Additional methods

Note

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This notebooks provides an overview of built-in clustering performance evaluation, ways of accessing individual labels resulting from clustering and saving the object to disk.

Clustering performance evaluation

Clustergam includes handy wrappers around a selection of clustering performance metrics offered by scikit-learn. Data which were originally computed on GPU are converted to numpy on the fly.

Let’s load the data and fit clustergram on Palmer penguins dataset. See the Introduction for its overview.

import seaborn
from sklearn.preprocessing import scale
from clustergram import Clustergram

seaborn.set(style='whitegrid')

df = seaborn.load_dataset('penguins')
data = scale(df.drop(columns=['species', 'island', 'sex']).dropna())

cgram = Clustergram(range(1, 12), verbose=False)
cgram.fit(data)

Silhouette score

Compute the mean Silhouette Coefficient of all samples. See scikit-learn documentation for details.

cgram.silhouette_score()

2     0.531540
3     0.447219
4     0.399584
5     0.377720
6     0.368665
7     0.335069
8     0.286170
9     0.285263
10    0.279539
11    0.273914
Name: silhouette_score, dtype: float64

Once computed, resulting Series is available as cgram.silhouette. Calling the original method will recompute the score.

cgram.silhouette.plot()

<AxesSubplot:>

Calinski and Harabasz score

Compute the Calinski and Harabasz score, also known as the Variance Ratio Criterion. See scikit-learn documentation for details.

cgram.calinski_harabasz_score()

2     482.191469
3     441.677075
4     400.410025
5     411.175066
6     382.297175
7     352.713169
8     331.796377
9     315.551827
10    298.178981
11    286.976897
Name: calinski_harabasz_score, dtype: float64

Once computed, resulting Series is available as cgram.calinski_harabasz. Calling the original method will recompute the score.

cgram.calinski_harabasz.plot()

<AxesSubplot:>

Davies-Bouldin score

Compute the Davies-Bouldin score. See scikit-learn documentation for details.

cgram.davies_bouldin_score()

2     0.714064
3     0.943553
4     0.944215
5     0.973248
6     0.976604
7     1.036676
8     1.175931
9     1.240331
10    1.201865
11    1.239891
Name: davies_bouldin_score, dtype: float64

Once computed, resulting Series is available as cgram.davies_bouldin. Calling the original method will recompute the score.

cgram.davies_bouldin.plot()

<AxesSubplot:>

Acessing labels

Clustergram stores resulting labels for each of the tested options, which can be accessed as:

cgram.labels

	1	2	3	4	5	6	7	8	9	10	11
0	0	1	0	3	4	5	1	1	3	0	5
1	0	1	0	3	4	5	1	1	3	4	1
2	0	1	0	3	4	5	1	1	3	4	1
3	0	1	0	3	4	5	1	1	4	0	5
4	0	1	0	3	1	0	4	4	4	7	4
...	...	...	...	...	...	...	...	...	...	...	...
337	0	0	1	1	0	1	3	0	8	8	0
338	0	0	1	1	0	1	3	0	8	8	0
339	0	0	1	2	3	3	0	3	5	3	3
340	0	0	1	1	0	1	6	6	2	1	7
341	0	0	1	2	3	3	6	6	2	1	7

342 rows × 11 columns

Saving clustergram

If you want to save your computed clustergram.Clustergram object to a disk, you can use pickle library:

import pickle

with open('clustergram.pickle','wb') as f:
    pickle.dump(cgram, f)

with open('clustergram.pickle','rb') as f:
    loaded = pickle.load(f)

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