linkage,

distance_threshold=0.5, n_clusters=None)

agglomerative.fit(X)

fig, axes = plt.subplots(1, 2)

fig.suptitle(ds_name+' dataset: '+ str(linkage)+' linkage')

axes[0].scatter(X[:, 0], X[:, 1], c=y, cmap='tab20b')

axes[0].set_title('Real clusters')

axes[1].scatter(X[:, 0], X[:, 1], c=agglomerative.labels_, cmap='tab20b')

axes[1].set_title('Clusters with Agglomerative')

X, y = make_moons(n_samples=500)

check_linkage_parameter(X, y, 'Moons', linkage='single')

check_linkage_parameter(X, y, 'Moons', linkage='complete')

check_linkage_parameter(X, y, 'Moons', linkage='average')

X, y = make_circles(n_samples=500)

check_linkage_parameter(X, y, 'Circles', linkage='single')

check_linkage_parameter(X, y, 'Circles', linkage='complete')

check_linkage_parameter(X, y, 'Circles', linkage='average')

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Section 2. Chapter 4

from sklearn.datasets import make_moons, make_circles

import matplotlib.pyplot as plt

import numpy as np

from sklearn.___ import ___

# this function will train agglomerative model with different linakges and plot the results

def check_linkage_parameter(X, y, ds_name, ___):

agglomerative = AgglomerativeClustering(linkage=linkage,

distance_threshold=0.5, n_clusters=None)

agglomerative.___(X)

fig, axes = plt.subplots(1, 2)

fig.suptitle(ds_name+' Dataset: '+str(linkage)+' linkage')

axes[0].scatter(X[:, 0], X[:,1 ], c=y, cmap='tab20b')