## Clustergram

The clustergram was later implemented in R by Tal Galili, who also gives a thorough explanation of the concept.

This is a Python translation of Tal's script written for scikit-learn and RAPIDS cuML implementations of K-Means, Mini Batch K-Means and Gaussian Mixture Model (scikit-learn only) clustering, plus hierarchical/agglomerative clustering using SciPy. Alternatively, you can create clustergram using from_* constructors based on alternative clustering algorithms.

## Getting started

You can install clustergram from `conda`

or `pip`

:

```
conda install clustergram -c conda-forge
```

```
pip install clustergram
```

In any case, you still need to install your selected backend

(`scikit-learn`

and `scipy`

or `cuML`

).

The example of clustergram on Palmer penguins dataset:

```
import seaborn
df = seaborn.load_dataset('penguins')
```

First we have to select numerical data and scale them.

```
from sklearn.preprocessing import scale
data = scale(df.drop(columns=['species', 'island', 'sex']).dropna())
```

And then we can simply pass the data to `clustergram`

.

```
from clustergram import Clustergram
cgram = Clustergram(range(1, 8))
cgram.fit(data)
cgram.plot()
```

## Styling

`Clustergram.plot()`

returns matplotlib axis and can be fully customised as any other matplotlib plot.

```
seaborn.set(style='whitegrid')
cgram.plot(
ax=ax,
size=0.5,
linewidth=0.5,
cluster_style={"color": "lightblue", "edgecolor": "black"},
line_style={"color": "red", "linestyle": "-."},
figsize=(12, 8)
)
```

## Mean options

On the `y`

axis, a clustergram can use mean values as in the original paper by Matthias Schonlau or PCA weighted mean values as in the implementation by Tal Galili.

```
cgram = Clustergram(range(1, 8))
cgram.fit(data)
cgram.plot(figsize=(12, 8), pca_weighted=True)
```

```
cgram = Clustergram(range(1, 8))
cgram.fit(data)
cgram.plot(figsize=(12, 8), pca_weighted=False)
```

## Scikit-learn, SciPy and RAPIDS cuML backends

Clustergram offers three backends for the computation - `scikit-learn`

and `scipy`

which use CPU and RAPIDS.AI `cuML`

, which uses GPU. Note that all are optional dependencies but you will need at least one of them to generate clustergram.

Using `scikit-learn`

(default):

```
cgram = Clustergram(range(1, 8), backend='sklearn')
cgram.fit(data)
cgram.plot()
```

Using `cuML`

:

```
cgram = Clustergram(range(1, 8), backend='cuML')
cgram.fit(data)
cgram.plot()
```

`data`

can be all data types supported by the selected backend (including `cudf.DataFrame`

with `cuML`

backend).

## Supported methods

Clustergram currently supports K-Means, Mini Batch K-Means, Gaussian Mixture Model and SciPy's hierarchical clustering methods. Note tha GMM and Mini Batch K-Means are supported only for `scikit-learn`

backend and hierarchical methods are supported only for `scipy`

backend.

Using K-Means (default):

```
cgram = Clustergram(range(1, 8), method='kmeans')
cgram.fit(data)
cgram.plot()
```

Using Mini Batch K-Means, which can provide significant speedup over K-Means:

```
cgram = Clustergram(range(1, 8), method='minibatchkmeans', batch_size=100)
cgram.fit(data)
cgram.plot()
```

Using Gaussian Mixture Model:

```
cgram = Clustergram(range(1, 8), method='gmm')
cgram.fit(data)
cgram.plot()
```

Using Ward's hierarchical clustering:

```
cgram = Clustergram(range(1, 8), method='hierarchical', linkage='ward')
cgram.fit(data)
cgram.plot()
```

## Manual input

Alternatively, you can create clustergram using `from_data`

or `from_centers`

methods based on alternative clustering algorithms.

Using `Clustergram.from_data`

which creates cluster centers as mean or median values:

```
data = numpy.array([[-1, -1, 0, 10], [1, 1, 10, 2], [0, 0, 20, 4]])
labels = pandas.DataFrame({1: [0, 0, 0], 2: [0, 0, 1], 3: [0, 2, 1]})
cgram = Clustergram.from_data(data, labels)
cgram.plot()
```

Using `Clustergram.from_centers`

based on explicit cluster centers.:

```
labels = pandas.DataFrame({1: [0, 0, 0], 2: [0, 0, 1], 3: [0, 2, 1]})
centers = {
1: np.array([[0, 0]]),
2: np.array([[-1, -1], [1, 1]]),
3: np.array([[-1, -1], [1, 1], [0, 0]]),
}
cgram = Clustergram.from_centers(centers, labels)
cgram.plot(pca_weighted=False)
```

To support PCA weighted plots you also need to pass data:

```
cgram = Clustergram.from_centers(centers, labels, data=data)
cgram.plot()
```

## Partial plot

`Clustergram.plot()`

can also plot only a part of the diagram, if you want to focus on a limited range of `k`

.

```
cgram = Clustergram(range(1, 20))
cgram.fit(data)
cgram.plot(figsize=(12, 8))
```

```
cgram.plot(k_range=range(3, 10), figsize=(12, 8))
```

## Additional 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.

### 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 0.400154
5 0.377720
6 0.372128
7 0.331575
Name: silhouette_score, dtype: float64
```

Once computed, resulting Series is available as `cgram.silhouette`

. Calling the original method will recompute the score.

### 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
4 400.392131
5 411.175066
6 382.731416
7 352.447569
Name: calinski_harabasz_score, dtype: float64
```

Once computed, resulting Series is available as `cgram.calinski_harabasz`

. Calling the original method will recompute the score.

### 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 0.943320
5 0.973248
6 0.950910
7 1.074937
Name: davies_bouldin_score, dtype: float64
```

Once computed, resulting Series is available as `cgram.davies_bouldin`

. Calling the original method will recompute the score.

## 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
0 0 0 2 2 3 2 1
1 0 0 2 2 3 2 1
2 0 0 2 2 3 2 1
3 0 0 2 2 3 2 1
4 0 0 2 2 0 0 3
.. .. .. .. .. .. .. ..
337 0 1 1 3 2 5 0
338 0 1 1 3 2 5 0
339 0 1 1 1 1 1 4
340 0 1 1 3 2 5 5
341 0 1 1 1 1 1 5
```

## Saving clustergram

You can save both plot and `clustergram.Clustergram`

to a disk.

### Saving plot

`Clustergram.plot()`

returns matplotlib axis object and as such can be saved as any other plot:

```
import matplotlib.pyplot as plt
cgram.plot()
plt.savefig('clustergram.svg')
```

### Saving object

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)
```

Then loading is equally simple:

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

## References

Schonlau M. The clustergram: a graph for visualizing hierarchical and non-hierarchical cluster analyses. The Stata Journal, 2002; 2 (4):391-402.

Schonlau M. Visualizing Hierarchical and Non-Hierarchical Cluster Analyses with Clustergrams. Computational Statistics: 2004; 19(1):95-111.